Global Journal of Emerging AI and Computing
Global Journal of Emerging AI and Computing The Global Journal of Emerging AI and Computing (GJEAC) is a leading international, peer-reviewed journal dedicated to publishing high-quality research in the rapidly evolving fields of artificial intelligence (AI) and computing which is published by Global Alliance for Scientific Studies. The journal serves as a platform for researchers, academicians, and industry professionals to share innovative ideas, cutting-edge developments, and transformative applications in these dynamic areas. Key Areas of Focus GJEAC emphasizes interdisciplinary research and covers a broad range of topics, including but not limited to: Machine learning and deep learning innovations. AI ethics, policies, and societal impacts. Emerging technologies in computing, such as quantum computing and edge computing. Natural language processing, computer vision, and robotics. AI-driven solutions in healthcare, education, finance, and other domains. Big data analytics and cloud computing advancements. Cybersecurity and AI integration. Features and Objectives Quality Research: Publishes original research articles, comprehensive reviews, and case studies. Global Perspective: Encourages contributions from diverse regions, fostering a global dialogue on AI and computing. Innovation-Driven: Focuses on emerging trends and disruptive technologies shaping the future of computing and AI. Open Access: Ensures wide dissemination of knowledge to academics, practitioners, and policymakers. The journal is committed to advancing the frontiers of knowledge in AI and computing, bridging the gap between theoretical research and real-world applications while promoting responsible and sustainable technological growth.
AI-Driven Cybersecurity in Healthcare: The Transformative Potential of Generative AI
The healthcare industry is experiencing increasing cybersecurity threats because of the increasing digitization of medical records, Telemedicine and connected devices. Conventional security tools are not usually effective at dealing with such advanced cybercrime as ransom ware, phishing, or advanced persistent attacks. Generative AI has the potential to be transformed through artificial intelligence (AI), in which it can be used to predict potential threats and identify anomalies and offer automated responses. This review examines the usage, advantages, and issues of generative AI in healthcare cybersecurity, its use in protecting sensitive patient information, improving operational efficiency, and promoting proactive protection. Ethical aspects, integration issues and future are also done.
AI, Generative AI, Cybersecurity, Healthcare, Data Privacy, Predictive Modeling, Digital Health.
Copyright (c) 2025 Murad Khan, Ahmad Bacha (Author) https://creativecommons.org/licenses/by-nd/4.0
AI in Quality Assurance: A Systematic Review
This review of the literature examines how Artificial Intelligence (AI) can be incorporated into Quality Assurance (QA) in the various sectors. It explores the application of AI methods (Machine Learning, Deep Learning, Computer Vision, and Natural Language Processing) in increasing the defect detection, optimization of processes, and forecast quality control. The review establishes important trends, applications, advantages and issues relating to AI-driven QA systems through a thorough review of the latest research works. The results indicate that AI can contribute greatly to the level of accuracy, speed, and decision-making and allows the proactive quality regulation. Nonetheless, there are problems of data quality, interpretability and implementation costs that persist. The paper is summed up with some insights into the emerging technologies and the research directions of the future.
Artificial Intelligence, Quality Assurance, Machine Learning, Deep Learning, Computer Vision, Predictive Quality Management.
Copyright (c) 2025 Muhammad Mohsin Kabeer (Author) https://creativecommons.org/licenses/by-nd/4.0
A Review of Secure Cloud-Based Deep Learning and ChatGPT Applications in Healthcare
The development of deep learning, natural language processing, and cloud computing security is changing the face of healthcare using artificial intelligence (AI). In this review, I discuss the intersection between deep learning and ChatGPT in cloud-based healthcare systems, the opportunities, the challenges, and future directions. Deep learning allows making correct diagnoses, predictive analytics, and treatment, and ChatGPT improves the process of patient interaction, clinical records, and medical education. The cloud offers the required infrastructure to support the activation of scalability and teamwork but it poses a challenge of privacy, security and compliance. There is a need to address matters of trust, interpretability and ethical governance. Collectively, the technologies present an avenue to a more secure path of transparent and patient-centered innovation in healthcare.
Artificial Intelligence, Deep Learning, ChatGPT, Cloud Computing, Healthcare Security, Explainable AI.
