Search Results - (( emotion selection model algorithm ) OR ( panel classification based algorithm ))
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A Parallel-Model Speech Emotion Recognition Network Based on Feature Clustering
Published 2023“…To address this issue, we proposed a novel algorithm called F-Emotion to select speech emotion features and established a parallel deep learning model to recognize different types of emotions. …”
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A parallel-model speech emotion recognition network based on feature clustering
Published 2023“…To address this issue, we proposed a novel algorithm called F-Emotion to select speech emotion features and established a parallel deep learning model to recognize different types of emotions. …”
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Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition
Published 2018“…Due to some limitations of current heuristics and evolutionary algorithms, this paper proposed a new swarm based algorithm for feature selection method called Social Spider Optimization (SSO-FS). …”
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Affective computation on EEG correlates of emotion from musical and vocal stimuli
Published 2009“…In this paper, an affective brain-computer interface (ABCI) is proposed to perform affective computation on electroencephalogram (EEG) correlates of emotion. The proposed ABCI extracts EEG features from subjects while exposed to 6 emotionally-related musical and vocal stimuli using kernel smoothing density estimation (KSDE) and Gaussian mixture model probability estimation (GMM). …”
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Proceeding Paper -
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Optimization of least squares support vector machine technique using genetic algorithm for electroencephalogram multi-dimensional signals
Published 2016“…Regardless the popularity of EEG in recognizing human emotion, this study field is relatively challenging as EEG signal is nonlinear, involves myriad factors and chaotic in nature.These issues have led to high dimensional problem and poor classification results.To address such problems, this study has proposed a novel computational model, which consist of three main stages, namely a) feature extraction; b) feature selection and c) classifier. …”
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Risk perception modeling based on physiological and emotional responses / Ding Huizhe
Published 2024“…A Pleasure-Arousal-Dominance (PAD) model was used to induced and expressed mixed emotions. …”
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Development of Image-Based Emotion Recognition using Convolutional Neural Networks
Published 2021“…First, the extended Cohn-Kanade image emotion database was selected with five defined emotions: happy, sad, anger, fear, surprise, and neutral. …”
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Proceeding Paper -
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Analysis of Feature Selection Methods for Sentiment Analysis Concerning Covid-19 Vaccination Issues
Published 2023“…It is also hoped that this can increase the quality of the prediction model that will be formed. In this study, the author will continue the research from another researcher by adding a feature selection process, such as two algorithms from the filtered method, chi-square, and information gain, and one algorithm from the wrapped method, which is Genetic Algorithms (GA). …”
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Mobile app of mood prediction based on menstrual cycle using machine learning algorithm / Nur Hazirah Amir
Published 2019“…It implemented Supervised Learning algorithm with Bayes’ Theorem model for the calculation of mood prediction using Python programming language. …”
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The classification of skateboarding trick manoeuvres: A K-nearest neighbour approach
“…A variation of k-NN algorithms were tested based on the number of neighbours, as well as the weight and the type of distance metric used. …”
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Development of electronic nose for classification of aromatic herbs using Artificial Intelligent techniques
Published 2018“…Two classification methods, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used in order to investigate the performance of classification accuracy for this E-nose system. …”
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A dataset for emotion recognition using virtual reality and EEG (DER-VREEG): Emotional state classification using low-cost wearable VR-EEG headsets
Published 2022“…Finally, we evaluate the emotion recognition system by using popular machine learning algorithms and compare them for both intra-subject and inter-subject classification. …”
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Personalized Recommendation Classification Model of Students’ Social Well-being Based on Personality Trait Determinants Using Machine Learning Algorithms
Published 2023“…In this study, how different personality trait models compare in terms of accuracy and reliability is explored using different machine learning algorithms using the WEKA tool. …”
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Affect classification using genetic-optimized ensembles of fuzzy ARTMAPs
Published 2015“…Speciation was implemented using subset selection of classification data attributes, as well as using an island model genetic algorithms method. …”
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Analytical framework for predicting online purchasing behavior in Malaysia using a machine learning approach
Published 2025“…The descriptive analysis examines purchasing behavior through correlation and regression analyses, while the predictive model uses decision trees (J48, Random Tree, REPTree), rule-based algorithms (JRip, OneR, PART), and clustering (K-Means) to identify patterns and predict trends. …”
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Audience Responses to Newspaper Coverage of Floods in China: Victims Versus Onlookers
Published 2026“…The findings show that both victims and onlookers are aware of media control and the role of big data algorithms to push selected national news but still trust newspapers over social media as sources of information during flood crises.…”
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AUDIENCE RESPONSES TO NEWSPAPER COVERAGE OF FLOODS IN CHINA: VICTIMS VERSUS ONLOOKERS
Published 2026“…The findings show that both victims and onlookers are aware of media control and the role of big data algorithms to push selected national news but still trust newspapers over social media as sources of information during flood crises.…”
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Accelerating color theory-based design through artificial intelligence: a conceptual welcome
Published 2025“…While traditional color selection relies heavily on human intuition, theoretical knowledge, and iterative experimentation, AI systems offer predictive modeling and data-driven recommendations that align with established color harmony principles (Jain et al., 2022). …”
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