NeuroSafeDrive: An Intelligent System Using fNIRS for Driver Distraction Recognition by Ghazal Bargshady, Hakki Gokalp Ustun, Yasaman Baradaran, Houshyar Asadi, Ravinesh C Deo, Jeroen Van Boxtel, Raul Fernandez Rojas

“…This study contributes to affective computing and intelligent transportation systems and could support the development of future driver distraction monitoring systems for safer and more adaptive vehicle control.…”
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A Multi-Epiphysiological Indicator Dog Emotion Classification System Integrating Skin and Muscle Potential Signals by Wenqi Jia, Yanzhi Hu, Zimeng Wang, Kai Song, Boyan Huang

“…The proposed system demonstrates high accuracy, efficiency, and portability, laying a robust groundwork for future advancements in cross-species affective computing and intelligent animal welfare technologies.…”
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Extending Cognitive Load Theory: The CLAM Framework for Biometric, Adaptive, and Ethical Learning by Eleni Vasilaki, Aristea Mavrogianni

“…Synthesizing insights from cognitive psychology, educational technology, and affective computing, CLAM supports the design of personalized, data-driven instructional systems attuned to learners’ cognitive and emotional states. …”
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Emotion Recognition Model of EEG Signals Based on Double Attention Mechanism by Yahong Ma, Zhentao Huang, Yuyao Yang, Shanwen Zhang, Qi Dong, Rongrong Wang, Liangliang Hu

“…Emotion recognition based on brain signals has become a significant challenge in the fields of affective computing and human-computer interaction. Methods: Addressing the issue of inaccurate feature extraction and low accuracy of existing deep learning models in emotion recognition, this paper proposes a multi-channel automatic classification model for emotion EEG signals named DACB, which is based on dual attention mechanisms, convolutional neural networks, and bidirectional long short-term memory networks. …”
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CAG-MoE: Multimodal Emotion Recognition with Cross-Attention Gated Mixture of Experts by Axel Gedeon Mengara Mengara, Yeon-kug Moon

“…Extensive theoretical analysis and rigorous experiments on benchmark datasets—the Korean Emotion Multimodal Database (KEMDy20) and the ASCERTAIN dataset—demonstrate that our approach significantly outperforms state-of-the-art methods in emotion recognition, setting new performance baselines in affective computing.…”
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Brain computer interface based emotion recognition with error analysis and challenges: an interdisciplinary review by Niharika Gudikandula, Ravichander Janapati, Rakesh Sengupta, Sridhar Chintala

“…Therefore, emotion recognition through BCIs holds significant promise for various domains, including affective computing, healthcare, and human–computer interaction, with numerous potential applications. …”
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Deep learning model for patient emotion recognition using EEG-tNIRS data by Mohan Raparthi, Nischay Reddy Mitta, Vinay Kumar Dunka, Sowmya Gudekota, Sandeep Pushyamitra Pattyam, Venkata Siva Prakash Nimmagadda

“…This research underscores the potential of EEG-tNIRS fusion in real-time, non-invasive emotion monitoring, paving the way for advanced applications in personalized healthcare and affective computing.…”
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Performance Analysis and Improvement of Machine Learning with Various Feature Selection Methods for EEG-Based Emotion Classification by Sherzod Abdumalikov, Jingeun Kim, Yourim Yoon

“…Emotion classification is a challenge in affective computing, with applications ranging from human–computer interaction to mental health monitoring. …”
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Detecting Anomalies in CPU Behavior Using Clustering Algorithms from the Scikit-Learn Library in Python Programming Language by Artem Turashev, Vladimir Sukhomlin

“…They provide us with many features, but sometimes anomalies in the system can negatively affect computer performance. In this case, the issue of anomaly detection is acute, since anomalous activity detected in time can prevent a cyber attack. …”
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Ethical dilemmas and the reconstruction of subjectivity in digital mourning in the age of AI: an empirical study on the acceptance intentions of bereaved family members of cancer p... by Kun Fu, Chenxi Ye, Zeyu Wang, Miaohui Wu, Zhen Liu, Yuan Yuan

