Pub Date : 2020-07-01DOI: 10.1109/ICCSP48568.2020.9182067
K. Sathya, J. Premalatha, S. Suwathika
In the past decade, the Machine Learning (ML) and Deep learning (DL) has produced much research interest in the society and attracted them. Now-a-days, the Internet and social life make a lead in most of their life but it has serious social threats. It is a challenging thing to protect the sensitive information, data network and the computers which are in unauthorized cyber-attacks. For protecting the data’s we need the cyber security. For these problems, the recent technologies of Deep learning and Machine Learning are integrated with the cyber-attacks to provide the solution for the problems. This paper gives a synopsis of utilizing deep learning to enhance the security of cyber world and various challenges in integrating deep learning into cyber security are analyzed.
{"title":"Reinforcing Cyber World Security with Deep Learning Approaches","authors":"K. Sathya, J. Premalatha, S. Suwathika","doi":"10.1109/ICCSP48568.2020.9182067","DOIUrl":"https://doi.org/10.1109/ICCSP48568.2020.9182067","url":null,"abstract":"In the past decade, the Machine Learning (ML) and Deep learning (DL) has produced much research interest in the society and attracted them. Now-a-days, the Internet and social life make a lead in most of their life but it has serious social threats. It is a challenging thing to protect the sensitive information, data network and the computers which are in unauthorized cyber-attacks. For protecting the data’s we need the cyber security. For these problems, the recent technologies of Deep learning and Machine Learning are integrated with the cyber-attacks to provide the solution for the problems. This paper gives a synopsis of utilizing deep learning to enhance the security of cyber world and various challenges in integrating deep learning into cyber security are analyzed.","PeriodicalId":321133,"journal":{"name":"2020 International Conference on Communication and Signal Processing (ICCSP)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131987250","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 : 2020-07-01DOI: 10.1109/ICCSP48568.2020.9182110
Sayantani Ghosh, Mousumi Laha, A. Konar
This paper intends to develop a novel methodology that helps to determine the variation of pain perception across various individuals using EEG signal analysis. Three types of touch stimuli: heat, bristles and pinch with varying intensity levels are utilized for the experiment. The brain signals acquired are analyzed using eLORETA software that confirms the involvement of frontal and parietal lobes for this cognitive activity. Additionally, frequency analysis undertaken infers the participation of alpha and theta bands for the said task. The signals are further evaluated to inspect the existence of any Event Related Potential (ERP) signal. A unique and notable ERP signal has been found when a subject finds the perceived stimuli to be painful. However, no relevant ERP component is generated when the subject finds the presented stimuli to be completely painless. A novel Interval Type-2 fuzzy classifier has been designed to classify these two distinct conditions (painful and non-painful). Performance analysis undertaken confirms the superlative behaviour of the proposed classifier with respect to other standard ones. Moreover, statistical evaluation also assures the superior performance of the proposed classifier model. Hence, this method can act as a neuronal marker to detect an individual’s pain sensitivity that can be used to diagnose and treat various neurological disorders and chronic pain based diseases.
