Over the last two decades, researchers are looking at imbalanced data learning as a prominent research area. Many critical real-world application areas like finance, health, network, news, online advertisement, social network media, and weather have imbalanced data, which emphasizes the research necessity for real-time implications of precise fraud/defaulter detection, rare disease/reaction prediction, network intrusion detection, fake news detection, fraud advertisement detection, cyber bullying identification, disaster events prediction, and more. Machine learning algorithms are based on the heuristic of equally-distributed balanced data and provide the biased result towards the majority data class, which is not acceptable considering imbalanced data is omnipresent in real-life scenarios and is forcing us to learn from imbalanced data for foolproof application design. Imbalanced data is multifaceted and demands a new perception using the novelty at sampling approach of data preprocessing, an active learning approach, and a cost perceptive approach to resolve data imbalance. Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance offers new aspects for imbalanced data learning by providing the advancements of the traditional methods, with respect to big data, through case studies and research from experts in academia, engineering, and industry. The chapters provide theoretical frameworks and the latest empirical research findings that help to improve the understanding of the impact of imbalanced data and its resolving techniques based on data preprocessing, active learning, and cost perceptive approaches. This book is ideal for data scientists, data analysts, engineers, practitioners, researchers, academicians, and students looking for more information on imbalanced data characteristics and solutions using varied approaches.

Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction algorithms, tree-based algorithms, and density-based algorithms. A number of machine learning related algorithms have been derived from these four algorithms. Consequently, they represent excellent underlying methods for extracting hidden knowledge from unstructured data, as essential data mining tasks. Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. It presents research of energy efficient accelerators for machine learning algorithms. Covering topics such as control-flow implementation, approximate computing, and decision tree algorithms, this book is an essential resource for computer scientists, engineers, students and educators of higher education, researchers, and academicians.

Even though many data analytics tools have been developed in the past years, their usage in the field of cyber twin warrants new approaches that consider various aspects including unified data representation, zero-day attack detection, data sharing across threat detection systems, real-time analysis, sampling, dimensionality reduction, resource-constrained data processing, and time series analysis for anomaly detection. Further study is required to fully understand the opportunities, benefits, and difficulties of data analytics and the internet of things in today’s modern world. New Approaches to Data Analytics and Internet of Things Through Digital Twin considers how data analytics and the internet of things can be used successfully within the field of digital twin as well as the potential future directions of these technologies. Covering key topics such as edge networks, deep learning, intelligent data analytics, and knowledge discovery, this reference work is ideal for computer scientists, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.

Medical imaging provides medical professionals the unique ability to investigate and diagnose injuries and illnesses without being intrusive. With the surge of technological advancement in recent years, the practice of medical imaging has only been improved through these technologies and procedures. It is essential to examine these innovations in medical imaging to implement and improve the practice around the world. The Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention investigates and presents the recent innovations, procedures, and technologies implemented in medical imaging. Covering topics such as automatic detection, simulation in medical education, and neural networks, this major reference work is an excellent resource for radiologists, medical professionals, hospital administrators, medical educators and students, librarians, researchers, and academicians.

This book includes high-quality papers presented at the Second International Symposium on Computer Vision and Machine Intelligence in Medical Image Analysis (ISCMM 2021), organized by Computer Applications Department, SMIT in collaboration with Department of Pathology, SMIMS, Sikkim, India, and funded by Indian Council of Medical Research, during 11 – 12 November 2021. It discusses common research problems and challenges in medical image analysis, such as deep learning methods. It also discusses how these theories can be applied to a broad range of application areas, including lung and chest x-ray, breast CAD, microscopy and pathology. The studies included mainly focus on the detection of events from biomedical signals.

The introduction of social media has given many communities the opportunity to connect and communicate with each other at a higher level than ever before. Many organizations, from businesses to governments, have taken advantage of this important tool to conduct research and enhance efficiency. Libraries and educational institutions have also made use of social media to enhance educational marketing, engage with learning communities, adapt educational tools, and more. The Research Anthology on Applying Social Networking Strategies to Classrooms and Libraries describes the applications, tools, and opportunities provided by the intersection of education and social media. It also considers the ways in which social media encourages learner engagement and community participation. Covering topics such as data collection, online professional learning networks, and reinforcement learning, this major reference work is a dynamic resource for pre-service teachers, teacher educators, faculty and administrators of both K-12 and higher education, librarians, archivists, government officials, researchers, and academicians.

The two-volume set LNAI 14115 and 14116 constitutes the refereed proceedings of the 22nd EPIA Conference on Progress in Artificial Intelligence, EPIA 2023, held in Faial Island, Azores, in September 2023. The 85 full papers presented in these proceedings were carefully reviewed and selected from 163 submissions. The papers have been organized in the following topical sections: ambient intelligence and affective environments; ethics and responsibility in artificial intelligence; general artificial intelligence; intelligent robotics; knowledge discovery and business intelligence; multi-agent Systems: theory and applications; natural language processing, text mining and applications; planning, scheduling and decision-making in AI; social simulation and modelling; artifical intelligence, generation and creativity; artificial intelligence and law; artificial intelligence in power and energy systems; artificial intelligence in medicine; artificial intelligence and IoT in agriculture; artificial intelligence in transportation systems; artificial intelligence in smart computing; artificial intelligence for industry and societies.

This book presents the proceedings of the 9th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2021), held at NIT Mizoram, Aizwal, Mizoram, India, during June 25 – 26, 2021. FICTA conference aims to bring together researchers, scientists, engineers, and practitioners to exchange their new ideas and experiences in the domain of intelligent computing theories with prospective applications to various engineering disciplines. This volume covers broad areas of Evolution in Computational Intelligence. The conference papers included herein presents both theoretical as well as practical aspects of different areas like ANN and genetic algorithms, human-computer interaction, intelligent control optimization, evolutionary computing, intelligent e-learning systems, machine learning, mobile computing, multi-agent systems, etc. The volume will also serve as a knowledge centre for students of post-graduate level in various engineering disciplines.

The rise of internet and social media usage in the past couple of decades has presented a very useful tool for many different industries and fields to utilize. With much of the world’s population writing their opinions on various products and services in public online forums, industries can collect this data through various computational tools and methods. These tools and methods, however, are still being perfected in both collection and implementation. Sentiment analysis can be used for many different industries and for many different purposes, which could better business performance and even society. The Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines discusses the tools, methodologies, applications, and implementation of sentiment analysis across various disciplines and industries such as the pharmaceutical industry, government, and the tourism industry. It further presents emerging technologies and developments within the field of sentiment analysis and opinion mining. Covering topics such as electronic word of mouth (eWOM), public security, and user similarity, this major reference work is a comprehensive resource for computer scientists, IT professionals, AI scientists, business leaders and managers, marketers, advertising agencies, public administrators, government officials, university administrators, libraries, students and faculty of higher education, researchers, and academicians.

Post COVID-19 pandemic, researchers have been evaluating the healthcare system for improvements that can be made. Understanding global healthcare systems’ operations is essential to preventative measures to be taken for the next global health crisis. A key part to bettering healthcare is the implementation of information management and One Health. The Handbook of Research on Essential Information Approaches to Aiding Global Health in the One Health Context evaluates the concepts in global health and the application of essential information management in healthcare organizational strategic contexts. This text promotes understanding in how evaluation health and information management are decisive for health planning, management, and implementation of the One Health concept. Covering topics like development partnerships, global health, and the nature of pandemics, this text is essential for health administrators, policymakers, government officials, public health officials, information systems experts, data scientists, analysts, health information science and global health scholars, researchers, practitioners, doctors, students, and academicians.