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31 articles for “Emotion detection”
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Facial Emotion Detection and Its Applications
Abstract: Facial emotion detection (FED) is an interdisciplinary field that integrates artificial intelligence, computer vision, and machine learning to recognize and interpret human emotions based on facial expressions. The development of FED systems has been propelled by advancements in deep learning, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which enhance recognition accuracy. Feature extraction techniques, including geometric and appearance-based methods, play a crucial role in classifying emotional states. …
Published in International Journal of Optical Innovations & Research · Vol. 3, Issue 1, 2025 · pp. 8–12 Read article
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Comparison and Analysis of Facial Emotion Detection Using Various Deep Learning Neural Networks
Abstract: Facial emotion recognition employs Convolutional Neural Networks (CNNs), Residual Networks (ResNet), Long Short-Term Memory (LSTM) networks, and Deep Neural Networks (DNNs) to automatically identify various emotions, including disgust, anger, fear, happiness, sadness, surprise, and neutrality. This study utilizes transfer learning along with data preprocessing techniques such as rotation, flipping, brightness adjustment, and enhancement methods. Traditional machine learning models achieve an accuracy range of 45 to 50%. In contrast, our proposed …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 2, 2025 · pp. 37–42 Read article
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Procedure for Conventional Facial Emotion Detection Algorithms Based on Machine Learning
Abstract: Researchers in psychology, computer science, linguistics, neurology, and allied fields have become more interested in a human-computer interface system for autonomous face recognition or facial expression recognition. This study has recommended an Automatic Facial Expression Recognition System (AFERS). The proposed methodology consists of face detection, feature extraction, and facial expression identification processes. The initial phases of the face detection procedure include skin color identification using the YCbCr color model, illumination …
Published in International Journal of Electronics Automation · Vol. 1, Issue 1, 2023 · pp. 07–13 Read article
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Face Emotion Recognition to Detect Depression
Abstract: In the current competitive world, one of the most familiar and grave mental illness we encounter in humans is Depression also called as major depression or major depressive disorder. It makes you feel depressed and disinterested all the time, which has a bad impact on your thoughts and behaviour. Thus affecting not only the victim but also people associated with them, such as family, friends and society. If not treated …
Published in Current Trends in Signal Processing · Vol. 14, Issue 1, 2024 · pp. 1–14 Read article
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AI-Powered Emotion Recognition in Dog
Abstract: Understanding animal emotions is important for improving veterinary care, human animal interaction, and overall pet well-being. Inspired by previous research that utilized a modified EfficientNetB5 model for emotion classification in cats and dogs, our study builds upon this foundation with a focus on real-time emotion recognition in dogs. While earlier approaches achieved high accuracy using Dense Residual and Squeeze-and-Excitation blocks, they often lacked real-time applicability and were not optimized for …
Published in International Journal of Machine Systems and Manufacturing Technology · Vol. 4, Issue 1, 2026 · pp. 20–32 Read article
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Explainable Sentiment Mining Model in Mental Health Forums for Emotion Classification and Justification
Abstract: Understanding and interpreting emotions expressed in online mental health discussions plays a crucial role in enabling early detection of psychological distress and facilitating timely interventions. As individuals increasingly turn to digital platforms to share personal experiences and seek support, automated systems capable of accurately identifying emotional states can significantly assist clinicians, moderators, and support communities. This paper presents a deep learning–based sentiment mining and emotion classification framework specifically designed to …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 22–32 Read article
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The Heart of Charani Poetry: An AI Interpretation of Emotional Resonance
Abstract: Poetry has long been a means of expressing emotions and ideas, yet understanding the emotional depth within poems can be challenging using traditional computer-based tools. This study explores the emotions embedded in Charani poems, an important genre in Indian literature, through the lens of their distinctive poetic style. The primary objective is to develop a novel method of analyzing emotions in Charani poetry, contributing to the broader field of emotion …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 1, 2025 · pp. 34–39 Read article
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Detection of Driver Emotion Using Deep Learning
Abstract: High level Driver-Help Frameworks (ADASs) are utilized for expanding security in the auto space, yet momentum ADASs quite work without considering drivers' states, e.g., whether she/he is genuinely able to drive. Feelings are a significant way of behaving of people and may emerge in driving circumstances. Uncontrolled feelings can prompt unsafe impacts. To control and decrease the adverse consequence of conduct. In this paper we will distinguish the driver’s conduct. …
Published in International Journal of Electronics Automation · Vol. 1, Issue 1, 2023 · pp. 01–06 Read article
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Real-time Facial Recognition with Convolutional Neural Networks for Personalized Music Therapy
Abstract: In this project, a web-based application has been developed that integrates computer vision-based facial recognition, multiple algorithms, and machine learning approaches. The given system obtains a user’s emotions in the real-time frame by analyzing facial expressions such as eyes, mouth, the forehead, and so on. It detects emotions like happiness, sadness, that is neutrality, or rock. For a given detected emotion, language, and a user’s chosen artist, the system recommends …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 2, 2024 · pp. 67–76 Read article
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Real-time Emotion-aware AI Counseling System with Memory Retention Polymer Composites
Abstract: The availability of mental health services is still a major barrier, with many individuals constrained by financial limitations, social stigma, and a shortage of accessible counselors. This work introduces an emotion-aware AI counselor designed to provide empathetic and personalized emotional support via voice-based interfaces. The system leverages Natural Language Processing (NLP) and sentiment analysis to detect emotional cues from speech and generate contextually appropriate, comforting responses. A key innovation is …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1395–1407 Read article