“…IntroductionWith the rapid advancement of AI replication, virtual memorials, and affective computing technologies, digital mourning has emerged as a prevalent mode of psychological reconstruction for families coping with the loss of terminally ill patients. …”
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UMEDNet: a multimodal approach for emotion detection in the Urdu language by Adil Majeed, Hasan Mujtaba

“…Emotion detection is a critical component of interaction between human and computer systems, more especially affective computing, and health screening. Integrating video, speech, and text information provides better coverage of the basic and derived affective states with improved estimation of verbal and non-verbal behavior. …”
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Deep learning techniques for speech emotion recognition: A review by Silviana Widya Lestari, Saliyah Kahar, Trismayanti Dwi

“…This advancement has significant implications for various applications, including human computer interaction, affective computing, call center analytics, psychological research, and clinical diagnosis.…”
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A comparative analysis of emotion recognition from EEG signals using temporal features and hyperparameter-tuned machine learning techniques by Rabita Hasan, Sheikh Md. Rabiul Islam

“…Classifying emotions based on EEG signals is really important for enhancing our interactions with computers, monitoring mental health and creating applications in affective computing field. This study explores improving emotion recognition performance by applying traditional machine learning classifiers and boosting techniques to EEG data from the DEAP dataset. …”
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Modeling Affective Mechanisms in Relaxing Video Games: Sentiment and Topic Analysis of User Reviews by Yuxin Xing, Wenbao Ma, Qiang You, Jiaxing Li

“…This research contributes to affective computing, digital mental health, and the design of emotionally aware interactive systems.…”
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Multilingual identification of nuanced dimensions of hope speech in social media texts by Grigori Sidorov, Fazlourrahman Balouchzahi, Luis Ramos, Helena Gómez-Adorno, Alexander Gelbukh

“…These findings underscore the value of language-specific fine-tuning for nuanced affective computing tasks. This study advances sentiment analysis by addressing a novel and underrepresented affective dimension-hope, and proposes robust multilingual benchmarks for future research. …”
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NeuroSense: A Novel EEG Dataset Utilizing Low-Cost, Sparse Electrode Devices for Emotion Exploration by Tommaso Colafiglio, Angela Lombardi, Paolo Sorino, Elvira Brattico, Domenico Lofu, Danilo Danese, Eugenio Di Sciascio, Tommaso Di Noia, Fedelucio Narducci

“…Emotion recognition is crucial in affective computing, aiming to bridge the gap between human emotional states and computer understanding. …”
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EEG-SKDNet: A Self-Knowledge Distillation Model With Scaled Weights for Emotion Recognition From EEG Signals by Thuong Duong Thi Mai, Duc-Quang Vu, Huy Nguyen Phuong, Trung-Nghia Phung

“…Electroencephalogram-based emotion recognition has garnered increasing attention due to its potential in human–computer interaction and affective computing. While recent deep learning methods have achieved remarkable performance in this task, most approaches emphasize accuracy at the expense of computational efficiency, making them impractical for real-time applications or deployment on resource-constrained devices. …”
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Machine Learning Applied to Improve Prevention of, Response to, and Understanding of Violence Against Women by Mariana Carolyn Cruz-Mendoza, Roberto Angel Melendez-Armenta, Juana Canul-Reich, Julio Muñoz-Benítez

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Biological Motion-Based Emotion Recognition Through a Deep Learning Approach by Amjaad T. Alotaibi, Suhare Solaiman

“…In this study, biological motion was employed to attain cutting-edge results in the field of emotion recognition tasks, highlighting its importance in various affective computing applications.…”
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Multimodal Knowledge Distillation for Emotion Recognition by Zhenxuan Zhang, Guanyu Lu

“…Multimodal emotion recognition has emerged as a prominent field in affective computing, offering superior performance compared to single-modality methods. …”
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