2026-11-06T05:09:03Z https://repository.globalsciencepress.com/cgi/oai2

oai:repository.globalsciencepress.com:2 2026-09-14T05:00:52Z

oai:repository.globalsciencepress.com:43 2026-09-11T06:43:44Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/43/ Combined imaging and molecular techniques for evaluating microbial function and composition Sharif, Javeria Bilal, Uzma Fatima, Hafiza Rida Muazzam, Abia Perveen, Aqsa Naveed, Areesha Saeed, Hafiza Arshi Haidar, Ayesha Atique, Rameen Muazzam, Ayesha Microorganisms take key positions among ecological communities, supplying biogeochemical activities such as nutrient circulations and environmental pollution decomposition and ensuring ecosystem stability. The usual ways of studying microbial communities use cultivation techniques, which are a hindrance to our grasp of almost all microorganisms unable to grow in the controlled conditions of the laboratory. This review is meant to summarize the current development in imaging and molecular techniques for assessing microbe communities.  The combined use of molecular and imaging techniques for microbial analysis was assessed through a review of literature reporting studies that employed this approach. The review suggested that there is a broad range of techniques that apply both imagery and molecular characteristics for microbial analysis which include fluorescence in situ hybridization (FISH), confocal laser scanning microscopy (CLSM), metagenomics, metatranscriptomics and metaproteomics. The applied combined imaging and molecular techniques represent an effective way of understanding microbial function and structure. By combining microscopic observation with molecular analysis, researchers can obtain significant information on the structure-function relationships of microbial communities and their interactions with the surroundings. These technologies are of great importance in solving the crucial issues of environmental surveillance, biodegradation, and human health. On the other hand, future technological progress and interdisciplinary cooperation are the key factors that will let us fully unleash the potential of these approaches and transform the findings of basic research into practical applications. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Sharif, Javeria and Bilal, Uzma and Fatima, Hafiza Rida and Muazzam, Abia and Perveen, Aqsa and Naveed, Areesha and Saeed, Hafiza Arshi and Haidar, Ayesha and Atique, Rameen and Muazzam, Ayesha (2024) Combined imaging and molecular techniques for evaluating microbial function and composition. Global Journal of Universal Studies, 1 (1). pp. 1-21. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/1

oai:repository.globalsciencepress.com:44 2026-09-11T06:43:45Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/44/ Brief overview of Coronavirus Sharif, Javeria Atique, Rameen Naveed, Areesha Saeed, Hafiza Arshi Anwar, Bushra Rana, Tehreem Kausar, Sana Talib, Ambreen Shah, Rabbya Rayan Haidar, Ayesha A worldwide health emergency, the coronavirus illness (COVID-19) is having a profound effect on economies, society, and healthcare systems. The virus, which causes the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was initially discovered in Wuhan, China, towards the end of 2019. It can cause everything from minor respiratory problems to severe pneumonia, and it frequently kills people, especially in vulnerable groups. Since respiratory droplets are the main way that the virus spreads, social distance and immunization are crucial protective measures. The epidemic has brought to light the shortcomings in public health infrastructures and the need for quick and open information sharing, underscoring the need for a coordinated worldwide response to infectious illnesses. The socioeconomic impact of COVID-19 is demonstrated by the disruptions to everyday routines, high rates of unemployment, and demands placed on healthcare services. Travel bans and quarantines have accelerated the trends in online learning, telemedicine, and remote work while also posing challenges to certain industries and aggravating social inequality. The quick thinking of the scientific community has been essential in comprehending the virus, producing diagnostic instruments, and designing vaccines. The situation highlights the need for improved global collaboration, strong healthcare systems, and efficient pandemic response plans. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Sharif, Javeria and Atique, Rameen and Naveed, Areesha and Saeed, Hafiza Arshi and Anwar, Bushra and Rana, Tehreem and Kausar, Sana and Talib, Ambreen and Shah, Rabbya Rayan and Haidar, Ayesha (2024) Brief overview of Coronavirus. Global Journal of Universal Studies, 1 (1). pp. 22-36. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/2

oai:repository.globalsciencepress.com:45 2026-09-11T06:43:45Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/45/ Unravelling the Mysteries of Zika Virus: A Comprehensive Overview Shah, Rabbya Rayan Talib, Ambreen Ferdos, Tehreem Akmal, Huda Yamin, Hamna Zahra, Maryam Nadeem, Ayesha Anwar, Bushra Muazzam, Abia hameed, Fatima Samad, Abdul The Zika virus (ZIKV), which emerged in an epidemic state in 2016, poses a serious risk to the health of society. It could appear as a serious condition with signs such as migraine and a high body temperature or it can appear benign. Aedes species of mosquitoes (family Flaviviridae, genus Flavivirus) are responsible for the transmission of ZIKA virus to humans. Even though over half of civilization lives in areas where Aedes aegypti is common, there is still concern about the possibility of its revival. Global accounts of Zika virus infections have recently been made, especially from America; French Polynesia and other Pacific Island nations; and south-East Asia. ZIKV was first discovered in 1947 in the Ugandan Zika Forest, in a rhesus monkey. Since the beginning of discoveries in Africa and Asia in 2007, the virus expanded around every continent.  A serious infection frequently results in a short-term febrile illness, but recent evidence reveals that it may also be linked to additional important adverse effects like microcephaly in infants and Guillain-Barré syndrome. This review provides knowledge about the pathophysiology of ZIKV, reveals essential cellular processes that develop during infections, and recalls the origins of the virus from when it was first discovered to its current worldwide propagation. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Shah, Rabbya Rayan and Talib, Ambreen and Ferdos, Tehreem and Akmal, Huda and Yamin, Hamna and Zahra, Maryam and Nadeem, Ayesha and Anwar, Bushra and Muazzam, Abia and hameed, Fatima and Samad, Abdul (2024) Unravelling the Mysteries of Zika Virus: A Comprehensive Overview. Global Journal of Universal Studies, 1 (1). pp. 37-58. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/3

oai:repository.globalsciencepress.com:46 2026-09-11T06:43:45Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/46/ Interactions between Gut Microbiota and Nanoparticles: Implications for Health and Disease Bilal, Uzma Shah, Rabbya Rayan Bilal, Bushra hameed, Fatima Talib, Ambreen Ijaz, Amara Saeed, Hafiza Arshi Fatima, Hafiza Rida Zahra, Maryam Muazzam, Ayesha Many bacteria share a healthy human body, generating natural flora. According to many predictions, bacterial genetic material is much more important in the human body than a certain amount of DNA. Any modification in the composition of this flora could result in a wide range of disorders, considering that it plays a crucial role in human health and illness. In many different sectors, nanoparticles are frequently used: industry, food, and platforms for the transportation of pharmaceutical products in the medical domain. Different industries, fields, and occupations, such as pharmaceutical products, cosmetics, and medicines, widely use nanoparticles. Nanoparticles could interact with the human body in multiple ways at various stages because of their many potential applications. Due to the shape, type, and concentration of nanoparticles, their interaction can cause the microbiota to become unstable, regenerate themselves, or show no harmful effects. Additionally, the microbiota can be controlled or some disorders can be treated with nanoparticles. Nanoparticles may also be used as a vehicle to regulate the microbiota or to treat some of its diseases. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Bilal, Uzma and Shah, Rabbya Rayan and Bilal, Bushra and hameed, Fatima and Talib, Ambreen and Ijaz, Amara and Saeed, Hafiza Arshi and Fatima, Hafiza Rida and Zahra, Maryam and Muazzam, Ayesha (2024) Interactions between Gut Microbiota and Nanoparticles: Implications for Health and Disease. Global Journal of Universal Studies, 1 (1). pp. 59-87. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/4

oai:repository.globalsciencepress.com:47 2026-09-11T06:43:45Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/47/ Host-parasite Interactions; From Co-evolutionary Changes to Genomic Insights Atique, Rameen Saeed, Hafiza Arshi Haidar, Ayesha Shareef, Javeria Naveed, Areesha Nadeem, Ayesha Shahzadi, Iram Kausar, Sana Ijaz, Amara Jamal, Ahmad Host-parasite interaction belongs to the association between two biologically different organisms in which the host delivers food and shelter for the parasite and in return the parasite reproduces itself in the host body. There are many kinds of host-parasite relationships including parasitism, mutualism, and commensalism. The potent interaction between the host and parasite is regulated by the evolutionary changes over space and time. Host-parasite interconnection is the basic feature of the maintenance of ecology and biodiversity. The association between the host and parasite establishes an equilibrium in which the host unfolds protective strategies against parasites and the parasites develop mechanisms to elude the host’s defenses and spread infections. This dynamic interaction between the host and parasite mainly depends on the host's immune system, the severity of infection, the load of parasites in the body, and the ecological requirements. Moreover, environmental conditions, locomotion, and genetic factors of the host also influence host-parasite interactions. The pathogenicity of parasites is determined by the sensitivity and resistivity of the host's immune response. As parasites evolve, they employ diverse strategies to manipulate the host’s behavior and adapt to changing atmospheres. The education of the host-parasite association is essential for proper control and surveillance of parasitic diseases. For the prevention of parasitic infections, different therapeutic measures have been devised. Researchers are performing many types of procedures to sustain the ecosystem and fitness of individuals using host-parasite interactions. The purpose of this review was to enhance the importance of the host-parasite interaction in our environment. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Atique, Rameen and Saeed, Hafiza Arshi and Haidar, Ayesha and Shareef, Javeria and Naveed, Areesha and Nadeem, Ayesha and Shahzadi, Iram and Kausar, Sana and Ijaz, Amara and Jamal, Ahmad (2024) Host-parasite Interactions; From Co-evolutionary Changes to Genomic Insights. Global Journal of Universal Studies, 1 (1). pp. 88-107. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/5

oai:repository.globalsciencepress.com:48 2026-09-11T06:43:45Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/48/ Renewable Energy Technologies: Present Patterns and Upcoming Paths in Ecological Power Production Lodhi, Shahrukh Khan Gill, Ahmad Yousaf Hussain, Ibrar In response to the increasing need for environmentally friendly and sustainable power sources, renewable energy technologies are developing quickly. This thorough analysis looks at the present and potential trends in a number of renewable energy areas, such as hydropower, geothermal, and biomass energy. Emerging as a flexible and sustainable energy source made from organic materials is biomass energy. Anaerobic digestion, pyrolysis, and gasification have all been improved by recent advances in biomass technology, increasing their effectiveness and enabling new uses. While biomass energy finds a variety of applications in power generation, transportation, heating, and industrial processes, sustainability concerns are being addressed by advanced approaches in feedstock management and lifetime emissions assessment. The internal heat of the Earth is captured by geothermal energy, which offers a steady and dependable power source. Geothermal heat pumps, better drilling methods, and upgraded geothermal systems (EGS) are examples of technological developments. Every one of these solutions helps to lower expenses and increase efficiency. Heat pumps, power plants, and direct use systems are the three types of geothermal systems; each has advantages and uses in particular. The global spread of geothermal energy is being facilitated by technological breakthroughs and regulations that promote it, addressing issues including resource exploration, environmental effect, and economic hurdles. Although new developments have redefined its place in contemporary energy systems, hydropower is still a key component of renewable energy sources. Technological advancements in turbines, such as hydrokinetic and fish-friendly models, are improving performance and lessening their negative environmental effects. Micro- and small-scale hydropower plants are expanding the versatility and accessibility of hydropower. These technologies are essential to developing a robust and diverse energy mix that supports sustainable development and climate goals as the global energy landscape moves toward cleaner and more sustainable solutions. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Lodhi, Shahrukh Khan and Gill, Ahmad Yousaf and Hussain, Ibrar (2024) Renewable Energy Technologies: Present Patterns and Upcoming Paths in Ecological Power Production. Global Journal of Universal Studies, 1 (1). pp. 108-131. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/6

oai:repository.globalsciencepress.com:49 2026-09-11T06:43:45Z 7375626A656374733D41:4149 7375626A656374733D4D:4D4C 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/49/ Under the titles for Risk Assessment, Pricing, and Claims Management, write Modern Analytics Valli, Latha Narayanan AI Indexes (General) ML Literature of music Modern analytics has become a revolutionary force in the insurance sector, redefining established methods and spurring innovation in all facets of the business in the ever-changing landscape. This abstract offers a thorough analysis of the function and implications of contemporary analytics in the insurance industry, with particular attention to how it is applied to risk assessment, pricing schemes, claims handling, and client involvement. With the help of cutting-edge algorithms, enormous data sets, and newly developed technology, insurers can now make better judgments, increase operational effectiveness, and provide individualized customer experiences. Insurance companies can improve client interactions, expedite workflows for claims management, establish dynamic pricing models, and improve risk assessment processes by utilizing AI-powered tools, predictive modelling, and machine learning. Modern analytics adoption in the insurance industry is not without its difficulties, though. To fully utilize modern analytics, insurers must traverse a number of important issues, including data protection and security, regulatory compliance, talent recruiting, and ethical considerations. The application of contemporary analytics in the insurance industry has a bright future ahead of it. Block chain, quantum computing, and augmented analytics are examples of emerging technologies that present insurers with new chances to spur innovation, reduce risk, and add value for stakeholders and clients. The path towards contemporary analytics in the insurance industry is demanding and fulfilling. In an industry that is changing quickly, insurers may achieve new heights of effectiveness, efficiency, and competitiveness by embracing innovation, teamwork, and customer-centricity. The future of modern analytics in insurance is bright, promising, and full of opportunity for those who dare to innovate and lead the path forward as we set out on this adventure together. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Valli, Latha Narayanan (2024) Under the titles for Risk Assessment, Pricing, and Claims Management, write Modern Analytics. Global Journal of Universal Studies, 1 (1). pp. 132-151. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/7

oai:repository.globalsciencepress.com:50 2026-09-11T06:43:45Z 7375626A656374733D41:4149 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/50/ AI-Powered Healthcare Reform: Lessons from Innovations in the Petroleum Industry and Fraud Detection Methods Fahad, Muhammad Ibrar, Muhammad Qayyum, Muhammad Umer Husnain, Ali AI Indexes (General) By improving predictive analytics, personalized medicine, diagnostics, and operational efficiency, artificial intelligence (AI) has the potential to revolutionize the healthcare industry. This review examines how AI can be applied to key healthcare challenges like fraud detection, data management, and predictive maintenance by drawing on innovations in the financial and petroleum sectors. AI has a wide range of applications in the healthcare industry, from real-time patient monitoring and personalized treatment plans to advanced medical imaging and operational optimization. However, despite these advantages, there are a number of technical and ethical issues that need to be resolved, such as managing algorithmic bias, ensuring data quality and integration, and protecting patient privacy and data security. Additionally, ethical issues pertaining to informed consent, transparency, and equity need to be carefully considered in order to uphold justice and trust. Similar to how fraud detection techniques from the financial sector can improve the identification and prevention of fraudulent activities in healthcare billing and claims processing, lessons from the petroleum industry can be applied to improve the reliability and efficiency of medical equipment and healthcare operations. Examples of these lessons include predictive maintenance and operational optimization. The integration of artificial intelligence (AI) in healthcare offers substantial opportunities for advancement but also poses significant challenges that must be addressed. Healthcare organizations can fully realize the potential of AI to improve patient care, operational efficiency, and system integrity by addressing both technical and ethical issues and leveraging cross-industry insights. The path forward involves a commitment to innovation, ethical practices, and interdisciplinary collaboration to ensure that AI benefits are distributed fairly and effectively across the healthcare system. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Fahad, Muhammad and Ibrar, Muhammad and Qayyum, Muhammad Umer and Husnain, Ali (2024) AI-Powered Healthcare Reform: Lessons from Innovations in the Petroleum Industry and Fraud Detection Methods. Global Journal of Universal Studies, 1 (1). pp. 152-171. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/8

oai:repository.globalsciencepress.com:51 2026-09-11T06:43:46Z 7375626A656374733D41:4149 7375626A656374733D4D:4D4C 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/51/ Integrating AI in Healthcare: Advancing Petroleum Fraud Detection and Enhancing Vaccine Development Qayyum, Muhammad Umer Fahad, Muhammad Ibrar, Muhammad Husnain, Ali AI Indexes (General) ML Literature of music Healthcare, the petroleum industry, and public health are just a few of the industries that artificial intelligence (AI) is revolutionizing with its cutting-edge solutions that promote efficiency and creativity. This article examines how AI is combining these many domains, with a special emphasis on healthcare, namely on vaccine research and delivery, and fraud detection in the petroleum sector. AI has completely changed vaccine development by speeding up the identification of strong candidates and streamlining clinical trials thanks to its rapid analysis of enormous volumes of data. Similar to this, AI has helped the petroleum industry by enhancing operational effectiveness and detecting fraud. The use of AI across industries demonstrates how it can promote cooperation and creativity across conventional industry boundaries. But integrating AI comes with a lot of work and ethical issues to work through, such protecting data privacy, dealing with algorithmic bias, and preserving openness in AI decision-making. These difficulties are crucial, particularly in industries like healthcare where judgments made using AI can significantly affect patient outcomes. The emphasis is on the necessity of strong legal frameworks and moral governance to manage these difficulties and guarantee the proper application of AI technologies. Artificial intelligence (AI) has the potential to revolutionize a wide range of industries, but it also comes with hazards and moral conundrums that need to be carefully managed. AI may be used to build a more just and sustainable future by tackling these issues through cooperation and moral behavior. This will advance both technical innovation and societal well-being. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Qayyum, Muhammad Umer and Fahad, Muhammad and Ibrar, Muhammad and Husnain, Ali (2024) Integrating AI in Healthcare: Advancing Petroleum Fraud Detection and Enhancing Vaccine Development. Global Journal of Universal Studies, 1 (1). pp. 172-189. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/9

