Visual And Text Sentiment Analysis Through Hierarchical Deep Learning Networks
DOWNLOAD
Download Visual And Text Sentiment Analysis Through Hierarchical Deep Learning Networks PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Visual And Text Sentiment Analysis Through Hierarchical Deep Learning Networks book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Visual And Text Sentiment Analysis Through Hierarchical Deep Learning Networks
DOWNLOAD
Author : Arindam Chaudhuri
language : en
Publisher: Springer
Release Date : 2019-04-06
Visual And Text Sentiment Analysis Through Hierarchical Deep Learning Networks written by Arindam Chaudhuri and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-06 with Computers categories.
This book presents the latest research on hierarchical deep learning for multi-modal sentiment analysis. Further, it analyses sentiments in Twitter blogs from both textual and visual content using hierarchical deep learning networks: hierarchical gated feedback recurrent neural networks (HGFRNNs). Several studies on deep learning have been conducted to date, but most of the current methods focus on either only textual content, or only visual content. In contrast, the proposed sentiment analysis model can be applied to any social blog dataset, making the book highly beneficial for postgraduate students and researchers in deep learning and sentiment analysis. The mathematical abstraction of the sentiment analysis model is presented in a very lucid manner. The complete sentiments are analysed by combining text and visual prediction results. The book’s novelty lies in its development of innovative hierarchical recurrent neural networks for analysing sentiments; stacking of multiple recurrent layers by controlling the signal flow from upper recurrent layers to lower layers through a global gating unit; evaluation of HGFRNNs with different types of recurrent units; and adaptive assignment of HGFRNN layers to different timescales. Considering the need to leverage large-scale social multimedia content for sentiment analysis, both state-of-the-art visual and textual sentiment analysis techniques are used for joint visual-textual sentiment analysis. The proposed method yields promising results from Twitter datasets that include both texts and images, which support the theoretical hypothesis.
Proceedings Of Fifth International Conference On Computing Communications And Cyber Security
DOWNLOAD
Author : Paulo J. Sequeira Gonçalves
language : en
Publisher: Springer Nature
Release Date : 2024-12-04
Proceedings Of Fifth International Conference On Computing Communications And Cyber Security written by Paulo J. Sequeira Gonçalves and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-04 with Technology & Engineering categories.
This book features selected research papers presented at the Fifth International Conference on Computing, Communications, and Cyber-Security (IC4S'05) Volume 2, organized in India, during 8th–9th April, 2024. The conference was hosted at GEHU, Bhimtal Campus in India . It includes innovative work from researchers, leading innovators, and professionals in the areas of communication and network technologies, advanced computing technologies, data analytics and intelligent learning, the latest electrical and electronics trends, and security and privacy issues. The work is presented in two volumes.
Deep Learning Based Approaches For Sentiment Analysis
DOWNLOAD
Author : Basant Agarwal
language : en
Publisher: Springer Nature
Release Date : 2020-01-24
Deep Learning Based Approaches For Sentiment Analysis written by Basant Agarwal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-24 with Technology & Engineering categories.
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.
Emerging Technologies In Data Mining And Information Security
DOWNLOAD
Author : Aboul Ella Hassanien
language : en
Publisher: Springer Nature
Release Date : 2021-05-04
Emerging Technologies In Data Mining And Information Security written by Aboul Ella Hassanien and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-04 with Technology & Engineering categories.
This book features research papers presented at the International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS 2020) held at the University of Engineering & Management, Kolkata, India, during July 2020. The book is organized in three volumes and includes high-quality research work by academicians and industrial experts in the field of computing and communication, including full-length papers, research-in-progress papers and case studies related to all the areas of data mining, machine learning, Internet of things (IoT) and information security.
Deep Learning And Reinforcement Learning
DOWNLOAD
Author :
language : en
Publisher: BoD – Books on Demand
Release Date : 2023-11-15
Deep Learning And Reinforcement Learning written by and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-15 with Computers categories.
Deep learning and reinforcement learning are some of the most important and exciting research fields today. With the emergence of new network structures and algorithms such as convolutional neural networks, recurrent neural networks, and self-attention models, these technologies have gained widespread attention and applications in fields such as natural language processing, medical image analysis, and Internet of Things (IoT) device recognition. This book, Deep Learning and Reinforcement Learning examines the latest research achievements of these technologies and provides a reference for researchers, engineers, students, and other interested readers. It helps readers understand the opportunities and challenges faced by deep learning and reinforcement learning and how to address them, thus improving the research and application capabilities of these technologies in related fields.
Multi Modal Sentiment Analysis
DOWNLOAD
Author : Hua Xu
language : en
Publisher: Springer Nature
Release Date : 2023-11-26
Multi Modal Sentiment Analysis written by Hua Xu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-26 with Computers categories.
The natural interaction ability between human and machine mainly involves human-machine dialogue ability, multi-modal sentiment analysis ability, human-machine cooperation ability, and so on. To enable intelligent computers to have multi-modal sentiment analysis ability, it is necessary to equip them with a strong multi-modal sentiment analysis ability during the process of human-computer interaction. This is one of the key technologies for efficient and intelligent human-computer interaction. This book focuses on the research and practical applications of multi-modal sentiment analysis for human-computer natural interaction, particularly in the areas of multi-modal information feature representation, feature fusion, and sentiment classification. Multi-modal sentiment analysis for natural interaction is a comprehensive research field that involves the integration of natural language processing, computer vision, machine learning, pattern recognition, algorithm, robot intelligent system, human-computer interaction, etc. Currently, research on multi-modal sentiment analysis in natural interaction is developing rapidly. This book can be used as a professional textbook in the fields of natural interaction, intelligent question answering (customer service), natural language processing, human-computer interaction, etc. It can also serve as an important reference book for the development of systems and products in intelligent robots, natural language processing, human-computer interaction, and related fields.
