Global Insights in Artificial Intelligence and Computing
Global Insights in Artificial Intelligence and Computing (GIAIC) is a leading international peer-reviewed journal dedicated to showcasing the latest advancements, groundbreaking ideas, and practical applications in artificial intelligence (AI) and computing. It serves as an essential resource for researchers, industry professionals, and policymakers exploring the dynamic relationship between AI technologies and computing systems. Aims and Scope GIAIC promotes interdisciplinary research and covers a broad range of topics, including: Innovations in machine learning, deep learning, and AI algorithms. Cutting-edge computing paradigms like quantum computing, cloud computing, and edge computing. AI integration in robotics, IoT, and autonomous systems. Advances in natural language processing, computer vision, and speech recognition. Ethical challenges, policy development, and societal impacts of AI and computing. Cybersecurity solutions and AI-driven defense technologies. Applications of AI and computing in healthcare, education, smart cities, and Industry 4.0. Features and Objectives Innovative Research: Publishes original studies, in-depth reviews, and insightful case studies. Global Collaboration: Encourages diverse contributions to facilitate knowledge exchange across disciplines and regions. Real-World Impact: Focuses on the practical applications of AI and computing technologies. Open Access: Ensures free and unrestricted access to high-quality research, supporting academic and professional communities worldwide. GIAIC is committed to advancing the fields of AI and computing by providing a platform for impactful research, fostering ethical developments, and addressing global challenges with intelligent, innovative solutions.
Artificial Intelligence Techniques in Data Science: Trends, Challenges, and Future Directions
The concept of Artificial Intelligence (AI) has been incorporated into data science, allowing one to analyze large and complex data to take action. This review examines the basic AI concepts which are machine learning, deep learning and reinforcement learning and how they are used in prediction, classification, and decision-making. The new trends in this area like AutoML, Explainable AI, Edge AI, and integration with big data and IoT are mentioned, and the problem of data quality, model interpretability, scalability, and ethical issues are also addressed. Lastly, the future directions point at sophisticated algorithms, AI-based decision support, the integration of quantum computing, and governance models, which reflects the radical scope of AI in the formation of data-driven innovation.
Artificial Intelligence, Data Science, Machine Learning, Deep Learning, Predictive Analytics, Ethical AI, Quantum Computing.
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Artificial Intelligence Driven Healthcare: A Comprehensive Review of Data Analytics, Clinical Applications, and Future Directions
Artificial intelligence (AI) and data analytics are changing the nature of healthcare by providing opportunities to predict diagnostics, personalized treatment, and the efficiency of work. In this review, present-day applications are examined such as machine learning model to detect diseases, support clinical decision-making and monitor patients, and the importance of the big data in enhancing quality and accuracy. It also looks at ethical, legal, and social factors, like bias, transparency, and privacy of data, and issues, such as interoperability, regulatory obstacles, and model generalizability. Future trends are focused on federated learning, multimodal AI, digital twins, and AI-robotics integration, which are in the direction of intelligent and patient-centered healthcare systems. Deployment is a crucial responsibility that should be deployed to get the maximum.
AI, healthcare, data analytics, machine learning, predictive diagnostics, patient privacy, ethical considerations
Bridging Pharmaceutics, Computing, and Environmental Health: A Comprehensive Review of Oral Organogels, AI & Cybersecurity Applications, and Machine-Learning–Driven Insights
This is a review of interdisciplinary convergence of the field of pharmaceutics, cancer medicine, computing, artificial intelligence (AI), and machine learning (ML), as well as environmental health with respect to innovative approaches to improving healthcare. Oral organogels are mentioned as potential effective drug-delivery systems with the ability to enhance solubility, stability, and controlled-release, especially of anticancer agents. The developments of cancer medicine such as precision oncology and immunotherapy prove the usefulness of tailored therapies, whereas AI and ML contribute to diagnostics, predictive modeling and treatment planning. Holistic viewpoint on external factors affecting disease and treatment is offered by environmental health insights coupled with the use of computational tools. The review highlights synergistic strategies, issues, and the perspective, presenting the opportunities of interdisciplinary innovation to safer, more efficient, and patient-centered medical solutions.
Oral organogels, cancer medicine, pharmaceutics, artificial intelligence, machine learning, cybersecurity, drug delivery, precision oncology.
