Search Results - (( emotion detection a algorithm ) OR ( gender classification based algorithm ))

Multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based EEGs by Al-Qazzaz, Noor Kamal, Sabir, Mohannad K., Mohd Ali, Sawal Hamid, Ahmad, Siti Anom, Grammer, Karl

“…Finally, the k-nearest neighbors ( kNN) classification technique was used for automatic gender identification of an emotional-based EEG dataset. …”
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Facial geometry and speech analysis for depression detection by Pampouchidou, A., Simantiraki, O., Vazakopoulou, C.-M., Chatzaki, C., Pediaditis, M., Maridaki, A., Marias, K., Simos, P., Yang, F., Meriaudeau, F., Tsiknakis, M.

“…The algorithms were evaluated for several combinations of parameters and classification schemes, on the dataset provided by the Audio/Visual Emotion Challenge of 2013 and 2014. …”
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Gender classification on skeletal remains: efficiency of metaheuristic algorithm method and optimized back propagation neural network by Hairuddin, Nurul Liyana, Yusuf, Lizawati Mi, Othman, Mohd Shahizan

“…Thus, this paper proposed metaheuristic algorithms such as Particle Swarm Optimization, Ant Colony Algorithm and Harmony Search Algorithm based feature selection to identify the most significant features of skeleton remains. …”
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A study of frequency radiation of human torso for gender classification by Siti Zura, A. Jalil, Mohd Nasir, Taib, Prof. Dr., Hasnain, Abdulllah@Idris, Megawati, Mohd Yunus

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Multilayer Perceptron Neural Network In Classifying Gender Using Fingerprint Global Level Features by Siti Fairuz, Abdullah, Ahmad Fadzli Nizam, Abdul Rahman, Zuraida, Abal Abas, Wira Hidayat, Mohd Saad

“…Background/Objective: A new algorithms of gender classification from fingerprint is proposed based on Acree 25mm2 square area. …”
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Multilanguage speech-based gender classification using time-frequency features and SVM classifier by Wani, Taiba, Gunawan, Teddy Surya, Mansor, Hasmah, Ahmad Qadri, Syed Asif, Sophian, Ali, Ambikairajah, Eliathamby, Ihsanto, Eko

“…The classification is done based on features derived from the frequency and time domain processing using the Support Vector Machines (SVM) algorithm. …”
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Real-time system for facial emotion detection using GPSO algorithm by Bashir, Mohammed Ghandi, R., Nagarajan, Prof. Dr., Hazry, Desa, Assoc. Prof. Dr.

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Gender classification based on asian faces using deep learning by Janahiraman T.V., Subramaniam P.

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

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Particle Swarm Optimization algorithm for facial emotion detection by Ghandi, B.M., Nagarajan, R., Hazry, Desa, Prof. Madya Dr.

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First Semester Computer Science Students’ Academic Performances Analysis by Using Data Mining Classification Algorithms by Azwa, Abdul Aziz, Fadhilah, Ahmad

“…The comparative analysis is also conducted to discover the best classification model for prediction. From the experiment, the models develop using Rule Based and Decision Tree algorithm shows the best result compared to the model develop from the Naïve Bayes algorithm. …”
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Emotion Detection Based on EEG Signal by Mohamad Nasaruddin, Noradila

“…Thus, this project aimed to study the emotion detection through EEG signal and proposed the right algorithm to process the signal. …”
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Development of Low Cost Heart Rate Monitoring Device and Classification Technique Using Fuzzy Logics Algorithm by Faili, Zahra

“…The classification of the signal being obtained is achieved through fuzzy logics algorithm inside the MATLAB Fuzzy Logic Toolbox. …”
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Classification Of Gender Using Global Level Features In Fingerprint For Malaysian Population by Abdullah, Siti Fairuz

“…A new approach of algorithm based on the Mark Acree’s theory, focusing on fingerprint global extracted features is proposed and implemented for enhancing gender classification method. …”
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Human Spontaneous Emotion Detection System by Radin Monawir, Radin Puteri Hazimah

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

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Gender Classification: A Convolutional Neural Network Approach by Shan, Sung Liew, Mohamed, Khalil-Hani, Syafeeza, Ahmad Radzi, Rabia, Bakhteri

“…An approach using a convolutional neural network (CNN) is proposed for real-time gender classification based on facial images. The proposed CNN architecture exhibits a much reduced design complexity when compared with other CNN solutions applied in pattern recognition. …”
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Human faces: a review from detection to recognition by Mohamed Rizon, Mohamed Juhari, Muthukaruppan, Karthigayan, Sazali, Yaacob, Ramachandran, Nagarajan, Puteh, Saad, P. L. EhKan

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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

Making programmer effective for software development teams: An extended study by Gilal, A.R., Jaafar, J., Abro, A., Umrani, W.A., Basri, S., Omar, M.

“…Basically, two types of decision rules were formed: rules without gender classification of programmer but they only discussed the personality types of team-leader and programmer. …”
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