Copyright (c) 2025 Mohammad Ali (Author) https://creativecommons.org/licenses/by-nd/4.0
Fifty Years of Computer Science: Trends, Milestones, and Emerging Challenges
The article under a review examines the development of computer science, the latest tendencies, and the prospects of computer science utilizing its revolutionary potential impact in a variety of realms. It perceives facilities like algorithms, hardware, and artificial intelligence, in addition to the emerging research opportunities, including quantum computing and human-computer interactions. The article focuses on the societal impacts of computing with reference to the contribution in the area of healthcare, education and governance, and includes the most critical issues in the field of privacy, ethics and sustainability. In addition, it also indicates that cross-disciplinary research is a must to develop responsible innovation to overcome some of the life-threatening issues in the world. With the ongoing impact of computer science in the future, it will be necessary to balance development and technological advancement and ethics. This review provides a wide scope of this rapidly evolving subject, and the goal of extensive information is to provide the researcher, practitioners, and policymakers about the influence of computer science in the current world so profound.
Computer science, artificial intelligence, quantum computing, cybersecurity, sustainable computing, societal impact.
Copyright (c) 2025 Ankur singh (Author) https://creativecommons.org/licenses/by-nd/4.0
Advances and Challenges in Health Informatics: Shaping the Future of Digital Healthcare
Health informatics combines healthcare practices with IT solutions to enhance the delivery of medical services by increasing both their operational effectiveness and quality and their accessible reach. The medical landscape underwent major transformation because of its progress from basic computer administrative applications toward including AI technology and machine learning algorithms and big data analysis functionalities. Medical advancement depends on four essential elements which include Electronic Health Records (EHRs) and Clinical Decision Support Systems (CDSS) and telemedicine and mobile health (mHealth) for better patient services and clinical operational enhancement. The implementation of health informatics deals with multiple barriers which consist of patients' privacy issues alongside security threats and complicated system integration and expensive implementation and provider reluctance to change. Reaching equal access to advanced technologies represents an essential challenge that needs successful resolution among diverse geographic areas and communities. Achieving complete health informatics benefits requires addressing current obstacles which stand as barriers to improved patient results and more efficient and less expensive health services at both international and global levels.
Health Informatics, Electronic Health Records (EHRs), Artificial Intelligence (AI), Data Privacy and Security, Interoperability.
Copyright (c) 2025 Mohammed Javeedullah (Author) https://creativecommons.org/licenses/by-nd/4.0
Modern AI in Green Logistics: Smart Systems for Sustainability and Carbon Reduction
Green AI has emerged as a new powerful tool of logistics in this present modern world which has helped optimize supply chain operations and at the same time the emission of greenhouse gases had also been reduced. Mores, artificial intelligence systems such as integrated intelligent predictive analytics, the IoT Approach, application of optimal routes, and automated warehouses are making a positive contribution to sustainability through the efficiency reduction of fuel consumption, optimal inventory, and management of transportation networks. This paper review is based on the topic of AI and the way it is being employed in green logistics, particularly in the reduction of environmental footprint, optimization of resources and setting up of environmentally friendly last one kilometer conveying solutions. In this regard, it also provides faces of the most successful companies that have been adopting sustainable AI-facilitated initiatives. There are certain challenges that organization face while adopting AI in logistics some of which are the high cost of the implementation, data security, skills of the workers, and compatibility with legacy systems. With a focus on corporate social responsibility as well as the effective management of resources at the government level for sustainable supply chain, Artificial Intelligence technology will have a big part to play in the development of new and efficient transport system.
Copyright (c) 2025 Akshay Anil Kale (Author) https://creativecommons.org/licenses/by-nc/4.0
Role of Artificial Intelligence and Deep Learning in Healthcare, Cyber security, and Food Systems: Innovations, Challenges, and Future Perspectives
Through deep learning and artificial intelligence the healthcare and cyber security sectors can work faster and smarter while maintaining secure food systems across our planet. Al technology helps medical specialists diagnose better and find security threats early while guiding farmers with precise crop actions. Several important barriers including ethical problems and legal restrictions make progress more difficult. The future will develop AI systems that work fairly with clear processes while uniting with new technology types. AI development require responsible methods and ethics to create value with minimal safety hazards. Well-planned use of AI systems helps companies solve global issues in their business sectors.