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Emotion Recognition from Electroencephalogram Signal and Eye Movement Based on Deep Learning
Abstract: Emotion recognition from electroencephalogram (EEG) signals has gained significant attention due to its potential in human- computer interaction (HCI), mental health monitoring, and personalized content delivery. This paper presents the use of Convolutional neural networks (CNNs) to classify emotions such as happiness, sadness, fear, neutral, and disgust by leveraging a fusion of EEG signals and eye movements data. Compared to conventional methods of emotion detection, such as those that rely …
Published in International Journal of Optical Innovations & Research · Vol. 3, Issue 2, 2025 · pp. 8–15 Read article
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Emotionally Intelligent AI: The Future of Mental Health Care and Emotional Well-being
Abstract: With the potential to improve emotional well-being through sophisticated AI systems, emotionally intelligent AI (EI-AI) represents a revolutionary frontier in mental health treatment. EI-AI can recognize, understand, and react to human emotions in real- time by utilizing recent advancements in machine learning, natural language processing, and emotion detection. These features are being used more and more in mental health settings, where chatbots and other AI-driven interventions help with emotional regulation, …
Published in Recent Trends in Social Studies · Vol. 2, Issue 1, 2025 · pp. 17–21 Read article
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Depression Detection Using AI with Chatbot Support
Abstract: Depression is a major global health concern and a significant contributor to suicide rates worldwide. India reports a high number of suicide cases, making the early detection of mental distress and depression essential for timely intervention. This research presents an AI-based system for depression detection that integrates deep learning, natural language processing (NLP), and a chatbot for user support. The system analyzes facial expressions using convolutional neural networks (CNNs) and …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 14, Issue 1, 2025 · pp. 01–08 Read article
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Comparative Analysis Between Librosa and OpenSMILE
Abstract: This research work focuses on comparative study of Librosa, a python-based library, and openSMILE, a C++ toolkit, with python bindings used in audio speech analysis. Librosa is ideal for beginners due to its simple structure and flexibility with strong integration with machine learning frameworks like TensorFlow and PyTorch. On the other hand, OpenSMILE is ideal for speech-centric tasks like speech-emotion recognition or paralinguistic studies, offering a wide range of pre-defined …
Published in Journal of Open Source Developments · Vol. 12, Issue 3, 2025 · pp. 06–10 Read article
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Mood Mate: A Solid-State Edge-AI System for Real-Time Facial Emotion Recognition
Abstract: Recent progress in solid-state electronics and embedded vision systems has enabled real-time emotion-aware applications at the edge. This paper presents MoodMate, a solid-state edge-AI framework for real-time facial emotion recognition using camera-based sensing and embedded processing. The proposed system integrates a solid-state image sensor with an AI- driven emotion classification pipeline optimized for low-latency and resource-constrained environments. Intelligent, emotion-aware apps can now be deployed right at the network edge thanks …
Published in International Journal of Solid State Innovations & Research · Vol. 3, Issue 2, 2025 · pp. 24–30 Read article
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Employee Well-Being: Deep Learning Approaches to Stress Detection
Abstract: Stress has become a major concern for employee health, productivity, and overall well-being in today's fast-paced work environment. It is a growing global issue, affecting both individual employees and the productivity of organizations. Work-related stress occurs when the demands of a job surpass an individual's ability to manage, whether because of long hours, overwhelming responsibilities, or other pressures. Factors such as conflicts with coworkers or supervisors, constant changes, and job …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 12, Issue 1, 2025 · pp. 52–58 Read article
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Empowering Vehicle: The Impact of Deep and Reinforcement Learning in IoV
Abstract: Deep learning and reinforcement learning represent two pivotal pillars within the realm of artificial intelligence and machine learning, bearing transformative potential in the domain of the Internet of Vehicles (IoV). This abstract explores the multifaceted applications of these cutting-edge techniques within the IoV framework. Deep learning, exemplified by convolution neural networks (CNNs) and recurrent neural networks (RNNs), empowers IoV systems with the prowess to discern complex patterns in sensory data. …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 3, Issue 2, 2025 · pp. 1–12 Read article
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Digital Psychiatry: A Narrative Review on AI Positive Role in Mental Health
Abstract: Artificial Intelligence has rapidly evolved into a formidable instrument within the domain of mental healthcare, fundamentally altering the way we understand awareness, diagnosis, intervention and emotional regulation. This narrative review explores AI’s potential to foster positive mental health through tools such as natural language processing, machine learning, deep learning and computer vision. These technologies promise earlier detection of mental disorders, customized treatment plans and responsive emotional support. Yet, alongside these …
Published in Current Trends in Signal Processing · Vol. 15, Issue 3, 2025 · pp. 1–13 Read article
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Real-Time Browser-Based Early Warning System for Cyberbullying Detection in Online Platforms
Abstract: The rise in social networking through internet-based communication tools, Instagram, and YouTube, to name a few, significantly increases the risk of cyberbullying, thereby increasing psychological trauma on users, especially children, through adverse emotional states like anxiety, depression, etc. For a long time, researchers have been enhancing detection tools to counter cyberbullying, but their ability to detect only after the fact, along with limited support for English-based architecture, is a major …
Published in International Journal of Computer Science Languages · Vol. 4, Issue 1, 2026 · pp. 09–15 Read article
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Animal Detection in Farms Using Opencv
Abstract: Agriculture plays a fundamental role in sustaining the Indian economy, providing employment and livelihood to a large portion of the population. Despite advancements in farming techniques, one of the persistent challenges faced by farmers is the intrusion of wild animals into agricultural fields. Such intrusions often lead to large-scale crop damage, financial loss, and emotional distress for farmers. Traditional animal deterrent methods, such as manual patrolling, fences, or scarecrows, have …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 13, Issue 2, 2025 Read article