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  1. 1
    EEG-based emotion recognition using machine learning algorithms

    EEG-based emotion recognition using machine learning algorithms by Lam, Yee Wei

    Published 2024
    “…Thus, this project proposed an optimised machine learning algorithms to classify emotion by analysing brain activity using Electroencephalogram (EEG) signals. …”
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    Final Year Project / Dissertation / Thesis
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  2. 2
    Novice programmers’ emotion and competency assessments using machine learning on physiological data / Fatima Jannat

    Novice programmers’ emotion and competency assessments using machine learning on physiological data / Fatima Jannat by Fatima, Jannat

    Published 2022
    “…Hyper-parameter tuning has been used in all the algorithms using k-fold cross validation to have the best accuracy and to avoid the over-fitting issue. …”
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  3. 3
    Deep learning based emotion recognition for image and video signals: matlab implementation

    Deep learning based emotion recognition for image and video signals: matlab implementation by Ashraf, Arselan, Gunawan, Teddy Surya, Kartiwi, Mira

    Published 2021
    “…Five emotions are considered for recognition: angry, happy, neutral, sad, and surprise, compared to previous algorithms. …”
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  4. 4
    EMOTION RECOGNITION USING GALVANIC SKIN RESPONSE (GSR) SIGNAL

    EMOTION RECOGNITION USING GALVANIC SKIN RESPONSE (GSR) SIGNAL by RAMOS UKAR, YAKOBUS

    Published 2022
    “…The classified affective GSR signals with labels were obtained from the arousal seven-point emotional scale approach using machine learning algorithms. …”
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    Final Year Project Report / IMRAD
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  5. 5
    Learner’s emotion prediction using production rules classification algorithm through brain computer interface tool

    Learner’s emotion prediction using production rules classification algorithm through brain computer interface tool by Nurshafiqa Saffah, Mohd Sharif

    Published 2018
    “…From the data analysis using WEKA software, the production rules classifier (PART) is found to be the most accurate classification algorithm in classifying the emotion which yields the highest precision percentage of 99.6% compared to J48 (99.5%) and Naïve Bayes (96.2%). …”
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  6. 6
    Emotion recognition and analysis of netizens based on micro-blog during covid-19 epidemic

    Emotion recognition and analysis of netizens based on micro-blog during covid-19 epidemic by Jiao, BianBian, Leelavathi, R., Lohgheswary, N., Nopiah, Z. M.

    Published 2022
    “…This research uses machine learning algorithm combined with statistical analysis to analyze current events in real time. …”
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  7. 7
    A Parallel-Model Speech Emotion Recognition Network Based on Feature Clustering

    A Parallel-Model Speech Emotion Recognition Network Based on Feature Clustering by Li-Min Zhang, Giap Weng Ng, Yu-Beng Leau, Hao Yan

    Published 2023
    “…To address this issue, we proposed a novel algorithm called F-Emotion to select speech emotion features and established a parallel deep learning model to recognize different types of emotions. …”
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  8. 8
    A parallel-model speech emotion recognition network based on feature clustering

    A parallel-model speech emotion recognition network based on feature clustering by Li-Min Zhang, Giap Weng Ng, Yu-Beng Leau, Hao Yan

    Published 2023
    “…To address this issue, we proposed a novel algorithm called F-Emotion to select speech emotion features and established a parallel deep learning model to recognize different types of emotions. …”
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  9. 9
    Emotion Modelling Using Neural Network

    Emotion Modelling Using Neural Network by Lam, Choong Kee

    Published 2005
    “…The dataset was tested on Multipayer Perceptron with backpropagation learning algorithm. The emotion model obtained in this study uses parameters such as; learning rate 0.1, momentum rate 0.1, Sigmoid activation function, 200 epoch learning stopping criteria, with its architecture, 82 input units, 10 hidden units and 6 output layer units. …”
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  10. 10
    A comparative investigation of eye fixation-based 4-class emotion recognition in virtual reality using machine learning

    A comparative investigation of eye fixation-based 4-class emotion recognition in virtual reality using machine learning by Lim Jia Zheng, James Mountstephens, Jason Teo

    Published 2021
    “…This paper proposes a novel approach for 4-class emotion classification using eye-tracking data solely in virtual reality (VR) with machine learning algorithms. …”
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    Proceedings
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  11. 11
    Predicting game-induced emotions using EEG, data mining and machine learning

    Predicting game-induced emotions using EEG, data mining and machine learning by Min, Xuan Lim, Jason Teo

    Published 2024
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  12. 12
    Speech emotion recognition using spectrogram based neural structured learning