oai:repository.globalsciencepress.com:52 2026-09-11T06:43:46Z 7375626A656374733D41:4149 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/52/ Advancements and Best Practices in Data Loss Prevention: A Comprehensive Review Abid, Noman AI Indexes (General) DLP solutions are needed because there is more data and more reliance on it which people and businesses need. This paper will provide a further account of what DLP is, its significance, issues it encounters, and prospects in what follows from this article about DLP. Globalization and the rise in the kind and level of cyber threats, uptick in cloud use, and the enhancement of legal requirements for data protection have all moved DLP to higher prominence as a cybersecurity mechanism. In more detail, some challenges that are associated with DLP implementation and that refers to initiation and management, are described, such as data classification, security needs and organization productivity, insiders and their threats, regulation compliance, and others. The article also examines how data protection paradigms are revolutionized by AI, ML, cloud native DLP solutions, Zero Trust architecture, and XDR. Also, it underlines the need of privacy by design, and the controls concerning insider threats in the shift of the paradigm. As the type and functionality of DLP systems will further advance not in the distant future, utilization of such state of the art technologies shall assist organizations to secure data while the latter focus on continued operation. Therefore to meet this ever present need for such smart and dynamic DLP solutions to address data loss consideration in the multiple complex cloud environment there is need to rise new and complex cyber threats. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Abid, Noman (2024) Advancements and Best Practices in Data Loss Prevention: A Comprehensive Review. Global Journal of Universal Studies, 1 (1). pp. 190-225. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/10

oai:repository.globalsciencepress.com:53 2026-09-11T06:43:46Z 7375626A656374733D41:4149 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/53/ AI in Clinical Practice: Current Uses and the Path Forward Sherani, Abdul Mannan Khan Khan, Murad AI Indexes (General) Machine learning is developing very quickly in the healthcare field in the field’s diagnostics, therapy and caring of patients and new drug development. Machine learning, deep learning, and so on have opened up new areas of diagnosis of diseases, understanding what particular treatment is required for a specific patient, and prognosis of disease. Radiology, Computer Aided Diagnosis and chronic disease applications are the current areas covered by AI to improve the health of patients and to hasten the speed of the health systems. Nevertheless, there are some of the issues that are associated with AI in clinical practice they include; Data privacy and protection, Issue of bias in machine learning algorithms, the black challenge. Some of the professional factors that are important when implementing artificial intelligence are: equity non –discrimination and patient engagement. Nonetheless, policies regarding AI in the healthcare context are still in their development process, and about responsibility or purse or assurance has been put forward, and it involves security. The future of AI in healthcare is consequently expected to continue evolution of precision medicine, enhance the speed of drug development, as well as enhance patient satisfaction through smart health including next generations of virtual assistants and telemedicine. For AI to be used in a way that optimally takes advantage of the potential that exists in significantly improving health care delivery as well as productivity, the technological, ethical, and regulatory factors that characterize the technology’s application need to be tamed cohesively to realize safe uses that are patient centered. Heated debates over the life of the next few decades are required to ensure great advancements in the future of AI in healthcare, but these advancements need to be managed carefully to safeguard the best interests of each patient/healthcare consumer and provider. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Sherani, Abdul Mannan Khan and Khan, Murad (2024) AI in Clinical Practice: Current Uses and the Path Forward. Global Journal of Universal Studies, 1 (1). pp. 226-245. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/11

oai:repository.globalsciencepress.com:54 2026-09-11T06:43:46Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/54/ Transformative AI Applications: Promising Value Faster Healthcare Development, Affording Petroleum Deception Suppression, Improving Cybersecurity, and Integrating Humanized Chatgpt for Conversational AI Ali, Mohammad In this field, the use of Artificial Intelligence (AI) is revolutionizing a number of industries bringing novel solutions that challenge conventional approaches to order. This review explores the significant impact of AI in five key sectors: healthcare, petroleum industry, credit card fraud, Cybersecurity, Chatgpt. AI is useful for improving diagnostic and clinical decisions and for making drug development and patient comparison faster. In the petroleum industry, by applying AI in Fraud management, companies are reducing their exposure to fraud, and increasing accountability, therefore reducing their frequency of finsancial loss and poor operational flow. In cybersecurity, AI is stepping up as the frontline technology to identify threats in real-time, analyze patterns, and respond proactively while organizations are fighting off more complex cyber threats. Furthermore, with advancements like Chatgpt, or AI tools it is clear that the dynamic of interaction between human and computer has also begun changing for the better generalizing human-computer-interaction for different contexts such as customer support, learning, and mental health. However, AI has its own issue such as data privacy, the ethical issues involved in adopting AI, issue of bias in data and risk of misuse of AI, all of which have to be taken into consideration. Thus, as the AI technologies develop individuals represent a great potential to positively progress and improve if the usage of AI technologies is reasonable and ethical. The focus of this review is on the value of integration AI in updating industries, potential vulnerabilities, and the role of future investigation, government control, and cross-discipline cooperation in enhancing positive outcomes of these technologies as well as the management of negative consequences. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Ali, Mohammad (2024) Transformative AI Applications: Promising Value Faster Healthcare Development, Affording Petroleum Deception Suppression, Improving Cybersecurity, and Integrating Humanized Chatgpt for Conversational AI. Global Journal of Universal Studies, 1 (1). pp. 1-20. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/12

oai:repository.globalsciencepress.com:55 2026-09-11T06:43:46Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/55/ Transformative AI Applications in Healthcare, Petroleum, Fraud Detection, Cybersecurity, and Conversational AI: Advancing Industries Hussain, Ibrar AI is the most disruptive technology of the decade as it changes industries for the better by upgrading processes, increasing efficiencies, and introducing creativity in many industries. The liberties, in this case, are seen in health care where artificial intelligences is changing the ways diagnosis’s are done, treatment methods, drug discovery and many more. Some of the potential applications of AI in the petroleum industry include; Efficiency in the production of energy, issues to do with safety and even with those of maintenance with emphasis on prevention of future mishaps that may be costly or fatal. AI has also found its use in anti-fraud where it is applied to identify new financial crimes and encode machine learning models to adapt to their new schemes. Categorized under cyber security, Artificial Intelligence is strengthening the cybersecurity by identifying new threats, making accurate forecasts, and responding to threats proactively to protect sensitive data. A well-known example of conversational AI is the Chatgpt is one of the key applications of the AI industry. Chatgpt is disintermediating customer service, sales and internal communication by providing a highly personalized many turn contextually aware conversation. Making communications with customers faster, and more efficient and thus is a win-win for customers as well as the organization. It also enhances the issue of accessibility since it facilitates communication between different user groups. At the same time, when AI became more successful and extensively integrated into multiple company processes, it created new specific ethical concerns, such as data privacy, bias, and insufficient transparency. It is crucial to solve these problems to get the highest advantage from AI and do not cause distrust and misunderstanding. This current role of AI is set to grow in the future, across sectors, to deliver even higher levels of effectiveness, creativity and convenience, unlocking the era of a smarter connected world. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Hussain, Ibrar (2024) Transformative AI Applications in Healthcare, Petroleum, Fraud Detection, Cybersecurity, and Conversational AI: Advancing Industries. Global Journal of Universal Studies, 1 (1). pp. 21-43. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/13

oai:repository.globalsciencepress.com:56 2026-09-11T06:43:46Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/56/ Investigation of Heat Transfer in Pipes Using Dimensionless Numbers Asif, Syed Muhammad This study investigates heat transfer in pipes, focusing on the calculation of three key dimensionless numbers to analyze thermal behavior. Although the experiment was conducted only once, measures were taken to ensure accuracy, and potential sources of error were minimized. Repeating the experiment multiple times and averaging the results would have further improved reliability by reducing the impact of anomalies. Despite this limitation, the primary objective of understanding heat transfer mechanisms through dimensionless numbers was successfully achieved, providing valuable insights into thermal processes. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Asif, Syed Muhammad (2024) Investigation of Heat Transfer in Pipes Using Dimensionless Numbers. Global Journal of Universal Studies, 1 (1). pp. 44-67. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/14

oai:repository.globalsciencepress.com:57 2026-09-11T06:43:46Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/57/ Leveraging AI and Machine Learning to Predict and Prevent Sudden Cardiac Arrest in High-Risk Populations Shah, Heta Hemang Bacha, Ahmad Cardiac arrest claims a lot of lives, mainly among people with heart diseases, diabetes, or abnormal heartbeats. Using AI and ML helps us find people at risk of heart failure and helps them avoid experiencing this condition. The research review looks at what AI and ML methods currently do to forecast SCA and checks how well they work. The models work by using lots of collected health information - from medical records, user monitors, and genetic studies - to forecast SCA danger right when needed. AI technology with machine learning algorithms can study large patient data to spot unique patterns that show which people are more likely to develop SCA. These models show two abilities: they estimate chance of events happening and suggest what steps to take to reduce risks. The study looks at AI implementation issues, including keeping patient data confidential, connecting with how doctors work today, and making sure doctors understand how AI systems make decisions. The research stresses that professionals should make transparent how their AI decisions work. Robust artificial intelligence and machine learning tools can help healthcare improve patient care by spotting those at highest risk of dangerous heart failure sooner and lowering deaths linked to cardiac emergencies. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Shah, Heta Hemang and Bacha, Ahmad (2024) Leveraging AI and Machine Learning to Predict and Prevent Sudden Cardiac Arrest in High-Risk Populations. Global Journal of Universal Studies, 1 (1). pp. 87-107. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/15

oai:repository.globalsciencepress.com:58 2026-09-11T06:43:46Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/58/ AI-Enhanced Liquid Biopsy: Advancements in Early Detection and Monitoring of Cancer through Blood-based Markers Bacha, Ahmad Shah, Heta Hemang The inclusion of AI technology into liquid biopsy brings us important new ways to find and track cancer. The non-invasive blood test called liquid biopsy shows great potential for early cancer detection and helps doctors track disease changes and create custom treatments. Machine learning algorithms in AI technology helps liquid biopsy find better biomarkers and detect cancer earlier while also monitoring patient health in real time. Advanced technologies find cancer-related genetic faults along with tumor markers and detect remaining disease cells accurately. Using advanced AI to solve quality and accuracy problems helps liquid biopsy systems transform how doctors treat cancer by identifying diseases sooner and giving better treatment plans for patients. These advancements help identify cancer-related genetic mutations, tumor markers, and minimal residual disease that may otherwise be missed. By addressing the challenges of data quality, algorithmic accuracy, and clinical integration, AI-enhanced liquid biopsy can transform cancer care, offering earlier detection, more precise monitoring, and tailored treatments for individual patients. This report explores latest advancements and explains how AI helps transform liquid biopsy use in medical oncology. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Bacha, Ahmad and Shah, Heta Hemang (2024) AI-Enhanced Liquid Biopsy: Advancements in Early Detection and Monitoring of Cancer through Blood-based Markers. Global Journal of Universal Studies, 1 (1). pp. 68-86. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/16

oai:repository.globalsciencepress.com:59 2026-09-11T06:43:46Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/59/ AI-Driven Approaches to Overcoming Tumor Heterogeneity in Breast Cancer: Modelling Resistance and Therapy Outcomes Abid, Noman Neoaz, Nahid Amin, Mohammad Hasan Breast cancer is a highly heterogeneous disease consisting of distinct molecular subtypes and differential response to treatment. The major challenge associated with predicting treatment responses and overcoming resistance mechanisms in tumor heterogeneity (genetic, phenotypic, and molecular diversity of a single tumor) remains yet to be overcome. Now artificial intelligence (AI) and machine learning (ML) promise to help us address these challenges by modelling complex interactions within the tumor. Using large-scale genomic, transcriptomic, imaging and clinical data, AI can incorporate cancer heterogeneity by characterizing and identifying key drivers for cancer heterogeneity and predicting resistance mechanisms. They help to generate more accurate prediction of chemotherapy, targeted therapy, and immunotherapy treatment responses and it can pave the way for more personalized treatment strategy. But AI is also capable of monitoring tumor evolution over time, and uncovering real-time insights about how effective treatment is and if relapse is starting, early. While promising, practitioners have been slow to adopt AI models for clinical work, due to data quality, model interpretability and compatibility in the current healthcare workflow. In translating AI based tools for practice, these challenges will need to be addressed. In this paper, we discuss how AI may be used to target tumor heterogeneity, model resistance mechanisms and optimize treatment responses in breast cancer as well as presenting obstacles to clinical implementation of such methods. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Abid, Noman and Neoaz, Nahid and Amin, Mohammad Hasan (2024) AI-Driven Approaches to Overcoming Tumor Heterogeneity in Breast Cancer: Modelling Resistance and Therapy Outcomes. Global Journal of Universal Studies, 1 (1). pp. 108-125. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/17

oai:repository.globalsciencepress.com:60 2026-09-11T06:43:46Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/60/ Understanding AI-Driven Cardiovascular Risk Prediction in Underserved Populations: Addressing Social Determinants of Health through Data Analytics Khan, Murad Sherani, Abdul Mannan Khan Cardiovascular disease (CVD) is a leading cause of morbidity and mortality internationally, and underserved populations bear an out sized burden of risk. In these communities, access to adequate healthcare, burdened by social determinants of health (SDOH) like socioeconomic status, education, and environmental factors, can pose a barrier to many individuals. The crucial role of Artificial Intelligence (AI) and Machine Learning (ML) to address problems of health disparity by leveraging large scale data to predict cardiovascular risks and impact appropriate interventions is discussed. The integration of diverse sources of information, such as electronic health records (EHR) social data, and behavioral data, allows AI models to identify at risk individuals even in settings with limited resources. In this paper, we investigate the use of AI driven cardiovascular risk prediction models in underserved populations and how they can consider the effect of SDOH. The paper also addresses ethical considerations, data challenges, and opportunities for use of these prediction tools in public health initiatives to address disparities in health. The potential of combining AI and data analytics is to enhance cardiovascular health outcomes, bridging gaps in equitable access to preventive care, especially for vulnerable communities. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Khan, Murad and Sherani, Abdul Mannan Khan (2024) Understanding AI-Driven Cardiovascular Risk Prediction in Underserved Populations: Addressing Social Determinants of Health through Data Analytics. Global Journal of Universal Studies, 1 (1). pp. 146-164. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/18

oai:repository.globalsciencepress.com:61 2026-09-11T06:43:47Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/61/ AI in Product Management: Efficiency, Quality, and Innovation Kabeer, Muhammad Mohsin Artificial Intelligence (AI) is transforming the way products are managed, manufactured, and quality controlled by providing data-driven choices, enabling intelligent automation, and preliminary information. This is a review of the way AI helps in increasing efficiency, improving the quality of products and also in promoting innovation throughout the product lifecycle. AI is useful in product management in supporting market forecasting, optimization by design, and personalization. In the factory industry, it initiates intelligent factories, proactive maintenance, and cost-efficiency. AI can be used in quality management to ensure real-time monitoring, detection of defects, and compliance. Although there are advantages, namely the cost savings, agility and sustainability, there still are challenges associated with the development like data dependency, integration, workforce adaptation, and ethical concerns. The future trends point to generative AI, digital twins and sustainability. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Kabeer, Muhammad Mohsin (2024) AI in Product Management: Efficiency, Quality, and Innovation. Global Journal of Universal Studies, 1 (1). pp. 165-185. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/19

oai:repository.globalsciencepress.com:62 2026-09-11T06:43:47Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/62/ Deep Learning for Multi-Modal Cancer Imaging: Integrating Radiomics, Genomics, and Clinical Data for Comprehensive Diagnosis and Prognosis Abid, Noman Neoaz, Nahid Amin, Mohammad Hasan At both the cellular and molecular levels, cancer is a heterogeneous disease. While traditional diagnostic and prognostic methods are often deficient in the ability to capture this complexity, limiting the potential to inform personalized treatment strategies. Deep learning (DL) results on multi-modal data sources have the potential to improve cancer diagnosis and prognosis by combining several types of data sources in the past few years: radiomics, genomics and clinical data. The first, radiomics, extracts quantitative features from medical images to reveal tumor characteristics invisible to the human eye; the second, genomic data, provides information about what genetic mutations are driving cancer progression. These modalities are complemented by clinical data with patient demographics and treatment history contributing context specific information to the data. The application of deep learning techniques on multi-modal cancer imaging is reviewed as well and their ability to combine radiomics, genomics and clinical data to improve diagnostic and prognostic accuracy is emphasized. We discuss the challenges and future directions for clinical implementation of this approach and its potential to transform cancer care, improve patient outcomes and empower precision medicine. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Abid, Noman and Neoaz, Nahid and Amin, Mohammad Hasan (2024) Deep Learning for Multi-Modal Cancer Imaging: Integrating Radiomics, Genomics, and Clinical Data for Comprehensive Diagnosis and Prognosis. Global Journal of Universal Studies, 1 (1). pp. 126-145. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/20

oai:repository.globalsciencepress.com:63 2026-09-11T06:43:47Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/63/ Integrating Health Informatics into Modern Healthcare Systems: A Comprehensive Review Javeedullah, Mohammed The multidisciplinary field of health informatics uses IT, data analytics with healthcare expertise to optimize health data management for better care services. Health informatics applications within contemporary healthcare organizations drive streamlined clinical operations while delivering high-quality decisions that boost patient recovery results. A review examines the developmental history and elementary elements and connective approaches of health informatics as well as the accomplishments and difficulties with new developing technologies in this field. Operations and patient care through improvements have become attainable using four critical health informatics components which consist of Electronic Health Records (EHRs), Health Information Exchange (HIE), Clinical Decision Support Systems (CDSS) and telehealth. Successful integration of healthcare systems withstands various obstacles such as technology difficulties as well as staff member opposition and security issues and limited financial means. The achievement of operational excellence demands proper interoperability standards and complete training programs as well as strict adherence to privacy guidelines. Modern healthcare depends on emerging technologies which include Artificial Intelligence (AI), Machine Learning, the Internet of Medical Things (IoMT), block chain, and genomics to transform patient-specific care and predictive healthcare and enhance patient engagement. Real-life healthcare implementations at Kaiser Permanente alongside the NHS along with the enhanced use of telehealth systems since the COVID-19 pandemic show that informatics successfully improves healthcare service quality while enhancing patient access to medical care and system efficiency. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Javeedullah, Mohammed (2024) Integrating Health Informatics into Modern Healthcare Systems: A Comprehensive Review. Global Journal of Universal Studies, 1 (1). pp. 1-21. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/21

oai:repository.globalsciencepress.com:64 2026-09-11T06:43:47Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/64/ The Convergence of AI and Medicine: Progress, Constraints, and Future Possibilities Malik, Yasir Ghayor Healthcare experiences a transformative shift because of Artificial Intelligence (AI) medical integration which improves diagnosis processes and enables better treatment strategies as well as patient care administration. Medical decision-making benefits from AI-driven advancements including robotic surgery together with virtual health assistance and predictive analytics which enhance efficiency and accuracy. The adoption of AI encounters important hurdles because it triggers privacy dangers to patient data and builds barriers to regulation while being complicated to integrate and subject to ethical doubts about AI systems. The exceptional power of AI computers needs human specialists to handle decisions based on ethics while delivering empathy and grasping the nature of situations. AI technology will find its best application point in medicine through combining forces with healthcare professionals rather than offering their position. Achieving responsible AI implementation demands solutions for bias-related problems and security measures and accountability systems and doctor-patient relationship maintenance. Medical professionals can reach patient-centered and safer healthcare while achieving more efficiency by implementing ethical AI practices and using AI as a supportive tool to provide equitable benefits to every individual. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Malik, Yasir Ghayor (2024) The Convergence of AI and Medicine: Progress, Constraints, and Future Possibilities. Global Journal of Universal Studies, 1 (1). pp. 22-47. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/22

oai:repository.globalsciencepress.com:65 2026-09-11T06:43:47Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/65/ Cognitive Computing in Healthcare: Past Lessons, Present Realities, and Future Horizons Malik, Yasir Ghayor The healthcare sector experiences transformation through cognitive computing which provides improved diagnosis methods and designs treatment plans as well as cares for patients and operates medical facilities. Modern medical decision-making has experienced a revolution through both early expert systems and current AI-driven diagnostics together with robotic surgery and precise medical treatments. The vast potential requires solution of privacy issues with datasets together with algorithmic discrimination prevention and adaptation of relevant regulations and resolution of ethical problems. AI healthcare development will focus on three main areas which involve predictive knowledge analysis and real-time illness prevention along with sophisticated drug innovation processes to deliver patient-specific proactive medical services in the future. The introduction of AI must meet both ethical requirements and legal responsibilities in order to gain public acceptance because patient rights and AI clarity and cybersecurity protection matter most. A complete adoption of cognitive computing requires global collaboration between healthcare professionals and strong regulatory frameworks as well as AI literacy for medical staff and cognitive computing implementation methods. A responsible implementation of AI healthcare will generate an enhanced system that provides more precise and efficient patient-focused medical treatment to enhance global health results. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Malik, Yasir Ghayor (2024) Cognitive Computing in Healthcare: Past Lessons, Present Realities, and Future Horizons. Global Journal of Universal Studies, 1 (1). pp. 48-69. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/23

oai:repository.globalsciencepress.com:66 2026-09-11T06:43:47Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/66/ The Role of Bioinformatics in Genetic Improvement and Tree Breeding for Sustainable Forestry Asif, Muhammad Raza, Zeshan Haider Munir, Akhtar Wang, Mengyuan Biological approach helps enhance tree breeding and genetic improvement through innovative analytical methods which sustain forest practices and increase their resistance rates. The application of genomic sequencing methods along with genetic editing techniques and genomic selection under bioinformatics control allows development of improved tree species with enhanced resistant features and growth dynamics and environmental tolerance. The new technologies play an essential role in solving difficulties which stem from climate change and biodiversity loss and ecosystem degradation. Bioinformatics allows forest managers to preserve tree genetic resources while creating sustainable climate-friendly forest practice methods. The review presents crucial uses of bioinformatics for forestry and outlines its developmental issues together with future possibility horizons while demonstrating worldwide exemplary cases. Bioinformatics technology will continue advancing tree breeding operations through precise breeding methods combined with improvements in forest resistance and global sustainable forest management and climate change reduction initiatives. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Asif, Muhammad and Raza, Zeshan Haider and Munir, Akhtar and Wang, Mengyuan (2024) The Role of Bioinformatics in Genetic Improvement and Tree Breeding for Sustainable Forestry. Global Journal of Universal Studies, 1 (1). pp. 70-100. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/24

oai:repository.globalsciencepress.com:67 2026-09-11T06:43:47Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/67/ Navigating New Frontiers in Poultry Health and Productivity Jamal, Ahmad The world depends on poultry farming as a foundation of food security but this sector battles with infections together with developing antimicrobial resistances while being affected by environmental problems. This paper assesses modern techniques for improving poultry wellness alongside techniques for sustainable production development. The poultry industry advances through new vaccine development together with probiotic implementation along with AI-based automatic farm monitoring systems. Genetic progress combined with optimized feeding methods accelerate the development of animals and their ability to resist illnesses. Keeping biosecurity standards and using sustainable practices in combination with reduced antibiotic use will sustain long-term productivity. Through innovation adoption the poultry sector can fulfill worldwide consumption needs by maintaining proper care of animals together with respect for ecological principles. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Jamal, Ahmad (2024) Navigating New Frontiers in Poultry Health and Productivity. Global Journal of Universal Studies, 1 (1). pp. 101-113. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/25

oai:repository.globalsciencepress.com:68 2026-09-11T06:43:47Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/68/ AI in Healthcare and Beyond: Advanced Approaches for Disease Detection and Management Edison, George Advanced data analysis done by Artificial Intelligence is now transforming healthcare and supporting animal health. Using a range of data such as sensor measurements, videos and behavior observations, it gives machine learning and computer vision the ability to notice diseases ahead of time. Using data from the past and the present, predictive analytics can foresee outbreaks and so help develop better prevention plans. Although problems like data quality, access and following regulations are not fully solved, these AI-driven solutions are expected to considerably boost health results for members of all species. Here, we point out AI technologies play a wide role and can be used to manage diseases in ways that go beyond the standard healthcare system—including monitoring poultry diseases and supporting conservation efforts for endangered species. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Edison, George (2024) AI in Healthcare and Beyond: Advanced Approaches for Disease Detection and Management. Global Journal of Universal Studies, 1 (1). pp. 114-133. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/26

oai:repository.globalsciencepress.com:69 2026-09-14T04:40:27Z

oai:repository.globalsciencepress.com:70 2026-09-11T06:43:47Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/70/ Ethnobotanical Survey of Common Medicinal Plants Used by People for the Treatment of COVID-19, in Faisalabad, Pakistan Akram, Muhammad Ahsan, Marium Ibrahim, Hamza Khalifa Saed, Abdulfatah This herb-rich plant has long been used to treat a variety of ailments. Since the spread of COVID-19 and death cases are constantly increasing, which prompted the Health and Food Administration to recommend taking drugs that inhibit viral activity, in addition to the World Health Organization suggesting the need to rely on alternative medicine, especially those medicinal plants that have antiviral properties. The goal of this study is to collect ethnic data that members of the Pakistani Faisal community may utilize to cure certain ailments. Method: Traditional herbalists and adults provided ethnic data. The pharmacy at Faisalabad University School of Medicine received the form letters gathered in this investigation. This research now includes 70 different tree species from 39 different families. Of the 12 species, the Rosaceaefamily has been discovered to be the most popular, and the most part of plants is more effective is leaves. Conclusion: The herbal business, herbalists, and export authorities for this therapeutic plant benefit from this research. This dissertation contains critical information for future phytochemical research that may be required to keep this crop alive. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Akram, Muhammad and Ahsan, Marium and Ibrahim, Hamza Khalifa and Saed, Abdulfatah (2024) Ethnobotanical Survey of Common Medicinal Plants Used by People for the Treatment of COVID-19, in Faisalabad, Pakistan. Global Journal of Universal Studies, 1 (1). pp. 154-172. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/28

oai:repository.globalsciencepress.com:71 2026-10-07T09:23:45Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/71/ Empowering Patients through Health Informatics: Trends, Challenges, and Opportunities Javeedullah, Mohammed By granting access to health records, empowering patients through connection and strining to improve health outcomes, health informatics plays a vital role. This article examines the growth of patient empowerment in healthcare, showing how important technologies such as electronic health records (EHRs), mobile health (mHealth) apps, wearable devices and telemedicine platforms have become. They allow patients to handle their health, talk with doctors more openly and make the right choices. Even though the advantages exist, digital inequalities, worries about private data and compatibility issues still need attention for proper and equal healthcare. New trends, for example, precision medicine, blockchain and artificial intelligence, may lead to better personalized healthcare and more choices for patients. As a result, health informatics helps rebuild the patient-provider partnership, opening doors for more personal, efficient and patient-friendly healthcare systems. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/71/1/article.pdf Javeedullah, Mohammed (2024) Empowering Patients through Health Informatics: Trends, Challenges, and Opportunities. Global Journal of Universal Studies, 1 (1). pp. 173-189. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/29

oai:repository.globalsciencepress.com:72 2026-09-11T06:43:48Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/72/ Big Data and Cloud Computing: A Review of Paradigms Transforming Computer Science singh, Ankur In this review, the author discusses the revolutionary conjunction between two emerging phenomena: Big Data and Cloud Computing, which, overturning the traditional paradigm in Computer Science, transform the outlook in the field. Big Data now allows us to process and analyses huge, varied volumes of data, and Cloud Computing can offer the scalable, on-demand infrastructure on which these can run. Combined them enable real-time analytics, AI applications, intelligent decisions, and smart city applications across industries including healthcare, finance, retail and real estate. The paper will explore some of the concepts on which it is based, how it has been integrated into technology, practical examples, advantages and some of the emerging trends like edge computing and server less architectures. It also deals with the major concerns relating to it implying the data privacy, security, vendor lock-in, and ethical issues. Finally, this review points out the synergy of the Big Data and the Cloud that is shaping a new age in innovation and power of computing. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed singh, Ankur (2024) Big Data and Cloud Computing: A Review of Paradigms Transforming Computer Science. Global Journal of Universal Studies, 1 (1). pp. 190-213. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/30

oai:repository.globalsciencepress.com:73 2026-09-11T06:43:48Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/73/ Catalysis and Mechanistic Insights in Contemporary Organic Chemistry Sayed, Javeriya The current state of organic chemistry is based on the principles of catalysis and mechanistic knowledge, which allow performing efficient, selective, and sustainable transformations. In this review, recent progress in transition metal catalysis, organocatalysis, photoredox catalysis, and bio-inspired systems is highlighted, with a focus on how transition metal catalysis, organocatalysis, photoredox catalysis, and bio-inspired systems are guided by mechanistic knowledge in reaction design, optimization, and substrate scope. Kinetic analysis, spectroscopy, and computational modeling techniques are important in order to gain critical information about intermediates, transition states, and reaction pathways; hence, to develop rational catalysts. The issues of catalyst stability, mechanistic complexity and scalability are addressed, as well as the new trends in AI-driven prediction and green chemistry. Combining catalysis with mechanistic information is still broadening the horizons of present-day organic synthesis. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Sayed, Javeriya (2024) Catalysis and Mechanistic Insights in Contemporary Organic Chemistry. Global Journal of Universal Studies, 1 (1). pp. 214-233. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/31

oai:repository.globalsciencepress.com:74 2026-09-11T06:43:48Z 7375626A656374733D41:4149 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/74/ Real-Time Data Analytics: Applications and Challenges in Smart Cities Raza, Hassan Erdenetsogt, Tsendayush Farooq, Mazhar Kabeer, Muhammad Mohsin Aslam, Muhammad Shahrukh AI Indexes (General) Smart cities have adopted real-time data analytics making them a critical component in the cities dynamic monitoring, sound decision-making, and effective management of urban services. Using information gathered by the IoT devices, sensors, social media, and infrastructure systems, cities are able to streamline how they transport, power, provide healthcare, govern, monitor their environment, and ensure their safety. This review examines some of the main concepts, technologies, and structures that support real-time analytics, brings out major applications, challenges associated with the volume of data, privacy, scalability, and interoperability. The trends in the future are focused on AI integration, edge computing, standardization, and sustainability. Eventually, real-time analytics facilitates the creation of efficient, responsive and sustainable cities. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Raza, Hassan and Erdenetsogt, Tsendayush and Farooq, Mazhar and Kabeer, Muhammad Mohsin and Aslam, Muhammad Shahrukh (2024) Real-Time Data Analytics: Applications and Challenges in Smart Cities. Global Journal of Universal Studies, 1 (1). pp. 1-30. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/32

oai:repository.globalsciencepress.com:75 2026-09-11T06:43:48Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/75/ Federated and Edge Learning for Real-Time Data Analytics in Clinical Environments Jamal, Mehtab Singh, A The new machine learning paradigms that allow privacy-guaranteed real-time data analytics in clinical settings are federated and edge learning. As electronic health records, wearable devices, and Internet of Things continue to multiply the volume of healthcare data, the conventional centralized data management methods are under a challenge due to privacy, latency, and scaling. In federated learning, collaborative model training is possible without exchanging raw patient information, whereas edge learning has the data processed on devices in real-time to provide a faster response. Their integration enables efficient, secure and scalable healthcare systems to be used in applications like remote monitoring, in ICUs and in medical diagnostics. These technologies also have issues such as communication overhead and resource constraints, however, it does not mean that they lack promise of improved patient care. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Jamal, Mehtab and Singh, A (2024) Federated and Edge Learning for Real-Time Data Analytics in Clinical Environments. Global Journal of Universal Studies, 1 (1). pp. 246-272. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/33

oai:repository.globalsciencepress.com:76 2026-09-11T06:43:48Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/76/ Deep Learning Applications in Medicine: Enhancing Diagnosis and Patient Outcomes Jamal, Mehtab Deep learning has transformed the modern medicine to accurately diagnose, treat patients with uniqueness, and provide better patient outcomes. With the use of big data in medicine, imaging, electronic health record and genomic data, deep learning models identify subtle features, disease progression, and aids in clinical decision-making. The uses can include medical imaging and pathology as well as predictive analytics, remote monitoring, and AI-assisted drug discovery. Although issues like data privacy, model interpretability, and algorithmic biasness persist, such new approaches as explainable AI, federated learning, and IoT integration have greater opportunities to be safe and effective to adopt. Deep learning is building a future in the field of accuracy, effectiveness, and patient-centric care. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Jamal, Mehtab (2024) Deep Learning Applications in Medicine: Enhancing Diagnosis and Patient Outcomes. Global Journal of Universal Studies, 1 (1). pp. 31-52. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/34

oai:repository.globalsciencepress.com:77 2026-09-11T06:43:48Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/77/ Role of Artificial Intelligence in Modern Data Science:  A Critical Review Oad, Surdesh Kumar The artificial intelligence (AI) has been used as a revolutionary technology in contemporary data science, which has improved capacity to process enormous and sophisticated data, forecasting and automation. Thse conceptual underpinnings, historical development, and the most important AI methods, including machine learning, deep learning, natural language processing, and computer vision, which enable data science are reviewed. It also discusses its use in practical healthcare, finance, marketing, transportation, and smart cities, and identifies such advantages as efficiency, predictive accuracy and automation. Challenges, such as data quality, bias, interpretability, and ethical considerations are also mentioned, and the future trends addressed are AutoML, explainable AI, and edge computing, which would allow a responsible and innovative future. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Oad, Surdesh Kumar (2024) Role of Artificial Intelligence in Modern Data Science: A Critical Review. Global Journal of Universal Studies, 1 (1). pp. 53-76. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/35

oai:repository.globalsciencepress.com:78 2026-09-11T06:43:48Z 7375626A656374733D41:4149 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/78/ Artificial Intelligence in Data Analytics: A Comprehensive Review Oad, Vidya Devi AI Indexes (General) Data analytics have become a strong, smart, and automatic field with the help of AI. In this review, the author discusses the development of data analytics since their traditional ways of operation to AI-based systems, including machine learning, deep learning, and natural language processing. It covers key AI methods, industry applications, tools and technologies, advantages, issues and future trends. AI boosts precision, real-time processing, and decision-making and facilitates predictive and prescriptive insights. Although issues such as the privacy of data, bias, and computational complexity remain, AI is proceeding at a very fast pace. All in all, AI is transforming data analytics to become more efficient, scalable, and intelligent. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Oad, Vidya Devi (2024) Artificial Intelligence in Data Analytics: A Comprehensive Review. Global Journal of Universal Studies, 1 (1). pp. 77-104. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/36

oai:repository.globalsciencepress.com:79 2026-09-11T06:43:48Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/79/ Quality Control and Process Validation in Medical Device Manufacturing Kunnipurayil, Salman Mohammed, Mannan Medical device manufacturers must incorporate quality control and process validation as a part of the process, which guarantees the safety of the product, its reliability, and regulatory compliance. Quality control aims at identifying defects by inspection and testing and process validation comes up with documented evidence that the manufacturing processes continuously produce devices within specifications that have been defined. Collectively, they create a system of quality that is integrated and reduces the risks and improves the consistency of products. This review identifies regulatory frameworks, methodologies, tools, challenges, and new trends of these practices. As the complexity of devices increases, and global regulations become stricter, implementing new technologies and lifecycle-oriented approach is essential to continue upholding high-quality standards and enhancing patient safety outcomes. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Kunnipurayil, Salman and Mohammed, Mannan (2024) Quality Control and Process Validation in Medical Device Manufacturing. Global Journal of Universal Studies, 1 (1). pp. 105-129. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/37