Advanced Data Mining And Applications
DOWNLOAD
Author : Xiaochun Yang
language : en
Publisher: Springer Nature
Release Date : 2021-01-05
Advanced Data Mining And Applications written by Xiaochun Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-05 with Computers categories.
This book constitutes the proceedings of the 16th International Conference on Advanced Data Mining and Applications, ADMA 2020, held in Foshan, China in November 2020. The 35 full papers presented together with 14 short papers papers were carefully reviewed and selected from 96 submissions. The papers were organized in topical sections named: Machine Learning; Text Mining; Graph Mining; Predictive Analytics; Recommender Systems; Privacy and Security; Query Processing; Data Mining Applications.
Machine Learning In Forensic Evidence Examination
DOWNLOAD
Author : Niha Ansari
language : en
Publisher: CRC Press
Release Date : 2025-09-15
Machine Learning In Forensic Evidence Examination written by Niha Ansari and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-15 with Social Science categories.
The availability of machine-learning algorithms, and the immense computational power required to develop robust models with high accuracy, has driven researchers to conduct extensive studies in forensic science, particularly in the identification and examination of evidence found at crime scenes. Machine Learning in Forensic Evidence Examination discusses methodologies for the application of machine learning to the field of forensic science. Evidence analysis is the cornerstone of forensic investigations, examined for either classification or individualization based on distinct characteristics. Artificial intelligence offers a powerful advantage by efficiently processing large datasets with multiple features, enhancing accuracy and speed in forensic analysis to potentially mitigate human errors. Algorithms have the potential to identify patterns and features in evidence such as firearms, explosives, trace evidence, narcotics, body fluids, etc. and catalogue them in various databases. Additionally, they can be useful in the reconstruction and detection of complex events, such as accidents and crimes, both during and after the event. This book provides readers with consolidated research data on the potential applications and use of machine learning for analyzing various types of evidence. Chapters focus on different methodologies of machine learning applied in different domains of forensic sciences such as biology, serology, physical sciences, fingerprints, trace evidence, ballistics, anthropology, odontology, digital forensics, chemistry and toxicology, as well as the potential use of big data analytics in forensics. Exploring recent advancements in machine learning, coverage also addresses the challenges faced by experts during routine examinations and how machine learning can help overcome these challenges. Machine Learning in Forensic Evidence Examination is a valuable resource for academics, forensic scientists, legal professionals and those working on investigations and analysis within law enforcement agencies.
Computational Vision And Bio Inspired Computing
DOWNLOAD
Author : S. Smys
language : en
Publisher: Springer Nature
Release Date : 2022-03-30
Computational Vision And Bio Inspired Computing written by S. Smys and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-30 with Technology & Engineering categories.
This book includes selected papers from the 5th International Conference on Computational Vision and Bio Inspired Computing (ICCVBIC 2021), held in Coimbatore, India, during November 25–26, 2021. This book presents state-of-the-art research innovations in computational vision and bio-inspired techniques. The book reveals the theoretical and practical aspects of bio-inspired computing techniques, like machine learning, sensor-based models, evolutionary optimization and big data modeling and management that make use of effectual computing processes in the bio-inspired systems. It also contributes to the novel research that focuses on developing bio-inspired computing solutions for various domains, such as human–computer interaction, image processing, sensor-based single processing, recommender systems and facial recognition, which play an indispensable part in smart agriculture, smart city, biomedical and business intelligence applications.
Generative Ai And Large Language Models
DOWNLOAD
Author : Aditya Pratap Bhuyan
language : en
Publisher: Aditya Pratap Bhuyan
Release Date : 2024-07-24
Generative Ai And Large Language Models written by Aditya Pratap Bhuyan and has been published by Aditya Pratap Bhuyan this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-24 with Computers categories.
Artificial Intelligence is reshaping our world, and at the forefront of this revolution are Generative AI and Large Language Models (LLMs). This book, "Generative AI and Large Language Models: Revolutionizing the Future," offers an in-depth exploration of these groundbreaking technologies, delving into their foundations, development, and profound implications for various industries and society as a whole. Starting with a historical overview of AI, the book traces the evolution of machine learning and deep learning, setting the stage for understanding the rise of generative AI. Readers will discover the inner workings of LLMs, from their advanced neural network architectures to the massive datasets and computational power required for their training. Key models, such as the Generative Pre-trained Transformer (GPT) series, are examined in detail, showcasing their remarkable capabilities in natural language processing and beyond. The book also addresses the ethical and social challenges posed by these powerful technologies. Issues such as bias, fairness, and privacy are discussed, alongside the need for transparent and accountable AI systems. Through real-world applications and case studies, readers will see how generative AI is transforming fields like healthcare, finance, content creation, and more. Looking ahead, the book explores future trends and innovations, highlighting potential advancements and the ongoing research aimed at enhancing AI's efficiency and multimodal capabilities. It envisions a future where AI and humans collaborate more closely, driving progress and innovation across all domains. "Generative AI and Large Language Models: Revolutionizing the Future" is an essential read for anyone interested in the cutting-edge of AI technology. Whether you are a researcher, practitioner, or simply curious about the future of AI, this book provides a comprehensive and accessible guide to the transformative power of generative AI and LLMs.