Global Journal of Computer Sciences and Artificial Intelligence
Global Journal of Computer Sciences and Artificial Intelligence The Global Journal of Computer Sciences and Artificial Intelligence (GJCSAI) is a renowned international, peer-reviewed journal published by the Global Alliance for Scientific Studies, which is dedicated to publishing cutting-edge research and advancements in computer sciences and artificial intelligence (AI). It serves as a vital platform for academics, researchers, and industry experts to exchange innovative ideas and foster global collaboration in these ever-evolving fields. Key Areas of Focus GJCSAI emphasizes interdisciplinary research and covers a diverse range of topics, including but not limited to: Theoretical and applied computer science. Artificial intelligence frameworks, algorithms, and models. Machine learning and deep learning innovations. Computer vision, natural language processing, and robotics. Computational theory and data structures. AI ethics, explainability, and societal impacts. Emerging computing paradigms, including cloud, edge, and quantum computing. AI-driven solutions for industries such as healthcare, education, and smart cities. Features and Objectives Quality Research: Publishes original articles, reviews, and case studies that contribute to the advancement of computer sciences and AI. Interdisciplinary Approach: Encourages research that integrates computer science with other disciplines, promoting holistic innovation. Global Outreach: Welcomes contributions from all regions, supporting a global exchange of knowledge and ideas. Open Access: Facilitates free and wide access to research, empowering academic and professional communities worldwide. The journal is dedicated to promoting excellence in research and application, driving innovation, and addressing global challenges through advancements in computer sciences and artificial intelligence.
Recent Advances in AI for Data Analytics: A Review
Artificial Intelligence (AI) has greatly changed the field of data analytics through the introduction of sophisticated methods of processing, analyzing and interpreting large and complex data. This review examines the history of AI in analytics, fundamental approaches to AI, including machine learning, deep learning, natural language processing, and reinforcement learning, and their application to predictive and prescriptive analytics. It also emphasizes AI-based data processing, integration of big data, and real-life applications in such fields as healthcare, finance, and marketing. New directions like AutoML, explainable AI, and generative models are talked about as well as some of the main challenges such as privacy, bias, and scalability. The conclusion of the study is the future directions and focuses on intelligent, autonomous and ethical analytics systems.
Copyright (c) 2025 Global Journal of Computer Sciences and Artificial Intelligence https://creativecommons.org/licenses/by-nc/4.0
Intelligent Data Science: A Review of AI-Based Approaches and Frameworks
Intelligent data science is a combination of Artificial Intelligence (AI) and conventional data science, exploiting big and multicomponent data sets to uncover actionable insights. The review discusses the background knowledge, such as machine learning, deep learning, and reinforcement learning, and the ways they are used in healthcare, finance, smart cities, and IoT. The major tools and framewor66ks, like TensorFlow, PyTorch, Scikit-learn and big data platforms, are talked about in terms of scalable models development and deployment. Young issues, such as data quality, interpretability, ethical issues, and computational requirements, are examined, and new directions, such as AutoML, multi-modal learning, and real-time analytics are discussed, with an eye to the potential of AI-driven intelligent data science in the future.
Artificial Intelligence, Machine Learning, Deep Learning, Reinforcement Learning, Intelligent Data Science, Big Data, Predictive Modeling.
Responsible Data Science: Governance, Risk Management, and AI Compliance in Modern Computing
In the present-day world of computing where AI and data-driven systems play an important role in shaping vital decisions in many industries, responsible data science is necessary. This review analyzes the perspectives and governance systems, risk management plans, and mechanisms of compliance that allow ethical, transparent and accountable use of data. The essential issues are examined, namely, bias, privacy issues, and the change of regulations, whereas the tools and techniques used to tackle them involve bias detection, explainable AI, and methods of privacy preservation. Organizations can reduce the risks and enhance innovation through incorporating governance, compliance and high-order monitoring practices. The review offers best practices and the direction to build trustful, fair, and mutually socially responsible AI systems.
Responsible data science, governance, risk management, AI compliance, data quality, bias detection, accountability.
