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Total Size:
17.8 MB
Info Hash:
E2CB12914826AE09F6E5850EFB91E4FD2D62E4EF
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Added:
Sept. 30, 2025, 10:08 a.m.
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(Last updated: Sept. 30, 2025, 10:09 a.m.)
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| Mahajan S. Next-Generation Computational Intelligence Trends...Technologies 2025.pdf | 17.8 MB |
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NOTE
SOURCE: Mahajan S. Next-Generation Computational Intelligence Trends...Technologies 2025
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COVER
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MEDIAINFO
Textbook in PDF format Next-Generation Computational Intelligence: Trends and Technologies explores the transformative potential of advanced Computational Intelligence (CI) methods and their application across modern industries. With the rapid evolution of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) frameworks, this book provides a comprehensive overview of emerging CI trends and their role in shaping intelligent decision-making systems, automation, and data-driven innovation. Contributors from academia and industry present cutting-edge research on neural networks, fuzzy systems, evolutionary algorithms, and hybrid intelligent systems, emphasizing real-world applications in domains such as healthcare, finance, smart manufacturing, cybersecurity, and intelligent transportation. The book also addresses challenges in scalability, interpretability, and sustainability, offering critical insights for researchers, practitioners, and policy-makers. AI covers a wider range of subfields like Machine Learning (ML), Deep Learning (DL), natural language processing (NLP), computer vision, robotics and many more. The increasing volumes and complexity of data has made AI a route through which efficiencies in information systems can be built beyond static-complete rule-based systems. Machine learning models trained mostly on historical data can identify patterns, predict, and also automate decisions. Deep learning is a subdivision of machine learning that gave the power to systems to process unstructured data such as text, image and sound with great accuracy. The NLP has paved the way for information systems to be able to understand, interpret, and even generate human languages, thus heralding the development of intelligent virtual assistants, chatbots and language translation tools. Artificial Intelligence in information systems marks the shift from being retroactive and depending on humans to proactive and autonomous systems like self-driving cars, robotic process automation, autonomous drones. These AI-enriched systems learn from user behavior and adapt to new data and improve over time automatically without explicit reprogramming. Reinforcement Learning (RL) has become a significant subject in Artificial Intelligence (AI), allowing systems to learn and adapt by interacting with their environment. This resembles how humans and animals learn through trial and error. RL helps machines improve their decision making by responding to rewards and penalties. What once seemed nearly impossible, like mastering video games using only raw pixel data, is now achievable through deep reinforcement learning. In fact, RL agents can outperform humans in complex tasks, often without direct supervision. In RL, agents learn how to improve decision making by trial-and-error to maximize cumulative rewards. It is different from supervised and unsupervised learning because it relies on trial-and-error experiences instead of labeled data or pattern discovery. In RL problems, the best actions are discovered by agents without clear instructions or knowledge of the environment. By incorporating concepts from optimal control, dynamic programming, and behavioral psychology, RL forms a unique bridge between theory and behavior-based learning. As part of the Information Systems Engineering and Management series, this volume serves as an essential resource for graduate students, researchers, engineers, and professionals seeking to harness the power of next-generation CI to drive digital transformation and competitive advantage
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