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Kumar, Tarun
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Computer Science & IT Electrical & Electronics Engineering

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Machine learning-based emotions recognition model using peripheral signals Kumar, Tarun; Kumar, Rajendra; Chandra Singh, Ram
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp976-984

Abstract

This work proposes a system for emotion recognition using four peripheral signals electromyography, galvanic skin response, blood volume pulse, and respiration. Peripheral signals cannot be modified, unlike other expression like voice and facial expression. The proposed method is applied to the DEAP datasets to verify the accuracy of emotion recognition. The proposed model focuses on accuracy and F1-score. DEAP dataset has more signals but only thirty-seven features from four peripheral signals were extracted for each trail and each video. On the DEAP datasets, the implementation found that the classification accuracy for arousal, valence, liking, and dominance was, respectively, 80%, 75%, 71%, and 78%. For two classes of problems, the corresponding F1-scores for arousal, valence, liking, and dominance are 0.50, 0.49, 0.47, and 0.47. The proposed model was implemented in MATLAB R2017a.
A variant of particle swarm optimization in cloud computing environment for scheduling workflow applications Tripathi, Ashish; Singh, Rajnesh; Moudgil, Suveg; Gupta, Pragati; Sondhi, Nitin; Kumar, Tarun; Srivastava, Arun Pratap
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1392-1401

Abstract

Cloud computing offers on-demand access to shared resources, with user costs based on resource usage and execution time. To attract users, cloud providers need efficient schedulers that minimize these costs. Achieving cost minimization is challenging due to the need to consider both execution and data transfer costs. Existing scheduling techniques often fail to balance these costs effectively. This study proposes a variant of the particle swarm optimization algorithm (VPSO) for scheduling workflow applications in a cloud computing environment. The approach aims to reduce both execution and communication costs. We compared VPSO with several PSO variants, including Inertia-weighted PSO, gaussian disturbed particle swarm optimization (GDPSO), dynamic-PSO, and dynamic adaptive particle swarm optimization with self-supervised learning (DAPSO-SSL). Results indicate that VPSO generally offers significant cost reductions and efficient workload distribution across resources, although there are specific scenarios where other algorithms perform better. VPSO provides a robust and cost-effective solution for cloud workflow scheduling, enhancing task-resource mapping and reducing costs compared to existing methods. Future research will explore further enhancements and additional PSO variants to optimize cloud resource management.
Machine learning-based hybrid emotions recognition model using electroencephalogram signals Kumar, Tarun; Kumar, Rajendra; Singh, Ram Chandra
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3180-3190

Abstract

This paper uses Hindi video clips to propose an electroencephalogram (EEG) signal-based hybrid system for emotion identification. EEG signals cannot be altered, unlike other forms of expressiveness-like voice and facial emotion. The suggested approach uses a self-created dataset under the control environments. Accuracy is the main objective of the proposed model. This study used a self-created constructed using an 8-channel unicorn black hybrid EEG machine on 30 participants while they viewed Hindi movie video clips mimicking emotions: happy, fearful, sad, and neutral. The proposed model used a two-hybrid classifier support vector machine (SVM) and k-nearest neighbor (KNN), implemented using MATLAB R2017a. In the proposed implementation, the four emotion classification categories (happy, sad, fear, and neutral) observed an average accuracy of 60.832%. The results of the presented study were compared with two recent systems. It was found that the proposed system observed better accuracy for the category of NHP five classes and the category of HP Five Classes.
Co-Authors Chandra Singh, Ram Gupta, Pragati Kumar, Rajendra Moudgil, Suveg Singh, Rajnesh Singh, Ram Chandra Sondhi, Nitin Srivastava, Arun Pratap Tripathi, Ashish