Harnessing ChatGPT for Pharmaceutics, Computing, and Environmental Health: Oral Organogels, AI-Driven Cybersecurity, and Machine Learning Insights
This review offers the interdisciplinary convergence of the pharmaceutics, computing, and environmental health, and outlines the potential transformative value of the integration of oral organogels, artificial intelligence (AI), and machine learning (ML)-driven insights. Oral organogels are novel drug delivery systems, which are much more soluble, stable, as well as releaseable. Pharmaceutical research, predictive modeling, and cybersecurity are improved with the help of AI and ML, and knowledge synthesis and interdisciplinary collaboration can be facilitated by such tools as ChatGPT. ML-based predictive analytics and risk assessment are useful in environmental health applications to enable proactive intervention. Improving the interconnection of these areas, researchers will be able to optimize therapeutic approaches, protect sensitive data, and come up with actionable findings, and this proves the power of integrated strategies to achieve better healthcare, environmental leadership, and data-driven innovation.
Oral organogels, artificial intelligence, machine learning, environmental health, ChatGPT.
ChatGPT, Machine Learning, and Cloud AI Technologies for Smarter Healthcare and Enhanced Cybersecurity
This review discusses how ChatGPT, Machine Learning (ML), and Cloud AI technologies can be integrated in changing the current state of healthcare and improving cybersecurity. It points out the ways these innovations make it possible to detect diseases early, predictively, and intelligently engage patients, and manage data securely. ChatGPT promotes communication and access, ML contributes to the accuracy of diagnosis, and cloud computing offers scalable and interoperable infrastructure. Ethical, regulatory, and cybersecurity challenges are also discussed, and the importance of privacy-preserving solutions, including searchable encryption and federated learning, is highlighted. These technologies combined create a cohesive, smart, and safe healthcare ecosystem that will encourage efficiency, personalization, and patient confidence during the digital age.
ChatGPT, Machine Learning, Cloud Computing, Healthcare AI, Cybersecurity, Data Privacy, Digital Health.
Intelligent Machines: Shaping the Future of Computer Science and Society
This paper discusses how smart machines would emerge and become a revolutionary change in computer science and society. It explores the history of artificial intelligence (AI), the technologies around which intelligent systems revolve and the ubiquitous utilization of intelligent systems in healthcare, transportation, education and finance among others. Other important issues that are discussed are algorithmic bias, lack of transparency, disruption of workforce, and ethical issues. Further down the road, the paper puts up a spotlight on future trends, such as emotional AI, general intelligence, edge computing, and quantum integration. As much as intelligent machines hold immense potential to enhance the human life, it requires responsible development, regulation and education. To live with these systems, there must be a middle-ground between innovation, ethics and teamwork to ensure that intelligent machines serve as a human booster instead of inequality or aggression.
Artificial intelligence, machine learning, intelligent machines, automation, computer vision, ethical AI.
Evolution of Health Informatics: Exploring New Frontiers in Patient-Centered Data
Health informatics experienced a revolutionary change which involved moving from paper-based healthcare systems toward digital technologies that focus on patient outcomes. This research investigates health informatics development by focusing on four significant enhancements such as electronic health records (EHRs) in addition to artificial intelligence (AI) and predictive analytics together with wearable devices that deliver better healthcare to patients through more convenient and resource-efficient methods. The transformation of healthcare data into patient-centered information allows people to better oversee their health by getting clear access to their medical information. The field encounters ongoing obstacles in both data protection and system connection standards as well as healthcare participant involvement. Mutually ethical matters involving consent rights must be addressed together with concerns about data autonomy and correct execution throughout medical choices. Health informatics presents promising prospects which include implementing block chain technology and extending virtual healthcare services for the upcoming years. Health informatics designed for patients can drive better healthcare results while it decreases disparities in care and provides a superior experience from both medical providers and their patients.
Health informatics, patient-centered data, electronic health records, artificial intelligence, predictive analytics.