{"title":"P1000 Induced Brain Signal Analysis for Assessing Subjective Pain Sensitivity using Type-2 Fuzzy Classifier","authors":"Sayantani Ghosh, Mousumi Laha, A. Konar","doi":"10.1109/ICCSP48568.2020.9182110","DOIUrl":"https://doi.org/10.1109/ICCSP48568.2020.9182110","url":null,"abstract":"This paper intends to develop a novel methodology that helps to determine the variation of pain perception across various individuals using EEG signal analysis. Three types of touch stimuli: heat, bristles and pinch with varying intensity levels are utilized for the experiment. The brain signals acquired are analyzed using eLORETA software that confirms the involvement of frontal and parietal lobes for this cognitive activity. Additionally, frequency analysis undertaken infers the participation of alpha and theta bands for the said task. The signals are further evaluated to inspect the existence of any Event Related Potential (ERP) signal. A unique and notable ERP signal has been found when a subject finds the perceived stimuli to be painful. However, no relevant ERP component is generated when the subject finds the presented stimuli to be completely painless. A novel Interval Type-2 fuzzy classifier has been designed to classify these two distinct conditions (painful and non-painful). Performance analysis undertaken confirms the superlative behaviour of the proposed classifier with respect to other standard ones. Moreover, statistical evaluation also assures the superior performance of the proposed classifier model. Hence, this method can act as a neuronal marker to detect an individual’s pain sensitivity that can be used to diagnose and treat various neurological disorders and chronic pain based diseases.","PeriodicalId":321133,"journal":{"name":"2020 International Conference on Communication and Signal Processing (ICCSP)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134337986","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 : 2020-07-01DOI: 10.1109/ICCSP48568.2020.9182185
N. Geetha, D. Harinee Devi., S. Samyuktha, M. Vishnu
The cyberspace news consumption is increasing day by day all over the world. The main reason for cyber space news consumption is due to its rapid spread of information and its easy access which lead people to consume news rapidly without the knowledge of whether the news is false or true. Thus, it leads to the wide spread of false news which leads to the negative impacts on society. Therefore false news prediction on cyberspace is attracting a tremendous attention. The issue of fake-news prediction on cyberspace is both challenging and relevant as spreading of fake news occurs in various streams like text, audio, video, images etc. This model works on processing the text and images together by providing an interactive Application Interface (API), i.e. text by applying the model Logistic regression classifier and image by applying self-consistency algorithm. The natural language tool kit (NLTK) model is used for these implementation through python. Once the news is predicted fake, a report is redirected to the authorized website (cybercrime department) to take the immediate necessary actions required to stop these news from spreading.
{"title":"Cyberspace News Prediction of Text and Image with Report Generation","authors":"N. Geetha, D. Harinee Devi., S. Samyuktha, M. Vishnu","doi":"10.1109/ICCSP48568.2020.9182185","DOIUrl":"https://doi.org/10.1109/ICCSP48568.2020.9182185","url":null,"abstract":"The cyberspace news consumption is increasing day by day all over the world. The main reason for cyber space news consumption is due to its rapid spread of information and its easy access which lead people to consume news rapidly without the knowledge of whether the news is false or true. Thus, it leads to the wide spread of false news which leads to the negative impacts on society. Therefore false news prediction on cyberspace is attracting a tremendous attention. The issue of fake-news prediction on cyberspace is both challenging and relevant as spreading of fake news occurs in various streams like text, audio, video, images etc. This model works on processing the text and images together by providing an interactive Application Interface (API), i.e. text by applying the model Logistic regression classifier and image by applying self-consistency algorithm. The natural language tool kit (NLTK) model is used for these implementation through python. Once the news is predicted fake, a report is redirected to the authorized website (cybercrime department) to take the immediate necessary actions required to stop these news from spreading.","PeriodicalId":321133,"journal":{"name":"2020 International Conference on Communication and Signal Processing (ICCSP)","volume":"109 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134467933","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 : 2020-07-01DOI: 10.1109/ICCSP48568.2020.9182246
T. J. V. V. P. Reddy, C. S. Kumar, K. Suman, U. Avinash, Harisudha Kuresan
The electromagnetic(EM) waves are used for long distance communication by using air as a medium but when EM waves are used for communication through soil it cannot penetrate through soil due to various compositions of soil like red, black cotton soil etc. When these waves are used for data transmission in soil there will be loss in data because of high difraction. When there is increase in transmission distance there will be high path loss and high attenuation because of interior distance. In this present day to day communication underground communication system needs to play a key role for the effective data transmission. To establish this effective wireless connection wireless underground sensor networks(WUSN) has been introduced. To overcome problems in the electromagnetic waves, Magnetic induction(MI) has been proposed as it consists of magnetic induction coils which are used as transceivers for the effective data transmission.
