Global Transactions on Science and Advanced Technologies
Global Transactions on Science and Advanced Technologies (GTSAT) is an international, peer-reviewed multidisciplinary journal dedicated to publishing impactful research that advances scientific knowledge and cutting-edge technological innovation. The journal serves as a global forum for researchers, academicians, engineers, and industry professionals to disseminate transformative findings that address contemporary challenges and drive next-generation solutions. GTSAT focuses on high-quality original research articles, comprehensive review papers, technical communications, and applied case studies spanning core and emerging domains of science and technology. Key areas include Artificial Intelligence and Machine Learning, Advanced Computing Systems, Smart Infrastructure, Renewable and Sustainable Technologies, Nanotechnology, Robotics and Automation, Cybersecurity, Biomedical Innovations, Materials Science, and Digital Engineering. With a rigorous double-blind peer review process and adherence to international publication ethics, Global Transactions on Science and Advanced Technologies promotes interdisciplinary collaboration and knowledge exchange across global research communities. The journal aspires to become a distinguished platform for scientific excellence, technological leadership, and innovation-driven progress worldwide.
AI-Driven Predictive Maintenance in Industrial IoT: A Review of Techniques and Applications
Predictive maintenance has become a critical application of AI in Industrial Internet of Things (IIoT) environments, enabling early fault detection and reducing operational costs. This paper provides a detailed review of machine learning and deep learning techniques used for predictive maintenance, including time-series analysis, anomaly detection, and prognostics models. It examines data acquisition from IoT sensors, edge processing, and cloud integration. The study also discusses challenges such as data quality, real-time processing, and model generalization. Future directions include self-adaptive maintenance systems and integration with digital twin technologies.
Human-Centered AI Systems: Balancing Automation, Ethics, and User Trust in Intelligent Technologies
As AI systems become deeply embedded in everyday applications, ensuring they align with human values and expectations is critical. This paper reviews the concept of human-centered AI, focusing on usability, fairness, transparency, and accountability. It examines frameworks for designing AI systems that enhance user trust while maintaining high performance. Applications in healthcare, finance, and public services are explored. The study also discusses ethical concerns such as bias, privacy, and decision accountability. Future research directions include participatory AI design, regulatory compliance, and improved human-AI interaction models.
AutoML Techniques for Automated Model Selection and Hyperparameter Optimization
Selecting optimal machine learning models and tuning hyperparameters is a time-consuming process. This research presents an AutoML framework that automates model selection using Bayesian optimization and neural architecture search. The system evaluates multiple models and configurations to identify the best-performing solution with minimal human intervention. Results indicate significant improvements in efficiency and performance, enabling faster deployment of ML solutions.