About the Book
Since the invention of computers and other similar machines, scientists and researchers have been trying very hard to enhance their capabilities to perform various tasks. As a result, the capabilities of computers are growing exponentially day by day in terms of diverse working domains, versatile jobs, processing speed, and reduced size. Now, we are in the race to make these machines as intelligent as human beings. Artificial intelligence (AI) came up as a way of making a computer or computer software think in a similar manner to the way that humans think. AI is inspired by the study of human brain, including how humans think, learn, decide, and act while trying to solve a problem. The outcomes of this study are the basis of developing intelligent software and systems or intelligent computing (IC).
An IC system has the capabilities of reasoning, learning, problem-solving, perception, and linguistic intelligence. IC systems consist of AI techniques as well as other emerging techniques that make a system intelligent. The use of IC has been seen in almost every sub-domain of computer science such as networking, software engineering, gaming, natural language processing, computer vision, image processing, data science, robotics, expert systems, and security. Nowadays, IC is also useful for solving various complex problems in diverse domains such as for predicting disease in medical science, predicting land fertility or crop productivity in agricultural science, predicting market growth in economics, and weather forecasting.
For all these reasons, this book presents the advances in AI techniques, under the umbrella of IC. In this context, the book includes recent research that has been done in the areas of machine learning, neural networks, deep learning, evolutionary algorithms, genetic algorithms, swarm intelligence, fuzzy systems, and so on. This book discusses recent theoretical, algorithmic, simulation, and implementation-based advancements related to IC.
Table of Contents:
1. RNN Based Machine Translation for Indian Languages. 2. Dynamic Movie Ticket Price Prediction Using Machine Learning. 3. Designing a GUI for Offline Text Independent Writer Identification System. 4. Soil Fertility Prediction using Soft Computing Approach. 5. Psychological Distress Detection, Classification and Assistance using AI. 6. Fast and Efficient Text Searching in Compressed Data using Burrows Wheeler Transform. 7. A Comprehensive Exploration of Brain Tumor Segmentation using Deep Learning Technique. 8. Hand Sign Recognition using LeNet like Architecture. 9. Automated Kinship Recognition through Facial Feature Matching. 10. Ensemble Convolutional Neural Network – Bidimensional EMD Model for ECG Denoising. 11. Wrapper based Feature Selection for Big Data using Multi-core Environment. 12. MDAKB: A Metadata Driven Approach for Multi-Lingual Knowledge Base Generation for Agriculture Domain in the Indian Context .13. Melanoma Prediction from Skin Lesion using Convolutional Neural Network Against Support Vector Machine Learning Algorithm to Achieve Maximum Accuracy and Minimum Loss. 14. Fusion Based Morphological Watershed Segmentation Algorithm for Medical Images. 15. Music Recommendation System to Address the Problem of Long Tail Using Multi Stage Graph. 16. Speech based Access of Agricultural Commodity Prices and Weather Information in Kannada Language/Dialects. 17. Weather Classification: Image Embedding using Convolutional Autoencoder and Predictive Analysis using Stacked Generalization. 18. COVID-19 Confirmed Cases Prediction After Vaccination using Deep Learning Models. 19. Recognition Model for Facial Expression Using Machine Learning. 20. Sentiment Analysis and Sarcasm Detection of Indian General Election Tweets. 21. Image Generation Using Deep Convolutional Generative Adversarial Networks. 22. Person Activity Identification using Artificial Intelligence. 23. Heartbeat based Failure Detector of Perfect P Class for Synchronous Hierarchical Distributed Systems.
About the Author :
Dr. Anshul Verma received M.Tech. and Ph.D. degrees in Computer Science & Engineering from ABV-Indian Institute of Information Technology and Management Gwalior, India. He has done Post-doctorate from Indian Institute of Technology Kharagpur, India. Currently, he is serving as Assistant Professor in Department of Computer Science, Institute of Science, Banaras Hindu University Varanasi, India. He has also served as faculty member in Computer Science & Engineering Department at Motilal Nehru National Institute of Technology (MNNIT) Allahabad and National Institute of Technology (NIT) Jamshedpur, India. His research interests include IoT, Mobile ad-hoc networks, Distributed systems, Formal verification, and Mobile computing. He is serving as Editor of the Journal of Scientific Research of the Banaras Hindu University.
Dr. Pradeepika Verma received her Ph.D. degree in Computer Science & Engineering from Indian Institute of Technology (ISM) Dhanbad, India. She has received M.Tech in Computer Science & Engineering from Banasthali University, Rajasthan, India. Currently, she is working as Post-Doctoral Fellow in Department of Computer Science & Engineering at Indian Institute of Technology (BHU), Varanasi, India. She is also Associate Professor in Department of Computer Science at K L Deemed to be University, Andhra Pradesh, India and is presently on study leave. Prior to this, she has worked as Assistant Professor in Department of Computer Science & Engineering at Pranveer Singh Institute of Technology, Kanpur, India and as Faculty member in Department of Computer Application at Institute of Engineering and Technology, Lucknow, India. Her current research interests include IoT, Natural Language Processing, Optimization Approaches, and Information Retrieval.
Dr. Kiran Kumar Pattanaik holds Bachelor degree in Electrical & Electronics Engineering followed by Master and Doctorate in Computer Science & Engineering. He is presently working as Associate Professor at ABV-Indian Institute of Information Technology and Management, Gwalior, India. He is enthusiastic about exploring various engineering application domains involving Distributed and Mobile Computing, Wireless Sensor Network protocols, IoT, and Edge Computing. His Wireless Sensor Network laboratory is equipped with the necessary computing facilities (simulation and hardware) and is accessible round the clock for learning. The competitive ambiance of the laboratory is instrumental for a number of public funded research projects and high impact factor research publications. Dr. Pattanaik (Senior Member of IEEE) is a reviewer for several leading Journals in the areas of communication and networking.
Dr. Lalit Garg is a Senior Lecturer in Computer Information Systems at the University of Malta, Msida, Malta. He is also an honorary lecturer at the University of Liverpool, UK. He has also worked as a researcher at the Nanyang Technological University, Singapore and the University of Ulster, UK. He is skilled in solving complex problems using machine learning and data analytics, especially from medicine and healthcare domain. He has published over 90 technical papers in refereed high impact journals, conferences and books and has more than 622 citation count to publications, and some of his articles awarded best paper awards. He has delivered more than 20 keynote speeches in different countries, organised/chaired/co-chaired 16 international conferences and contributed as a technical committee member or a reviewer of several high impact journals and reputed conferences. He is awarded research studentship in Healthcare Modelling to carry out his PhD research studies in the faculty of computing and engineering at the University of Ulster, UK. He completed his Post Graduate in Information Technology from ABV-IIITM, Gwalior, India and his first degree in Electronics and Communication Engineering from Barkatullah University, Bhopal, India. He has also consulted numerous public and private organisations for their information system implementations.