{"title":"Wireless Underground Sensor Network Using Magnetic Induction","authors":"T. J. V. V. P. Reddy, C. S. Kumar, K. Suman, U. Avinash, Harisudha Kuresan","doi":"10.1109/ICCSP48568.2020.9182246","DOIUrl":"https://doi.org/10.1109/ICCSP48568.2020.9182246","url":null,"abstract":"The electromagnetic(EM) waves are used for long distance communication by using air as a medium but when EM waves are used for communication through soil it cannot penetrate through soil due to various compositions of soil like red, black cotton soil etc. When these waves are used for data transmission in soil there will be loss in data because of high difraction. When there is increase in transmission distance there will be high path loss and high attenuation because of interior distance. In this present day to day communication underground communication system needs to play a key role for the effective data transmission. To establish this effective wireless connection wireless underground sensor networks(WUSN) has been introduced. To overcome problems in the electromagnetic waves, Magnetic induction(MI) has been proposed as it consists of magnetic induction coils which are used as transceivers for the effective data transmission.","PeriodicalId":321133,"journal":{"name":"2020 International Conference on Communication and Signal Processing (ICCSP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133872417","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 : 2020-07-01DOI: 10.1109/ICCSP48568.2020.9182314
S. Dasgupta, Madhurupa Samaddar, C. Yogalakshmi, K. Vijayan, Subhiksha
In recent times hotel management has been pretty important when it comes to business meets and food chains. Currently in India multiple food chains are running out of which many food chains are running internationally too. One of the most important aspects of these chains is the management of customers and customer satisfaction. The first part of this project aims at intelligently predicting the choices and the personal details of the customer. This prediction is done through voice recognition where the voiceprint of a person is detected to access the database of the person which consists of his/her previous orders and special changes in the menu. This helps in knowing about the allergic reactions of a person to some specific food items and a customizable menu can be provided to the person. This would greatly help in management of customer databases and customer satisfaction as well as maintenance of the privacy of the person. The second part of the project deals with the food item detection algorithm using image processing so that veg and non veg food items do not get mixed up and the correct table receives the correct order.
{"title":"Autonomous Restaurant Serving System using Image Detection and Voiceprint Detection in Android Application","authors":"S. Dasgupta, Madhurupa Samaddar, C. Yogalakshmi, K. Vijayan, Subhiksha","doi":"10.1109/ICCSP48568.2020.9182314","DOIUrl":"https://doi.org/10.1109/ICCSP48568.2020.9182314","url":null,"abstract":"In recent times hotel management has been pretty important when it comes to business meets and food chains. Currently in India multiple food chains are running out of which many food chains are running internationally too. One of the most important aspects of these chains is the management of customers and customer satisfaction. The first part of this project aims at intelligently predicting the choices and the personal details of the customer. This prediction is done through voice recognition where the voiceprint of a person is detected to access the database of the person which consists of his/her previous orders and special changes in the menu. This helps in knowing about the allergic reactions of a person to some specific food items and a customizable menu can be provided to the person. This would greatly help in management of customer databases and customer satisfaction as well as maintenance of the privacy of the person. The second part of the project deals with the food item detection algorithm using image processing so that veg and non veg food items do not get mixed up and the correct table receives the correct order.","PeriodicalId":321133,"journal":{"name":"2020 International Conference on Communication and Signal Processing (ICCSP)","volume":"318 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115355877","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 : 2020-07-01DOI: 10.1109/ICCSP48568.2020.9182201
M. Murali, Shreya Sharma, Neel Nagansure
This work aims to assist the visually impaired people for reading a text material and detect objects in their surroundings. The input is taken in the form of an image captured from the web camera. This image is then processed either for the purpose of text reading or for object detection based on user choice. The Raspberry Pi acts as the microcontroller for processing of the entire process. The text reading is supported by software named OCR. The read text is changed into an audio output using the TTS Synthesis. Other dependencies required for the process include Tesseract Library. The Object Detection is another aspect of the project which is implemented using a TensorFlow Object Detection API. It is able to detect various objects in its surroundings and provide an audio feedback about the same. The dataset can be trained on various different situations depending on the user needs, thus making it scalable
{"title":"Reader and Object Detector for Blind","authors":"M. Murali, Shreya Sharma, Neel Nagansure","doi":"10.1109/ICCSP48568.2020.9182201","DOIUrl":"https://doi.org/10.1109/ICCSP48568.2020.9182201","url":null,"abstract":"This work aims to assist the visually impaired people for reading a text material and detect objects in their surroundings. The input is taken in the form of an image captured from the web camera. This image is then processed either for the purpose of text reading or for object detection based on user choice. The Raspberry Pi acts as the microcontroller for processing of the entire process. The text reading is supported by software named OCR. The read text is changed into an audio output using the TTS Synthesis. Other dependencies required for the process include Tesseract Library. The Object Detection is another aspect of the project which is implemented using a TensorFlow Object Detection API. It is able to detect various objects in its surroundings and provide an audio feedback about the same. The dataset can be trained on various different situations depending on the user needs, thus making it scalable","PeriodicalId":321133,"journal":{"name":"2020 International Conference on Communication and Signal Processing (ICCSP)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114293494","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 : 2020-07-01DOI: 10.1109/ICCSP48568.2020.9182103
Hao Liu, Zhisheng Zhang, Dini Duan
As the number of motor vehicles in developing countries increases year by year, the increase in the number of vehicles has brought a series of problems to urban traffic, such as traffic congestion and road safety issues. The road traffic information is effectively transmitted to the on-board self-organizing network. The on-board self-organizing network can communicate with people, vehicles, vehicles and vehicles, and vehicles and roadside facilities to optimize the road network reasonably and reduce traffic The purpose of congestion. Starting from the road traffic network, this paper mainly studies a routing protocol for road information acquisition with roadside unit vehicles as SINK nodes. Based on this information, a road network optimization for the global road network is proposed. algorithm. From a global perspective, reduce road congestion and improve road utilization.
