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Emotional AI and Human-AI Interactions in Social Networking makes readers aware of recent progress in this integrated discipline. Filling the existing vacuum in research in artificial intelligence with the application of social science, this book provides in-depth knowledge of human-AI interactions with social networking and increased use of the internet. Chapters integrating Emotional Artificial Intelligence, examining behavioral interventions, compassion, education, and healthcare, as well as social cognitive networking, including social brain networks, play a pivotal role in enhancing interdisciplinary studies in the field of social neuroscience and Emotional AI.

This volume is a must for those wanting to dive into this exciting field of social neuroscience AI.

Serves as a guide on social cognitive neuroscience for mental health and emotional AI for behavioral interventions Details various technologies of human-AI interactions with social networking Includes sections on emotional AI in behavioral interventions, compassion, education and healthcare

Impact of Climate Change on Social and Mental Well-Being, increases the awareness and understanding of how global climate change, including natural disasters, loss of habitat, and displacement can affect social and mental well-being. This book provides valuable insights into the connection between environmental issues and mental health, and the long-term implications of these issues. Sections dedicated to adaptation and solutions, offer coping mechanisms and strategies for eco-anxiety and climate grief. This book also explores the social, economic, and political factors that contribute to climate change and how these factors impact human well-being.Reviews the social impacts of climate change including forced migration, and social equity. Outlines the links between climate change and suicidal ideationIdentifies coping strategies for climate-related stressors

This book presents the basics and recent advancements in natural language processing and information retrieval in a single volume. It will serve as an ideal reference text for graduate students and academic researchers in interdisciplinary areas of electrical engineering, electronics engineering, computer engineering, and information technology. This text emphasizes the existing problem domains and possible new directions in natural language processing and information retrieval. It discusses the importance of information retrieval with the integration of machine learning, deep learning, and word embedding. This approach supports the quick evaluation of real-time data. It covers important topics including rumor detection techniques, sentiment analysis using graph-based techniques, social media data analysis, and language-independent text mining.Features:• Covers aspects of information retrieval in different areas including healthcare, data analysis, and machine translation• Discusses recent advancements in language- and domain-independent information extraction from textual and/or multimodal data• Explains models including decision making, random walk, knowledge graphs, word embedding, n-grams, and frequent pattern mining• Provides integrated approaches of machine learning, deep learning, and word embedding for natural language processing• Covers latest datasets for natural language processing and information retrieval for social media like TwitterThe text is primarily written for graduate students and academic researchers in interdisciplinary areas of electrical engineering, electronics engineering, computer engineering, and information technology.

This book presents the basics and recent advancements in natural language processing and information retrieval in a single volume. It will serve as an ideal reference text for graduate students and academic researchers in interdisciplinary areas of electrical engineering, electronics engineering, computer engineering, and information technology. This text emphasizes the existing problem domains and possible new directions in natural language processing and information retrieval. It discusses the importance of information retrieval with the integration of machine learning, deep learning, and word embedding. This approach supports the quick evaluation of real-time data. It covers important topics including rumor detection techniques, sentiment analysis using graph-based techniques, social media data analysis, and language-independent text mining.Features:• Covers aspects of information retrieval in different areas including healthcare, data analysis, and machine translation• Discusses recent advancements in language- and domain-independent information extraction from textual and/or multimodal data• Explains models including decision making, random walk, knowledge graphs, word embedding, n-grams, and frequent pattern mining• Provides integrated approaches of machine learning, deep learning, and word embedding for natural language processing• Covers latest datasets for natural language processing and information retrieval for social media like TwitterThe text is primarily written for graduate students and academic researchers in interdisciplinary areas of electrical engineering, electronics engineering, computer engineering, and information technology.

Advances in graph-based natural language processing (NLP) and information retrieval tasks have shown the importance of processing using the Graph of Words method. This book covers recent concrete information, from the basics to advanced level, about graph-based learning, such as neural network-based approaches, computational intelligence for learning parameters and feature reduction, and network science for graph-based NPL. It also contains information about language generation based on graphical theories and language models.Features:Presents a comprehensive study of the interdisciplinary graphical approach to NLPCovers recent computational intelligence techniques for graph-based neural network modelsDiscusses advances in random walk-based techniques, semantic webs, and lexical networksExplores recent research into NLP for graph-based streaming dataReviews advances in knowledge graph embedding and ontologies for NLP approachesThis book is aimed at researchers and graduate students in computer science, natural language processing, and deep and machine learning.

Advances in graph-based natural language processing (NLP) and information retrieval tasks have shown the importance of processing using the Graph of Words method. This book covers recent concrete information, from the basics to advanced level, about graph-based learning, such as neural network-based approaches, computational intelligence for learning parameters and feature reduction, and network science for graph-based NPL. It also contains information about language generation based on graphical theories and language models.Features:Presents a comprehensive study of the interdisciplinary graphical approach to NLPCovers recent computational intelligence techniques for graph-based neural network modelsDiscusses advances in random walk-based techniques, semantic webs, and lexical networksExplores recent research into NLP for graph-based streaming dataReviews advances in knowledge graph embedding and ontologies for NLP approachesThis book is aimed at researchers and graduate students in computer science, natural language processing, and deep and machine learning.