oai:repository.globalsciencepress.com:80 2026-09-11T06:43:48Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/80/ Deep Learning Applications in Power Systems: A Review Study Khan, Fawad This review paper discusses the application of deep learning in modern power systems, and how it can be used to enhance forecasting, fault detection, stability analysis, and renewable energy integration. It addresses some of the basic concepts of deep learning and power system structure, then goes on to discuss some of the key techniques: CNNs, RNNs, LSTMs, and reinforcement learning. The study compares against the existing research showing that deep learning has superior performance over traditional methods but also indicates that deep learning has several challenges such as high cost of computation, dependence on data, and lack of interpretability. The directions in the future are smart grid integration, explainable AI, and hybrid models. Altogether, deep learning is a promising technology to improve the efficiency, reliability, and intelligence of power systems. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Khan, Fawad (2024) Deep Learning Applications in Power Systems: A Review Study. Global Journal of Universal Studies, 1 (1). pp. 130-151. ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/38

oai:repository.globalsciencepress.com:81 2026-09-11T06:43:48Z 7375626A656374733D41:4149 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/81/ Artificial Intelligence in Electrical Power Systems: A Comprehensive Review of Machine Learning Techniques and Applications Khan, Fawad AI Indexes (General) The electrical power system is undergoing a significant transformation through the use of Artificial Intelligence (AI) and Machine Learning (ML) for advanced forecasting, monitoring, optimization, fault detection and intelligent control of electrical power systems. It reviews the broadly used ML methods such as regression, decision trees, support vector machines, neural networks, deep learning, ensemble methods, and reinforcement learning, and their implementations in the field of load forecasting, renewable energy integration, stability assessment, predictive maintenance, and smart grids. The review also addresses issues of data quality, computational complexity, cybersecurity, interpretability and model generalization. The future directions of intelligent power systems are highlighted to be promising with emerging approaches such as explainable AI, physics-informed learning, federated learning, digital twins, edge AI and autonomous operation. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed Khan, Fawad (2024) Artificial Intelligence in Electrical Power Systems: A Comprehensive Review of Machine Learning Techniques and Applications. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/39

oai:repository.globalsciencepress.com:82 2026-09-11T07:10:11Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/82/ Combined imaging and molecular techniques for evaluating microbial function and composition Sharif, Javeria Bilal, Uzma Fatima, Hafiza Rida Muazzam, Abia Perveen, Aqsa Naveed, Areesha Saeed, Hafiza Arshi Haidar, Ayesha Atique, Rameen Muazzam, Ayesha Microorganisms take key positions among ecological communities, supplying biogeochemical activities such as nutrient circulations and environmental pollution decomposition and ensuring ecosystem stability. The usual ways of studying microbial communities use cultivation techniques, which are a hindrance to our grasp of almost all microorganisms unable to grow in the controlled conditions of the laboratory. This review is meant to summarize the current development in imaging and molecular techniques for assessing microbe communities.  The combined use of molecular and imaging techniques for microbial analysis was assessed through a review of literature reporting studies that employed this approach. The review suggested that there is a broad range of techniques that apply both imagery and molecular characteristics for microbial analysis which include fluorescence in situ hybridization (FISH), confocal laser scanning microscopy (CLSM), metagenomics, metatranscriptomics and metaproteomics. The applied combined imaging and molecular techniques represent an effective way of understanding microbial function and structure. By combining microscopic observation with molecular analysis, researchers can obtain significant information on the structure-function relationships of microbial communities and their interactions with the surroundings. These technologies are of great importance in solving the crucial issues of environmental surveillance, biodegradation, and human health. On the other hand, future technological progress and interdisciplinary cooperation are the key factors that will let us fully unleash the potential of these approaches and transform the findings of basic research into practical applications. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/82/1/article.pdf Sharif, Javeria and Bilal, Uzma and Fatima, Hafiza Rida and Muazzam, Abia and Perveen, Aqsa and Naveed, Areesha and Saeed, Hafiza Arshi and Haidar, Ayesha and Atique, Rameen and Muazzam, Ayesha (2024) Combined imaging and molecular techniques for evaluating microbial function and composition. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/1

oai:repository.globalsciencepress.com:83 2026-09-11T07:10:12Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/83/ Brief overview of Coronavirus Sharif, Javeria Atique, Rameen Naveed, Areesha Saeed, Hafiza Arshi Anwar, Bushra Rana, Tehreem Kausar, Sana Talib, Ambreen Shah, Rabbya Rayan Haidar, Ayesha A worldwide health emergency, the coronavirus illness (COVID-19) is having a profound effect on economies, society, and healthcare systems. The virus, which causes the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was initially discovered in Wuhan, China, towards the end of 2019. It can cause everything from minor respiratory problems to severe pneumonia, and it frequently kills people, especially in vulnerable groups. Since respiratory droplets are the main way that the virus spreads, social distance and immunization are crucial protective measures. The epidemic has brought to light the shortcomings in public health infrastructures and the need for quick and open information sharing, underscoring the need for a coordinated worldwide response to infectious illnesses. The socioeconomic impact of COVID-19 is demonstrated by the disruptions to everyday routines, high rates of unemployment, and demands placed on healthcare services. Travel bans and quarantines have accelerated the trends in online learning, telemedicine, and remote work while also posing challenges to certain industries and aggravating social inequality. The quick thinking of the scientific community has been essential in comprehending the virus, producing diagnostic instruments, and designing vaccines. The situation highlights the need for improved global collaboration, strong healthcare systems, and efficient pandemic response plans. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/83/1/article.pdf Sharif, Javeria and Atique, Rameen and Naveed, Areesha and Saeed, Hafiza Arshi and Anwar, Bushra and Rana, Tehreem and Kausar, Sana and Talib, Ambreen and Shah, Rabbya Rayan and Haidar, Ayesha (2024) Brief overview of Coronavirus. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/2

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https://repository.globalsciencepress.com/id/eprint/84/ Unravelling the Mysteries of Zika Virus: A Comprehensive Overview Shah, Rabbya Rayan Talib, Ambreen Ferdos, Tehreem Akmal, Huda Yamin, Hamna Zahra, Maryam Nadeem, Ayesha Anwar, Bushra Muazzam, Abia hameed, Fatima Samad, Abdul The Zika virus (ZIKV), which emerged in an epidemic state in 2016, poses a serious risk to the health of society. It could appear as a serious condition with signs such as migraine and a high body temperature or it can appear benign. Aedes species of mosquitoes (family Flaviviridae, genus Flavivirus) are responsible for the transmission of ZIKA virus to humans. Even though over half of civilization lives in areas where Aedes aegypti is common, there is still concern about the possibility of its revival. Global accounts of Zika virus infections have recently been made, especially from America; French Polynesia and other Pacific Island nations; and south-East Asia. ZIKV was first discovered in 1947 in the Ugandan Zika Forest, in a rhesus monkey. Since the beginning of discoveries in Africa and Asia in 2007, the virus expanded around every continent.  A serious infection frequently results in a short-term febrile illness, but recent evidence reveals that it may also be linked to additional important adverse effects like microcephaly in infants and Guillain-Barré syndrome. This review provides knowledge about the pathophysiology of ZIKV, reveals essential cellular processes that develop during infections, and recalls the origins of the virus from when it was first discovered to its current worldwide propagation. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/84/1/article.pdf Shah, Rabbya Rayan and Talib, Ambreen and Ferdos, Tehreem and Akmal, Huda and Yamin, Hamna and Zahra, Maryam and Nadeem, Ayesha and Anwar, Bushra and Muazzam, Abia and hameed, Fatima and Samad, Abdul (2024) Unravelling the Mysteries of Zika Virus: A Comprehensive Overview. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/3

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https://repository.globalsciencepress.com/id/eprint/85/ Interactions between Gut Microbiota and Nanoparticles: Implications for Health and Disease Bilal, Uzma Shah, Rabbya Rayan Bilal, Bushra hameed, Fatima Talib, Ambreen Ijaz, Amara Saeed, Hafiza Arshi Fatima, Hafiza Rida Zahra, Maryam Muazzam, Ayesha Many bacteria share a healthy human body, generating natural flora. According to many predictions, bacterial genetic material is much more important in the human body than a certain amount of DNA. Any modification in the composition of this flora could result in a wide range of disorders, considering that it plays a crucial role in human health and illness. In many different sectors, nanoparticles are frequently used: industry, food, and platforms for the transportation of pharmaceutical products in the medical domain. Different industries, fields, and occupations, such as pharmaceutical products, cosmetics, and medicines, widely use nanoparticles. Nanoparticles could interact with the human body in multiple ways at various stages because of their many potential applications. Due to the shape, type, and concentration of nanoparticles, their interaction can cause the microbiota to become unstable, regenerate themselves, or show no harmful effects. Additionally, the microbiota can be controlled or some disorders can be treated with nanoparticles. Nanoparticles may also be used as a vehicle to regulate the microbiota or to treat some of its diseases. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/85/1/article.pdf Bilal, Uzma and Shah, Rabbya Rayan and Bilal, Bushra and hameed, Fatima and Talib, Ambreen and Ijaz, Amara and Saeed, Hafiza Arshi and Fatima, Hafiza Rida and Zahra, Maryam and Muazzam, Ayesha (2024) Interactions between Gut Microbiota and Nanoparticles: Implications for Health and Disease. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/4

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https://repository.globalsciencepress.com/id/eprint/86/ Host-parasite Interactions; From Co-evolutionary Changes to Genomic Insights Atique, Rameen Saeed, Hafiza Arshi Haidar, Ayesha Shareef, Javeria Naveed, Areesha Nadeem, Ayesha Shahzadi, Iram Kausar, Sana Ijaz, Amara Jamal, Ahmad Host-parasite interaction belongs to the association between two biologically different organisms in which the host delivers food and shelter for the parasite and in return the parasite reproduces itself in the host body. There are many kinds of host-parasite relationships including parasitism, mutualism, and commensalism. The potent interaction between the host and parasite is regulated by the evolutionary changes over space and time. Host-parasite interconnection is the basic feature of the maintenance of ecology and biodiversity. The association between the host and parasite establishes an equilibrium in which the host unfolds protective strategies against parasites and the parasites develop mechanisms to elude the host’s defenses and spread infections. This dynamic interaction between the host and parasite mainly depends on the host's immune system, the severity of infection, the load of parasites in the body, and the ecological requirements. Moreover, environmental conditions, locomotion, and genetic factors of the host also influence host-parasite interactions. The pathogenicity of parasites is determined by the sensitivity and resistivity of the host's immune response. As parasites evolve, they employ diverse strategies to manipulate the host’s behavior and adapt to changing atmospheres. The education of the host-parasite association is essential for proper control and surveillance of parasitic diseases. For the prevention of parasitic infections, different therapeutic measures have been devised. Researchers are performing many types of procedures to sustain the ecosystem and fitness of individuals using host-parasite interactions. The purpose of this review was to enhance the importance of the host-parasite interaction in our environment. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/86/1/article.pdf Atique, Rameen and Saeed, Hafiza Arshi and Haidar, Ayesha and Shareef, Javeria and Naveed, Areesha and Nadeem, Ayesha and Shahzadi, Iram and Kausar, Sana and Ijaz, Amara and Jamal, Ahmad (2024) Host-parasite Interactions; From Co-evolutionary Changes to Genomic Insights. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/5

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https://repository.globalsciencepress.com/id/eprint/87/ Renewable Energy Technologies: Present Patterns and Upcoming Paths in Ecological Power Production Lodhi, Shahrukh Khan Gill, Ahmad Yousaf Hussain, Ibrar In response to the increasing need for environmentally friendly and sustainable power sources, renewable energy technologies are developing quickly. This thorough analysis looks at the present and potential trends in a number of renewable energy areas, such as hydropower, geothermal, and biomass energy. Emerging as a flexible and sustainable energy source made from organic materials is biomass energy. Anaerobic digestion, pyrolysis, and gasification have all been improved by recent advances in biomass technology, increasing their effectiveness and enabling new uses. While biomass energy finds a variety of applications in power generation, transportation, heating, and industrial processes, sustainability concerns are being addressed by advanced approaches in feedstock management and lifetime emissions assessment. The internal heat of the Earth is captured by geothermal energy, which offers a steady and dependable power source. Geothermal heat pumps, better drilling methods, and upgraded geothermal systems (EGS) are examples of technological developments. Every one of these solutions helps to lower expenses and increase efficiency. Heat pumps, power plants, and direct use systems are the three types of geothermal systems; each has advantages and uses in particular. The global spread of geothermal energy is being facilitated by technological breakthroughs and regulations that promote it, addressing issues including resource exploration, environmental effect, and economic hurdles. Although new developments have redefined its place in contemporary energy systems, hydropower is still a key component of renewable energy sources. Technological advancements in turbines, such as hydrokinetic and fish-friendly models, are improving performance and lessening their negative environmental effects. Micro- and small-scale hydropower plants are expanding the versatility and accessibility of hydropower. These technologies are essential to developing a robust and diverse energy mix that supports sustainable development and climate goals as the global energy landscape moves toward cleaner and more sustainable solutions. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/87/1/article.pdf Lodhi, Shahrukh Khan and Gill, Ahmad Yousaf and Hussain, Ibrar (2024) Renewable Energy Technologies: Present Patterns and Upcoming Paths in Ecological Power Production. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/6

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https://repository.globalsciencepress.com/id/eprint/88/ Under the titles for Risk Assessment, Pricing, and Claims Management, write Modern Analytics Valli, Latha Narayanan AI Indexes (General) ML Literature of music Modern analytics has become a revolutionary force in the insurance sector, redefining established methods and spurring innovation in all facets of the business in the ever-changing landscape. This abstract offers a thorough analysis of the function and implications of contemporary analytics in the insurance industry, with particular attention to how it is applied to risk assessment, pricing schemes, claims handling, and client involvement. With the help of cutting-edge algorithms, enormous data sets, and newly developed technology, insurers can now make better judgments, increase operational effectiveness, and provide individualized customer experiences. Insurance companies can improve client interactions, expedite workflows for claims management, establish dynamic pricing models, and improve risk assessment processes by utilizing AI-powered tools, predictive modelling, and machine learning. Modern analytics adoption in the insurance industry is not without its difficulties, though. To fully utilize modern analytics, insurers must traverse a number of important issues, including data protection and security, regulatory compliance, talent recruiting, and ethical considerations. The application of contemporary analytics in the insurance industry has a bright future ahead of it. Block chain, quantum computing, and augmented analytics are examples of emerging technologies that present insurers with new chances to spur innovation, reduce risk, and add value for stakeholders and clients. The path towards contemporary analytics in the insurance industry is demanding and fulfilling. In an industry that is changing quickly, insurers may achieve new heights of effectiveness, efficiency, and competitiveness by embracing innovation, teamwork, and customer-centricity. The future of modern analytics in insurance is bright, promising, and full of opportunity for those who dare to innovate and lead the path forward as we set out on this adventure together. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/88/1/article.pdf Valli, Latha Narayanan (2024) Under the titles for Risk Assessment, Pricing, and Claims Management, write Modern Analytics. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/7

oai:repository.globalsciencepress.com:89 2026-09-11T07:10:13Z 7375626A656374733D41:4149 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/89/ AI-Powered Healthcare Reform: Lessons from Innovations in the Petroleum Industry and Fraud Detection Methods Fahad, Muhammad Ibrar, Muhammad Qayyum, Muhammad Umer Husnain, Ali AI Indexes (General) By improving predictive analytics, personalized medicine, diagnostics, and operational efficiency, artificial intelligence (AI) has the potential to revolutionize the healthcare industry. This review examines how AI can be applied to key healthcare challenges like fraud detection, data management, and predictive maintenance by drawing on innovations in the financial and petroleum sectors. AI has a wide range of applications in the healthcare industry, from real-time patient monitoring and personalized treatment plans to advanced medical imaging and operational optimization. However, despite these advantages, there are a number of technical and ethical issues that need to be resolved, such as managing algorithmic bias, ensuring data quality and integration, and protecting patient privacy and data security. Additionally, ethical issues pertaining to informed consent, transparency, and equity need to be carefully considered in order to uphold justice and trust. Similar to how fraud detection techniques from the financial sector can improve the identification and prevention of fraudulent activities in healthcare billing and claims processing, lessons from the petroleum industry can be applied to improve the reliability and efficiency of medical equipment and healthcare operations. Examples of these lessons include predictive maintenance and operational optimization. The integration of artificial intelligence (AI) in healthcare offers substantial opportunities for advancement but also poses significant challenges that must be addressed. Healthcare organizations can fully realize the potential of AI to improve patient care, operational efficiency, and system integrity by addressing both technical and ethical issues and leveraging cross-industry insights. The path forward involves a commitment to innovation, ethical practices, and interdisciplinary collaboration to ensure that AI benefits are distributed fairly and effectively across the healthcare system. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/89/1/article.pdf Fahad, Muhammad and Ibrar, Muhammad and Qayyum, Muhammad Umer and Husnain, Ali (2024) AI-Powered Healthcare Reform: Lessons from Innovations in the Petroleum Industry and Fraud Detection Methods. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/8

oai:repository.globalsciencepress.com:90 2026-09-11T07:10:13Z 7375626A656374733D41:4149 7375626A656374733D4D:4D4C 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/90/ Integrating AI in Healthcare: Advancing Petroleum Fraud Detection and Enhancing Vaccine Development Qayyum, Muhammad Umer Fahad, Muhammad Ibrar, Muhammad Husnain, Ali AI Indexes (General) ML Literature of music Healthcare, the petroleum industry, and public health are just a few of the industries that artificial intelligence (AI) is revolutionizing with its cutting-edge solutions that promote efficiency and creativity. This article examines how AI is combining these many domains, with a special emphasis on healthcare, namely on vaccine research and delivery, and fraud detection in the petroleum sector. AI has completely changed vaccine development by speeding up the identification of strong candidates and streamlining clinical trials thanks to its rapid analysis of enormous volumes of data. Similar to this, AI has helped the petroleum industry by enhancing operational effectiveness and detecting fraud. The use of AI across industries demonstrates how it can promote cooperation and creativity across conventional industry boundaries. But integrating AI comes with a lot of work and ethical issues to work through, such protecting data privacy, dealing with algorithmic bias, and preserving openness in AI decision-making. These difficulties are crucial, particularly in industries like healthcare where judgments made using AI can significantly affect patient outcomes. The emphasis is on the necessity of strong legal frameworks and moral governance to manage these difficulties and guarantee the proper application of AI technologies. Artificial intelligence (AI) has the potential to revolutionize a wide range of industries, but it also comes with hazards and moral conundrums that need to be carefully managed. AI may be used to build a more just and sustainable future by tackling these issues through cooperation and moral behavior. This will advance both technical innovation and societal well-being. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/90/1/article.pdf Qayyum, Muhammad Umer and Fahad, Muhammad and Ibrar, Muhammad and Husnain, Ali (2024) Integrating AI in Healthcare: Advancing Petroleum Fraud Detection and Enhancing Vaccine Development. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/9