A Comprehensive Review of AI-Driven Healthcare and Health Informatics with Emphasis on Machine Learning, Nanocarrier Drug Delivery, Computational Methods, Cybersecurity, and Quality Assurance
Healthcare and health informatics AI solutions are changing the future of the medical profession by facilitating predictive, personalized, and efficient medical care. This review examines the incorporation of machine learning, nanocarrier drug delivery, computational methods, cybersecurity, and quality assurance of healthcare systems. Machine learning improves diagnostics, predictive analytics, and personalization of treatment, and computational methods improve the process of molecular modeling, data analysis, and optimization of nanocarriers. Nanocarriers allow them to deliver drugs that will have a high level of therapeutic effectiveness and safety because they are delivered effectively and in a controlled way. Cybersecurity protects sensitive medical information and AI systems by threats, and quality assurance mechanisms guarantee the model reliability, regulatory, and ethical use. These technologies have the ability to approach the future of precision and intelligent healthcare delivery because of its synergistic integration which enhances an overall healthcare ecosystem that promotes innovation, patient safety, and better clinical outcomes.
AI-driven healthcare, health informatics, machine learning, nanocarrier drug delivery, computational methods, cybersecurity, quality assurance.
Integrating Quality Assurance and Cyber Defense in Generative AI Applications for Healthcare Systems
Generative Artificial Intelligence (AI) is transforming the medical sphere, making it possible to achieve high-quality diagnostics, individual treatment, and informed choices. But its increased usage is causing a serious issue of data integrity, system reliability, and cybersecurity. The paper reviews how Quality Assurance (QA) and the Cyber Defense models can be combined to make healthcare systems using generative AI to be safe, accurate, and trustworthy. It looks at major methodologies, issues and new solutions that reconcile AI innovation to ethical, regulatory and security norms. Through the integration of QA validation and military-grade cyber defense, healthcare organizations will be able to have resilient, transparent, and secure AI applications that will eventually result in patient trust and improvement of overall quality of digital healthcare delivery.
Generative Artificial Intelligence, Quality Assurance, Cyber Defense, Healthcare Systems, Patient Safety, Regulatory Frameworks.
Automation Meets Accuracy: A Deep Dive into AI for Quality Assurance
This review discusses the transformative quality assurance (QA) of the Artificial Intelligence (AI) through the lens of the way intelligent technologies can make industries more precise, efficient, and decision-making. With a combination of machine learning, computer vision, as well as predictive analytics, AI-powered QA systems transition to proactive quality control as opposed to reactive inspection. The paper explains the history of QA, fundamental AI technologies, industry applications, advantages and the challenges related to it. It also describes the future trends that focus on explainable AI, human-machine cooperation, and real-time monitoring. All in all, the research highlights how AI has the potential to transform the quality systems to make them accurate, consistent, and continuously improved in contemporary industries.
Artificial Intelligence, Quality Assurance, Machine Learning, Industry 4.0, Explainable AI, Continuous Improvement.
AI, ChatGPT, and Machine Learning in Healthcare and Cybersecurity: Insights from Cloud Analytics, Graph Neural Networks, and Intelligent Systems
This review examines the role of Artificial Intelligence (AI), ChatGPT, Machine Learning (ML), and cybersecurity in the contemporary healthcare systems. Graph Neural Networks (GNN) or cloud-based analytics, which are AI-based models, improve disease detection, predictive diagnostics, and personalized treatment. ChatGPT assists with clinical decision-making, interaction with patients, and interpretation of data by means of natural language processing. At the same time, intrusion detection and searchable encryption security systems based on graphs protect confidential medical information. Combining these technologies, it becomes possible to have intelligent, secure, and adaptable healthcare ecosystems. The review identifies the developments in the present, ethical concerns, and future perspectives on the realization of trustworthy, data-driven, and cyber-resilient healthcare innovations in the quest to enhance clinical efficiency and patient safety.
AI, ChatGPT, Machine Learning, Healthcare, Cybersecurity, Cloud Analytics, Graph Neural Networks, Data Encryption
Foundations and Frontiers in Theoretical Computer Science: A Review of Key Concepts and Open Problems
The survey looks into the foundations and the frontiers of Theoretical Computer Science (TCS) which is a central bit of computing to understand capabilities, limitations, and models of computation. Its concentration areas are the automata theory, computability, complexity theory, logic, and algorithms, along with the multidisciplinary overlap with quantum computing, cryptography and geometry, and machine learning. The new advances--in interactive proofs and zero-knowledge protocols and in quantum complexity--all indicate that the field still has its youthful vitality. Such important open issues as P vs NP and circuit lower bounds still exist to make the study. First-order effects include allowing TCS to provide leverage to secure communication, efficient algorithms, formal verification and theory of artificial intelligence. Looking to the future, it can be said that there are several possible issues on the horizon in the sphere in the sense of both quantum computing and ethical AI and biological computing, respectively. The design, analysis and development of computer science is still centered on TCS, and it is important to consider theory and practice through the integration of the two.