    Speech emotion recognition using spectrogram based neural structured learning by Sivan, Dawn, Haripriya, P. H., Jose, Rajan

    Published 2022
    “…The generated features are then used to train and understand the emotions via Neural Structured Learning (NSL), a fast and accurate deep learning approach. …”
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  13. 13
    Fundamental Research Grant Scheme (FRGS) - FRGS19-076-0684, Speech Emotion Recognition and Depression Prediction Based on Speech Analysis using Deep Neural Networks

    Fundamental Research Grant Scheme (FRGS) - FRGS19-076-0684, Speech Emotion Recognition and Depression Prediction Based on Speech Analysis using Deep Neural Networks by Gunawan, Teddy Surya, Draman, Samsul, Kartiwi, Mira, Borhan, Lihanna, Abdul Malik, Noreha, Abdul Rahman, Farah Diyana, Elsheikh, Elsheikh Mohamed Ahmed, Alghifari, Muhammad Fahreza, Ahmad Qadri, Syed Asif, Ashraf, Arselan, Wani, Taiba Majid

    Published 2022
    “…The deep learning model was trained using well-known databases such as the Berlin Emotion Database and the DAIC-WOZ Depression Dataset. …”
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    Monograph
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  14. 14
    Detecting emotions and depression through voice

    Detecting emotions and depression through voice by Gunawan, Teddy Surya

    Published 2021
    “…A deep learning algorithm can detect emotion, including depression, using a voice signal. …”
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  15. 15
    Algorithms for moderating effect of emotional value from a cross-media data fusion perspective: a case study of Chinese dating reality shows

    Algorithms for moderating effect of emotional value from a cross-media data fusion perspective: a case study of Chinese dating reality shows by Zhang, Shasha, Dong, Qiming, Yasin, Megat Al Imran, Fang, Ng Chwee

    Published 2026
    “…The Multimodal Transformer Fusion (MMTF) model uses the cross-modal attention mechanisms to combine these streams of data to produce unified emotional representations. …”
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  16. 16
    Utilizing machine learning technique for emotion learning and aiding mental health issues

    Utilizing machine learning technique for emotion learning and aiding mental health issues by Husin, Nor Azura, Wan, Gibson Liang, Kamaruzaman, Nurul Nadhrah

    Published 2022
    “…EMOICE can also be used for emotional learning, where people can use empathy and understanding to deal with mental health concerns. …”
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  17. 17
    Detection of mental disorders based on the analysis of emotion, facial expressions and facial movements in a video stream

    Detection of mental disorders based on the analysis of emotion, facial expressions and facial movements in a video stream by Nurzhanova, Aizhan, Mussabek, Miras, Ince, Gokhan, Mustaffa, Mas Rina, Zhumadillayeva, Ainur

    Published 2025
    “…Using machine learning techniques and deep learning algorithms, we aim to create an algorithm for emotion recognition using a personalized approach. …”
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  18. 18
    DESIGN AND ANALYSIS OF BRAIN EMOTIONAL LEARNING BASED INTELLIGENT CONTROLLER (BELBIC) FOR TEMPERATURE CONTROL

    DESIGN AND ANALYSIS OF BRAIN EMOTIONAL LEARNING BASED INTELLIGENT CONTROLLER (BELBIC) FOR TEMPERATURE CONTROL by Siti Rasyidah binti Syed Rabi'i, Siti Rasyidah

    Published 2010
    “…This report presents the project undertaken to design and analyze the performance of temperature control using brain emotional learning control approach. …”
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    Final Year Project
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  19. 19
    A review of recent approaches for emotion classification using electrocardiography and electrodermography signals

    A review of recent approaches for emotion classification using electrocardiography and electrodermography signals by Aaron Frederick Bulagang, Ng, Giap Weng, James Mountstephens, Jason Teo

    Published 2020
    “…Fewer studies have been conducted using the ECG and EDG to this end. These physiological signals will be reviewed to compare the ECG and EDG approach, equipment, and stimuli used, as well as machine learning algorithms utilized to perform the classification task. …”
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  20. 20
    Text-based emotion prediction system using machine learning approach

    Text-based emotion prediction system using machine learning approach by Ahmad Fakhri, Ab. Nasir, Eng, Seok Nee, Chun, Sern Choong, Ahmad Shahrizan, Abdul Ghani, Anwar, P. P. Abdul Majeed, Asrul, Adam, Mhd, Furqan

    Published 2020
    “…A benchmark of ISEAR (International Survey on Emotion Antecedents and Reactions) dataset was used to test all models. …”
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