Pub Date : 2023-02-22DOI: 10.1109/ICECCT56650.2023.10179695
A. Sahu, D. Joshi
In this paper, the performance of a three-phase induction motor with space vector pulse width modulation (SVPWM) technique under artificial neural network (ANN) control is studied. The use of ANN control allows for improved performance of the induction motor, including enhanced speed control. The SVPWM technique is used to accurately control the voltage applied to the motor, resulting in improved performance of the induction motor. The operation of the induction motor is compared with proportional-integral (PI) controller. The results of the study show that the use of ANN control in conjunction with SVPWM leads to improved performance of the three-phase induction motor. The system's complete mathematical model is outlined and simulated using the MATLAB/Simulink platform.
{"title":"Performance of Three-Phase Induction Motor with Space Vector Pulse Width Modulation under Artificial Neural Network Control","authors":"A. Sahu, D. Joshi","doi":"10.1109/ICECCT56650.2023.10179695","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179695","url":null,"abstract":"In this paper, the performance of a three-phase induction motor with space vector pulse width modulation (SVPWM) technique under artificial neural network (ANN) control is studied. The use of ANN control allows for improved performance of the induction motor, including enhanced speed control. The SVPWM technique is used to accurately control the voltage applied to the motor, resulting in improved performance of the induction motor. The operation of the induction motor is compared with proportional-integral (PI) controller. The results of the study show that the use of ANN control in conjunction with SVPWM leads to improved performance of the three-phase induction motor. The system's complete mathematical model is outlined and simulated using the MATLAB/Simulink platform.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133278293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-22DOI: 10.1109/ICECCT56650.2023.10179707
A. B, Vidhya. M
In this research, the concept of hesitant triangular fuzzy set (HTFS) by integrating HFS and TFS concepts, and we give various HTFS set theoretical operations. On HTFSs, we also create Dombi operations. We describe some Dombi-based aggreagation operators, such as the hesitant triangular fuzzy Dombi weighted averaging operator (HTFDW A) and the hesitant triangular fuzzy Dombi weighted Geometric operator (HTFDWG). Additionally, we add a score for hesitant triangular dombi numbers to the ranking system. In order to choose the most preferable option, we develop a MADM approach where the alternatives are ranked according to the values of the score of HTFDO. The accuracy and efficiency of the created aggregation operators and decision-making approach are finally shown through real-world examples.
{"title":"Hesitant Triangular Fuzzy Dombi Operators and Its Applications","authors":"A. B, Vidhya. M","doi":"10.1109/ICECCT56650.2023.10179707","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179707","url":null,"abstract":"In this research, the concept of hesitant triangular fuzzy set (HTFS) by integrating HFS and TFS concepts, and we give various HTFS set theoretical operations. On HTFSs, we also create Dombi operations. We describe some Dombi-based aggreagation operators, such as the hesitant triangular fuzzy Dombi weighted averaging operator (HTFDW A) and the hesitant triangular fuzzy Dombi weighted Geometric operator (HTFDWG). Additionally, we add a score for hesitant triangular dombi numbers to the ranking system. In order to choose the most preferable option, we develop a MADM approach where the alternatives are ranked according to the values of the score of HTFDO. The accuracy and efficiency of the created aggregation operators and decision-making approach are finally shown through real-world examples.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"17 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114052367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-22DOI: 10.1109/ICECCT56650.2023.10179667
Abir AlSideiri, R. M. Tawafak, G. Alfarsi, B. Khudayer, Z. C. Cob
Computer Software, an Online-based clearance system, is an internet-based system that effectively manages information for colleges and universities. The aim of this study is to develop software for graduated services and follow up to replace the manual method of clearance for graduating students. The system serves as a more reliable and effective means of removing all forms of delay and enabling an understanding of the procedures involved and how to do online clearance. The data was collected from the BUC collage. the method used to develop software for easy service after graduation. The online clearance system was implemented using Basic Visual 2015 for all static and dynamic programming and MYSQL to manage the database. The finding reveals a significant use of the software. The application reveals the effectiveness and efficiency of these services' software.
