Download or read book Machine Learning Approaches to Emotion Recognition Searching for a Hierarchy in Emotions written by Barbara Verstraeten and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatic emotion recognition is part of the wider framework of sentiment analysis and deals with the computational treatment of emotions in text. Some studies have brought the idea forward of a hierarchy in human emotion detection. We tried to see if a machine learning approach could provide insights on the difference and similarities of emotions. We approached the problem as a text classification task and employed simple feature vectors constructed with the Natural Language ToolKit. We used two supervised classifiers: Naïve Bayes and Support Vector Machines. Data was made available by Aman & Szpakowicz (2007). The data was annotated for seven emotion categories: happiness, sadness, anger, fear, disgust, surprise and no emotion. The highest obtained accuracy for the entire data was 94,23% using a SVM on balanced data, the lowest accuracy was 46,11% for Naïve Bayes on unbalanced data. Performance differed greatly between classifiers and if data was resampled or not.

Download or read book Emotion and Stress Recognition Related Sensors and Machine Learning Technologies written by Kyandoghere Kyamakya and published by MDPI. This book was released on 2021-09-01 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective. This book, emerging from the Special Issue of the Sensors journal on “Emotion and Stress Recognition Related Sensors and Machine Learning Technologies” emerges as a result of the crucial need for massive deployment of intelligent sociotechnical systems. Such technologies are being applied in assistive systems in different domains and parts of the world to address challenges that could not be addressed without the advances made in these technologies.

Download or read book Machine and Deep Learning Techniques for Emotion Detection written by Rai, Mritunjay and published by IGI Global. This book was released on 2024-05-14 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Emotion Recognition written by Amit Konar and published by John Wiley & Sons. This book was released on 2015-01-27 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: A timely book containing foundations and current research directions on emotion recognition by facial expression, voice, gesture and biopotential signals This book provides a comprehensive examination of the research methodology of different modalities of emotion recognition. Key topics of discussion include facial expression, voice and biopotential signal-based emotion recognition. Special emphasis is given to feature selection, feature reduction, classifier design and multi-modal fusion to improve performance of emotion-classifiers. Written by several experts, the book includes several tools and techniques, including dynamic Bayesian networks, neural nets, hidden Markov model, rough sets, type-2 fuzzy sets, support vector machines and their applications in emotion recognition by different modalities. The book ends with a discussion on emotion recognition in automotive fields to determine stress and anger of the drivers, responsible for degradation of their performance and driving-ability. There is an increasing demand of emotion recognition in diverse fields, including psycho-therapy, bio-medicine and security in government, public and private agencies. The importance of emotion recognition has been given priority by industries including Hewlett Packard in the design and development of the next generation human-computer interface (HCI) systems. Emotion Recognition: A Pattern Analysis Approach would be of great interest to researchers, graduate students and practitioners, as the book Offers both foundations and advances on emotion recognition in a single volume Provides a thorough and insightful introduction to the subject by utilizing computational tools of diverse domains Inspires young researchers to prepare themselves for their own research Demonstrates direction of future research through new technologies, such as Microsoft Kinect, EEG systems etc.

Download or read book Deep Learning Techniques Applied to Affective Computing written by Zhen Cui and published by Frontiers Media SA. This book was released on 2023-06-14 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Music Emotion Recognition written by Yi-Hsuan Yang and published by CRC Press. This book was released on 2011-02-22 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing a complete review of existing work in music emotion developed in psychology and engineering, Music Emotion Recognition explains how to account for the subjective nature of emotion perception in the development of automatic music emotion recognition (MER) systems. Among the first publications dedicated to automatic MER, it begins with

Download or read book Real Time Computer Vision written by Christopher M. Brown and published by Cambridge University Press. This book was released on 1995-03-30 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: This first book on real-time computer vision will interest all involved in the design and programming of visually guided systems.

