Pub Date : 2023-02-23DOI: 10.1109/ECCE57851.2023.10101627
Samanta Paul, Torikul Islam Palash, Amit Dutta Roy, Arup Kumar Debnath
Atherosclerosis has been increasing rapidly in the past couple of decades. To treat this pathology, a stent with an expandable balloon has been mostly utilized among all other techniques. However, the performance of the stent is predominantly contingent upon the materials that are utilized for stent development. Though stainless steel and Co-Cr are used the most in stent manufacturing, there has been the development of new prospective biomaterials, mostly alloys, for coronary stent deployment. In this paper, four different alloys have been selected as those materials are considered to have better properties. The behavior of disparate alloy materials such as Co-Cr, AZ31, WE43 as well as Fe-Mn-Si unprecedentedly in stent development is studied through Finite Element Simulation in COMSOL Multiphysics. For the simulation, Palmaz- Schatz model is used and performance properties of the stent after simulation including stress distribution, dogboning, foreshortening along with recoil are evaluated. With the results of the simulation, it is perceived that WE43 is exhibiting better performance during and after stent expansion than other material alloys. It has lesser Von misses stress (400 MPa) with the lowest dogboning (0.48) and medium ranged foreshortening (-0.29) compared to other material alloys. AZ31 is observed to have shown closer results to WE43 due to their similar mechanical properties. Therefore, WE43 along with AZ31- these Mg alloys can be used as stent materials alongside commercially used materials because of their better performances, with the requirement of testing it experimentally beforehand.
{"title":"Numerical Analysis of Coronary Stent Alloy Materials During Radial Expansion","authors":"Samanta Paul, Torikul Islam Palash, Amit Dutta Roy, Arup Kumar Debnath","doi":"10.1109/ECCE57851.2023.10101627","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101627","url":null,"abstract":"Atherosclerosis has been increasing rapidly in the past couple of decades. To treat this pathology, a stent with an expandable balloon has been mostly utilized among all other techniques. However, the performance of the stent is predominantly contingent upon the materials that are utilized for stent development. Though stainless steel and Co-Cr are used the most in stent manufacturing, there has been the development of new prospective biomaterials, mostly alloys, for coronary stent deployment. In this paper, four different alloys have been selected as those materials are considered to have better properties. The behavior of disparate alloy materials such as Co-Cr, AZ31, WE43 as well as Fe-Mn-Si unprecedentedly in stent development is studied through Finite Element Simulation in COMSOL Multiphysics. For the simulation, Palmaz- Schatz model is used and performance properties of the stent after simulation including stress distribution, dogboning, foreshortening along with recoil are evaluated. With the results of the simulation, it is perceived that WE43 is exhibiting better performance during and after stent expansion than other material alloys. It has lesser Von misses stress (400 MPa) with the lowest dogboning (0.48) and medium ranged foreshortening (-0.29) compared to other material alloys. AZ31 is observed to have shown closer results to WE43 due to their similar mechanical properties. Therefore, WE43 along with AZ31- these Mg alloys can be used as stent materials alongside commercially used materials because of their better performances, with the requirement of testing it experimentally beforehand.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127206843","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 : 2023-02-23DOI: 10.1109/ECCE57851.2023.10101605
Liang Jiang, Bo Huo, Qing Sun, Xue Chen, Xianzhi Gao
In this study, an intelligent training analysis system for snowboard big air was established. Based on the non-contact measurement method of vision, the system used high-speed cameras to build a multi-camera array, combined with 3D space camera calibration technology, human body projection matching, and tracking algorithm to track athletes' movements, capture, identify, and analyze athletes' gestures. In addition, based on the technology of kinematic calculation of external force, this study calculates the kinetics parameters of athletes in the process of take-off through the kinematic parameters of the human body, such as the moment of inertia, rotational kinetic energy, ground reaction force and so on. Finally, it establishes the calculation and analysis system of the kinetics parameters of the snowboard big air at the take-off stage. The system is of great significance to monitoring snowboard big air training.
