- 24 Janvier 2020
This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field.
- 13 Novembre 2022
This book presents novel work of academicians, researchers, industry professionals, practitioners, and budding engineers to disseminate the most recent innovations, trends, and concerns along with the present-day challenges and the solving approaches for implementation in the domains of data science, intelligent computing, and computer networks and security. It is a collection of selected high-quality research papers from the International Conference on Data Science, Intelligent Computing and Cyber Security (ICDIC 2020) organized by Sree Vidyanikethan Engineering College, Tirupati, India, during 27-29 February 2020. It discusses the latest challenges and solutions in the field of data innovation, data management, data analysis, data security, and intelligent methods and applications.
- 25 Mai 2023
This book presents select peer-reviewed papers from the International Conference on Robotics, Control, and Computer Vision (ICRCCV 2022). The contents focus on the latest research in the field of Robotics, their control, and computer vision in the context of robotics. The contributed papers have been arranged to give a flow to the reader. This book will be useful for students, researchers, and professionals from multidisciplinary fields such as mechanical engineering, electronics engineering, electrical engineering, computer science, and mathematics.
This book offers a detailed and comprehensive analysis of multi-aspect data learning, focusing especially on representation learning approaches for unsupervised machine learning. It covers state-of-the-art representation learning techniques for clustering and their applications in various domains. This is the first book to systematically review multi-aspect data learning, incorporating a range of concepts and applications. Additionally, it is the first to comprehensively investigate manifold learning for dimensionality reduction in multi-view data learning. The book presents the latest advances in matrix factorization, subspace clustering, spectral clustering and deep learning methods, with a particular emphasis on the challenges and characteristics of multi-aspect data. Each chapter includes a thorough discussion of state-of-the-art of multi-aspect data learning methods and important research gaps. The book provides readers with the necessary foundational knowledge to apply these methods to new domains and applications, as well as inspire new research in this emerging field.