Download or read book Machine Learning Solutions for Emotional Speech written by Reza Lotfian and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Speech emotion recognition solutions designed under controlled conditions do not translate well into real-life applications. The first step to address this issue is developing a large, natural emotional database. Current approaches used to collect spontaneous databases tend to provide unbalanced emotional content, which is dictated by the given recording protocol. The size and speaker diversity are also limited. We propose a novel approach to effectively build a large, naturalistic emotional database with balanced emotional content, reduced cost and reduced manual labor. It relies on existing spontaneous recordings obtained from audio-sharing websites. To balance the emotional content, we apply preference learning methods to retrieve speech samples with emotions that are less frequent in everyday conversation. The target emotions in this approach can be represented with attribute based labels (e.g., arousal, valence, dominance) or by categorical emotion (e.g., happy, angry, sad). We address both of these problems. Motivated by positive results from applying preference learning to emotion recognition problem, we propose to use neutral reference to detect the absolute emotional content of speech samples. We use synthetic speech which synthesized using same transcript and use it as reference to account for the lexical dependencies which is considered a nuisance factor in detecting emotions. The collected MSP-PODCAST database opens new research opportunity to address some challenging aspects of emotion recognition. One challenge is that expressive behaviors tend to be ambiguous with blended emotions during spontaneous conversations. Therefore, evaluators disagree on the perceived emotion, assigning multiple emotional classes to the same stimuli. These observations have clear implications on emotion classification, where assigning a single descriptor per stimuli oversimplifies the intrinsic subjectivity in emotion perception. We propose a new formulation, where the emotional perception of a stimuli is a multidimensional Gaussian random variable with an unobserved distribution. Each dimension corresponds to an emotion characterized by a numerical scale. We train the deep network to predict the mean vector of this distribution instead of single label. The second challenge we address is the limited examples from minority emotional for training. We argue that individual labels convey more information than the consensus labels. We present a novel over-sampling approach, where the samples are over-sampled according to the labels from individual evaluations. This approach (1) increases the number of samples from classes with underrepresented consensus labels, and (2) efficiently uses samples with ambiguous emotional content. The next machine learning concept we discuss in this study is the order which the samples need to be introduced to the learning process. We introduce a method to design a curriculum for machine learning to maximize the efficiency during learning. The curriculum is arranged to gradually learn samples from easy to difficult. For emotion recognition, the challenge is to establish an order of difficulty in the training set. We propose to use the disagreement between evaluators as a measure of difficulty of the classification. Our experimental results show that relying on a curriculum based on human judgment of emotion consistently improves the classification performance across emotion recognition task, and increase the convergence rate.

Download or read book High Performance Modelling and Simulation for Big Data Applications written by Joanna Kołodziej and published by Springer. This book was released on 2019-03-25 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications.

Download or read book Cognitive Analytics Concepts Methodologies Tools and Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2020-03-06 with total page 1961 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries, including business and healthcare. It is necessary to develop specific software programs that can analyze and interpret large amounts of data quickly in order to ensure adequate usage and predictive results. Cognitive Analytics: Concepts, Methodologies, Tools, and Applications provides emerging perspectives on the theoretical and practical aspects of data analysis tools and techniques. It also examines the incorporation of pattern management as well as decision-making and prediction processes through the use of data management and analysis. Highlighting a range of topics such as natural language processing, big data, and pattern recognition, this multi-volume book is ideally designed for information technology professionals, software developers, data analysts, graduate-level students, researchers, computer engineers, software engineers, IT specialists, and academicians.

Download or read book Machine Learning for Health Informatics written by Andreas Holzinger and published by Springer. This book was released on 2016-12-09 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.

