Uddin M. Computational Intelligent Systems. Apps of AI and Machine Learning 2026

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Uddin M. Computational Intelligent Systems. Apps of AI and Machine Learning 2026

Textbook in PDF format This book contains select chapters on a broad spectrum of topics on Artificial Intelligence, Machine Learning, and computational methodologies. Each chapter offers insightful perspectives on tackling real-world challenges through intelligent algorithms and systems, from optimizing traffic management in smart cities and enhancing healthcare diagnostics with Machine Learning to advancing information security and leveraging Quantum Deep Learning for innovative applications. The book explores practical implementations, such as intelligent sensors for highway monitoring, adaptive systems for residential energy efficiency, and groundbreaking technologies for human–computer interaction. It provides a detailed analysis of the potential of computational intelligent systems to address complex issues across diverse domains, including healthcare, robotics, cybersecurity, and e-agriculture. The book is designed for researchers, students, and practitioners encapsulating the latest research findings and methodologies in the evolving landscape of intelligent systems. Whether understanding the theoretical concepts or exploring hands-on applications, readers will find this book a valuable resource for understanding the cutting-edge developments shaping the future of Computational Intelligent. Automatic Text Summarization (ATS) involves condensing long texts into concise summaries while retaining the core meaning, addressing the challenge of processing vast amounts of textual data. Traditional methods often lack the precision and efficiency required for such tasks, which requires the development of scalable result using modern Natural Language Processing models. This study proposed a deep learning-based ATS system, leveraging advanced models such as Text-to-Text Transfer Transformer (T5-small) and T5-base, Generative Pre-trained Transformers-2 (GPT-2), and Longformer Encoder-Decoder (LED) to generate summaries from a specialized psychological dataset. The LED model has been deployed via a mobile and web application using FastAPI, offering users an efficient tool for summarization and FAQs. This work demonstrates the potential of deep learning models in enhancing the quality and accuracy of text summarization, providing a robust solution for handling textual data. In the context of Automatic Text Summarization (ATS), generative models, including the Encoder-Decoder transformers, are employed to generate summaries, which are semantically equivalent to the original texts. Text summarization is primarily observed on three methodologies; extractive, abstractive, and hybrid methods. Extractive techniques focus on selecting and extracting words directly by the text to create a summary through replication. On the other hand, abstractive approaches take an innovative approach by generating fresh coherent text that captures the essence of the source material. This method typically requires comprehension of the content as it involves rewriting and rephrasing in a more concise manner while hybrid summarization combined the extractive and abstractive processes to create new sentences, resulting in more thorough and informative summaries. There has been an essential development in the amount of textual data accessible on the Internet which makes it practically impossible to process, therefore, requiring the ATS