{"title":"Development of Online Clearance System Using Web-Based System","authors":"Abir AlSideiri, R. M. Tawafak, G. Alfarsi, B. Khudayer, Z. C. Cob","doi":"10.1109/ICECCT56650.2023.10179667","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179667","url":null,"abstract":"Computer Software, an Online-based clearance system, is an internet-based system that effectively manages information for colleges and universities. The aim of this study is to develop software for graduated services and follow up to replace the manual method of clearance for graduating students. The system serves as a more reliable and effective means of removing all forms of delay and enabling an understanding of the procedures involved and how to do online clearance. The data was collected from the BUC collage. the method used to develop software for easy service after graduation. The online clearance system was implemented using Basic Visual 2015 for all static and dynamic programming and MYSQL to manage the database. The finding reveals a significant use of the software. The application reveals the effectiveness and efficiency of these services' software.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122584299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-22DOI: 10.1109/ICECCT56650.2023.10179665
S. Kuzhaloli, S. Thenappan, Premavathi T, V. Nivedita, M. Mageshbabu, S. Navaneethan
Malaria, caused by Plasmodium parasites in the bloodstream spread by infected mosquitoes, is a highly severe and sometimes deadly disease. Image analysis and machine learning can enhance diagnosis by quantifying parasitemia on blood slides. The building of an autonomous, accurate, and effective model can significantly reduce the need for trained laborers. This article discusses computer-assisted approaches for finding malaria parasites in blood smear images. These procedures consist of obtaining the dataset, preprocessing the images, segmenting the red blood cells, extracting and choosing features, and classifying the images. The approach is based on well-known Convolutional neural network (CNN) models of Plasmodium parasites and erythrocytes. The trained CNN and VGG-19 are given images of infected and uninfected erythrocytes from the same dataset. VGG 19 gives 96% detection accuracy where CNN achieves 94%.
{"title":"Identification of Malaria Disease Using Machine Learning Models","authors":"S. Kuzhaloli, S. Thenappan, Premavathi T, V. Nivedita, M. Mageshbabu, S. Navaneethan","doi":"10.1109/ICECCT56650.2023.10179665","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179665","url":null,"abstract":"Malaria, caused by Plasmodium parasites in the bloodstream spread by infected mosquitoes, is a highly severe and sometimes deadly disease. Image analysis and machine learning can enhance diagnosis by quantifying parasitemia on blood slides. The building of an autonomous, accurate, and effective model can significantly reduce the need for trained laborers. This article discusses computer-assisted approaches for finding malaria parasites in blood smear images. These procedures consist of obtaining the dataset, preprocessing the images, segmenting the red blood cells, extracting and choosing features, and classifying the images. The approach is based on well-known Convolutional neural network (CNN) models of Plasmodium parasites and erythrocytes. The trained CNN and VGG-19 are given images of infected and uninfected erythrocytes from the same dataset. VGG 19 gives 96% detection accuracy where CNN achieves 94%.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124178858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-22DOI: 10.1109/ICECCT56650.2023.10179655
K. Devi, K. Balasamy, M. Prathyusha, R. Jeevitha, P. Balasubramanie, M. Eswaran
Data mining is one of the significant area where it plays a predominant role in extracting important factors and trends from large volume of data. This covers various areas such as healthcare, education, entertainment, finance, e-commerce applications etc., The data mining domain has used a variety of algorithms, including supervised, unsupervised, semi-supervised, and reinforcement learning techniques. Under healthcare arena, it deals with huge amount of sensitive data such as patients' data such as their name, age, health records. Those sensitive data have been utilized by the intruders for extracting the original data and also became a prey for the authorized access. Hence, the privacy is one of the serious concern that should be addressed. Various privacy preserving in data mining (PPDM) techniques such as anonymization, perturbation, condensation and cryptographic methods are available to protect those data. In this paper, the optimization techniques such as Genetic algorithm(GA) under evolutionary method and Particle swarm optimization(PSO) under meta heuristic method have been discussed and how it plays an important part in providing more optimal results by securing those sensitive and important information from the unauthorized access.