{"title":"Intelligent training system for snowboard big air","authors":"Liang Jiang, Bo Huo, Qing Sun, Xue Chen, Xianzhi Gao","doi":"10.1109/ECCE57851.2023.10101605","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101605","url":null,"abstract":"In this study, an intelligent training analysis system for snowboard big air was established. Based on the non-contact measurement method of vision, the system used high-speed cameras to build a multi-camera array, combined with 3D space camera calibration technology, human body projection matching, and tracking algorithm to track athletes' movements, capture, identify, and analyze athletes' gestures. In addition, based on the technology of kinematic calculation of external force, this study calculates the kinetics parameters of athletes in the process of take-off through the kinematic parameters of the human body, such as the moment of inertia, rotational kinetic energy, ground reaction force and so on. Finally, it establishes the calculation and analysis system of the kinetics parameters of the snowboard big air at the take-off stage. The system is of great significance to monitoring snowboard big air training.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130668263","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 : 2023-02-23DOI: 10.1109/ECCE57851.2023.10101606
S. Ishrak, Muhaimin Bin Munir, M. H. Kabir
Hand gestures have been used as a natural and spontaneous form of nonverbal communication since the beginning of humanity. The interest in this field of study is expanding as a result of recent research endeavors. The method for dynamic hand gesture identification in this paper is based on a 3D skeletal model and uses depth pictures. The series of spatiotemporal changes in the relative angles of several skeletal joints with respect to a reference joint is used to suggest a new gesture representation. Over a predetermined number of frames, a series of significant Joint Relative Angles (JRA) between two skeletal joints is calculated. We identified a collection of 12 dynamic gestures with 98.6% accuracy using machine learning algorithms to analyze this sequential data.
{"title":"Dynamic Hand Gesture Recognition using Sequence of Human Joint Relative Angles","authors":"S. Ishrak, Muhaimin Bin Munir, M. H. Kabir","doi":"10.1109/ECCE57851.2023.10101606","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101606","url":null,"abstract":"Hand gestures have been used as a natural and spontaneous form of nonverbal communication since the beginning of humanity. The interest in this field of study is expanding as a result of recent research endeavors. The method for dynamic hand gesture identification in this paper is based on a 3D skeletal model and uses depth pictures. The series of spatiotemporal changes in the relative angles of several skeletal joints with respect to a reference joint is used to suggest a new gesture representation. Over a predetermined number of frames, a series of significant Joint Relative Angles (JRA) between two skeletal joints is calculated. We identified a collection of 12 dynamic gestures with 98.6% accuracy using machine learning algorithms to analyze this sequential data.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132185890","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 : 2023-02-23DOI: 10.1109/ECCE57851.2023.10101588
Tanveer Ahmed Belal, G. M. Shahariar, M. H. Kabir
This paper presents a deep learning-based pipeline for categorizing Bengali toxic comments, in which at first a binary classification model is used to determine whether a comment is toxic or not, and then a multi-label classifier is employed to determine which toxicity type the comment belongs to. For this purpose, we have prepared a manually labeled dataset consisting of 16,073 instances among which 8,488 are Toxic and any toxic comment may correspond to one or more of the six toxic categories - vulgar, hate, religious, threat, troll, and insult simulta-neously. Long Short Term Memory (LSTM) with BERT Embedding achieved 89.42% accuracy for the binary classification task while as a multi-label classifier, a combination of Convolutional Neural Network and Bi-directional Long Short Term Memory (CNN-BiLSTM) with attention mechanism achieved 78.92% accuracy and 0.86 as weighted F1-score. To explain the predictions and interpret the word feature importance during classification by the proposed models, we utilized Local Interpretable Model-Agnostic Explanations (LIME) framework. We have made our dataset public and can be accessed at - https://github.com/deepu099cse/Multi-Labeled-Bengali-Toxic-Comments-Classification
{"title":"Interpretable Multi Labeled Bengali Toxic Comments Classification using Deep Learning","authors":"Tanveer Ahmed Belal, G. M. Shahariar, M. H. Kabir","doi":"10.1109/ECCE57851.2023.10101588","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101588","url":null,"abstract":"This paper presents a deep learning-based pipeline for categorizing Bengali toxic comments, in which at first a binary classification model is used to determine whether a comment is toxic or not, and then a multi-label classifier is employed to determine which toxicity type the comment belongs to. For this purpose, we have prepared a manually labeled dataset consisting of 16,073 instances among which 8,488 are Toxic and any toxic comment may correspond to one or more of the six toxic categories - vulgar, hate, religious, threat, troll, and insult simulta-neously. Long Short Term Memory (LSTM) with BERT Embedding achieved 89.42% accuracy for the binary classification task while as a multi-label classifier, a combination of Convolutional Neural Network and Bi-directional Long Short Term Memory (CNN-BiLSTM) with attention mechanism achieved 78.92% accuracy and 0.86 as weighted F1-score. To explain the predictions and interpret the word feature importance during classification by the proposed models, we utilized Local Interpretable Model-Agnostic Explanations (LIME) framework. We have made our dataset public and can be accessed at - https://github.com/deepu099cse/Multi-Labeled-Bengali-Toxic-Comments-Classification","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"2007 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134618713","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 : 2023-02-23DOI: 10.1109/ECCE57851.2023.10101540
