The book is a collection of papers presented at First Doctoral Symposium on Natural Computing Research (DSNCR 2020), held during 8 August 2020 in Pune, India. The book covers different topics of applied and natural computing methods having applications in physical sciences and engineering. The book focuses on computer vision and applications, soft computing, security for Internet of Things, security in heterogeneous networks, signal processing, intelligent transportation system, VLSI design and embedded systems, privacy and confidentiality, big data and cloud computing, bioinformatics and systems biology, remote healthcare, software security, mobile and pervasive computing, biometrics-based authentication, natural language processing, analysis and verification techniques, large scale networking, distributed systems, digital forensics, and human–computer interaction.

Multinational organizations have begun to realize that sentiment mining plays an important role for decision making and market strategy. The revolutionary growth of digital marketing not only changes the market game, but also brings forth new opportunities for skilled professionals and expertise. Currently, the technologies are rapidly changing, and artificial intelligence (AI) and machine learning are contributing as game-changing technologies. These are not only trending but are also increasingly popular among data scientists and data analysts. New Opportunities for Sentiment Analysis and Information Processing provides interdisciplinary research in information retrieval and sentiment analysis including studies on extracting sentiments from textual data, sentiment visualization-based dimensionality reduction for multiple features, and deep learning-based multi-domain sentiment extraction. The book also optimizes techniques used for sentiment identification and examines applications of sentiment analysis and emotion detection. Covering such topics as communication networks, natural language processing, and semantic analysis, this book is essential for data scientists, data analysts, IT specialists, scientists, researchers, academicians, and students.

The book is a collection of peer-reviewed best-selected research papers presented at the International Conference on Advances in IoT and Security with AI (ICAISA 2023), organized by Deen Dayal Upadhyaya College, University of Delhi, New Delhi, India, in collaboration with University of Canberra, Canberra, Australia, and NIT, Arunachal Pradesh, Itanagar, AP, India, during March 24–25, 2023. The book includes various applications and technologies in this specialized sector of Industry 4.0. The book is divided into two volumes. It focuses on recent advances in Internet of Things and security with its applications using artificial intelligence.

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.

This book is focused on the impact of the COVID-19 pandemic on different sectors, i.e., education, real estate, health, and agriculture. The lockdown has been announced to control the spread of COVID-19 infections, however people/industries/organizations were not ready for lockdown and it has greatly affected their growth. The front workers in the healthcare sector suffered a lot as major responsibilities they needed to carry on. The education sector is also hampered due to the pandemic as schools, colleges were closed and teaching, examinations were carried out on online platforms. These platforms were new to teachers as well as students. The real estate sector faced tremendous loss in this pandemic as people were scared and no one ready to invest their money in such an uncertain time. The agriculture filed is also suffered as raw materials required for agriculture were not available readily due to pandemic. This book presents a survey that is conducted to understand the impact of COVID-19 on the quality of work-life in various sectors. The survey is focused majorly on four sectors, i.e. education, healthcare, real estate and agriculture. Data analysis is done based on responses of survey and mathematical modeling is provided for each case study.

DIGITIZATION OF HEALTHCARE DATA USING BLOCKCHAIN The book gives a detailed description of the integration of blockchain technology for Electronic Health Records and provides the research challenges to consider in various disciplines such as supply chain, drug discovery, and data management. The aim of the book is to investigate the concepts of blockchain technology and its association with the recent development and advancements in the medical field. Moreover, it focuses on the integration of workflow strategies like NLP, and AI which could be adopted for boosting the clinical documentation and electronic healthcare records (EHR) usage by bringing down the physician EHR data entry. Also, the book covers the usage of smart contracts for securing patient records. Digitization of Healthcare Data Using Blockchain presents the practical implementations that deal with developing a web framework for building highly usable healthcare applications, a simple blockchain-powered EHR system. Audience Researchers in information technology, artificial intelligence, electronics engineering, medical informatics, as well as policymakers and healthcare providers and management systems.