Search Results - (( emotion detection system algorithm ) OR ( level classification system algorithm ))*

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  1. 1
    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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  2. 2
    Vader lexicon and support vector machine algorithm to detect customer sentiment orientation

    Vader lexicon and support vector machine algorithm to detect customer sentiment orientation by Vivine Nurcahyawati, ., Zuriani, Mustaffa

    Published 2023
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  3. 3
    Real-time system for facial emotion detection using GPSO algorithm

    Real-time system for facial emotion detection using GPSO algorithm by Bashir, Mohammed Ghandi, R., Nagarajan, Prof. Dr., Hazry, Desa, Assoc. Prof. Dr.

    Published 2012
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  4. 4
    Facial emotion detection using GPSO and Lucas-Kanade algorithms

    Facial emotion detection using GPSO and Lucas-Kanade algorithms by Bashir, Mohammed Ghandi, Nagarajan, Ramachandran, Prof. Dr., Hazry, Desa, Prof. Madya Dr.

    Published 2010
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  5. 5
    Human Spontaneous Emotion Detection System

    Human Spontaneous Emotion Detection System by Radin Monawir, Radin Puteri Hazimah

    Published 2018
    “…Having smart computerized system which can understand and instantly gives appropriate response to human is the utmost motive in human and computer interaction (HCI) field.It is argued either HCI is considered advance if human could not have natural and comfortable interaction like human to human interaction.Besides,despite of several studies regarding emotion detection system, current system mostly tested in laboratory environment and using mimic emotion.Realizing the current system research lack of real life or genuine emotion input,this research work comes up with the idea of developing a system that able to recognize human emotion through facial expression.Therefore,the aims of this study are threefold which are to enhance the algorithm to detect spontaneous emotion,to develop spontaneous facial expression database and to verify the algorithm performance.This project used Matlab programming language,specifically Viola Jones method for features tracking and extraction,then pattern matching for emotion classification purpose.Mouth feature is used as main features to identify the emotion of the expression.For verification purpose,the mimic and spontaneous database which are obtained from internet,open source database or novel (own) developed databases are used.Basically,the performance of the system is indicated by emotion detection rate and average execution time.At the end of this study,it is found that this system is suitable for recognizing spontaneous facial expression (63.28%) compared to posed facial expression (51.46%).The verification even better for positive emotion with 71.02% detection rate compared to 48.09% for negative emotion detection rate.Finally,overall detection rate of 61.20% is considered good since this system can execute result within 3s and use spontaneous input data which known as highly susceptible to noise.…”
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  6. 6
    Emotion detecting humanoid – the use of GPSO Algorithm

    Emotion detecting humanoid – the use of GPSO Algorithm by Bashir, Mohammed Ghandi, Ramachandran, Nagarajan, Prof. Dr., Hazry, Desa, Assoc. Prof. Dr.

    Published 2012
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  7. 7
    Various Recognition approach in human faces

    Various Recognition approach in human faces by Karthigayan, M., Mohd Rizon, Mohamed Juhari, Sazali, Yaacob, Nagarajan, R., Puteh, Saad, Muhammed Firdaus, Hashim, Haniza, Yazid

    Published 2009
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  8. 8
    Human faces: a review from detection to recognition

    Human faces: a review from detection to recognition by Mohamed Rizon, Mohamed Juhari, Muthukaruppan, Karthigayan, Sazali, Yaacob, Ramachandran, Nagarajan, Puteh, Saad, P. L. EhKan

    Published 2009
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  9. 9
    GPSO versus GA in facial emotion detection

    GPSO versus GA in facial emotion detection by Bashir, Mohammed Ghandi, Ramachandran, Nagarajan, Prof. Dr., Sazali, Yaacob, Prof. Dr., Hazry, Desa, Assoc. Prof. Dr.

    Published 2012
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  10. 10
    A Simple Yet Reliable Facial Emotion Detection For Campus Environment

    A Simple Yet Reliable Facial Emotion Detection For Campus Environment by Amar Lokman, Mus’ab Sahrim, Wan Zakiah Wan Ismail, Sharma Rao Balakrishnan, Juliza Jamaludin

    Published 2024
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  11. 11
    Facial emotion detection using Guided Particle Swarm Optimization (GPSO)

    Facial emotion detection using Guided Particle Swarm Optimization (GPSO) by Bashir, Mohammed Ghandi, Nagarajan, R., Hazry, Desa

    Published 2009
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  12. 12
    Stress Level Detection using Fuzzy Logic / Nor Husna Nabila Khuzaini

    Stress Level Detection using Fuzzy Logic / Nor Husna Nabila Khuzaini by Khuzaini, Nor Husna Nabila

    Published 2020
    “…This fuzzy logic system were successful develop. The result indicate in stress level detection based on fuzzy logic system that provide the symptom of stress and the facial emotion. …”
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  13. 13
    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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  14. 14
    Development of a personified face emotion recognition technique using fitness function

    Development of a personified face emotion recognition technique using fitness function by Muthukaruppan, Karthigayan, Mohd Rizon, Mohamed Juhari, Ramachandran, Nagarajan, Masanori, Sugisaka, Sazali, Yaacob, Mohd Rozailan, Mamat, Hazry, Desa

    Published 2009
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  15. 15
    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
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  16. 16
    Multiview face emotion recognition using geometrical and texture features

    Multiview face emotion recognition using geometrical and texture features by Goodarzi, Farhad

    Published 2017
    “…A 3D face pose estimation algorithm detects head rotations of Yaw, Roll and Pitch for emotion recognition. …”
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  17. 17
    An intra-severity classification and adaptation technique to improve dysarthric speech recognition accuracy / Bassam Ali Qasem Al-Qatab

    An intra-severity classification and adaptation technique to improve dysarthric speech recognition accuracy / Bassam Ali Qasem Al-Qatab by Bassam Ali Qasem, Al-Qatab

    Published 2020
    “…Firstly, intra-severity classification intended to identify the level of severity of the dysarthric speakers. …”
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  18. 18
    Jogging activity recognition using k-NN algorithm

    Jogging activity recognition using k-NN algorithm by Afifah Ismail

    Published 2022
    “…Jogging activity recognition using the k-NN algorithm is a system that can help users collect information data of user speed movement using speed sensor and give the classification of jogging activity to the user. …”
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  19. 19
    A Real time specific weed discrimination system using multi-Level wavelet decomposition

    A Real time specific weed discrimination system using multi-Level wavelet decomposition by Siddiqi, M.H., Sulaiman, S., Faye, Ibrahima, Ahmad, I.

    Published 2009
    “…The developed algorithm was used for the real time specific weed discrimination employing multi-level wavelet decomposition. …”
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  20. 20
    A Real time specific weed discrimination system using multi-Level wavelet decomposition

    A Real time specific weed discrimination system using multi-Level wavelet decomposition by Siddiqi, M.H., Sulaiman, S., Faye, Ibrahima, Ahmad, I.

    Published 2009
    “…The developed algorithm was used for the real time specific weed discrimination employing multi-level wavelet decomposition. …”
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