Md. Anwar Hussen Wadud, Anichur Rahman, Md. Jahidul Islam, T. M. Amir-Ul-Haque Bhuiyan, Md. Jobaer Hossain, Rakib Hossen
In recent times, Blockchain (BC) technology has been one of the fastest-growing technologies poised to perform a significant role in the near future. In the age of Cloud Computing (CC) solutions, the Internet of Things (IoT), and big data analytics, BC guarantees data safety, transaction security, and self-regulation development. CC solutions initiated its role in the health industry due to its flexibility and efficient energy consumption paradigm. Nevertheless, CC operations have an issue with preserving confidential data and sharing patients' critical medical data with other healthcare facilities. BC provides solutions in regard to cloud protection and privacy issues of such decentralization features coupled with information protection and privacy, while the cloud contributes towards the resolution of BC's measurability and efficiency difficulties, thereby introducing the idea of an innovative BC-Cloud integration to track and preserve the electronic health records of patients in a reliable manner. In this paper, we present a BC-cloud combination for the electronic healthcare service to provide healthcare officials with the impulses following the rise of this most delinquent model, propose an arrangement of surviving structures, and their applicability for more reliable medical healthcare services. We then evaluate the improvement stages and co-operations and focus on the analysis difficulties for the combined BC-cloud structure, potential solutions, and prospective analysis objectives. This paper's outcome will benefit the health service enterprise to create and improve data administration systems to address patient concerns reliably.
{"title":"A Decentralized Secure Blockchain-based Privacy-Preserving Healthcare Clouds and Applications","authors":"Md. Anwar Hussen Wadud, Anichur Rahman, Md. Jahidul Islam, T. M. Amir-Ul-Haque Bhuiyan, Md. Jobaer Hossain, Rakib Hossen","doi":"10.1109/ECCE57851.2023.10101540","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101540","url":null,"abstract":"In recent times, Blockchain (BC) technology has been one of the fastest-growing technologies poised to perform a significant role in the near future. In the age of Cloud Computing (CC) solutions, the Internet of Things (IoT), and big data analytics, BC guarantees data safety, transaction security, and self-regulation development. CC solutions initiated its role in the health industry due to its flexibility and efficient energy consumption paradigm. Nevertheless, CC operations have an issue with preserving confidential data and sharing patients' critical medical data with other healthcare facilities. BC provides solutions in regard to cloud protection and privacy issues of such decentralization features coupled with information protection and privacy, while the cloud contributes towards the resolution of BC's measurability and efficiency difficulties, thereby introducing the idea of an innovative BC-Cloud integration to track and preserve the electronic health records of patients in a reliable manner. In this paper, we present a BC-cloud combination for the electronic healthcare service to provide healthcare officials with the impulses following the rise of this most delinquent model, propose an arrangement of surviving structures, and their applicability for more reliable medical healthcare services. We then evaluate the improvement stages and co-operations and focus on the analysis difficulties for the combined BC-cloud structure, potential solutions, and prospective analysis objectives. This paper's outcome will benefit the health service enterprise to create and improve data administration systems to address patient concerns reliably.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132257608","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 : 2023-02-23DOI: 10.1109/ECCE57851.2023.10100745
Md. Rahat Imrose, Md. Tarek Hassan, Md. Rajibul Hassen, M. Mowla
Due to its broad (10-300 GHz) spectrum and potential for widespread use in 5G applications, millimeter wave (mmWave) technology is proposed as an alternative to optical fiber for backhaul. However, human blockage as well as rapid channel fluctuation caused by user movement are two of the biggest obstacles to mmWave communication implementation in the network. Spatial consistency is one of the most crucial factors for achieving smooth channel fluctuations and contributing in the design of channel estimation and beam tracking. This paper investigates a clear understanding of how human blockage and spatial consistency affect the reliability of 5G mmWave backhauling in real-life scenarios for an urban macrocell (UMa) of busy airport areas. An extensive simulation on mmWave backhauling considering 28 GHz and 60 GHz mmWave has been carried out which will significantly affect the channel modeling of next-generation complex communication networks.
