Using Machine Learning to Detect Emotions and Predict Human Psychology PDF Download

Author: Rai, Mritunjay
Publisher: IGI Global
ISBN:
Category : Psychology
Languages : en
Pages : 332

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Book Description
In the realm of analyzing human emotions through Artificial Intelligence (AI), a myriad of challenges persist. From the intricate nuances of emotional subtleties to the broader concerns of ethical considerations, privacy implications, and the ongoing battle against bias, AI faces a complex landscape when venturing into the understanding of human emotions. These challenges underscore the intricate balance required to navigate the human psyche with accuracy. The book, Using Machine Learning to Detect Emotions and Predict Human Psychology, serves as a guide for innovative solutions in the field of emotion detection through AI. It explores facial expression analysis, where AI decodes real-time emotions through subtle cues such as eyebrow movements and micro-expressions. In speech and voice analysis, the book unveils how AI processes vocal nuances to discern emotions, considering elements like tone, pitch, and language intricacies. Additionally, the power of text analysis is of great importance, revealing how AI extracts emotional tones from diverse textual communications. By weaving these systems together, the book offers a holistic solution to the challenges faced by AI in understanding the complex landscape of human emotions.

Author: Zhen Cui
Publisher: Frontiers Media SA
ISBN: 2832526365
Category : Science
Languages : en
Pages : 151

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Book Description
Affective computing refers to computing that relates to, arises from, or influences emotions. The goal of affective computing is to bridge the gap between humans and machines and ultimately endow machines with emotional intelligence for improving natural human-machine interaction. In the context of human-robot interaction (HRI), it is hoped that robots can be endowed with human-like capabilities of observation, interpretation, and emotional expression. The research on affective computing has recently achieved extensive progress with many fields contributing including neuroscience, psychology, education, medicine, behavior, sociology, and computer science. Current research in affective computing concentrates on estimating human emotions through different forms of signals such as speech, face, text, EEG, fMRI, and many others. In neuroscience, the neural mechanisms of emotion are explored by combining neuroscience with the psychological study of personality, emotion, and mood. In psychology and philosophy, emotion typically includes a subjective, conscious experience characterized primarily by psychophysiological expressions, biological reactions, and mental states. The multi-disciplinary features of understanding “emotion” result in the fact that inferring the emotion of humans is definitely difficult. As a result, a multi-disciplinary approach is required to facilitate the development of affective computing. One of the challenging problems in affective computing is the affective gap, i.e., the inconsistency between the extracted feature representations and subjective emotions. To bridge the affective gap, various hand-crafted features have been widely employed to characterize subjective emotions. However, these hand-crafted features are usually low-level, and they may hence not be discriminative enough to depict subjective emotions. To address this issue, the recently-emerged deep learning (also called deep neural networks) techniques provide a possible solution. Due to the used multi-layer network structure, deep learning techniques are capable of learning high-level contributing features from a large dataset and have exhibited excellent performance in multiple application domains such as computer vision, signal processing, natural language processing, human-computer interaction, and so on. The goal of this Research Topic is to gather novel contributions on deep learning techniques applied to affective computing across the diverse fields of psychology, machine learning, neuroscience, education, behavior, sociology, and computer science to converge with those active in other research areas, such as speech emotion recognition, facial expression recognition, Electroencephalogram (EEG) based emotion estimation, human physiological signal (heart rate) estimation, affective human-robot interaction, multimodal affective computing, etc. We welcome researchers to contribute their original papers as well as review articles to provide works regarding the neural approach from computation to affective computing systems. This Research Topic aims to bring together research including, but not limited to: • Deep learning architectures and algorithms for affective computing tasks such as emotion recognition from speech, face, text, EEG, fMRI, and many others. • Explainability of deep Learning algorithms for affective computing. • Multi-task learning techniques for emotion, personality and depression detection, etc. • Novel datasets for affective computing • Applications of affective computing in robots, such as emotion-aware human-robot interaction and social robots, etc.