Download or read book Social Emotions in Nature and Artifact written by Jonathan Gratch and published by Oxford University Press. This book was released on 2013-11-01 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent years have seen the rise of a remarkable partnership between the social and computational sciences on the phenomena of emotions. Rallying around the term Affective Computing, this research can be seen as revival of the cognitive science revolution, albeit garbed in the cloak of affect, rather than cognition. Traditional cognitive science research, to the extent it considered emotion at all, cases it as at best a heuristic but more commonly a harmful bias to cognition. More recent scholarship in the social sciences has upended this view. Increasingly, emotions are viewed as a form of information processing that serves a functional role in human cognition and social interactions. Emotions shape social motives and communicate important information to social partners. When communicating face-to-face, people can rapidly detect nonverbal affective cues, make inferences about the other party's mental state, and respond in ways that co-construct an emotional trajectory between participants. Recent advances in biometrics and artificial intelligence are allowing computer systems to engage in this nonverbal dance, on the one hand opening a wealth of possibilities for human-machine systems, and on the other, creating powerful new tools for behavioral science research. Social Emotions in Nature and Artifact reports on the state-of-the-art in both social science theory and computational methods, and illustrates how these two fields, together, can both facilitate practical computer/robotic applications and illuminate human social processes.

Download or read book Brain Informatics written by Mufti Mahmud and published by Springer Nature. This book was released on 2020-09-18 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 13th International Conference on Brain Informatics, BI 2020, held in Padua, Italy, in September 2020. The conference was held virtually due to the COVID-19 pandemic. The 33 full papers were carefully reviewed and selected from 57 submissions. The papers are organized in the following topical sections: cognitive and computational foundations of brain science; investigations of human information processing systems; brain big data analytics, curation and management; informatics paradigms for brain and mental health research; and brain-machine intelligence and brain-inspired computing.

Download or read book Identifying Expressions of Emotions and Their Stimuli in Text written by Diman Ghazi and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Emotional Intelligence written by Aruna Chakraborty and published by Springer Science & Business Media. This book was released on 2009-11-11 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Emotional Intelligence is a new discipline of knowledge, dealing with modeling, recognition and control of human emotions. The book Emotional Intelligence: A Cybernetic Approach, to the best of the authors’ knowledge is a first compreh- sive text of its kind that provides a clear introduction to the subject in a precise and insightful writing style. It begins with a philosophical introduction to E- tional Intelligence, and gradually explores the mathematical models for emotional dynamics to study the artificial control of emotion using music and videos, and also to determine the interactions between emotion and logic from the points of view of reasoning. The later part of the book covers the chaotic behavior of - existing emotions under certain conditions of emotional dynamics. Finally, the book attempts to cluster emotions using electroencephalogram signals, and d- onstrates the scope of application of emotional intelligence in several engineering systems, such as human-machine interfaces, psychotherapy, user assistance s- tems, and many others. The book includes ten chapters. Chapter 1 provides an introduction to the s- ject from a philosophical and psychological standpoint. It outlines the fundamental causes of emotion arousal, and typical characteristics of the phenomenon of an emotive experience. The relation between emotion and rationality of thoughts is also introduced here. Principles of natural regulation of emotions are discussed in brief, and the biological basis of emotion arousal using an affective neu- scientific model is introduced next.

Download or read book Deep Learning Based Approaches for Sentiment Analysis written by Basant Agarwal and published by Springer Nature. This book was released on 2020-01-24 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Cyberspace Safety and Security written by Jaideep Vaidya and published by Springer. This book was released on 2020-01-04 with total page 602 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two volumes LNCS 11982 and 11983 constitute the proceedings of the 11th International Symposium on Cyberspace Safety and Security, CSS 2019, held in Guangzhou, China, in December 2019. The 61 full papers and 40 short papers presented were carefully reviewed and selected from 235 submissions. The papers cover a broad range of topics in the field of cyberspace safety and security, such as authentication, access control, availability, integrity, privacy, confidentiality, dependability and sustainability issues of cyberspace. They are organized in the following topical sections: network security; system security; information security; privacy preservation; machine learning and security; cyberspace safety; big data and security; and cloud and security;