{"title":"A Statistical Investigation of Spatial Consistency and Human Blockage Consideration based mmWave Channel Modeling for 5G Back-Haul Networks","authors":"Md. Rahat Imrose, Md. Tarek Hassan, Md. Rajibul Hassen, M. Mowla","doi":"10.1109/ECCE57851.2023.10100745","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10100745","url":null,"abstract":"Due to its broad (10-300 GHz) spectrum and potential for widespread use in 5G applications, millimeter wave (mmWave) technology is proposed as an alternative to optical fiber for backhaul. However, human blockage as well as rapid channel fluctuation caused by user movement are two of the biggest obstacles to mmWave communication implementation in the network. Spatial consistency is one of the most crucial factors for achieving smooth channel fluctuations and contributing in the design of channel estimation and beam tracking. This paper investigates a clear understanding of how human blockage and spatial consistency affect the reliability of 5G mmWave backhauling in real-life scenarios for an urban macrocell (UMa) of busy airport areas. An extensive simulation on mmWave backhauling considering 28 GHz and 60 GHz mmWave has been carried out which will significantly affect the channel modeling of next-generation complex communication networks.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129506511","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 : 2023-02-23DOI: 10.1109/ECCE57851.2023.10101490
Tahsen Islam Sajon, Maria Chowdhury, Azmain Yakin Srizon, Md. Farukuzzaman Faruk, S. M. Mahedy Hasan, Abu Sayeed, A. F. M. Minhazur Rahman
Although extensive research has been conducted on leukemia, the disease still accounts for more than 350,000 fatalities annually. Automated Leukemia diagnosis may alter the situation because actions can be taken immediately; as a result, accurate detection of Leukemia has been a subject of interest for researchers. As statistics grow and expand, the need for precise leukemia identification continues to increase. In this study, we investigated a dataset of leukemia that used the WHO classification scheme. We developed a modified DenseNet201 design that achieved an overall accuracy of 99.69% without relying on data augmentation. Additionally, we identified and validated key features for leukemia classification by utilizing three feature extraction approaches (i.e., hu moments, haralick texture and parameter-free threshold adjacency statistics) and several machine learning classifiers (i.e., Gaussian Process, Support Vector Machine, K-Nearest Neighbor or KNN, Extra Trees Classifier, and Logistic regression) that outperformed earlier feature extraction-based techniques.