Unpacking the Organizational Factors Influencing Predictive Analytics Adoption for FX Exposure Management in SMEs: A Non-Empirical Appraisal
Predominant enterprises use predictive analytics as their main instrument to control monetary perils starting from foreign exchange (FX) exposure. Small and medium-sized enterprises (SMEs) encounter substantial obstacles when it comes to implementing data-driven approaches although large corporations have already accepted this methodology. The evaluation analyzes how organizational aspects contribute to the implementation of predictive analytics for FX exposure management within SMEs along with major adoption hindrances and assistance elements. The potential of predictive analytics for risk management in SMEs depends strongly on factors which include their technological capabilities and financial resources and data access limitations and workforce expertise alongside regulatory demands. Predictive analytics solutions for SMEs need dedicated development to match their needs while training programs and policy changes will help increase wide-spread implementation. Future studies need to concentrate on building both economical and easy-to-use technology models while investigating the behavioral aspects which determine acceptance rates. SMEs who successfully overcome obstacles in adopting predictive analytics technology will build better financial stability while keeping their market competitiveness strong.
Harnessing AI-Driven Analytics, Cybersecurity, and Heat Transfer Optimization: A Multidisciplinary Strategy for Revolutionizing Healthcare, Strengthening Risk Management, and Enhancing Industrial Performance
The integration of AI-driven analytics, cybersecurity, and heat transfer optimization is transforming modern industries by enhancing efficiency, security, and sustainability. AI plays a crucial role in cybersecurity by detecting and mitigating threats while also improving thermal management systems across various sectors, including healthcare, manufacturing, and energy. This multidisciplinary approach enables real-time monitoring, predictive maintenance, and automated decision-making, leading to optimized industrial performance and risk mitigation. However, the widespread adoption of AI comes with ethical concerns, security vulnerabilities, and implementation challenges. AI biases in decision-making, lack of transparency, and privacy risks raise concerns about fairness and reliability. Additionally, AI-powered systems are vulnerable to adversarial attacks, data poisoning, and cyber threats, necessitating robust security frameworks. In heat transfer optimization, AI-driven models enhance energy efficiency, smart cooling, and industrial process management, promising significant advancements in renewable energy and sustainable technologies. This paper highlights key insights, challenges, and future prospects for AI integration in cybersecurity and heat transfer, offering recommendations for further research and industry adoption to build resilient, efficient, and secure technological ecosystems.
Comprehensive Integration of AI-Driven Analytics, Cybersecurity, and Heat Transfer Optimization: A Multidisciplinary Strategy for Advancing Healthcare, Risk Management, and Industrial Efficiency
Healthcare, risk management, and industrial efficiency are among the important areas that are changing as a result of the combination of AI-driven analytics, cybersecurity, and heat transfer optimization. While cybersecurity guarantees data protection and resistance against cyber-attacks, artificial intelligence (AI) improves predictive analytics, process automation, and decision-making. In many different industries, heat transfer optimization is essential for thermal management, energy efficiency, and operational sustainability. This multidisciplinary approach improves operational efficiency in industrial settings, fraud detection and risk reduction in finance, and diagnostics and patient data protection in healthcare. To realize its full potential, however, obstacles including data privacy, high processing needs, system integration problems, and ethical considerations must be resolved. These technologies will be further improved by next developments like as federated learning, quantum computing, and sustainable AI-driven thermal management. Collaboration between professionals in these domains will be crucial as firms embrace self-optimizing heat management systems and cybersecurity measures driven by artificial intelligence. Regulations must also change to guarantee responsible innovation, data security, and the application of AI in an ethical manner. Modern industry will continue to shape a more efficient, safe, and sustainable future through the multidisciplinary convergence of AI, cybersecurity, and heat transfer optimization.
Synergizing AI-Driven Insights, Cybersecurity, and Thermal Management: A Holistic Framework for Advancing Healthcare, Risk Mitigation, and Industrial Performance
This review explores the integration of artificial intelligence (AI), cybersecurity, and heat transfer optimization in modern industries and healthcare. AI-driven analytics enhance diagnostic accuracy, predictive maintenance, and real-time monitoring, while cybersecurity frameworks protect critical data and systems from cyber threats. Heat transfer optimization ensures energy efficiency and system reliability in healthcare devices, manufacturing processes, and data centers. The synergy among these technologies leads to improved operational safety, sustainability, and efficiency. However, challenges such as ethical concerns, data privacy, and the complexity of integrating these technologies persist. Future directions include developing autonomous systems, AI in quantum computing, and advanced cybersecurity for IoT. This multidisciplinary approach promises to transform industries, healthcare, and energy sectors, creating smarter, more resilient systems. The review highlights the potential for continuous innovation while addressing key challenges in AI, cybersecurity, and thermal management.