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https://repository.globalsciencepress.com/id/eprint/91/ Advancements and Best Practices in Data Loss Prevention: A Comprehensive Review Abid, Noman AI Indexes (General) DLP solutions are needed because there is more data and more reliance on it which people and businesses need. This paper will provide a further account of what DLP is, its significance, issues it encounters, and prospects in what follows from this article about DLP. Globalization and the rise in the kind and level of cyber threats, uptick in cloud use, and the enhancement of legal requirements for data protection have all moved DLP to higher prominence as a cybersecurity mechanism. In more detail, some challenges that are associated with DLP implementation and that refers to initiation and management, are described, such as data classification, security needs and organization productivity, insiders and their threats, regulation compliance, and others. The article also examines how data protection paradigms are revolutionized by AI, ML, cloud native DLP solutions, Zero Trust architecture, and XDR. Also, it underlines the need of privacy by design, and the controls concerning insider threats in the shift of the paradigm. As the type and functionality of DLP systems will further advance not in the distant future, utilization of such state of the art technologies shall assist organizations to secure data while the latter focus on continued operation. Therefore to meet this ever present need for such smart and dynamic DLP solutions to address data loss consideration in the multiple complex cloud environment there is need to rise new and complex cyber threats. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/91/1/article.pdf Abid, Noman (2024) Advancements and Best Practices in Data Loss Prevention: A Comprehensive Review. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/10

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https://repository.globalsciencepress.com/id/eprint/92/ AI in Clinical Practice: Current Uses and the Path Forward Sherani, Abdul Mannan Khan Khan, Murad AI Indexes (General) Machine learning is developing very quickly in the healthcare field in the field’s diagnostics, therapy and caring of patients and new drug development. Machine learning, deep learning, and so on have opened up new areas of diagnosis of diseases, understanding what particular treatment is required for a specific patient, and prognosis of disease. Radiology, Computer Aided Diagnosis and chronic disease applications are the current areas covered by AI to improve the health of patients and to hasten the speed of the health systems. Nevertheless, there are some of the issues that are associated with AI in clinical practice they include; Data privacy and protection, Issue of bias in machine learning algorithms, the black challenge. Some of the professional factors that are important when implementing artificial intelligence are: equity non –discrimination and patient engagement. Nonetheless, policies regarding AI in the healthcare context are still in their development process, and about responsibility or purse or assurance has been put forward, and it involves security. The future of AI in healthcare is consequently expected to continue evolution of precision medicine, enhance the speed of drug development, as well as enhance patient satisfaction through smart health including next generations of virtual assistants and telemedicine. For AI to be used in a way that optimally takes advantage of the potential that exists in significantly improving health care delivery as well as productivity, the technological, ethical, and regulatory factors that characterize the technology’s application need to be tamed cohesively to realize safe uses that are patient centered. Heated debates over the life of the next few decades are required to ensure great advancements in the future of AI in healthcare, but these advancements need to be managed carefully to safeguard the best interests of each patient/healthcare consumer and provider. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/92/1/article.pdf Sherani, Abdul Mannan Khan and Khan, Murad (2024) AI in Clinical Practice: Current Uses and the Path Forward. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/11

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https://repository.globalsciencepress.com/id/eprint/93/ Transformative AI Applications: Promising Value Faster Healthcare Development, Affording Petroleum Deception Suppression, Improving Cybersecurity, and Integrating Humanized Chatgpt for Conversational AI Ali, Mohammad In this field, the use of Artificial Intelligence (AI) is revolutionizing a number of industries bringing novel solutions that challenge conventional approaches to order. This review explores the significant impact of AI in five key sectors: healthcare, petroleum industry, credit card fraud, Cybersecurity, Chatgpt. AI is useful for improving diagnostic and clinical decisions and for making drug development and patient comparison faster. In the petroleum industry, by applying AI in Fraud management, companies are reducing their exposure to fraud, and increasing accountability, therefore reducing their frequency of finsancial loss and poor operational flow. In cybersecurity, AI is stepping up as the frontline technology to identify threats in real-time, analyze patterns, and respond proactively while organizations are fighting off more complex cyber threats. Furthermore, with advancements like Chatgpt, or AI tools it is clear that the dynamic of interaction between human and computer has also begun changing for the better generalizing human-computer-interaction for different contexts such as customer support, learning, and mental health. However, AI has its own issue such as data privacy, the ethical issues involved in adopting AI, issue of bias in data and risk of misuse of AI, all of which have to be taken into consideration. Thus, as the AI technologies develop individuals represent a great potential to positively progress and improve if the usage of AI technologies is reasonable and ethical. The focus of this review is on the value of integration AI in updating industries, potential vulnerabilities, and the role of future investigation, government control, and cross-discipline cooperation in enhancing positive outcomes of these technologies as well as the management of negative consequences. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/93/1/article.pdf Ali, Mohammad (2024) Transformative AI Applications: Promising Value Faster Healthcare Development, Affording Petroleum Deception Suppression, Improving Cybersecurity, and Integrating Humanized Chatgpt for Conversational AI. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/12

oai:repository.globalsciencepress.com:94 2026-09-11T07:10:13Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/94/ Transformative AI Applications in Healthcare, Petroleum, Fraud Detection, Cybersecurity, and Conversational AI: Advancing Industries Hussain, Ibrar AI is the most disruptive technology of the decade as it changes industries for the better by upgrading processes, increasing efficiencies, and introducing creativity in many industries. The liberties, in this case, are seen in health care where artificial intelligences is changing the ways diagnosis’s are done, treatment methods, drug discovery and many more. Some of the potential applications of AI in the petroleum industry include; Efficiency in the production of energy, issues to do with safety and even with those of maintenance with emphasis on prevention of future mishaps that may be costly or fatal. AI has also found its use in anti-fraud where it is applied to identify new financial crimes and encode machine learning models to adapt to their new schemes. Categorized under cyber security, Artificial Intelligence is strengthening the cybersecurity by identifying new threats, making accurate forecasts, and responding to threats proactively to protect sensitive data. A well-known example of conversational AI is the Chatgpt is one of the key applications of the AI industry. Chatgpt is disintermediating customer service, sales and internal communication by providing a highly personalized many turn contextually aware conversation. Making communications with customers faster, and more efficient and thus is a win-win for customers as well as the organization. It also enhances the issue of accessibility since it facilitates communication between different user groups. At the same time, when AI became more successful and extensively integrated into multiple company processes, it created new specific ethical concerns, such as data privacy, bias, and insufficient transparency. It is crucial to solve these problems to get the highest advantage from AI and do not cause distrust and misunderstanding. This current role of AI is set to grow in the future, across sectors, to deliver even higher levels of effectiveness, creativity and convenience, unlocking the era of a smarter connected world. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/94/1/article.pdf Hussain, Ibrar (2024) Transformative AI Applications in Healthcare, Petroleum, Fraud Detection, Cybersecurity, and Conversational AI: Advancing Industries. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/13

oai:repository.globalsciencepress.com:95 2026-09-11T07:10:13Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/95/ Investigation of Heat Transfer in Pipes Using Dimensionless Numbers Asif, Syed Muhammad This study investigates heat transfer in pipes, focusing on the calculation of three key dimensionless numbers to analyze thermal behavior. Although the experiment was conducted only once, measures were taken to ensure accuracy, and potential sources of error were minimized. Repeating the experiment multiple times and averaging the results would have further improved reliability by reducing the impact of anomalies. Despite this limitation, the primary objective of understanding heat transfer mechanisms through dimensionless numbers was successfully achieved, providing valuable insights into thermal processes. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/95/1/article.pdf Asif, Syed Muhammad (2024) Investigation of Heat Transfer in Pipes Using Dimensionless Numbers. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/14

oai:repository.globalsciencepress.com:96 2026-09-11T07:10:13Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/96/ Leveraging AI and Machine Learning to Predict and Prevent Sudden Cardiac Arrest in High-Risk Populations Shah, Heta Hemang Bacha, Ahmad Cardiac arrest claims a lot of lives, mainly among people with heart diseases, diabetes, or abnormal heartbeats. Using AI and ML helps us find people at risk of heart failure and helps them avoid experiencing this condition. The research review looks at what AI and ML methods currently do to forecast SCA and checks how well they work. The models work by using lots of collected health information - from medical records, user monitors, and genetic studies - to forecast SCA danger right when needed. AI technology with machine learning algorithms can study large patient data to spot unique patterns that show which people are more likely to develop SCA. These models show two abilities: they estimate chance of events happening and suggest what steps to take to reduce risks. The study looks at AI implementation issues, including keeping patient data confidential, connecting with how doctors work today, and making sure doctors understand how AI systems make decisions. The research stresses that professionals should make transparent how their AI decisions work. Robust artificial intelligence and machine learning tools can help healthcare improve patient care by spotting those at highest risk of dangerous heart failure sooner and lowering deaths linked to cardiac emergencies. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/96/1/article.pdf Shah, Heta Hemang and Bacha, Ahmad (2024) Leveraging AI and Machine Learning to Predict and Prevent Sudden Cardiac Arrest in High-Risk Populations. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/15

oai:repository.globalsciencepress.com:97 2026-09-11T07:10:14Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/97/ AI-Enhanced Liquid Biopsy: Advancements in Early Detection and Monitoring of Cancer through Blood-based Markers Bacha, Ahmad Shah, Heta Hemang The inclusion of AI technology into liquid biopsy brings us important new ways to find and track cancer. The non-invasive blood test called liquid biopsy shows great potential for early cancer detection and helps doctors track disease changes and create custom treatments. Machine learning algorithms in AI technology helps liquid biopsy find better biomarkers and detect cancer earlier while also monitoring patient health in real time. Advanced technologies find cancer-related genetic faults along with tumor markers and detect remaining disease cells accurately. Using advanced AI to solve quality and accuracy problems helps liquid biopsy systems transform how doctors treat cancer by identifying diseases sooner and giving better treatment plans for patients. These advancements help identify cancer-related genetic mutations, tumor markers, and minimal residual disease that may otherwise be missed. By addressing the challenges of data quality, algorithmic accuracy, and clinical integration, AI-enhanced liquid biopsy can transform cancer care, offering earlier detection, more precise monitoring, and tailored treatments for individual patients. This report explores latest advancements and explains how AI helps transform liquid biopsy use in medical oncology. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/97/1/article.pdf Bacha, Ahmad and Shah, Heta Hemang (2024) AI-Enhanced Liquid Biopsy: Advancements in Early Detection and Monitoring of Cancer through Blood-based Markers. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/16

oai:repository.globalsciencepress.com:98 2026-09-11T07:10:14Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/98/ AI-Driven Approaches to Overcoming Tumor Heterogeneity in Breast Cancer: Modelling Resistance and Therapy Outcomes Abid, Noman Neoaz, Nahid Amin, Mohammad Hasan Breast cancer is a highly heterogeneous disease consisting of distinct molecular subtypes and differential response to treatment. The major challenge associated with predicting treatment responses and overcoming resistance mechanisms in tumor heterogeneity (genetic, phenotypic, and molecular diversity of a single tumor) remains yet to be overcome. Now artificial intelligence (AI) and machine learning (ML) promise to help us address these challenges by modelling complex interactions within the tumor. Using large-scale genomic, transcriptomic, imaging and clinical data, AI can incorporate cancer heterogeneity by characterizing and identifying key drivers for cancer heterogeneity and predicting resistance mechanisms. They help to generate more accurate prediction of chemotherapy, targeted therapy, and immunotherapy treatment responses and it can pave the way for more personalized treatment strategy. But AI is also capable of monitoring tumor evolution over time, and uncovering real-time insights about how effective treatment is and if relapse is starting, early. While promising, practitioners have been slow to adopt AI models for clinical work, due to data quality, model interpretability and compatibility in the current healthcare workflow. In translating AI based tools for practice, these challenges will need to be addressed. In this paper, we discuss how AI may be used to target tumor heterogeneity, model resistance mechanisms and optimize treatment responses in breast cancer as well as presenting obstacles to clinical implementation of such methods. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/98/1/article.pdf Abid, Noman and Neoaz, Nahid and Amin, Mohammad Hasan (2024) AI-Driven Approaches to Overcoming Tumor Heterogeneity in Breast Cancer: Modelling Resistance and Therapy Outcomes. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/17

oai:repository.globalsciencepress.com:99 2026-09-11T07:10:14Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/99/ Understanding AI-Driven Cardiovascular Risk Prediction in Underserved Populations: Addressing Social Determinants of Health through Data Analytics Khan, Murad Sherani, Abdul Mannan Khan Cardiovascular disease (CVD) is a leading cause of morbidity and mortality internationally, and underserved populations bear an out sized burden of risk. In these communities, access to adequate healthcare, burdened by social determinants of health (SDOH) like socioeconomic status, education, and environmental factors, can pose a barrier to many individuals. The crucial role of Artificial Intelligence (AI) and Machine Learning (ML) to address problems of health disparity by leveraging large scale data to predict cardiovascular risks and impact appropriate interventions is discussed. The integration of diverse sources of information, such as electronic health records (EHR) social data, and behavioral data, allows AI models to identify at risk individuals even in settings with limited resources. In this paper, we investigate the use of AI driven cardiovascular risk prediction models in underserved populations and how they can consider the effect of SDOH. The paper also addresses ethical considerations, data challenges, and opportunities for use of these prediction tools in public health initiatives to address disparities in health. The potential of combining AI and data analytics is to enhance cardiovascular health outcomes, bridging gaps in equitable access to preventive care, especially for vulnerable communities. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/99/1/article.pdf Khan, Murad and Sherani, Abdul Mannan Khan (2024) Understanding AI-Driven Cardiovascular Risk Prediction in Underserved Populations: Addressing Social Determinants of Health through Data Analytics. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/18

oai:repository.globalsciencepress.com:100 2026-09-11T07:10:14Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/100/ AI in Product Management: Efficiency, Quality, and Innovation Kabeer, Muhammad Mohsin Artificial Intelligence (AI) is transforming the way products are managed, manufactured, and quality controlled by providing data-driven choices, enabling intelligent automation, and preliminary information. This is a review of the way AI helps in increasing efficiency, improving the quality of products and also in promoting innovation throughout the product lifecycle. AI is useful in product management in supporting market forecasting, optimization by design, and personalization. In the factory industry, it initiates intelligent factories, proactive maintenance, and cost-efficiency. AI can be used in quality management to ensure real-time monitoring, detection of defects, and compliance. Although there are advantages, namely the cost savings, agility and sustainability, there still are challenges associated with the development like data dependency, integration, workforce adaptation, and ethical concerns. The future trends point to generative AI, digital twins and sustainability. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/100/1/article.pdf Kabeer, Muhammad Mohsin (2024) AI in Product Management: Efficiency, Quality, and Innovation. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/19

oai:repository.globalsciencepress.com:101 2026-09-11T07:10:14Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/101/ Deep Learning for Multi-Modal Cancer Imaging: Integrating Radiomics, Genomics, and Clinical Data for Comprehensive Diagnosis and Prognosis Abid, Noman Neoaz, Nahid Amin, Mohammad Hasan At both the cellular and molecular levels, cancer is a heterogeneous disease. While traditional diagnostic and prognostic methods are often deficient in the ability to capture this complexity, limiting the potential to inform personalized treatment strategies. Deep learning (DL) results on multi-modal data sources have the potential to improve cancer diagnosis and prognosis by combining several types of data sources in the past few years: radiomics, genomics and clinical data. The first, radiomics, extracts quantitative features from medical images to reveal tumor characteristics invisible to the human eye; the second, genomic data, provides information about what genetic mutations are driving cancer progression. These modalities are complemented by clinical data with patient demographics and treatment history contributing context specific information to the data. The application of deep learning techniques on multi-modal cancer imaging is reviewed as well and their ability to combine radiomics, genomics and clinical data to improve diagnostic and prognostic accuracy is emphasized. We discuss the challenges and future directions for clinical implementation of this approach and its potential to transform cancer care, improve patient outcomes and empower precision medicine. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/101/1/article.pdf Abid, Noman and Neoaz, Nahid and Amin, Mohammad Hasan (2024) Deep Learning for Multi-Modal Cancer Imaging: Integrating Radiomics, Genomics, and Clinical Data for Comprehensive Diagnosis and Prognosis. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/20