Author: Aslam, Muhammad Shahzad
Publisher: IGI Global
ISBN:
Category : Business & Economics
Languages : en
Pages : 301

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Book Description
In the modern healthcare system, a pervasive problem takes new shape as cyberbullying. Healthcare professionals, those dedicated to caring for the well-being of others, are increasingly falling victim to online harassment, intimidation, and harmful behavior. This corrosive issue disrupts team dynamics, undermines workplace culture, and poses severe psychological and emotional consequences for its targets. Academic scholars and healthcare decision-makers must grapple with the pressing need to address this burgeoning crisis. Workplace Cyberbullying and Behavior in Health Professions is a comprehensive and meticulously researched book that presents itself as the definitive solution to the ever-growing challenge of cyberbullying within healthcare. This book is aimed at postgraduate and post-doctorate researchers as well as policymakers, providing a solid foundation for understanding, addressing, and ultimately eliminating cyberbullying in healthcare environments.

Author: Burrell, Darrell Norman
Publisher: IGI Global
ISBN:
Category : Medical
Languages : en
Pages : 348

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Book Description
In a world characterized by complexity and rapid change, the intersection of healthcare, social sciences, and technology presents a formidable challenge. The vast array of interconnected issues, ethical dilemmas, and technological advancements often evade comprehensive understanding within individual disciplines. The problem lies in the siloed approach to these critical domains, hindering our ability to navigate the complexities of our modern world effectively. Change Dynamics in Healthcare, Technological Innovations, and Complex Scenarios emerges as a transformative solution, offering a beacon of insight and knowledge to those grappling with the intricate dynamics of our interconnected society. Change Dynamics in Healthcare, Technological Innovations, and Complex Scenarios dives into organizational narratives, ethical challenges, and technological promises across healthcare, social sciences, and technology. It doesn't merely acknowledge the interplay between these disciplines; it celebrates their interconnectedness. By dissecting, analyzing, and synthesizing critical developments, this book serves as a compass, providing a rich resource for comprehending the multifaceted impacts of emerging changes.

Author: Mritunjay Rai
Publisher: Medical Information Science Reference
ISBN:
Category : Computers
Languages : en
Pages : 0

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Book Description
Computer understanding of human emotions has become crucial and complex within the era of digital interaction and artificial intelligence. Emotion detection, a field within AI, holds promise for enhancing user experiences, personalizing services, and revolutionizing industries. However, navigating this landscape requires a deep understanding of machine and deep learning techniques and the interdisciplinary challenges accompanying them. Machine and Deep Learning Techniques for Emotion Detection offer a comprehensive solution to this pressing problem. Designed for academic scholars, practitioners, and students, it is a guiding light through the intricate terrain of emotion detection. By blending theoretical insights with practical implementations and real-world case studies, our book equips readers with the knowledge and tools needed to advance the frontier of emotion analysis using machine and deep learning methodologies. Focusing on addressing challenges such as cross-cultural variability, data privacy, and model interpretability, Machine and Deep Learning Techniques for Emotion Detection provide a holistic perspective on the ethical, legal, and societal implications of deploying emotion detection technologies. Whether readers are researchers exploring convolutional neural networks for facial expression analysis or practitioners integrating emotion detection into healthcare or marketing, this book provides a comprehensive guide for unlocking the transformative potential of this burgeoning field.

Author: Prabhakar, Pranav Kumar
Publisher: IGI Global
ISBN:
Category : Computers
Languages : en
Pages : 411

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Book Description
A chasm grows between the currently established knowledge and the rapidly evolving landscape of healthcare. As the field of biomedical research hurtles forward with groundbreaking discoveries and transformative technologies, academic scholars find themselves grappling with a significant dilemma. There exists a disconnect between traditional educational resources and the need to keep pace with the latest innovations that are reshaping medicine, diagnosis, and treatment. This widening gap inhibits scholars from adequately preparing their students and hampers their ability to engage in relevant, cutting-edge research, ultimately impeding the advancement of healthcare as a whole. Biomedical Research Developments for Improved Healthcare serves as the ultimate solution to this academic challenge. This book offers a compelling bridge between the realm of academic theory and the dynamic world of practical, real-world biomedical research. Its primary objective is to equip scholars with the knowledge, insights, and materials needed to inspire the next generation of healthcare professionals. By presenting a comprehensive overview of the most recent and groundbreaking advancements in biomedical research, the book enables scholars to transcend the limitations of traditional academia and empower their students with up-to-date, practical knowledge.