{"title":"Recognition of Leukemia Sub-types Using Transfer Learning and Extraction of Distinguishable Features Using an Effective Machine Learning Approach","authors":"Tahsen Islam Sajon, Maria Chowdhury, Azmain Yakin Srizon, Md. Farukuzzaman Faruk, S. M. Mahedy Hasan, Abu Sayeed, A. F. M. Minhazur Rahman","doi":"10.1109/ECCE57851.2023.10101490","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101490","url":null,"abstract":"Although extensive research has been conducted on leukemia, the disease still accounts for more than 350,000 fatalities annually. Automated Leukemia diagnosis may alter the situation because actions can be taken immediately; as a result, accurate detection of Leukemia has been a subject of interest for researchers. As statistics grow and expand, the need for precise leukemia identification continues to increase. In this study, we investigated a dataset of leukemia that used the WHO classification scheme. We developed a modified DenseNet201 design that achieved an overall accuracy of 99.69% without relying on data augmentation. Additionally, we identified and validated key features for leukemia classification by utilizing three feature extraction approaches (i.e., hu moments, haralick texture and parameter-free threshold adjacency statistics) and several machine learning classifiers (i.e., Gaussian Process, Support Vector Machine, K-Nearest Neighbor or KNN, Extra Trees Classifier, and Logistic regression) that outperformed earlier feature extraction-based techniques.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130989353","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 : 2023-02-23DOI: 10.1109/ECCE57851.2023.10101563
Farhan Ishtiaque, Fazla Rabbi Mashrur, Mohammad Tohidul Islam Miya, Khandoker Mahmudur Rahman, R. Vaidyanathan, S. Anwar, F. Sarker, K. Mamun
In Neuromarketing, BCI technology is used to analyze how a consumer behaves in response to a marketing stimulus, to evaluate the stimuli itself. Traditionally it can be achieved by different marketing research techniques such as questioner-based surveys, interviews, field surveys, etc. But since these procedures are time-consuming and prone to human error, neuromarketing promises a more advanced, automated, and accurate solution. Most of the neuromarketing solutions use research-grade EEG devices to analyze consumer preferences, but their effectiveness using consumer-grade EEG devices is unknown. In this study, we designed an experiment to compare a research-grade EEG device with a consumer-grade EEG device for predicting consumer preference stated as affective attitude (AA) and purchase intention (PI). We determined what type of setup, processing, and algorithm brings out the best result using the two devices. EEG signals were collected while the participants were shown pictures of different products in two different setups After that several signal-processing techniques were applied to remove artifacts and multi-domain features were extracted. 50 features were selected using Recursing Feature Elimination techniques. SMOTE was used to balance out the data. After that SVM classifier was used to classify Positive and Negative consumer preferences. With the first setup, we managed to achieve 82.4 % and 85.23 % accuracy for predicting purchase intention and affective attitude respectively with the research-grade EEG device whereas we achieved 75.43% and 79.5% accuracy with the commercial-grade EEG device. With the second setup, it's 78.75% and 83.75% using the research-grade EEG device whereas it's 75% and 82.97% using the commercial-grade EEG device for purchase intention and affective attitude respectively.
{"title":"AI-based Consumers' Preference Prediction Using a Research-grade BCI and a Commercial-grade BCI for Neuromarketing: A Systematic Comparison","authors":"Farhan Ishtiaque, Fazla Rabbi Mashrur, Mohammad Tohidul Islam Miya, Khandoker Mahmudur Rahman, R. Vaidyanathan, S. Anwar, F. Sarker, K. Mamun","doi":"10.1109/ECCE57851.2023.10101563","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101563","url":null,"abstract":"In Neuromarketing, BCI technology is used to analyze how a consumer behaves in response to a marketing stimulus, to evaluate the stimuli itself. Traditionally it can be achieved by different marketing research techniques such as questioner-based surveys, interviews, field surveys, etc. But since these procedures are time-consuming and prone to human error, neuromarketing promises a more advanced, automated, and accurate solution. Most of the neuromarketing solutions use research-grade EEG devices to analyze consumer preferences, but their effectiveness using consumer-grade EEG devices is unknown. In this study, we designed an experiment to compare a research-grade EEG device with a consumer-grade EEG device for predicting consumer preference stated as affective attitude (AA) and purchase intention (PI). We determined what type of setup, processing, and algorithm brings out the best result using the two devices. EEG signals were collected while the participants were shown pictures of different products in two different setups After that several signal-processing techniques were applied to remove artifacts and multi-domain features were extracted. 50 features were selected using Recursing Feature Elimination techniques. SMOTE was used to balance out the data. After that SVM classifier was used to classify Positive and Negative consumer preferences. With the first setup, we managed to achieve 82.4 % and 85.23 % accuracy for predicting purchase intention and affective attitude respectively with the research-grade EEG device whereas we achieved 75.43% and 79.5% accuracy with the commercial-grade EEG device. With the second setup, it's 78.75% and 83.75% using the research-grade EEG device whereas it's 75% and 82.97% using the commercial-grade EEG device for purchase intention and affective attitude respectively.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117132079","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 : 2023-02-23DOI: 10.1109/ECCE57851.2023.10101647
Sharith Dhar, Md. Saiful Islam
The industrial revolution has increased the use of induction motors enormously. Today's soft starter is used for controlling starting current, acceleration torque, and acceleration time of the induction motor. Intelligent techniques are used in soft starters for controlling starting parameters of the induction motor smoothly. But developed intelligent algorithm based soft starter takes more acceleration time, and due to this induction motor can not accelerate the load properly during the starting period. To solve this problem fuzzy logic-based soft starter is proposed in this paper. This proposed starting technique reaches the target through its instinctive decision making capability. The fuzzy logic controller takes stator phase current and torque from the three phase induction motor (IM) and gives firing angles to the thyristor unit in the soft starter by using the Mamdani fuzzy inference system and the mean of maximum method in defuzzification. The proposed technique accelerates the IM with the load smoothly by decreasing acceleration time. The proposed intelligent soft starter reduces the starting current of IM with a Direct on line (DOL) starting technique by more than 10% at the constant load and also provides proper acceleration torque. The proposed soft starter provides a better response compared with another method.
{"title":"Fuzzy Logic-based Soft Starter for Controlling Starting Parameters of Induction Motor","authors":"Sharith Dhar, Md. Saiful Islam","doi":"10.1109/ECCE57851.2023.10101647","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101647","url":null,"abstract":"The industrial revolution has increased the use of induction motors enormously. Today's soft starter is used for controlling starting current, acceleration torque, and acceleration time of the induction motor. Intelligent techniques are used in soft starters for controlling starting parameters of the induction motor smoothly. But developed intelligent algorithm based soft starter takes more acceleration time, and due to this induction motor can not accelerate the load properly during the starting period. To solve this problem fuzzy logic-based soft starter is proposed in this paper. This proposed starting technique reaches the target through its instinctive decision making capability. The fuzzy logic controller takes stator phase current and torque from the three phase induction motor (IM) and gives firing angles to the thyristor unit in the soft starter by using the Mamdani fuzzy inference system and the mean of maximum method in defuzzification. The proposed technique accelerates the IM with the load smoothly by decreasing acceleration time. The proposed intelligent soft starter reduces the starting current of IM with a Direct on line (DOL) starting technique by more than 10% at the constant load and also provides proper acceleration torque. The proposed soft starter provides a better response compared with another method.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122145469","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 : 2023-02-23DOI: 10.1109/ECCE57851.2023.10101581
M. Hasan, Md. Ali Hossain, Azmain Yakin Srizon, Abu Sayeed
Bengali, the seventh most spoken language in the world by the number of speakers, doesn't have a well-established Optical Character Recognition (OCR) system for handwritten texts. One of the major reasons behind this lacking is contributed to having no complete conjuncts database. No dataset available today covers all the conjunct characters that are used by authors around the globe. In this research, we prepared a complete dataset consisting of 292 consonant conjunct characters, which is the biggest consonant conjunct character dataset to date by the number of classes available in the literature to our knowledge. We applied Big Transfer-based M-ResNet-101x3 Deep Convolutional Neural Network (DCNN) which achieves 91.32% accuracy that outperforms the baseline EfficientNetB7 approach which obtained 81.05% accuracy.
{"title":"juktoMala: A Handwritten Bengali Consonant Conjuncts Dataset for Optical Character Recognition Using BiT-based M-ResNet-101x3 Architecture","authors":"M. Hasan, Md. Ali Hossain, Azmain Yakin Srizon, Abu Sayeed","doi":"10.1109/ECCE57851.2023.10101581","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101581","url":null,"abstract":"Bengali, the seventh most spoken language in the world by the number of speakers, doesn't have a well-established Optical Character Recognition (OCR) system for handwritten texts. One of the major reasons behind this lacking is contributed to having no complete conjuncts database. No dataset available today covers all the conjunct characters that are used by authors around the globe. In this research, we prepared a complete dataset consisting of 292 consonant conjunct characters, which is the biggest consonant conjunct character dataset to date by the number of classes available in the literature to our knowledge. We applied Big Transfer-based M-ResNet-101x3 Deep Convolutional Neural Network (DCNN) which achieves 91.32% accuracy that outperforms the baseline EfficientNetB7 approach which obtained 81.05% accuracy.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122151778","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：