{"title":"Optimization techniques for preserving privacy in data mining","authors":"K. Devi, K. Balasamy, M. Prathyusha, R. Jeevitha, P. Balasubramanie, M. Eswaran","doi":"10.1109/ICECCT56650.2023.10179655","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179655","url":null,"abstract":"Data mining is one of the significant area where it plays a predominant role in extracting important factors and trends from large volume of data. This covers various areas such as healthcare, education, entertainment, finance, e-commerce applications etc., The data mining domain has used a variety of algorithms, including supervised, unsupervised, semi-supervised, and reinforcement learning techniques. Under healthcare arena, it deals with huge amount of sensitive data such as patients' data such as their name, age, health records. Those sensitive data have been utilized by the intruders for extracting the original data and also became a prey for the authorized access. Hence, the privacy is one of the serious concern that should be addressed. Various privacy preserving in data mining (PPDM) techniques such as anonymization, perturbation, condensation and cryptographic methods are available to protect those data. In this paper, the optimization techniques such as Genetic algorithm(GA) under evolutionary method and Particle swarm optimization(PSO) under meta heuristic method have been discussed and how it plays an important part in providing more optimal results by securing those sensitive and important information from the unauthorized access.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127672540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, a novel III-V material-based polarity-controlled electrical doped Tunnel field effect transistor (PC- ED-GaAs-InAs-TFET) biosensor has been proposed. The N+ drain and P+ source regions are created in the intrinsic GaAs and InAs regions using the polarity-controlled concept. In addition, a nano-cavity is etched in the gate oxide towards the tunneling junction to modulate the tunneling mechanism via the immobilized biomolecules. The sensitivity of the proposed biosensor is analyzed using neutral and charged biomolecules, namely Biotin (k = 2.63), Ferro-cytochrome c (k = 4.7), Keratin (k = 8) and Gelatin (k = 12). The PC-ED-GaAs-InAs- TFET biosensor exhibits superior sensitivity in terms of drain current, threshold voltage, subthreshold swing, and $mathrm{I}_{ON}/mathrm{I}_{OFF}$ ratio. The sensitivity of the PC-ED-GaAs-InAs-TFET biosensor, in terms of various cavity dimensions (thickness and height), fill factors and the effect of temperature has also been investigated.
{"title":"Sensitivity Analysis of Polarity Control Electrically doped GaAs-InAs Tunnel Field Effect Transistor for Bio-sensing Application","authors":"Dharmender, Piyush Yadav, Rashi Gupta, Shivangi Singh","doi":"10.1109/ICECCT56650.2023.10179770","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179770","url":null,"abstract":"In this paper, a novel III-V material-based polarity-controlled electrical doped Tunnel field effect transistor (PC- ED-GaAs-InAs-TFET) biosensor has been proposed. The N+ drain and P+ source regions are created in the intrinsic GaAs and InAs regions using the polarity-controlled concept. In addition, a nano-cavity is etched in the gate oxide towards the tunneling junction to modulate the tunneling mechanism via the immobilized biomolecules. The sensitivity of the proposed biosensor is analyzed using neutral and charged biomolecules, namely Biotin (k = 2.63), Ferro-cytochrome c (k = 4.7), Keratin (k = 8) and Gelatin (k = 12). The PC-ED-GaAs-InAs- TFET biosensor exhibits superior sensitivity in terms of drain current, threshold voltage, subthreshold swing, and $mathrm{I}_{ON}/mathrm{I}_{OFF}$ ratio. The sensitivity of the PC-ED-GaAs-InAs-TFET biosensor, in terms of various cavity dimensions (thickness and height), fill factors and the effect of temperature has also been investigated.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127983654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-22DOI: 10.1109/ICECCT56650.2023.10179727
T. Singh, Ajay Mahaputra Kumar
Cloud computing security is a broad term that covers a wide range of security and privacy issues for businesses that use multi-cloud services. The service providers must take several factors into account when addressing security for their clients, including access and identity control, in-transit, and other security factors such as confidentiality, vulnerability management, threats, and audits. This paper takes a close look at each of these areas of cloud security and shows how multi-cloud service providers can protect themselves and move forward. It also talks about the problems with protecting systems that use more than one cloud and gives ways to deal with these problems. The reader should understand all of the security concerns that come with multi-cloud setups and how to deal with them in light of the latest cyber threats. The sole motivation of this paper is to raise awareness about multi-cloud service security in different aspects and its impact. Nessus, which is a well-known security tool, has been used to find vulnerabilities and other critical security concerns. Overall, experiments have been performed on three different websites: “www.cuchd.in”, “indiapost.gov.in” and “ajaykumar.in”. Despite this, the mitigation approaches for each vulnerability are explained systematically.