oai:repository.globalsciencepress.com:102 2026-09-11T07:10:14Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/102/ Integrating Health Informatics into Modern Healthcare Systems: A Comprehensive Review Javeedullah, Mohammed The multidisciplinary field of health informatics uses IT, data analytics with healthcare expertise to optimize health data management for better care services. Health informatics applications within contemporary healthcare organizations drive streamlined clinical operations while delivering high-quality decisions that boost patient recovery results. A review examines the developmental history and elementary elements and connective approaches of health informatics as well as the accomplishments and difficulties with new developing technologies in this field. Operations and patient care through improvements have become attainable using four critical health informatics components which consist of Electronic Health Records (EHRs), Health Information Exchange (HIE), Clinical Decision Support Systems (CDSS) and telehealth. Successful integration of healthcare systems withstands various obstacles such as technology difficulties as well as staff member opposition and security issues and limited financial means. The achievement of operational excellence demands proper interoperability standards and complete training programs as well as strict adherence to privacy guidelines. Modern healthcare depends on emerging technologies which include Artificial Intelligence (AI), Machine Learning, the Internet of Medical Things (IoMT), block chain, and genomics to transform patient-specific care and predictive healthcare and enhance patient engagement. Real-life healthcare implementations at Kaiser Permanente alongside the NHS along with the enhanced use of telehealth systems since the COVID-19 pandemic show that informatics successfully improves healthcare service quality while enhancing patient access to medical care and system efficiency. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/102/1/article.pdf Javeedullah, Mohammed (2024) Integrating Health Informatics into Modern Healthcare Systems: A Comprehensive Review. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/21

oai:repository.globalsciencepress.com:103 2026-09-11T07:10:14Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/103/ The Convergence of AI and Medicine: Progress, Constraints, and Future Possibilities Malik, Yasir Ghayor Healthcare experiences a transformative shift because of Artificial Intelligence (AI) medical integration which improves diagnosis processes and enables better treatment strategies as well as patient care administration. Medical decision-making benefits from AI-driven advancements including robotic surgery together with virtual health assistance and predictive analytics which enhance efficiency and accuracy. The adoption of AI encounters important hurdles because it triggers privacy dangers to patient data and builds barriers to regulation while being complicated to integrate and subject to ethical doubts about AI systems. The exceptional power of AI computers needs human specialists to handle decisions based on ethics while delivering empathy and grasping the nature of situations. AI technology will find its best application point in medicine through combining forces with healthcare professionals rather than offering their position. Achieving responsible AI implementation demands solutions for bias-related problems and security measures and accountability systems and doctor-patient relationship maintenance. Medical professionals can reach patient-centered and safer healthcare while achieving more efficiency by implementing ethical AI practices and using AI as a supportive tool to provide equitable benefits to every individual. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/103/1/article.pdf Malik, Yasir Ghayor (2024) The Convergence of AI and Medicine: Progress, Constraints, and Future Possibilities. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/22

oai:repository.globalsciencepress.com:104 2026-09-11T07:10:14Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/104/ Cognitive Computing in Healthcare: Past Lessons, Present Realities, and Future Horizons Malik, Yasir Ghayor The healthcare sector experiences transformation through cognitive computing which provides improved diagnosis methods and designs treatment plans as well as cares for patients and operates medical facilities. Modern medical decision-making has experienced a revolution through both early expert systems and current AI-driven diagnostics together with robotic surgery and precise medical treatments. The vast potential requires solution of privacy issues with datasets together with algorithmic discrimination prevention and adaptation of relevant regulations and resolution of ethical problems. AI healthcare development will focus on three main areas which involve predictive knowledge analysis and real-time illness prevention along with sophisticated drug innovation processes to deliver patient-specific proactive medical services in the future. The introduction of AI must meet both ethical requirements and legal responsibilities in order to gain public acceptance because patient rights and AI clarity and cybersecurity protection matter most. A complete adoption of cognitive computing requires global collaboration between healthcare professionals and strong regulatory frameworks as well as AI literacy for medical staff and cognitive computing implementation methods. A responsible implementation of AI healthcare will generate an enhanced system that provides more precise and efficient patient-focused medical treatment to enhance global health results. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/104/1/article.pdf Malik, Yasir Ghayor (2024) Cognitive Computing in Healthcare: Past Lessons, Present Realities, and Future Horizons. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/23

oai:repository.globalsciencepress.com:105 2026-09-11T07:10:14Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/105/ The Role of Bioinformatics in Genetic Improvement and Tree Breeding for Sustainable Forestry Asif, Muhammad Raza, Zeshan Haider Munir, Akhtar Wang, Mengyuan Biological approach helps enhance tree breeding and genetic improvement through innovative analytical methods which sustain forest practices and increase their resistance rates. The application of genomic sequencing methods along with genetic editing techniques and genomic selection under bioinformatics control allows development of improved tree species with enhanced resistant features and growth dynamics and environmental tolerance. The new technologies play an essential role in solving difficulties which stem from climate change and biodiversity loss and ecosystem degradation. Bioinformatics allows forest managers to preserve tree genetic resources while creating sustainable climate-friendly forest practice methods. The review presents crucial uses of bioinformatics for forestry and outlines its developmental issues together with future possibility horizons while demonstrating worldwide exemplary cases. Bioinformatics technology will continue advancing tree breeding operations through precise breeding methods combined with improvements in forest resistance and global sustainable forest management and climate change reduction initiatives. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/105/1/article.pdf Asif, Muhammad and Raza, Zeshan Haider and Munir, Akhtar and Wang, Mengyuan (2024) The Role of Bioinformatics in Genetic Improvement and Tree Breeding for Sustainable Forestry. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/24

oai:repository.globalsciencepress.com:106 2026-09-11T07:10:14Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/106/ Navigating New Frontiers in Poultry Health and Productivity Jamal, Ahmad The world depends on poultry farming as a foundation of food security but this sector battles with infections together with developing antimicrobial resistances while being affected by environmental problems. This paper assesses modern techniques for improving poultry wellness alongside techniques for sustainable production development. The poultry industry advances through new vaccine development together with probiotic implementation along with AI-based automatic farm monitoring systems. Genetic progress combined with optimized feeding methods accelerate the development of animals and their ability to resist illnesses. Keeping biosecurity standards and using sustainable practices in combination with reduced antibiotic use will sustain long-term productivity. Through innovation adoption the poultry sector can fulfill worldwide consumption needs by maintaining proper care of animals together with respect for ecological principles. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/106/1/article.pdf Jamal, Ahmad (2024) Navigating New Frontiers in Poultry Health and Productivity. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/25

oai:repository.globalsciencepress.com:107 2026-09-11T07:10:15Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/107/ AI in Healthcare and Beyond: Advanced Approaches for Disease Detection and Management Edison, George Advanced data analysis done by Artificial Intelligence is now transforming healthcare and supporting animal health. Using a range of data such as sensor measurements, videos and behavior observations, it gives machine learning and computer vision the ability to notice diseases ahead of time. Using data from the past and the present, predictive analytics can foresee outbreaks and so help develop better prevention plans. Although problems like data quality, access and following regulations are not fully solved, these AI-driven solutions are expected to considerably boost health results for members of all species. Here, we point out AI technologies play a wide role and can be used to manage diseases in ways that go beyond the standard healthcare system—including monitoring poultry diseases and supporting conservation efforts for endangered species. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/107/1/article.pdf Edison, George (2024) AI in Healthcare and Beyond: Advanced Approaches for Disease Detection and Management. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/26

oai:repository.globalsciencepress.com:108 2026-09-11T07:10:15Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/108/ AI for Smarter Product Development and Manufacturing Kabeer, Muhammad Mohsin Artificial Intelligence (AI) is making product development and manufacturing more advanced by helping with smarter design, flexible production and better supply chains. This review focuses on important technologies such as machine learning, deep learning and generative design and how they bring about change in the manufacturing of products. AI is demonstrated in real situations to help businesses innovate, improve work quality and operate more efficiently, as it also manages problems like data quality, bias, creating clear systems and the effect on employees. The importance of ensuring trust and accountability in AI is better explained when using an approach that focuses on people, ethics, understandable AI and close human-AI cooperation. In the end, it proves that using AI responsibly leads to more flexible, open and equitable manufacturing systems. If we use AI responsibly, it could help industries produce creative and durable products which would benefit society and help the economy grow. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/108/1/article.pdf Kabeer, Muhammad Mohsin (2024) AI for Smarter Product Development and Manufacturing. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/27

oai:repository.globalsciencepress.com:109 2026-09-11T07:10:15Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/109/ Ethnobotanical Survey of Common Medicinal Plants Used by People for the Treatment of COVID-19, in Faisalabad, Pakistan Akram, Muhammad Ahsan, Marium Ibrahim, Hamza Khalifa Saed, Abdulfatah This herb-rich plant has long been used to treat a variety of ailments. Since the spread of COVID-19 and death cases are constantly increasing, which prompted the Health and Food Administration to recommend taking drugs that inhibit viral activity, in addition to the World Health Organization suggesting the need to rely on alternative medicine, especially those medicinal plants that have antiviral properties. The goal of this study is to collect ethnic data that members of the Pakistani Faisal community may utilize to cure certain ailments. Method: Traditional herbalists and adults provided ethnic data. The pharmacy at Faisalabad University School of Medicine received the form letters gathered in this investigation. This research now includes 70 different tree species from 39 different families. Of the 12 species, the Rosaceaefamily has been discovered to be the most popular, and the most part of plants is more effective is leaves. Conclusion: The herbal business, herbalists, and export authorities for this therapeutic plant benefit from this research. This dissertation contains critical information for future phytochemical research that may be required to keep this crop alive. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/109/1/article.pdf Akram, Muhammad and Ahsan, Marium and Ibrahim, Hamza Khalifa and Saed, Abdulfatah (2024) Ethnobotanical Survey of Common Medicinal Plants Used by People for the Treatment of COVID-19, in Faisalabad, Pakistan. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/28

oai:repository.globalsciencepress.com:110 2026-09-11T07:10:15Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/110/ Empowering Patients through Health Informatics: Trends, Challenges, and Opportunities Javeedullah, Mohammed By granting access to health records, empowering patients through connection and strining to improve health outcomes, health informatics plays a vital role. This article examines the growth of patient empowerment in healthcare, showing how important technologies such as electronic health records (EHRs), mobile health (mHealth) apps, wearable devices and telemedicine platforms have become. They allow patients to handle their health, talk with doctors more openly and make the right choices. Even though the advantages exist, digital inequalities, worries about private data and compatibility issues still need attention for proper and equal healthcare. New trends, for example, precision medicine, blockchain and artificial intelligence, may lead to better personalized healthcare and more choices for patients. As a result, health informatics helps rebuild the patient-provider partnership, opening doors for more personal, efficient and patient-friendly healthcare systems. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/110/1/article.pdf Javeedullah, Mohammed (2024) Empowering Patients through Health Informatics: Trends, Challenges, and Opportunities. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/29

oai:repository.globalsciencepress.com:111 2026-09-11T07:10:15Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/111/ Big Data and Cloud Computing: A Review of Paradigms Transforming Computer Science singh, Ankur In this review, the author discusses the revolutionary conjunction between two emerging phenomena: Big Data and Cloud Computing, which, overturning the traditional paradigm in Computer Science, transform the outlook in the field. Big Data now allows us to process and analyses huge, varied volumes of data, and Cloud Computing can offer the scalable, on-demand infrastructure on which these can run. Combined them enable real-time analytics, AI applications, intelligent decisions, and smart city applications across industries including healthcare, finance, retail and real estate. The paper will explore some of the concepts on which it is based, how it has been integrated into technology, practical examples, advantages and some of the emerging trends like edge computing and server less architectures. It also deals with the major concerns relating to it implying the data privacy, security, vendor lock-in, and ethical issues. Finally, this review points out the synergy of the Big Data and the Cloud that is shaping a new age in innovation and power of computing. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/111/1/article.pdf singh, Ankur (2024) Big Data and Cloud Computing: A Review of Paradigms Transforming Computer Science. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/30

oai:repository.globalsciencepress.com:112 2026-09-11T07:10:15Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/112/ Catalysis and Mechanistic Insights in Contemporary Organic Chemistry Sayed, Javeriya The current state of organic chemistry is based on the principles of catalysis and mechanistic knowledge, which allow performing efficient, selective, and sustainable transformations. In this review, recent progress in transition metal catalysis, organocatalysis, photoredox catalysis, and bio-inspired systems is highlighted, with a focus on how transition metal catalysis, organocatalysis, photoredox catalysis, and bio-inspired systems are guided by mechanistic knowledge in reaction design, optimization, and substrate scope. Kinetic analysis, spectroscopy, and computational modeling techniques are important in order to gain critical information about intermediates, transition states, and reaction pathways; hence, to develop rational catalysts. The issues of catalyst stability, mechanistic complexity and scalability are addressed, as well as the new trends in AI-driven prediction and green chemistry. Combining catalysis with mechanistic information is still broadening the horizons of present-day organic synthesis. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/112/1/article.pdf Sayed, Javeriya (2024) Catalysis and Mechanistic Insights in Contemporary Organic Chemistry. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/31

oai:repository.globalsciencepress.com:113 2026-09-11T07:10:15Z 7375626A656374733D41:4149 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/113/ Real-Time Data Analytics: Applications and Challenges in Smart Cities Raza, Hassan Erdenetsogt, Tsendayush Farooq, Mazhar Kabeer, Muhammad Mohsin Aslam, Muhammad Shahrukh AI Indexes (General) Smart cities have adopted real-time data analytics making them a critical component in the cities dynamic monitoring, sound decision-making, and effective management of urban services. Using information gathered by the IoT devices, sensors, social media, and infrastructure systems, cities are able to streamline how they transport, power, provide healthcare, govern, monitor their environment, and ensure their safety. This review examines some of the main concepts, technologies, and structures that support real-time analytics, brings out major applications, challenges associated with the volume of data, privacy, scalability, and interoperability. The trends in the future are focused on AI integration, edge computing, standardization, and sustainability. Eventually, real-time analytics facilitates the creation of efficient, responsive and sustainable cities. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/113/1/article.pdf Raza, Hassan and Erdenetsogt, Tsendayush and Farooq, Mazhar and Kabeer, Muhammad Mohsin and Aslam, Muhammad Shahrukh (2024) Real-Time Data Analytics: Applications and Challenges in Smart Cities. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/32

oai:repository.globalsciencepress.com:114 2026-09-11T07:10:15Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/114/ Federated and Edge Learning for Real-Time Data Analytics in Clinical Environments Jamal, Mehtab Singh, A The new machine learning paradigms that allow privacy-guaranteed real-time data analytics in clinical settings are federated and edge learning. As electronic health records, wearable devices, and Internet of Things continue to multiply the volume of healthcare data, the conventional centralized data management methods are under a challenge due to privacy, latency, and scaling. In federated learning, collaborative model training is possible without exchanging raw patient information, whereas edge learning has the data processed on devices in real-time to provide a faster response. Their integration enables efficient, secure and scalable healthcare systems to be used in applications like remote monitoring, in ICUs and in medical diagnostics. These technologies also have issues such as communication overhead and resource constraints, however, it does not mean that they lack promise of improved patient care. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/114/1/article.pdf Jamal, Mehtab and Singh, A (2024) Federated and Edge Learning for Real-Time Data Analytics in Clinical Environments. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/33

oai:repository.globalsciencepress.com:115 2026-09-11T07:10:15Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/115/ Deep Learning Applications in Medicine: Enhancing Diagnosis and Patient Outcomes Jamal, Mehtab Deep learning has transformed the modern medicine to accurately diagnose, treat patients with uniqueness, and provide better patient outcomes. With the use of big data in medicine, imaging, electronic health record and genomic data, deep learning models identify subtle features, disease progression, and aids in clinical decision-making. The uses can include medical imaging and pathology as well as predictive analytics, remote monitoring, and AI-assisted drug discovery. Although issues like data privacy, model interpretability, and algorithmic biasness persist, such new approaches as explainable AI, federated learning, and IoT integration have greater opportunities to be safe and effective to adopt. Deep learning is building a future in the field of accuracy, effectiveness, and patient-centric care. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/115/1/article.pdf Jamal, Mehtab (2024) Deep Learning Applications in Medicine: Enhancing Diagnosis and Patient Outcomes. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/34

oai:repository.globalsciencepress.com:116 2026-09-11T07:10:16Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/116/ Role of Artificial Intelligence in Modern Data Science:  A Critical Review Oad, Surdesh Kumar The artificial intelligence (AI) has been used as a revolutionary technology in contemporary data science, which has improved capacity to process enormous and sophisticated data, forecasting and automation. Thse conceptual underpinnings, historical development, and the most important AI methods, including machine learning, deep learning, natural language processing, and computer vision, which enable data science are reviewed. It also discusses its use in practical healthcare, finance, marketing, transportation, and smart cities, and identifies such advantages as efficiency, predictive accuracy and automation. Challenges, such as data quality, bias, interpretability, and ethical considerations are also mentioned, and the future trends addressed are AutoML, explainable AI, and edge computing, which would allow a responsible and innovative future. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/116/1/article.pdf Oad, Surdesh Kumar (2024) Role of Artificial Intelligence in Modern Data Science: A Critical Review. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/35