Computability, complexity, algorithms, cryptography, quantum computing, learning theory, formal verification, interactive proofs.
Bridging Technology and Medicine: A Review of Emerging Trends in Health Informatics
Health informatics transforms healthcare practice through its combination of advanced technology features which include artificial intelligence (AI) and big data and telemedicine and wearable devices. The innovative healthcare solutions improve diagnosis precision as well as individualized medical care to better serve patients. Health informatics faces ongoing barriers which involve protecting patient data and making different systems communicate properly and bridging the gap between individuals who have access to technology and those who do not. Health informatics' prospective developments will create future systems that use predictions and prevention as well as provide virtual hospital services and simplify global healthcare data access and sharing while healthcare operators need to solve privacy ethics issues that stem from AI bias and transparency problems. Assurance of accessibility along with security and effectiveness will become essential criteria as these health technologies continue to develop so they can improve health outcomes for diverse community populations. Various sectors must work together to establish an integrated efficient healthcare system based on equity during the upcoming years.
Health informatics, artificial intelligence, big data, telemedicine, wearable devices, predictive healthcare.
Virtual Replicas in Manufacturing: AI-Powered Modeling for Workflow Enhancement and Predictive Optimization
Original and Virtual Replicas that are managed by Artificial Intelligence are changing the shape or rather the functioning of manufacturing industries or enterprises for better by giving simulations, real-time optimization, and improved decisions. These intelligent models employ AI, IoT and machine learning features for more streamlined working, less time-off, and for managing available resources. In this case, Virtual Replicas contribute to improving the quality control, cutting down on wastage, and reducing the overall expenses of operation through efficient real-time and supply chain management for maintenance and product predictability. That being said, edge computing, 5G, and self-learning AI are new and better ways to create Virtual Replicas. They reveal that its usage will continue to grow in various sectors within the near future and its benefit in enhancing energy efficient and minimizing wastes in SMEs. Considering the concept of Virtual Replicas become more and more popular, the AI-driven Virtual Replicas will be even more significant for the future of smart manufacturing. Companies that will adopt this technology will be at an advantage than their competitors and organization will experience better productivity, stability and robustness in the face of a more digitalized and bureaucratic industrial environment.
Securing Healthcare in the Age of AI: A Comprehensive Review of Cybersecurity Challenges and Solutions
AI in the context of healthcare can be defined as a process of broadening the options on the improvement of the patients’ quality of their treatment, an increase in productivity and decrease in the rates of diagnosis failure. But there are huge cyber risks, including the protection of patients’ data and information, the model it adopted in AI, and primarily the security risks of healthcare networks. This review looks at the policy, which applies to the AI healthcare cybersecurity, how health data security is addressed, how the core of the AI model is protected, the issue of access, and lastly, monitoring as the final control. Subsequently, it presents new directions and opportunities in AI-based healthcare cybersecurity such as federated learning and homomorphic encryption and block chain. In addition, the review also explains the place of explanation and ethical evaluation regarding the construction of trust and accountabilities in AI solutions. Future endeavors imply that with the advance of applications and technologies concerning artificial intelligence, healthcare of today in both its practical implementation and future research, modern care providers, AI software developers, legislators, and IT security experts must cooperate to identify existing and potentially malicious threats towards introducing artificial intelligence into the healthcare systems of the future safely, securely, and in compliance with certain ethical standards. When these firms apply IT technical professional in AI to respect the regulations and ethical conducts the fn is subjected to cyber security tests and still maintain the data of the patients require treatment while using AI.
Artificial Intelligence in Healthcare: Safeguarding Data and Enhancing Information Access through Cybersecurity
The use of AI in healthcare cybersecurity is rapidly growing, transforming how organizations protect patient data from emerging threats. This article highlights key considerations when integrating AI to enhance cybersecurity, focusing on risk prediction, vulnerability detection, cryptography, and compliance with essential security requirements. AI can proactively predict risks, enabling mitigation of cyber-attacks. Additionally, block chain enhances data integrity for IoT and IoMT security, while AI strengthens user authentication. These innovations offer promising solutions to future threats. However, improving AI’s role in managing cybersecurity requires expanding AI-based technologies to enhance data protection and response times. Advances such as federated learning ensure patient privacy while improving data security. Despite AI’s benefits, healthcare organizations must navigate ethical, legal, and technological challenges to prevent further erosion of patient trust. A balanced approach is essential to leveraging AI’s potential while maintaining security and compliance in an evolving digital healthcare landscape.