Copyright (c) 2025 George Edison (Author) https://creativecommons.org/licenses/by-nc/4.0
Artificial Intelligence and Deep Learning in Healthcare, Cyber security, and Food Systems: A Comprehensive Review of Applications, Challenges, and Future Directions
The implementation of Artificial Intelligence in healthcare alongside cyber security and food systems produces enhanced efficiency and security functions together with automated processes. Healthcare organizations use AI-powered diagnostic instruments together with predictive data tools and individualized medical approaches to enhance therapeutic results while shortening drug development cycles. AI applications need proper solution to algorithmic biases while protecting data privacy and respecting ethical values for the sake of transparent and fair implementation. Security frameworks which are resilient to emerging exploitative AI techniques together with human oversight have become essential because AI-powered threat detection and automated responses enhance cyber security defenses against cyber-attacks. The upcoming stage of AI advancement will concentrate on three main areas which combine explain ability, trustworthy systems and knowledge transfer between different sectors. Global healthcare advancement along with improved cyber security protection and enhanced food system management are possible through interdisciplinary AI techniques and appropriate challenge management which ensures transparency, security and sustainable practices.
Copyright (c) 2025 Ali Husnain (Author) https://creativecommons.org/licenses/by-nc/4.0
Revolutionizing Healthcare, Cyber security, and Food Systems: The Power of AI, Deep Learning, and Robotics for a Sustainable Future
AI systems along with deep learning and robotics technologies help companies innovate across healthcare and cyber security while developing better food production methods and fighting against global environmental issues. These technologies show great potential in saving time and resources plus making better decisions. Healthcare adapts faster by using AI technology that helps doctors identify illnesses, supports surgery robots, and predicts diseases. AI technologies now defend our digital systems better than ever by discovering and reacting to new forms of cyber dangers. Both AI automation systems make agriculture more productive while boosting environmental protection throughout food supply chains. The benefits AI brings come with important challenges and moral concerns. Our focus needs to remain on AI model fairness to protect data privacy and preserve jobs to make sure AI systems work correctly. The General Data Protection Regulation (GDPR) and privacy-enhancing AI methods must be enforced to protect personal data from security threats. The future outcome of AI technology depends on its successful present-day use to create sustainable benefits that everyone can access fairly. Our present decisions set the course for AI development which determines its effects on industries, economies and society until the next generations.
Copyright (c) 2025 Bimalendu Pendy (Author) https://creativecommons.org/licenses/by-nc/4.0
Integrating AI, Deep Learning, and Robotics: Transforming Healthcare, Cyber security, and Food Systems for a Sustainable Future
AI, deep learning and robotics are rising as significant shifts across the healthcare, information security, the food industry and supply chain. These technologies facilitate automation, increase effectiveness and effectiveness in decision making process, and therefore tackle global issues such as food insecurity, cyber-crime, and management of natural resources. AI application in farming and food storage increases efficiency and decreases spoilage; AI has a strong aspect of security as it is capable of identifying and analyzing threats within a short span of time. In the more general scope of health care, the uses include diagnostics, endoscopic and robotic surgeries, drug discovery, etc., to help the patients. However, it is crucial that problems like algorithm and data prejudice, privacy, employment losses, restrictions, and rules of ethics should be solving before implementing them. It is significant for the policymakers, industry giant and the researchers to come together to reap the benefits in order to avoid or reduce the risks that are associated with poor implementation of the policy. IT the responsible approach to embedding artificial intelligence allows industries to progress deeply, protect the natural environment, and affect economic development, thus shaping a more liberal, safe, and wise future.