{"title":"Analyzing Security and Privacy issues for Multi-Cloud Service Providers Using Nessus","authors":"T. Singh, Ajay Mahaputra Kumar","doi":"10.1109/ICECCT56650.2023.10179727","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179727","url":null,"abstract":"Cloud computing security is a broad term that covers a wide range of security and privacy issues for businesses that use multi-cloud services. The service providers must take several factors into account when addressing security for their clients, including access and identity control, in-transit, and other security factors such as confidentiality, vulnerability management, threats, and audits. This paper takes a close look at each of these areas of cloud security and shows how multi-cloud service providers can protect themselves and move forward. It also talks about the problems with protecting systems that use more than one cloud and gives ways to deal with these problems. The reader should understand all of the security concerns that come with multi-cloud setups and how to deal with them in light of the latest cyber threats. The sole motivation of this paper is to raise awareness about multi-cloud service security in different aspects and its impact. Nessus, which is a well-known security tool, has been used to find vulnerabilities and other critical security concerns. Overall, experiments have been performed on three different websites: “www.cuchd.in”, “indiapost.gov.in” and “ajaykumar.in”. Despite this, the mitigation approaches for each vulnerability are explained systematically.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121327378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-22DOI: 10.1109/ICECCT56650.2023.10179696
Muralidharan D, B. R, Vijay Sai R, C. K
Hill cipher is one of the classical ciphers which is considered as a secured one till date apart from its vulnerability of known plaintext attack. Because of its simplicity, in recent days also it is used as one of the building block of cryptography and steganography based algorithms. Many research articles are available to minimize the known plaintext attack vulnerability of Hill cipher. One of the research works suggests to use a second key which varies exponentially with block numbers of the plaintext. Though the proposal is very simple, it is a time consuming process. Furthermore, it has some weak blocks which deteriorates the security. In this research article both issues are rectified by the inclusion of a look-up-table method. The proposed method saves power and enhances the speed by slightly compromising with area. Implementation results show that the proposed method reduces the performance complexity from O(nlogn) to O(n).
{"title":"Performance And Security Enhanced Improved Hill Cipher","authors":"Muralidharan D, B. R, Vijay Sai R, C. K","doi":"10.1109/ICECCT56650.2023.10179696","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179696","url":null,"abstract":"Hill cipher is one of the classical ciphers which is considered as a secured one till date apart from its vulnerability of known plaintext attack. Because of its simplicity, in recent days also it is used as one of the building block of cryptography and steganography based algorithms. Many research articles are available to minimize the known plaintext attack vulnerability of Hill cipher. One of the research works suggests to use a second key which varies exponentially with block numbers of the plaintext. Though the proposal is very simple, it is a time consuming process. Furthermore, it has some weak blocks which deteriorates the security. In this research article both issues are rectified by the inclusion of a look-up-table method. The proposed method saves power and enhances the speed by slightly compromising with area. Implementation results show that the proposed method reduces the performance complexity from O(nlogn) to O(n).","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128531264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-22DOI: 10.1109/ICECCT56650.2023.10179836
V. Ranganayaki, Jency Rubia J, P. S. Ramesh, K. Rammohan, R.Babitha Lincy, A. Deepak
In autonomous driving, detecting pedestrians is a safety-critical activity, and the decision to avoid a person must be made as quickly as possible with as little delay as possible. In this work, INRIA and PETA datasets are taken. The progression of the work that is being proposed is broken up into three phases. The first step is to detect edges, the second step is to group colours, and the third step is extracting the feature, which includes screening body parts of pedestrians and detecting shoulder lines. The machine learning classifiers such as SVM, Naïve Bayes and KNN are taken for predicting the pedestrian in the road. The accuracy for SVM, Naïve Bayes and KNN are calculated as 93.58, 94.42 and 98.44 respectively. With the KNN model, it achieves the highest accuracy for predicting the exact images.
