Satchidananda Dehuri – författare

EEG Brain Signal Classification for Epileptic Seizure Disorder Detection provides the knowledge necessary to classify EEG brain signals to detect epileptic seizures using machine learning techniques. Chapters present an overview of machine learning techniques and the tools available, discuss previous studies, present empirical studies on the performance of the NN and SVM classifiers, discuss RBF neural networks trained with an improved PSO algorithm for epilepsy identification, and cover ABC algorithm optimized RBFNN for classification of EEG signal. Final chapter present future developments in the field.

This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need the most recent and promising automated techniques for EEG classification.

This book addresses many-criteria decision-making (MCDM), a process used to find a solution in an environment with several criteria. In many real-world problems, there are several different objectives that need to be taken into account. Solving these problems is a challenging task and requires careful consideration. In real applications, often simple and easy to understand methods are used; as a result, the solutions accepted by decision makers are not always optimal solutions. On the other hand, algorithms that would provide better outcomes are very time consuming. The greatest challenge facing researchers is how to create effective algorithms that will yield optimal solutions with low time complexity. Accordingly, many current research efforts are focused on the implementation of biologically inspired algorithms (BIAs), which are well suited to solving uni-objective problems.

This book introduces readers to state-of-the-art developments in biologically inspired techniques and their applications, with a major emphasis on the MCDM process. To do so, it presents a wide range of contributions on e.g. BIAs, MCDM, nature-inspired algorithms, multi-criteria optimization, machine learning and soft computing. 

This book covers a range of basic and advanced topics in software engineering. The field has undergone several phases of change and improvement since its invention, and there is significant ongoing research in software development, addressing aspects such as analysis, design, testing and maintenance. Rather than focusing on a single aspect of software engineering, this book provides a systematic overview of recent techniques, including requirement gathering in the form of story points in agile software, and bio-inspired techniques for estimating the effort, cost, and time required for software development. As such it is a valuable resource for new researchers interested in advances in software engineering — particularly in the area of bio-inspired techniques.

This book contains the papers presented at the First International Conference on Innovations in Intelligent Computing and Communication, ICIICC 2021, held in Bhubaneswar, Odisha, India, in December, 2022.

The 31 full papers presented were thoroughly reviewed and selected from 78 submissions. They are divided in three tracks with the following topics: ​Intelligent Computing; Communications; and Machine Learning and Data Analytics.

This book presents the recent advances including tools and techniques in the constantly changing landscape of machine learning (ML). This would enable the readers with a strong understanding of critical issues in ML by providing both broad and detailed perspectives on cutting-edge theories, algorithms, and tools. This will become a single source of reference on conceptual, methodological, technical, and managerial issues, as well as provide insight into emerging trends and future opportunities in the discipline of ML. This book contains altogether 36 chapters in the area of ML and its applications.

This book includes selected high-quality research papers presented at 3rd International Conference on Biologically Inspired Techniques in Many Criteria Decision Making (BITMDM 2024) organized by School of Engineering and Technology, Nagaland University, Dimapur, India on 6th and 7th December 2024. This book presents the recent advances in techniques which are biologically inspired and their usage in the field of single and many criteria decision making. Further, the topics covered in this book are divided into different sections like: i) healthcare and biomedical applications, ii) security, fraud detection, and cybersecurity, iii) intelligent systems and decision support, iv) agriculture and environment, v) image processing and multi-media analysis, and vi) emerging technologies and applications.

This book explores the transformative effects of AI and ML on software engineering. It emphasizes the potential of cutting-edge software development technologies such as Generative AI and ML applications. This book incorporates data-driven strategies across the entire software development life cycle, from requirements elicitation and design to coding, testing, and deployment. It illustrates the evolution from traditional frameworks to agile and DevOps methodologies. The potential of Generative AI for automating repetitive tasks and enhancing code quality is highlighted, along with ML applications in optimizing testing, effort estimation, design pattern recognition, fault prediction, debugging, and security through anomaly detection. These techniques have significantly improved software development efficiency, predictability, and project management effectiveness. While remarkable progress has been made, much remains to be done in this evolving area. This edited book is a timely effort toward advancing the field and promoting interdisciplinary collaboration in addressing ethical, security, and technical challenges.

This book advances the artificial intelligence techniques by strongly inspired from quantum computing. Quantum computing (QC), rooted in the mysterious principles of quantum mechanics, has fascinated scientists, researchers, and visionaries for a long time. Its potential lies in changing how we compute, moving from classical bits to quantum bits (qubits), and unlocking incredible processing capabilities. At the same time, artificial intelligence (AI) has been on a trajectory of rapid advancement, with deep learning becoming proficient at tasks that were once deemed impossible. At this juncture, integrating these two fields through various means can play a pivotal role to bring a technological revolution.