oai:repository.globalsciencepress.com:117 2026-09-11T07:10:16Z 7375626A656374733D41:4149 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/117/ Artificial Intelligence in Data Analytics: A Comprehensive Review Oad, Vidya Devi AI Indexes (General) Data analytics have become a strong, smart, and automatic field with the help of AI. In this review, the author discusses the development of data analytics since their traditional ways of operation to AI-based systems, including machine learning, deep learning, and natural language processing. It covers key AI methods, industry applications, tools and technologies, advantages, issues and future trends. AI boosts precision, real-time processing, and decision-making and facilitates predictive and prescriptive insights. Although issues such as the privacy of data, bias, and computational complexity remain, AI is proceeding at a very fast pace. All in all, AI is transforming data analytics to become more efficient, scalable, and intelligent. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/117/1/article.pdf Oad, Vidya Devi (2024) Artificial Intelligence in Data Analytics: A Comprehensive Review. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/36

oai:repository.globalsciencepress.com:118 2026-09-11T07:10:16Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/118/ Quality Control and Process Validation in Medical Device Manufacturing Kunnipurayil, Salman Mohammed, Mannan Medical device manufacturers must incorporate quality control and process validation as a part of the process, which guarantees the safety of the product, its reliability, and regulatory compliance. Quality control aims at identifying defects by inspection and testing and process validation comes up with documented evidence that the manufacturing processes continuously produce devices within specifications that have been defined. Collectively, they create a system of quality that is integrated and reduces the risks and improves the consistency of products. This review identifies regulatory frameworks, methodologies, tools, challenges, and new trends of these practices. As the complexity of devices increases, and global regulations become stricter, implementing new technologies and lifecycle-oriented approach is essential to continue upholding high-quality standards and enhancing patient safety outcomes. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/118/1/article.pdf Kunnipurayil, Salman and Mohammed, Mannan (2024) Quality Control and Process Validation in Medical Device Manufacturing. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/37

oai:repository.globalsciencepress.com:119 2026-09-11T07:10:16Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/119/ Deep Learning Applications in Power Systems: A Review Study Khan, Fawad This review paper discusses the application of deep learning in modern power systems, and how it can be used to enhance forecasting, fault detection, stability analysis, and renewable energy integration. It addresses some of the basic concepts of deep learning and power system structure, then goes on to discuss some of the key techniques: CNNs, RNNs, LSTMs, and reinforcement learning. The study compares against the existing research showing that deep learning has superior performance over traditional methods but also indicates that deep learning has several challenges such as high cost of computation, dependence on data, and lack of interpretability. The directions in the future are smart grid integration, explainable AI, and hybrid models. Altogether, deep learning is a promising technology to improve the efficiency, reliability, and intelligence of power systems. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/119/1/article.pdf Khan, Fawad (2024) Deep Learning Applications in Power Systems: A Review Study. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/38

oai:repository.globalsciencepress.com:120 2026-09-11T07:10:16Z 7375626A656374733D41:4149 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/120/ Artificial Intelligence in Electrical Power Systems: A Comprehensive Review of Machine Learning Techniques and Applications Khan, Fawad AI Indexes (General) The electrical power system is undergoing a significant transformation through the use of Artificial Intelligence (AI) and Machine Learning (ML) for advanced forecasting, monitoring, optimization, fault detection and intelligent control of electrical power systems. It reviews the broadly used ML methods such as regression, decision trees, support vector machines, neural networks, deep learning, ensemble methods, and reinforcement learning, and their implementations in the field of load forecasting, renewable energy integration, stability assessment, predictive maintenance, and smart grids. The review also addresses issues of data quality, computational complexity, cybersecurity, interpretability and model generalization. The future directions of intelligent power systems are highlighted to be promising with emerging approaches such as explainable AI, physics-informed learning, federated learning, digital twins, edge AI and autonomous operation. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/120/1/article.pdf Khan, Fawad (2024) Artificial Intelligence in Electrical Power Systems: A Comprehensive Review of Machine Learning Techniques and Applications. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/39

oai:repository.globalsciencepress.com:121 2026-09-14T01:31:07Z

oai:repository.globalsciencepress.com:122 2026-09-14T01:45:34Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/122/ Enforcing IP Rights in Nanotechnology: Evidentiary Challenges in Proving Infringement at the Atomic Scale Chowdhury, Md. Ekram Uddin Khan IP protection poses a serious challenge in nanotechnology as patented inventions in the area are frequently atomic and molecular level structures and processes. This review explores the problems in establishing evidence of infringement in patent litigation in nanotechnology. It explores issues of nanoscale measurements, scientific testing, and access to evidence, expert testimony, confidentiality and interpreting specialist technical information. The review also includes the effect of microscopy and spectroscopy and other analytical techniques on the determination of infringement. It highlights the importance of scientific certainty, technical clarity of patent claims, experts and sound legal processes for the proper protection of innovations in nanotechnology. Global Alliance for Scientific Studies 2024-06-15 Article PeerReviewed text en https://repository.globalsciencepress.com/id/eprint/122/1/article.pdf Chowdhury, Md. Ekram Uddin Khan (2024) Enforcing IP Rights in Nanotechnology: Evidentiary Challenges in Proving Infringement at the Atomic Scale. Global Journal of Universal Studies, 1 (1). ISSN 3008-0509 https://gjus.org/index.php/gjus/article/view/40

oai:repository.globalsciencepress.com:198 2026-09-14T05:45:45Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/198/ Artificial Intelligence in Electrical Systems: A Review of Machine Learning Methods, Applications, and Future Perspectives Khan, Fawad The integration of Artificial Intelligence (AI) has emerged as a crucial technology for making electrical systems intelligent and efficient.AI has been a pivotal technology in making electrical systems intelligent and efficient, enhancing automation, reliability, and decision-making. In this review, the integration of AI within the field of electrical engineering is explored, highlighting the role of machine learning techniques, deep learning models, and their applications in various areas such as smart grid, renewable energy prediction, fault detection, energy prediction, and predictive maintenance. It also covers benchmark datasets, evaluation platforms, and the performance of AI algorithms. In addition, the review identifies key challenges, such as Interpretability of the Models, Data Quality, Security of Data and Computational Complexity, and investigates new trends like Explainable AI, Digital Twins, Federated Learning and Edge AI for future electrical systems. Global Alliance for Scientific Studies Article PeerReviewed en https://repository.globalsciencepress.com/id/eprint/198/1/article.pdf Khan, Fawad Artificial Intelligence in Electrical Systems: A Review of Machine Learning Methods, Applications, and Future Perspectives. Global Trends in Science and Technology, 2 (4). ISSN 3079-5761 https://globaltrendsst.com/index.php/GTST/article/view/70 10.70445/gtst.2.4.2026.1-23 10.70445/gtst.2.4.2026.1-23

oai:repository.globalsciencepress.com:199 2026-09-14T05:45:47Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/199/ Decoding the Nanotech Patent Thicket: Strategies for Navigating Complex Intellectual Property Landscapes Chowdhury, Md. Ekram Uddin Khan The scientific and commercial progress of nanotechnology has been fast, and led to the development of constantly evolving intellectual property (IP) issues. The review looks at the rise of nanotech patent thickets: multiple overlapping patents, pieces of the patent puzzle held by various parties, and several interrelated technology claims. It covers the major issues of patentability, freedom to operate, licensing, regulatory considerations and commercialization. Various strategies for effective IP management are considered, such as patent landscape analysis, strategic licensing, cross-licensing, design-around solutions, and collaborative research. The review also notes the increasing importance of Artificial Intelligence and Digital Tools in Patent Analysis and Monitoring. In conclusion, balanced IP approaches and policies are critical to fostering sustainable innovation and commercialization of nanotechnology. Global Alliance for Scientific Studies Article PeerReviewed en https://repository.globalsciencepress.com/id/eprint/199/1/article.pdf Chowdhury, Md. Ekram Uddin Khan Decoding the Nanotech Patent Thicket: Strategies for Navigating Complex Intellectual Property Landscapes. Global Trends in Science and Technology, 2 (4). ISSN 3079-5761 https://globaltrendsst.com/index.php/GTST/article/view/71 10.70445/gtst.2.4.2026.24-44 10.70445/gtst.2.4.2026.24-44

oai:repository.globalsciencepress.com:200 2026-09-14T06:03:57Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/200/ AI in Predictive Healthcare Analytics: Forecasting Disease Outbreaks and Patient Outcomes Bacha, Ahmad Sherani, Abdul Manan Khan Advanced technology in the form of Artificial Intelligence is currently being used in health care in a way that allows for A/B testing of various scenarios with the expectation of predicting disease breakout and necessary staffing and resource distribution. Examples include ML and deep learning algorithms that process large amounts and types of data as ways healthcare systems can then anticipate proactive solutions. Public health responses are artificially intelligent to forecast the detection of epidemics by analyzing past and current data. Also, the AI models in healthcare prognosis of the expected health condition for a specific patient which results into better patient care. AI also helps to manage scarce resources such as; beds in hospitals and medications. This paper discusses these application areas and lays out how AI can strengthen public health interventions and refine the methods by which care is provided, despite issues like data protection and unfair model bias. Global Alliance for Scientific Studies Article PeerReviewed en https://repository.globalsciencepress.com/id/eprint/200/1/article.pdf Bacha, Ahmad and Sherani, Abdul Manan Khan AI in Predictive Healthcare Analytics: Forecasting Disease Outbreaks and Patient Outcomes. Global Trends in Science and Technology, 1 (1). ISSN 3079-5761 https://globaltrendsst.com/index.php/GTST/article/view/1 10.70445/gtst.1.1.2025.1-14 10.70445/gtst.1.1.2025.1-14

oai:repository.globalsciencepress.com:201 2026-09-14T06:04:00Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/201/ AI in Personalized Medicine: Tailoring Treatment Plans Based on Individual Patient Data Shah, Heta Hemang Lodhi, Shahrukh Khan The application of AI in personalized medicine is enhancing advanced treatment solutions with respect to every patient’s uniqueness manifested in genetic makeup, life style factors, and clinical background among others. Precision medicine or individualized medicine aims for more effective treatment, fewer side effects of drugs and better utilization of resources in a health care setting. Machine learning (ML) and deep learning (DL) are subsets of AI technologies that assess a tremendous number of factors, including genomics, patients’ EHRs, and lifestyle indicators, to determine which patients will respond better to treatments and which patients are most vulnerable to adverse outcomes. They enhance healthcare practitioners’ ability to provide tailored care to patients to get better results that are less costly in the healthcare system. The purpose of this paper is to review how artificial intelligence will work towards enabling personalized medicine, the issues that exist, and the prospects in the pipeline. Challenges like data privacy, algorithmic bias, and integration into the current healthcare systems are presented, and prospects of the AI –driven personalized medicine in the future improvement of patient care and the development of medical research are examined. Global Alliance for Scientific Studies Article PeerReviewed en https://repository.globalsciencepress.com/id/eprint/201/1/article.pdf Shah, Heta Hemang and Lodhi, Shahrukh Khan AI in Personalized Medicine: Tailoring Treatment Plans Based on Individual Patient Data. Global Trends in Science and Technology, 1 (1). ISSN 3079-5761 https://globaltrendsst.com/index.php/GTST/article/view/2 10.70445/gtst.1.1.2025.15-29 10.70445/gtst.1.1.2025.15-29

oai:repository.globalsciencepress.com:202 2026-09-14T06:04:02Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/202/ From Theory to Implementation: Optimizing AI-Driven Depression Detection Using Facial Recognition, EEG, and Algorithmic Innovations Neoaz, Nahid Amin, Mohammad Hasan Our research demonstrates the power of artificial intelligence to identify depression through improved detection of facial features and brain wave patterns. The exploration builds on current algorithm advances to develop depression detection methods that work accurately and quickly. This project examines AI's theoretical framework for mental health diagnosis plus reviews modern methods and studies practical uses and computational issues. Global Alliance for Scientific Studies Article PeerReviewed en https://repository.globalsciencepress.com/id/eprint/202/1/article.pdf Neoaz, Nahid and Amin, Mohammad Hasan From Theory to Implementation: Optimizing AI-Driven Depression Detection Using Facial Recognition, EEG, and Algorithmic Innovations. Global Trends in Science and Technology, 1 (1). ISSN 3079-5761 https://globaltrendsst.com/index.php/GTST/article/view/3 10.70445/gtst.1.1.2025.30-39 10.70445/gtst.1.1.2025.30-39

oai:repository.globalsciencepress.com:203 2026-09-14T06:04:05Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/203/ Advances in Regenerative Medicine: Paving the Way for Tissue Repair and Organ Regeneration Alomari, Ghaith Artificial Intelligence or AI has quickly established itself as an innovative approach in regenerative medicine and brings new hopes for tissue repair and organ regeneration. This paper examines multiple instances of how the AI technology is being incorporated into regenerative medicine and the effects thereof on stem cell biology, tissue engineering, and the development of patient-tailored regenerative therapies. Due to data analysis, biological outcome prediction, and protocol optimization, AI contributed tremendously to regenerating human tissues and organs. Here AI is finding ways of helping in cases such as replenishing scarce organs and treating diseases like those that cause degeneration, thus holding a promise to better patient outcomes. Moreover, this paper explores the implications of the application of AI in tissue engineering to bio printing and differentiation of stem cells. Nevertheless, with the advancement of this sector, several issues surface as the use of AI in regenerative medicine presents some difficulties such as data privacy issues, algorithms’ biases, and the question of regulation among others. All these issues should be handled in a very good manner for the benefit of regenerative medicine to be enhanced through implementation of Artificial Intelligence. Global Alliance for Scientific Studies Article PeerReviewed en https://repository.globalsciencepress.com/id/eprint/203/1/article.pdf Alomari, Ghaith Advances in Regenerative Medicine: Paving the Way for Tissue Repair and Organ Regeneration. Global Trends in Science and Technology, 1 (1). ISSN 3079-5761 https://globaltrendsst.com/index.php/GTST/article/view/4 10.70445/gtst.1.1.2025.40-52 10.70445/gtst.1.1.2025.40-52

oai:repository.globalsciencepress.com:204 2026-09-14T06:04:08Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/204/ Neural Pathways to Emotional Wellness: Merging AI-Driven VPSYC Systems with EEG and Facial Recognition Khan, Murad Bacha, Ahmad Our study brings together AI-driven VPSYC systems with EEG brain monitoring and facial image recognition to improve emotional health. The research shows how our current technology can help us understand emotional states while offering fast solutions because it follows natural brain pathways and improves machine learning functions. VPSYC systems with their advanced algorithms examine EEG readings and facial movement patterns to create valuable intelligence for better mental health outcomes. Doctors rely on this technology to create custom emotional treatments and monitor stress levels plus offer instant therapeutic support. The exploration discusses system efficiency problems plus data blending issues, while analyzing how to maintain performance at scale alongside ethical requirements for privacy security and bias avoidance. This study highlights both tech breakthroughs and social effects to show how robust systems need to protect emotional wellness and reliable AI systems. Global Alliance for Scientific Studies Article PeerReviewed en https://repository.globalsciencepress.com/id/eprint/204/1/article.pdf Khan, Murad and Bacha, Ahmad Neural Pathways to Emotional Wellness: Merging AI-Driven VPSYC Systems with EEG and Facial Recognition. Global Trends in Science and Technology, 1 (1). ISSN 3079-5761 https://globaltrendsst.com/index.php/GTST/article/view/5 10.70445/gtst.1.1.2025.53-62 10.70445/gtst.1.1.2025.53-62

oai:repository.globalsciencepress.com:205 2026-09-14T06:04:11Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/205/ Ethical Considerations and Data Privacy Challenges in AI-Powered Healthcare Solutions for Cancer and Cardiovascular Diseases Zainab, Hira Khan, Ali Raza A Khan, Muhammad Ismaeel Arif, Aftab Integrating artificial intelligence (AI) at the speed at which it is being done today has advantages along with potential issues in its application to health care – notably in diagnosing and treating cancer and heart disease. The development of AI health systems, such as the Clinical Decision Support Systems (CDSS), is transforming the efficiency of care delivery by increasing individual patient treatment options. Nevertheless, these advancements raise ethical challenges and data protection problems that should be solved to use the technology. This article discusses the ethical aspects of AI in medicine, including algorithmic bias, transparency of decision-making processes, and patient independence, while bringing to light the difficulties protecting sensitive health information. The paper highlights the possible methods that can be applied by healthcare organizations to pension and manage the risks through governance, practice transparency and compliance with privacy legislation. It also highlights the ethical principles and laws that should be adhered to by healthcare organizations in the deployment of AI technologies. Global Alliance for Scientific Studies Article PeerReviewed en https://repository.globalsciencepress.com/id/eprint/205/1/article.pdf Zainab, Hira and Khan, Ali Raza A and Khan, Muhammad Ismaeel and Arif, Aftab Ethical Considerations and Data Privacy Challenges in AI-Powered Healthcare Solutions for Cancer and Cardiovascular Diseases. Global Trends in Science and Technology, 1 (1). ISSN 3079-5761 https://globaltrendsst.com/index.php/GTST/article/view/6 10.70445/gtst.1.1.2025.63-74 10.70445/gtst.1.1.2025.63-74

