Search Results - (( emotion detection system algorithm ) OR ( based notification system algorithm ))

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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An indoor based push notification for location-based marketing using bluetooth smart ready technology / Nur Farehah Alias by Alias, Nur Farehah

“…Therefore, this project has proposed a method called token based alert notification. There are three algorithm developed for token based alert notification which is tokenization algorithm, token matching algorithm and item identification algorithm. …”
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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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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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Space Security System using Motion Sensor and Notification of Short Message Service with Arduino-Based Fuzzy Logic Algorithm by Erlan, Darmawan, Rivandi, Taufan

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

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

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

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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

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

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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. …”
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Detecting emotions and depression through voice by Gunawan, Teddy Surya

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

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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

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Development of mobile location based notification for crime cases tracking as an alternative alert for public safety / Noraini Ismail by Ismail, Noraini

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

“…A 3D face pose estimation algorithm detects head rotations of Yaw, Roll and Pitch for emotion recognition. …”
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A real-time mobile notification system for inventory stock out detection using SIFT and RANSAC by Merrad, Yacine, Habaebi, Mohamed Hadi, Islam, Md Rafiqul, Gunawan, Teddy Surya

“…The proposed method is a machine learning based real-time notification system using the exciting Scale Invariant Feature Transform feature detector (SIFT) and Random Sample Consensus (RANSAC) algorithms. …”
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Enhancement load balancing in failover web server by using HTTP-response status techniques with pfsense opensource / Muhammad Hafiz Ramli by Ramli, Muhammad Hafiz

“…Increasing the number of failures causes the number of failures of successfulness rate in ICMP based and congestion in HTTP based monitoring. Meanwhile, an email notification was introduced in ICMP and HTTP based monitoring as a notification to the administrator when the failure occurs.…”
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Learner’s emotion prediction using production rules classification algorithm through brain computer interface tool by Nurshafiqa Saffah, Mohd Sharif

“…In future, this research can be an initial work in automating tutorial decisions in an intelligent tutoring system which are able to adapt to the behaviour of the learners based on the detected mental states. …”
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