Author: Sharma, Avinash Kumar
Publisher: IGI Global
ISBN:
Category : Computers
Languages : en
Pages : 327

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Book Description
Academic scholars and professionals are currently grappling with hurdles in optimizing diagnostic processes, as traditional methodologies prove insufficient in managing the intricate and voluminous nature of medical data. The diverse range of imaging techniques, spanning from endoscopy to magnetic resonance imaging, necessitates a more unified and efficient approach. This complexity has created a pressing need for streamlined methodologies and innovative solutions. Academic scholars find themselves at the forefront of addressing these challenges, seeking ways to leverage AI's full potential in improving the accuracy of medical imaging diagnostics and, consequently, enhancing overall patient outcomes. Future of AI in Medical Imaging, stands as a solution to the challenges faced by academic scholars in the realm of medical imaging. The book lays a solid groundwork for understanding the complexities of medical imaging systems. Through an exploration of various imaging modalities, it not only addresses the current issues but also serves as a guide for scholars to navigate the landscape of AI-integrated medical diagnostics. This collaborative effort not only illuminates the existing hurdles of medical imaging but also looks towards a future where AI-driven diagnostics and personalized medicine become indispensable tools, significantly elevating patient outcomes.

Author: Papalois, Vassilios
Publisher: IGI Global
ISBN: 1799896544
Category : Medical
Languages : en
Pages : 347

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Book Description
The COVID-19 pandemic clearly shows the vital role of accurate and reliable information in public health. Health literacy addresses not only patient needs but also the needs of the general population, who must not only comply with advice and instructions but also understand the severity of health crises and respond accordingly. A variety of crises imposed on healthcare systems constantly arise ranging from pandemics to natural catastrophes, terrorist attacks, and outbreaks of illnesses. In addition, there are crises within the healthcare systems, such as a lack of resources and an appropriate workforce. Crises in healthcare systems that are not efficiently dealt with may result in inefficiencies and inequalities in health provision. The Role of Health Literacy in Major Healthcare Crises examines the role of health literacy not only in informing the public but also in building a culture of cooperation between the healthcare systems and their users. The book also investigates the role of communication strategies and educational activities of multiple agencies at local, national, and global levels and explores ethical issues associated with healthcare crises and how they are negotiated in health campaigns. Covering key topics such as digital media, health information, and e-health, this premier reference source is ideal for healthcare professionals, nurses, policymakers, researchers, scholars, academicians, practitioners, instructors, and students.

Author: Chatterjee, Lagnajita
Publisher: IGI Global
ISBN:
Category : Medical
Languages : en
Pages : 315

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Book Description
In the wake of the digital healthcare revolution, a critical challenge has emerged: the lack of a comprehensive understanding stemming from fragmented research. Despite the industry's meteoric rise, existing literature often compartmentalizes insights, neglecting the intricate multi-sector collaborations that fuel its progress. This gap hinders scholars and industry professionals, leaving them with a myopic view of the digital healthcare landscape. The urgent need for a holistic exploration has never been more apparent. Multi-Sector Analysis of the Digital Healthcare Industry is a groundbreaking book that will uncover the complexities of digital healthcare with a panoramic lens. This carefully curated collection of cross-functional chapters is a beacon guiding academics and industry specialists through the difficulties of the industry's past, present, and future. With experts from fields spanning medicine, technology, business, and regulatory sectors, this book addresses the limitations of current research but serves as a compass for those seeking a more profound comprehension of digital healthcare's collaborative dynamics.

Author: Filipe, Helena Prior
Publisher: IGI Global
ISBN: 1668467577
Category : Medical
Languages : en
Pages : 305

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Book Description
Health professionals grapple with a critical challenge: the traditional Continuous Medical Education (CME) model falls short of fostering the unique skills and self-directed learning required for a dynamic career. As medical practitioners navigate a world of new epidemiological models, technologies, and strategies, the need for a transformative solution becomes evident. The Lifelong Learning Journey of Health Professionals: Continuing Education and Professional Development is a book that not only identifies the limitations of existing education models but also provides a comprehensive solution for ushering in a new era of lifelong learning. This compelling book advocates for a paradigm shift towards Continuous Professional Development (CPD), a contemporary concept that embraces non-traditional learning formats. It dismantles the inadequacies of credit-based training by emphasizing the importance of self-direction and self-assessment for adult learners. From core principles for designing a robust CPD system to exploring successful models, alternative credentials, and the role of learning communities, the book offers a holistic approach to reshaping medical education.