期刊信息

Neural Networks

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影响因子:
7.2
出版商:
Elsevier
ISSN:
0893-6080
浏览:
42571
关注:
70

征稿

Neural Networks is an academic journal published by Elsevier. (ISSN 0893-6080, impact factor 7.2, CCF B).

Aims & Scope The journal Neural Networks provides a forum for developing and nurturing an international community of scholars and practitioners who are interested in all aspects of neural networks, including deep learning and related approaches to artificial intelligence and machine learning. Neural Networks welcomes submissions that contribute to the full range of neural networks research, from cognitive modeling and computational neuroscience, through deep learning algorithms and mathematical analyses, to engineering and technological applications of systems that significantly use neural network concepts and learning techniques. This uniquely broad range facilitates the cross-fertilization of ideas between biological and technological studies, and helps to foster the development of the interdisciplinary community that is interested in biologically-inspired artificial intelligence. Accordingly, the Neural Networks editorial board represents experts in fields including psychology, neurobiology, computer science, engineering, mathematics, and physics. On the other hand, neural networks should be central to submissions. The journal publishes articles, letters, and reviews/tutorials, as well as letters to the editor, editorials, and current events. Articles are published in one of five sections: learning systems, cognitive science, neuroscience, mathematical and computational analysis, engineering and applications. Neural Networks is the archival journal of three of the oldest and most prominent neural network societies: the International Neural Network Society (INNS), the Asia-Pacific Neural Network Society (APNNS), and the Japanese Neural Network Society (JNNS). A subscription to the journal is included with membership in each of these societies.
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Special Issues

Special Issue on Neural Networks for Human-Centric Perception, Affect, and Experience 截稿日期: 2026-12-01 Recent advances in neural networks have significantly improved machine perception and understanding of complex human-centered signals such as speech, images, videos, and multimodal behavioral data. However, many intelligent systems still struggle to effectively model the full chain of human perception, affect, and subjective experience, which plays a fundamental role in real-world human-AI interaction. Understanding how humans perceive signals, how these perceptions influence emotional responses, and how both contribute to overall user experience remains an important interdisciplinary challenge. This Special Issue aims to bring together recent progress in neural network-based approaches for modeling human-centric perception, affect, and experience across multiple modalities. The issue will focus on computational models that learn from human behavioral signals and multimedia data to better capture perceptual quality, emotional states, and subjective experiences in real-world environments. By integrating perspectives from multimedia signal processing, affective computing, and human-centered AI, this Special Issue aims to advance neural network methodologies that more effectively model and understand human perception, emotions, and experiences, ultimately enabling more robust, adaptive, and human-aware intelligent systems. Guest editors: Dr. Wei Zhou (Executive Guest Editor) Cardiff University, Cardiff, UK Email: [email protected] Areas of Expertise: Multimodal Processing, Human-Centric AI, Quality of Experience Prof. Björn W. Schuller Technische Universität München, Munich, Germany Email: [email protected] Areas of Expertise: Affective Computing, Computer Audition, Health Informatics Dr. Xavier Alameda-Pineda INRIA, Paris, France Email: [email protected] Areas of Expertise: Multimodal Learning, Audio-Visual Processing, Human-Robot Interaction Prof. Lu Yu Zhejiang University, Hangzhou, China Email: [email protected] Areas of Expertise: Video Coding, Multimedia Communication, Visual Quality Assessment Dr. Balu Adsumilli Google/YouTube, California, USA Email: [email protected] Areas of Expertise: Multimedia Processing, Video Quality, Video Compression Prof. Tat-Seng Chua National University of Singapore, Singapore Email: [email protected] Areas of Expertise: Robust and Trustable AI, Multimodal Analytics, Recommender Systems Special issue information: Topics of interest include but are not limited to: · Neural networks for multimedia perceptual quality assessment · Affective computing and emotion understanding with deep neural networks · Neural network approaches for quality of experience (QoE) and user response modeling · Cross-modal representation and multimodal fusion for human behavior understanding · Foundation models for human-centric perception and affect, including fine-grained emotion recognition, uncovering and leveraging emotional signals within large foundation models · Human-centered AI systems for interactive, immersive, and embodied applications (e.g., digital health, human-AI interaction, robotics) Manuscript submission information: Important Dates: Submission Open Date: April 24, 2026 Submission Deadline: December 1, 2026 Notification of Acceptance: June 1, 2027 Manuscripts must be submitted via the Neural Networks online submission system (Submit your manuscript | Neural Networks). Please select the article type “VSI: Human-Centric Neural Networks” when submitting your manuscript online. Please refer to the Guide for Authors to prepare your manuscript. All submitted papers under this call will undergo the standard review process of the journal. For any further information, the authors may contact the Guest Editors. Keywords: Neural networks, Human-centric AI, Affective computing, Emotion recognition, Multimodal learning, Perceptual quality assessment, Quality of Experience, Foundation models, Human behavior understanding, Multimedia signal processing, Human-AI interaction https://www.sciencedirect.com/special-issue/332976/neural-networks-for-human-centric-perception-affect-and-experience
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