Integrating AI-Driven Analytics, Cyber security, and Heat Transfer Optimization: A Multidisciplinary Approach to Modern Healthcare, Risk Management, and Industrial Efficiency
Through advanced technological combinations of dimensionless analysis with artificial intelligence as well as predictive analytics and machine learning organizations can optimize heat transfer processes in industry in addition to control operational risks and achieve enhanced efficiency operations. The development of energy and industrial thermal efficiency relies on dimensionless analysis because it uses essential dimensionless numbers to transform difficult heat transfer operations into simplified forms. AI systems when combined with predictive analytics tools create perfect heat transfer processes through their ability to observe data in real-time while doing automated predictive maintenance and adjusting system controls which deliver superior performance for sustainable industrial operations. Evidence of technological effectiveness exists in the sector where power plants combine with chemical plants and renewable energy facilities to show superior system performance and energy processing efficiency. Data quality issues and integration barriers create problems for the technology along with challenges against developing better model explanation functionalities. The resolution of research barriers for highly efficient industrial systems depends on both improved data acquisition technology and explainable AI models and reinforcement learning advancements. The research evaluates how thermal energy systems join big data platforms through technological alignment to optimize industrial heat transfer operations thus improving energy sustainability targets.
AI-Powered Food Contaminant Detection: A Review of Machine Learning Approaches
Food safety is being transformed by artificial intelligence (AI), which is boosting contamination detection, real time monitoring and transparency of food supply chain. AI based techniques like machine learning, deep learning and computer vision help to detect chemical, microbial and physical contaminants in food more accurately and efficiently. These advancements have led processes to be automated, minimize the impact of human error and facilitate better decision taking. Other innovations include rapid, automated detection and traceability using AI driven spectroscopy, sensor based monitoring and block chain integration. Challenges in adopting AI, however, include fragmented and proprietary data, lack of model interpretability, the sheer implementation costs, and regulatory hurdles. Implementing AI has cost and technical challenges for small and medium sized businesses. Also, the AI models must be explainable and FMV compliant to provide the necessary transparency and reliability. Future research will consist of building upon the AI models developed in this thesis, incorporation of AI with IoT and edge computing for real time monitoring as well as setting up of ethical and regulatory frameworks. Trust in AI driven food safety will be developed with standardized AI regulations, unbiased predictions, and data privacy protections. Although AI presents some hurdles, it has the power to contribute in building a much safer, more efficient and transparent global food supply chain.
Harnessing Artificial Intelligence for Tailored Cancer Treatment: Challenges and Future Prospects
Using AI tools to personalize cancer care is helping precision medicine advance in big ways. AI can work through big healthcare information from many places - like medical images, patient genes, and personal health records - to build custom treatment plans that are better than fixed-standard treatments. AI tools help oncologists find out which treatments will work best for patients, pick the right medications, and lower side effects by looking at both their genes and overall health condition. Even though AI has great promise in healthcare, major problems keep it from being used everywhere like it should be. This study studies how AI technology can help create more individualized cancer treatment plans, looks into the problems that stand in its way of becoming routine in healthcare, and talks about what will likely guide AI integration in cancer care over the coming years. This way, through AI, collaboration, and innovation in governance, the health sector can become a gold standard in ESG leadership of the 21st century.
Copyright (c) 2025 Global Journal of Computer Sciences and Artificial Intelligence
Leveraging Artificial Intelligence for Early Lung Cancer Detection Through Advanced Imaging Analysis
Lung cancer is still the worldwide leading cause of cancer deaths. Current testing methods can find lung cancer early, but standard tools like X-rays and CT scans often miss the right diagnosis or take too long for results. AI technology is revolutionizing how medical imaging helps find, diagnose, and track lung cancer, according to studies. This study looks at how AI boosts lung cancer imaging analysis by looking closely at machine learning (ML) and deep learning (DL) techniques, especially convolutional neural networks (CNNs). AI does three important things in medical imaging: it avoids human mistakes while doing analysis and speeds up work along with keeping results as accurate as possible. It has big potential, but first we need to fix three key problems: making sure our data is accurate, understanding how our models work, and making sure everything is done ethically. This article gives a complete look at the latest AI-based technology for lung cancer imaging, discussing both the progress made and the problems that need to be dealt with before AI can help healthcare professionals.
