Pub Date : 2022-11-04DOI: 10.1109/PhDEDITS56681.2022.9955252
Sricheta Parui, Uttam Ghosh, Puspita Chatterjee
One common neurological condition Parkinson is one of the diseases which might make it difficult for a patient to live a regular life like other people. It is a progressive neurodegenerative condition that is difficult to detect in the early stages. Traditional EEG-based PD diagnosis relies on arduous, time-consuming feature extraction that is done by hand. The ParkINN (Parkinson Identification Neural Network) has been proposed as a new EEG-based network for Parkinson’s screening that can quickly identify patients suffering from Parkinson’s or early stages of Parkinson’s. The suggested approach uses windowing and long-short term memory (LSTM) architectures for sequence learning, as well as 3 Dimensional Convolutional Neural Networks (CNN) for temporal learning of the EEG signal. The accuracy rate of the proposed 3D CNN-LSTM model is 94.64 percent, which is higher than the findings of the majority of other work in this area.
{"title":"ParkINN: An Integrated Neural Network Model for Parkinson Detection","authors":"Sricheta Parui, Uttam Ghosh, Puspita Chatterjee","doi":"10.1109/PhDEDITS56681.2022.9955252","DOIUrl":"https://doi.org/10.1109/PhDEDITS56681.2022.9955252","url":null,"abstract":"One common neurological condition Parkinson is one of the diseases which might make it difficult for a patient to live a regular life like other people. It is a progressive neurodegenerative condition that is difficult to detect in the early stages. Traditional EEG-based PD diagnosis relies on arduous, time-consuming feature extraction that is done by hand. The ParkINN (Parkinson Identification Neural Network) has been proposed as a new EEG-based network for Parkinson’s screening that can quickly identify patients suffering from Parkinson’s or early stages of Parkinson’s. The suggested approach uses windowing and long-short term memory (LSTM) architectures for sequence learning, as well as 3 Dimensional Convolutional Neural Networks (CNN) for temporal learning of the EEG signal. The accuracy rate of the proposed 3D CNN-LSTM model is 94.64 percent, which is higher than the findings of the majority of other work in this area.","PeriodicalId":373652,"journal":{"name":"2022 IEEE 4th PhD Colloquium on Emerging Domain Innovation and Technology for Society (PhD EDITS)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127731704","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 : 2022-11-04DOI: 10.1109/PhDEDITS56681.2022.9955283
Sudhan H V Madhu, S. S. Kumar, Monalin Pal, P. Rubini
Behavioral Activation is a method in Cognitive Behavioral Therapy which uses behavior to influence the emotional condition of the person. Behavioral Activation aids in engaging activities to activate a positive emotional state and overcome the depression. In this paper, we showcase a new method to recognize the right activity for behavioral activation by detecting emotion, sentiment and understanding the context and interests of the person during counseling. We showcase a multi-modal method to recognize activity for behavioral activation through speech and text modalities using artificial intelligence. Projected model attained an accuracy of 83% for emotion recognition, 81% for sentiment detection and 82% for identifying the right activity for behavioral activation.
{"title":"Activity Recognition for Behavioral Activation in Depression with Artificial Intelligence","authors":"Sudhan H V Madhu, S. S. Kumar, Monalin Pal, P. Rubini","doi":"10.1109/PhDEDITS56681.2022.9955283","DOIUrl":"https://doi.org/10.1109/PhDEDITS56681.2022.9955283","url":null,"abstract":"Behavioral Activation is a method in Cognitive Behavioral Therapy which uses behavior to influence the emotional condition of the person. Behavioral Activation aids in engaging activities to activate a positive emotional state and overcome the depression. In this paper, we showcase a new method to recognize the right activity for behavioral activation by detecting emotion, sentiment and understanding the context and interests of the person during counseling. We showcase a multi-modal method to recognize activity for behavioral activation through speech and text modalities using artificial intelligence. Projected model attained an accuracy of 83% for emotion recognition, 81% for sentiment detection and 82% for identifying the right activity for behavioral activation.","PeriodicalId":373652,"journal":{"name":"2022 IEEE 4th PhD Colloquium on Emerging Domain Innovation and Technology for Society (PhD EDITS)","volume":"90 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117296261","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 : 2022-11-04DOI: 10.1109/PhDEDITS56681.2022.9955296
G. Nayana, P. Vimala, V. Anandi
Biosensors has created a revolution in the area of research post pandemic situation. There are many ways to detect bio-molecules. The device that has gained huge popularity to detect the bio-molecules is the Field-Effect Transistor. It has higher ability to detect and its sensitivity is better with reduced device size and yields quick reactive and response time. But MOSFETs suffer from limitation of subthreshold swing of 60mV/decade. New device architecture with new device material is the need of the hour. Graphene Nanoribbon Tunnel Field Effect Transistor (GNR-TFET) device structure is presented and simulated for capturing device characteristics for bio-molecular application.
{"title":"Graphene Nano-ribbon Tunnel Field Effect Transistor based Bio-Sensors:Device Characteristics","authors":"G. Nayana, P. Vimala, V. Anandi","doi":"10.1109/PhDEDITS56681.2022.9955296","DOIUrl":"https://doi.org/10.1109/PhDEDITS56681.2022.9955296","url":null,"abstract":"Biosensors has created a revolution in the area of research post pandemic situation. There are many ways to detect bio-molecules. The device that has gained huge popularity to detect the bio-molecules is the Field-Effect Transistor. It has higher ability to detect and its sensitivity is better with reduced device size and yields quick reactive and response time. But MOSFETs suffer from limitation of subthreshold swing of 60mV/decade. New device architecture with new device material is the need of the hour. Graphene Nanoribbon Tunnel Field Effect Transistor (GNR-TFET) device structure is presented and simulated for capturing device characteristics for bio-molecular application.","PeriodicalId":373652,"journal":{"name":"2022 IEEE 4th PhD Colloquium on Emerging Domain Innovation and Technology for Society (PhD EDITS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134044477","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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