Affective Computing for Social Good: Enhancing Well-being, Empathy, and Equity offers an insightful journey into the intricate realm of affective computing. It covers a spectrum of topics ranging from foundational theories and technologies to ethical considerations and future possibilities.Beginning with "Deciphering the Emotional Spectrum: Advances in Emotion Science and Analysis," it sets the stage by tracing the evolution of understanding human emotions. Subsequent chapters explore practical applications, such as integrating clinical psychology with affective computing for therapeutic progress and leveraging affective computing in diagnosing and managing mood disorders more efficiently.As the narrative unfolds, the book emphasizes the crucial role of affective computing in fostering social justice and equity. It underscores the need for developing inclusive algorithms and databases while addressing ethical challenges like privacy, consent, and the risk of emotional manipulation. These discussions emphasize the significance of ethical deployment and regulation.The book also covers the technical aspects and applications of affective computing, including natural language processing for emotion recognition and analysis, voice emotion detection, and visual emotion recognition. It extends to applications, such as the use of affective computing in health management via recommender systems and personalized well-being interventions in mental health care.Addressing data challenges, "Enhancing Affective Computing with Data Augmentation: Strategies for Overcoming Limited Data Availability" presents solutions for imbalances affecting model performance. "Advancements in Multimodal Emotion Recognition" highlights the integration of facial expressions with physiological signals to improve emotion recognition accuracy and reliability.Concluding with "Ethical Considerations in Affective Computing" and "Cognitive Currents: A Path from Neuroscience to Consciousness," the book connects technical advancements in affective computing with broader ethical and philosophical inquiries surrounding consciousness and the human experience.Features:Helps readers understand the potential benefits of emotionally intelligent AI systems, such as improving mental health care, enhancing education, or promoting more ethical decision-making.Addresses ethical considerations related to the development and deployment of emotionally intelligent AI systems, helping readers to become more aware of the potential risks and trade-offs involved.Presents new approaches or frameworks for developing emotionally intelligent AI systems, providing readers with innovative ideas and perspectives.Provides examples of successful case studies where emotionally intelligent AI systems were used for social good, which may inspire readers to think about how they can contribute to society through AI development.Overall, this book will help readers gain a deeper understanding of the intersection between AI and human emotions, and how this technology can be used to create a more empathetic, compassionate, and socially responsible world.

Affective Computing for Social Good: Enhancing Well-being, Empathy, and Equity offers an insightful journey into the intricate realm of affective computing. It covers a spectrum of topics ranging from foundational theories and technologies to ethical considerations and future possibilities.Beginning with "Deciphering the Emotional Spectrum: Advances in Emotion Science and Analysis," it sets the stage by tracing the evolution of understanding human emotions. Subsequent chapters explore practical applications, such as integrating clinical psychology with affective computing for therapeutic progress and leveraging affective computing in diagnosing and managing mood disorders more efficiently.As the narrative unfolds, the book emphasizes the crucial role of affective computing in fostering social justice and equity. It underscores the need for developing inclusive algorithms and databases while addressing ethical challenges like privacy, consent, and the risk of emotional manipulation. These discussions emphasize the significance of ethical deployment and regulation.The book also covers the technical aspects and applications of affective computing, including natural language processing for emotion recognition and analysis, voice emotion detection, and visual emotion recognition. It extends to applications, such as the use of affective computing in health management via recommender systems and personalized well-being interventions in mental health care.Addressing data challenges, "Enhancing Affective Computing with Data Augmentation: Strategies for Overcoming Limited Data Availability" presents solutions for imbalances affecting model performance. "Advancements in Multimodal Emotion Recognition" highlights the integration of facial expressions with physiological signals to improve emotion recognition accuracy and reliability.Concluding with "Ethical Considerations in Affective Computing" and "Cognitive Currents: A Path from Neuroscience to Consciousness," the book connects technical advancements in affective computing with broader ethical and philosophical inquiries surrounding consciousness and the human experience.Features:Helps readers understand the potential benefits of emotionally intelligent AI systems, such as improving mental health care, enhancing education, or promoting more ethical decision-making.Addresses ethical considerations related to the development and deployment of emotionally intelligent AI systems, helping readers to become more aware of the potential risks and trade-offs involved.Presents new approaches or frameworks for developing emotionally intelligent AI systems, providing readers with innovative ideas and perspectives.Provides examples of successful case studies where emotionally intelligent AI systems were used for social good, which may inspire readers to think about how they can contribute to society through AI development.Overall, this book will help readers gain a deeper understanding of the intersection between AI and human emotions, and how this technology can be used to create a more empathetic, compassionate, and socially responsible world.

This unique book delves into the convergence of artificial intelligence (AI) principles—rooted in scientific knowledge and technological advancements—with the concept of spiritual wellness, exploring their significance in our increasingly automated and digitized world.

This unique book delves into the convergence of artificial intelligence (AI) principles—rooted in scientific knowledge and technological advancements—with the concept of spiritual wellness, exploring their significance in our increasingly automated and digitized world. The author offers a synthesis of two domains often perceived as distinct, appealing to both technologists and spiritual thinkers. Beginning with an exploration of the definitions and scope of "spiritual AI," the book encourages the quantification of spiritual wellness, illustrated through examples from current literature. It sheds light on the evolution of the spiritual quotient, presenting it as an integration of intelligence quotient (IQ) and emotional quotient (EQ), enhanced by dimensions of spirituality. The discussion spans various application domains and delves into the mind's entanglement-like phenomena, raising critical questions: Can a machine truly attain consciousness? How do spiritual wellness and quantum mechanics intertwine? The author invites readers to ask their own questions, contemplate the boundless possibilities of spiritual AI, and challenge existing paradigms.