Copyright (c) 2025 Alaxendra Harry (Author) https://creativecommons.org/licenses/by-nc/4.0
Quantum Computing in US Banking: The Future of Fraud Prevention and Financial Crime Detection
As an attempt to fight sophisticated financial crimes, US banks are getting into new technologies on this front, and quantum computing can be viewed as a game changer in this area. Unmatched computational power is available through quantum technology for use in fraud detection that is ahead of its time, real-time transaction monitoring, and quantum resistant encryption. Quantum computing, therefore, offers solutions for financial institutions that are currently facing threats of cyber-attacks, insider fraud, and compliance to various regulations with its quantum enhanced artificial intelligence (QAI) and quantum safe encryption. The adoption of the quantum computing, however, is met with a great number of challenges, such as high implementation costs, a shortage of qualified staff and constantly changing cybersecurity threats. Quantum powered cyber-attacks requires transition from the classical cryptography to the post quantum cryptography to protect sensitive banking data. Regulatory compliance is still a major issue even for banks, and the goal for the industry is to collaborate between banks, tech firms, and government agencies in order to create industry wide security standards. For US banks to prepare for this quantum powered future, they will need to spend in research, develop strategic partnerships, and train their cybersecurity professionals. As quantum technology is adopted at the beginning, quantum fraud prevention technologies
Copyright (c) 2025 Ashok Ghimire (Author) https://creativecommons.org/licenses/by-nc/4.0
Modern Trends in Food Production: The Role of AI in Smart Food Factories
The integration of Artificial Intelligence (AI) in food production is revolutionizing the industry by enhancing efficiency, improving food safety, and driving sustainability. Smart food factories powered by AI are optimizing production processes through automation, predictive maintenance, and real-time quality control. AI-driven supply chain management is reducing food waste, ensuring better resource allocation, and streamlining logistics. Furthermore, AI is playing a crucial role in developing personalized nutrition and alternative protein sources, catering to evolving consumer demands. Despite its numerous benefits, AI adoption in food manufacturing faces challenges such as high implementation costs, data privacy concerns, and workforce displacement. Overcoming these obstacles requires investment in AI training, regulatory frameworks, and ethical AI deployment. Looking ahead, advancements in robotics, block chain integration, and AI-powered 3D food printing will further shape the future of food production. By addressing these challenges and leveraging AI responsibly, the food industry can create safer, more efficient, and sustainable food production systems for the future.
Copyright (c) 2025 Khuram Shehzad, Akhtar Munir, Umair Ali (Author) https://creativecommons.org/licenses/by-nc/4.0
Synaptic Harmonies: Applying Graph Coloring Algorithms to Mental Health AI Systems
Current healthcare systems need advanced technology answers to treat effectively the mental disorders patients’ experience. Artificial Intelligence shows us multiple ways to improve mental health treatment customized specifically for everyone. Graph coloring principles help us design better mental health AI technologies. Our graph theory techniques help improve how AI mental health solutions match patients to therapists while distributing resources effectively. Graph coloring lets us structure how patients, therapists, and resources work together so our scheduling and resource use stay conflict-free and efficient. Our approach lets healthcare providers design unique treatment sessions that cater to different patient requirements. Our research shows how combining medical treatment systems and computer algorithms can organize better mental healthcare delivery. By combining mathematical systems and mental health AI tools we create a better way to handle both large numbers of patients and improve their treatment quality.
Copyright (c) 2025 Shahrukh Khan Lodhi (Author) https://creativecommons.org/licenses/by-nc/4.0
Hues of Mental Health: Harnessing Cat Swarm Optimization and Deep Learning for Anxiety and Depression Detection
Systems that provide health care face major problems because anxiety and depression disorders affect people more now across the entire world. Researchers find that traditional testing approaches based on patient interviews tend to produce doubtful results. This study looks at how combining Cat Swarm Optimization and Deep Learning algorithms can help doctors find anxiety and depression sooner at the start of the problem. The CSO optimization technique finds the best features of data and deep neural networks help study and process complex datasets. The researchers develop a better mental health diagnosis system using both Deep Learning and Cat Swarm Optimization. The integrated system produces better results than traditional methods and detects mental health conditions at higher speed.
Copyright (c) 2025 Ghaith Alomari (Author) https://creativecommons.org/licenses/by-nc/4.0
AI-Enhanced Depression and Anxiety Detection: Integrating EEG Systems, Performance-Cost Trade-Offs, and Optimization Algorithms
Millions of people worldwide now experience depression and anxiety as primary mental health conditions. The early detection of mental health issues lets us stop their continued deterioration. Modern medical practitioners choose EEG as their preferred noninvasive brain activity assessment tool which helps detect mental health disorder patterns. The research presents a new EEG-based sadness and anxiety detection system which integrates artificial intelligence technology. Through our study we built a real-time mental health detection system that unites system functioning with optimal solutions to deliver efficient diagnostic results immediately. We integrated advanced machine learning technologies with EEG data to build a framework that enhances both the affordability and accessibility of detection processes across multiple medical settings.