Leveraging AI for Efficient Healthcare Workforce Management: Addressing Staffing Shortages and Reducing Burnout
Artificial intelligence (AI), health care, workforce, management, burnout Artificial Intelligence (AI) is used in the healthcare workforce management to forecast the staffing requirements and reduce burnout among healthcare workers. In clinical practice areas of the organizations, lack of personnel, staff overwork and fatigue are the main culprits of employee burnout, which has significant damaging effects to patients and healthcare employees. AI technologies, primarily ML models, can leverage past data, patients’ throughput, and staff availability, to make a better prediction of the appropriate staffing. Through AI constant forecasting of adequate staffing levels, healthcare managers can improve provision of staff scheduling and improve on overworking, or lack of sufficient workforce. Moreover, AI can help in detectable burnout causative factors that may be prevented by changing workload and providing mental health care. The subject of this paper is the use of AI in managing the healthcare workforce, including ways it can be utilized to protect against staff burnout and to maintain the functionality of healthcare systems to provide proper care for patients and sustain their employees.
Deep Learning in Precision Nutrition: Tailoring Diet Plans Based on Genetic and Microbiome Data
Personalized nutrition with the help of Artificial Intelligence (AI) is rapidly growing due to the opportunity to create individual diet plans based on genes, microbiota, and other specific indicators. Autonomous nutrition suggestions can be particularly beneficial when contrasted against outdated approaches to diets that incorporate general heath guidelines to a population. AI when applied to genetics data and microbiome sequences can offer highly customized nutrition solutions to improve not only health and avoid certain illnesses but also improve the quality of one’s life. Smart helps nutritionists and dietitians use real-time tracking technology to adjust plan, goals, and preferences of a user. In this paper, the author looks at enhancement of precision nutrition with the aid of AI in relation to metabolic health, disease, and performance. In addition, it considers the ethical issues connected to data protection and algorithms in the context of AIs used in the global approach to nutrition.
AI in Surgical Robotics: Advancing Precision and Minimizing Human Error
AI being used in surgical robotics is rapidly changing the face of surgery in the modern society, improving the results of surgery by eliminating human influence and errors, and offering numerous advantages than manual surgery. In this paper, I delve into the possibility of using AI in surgical robotics with a view to reviewing how these surgical robots enhance surgical precision, shorten healing time and enhance general patient benefits. Machines, including robotic surgeries, like the da Vinci Surgical System, offer data feedback, risk assessment, analysis, and decision regarding improved safety and efficiency of surgeries. AI thus allows more accurate procedures in such surgeries depending with Surgeons’ handiwork, lessening of variation in results, fewer cases of complications, and quicker recovery. Nevertheless, there are limits or, more precisely, issues, which relate to the subject information protection, trust to the technologies, more significant and global ethical dilemmas concerning autonomy and responsibility. Further prospects for the development of AI in surgical robots involve complete robotic operations, more accurate simulation of surgeon’s skills and improved availability of high end healthcare. In future, AI is expected play a more significant role in surgical robotics and as technology advances similarly we can expect better care and enhanced healthcare delivery.
AI for Remote Patient Monitoring: Enabling Continuous Healthcare outside the Hospital
Followings are the consequences; Over the last few years, the application of Artificial Intelligence (AI) in healthcare niche is considerably increased, particularly in telemedicine and remote patient monitoring (RPM). In this article the author discusses how AI technologies help maintain constant supervision of the patient’s condition using data collected between hospital stays. This paper reveals that RPM coupled with AI investments significantly contributes to the enhancement of chronic disease management, patient experiences, decreased readmissions and overall health care provision. Analyzing the examples of AI devices, Machine learning algorithms, and Predictive analytics this paper looks into the utility of RPM and its impact on the future of healthcare by bringing constant care and necessary timely interventions. This paper also covers the present trend of AI integration in RPM, its practical implementation, associated problems, and its potential advancement in the future.