Next Generation Ai
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Next Generation AI Language Models in Research
In this comprehensive and cutting-edge volume, Qureshi and Jeon bring together experts from around the world to explore the potential of artificial intelligence models in research and discuss the potential benefits and the concerns and challenges that the rapid development of this field has raised. The international chapter contributor group provides a wealth of technical information on different aspects of AI, including key aspects of AI, deep learning and machine learning models for AI, natural language processing and computer vision, reinforcement learning, ethics and responsibilities, security, practical implementation, and future directions. The contents are balanced in terms of theory, methodologies, and technical aspects, and contributors provide case studies to clearly illustrate the concepts and technical discussions throughout. Readers will gain valuable insights into how AI can revolutionize their work in fields including data analytics and pattern identification, healthcare research, social science research, and more, and improve their technical skills, problem-solving abilities, and evidence-based decision-making. Additionally, they will be cognizant of the limitations and challenges, the ethical implications, and security concerns related to language models, which will enable them to make more informed choices regarding their implementation. This book is an invaluable resource for undergraduate and graduate students who want to understand AI models, recent trends in the area, and technical and ethical aspects of AI. Companies involved in AI development or implementing AI in various fields will also benefit from the book’s discussions on both the technical and ethical aspects of this rapidly growing field.
Next-Generation AI Methodologies in Education
Next-generation artificial intelligence (AI) methodologies revolutionize education landscapes, offering innovative approaches to enhance learning experiences and streamline administrative processes. These cutting-edge technologies utilize machine learning, natural language processing, and adaptive algorithms to create personalized learning environments tailored to individual student needs. By harnessing data-driven insights, educators can identify learning gaps, facilitate collaborative experiences, and foster critical thinking skills. As AI evolves, further research into its integration in education may improve academic outcomes while preparing students for digital literacy and adaptability. Next-Generation AI Methodologies in Education explores the intersection of AI and educational practices, examining how AI revolutionizes teaching methodologies in secondary schools and colleges. It investigates the emerging discipline of AI-driven education, highlighting its potential to enhance personalized learning, increase educational accessibility, and transform traditional pedagogical methods. This book covers topics such as pre-service teaching, professional development, and digital technology, and is a useful resource for computer engineers, education professionals, academicians, business owners, researchers, and scientists.
Next-Generation Artificial Intelligence Driven Smart and Renewable Energy
To provide for a sustainable future, the potential synergies at the dynamic intersection of renewable energy (RE) incorporated with smart energy and artificial intelligence (AI) must be exploited. RE is crucial to preserve the environment. Energy involving various systems must be optimized and assessed to provide better performance. However, the design and development of RE systems remains a challenge. Advanced optimization techniques, AI, and machine learning (ML) plays a crucial role in implementing the latest innovative research in the field of renewable energy-integrated electrical systems. This book also describes the practical challenges encountered, and the solutions and future scope to be adopted. Applications of a variety of advanced optimization and AI techniques in the design and development of RE-integrated systems are discussed to provide new solutions in the RE domain. Key features: Discusses modern modeling/control approaches for improving renewable energy integrating artificial intelligence-driven power systems Describes the principles and methods of renewable energy generation technologies, and an analysis of their implementation, management, and optimization, and related economic advantages Presents critical information on the technological design and policy issues that must be taken into considered while implementing a smart grid Explains of the metaheuristic optimization algorithm for complex electrical systems, and the whale optimization algorithm-based multi-objective hydrothermal scheduling Covers the electric vehicle charging station in the distribution network, and transient stability constraint optimal power flow problem using chaotic quasi-oppositional chemical reaction optimization The topics covered including microgrids, wind power, solar photo voltaic (PV), optimal power flow (OPF), grid connected inverter, electric vehicle, combined heat and power economic dispatch, FACTS tools for smart energy, harmonic impedance of a salient pole synchronous generator (HI), maximum power point tracking (MPPT) and advanced optimization techniques. Next Generation Artificial Intelligence-Driven Smart and Renewable Energy is ideal for academicians, practitioners, teachers, engineers, industry professionals, researchers, and students in diverse fields, including electrical engineering, electronics and communications engineering, energy, and environmental engineering.