oai:repository.globalsciencepress.com:206 2026-09-14T06:04:14Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/206/ Predictive AI Models for Food Spoilage and Shelf-Life Estimation Shehzad, Khuram Food spoilage is a global problem which causes food waste, economic loss and foodborne illness. The shelf life and spoilage estimation of food is traditionally done with fixed expiration dates and this leads to disposal of still eatable food or eating spoiled food. Recently, with the development of the Artificial Intelligence (AI), the predictive models have been developed to better evaluate the food spoilage based on such factors as temperature, humidity, microbial activities and gas emissions. This paper discusses the part played by AI in the prediction of food spoilage, while also outlining various machine learning and deep learning models (regression, classification, convolutional neural network – CNN and hybrid AI). Food spoilage estimation powered by AI relies on multiple sources of data including IoT enabled sensors, Spectroscopy as well as real time environmental monitoring. The practical use in the food industry of such data driven models is in the context of real life applications as smart packaging, AI powered quality in supply chains, retail inventory product optimization. However, the adoption of AI in this field is limited as the data is scarce and of low quality, the models have limited accuracy, ethical concerns exist, and implementation is expensive. In this review, potential for AI in transforming food spoilage estimation is highlighted and this could be achieved by working on obtaining greater accuracy, scalability, and adoption of the model in different food sectors. The role of AI in enhancing food security, sustainability and efficient use of resources, waste reduction and increasing accessibility of good quality perishables to every consumer will gain increasing feasibility with the improvement in AI. Global Alliance for Scientific Studies Article PeerReviewed en https://repository.globalsciencepress.com/id/eprint/206/1/article.pdf Shehzad, Khuram Predictive AI Models for Food Spoilage and Shelf-Life Estimation. Global Trends in Science and Technology, 1 (1). ISSN 3079-5761 https://globaltrendsst.com/index.php/GTST/article/view/7 10.70445/gtst.1.1.2025.75-94 10.70445/gtst.1.1.2025.75-94

oai:repository.globalsciencepress.com:207 2026-09-14T06:04:18Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/207/ AI-Powered Anomaly Detection for AML Compliance in US Banking: Enhancing Accuracy and Reducing False Positives Ghimire, Ashok AI detecting anomalies hasn’t just been used to transform fraud detection for US banks, it’s being used to reduce false positives. Current rule based systems create excessive alerts and it leads to inefficient costs and time. Machine learning, deep learning, and real-time analytics along with AI solve the problem with behavioral profiling, network analysis, and adaptive risk scoring to dramatically increase accuracy and efficiency. Explainable AI (XAI) still matters in order to maintain transparency and fairness, as per FinCEN and OCC. To comply with regulations, banks are coming up with regulatory sandboxes and AI governance frameworks. Crypto money laundering is becoming a growing issue as monitoring tools, such as block chain analytics and AI-driven crypto currency monitoring appear as the most effective way of tackling it. Real-time AI monitoring, predictive analytics, and federated learning for real collaboration without compromise to data privacy are also future trends of AML. With the integration of AI within regulatory compliant frameworks, US banks are able to improve the AML effectiveness, reduce costs and increase the financial security. In the banking world, AI powered AML solutions will transform the financial crime prevention, ensuring a more secure, efficient, and compliant banking system. Global Alliance for Scientific Studies Article PeerReviewed en https://repository.globalsciencepress.com/id/eprint/207/1/article.pdf Ghimire, Ashok AI-Powered Anomaly Detection for AML Compliance in US Banking: Enhancing Accuracy and Reducing False Positives. Global Trends in Science and Technology, 1 (1). ISSN 3079-5761 https://globaltrendsst.com/index.php/GTST/article/view/8 10.70445/gtst.1.1.2025.95-120 10.70445/gtst.1.1.2025.95-120

oai:repository.globalsciencepress.com:208 2026-09-14T06:04:22Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/208/ Deciphering the AI Healthcare Evolution: Opportunities, Risks, and the Path Forward Neoaz, Nahid Husnain, Ali Medical healthcare experiences transformation through Artificial Intelligence (AI) which delivers improvements in diagnosing patients as well as treatment delivery and medication discoveries and customized medicine delivery. Artificial intelligence technologies boost operational speed and precision together with strategic choices and they also make possible the execution of predictive models and automated tasks. Medical practitioners must address several barriers when implementing AI such as ethical problems along with privacy issues and biases in algorithms as well as regulatory obstacles. The adoption of AI technology in healthcare entails support for medical decisions instead of taking over responsibilities from healthcare providers. The integration of AI systems into healthcare requires close cooperation between members of the medical field alongside the developers and government decision makers who create the policies. The delivery of equitable healthcare to all populations depends on placing moral standards regarding data protection and treating citizens fairly along with being fully transparent. AI systems need appropriate oversight from regulators before their safe implementation. Moving toward the future requires a balanced application method which unites artificial intelligence technology potential with medical professional expertise. Healthcare systems will become smarter and accessible to patients through AI applications provided that appropriate responsible implementation guidelines exist. Global Alliance for Scientific Studies Article PeerReviewed en https://repository.globalsciencepress.com/id/eprint/208/1/article.pdf Neoaz, Nahid and Husnain, Ali Deciphering the AI Healthcare Evolution: Opportunities, Risks, and the Path Forward. Global Trends in Science and Technology, 1 (1). ISSN 3079-5761 https://globaltrendsst.com/index.php/GTST/article/view/9 10.70445/gtst.1.1.2025.121-142 10.70445/gtst.1.1.2025.121-142

oai:repository.globalsciencepress.com:209 2026-09-14T06:04:24Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/209/ Future of Health Informatics: Bridging Technology and Healthcare Javeedullah, Mohammed Health informatics is transforming healthcare by integrating technology, data analytics, and healthcare delivery systems. The review examines healthcare informatics development through discussions of EHRs as well as telehealth and mobile health device implementation. Medical diagnostic innovation along with patient treatment along with data security improvement have emerged through developing technologies such as artificial intelligence (AI) machine learning (ML) and block chain systems and the Internet of Medical Things (IoMT). These advanced healthcare technologies bring important advantages to medical care together with operational effectiveness yet the barriers of data protection along with system interface capabilities and official requirements and staff capability development persist. The potential of health informatics to develop an advanced healthcare platform depends on conquering current obstacles that launch its complete capabilities. The review stresses the requirement for innovation together with teamwork to achieve full benefits of health informatics solutions within the healthcare sector. Global Alliance for Scientific Studies Article PeerReviewed en https://repository.globalsciencepress.com/id/eprint/209/1/article.pdf Javeedullah, Mohammed Future of Health Informatics: Bridging Technology and Healthcare. Global Trends in Science and Technology, 1 (1). ISSN 3079-5761 https://globaltrendsst.com/index.php/GTST/article/view/10 10.70445/gtst.1.1.2025.143-159 10.70445/gtst.1.1.2025.143-159

oai:repository.globalsciencepress.com:210 2026-09-14T06:04:27Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/210/ Bioinformatics in Forest Biodiversity: From DNA Barcoding to Genomic Selection Raza, Zeshan Haider Asif, Muhammad Munir, Akhtar Wang, Mengyuan The investigation along with preservation of forest biodiversity benefits tremendously from bioinformatics. The paper reviews essential bioinformatics applications which include DNA barcoding and genomic selection within forest ecosystems. DNA barcoding helps scientists identify species correctly yet environmental DNA (eDNA) and metabarcoding methods let scientists study diverse species through non-destructive monitoring procedures. Population genomics operates as a method to investigate genetic diversity which helps maintain populations in addition to adaptive management functions. Biological and evolutionary analyses demonstrate species family links while showing the historical movements of living things which supports better planning of conservation efforts. Genomic selection speeds up tree breeding activities through which breeders can improve traits involving growth patterns and disease immunity. Research in forest biodiversity progresses due to BOLD and GenBank and QIIME as well as bioinformatics tools which analyze and interpret data. Forest conservation together with management and sustainable forestry practice benefits from genomic technologies even though data standards and computing requirements create present-day obstacles Global Alliance for Scientific Studies Article PeerReviewed en https://repository.globalsciencepress.com/id/eprint/210/1/article.pdf Raza, Zeshan Haider and Asif, Muhammad and Munir, Akhtar and Wang, Mengyuan Bioinformatics in Forest Biodiversity: From DNA Barcoding to Genomic Selection. Global Trends in Science and Technology, 1 (1). ISSN 3079-5761 https://globaltrendsst.com/index.php/GTST/article/view/11 10.70445/gtst.1.1.2025.160-175 10.70445/gtst.1.1.2025.160-175

oai:repository.globalsciencepress.com:211 2026-09-14T06:04:30Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/211/ Interoperability Solutions for Efficient Health Informatics Systems Javeedullah, Mohammed National health informatics interoperability establishes uninterrupted information transfer between different health care systems which enhances coordinated patient health management and treatment results. The research investigates current challenges of interoperability together with proposed solutions and identifies projections for its future development in healthcare. The barrier of technical issues together with standardization problems of healthcare data and privacy risks and administrative restrictions prevent quick and effective health information sharing. Health Level Seven (HL7) and Fast Healthcare Interoperability Resources (FHIR) together with Systematized Nomenclature of Medicine (SNOMED CT) have established standards that enhance data security during the process of standard data exchange. Multiple healthcare facilities worldwide such as the United States, the United Kingdom, Sweden and Canada showcase how their operable systems succeed and encounter obstacles within their medical infrastructure. Future healthcare systems will benefit from advanced technologies including AI and block chain and machine learning because these tools will improve system scalability and security and efficiency. Ongoing workforce cooperation together with financial support and continual technology transformation ensures true interoperable solutions can be obtained. Healthcare delivery will improve and patient outcomes will strengthen alongside a patient-centered care approach because of complete health informatics interoperability. Global Alliance for Scientific Studies Article PeerReviewed en https://repository.globalsciencepress.com/id/eprint/211/1/article.pdf Javeedullah, Mohammed Interoperability Solutions for Efficient Health Informatics Systems. Global Trends in Science and Technology, 1 (1). ISSN 3079-5761 https://globaltrendsst.com/index.php/GTST/article/view/12 10.70445/gtst.1.1.2025.176-194 10.70445/gtst.1.1.2025.176-194

oai:repository.globalsciencepress.com:212 2026-09-14T06:04:33Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/212/ Smarter Healthcare: Harnessing AI for Faster, More Accurate, and Accessible Medicine Husnain, Ali Medicine receives a transformative overhaul from Artificial Intelligence technology because it enhances testing processes as well as tailors specific treatments and improves medical imaging while introducing virtual medical aid platforms and robotic intervention in operations. The deployment of AI-powered tools permits better identification of diseases in addition to enhanced treatments for patients that leads to quicker medical care available to all. The utilization of AI in radiology through imaging systems enables physicians to detect medical conditions early while predictive analysis allows the creation of customized treatment approaches for patients. Healthcare facilities utilize virtual health assistants together with chatbots to deliver continuous support systems and handle chronic disease care as well as mental health interventions. Robotic surgery guided by artificial intelligence technology enhances treatment accuracy and minimizes healthcare risks before it brings better results to patients in their recovery journey. AI continues its progress by optimizing hospital operations and surgical decisions despite facing privacy and ethical problems and approval hurdles. AI healthcare applications will succeed by fusing technology with human patient providers for superior treatment quality along with reduced health mistakes and optimized therapeutic solutions. Medical field achievement of an efficient patient-centered innovative healthcare system depends on their ability to handle challenges and follow responsible AI adoption practices. Global Alliance for Scientific Studies Article PeerReviewed en https://repository.globalsciencepress.com/id/eprint/212/1/article.pdf Husnain, Ali Smarter Healthcare: Harnessing AI for Faster, More Accurate, and Accessible Medicine. Global Trends in Science and Technology, 1 (2). ISSN 3079-5761 https://globaltrendsst.com/index.php/GTST/article/view/13 10.70445/gtst.1.2.2025.1-22 10.70445/gtst.1.2.2025.1-22

oai:repository.globalsciencepress.com:213 2026-09-14T06:04:36Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/213/ From Computational Models to Clinical Impact: The Influence of AI on Modern Healthcare Neoaz, Nahid Amin, Mohammad Hasan The healthcare field transforms with Artificial Intelligence (AI) because it enhances patient diagnosis together with treatment methods and hospital administration while delivering customized medical care. The introduction of AI-based technology in healthcare produces better medical images along with new medication developments and robot-aided surgery along with patient observation systems which result in both higher operational effectiveness and enhanced medical results. AI systems deliver multiple benefits to healthcare by enhancing hospital operation efficiency and enabling predictive analysis followed by inexpensive telemedicine systems which increase health service accessibility. AI implementation faces hurdles because it generates problems about ethics along with data privacy risks and includes biases in algorithms and creates difficulties in regulation and demands costly deployments. AI deployment requires doctors to maintain medical regulations compliance along with transparency and accountability practices. Ethical AI deployment needs policymaker and healthcare provider and AI developer joint efforts to build solid frameworks for responsible AI mechanisms. Modern medical science and treatment processes along with research advancement have not removed but remain possible because of AI's revolutionary capabilities. The future of healthcare depends on AI to build an improved system based on accuracy and efficiency and patient-centered care because of ongoing innovation with proper governance. Global Alliance for Scientific Studies Article PeerReviewed en https://repository.globalsciencepress.com/id/eprint/213/1/article.pdf Neoaz, Nahid and Amin, Mohammad Hasan From Computational Models to Clinical Impact: The Influence of AI on Modern Healthcare. Global Trends in Science and Technology, 1 (2). ISSN 3079-5761 https://globaltrendsst.com/index.php/GTST/article/view/14 10.70445/gtst.1.2.2025.23-48 10.70445/gtst.1.2.2025.23-48

oai:repository.globalsciencepress.com:214 2026-09-14T06:04:39Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/214/ Utilizing Machine Learning for Continuous Process Improvement in Lean Six Sigma Kabeer, Muhammad Mohsin A potent framework for enduring process enhancement results from combining Lean Six Sigma (LSS) with Machine Learning (ML) systems by uniting Lean operational effectiveness with decision-making approaches based on data analytics. Through predictive analytics and real-time monitoring and adaptive process control Machine Learning enhances the waste reduction and process optimization capabilities of Lean Six Sigma. The combination offers exceptional benefits within production settings and healthcare and finance sectors because these industries put priority on quality and efficiency performance. The successful implementation of these technologies demands solutions for addressing data quality issues and system integration problems and organizational resistance. The evolution of accessible real-time processing through ML tools will shape Lean Six Sigma through increasingly autonomous systems which make predictive and proactive decisions on their own. The union between Machine Learning and Lean Six Sigma will transform process optimization to deliver businesses better efficiency alongside superior quality standards along with stronger market positions. Global Alliance for Scientific Studies Article PeerReviewed en https://repository.globalsciencepress.com/id/eprint/214/1/article.pdf Kabeer, Muhammad Mohsin Utilizing Machine Learning for Continuous Process Improvement in Lean Six Sigma. Global Trends in Science and Technology, 1 (2). ISSN 3079-5761 https://globaltrendsst.com/index.php/GTST/article/view/15 10.70445/gtst.1.2.2025.49-63 10.70445/gtst.1.2.2025.49-63

oai:repository.globalsciencepress.com:215 2026-09-14T06:04:41Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/215/ Decoding Medical AI: The Science, Ethics, and Future of Intelligent Healthcare Malik, Yasir Ghayor Healthcare experiences a significant transformation through Artificial Intelligence which both develops medical diagnoses and optimizes therapy methods as well as enhances healthcare services for patients. Wealthy tools that combine artificial intelligence elements employ machine learning algorithms together with robotic surgery and predictive analytics which provide physicians with prompter more reliable medical choices. The innovative healthcare advancements enhance operational efficiency along with increased accessibility to medical services and decreased occurrence of mistakes. The implementation of AI systems produces obstacles involving data security risks together with prejudice in algorithms and moral problems and administrative issues for proper governance. Patients need their medical information to remain confidential with fairness and accountability maintained during AI-based medical decisions for trust to continue existing. The FDA together with EMA and GDPR enforce policies which protect AI medical applications through their established compliance requirements. The power of artificial intelligence extends to transforming healthcare personalization as well as disease control and delivering more effective clinical service. The complete utilization of intelligent healthcare requires maintaining equilibrium between innovation practices and responsibility measures. Global Alliance for Scientific Studies Article PeerReviewed en https://repository.globalsciencepress.com/id/eprint/215/1/article.pdf Malik, Yasir Ghayor Decoding Medical AI: The Science, Ethics, and Future of Intelligent Healthcare. Global Trends in Science and Technology, 1 (2). ISSN 3079-5761 https://globaltrendsst.com/index.php/GTST/article/view/16 10.70445/gtst.1.2.2025.66-83 10.70445/gtst.1.2.2025.66-83

oai:repository.globalsciencepress.com:216 2026-09-14T06:04:44Z 74797065733D61727469636C65

https://repository.globalsciencepress.com/id/eprint/216/ Exploring AI Applications: Machine Learning in Healthcare, Food Technology, and Conversational Agents Harry, Alexandra Artificial Intelligence (AI) and machine learning (ML) are rapidly transforming healthcare, food technology, and conversational systems. In healthcare, AI enhances diagnostics, predictive analytics, and personalized treatments, leading to improved patient outcomes. In food technology, AI is optimizing agriculture, food safety, and supply chains while promoting sustainability and personalized nutrition. Conversational AI, exemplified by models like ChatGPT, is enhancing user engagement by providing tailored health and dietary advice. This integration of AI across sectors is creating synergies that offer more efficient, accessible, and personalized solutions. However, challenges related to data quality, privacy, algorithmic bias, and model transparency remain. The future of AI in these fields looks promising, with advancements in multimodal AI, sustainable food production, and drug discovery. Responsible deployment of AI will be key to realizing its full potential in improving human well-being. Global Alliance for Scientific Studies Article PeerReviewed en https://repository.globalsciencepress.com/id/eprint/216/1/article.pdf Harry, Alexandra Exploring AI Applications: Machine Learning in Healthcare, Food Technology, and Conversational Agents. Global Trends in Science and Technology, 1 (2). ISSN 3079-5761 https://globaltrendsst.com/index.php/GTST/article/view/17 10.70445/gtst.1.2.2025.97-112 10.70445/gtst.1.2.2025.97-112 metadataPrefix%3Doai_dc%26offset%3D217