Intersection of AI and Cybersecurity: Protecting Healthcare Data in a Connected World
The release of Artificial Intelligence in healthcare cybersecurity is innovative as it safeguards the patient’s data, recognizes threats while its occurrence and address cyber threats. However, its adoption raises concern with the following issues and concerns relating to data privacy, the law and ethics. In the following review, the different ways that AI enhances healthcare cybersecurity threat enumeration, data safeguarding, and reaction are examined. The principles of HIPAA and GDPR as well as the remaining principles concerning legislative requirements are also introduced in order to establish the knowledge on how healthcare organizations are required to keep with the rules of privacy and security of data. Some of the ethical concerns which arise when it comes to the issue of use of artificial intelligence in a healthcare organization include; In addition, the review outlines emerging AI solutions two of which are efficient but safeguard privacy preservation methods which are federated learning and differential privacy. Thus while relying on growth potential of AI in the enhancement of health sector cybersecurity: The implementation should be done carefully to respect patients’ rights and privacy, Shield laws and ethical standards.
Revolutionizing Healthcare, Risk Management, and Industrial Efficiency through AI-Driven Analytics, Cybersecurity, and Heat Transfer Optimization
The integration of AI-driven analytics, cybersecurity, and heat transfer optimization is revolutionizing healthcare, risk management, and industrial efficiency. AI enhances data-driven decision-making, enabling predictive analytics for improved diagnostics, risk assessment, and operational efficiency. Cybersecurity safeguards critical digital infrastructures, protecting sensitive data from evolving threats in healthcare and industrial sectors. Meanwhile, heat transfer optimization enhances energy efficiency, ensuring optimal thermal management in medical equipment, manufacturing processes, and energy systems. The synergy of these interdisciplinary technologies fosters innovation, resilience, and sustainability, driving progress in modern industries. However, challenges such as ethical concerns, regulatory compliance, and implementation barriers must be addressed to maximize their potential. Future research will focus on enhancing AI transparency, strengthening cybersecurity frameworks, and advancing thermal management techniques. This multidisciplinary approach promises to transform industries by improving security, efficiency, and sustainability, paving the way for a smarter, safer, and more resilient future.
Role of AI in Food Production and Preservation
Through the revolution of the Artificial Intelligence (AI) in the food production and preservation, efficiency, sustainability and food security are enhanced. In the real world, AI powered precision agriculture and autonomous crop monitoring, and robotics for best utilization of resources and production yields. The food processing also is made AI driven so that the quality is controlled, less waste is generated, and supply chain logistics is improved. From preservation, AI offers chilled chain management, spoilage detection and smart packaging improving shelf life and fighting food waste. While AI comes with many benefits, it faces challenges like high implementation costs, data privacy concerns, and ethical concerns that prevent their adoption. Yet, there is a promising future of AI in food industries, with autonomous farming, predictive analytics, and personalization of nutrition brought into play to facilitate a more efficient and sustainable food system. As AI continues to fill the role of reversing food security, decreasing environmental impact, and increasing global food quality, it is crucial for application in agriculture and food preservation in the future.
Pioneering Advances in AI-Driven Detection and Therapy for Mental Health Challenges
Mental health disorders create widespread health problems for hundreds of millions of people every year across our world. Traditional clinical processes work well but specific issues make them difficult to access and hard to scale up. Our study shows how AI technologies can improve mental health diagnosis and treatment by overcoming present system limitations. Through AI technology that combines machine learning and natural language processing with neural networks the system can spot health problems sooner, design unique care plans, and provide immediate therapeutic help. The research investigates both ethical issues and technical challenges when integrating AI technologies into mental health care while showing the need for AI implementation that serves everyone fairly. Through this research we forecast artificial intelligence will enhance mental health treatment delivery and make it both more effective and easier to reach while tailoring services to individual patients.