Copyright (c) 2025 Heta Hemang Shah (Author) https://creativecommons.org/licenses/by-nc/4.0
Innovative AI Solutions for Mental Health: Bridging Detection and Therapy
This paper examines how artificial intelligence systems can spot depression symptoms and develop treatment plans. We analyze the combination of physical healthcare devices with software applications to evaluate their worth and costs alongside AI face detection tools and optimization methods. The analysis reveals how expert teams combine their knowledge to develop effective artificial intelligence tools that assist people with mental health challenges. Our research proved that combining mental healthcare and AI technology leads to better diagnosis outcomes while providing tailored treatment solutions for global mental health support.
Copyright (c) 2025 Hira Zainab, Ali Raza A Khan, Muhammad Ismaeel Khan, Aftab Arif (Author) https://creativecommons.org/licenses/by-nc/4.0
AI in Personalized Medicine: Transforming Treatment Plans through Precision Health
Integrating The application of AI in healthcare has advanced over the last decade and is most prominent in personalized medicine. Due to ML and DL algorithms, AI is helping in better prognosis, customized interventions, and better results in a number of health disorders. It seeks to offer customized treatment according to the various features of each patient, and not generic methods like were initially used. Big data is being incorporated with the help of AI in individualized medicine by using elements like genetics, past health records, and surroundings to constitute threats of diseases and recommend particular courses of treatment. This paper seeks to review the various aspects of AI in personalized medicine, the application of AI, the advantages of AI, the disadvantages of AI, and the future of AI in precision health.
Copyright (c) 2025 Global Journal of Emerging AI and Computing
Leveraging AI for Real-Time Depression Detection in Healthcare Systems; a Systematic Review
Pursuant to this rationale, this paper examines the possibilities of AI application in mental health assessment with special reference to proactive monitoring of depression among health-care systems. Depression is still one of the most widespread and most serious mental health disorders in the modern society. Routine assessment techniques are critical, but they can be conventional, and so it, diagnosis of depression may not be made when it is still in its early stage. Thus, for the first time, AI provides a chance to overcome this trend by allowing constant monitoring and early detection in practice. This study examines using AI approaches, including machine learning algorithms, natural language processing, and sentiment analysis to improve depression detection, treatment recommendations and managing chronic care. The paper also presents the limitations of AI implementation in the context of health care, potential ethical concerns, data protection problems, and integration problems. Last but not the least, envisagement’s of how the future AI is going to transform the mental health care sector are presented in the last section.
Predicting Depression Trajectories: A Novel AI Approach for Personalized Mental Health Treatment
Depression is a widespread mental illness that affects millions of people worldwide and is a problem for health care. In contrast with modern approaches, traditional treatment strategies do not rely on individuality, specifically about the course of the disease. In this paper, a new AI-based method to identify individual-specific depression trajectories for improving the outcomes of depression treatments is proposed. This, we believe, makes our strategy more effective for predicting the outcomes of specific treatment techniques for each patient since it employs up-to-date machine learning techniques and integrates a vast volume of patient information into modeling patient response. The paper shows how using customized estimations can increase the effectiveness of a treatment plan as opposed to relying on trial and error as it is today. AI in mental health care not only has potential to increase the quality of treated condition, it also has the ability to provide efficient solution to increase demand of depressants all over the world. This paper supports the view that AI can revolutionize mental health treatment since it makes it targeted, time and effectiveness oriented.
The Role of Artificial Intelligence in Early Disease Detection: Current Applications and Future Prospects
Artificial intelligence (AI) has evolved as an innovative technology in the field of health care and the diagnosis of diseases at an early stage. AI systems employ large data sets, including medical images, patient history, and continuous real-time tracking, to diagnose diseases at early stages and thus learn how to act and bring about a healthier change. This paper focuses on the current uses of AI in early disease detection, its efficiency, and its drawbacks. The paper also attempts to conceptualize the future application of AI technologies for enhancing diagnostic precision, customizing treatment regimens, and providing economical solutions to healthcare organizations globally. Yet, there are certain drawbacks, such as data privacy, the issue of algorithm bias, and the problem of regulative efficiency in health care with the help of AI. The further advancement and evolution of AI applications in disease diagnosis are in machine learning and deep learning technologies, including establishing guidelines as prerequisites for making diagnostic AI services safe and effective in healthcare.