Search Results - (( emotion detection system algorithm ) OR ( label classification system algorithm ))

Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition by Leong, Shi Xiang

“…In pattern recognition system, achieving high accuracy in pattern classification is crucial. …”
Get full text
Get full text

Real-time classification improvement of Indonesian sign system letters (SIBI) using K-Nearest Neighbor algorithm by Dhewa, Oktaf Agni, Utama, Safitri Yuliana, Nasuha, Aris, Gunawan, Teddy Surya, Pratama, Gilang Nugraha Putu

Get full text
Get full text
Get full text

Performances of machine learning algorithms for binary classification of network anomaly detection system by Nawir, M., Amir, A., Lynn, O.B., Yaakob, N., Ahmad, R.B.

“…The finding showed that AODE algorithm is performed well in term of accuracy and processing time for binary classification towards UNSW-NB15 dataset.…”
Get full text
Get full text
Get full text

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

Get full text

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

Get full text

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.…”
Get full text
Get full text
Get full text
Get full text

Classification and visualization on eligibility rate of applicant’s LinkedIn account using Naïve Bayes / Nurul Atirah Ahmad by Ahmad, Nurul Atirah

“…It is set to label since it has no label class. The classification is set to two categories: Eligible or Ineligible. …”
Get full text
Get full text

Multi-label risk diabetes complication prediction model using deep neural network with multi-channel weighted dropout by Dzakiyullah, Nur Rachman

“…Seven machine learning models—Artificial Neural Network (ANN), Random Forest (RF), Decision Tree (DTT), k-Nearest Neighbors (k-NN), Naïve Bayes (NB), Support Vector Machine (SVM), and Deep Neural Network (DNN)—were used for multi-label classification of the complications. The study employed two MLC frameworks: Problem Transformation methods (Binary Relevance, Classifier Chains, Label Power Set, and Calibrated Label Ranking) and Algorithm Adaptation. …”
Get full text
Get full text

Classification of JPEG files by using extreme learning machine by Ali, Rabei Raad, Mohamad, Kamaruddin Malik, Jamel, Sapiee, Ahmad Khalid, Shamsul Kamal

“…This paper proposes an Extreme Learning Machine (ELM) algorithm to assign a class label of JPEG or Non-JPEG image for files in a continuous series of data clusters. …”
Get full text
Get full text

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

Get full text

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

Get full text

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

Get full text

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

Get full text

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

VEHICLE CLASSIFICATION USING NEURAL NETWORKS AND IMAGE PROCESSING by ONG KANG WEI, ONG KANG WEI, LOH SER LEE, LOH SER LEE

“…The aim of this study is to propose a vehicle classification scheme where YOLO v5 algorithm and Faster R-CNN algorithm are being implemented separately into vehicle classification, followed by comparison of result between these two algorithms. …”
Get full text
Get full text
Get full text

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

Get full text

Extreme learning machine classification of file clusters for evaluating content-based feature vectors by Ali, Rabei Raad, Mohamad, Kamaruddin Malik, Jamel, Sapiee, Ahmad Khalid, Shamsul Kamal

“…The files are allocated in a continuous series of clusters. The ELM algorithm is applied to the DFRWS (2006) dataset and the results show that the combination of the three methods produces 93.46% classification accuracy.…”
Get full text

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

“…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. …”
Get full text
Get full text

Detecting emotions and depression through voice by Gunawan, Teddy Surya

“…A deep learning algorithm can detect emotion, including depression, using a voice signal. …”
Get full text
Get full text

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

Get full text