The Neurological Nexus: Exploring EEG, Facial Recognition, and Graph Algorithms in Mental Health AI
The spread of depression anxiety and schizophrenia as mental health disorders creates demanding diagnostic and therapeutic hurdles for healthcare providers. Conventional patient examination requires patient words and doctor observation next to being slow and feeling dependent. New artificial intelligence technology helps doctors make better mental health diagnoses. This research studies how combining EEG readings with facial detection technology and graph analysis helps doctors better recognize mental health problems and treat them. EEG machines measure brain activity whereas facial recognition tools study emotional reactions to predict psychological states. Graph algorithms help us understand and interpret connections between neurological measurements. This work studies how different technologies work together and what computer processing problems they bring alongside ethical risks during mental health programs. By bringing these tools together we aim to improve disease detection methods and support both immediate medical tracking plus individual treatment programs.
Advanced AI Paradigms in Mental Health: An In-depth Exploration of Detection, Therapy, and Computational Efficacy
This study reveals how AI changes mental health diagnosis and therapy through advanced systems that find and help people with depression. We explore how the VPSYC platform uses AI to assess mental health conditions and then show an EEG-based solution that can detect depression and anxiety. The research looks at how well facial recognition can perform while considering its expenses and how cat swarm optimization helps resolve difficult computing issues like graph coloring. Our analysis studies the algorithms' behavior and effectiveness as we identify their value in developing mental health tools. This research method helps advance discussions about using AI in mental health services by showing what AI can do today and what it might achieve in the future.
AI-Driven Mental Health Detection: Integrating Brain Signals, Facial Data, and Advanced Optimization Techniques
The use of Artificial intelligence technology has revolutionized the methods we use to detect and handle mental health problems. This book studies how AI technologies detect mental health conditions including depression and anxiety at their most advanced level today. EEG detectors, facial recognition systems, and algorithms like the cat swarm algorithm form a complete mental healthcare framework according to this book. Researchers examine how the VPSYC AI platform affects personalized mental health support through real-time detection and treatment of depression. The book examines how AI works with EEG systems that track brain wave patterns to identify mental health conditions. This research compares how deep learning and traditional computer vision facial recognition systems work to detect emotional distress. The book evaluates optimization methods to find how the cat swarm algorithm might improve mental health evaluation processes.
AI-Powered Virtual Health Assistants: Transforming Patient Care and Engagement
Analyzing the possibilities of virtual health assistants and chatbots, this research paper reveals the importance of innovation in the sphere of patient interaction with the help of AI. An example of virtual health assistants is artificial intelligence (AI) and ML, being highly valuable in promoting patient engagement, informing patients via the internet, and providing early and instant medical advice. These advanced technologies powered by Artificial Intelligence are allowing patients to engage with their doctors and overall healthcare applications in such a localized and efficient way that the communications divide between patients and healthcare practitioners, especially for regular appointments, check-ups, or simple questions and answers, is removed. The paper reviews the many functionalities of otherwise health-related AI-based virtual assistants, including symptom checkers and administration, medication and disease management, and lifestyle modification, so as to demonstrate the benefits of this technology for healthcare delivery and for increasing health access, especially in marginalized settings. Nonetheless, this paper is to show the drawbacks and risks which are associated with implementation of AI, such as the question of patients’ data confidentiality, security issues of personal health information, and the questions of AI’s proper regulation in decision-making processes. These challenges explain why proper regulatory requirements and patients’ awareness have to be developed to increase belief in those technologies.
Integration of AI in Medical Imaging: Enhancing Diagnostic Accuracy and Workflow Efficiency
The application of AI in medical imaging has gained momentum over the years and is revolutionizing the quality of health facilities' diagnosis quality and increasing the efficiency of work experience. Advanced technologies such as ML and DL are used to study big volumes of medical data more effectively than conventional approaches. This paper discusses the use of AI in medical imaging with particular reference to diagnostic precision and efficiency. A number of techniques provided by machine learning, such as pattern identification, decrease the rate of misdiagnosis and offer clinicians means to make more accurate diagnoses in a timely manner. Furthermore, applying artificial intelligence in image recognition and initial evaluation takes a large load on radiologists and can help them concentrate on difficult cases only. Data privacy is another challenge; not all the data can be shared publicly and accessed by anyone, Algorithmic transparency, and Bias. This paper also brings into focus the further prospects of AI use in medical imaging, as the deployment of this technology is expected to develop superior diagnosis techniques in the future. In conclusion, the use of AI in medical imaging has the potential to improve health outcomes, cut operation costs, and increase efficiency in medical practices internationally.