{"title":"Machine Learning Approaches on Pedestrian Detection in an autonomous vehicle","authors":"V. Ranganayaki, Jency Rubia J, P. S. Ramesh, K. Rammohan, R.Babitha Lincy, A. Deepak","doi":"10.1109/ICECCT56650.2023.10179836","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179836","url":null,"abstract":"In autonomous driving, detecting pedestrians is a safety-critical activity, and the decision to avoid a person must be made as quickly as possible with as little delay as possible. In this work, INRIA and PETA datasets are taken. The progression of the work that is being proposed is broken up into three phases. The first step is to detect edges, the second step is to group colours, and the third step is extracting the feature, which includes screening body parts of pedestrians and detecting shoulder lines. The machine learning classifiers such as SVM, Naïve Bayes and KNN are taken for predicting the pedestrian in the road. The accuracy for SVM, Naïve Bayes and KNN are calculated as 93.58, 94.42 and 98.44 respectively. With the KNN model, it achieves the highest accuracy for predicting the exact images.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128607263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-22DOI: 10.1109/ICECCT56650.2023.10179711
Abgeena Abgeena, S. Garg
As life continues to change in the digital era, it is crucial to perceive a person's emotional state. Affective computing is receiving more attention with the increase in the human-computer interface (HCI). Human emotion recognition employing electroen-cephalogram (EEG) signals has been studied to obtain a person's emotional status for different stimuli. However, it is difficult to identify clear patterns in EEG signals because they have low electrical impulses and are highly sensitive to noise. A deep convolutional neural network (DCNN) was employed in the present study to recognize emotions in EEG signals. For this purpose, a publicly available dataset, DREAMER, was utilized in this study to assess the applicability of the model for emotion classification. The dataset consisted of three-dimensional emotions, that is, valence, arousal, and dominance (VAD). 2D emotions arousal and valence were the most-recognized emotions in existing research. The present study identified the 3D emotions present in the above-mentioned dataset. In this study, raw EEG signals from the DREAMER dataset were pre-processed. Subsequently, three EEG rhythms, theta, alpha, and beta, were extracted using a bandpass filter. The power spectral density (PSD) was computed using fast Fourier transform (FFT) in the feature extraction. Finally, a 1D CNN model is applied to the classification of emotions. In addition, the performance of the proposed model was compared with two machine learning (ML) classifiers: random forest (RF) and extreme Gradient Boosting (XGBoost) classifiers. The highest accuracy (ACC) of 97.6% was obtained using the proposed model in the dominance dimension. The working principles were compared and discussed to determine the suitability of the model for emotion recognition applications.
{"title":"EEG evoked automated emotion recognition using deep convolutional neural network","authors":"Abgeena Abgeena, S. Garg","doi":"10.1109/ICECCT56650.2023.10179711","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179711","url":null,"abstract":"As life continues to change in the digital era, it is crucial to perceive a person's emotional state. Affective computing is receiving more attention with the increase in the human-computer interface (HCI). Human emotion recognition employing electroen-cephalogram (EEG) signals has been studied to obtain a person's emotional status for different stimuli. However, it is difficult to identify clear patterns in EEG signals because they have low electrical impulses and are highly sensitive to noise. A deep convolutional neural network (DCNN) was employed in the present study to recognize emotions in EEG signals. For this purpose, a publicly available dataset, DREAMER, was utilized in this study to assess the applicability of the model for emotion classification. The dataset consisted of three-dimensional emotions, that is, valence, arousal, and dominance (VAD). 2D emotions arousal and valence were the most-recognized emotions in existing research. The present study identified the 3D emotions present in the above-mentioned dataset. In this study, raw EEG signals from the DREAMER dataset were pre-processed. Subsequently, three EEG rhythms, theta, alpha, and beta, were extracted using a bandpass filter. The power spectral density (PSD) was computed using fast Fourier transform (FFT) in the feature extraction. Finally, a 1D CNN model is applied to the classification of emotions. In addition, the performance of the proposed model was compared with two machine learning (ML) classifiers: random forest (RF) and extreme Gradient Boosting (XGBoost) classifiers. The highest accuracy (ACC) of 97.6% was obtained using the proposed model in the dominance dimension. The working principles were compared and discussed to determine the suitability of the model for emotion recognition applications.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128521571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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