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Human emotion recognition for computer games player using AdaBoost algorithm / Muhamad Nur Adhwa Muhamad Shukri by Muhamad Shukri, Muhamad Nur Adhwa

“…This application focusing on developing further implementation of human emotions recognition so that it will be more efficient and effective to be use in the future. …”
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An Algorithm to Define Emotions Based on Facial Gestures as Automated Input in Survey Instrument by Tayib, Saifulazmi, Jamaludin, Zulikha

“…Features of facial gestures that play important role in creating emotions are selected to be formulated in the algorithm.The aim of this paper is to produce an algorithm to define emotions based on three facial features namely mouth, eyes, and eyebrows.This is done by studying users and the problem domain, analyzing the relationship between emotions and facial gestures, and formulating solutions to the problem.This algorithm will be useful in developing a more interactive survey instrument and improving user interaction between researchers and dyslexic children.…”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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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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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

“…Firstly, it aims to use both linear and nonlinear features of EEG signals to identify emotional influences on gender behavior. Secondly, it aims to develop an automatic gender recognition model by employing optimization algorithms to identify the most effective channels for gender identification from emotional-based EEG signals. …”
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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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Deep learning based emotion recognition for image and video signals: matlab implementation by Ashraf, Arselan, Gunawan, Teddy Surya, Kartiwi, Mira

“…Five emotions are considered for recognition: angry, happy, neutral, sad, and surprise, compared to previous algorithms. …”
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Learner’s emotion prediction using production rules classification algorithm through brain computer interface tool by Nurshafiqa Saffah, Mohd Sharif

“…The objectives of this research are to classify the user emotion characteristics by using EEG signals based on children’s behaviour, to develop a prototype of an emotion prediction system named as MYEmotion and to validate the developed prototype in predicting the positive and negative emotions of the children. 16 datasets of attention and meditation levels were collected from a qualitative sampling of 10 years old school children in Pekan, Pahang using a BCI headset tool. …”
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An enhanced computational model based on social spider optimisation algorithm for EEG- based emotion recognition by Al-Qammaz, Abdullah Yousef Awwad

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Heart-design nexus: the emotion algorithm by Mohd Lokman, Anitawati

“…This book, "Heart-Design Nexus: The Emotion Algorithm", represents both a culmination and a continuation of that journey. …”
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Face emotion recognition by Mohd Rizon, Mohamed Juhari, M., Karthigayan, Ramachandran, Nagarajan, Prof. Dr., Sazali, Yaacob, Prof. Dr., M., Rozailan, M., Sugisaka, Hazry, Desa, Dr.

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Development of Image-Based Emotion Recognition using Convolutional Neural Networks by Latif, Atiya, Gunawan, Teddy Surya, Kartiwi, Mira, Arifin, Fatchul, Mansor, Hasmah

“…This paper presented the development of emotion recognition using Convolutional Neural Networks (CNN) on image input. …”
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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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Face emotion recognition using artificial intelligence techniques by Kartigayan Muthukaruppan

“…In order to understand the personalized face emotion recognition, the developed fitness functions are applied on two SEA subjects. …”
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A critical insight into multi-languages speech emotion databases by Ahmad Qadri, Syed Asif, Gunawan, Teddy Surya, Alghifari, Muhammad Fahreza, Mansor, Hasmah, Kartiwi, Mira, Janin, Zuriati

“…This paper reviews 34 `speech emotion databases for their characteristics and specifications. …”
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Industry 5.0 and Education 5.0: Transforming Vocational Education through Intelligent Technology by Zhang, Hongli, Leong, Wai Yie

“…By analyzing the research gaps in personalized learning paths, emotion-driven learning, crossdisciplinary integration, and long-term learning behavior analysis, the paper proposes four improved algorithms: the adaptive learning path generation algorithm, the emotion-driven personalized learning algorithm, the cross-disciplinary knowledge graph algorithm, and the long-term learning behavior prediction algorithm. …”
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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

“…Text-based emotion prediction system to interpret and understand human emotions was successfully developed.…”
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Enhanced emotion recognition in videos: a convolutional neural network strategy for human facial expression detection and classification by Ashraf, Arselan, Gunawan, Teddy Surya, Arifin, Fatchul, Kartiwi, Mira, Sophian, Ali, Habaebi, Mohamed Hadi

“…The human face is essential in conveying emotions, as facial expressions serve as effective, natural, and universal indicators of emotional states. …”
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Quantum-inspired associative memories for incorporating emotion in a humanoid / Naoki Masuyama by Naoki, Masuyama

“…In regards to the functional improvements, the emotion affected association model is developed in an interactive robot system based on the relationship between memory and emotion from the viewpoint of psychology and neuroscience, which is called the mood-congruency effect. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush