F.I.M. Robi, M. Dali, K. A. Ahmad, S. Z. Yahaya, Yahaya AbdRahman
{"title":"Development of an Optofluidic System for Concentration Measurement of Colorimetric based Solution targeted for Water Quality Assessment","authors":"F.I.M. Robi, M. Dali, K. A. Ahmad, S. Z. Yahaya, Yahaya AbdRahman","doi":"10.11159/eee23.113","DOIUrl":"https://doi.org/10.11159/eee23.113","url":null,"abstract":"","PeriodicalId":294100,"journal":{"name":"World Congress on Electrical Engineering and Computer Systems and Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131278372","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}
{"title":"A Computational Simulation of the Urine Output Flow Rate","authors":"Poupak Kermani","doi":"10.11159/icbes23.115","DOIUrl":"https://doi.org/10.11159/icbes23.115","url":null,"abstract":"","PeriodicalId":294100,"journal":{"name":"World Congress on Electrical Engineering and Computer Systems and Science","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131453869","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}
Greg Barish, Lauren Marlotte, Miguel Drayton, Catherine Mogil, P. Lester
{"title":"Automatically Enriching Content for a Behavioral Health Learning Management System: a First Look","authors":"Greg Barish, Lauren Marlotte, Miguel Drayton, Catherine Mogil, P. Lester","doi":"10.11159/cist23.125","DOIUrl":"https://doi.org/10.11159/cist23.125","url":null,"abstract":"","PeriodicalId":294100,"journal":{"name":"World Congress on Electrical Engineering and Computer Systems and Science","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131587376","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}
Dong Yong Park, H. Lee, Hyejin Song, Kyoung Je Cha, Sun Kwang Hwang, Chihun Lee
{"title":"Artificial Neural Network-Based Process Recommender System for Addictive Manufacturing","authors":"Dong Yong Park, H. Lee, Hyejin Song, Kyoung Je Cha, Sun Kwang Hwang, Chihun Lee","doi":"10.11159/cist23.133","DOIUrl":"https://doi.org/10.11159/cist23.133","url":null,"abstract":"","PeriodicalId":294100,"journal":{"name":"World Congress on Electrical Engineering and Computer Systems and Science","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114169117","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}
Nicholas V. Scott, Bradon Thymes, Joseph Peter Salisbury
{"title":"Statistical Modelling of Air-Ground Remotely Sensed Geo-Intelligence Information Using Naïve Bayesian Classification: A Decision-Making Approach","authors":"Nicholas V. Scott, Bradon Thymes, Joseph Peter Salisbury","doi":"10.11159/mvml23.102","DOIUrl":"https://doi.org/10.11159/mvml23.102","url":null,"abstract":"","PeriodicalId":294100,"journal":{"name":"World Congress on Electrical Engineering and Computer Systems and Science","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128396109","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}
{"title":"Multi-Material 3D Printing of Highly Sensitive Flexible Multi-Layered Tactile Sensors","authors":"Meshari Alsharari","doi":"10.11159/eee23.115","DOIUrl":"https://doi.org/10.11159/eee23.115","url":null,"abstract":"","PeriodicalId":294100,"journal":{"name":"World Congress on Electrical Engineering and Computer Systems and Science","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131681985","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}
{"title":"EEG Channel Selection Method for Subject-Independent Motor Imagery Classification using Shapley Additive exPlanations","authors":"Vishnupriya R, Neethu Robinson, Ramasubba Reddy M","doi":"10.11159/icbes23.126","DOIUrl":"https://doi.org/10.11159/icbes23.126","url":null,"abstract":"","PeriodicalId":294100,"journal":{"name":"World Congress on Electrical Engineering and Computer Systems and Science","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130278621","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}
{"title":"A systematic review of the predictors, mediators, and moderators of the Uncanny Valley Effect in human-embodied conversational agent interaction","authors":"Stefania Stefanache, Ioana R. Podina","doi":"10.11159/mhci23.109","DOIUrl":"https://doi.org/10.11159/mhci23.109","url":null,"abstract":"","PeriodicalId":294100,"journal":{"name":"World Congress on Electrical Engineering and Computer Systems and Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130966498","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}
Mathy Vandhana Sannasi, M. Kyritsis, Katie L. H. Gray
- The objective of this research is to understand the current capabilities of artificial neural network algorithms and contrast them to the human visual system, in order to identify the most effective features to support affective automation. This can, in turn, aid in optimisation of resources used for storage and transmission by understanding which level of information can be used to augment and potentially accelerate accurate identification of emotional facial expressions. For the first part of our experiment, which we present in this paper, we focused on evaluating feature selection of facial expression images using machine learning. 70 (10 examples of each basic emotion) randomly selected from the NIMSTIM dataset images were used, which were split into train (56) and test (14) sets. The testing images were then processed using Singular Vector Decomposition to vary the levels of information shown in the image. Next, the training dataset was used to train a Convolutional Neural Network algorithm with 18 layers (with convolutional, max pooling, dropout, flattening and activation layers) and 66,884,615 trainable parameters. The validation accuracy was 45% and the confusion matrix showed that the emotion disgust was predicted at almost 100% accuracy, surprise at 55%, and sorrow/happiness/neutral at 46-47%. As expected, the granularity level of the test images had an effect on the successful predictions. A feature map visualisation was performed to demonstrate what the CNN “sees” (i.e., the feature selection) in the image in order to accurately predict the emotional expression type. For the next phase of our experiment, we plan on contrasting the features and performance to that of the human visual system using an experimental design with eye tracking.
{"title":"What Does A Typical CNN “See” In An Emotional Facial Image?","authors":"Mathy Vandhana Sannasi, M. Kyritsis, Katie L. H. Gray","doi":"10.11159/mvml23.114","DOIUrl":"https://doi.org/10.11159/mvml23.114","url":null,"abstract":"- The objective of this research is to understand the current capabilities of artificial neural network algorithms and contrast them to the human visual system, in order to identify the most effective features to support affective automation. This can, in turn, aid in optimisation of resources used for storage and transmission by understanding which level of information can be used to augment and potentially accelerate accurate identification of emotional facial expressions. For the first part of our experiment, which we present in this paper, we focused on evaluating feature selection of facial expression images using machine learning. 70 (10 examples of each basic emotion) randomly selected from the NIMSTIM dataset images were used, which were split into train (56) and test (14) sets. The testing images were then processed using Singular Vector Decomposition to vary the levels of information shown in the image. Next, the training dataset was used to train a Convolutional Neural Network algorithm with 18 layers (with convolutional, max pooling, dropout, flattening and activation layers) and 66,884,615 trainable parameters. The validation accuracy was 45% and the confusion matrix showed that the emotion disgust was predicted at almost 100% accuracy, surprise at 55%, and sorrow/happiness/neutral at 46-47%. As expected, the granularity level of the test images had an effect on the successful predictions. A feature map visualisation was performed to demonstrate what the CNN “sees” (i.e., the feature selection) in the image in order to accurately predict the emotional expression type. For the next phase of our experiment, we plan on contrasting the features and performance to that of the human visual system using an experimental design with eye tracking.","PeriodicalId":294100,"journal":{"name":"World Congress on Electrical Engineering and Computer Systems and Science","volume":"211 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133891461","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}
{"title":"Non-invasive Assessment of Diabetes from sub- Heart Rate Variability: Coherence with HbA1c Test","authors":"Debadutta Subudhi, M. Manivannan, K. Deepak","doi":"10.11159/icbes23.164","DOIUrl":"https://doi.org/10.11159/icbes23.164","url":null,"abstract":"","PeriodicalId":294100,"journal":{"name":"World Congress on Electrical Engineering and Computer Systems and Science","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133656474","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：