IEEE Transactions on Affective Computing - Impact Factor

IEEE Transactions on Affective Computing | 2024 Impact Factor:9.8 | Cite Score:18.5 | Q1

IEEE Transactions on Affective Computing Journal

Impact Factor and Journal Rank of IEEE Transactions on Affective Computing

About: The IEEE Transactions on Affective Computing(TAC) is a distinguished scholarly journal published by the IEEE Computer Society. It focuses on research related to the development and application of systems that can recognize, interpret, and simulate human emotions and affective states. This interdisciplinary field, known as affective computing, blends computer science, psychology, cognitive science, and related disciplines to enhance human-computer interaction and create emotionally intelligent systems.
Content: The journal publishes a variety of article types, including original research papers, review articles, and technical notes. These articles present novel contributions, comprehensive reviews, and significant advancements in the theory, design, implementation, and application of affective computing technologies.
Audience: TAC is aimed at a diverse audience, including researchers, practitioners, educators, and students in the fields of computer science, psychology, cognitive science, human-computer interaction, artificial intelligence, and related areas. The journals articles are designed to be accessible to both specialists deeply involved in affective computing research and practitioners applying these technologies in real-world settings.
Interdisciplinary Approach: The journal emphasizes an interdisciplinary approach, encouraging contributions that integrate insights from various fields to advance the understanding and capabilities of affective computing systems. This fosters a comprehensive exploration of how emotions can be effectively modeled and utilized in computing environments.
High Standards and Impact: TAC maintains rigorous standards of quality through a thorough peer-review process, ensuring the publication of high-quality, impactful research. The journal is recognized for its significant contributions to the field of affective computing, with its articles frequently cited in both academic research and industry applications.
Global Reach: As part of the IEEE Computer Society, TAC enjoys a global readership and attracts contributions from leading researchers and institutions worldwide. This international perspective helps facilitate the exchange of ideas and advancements in affective computing across different regions and cultures.
Significance: IEEE Transactions on Affective Computing is a leading journal that significantly impacts the field of affective computing by publishing high-quality research on the recognition, interpretation, and simulation of human emotions. Its interdisciplinary and global approach makes it a valuable resource for researchers and practitioners aiming to develop emotionally intelligent systems and enhance human-computer interaction.

Journal Home: Journal Homepage

Editor-in-Chief: Jesse Hoey David R.

Scope: IEEE Transactions on Affective Computing (TAC) is a prominent journal that focuses on the interdisciplinary field of affective computing, which involves the study and development of systems and devices that can recognize, interpret, process, and simulate human emotions. The scope of the journal encompasses a wide range of topics related to the computational understanding and generation of affective and emotional states.
Here are some key areas covered by IEEE Transactions on Affective Computing:
Emotion Recognition and Detection: Research on techniques and algorithms for detecting and recognizing human emotions from various data sources, including facial expressions, voice, body gestures, physiological signals (e.g., heart rate, galvanic skin response), text, and multimodal data.
Affective Signal Processing: Development of methods for processing and analyzing affective signals to extract meaningful emotional information. This includes signal processing techniques for audio, video, and physiological data.
Emotion Modeling and Representation: Studies on theoretical models and computational representations of emotions, including psychological and cognitive models of affect, emotion ontologies, and frameworks for representing emotional states in machines.
Affective Interaction and Human-Computer Interaction (HCI): Research on how affective computing can enhance human-computer interaction, including affective interfaces, emotion-aware systems, adaptive user interfaces, and interactive environments that respond to users emotional states.
Affective Computing Applications: Exploration of practical applications of affective computing in various domains, such as healthcare (e.g., mental health monitoring, emotion-based therapy), education (e.g., adaptive learning systems), entertainment (e.g., emotion-driven games), customer service (e.g., sentiment analysis), and robotics (e.g., social robots).
Emotion Synthesis and Generation: Techniques for synthesizing and generating affective responses in machines, including expressive speech synthesis, emotion-driven avatar animation, and affective dialogue systems.
Psychophysiology and Neuroscience of Emotions: Studies that bridge affective computing with psychophysiology and neuroscience, focusing on understanding the biological and neurological underpinnings of emotions and how they can be measured and modeled computationally.
Affective Learning and Adaptation: Research on systems that can learn and adapt based on emotional feedback, including machine learning algorithms that incorporate affective information and systems that personalize experiences based on users emotional responses.
Ethics and Social Implications of Affective Computing: Examination of the ethical, legal, and societal issues related to affective computing, including privacy concerns, emotional manipulation, bias in emotion recognition systems, and the broader impact of emotion-aware technologies on society.