{"title":"Research on Road Network Optimization of Traffic Congestion Reduction based on Vehicle as Sink Node","authors":"Hao Liu, Zhisheng Zhang, Dini Duan","doi":"10.1109/ICCSP48568.2020.9182103","DOIUrl":"https://doi.org/10.1109/ICCSP48568.2020.9182103","url":null,"abstract":"As the number of motor vehicles in developing countries increases year by year, the increase in the number of vehicles has brought a series of problems to urban traffic, such as traffic congestion and road safety issues. The road traffic information is effectively transmitted to the on-board self-organizing network. The on-board self-organizing network can communicate with people, vehicles, vehicles and vehicles, and vehicles and roadside facilities to optimize the road network reasonably and reduce traffic The purpose of congestion. Starting from the road traffic network, this paper mainly studies a routing protocol for road information acquisition with roadside unit vehicles as SINK nodes. Based on this information, a road network optimization for the global road network is proposed. algorithm. From a global perspective, reduce road congestion and improve road utilization.","PeriodicalId":321133,"journal":{"name":"2020 International Conference on Communication and Signal Processing (ICCSP)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114386024","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 : 2020-07-01DOI: 10.1109/ICCSP48568.2020.9182258
Amrit Sreekumar, Karthika Nair, S. Sudheer, H. Ganesh Nayar, J. J. Nair
Lung Carcinoma, commonly known as Lung Cancer is an infectious lung tumour caused by uncontrollable tissue growth in the lungs. Presented here is an approach to detect malignant pulmonary nodules from CT scans using Deep Learning. A preprocessing pipeline was used to mask out the lung regions from the scans. The features were then extracted using a 3D CNN model based on the C3D network architecture. The LIDC-IDRI is the primary dataset used along with a few resources from the LUNA16 grand challenge for the reduction of false-positives. The end product is a model that predicts the coordinates of malignant pulmonary nodules and demarcates the corresponding areas from the CT scans. The final model achieved a sensitivity of 86 percent for detecting malignant Lung Nodules and predicting its malignancy scores.
{"title":"Malignant Lung Nodule Detection using Deep Learning","authors":"Amrit Sreekumar, Karthika Nair, S. Sudheer, H. Ganesh Nayar, J. J. Nair","doi":"10.1109/ICCSP48568.2020.9182258","DOIUrl":"https://doi.org/10.1109/ICCSP48568.2020.9182258","url":null,"abstract":"Lung Carcinoma, commonly known as Lung Cancer is an infectious lung tumour caused by uncontrollable tissue growth in the lungs. Presented here is an approach to detect malignant pulmonary nodules from CT scans using Deep Learning. A preprocessing pipeline was used to mask out the lung regions from the scans. The features were then extracted using a 3D CNN model based on the C3D network architecture. The LIDC-IDRI is the primary dataset used along with a few resources from the LUNA16 grand challenge for the reduction of false-positives. The end product is a model that predicts the coordinates of malignant pulmonary nodules and demarcates the corresponding areas from the CT scans. The final model achieved a sensitivity of 86 percent for detecting malignant Lung Nodules and predicting its malignancy scores.","PeriodicalId":321133,"journal":{"name":"2020 International Conference on Communication and Signal Processing (ICCSP)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114738265","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 : 2020-07-01DOI: 10.1109/ICCSP48568.2020.9182089
P. Vijayakumar, Ditipriya Gorai, Nitin Chauhan, Ujjawal Kant
Spectrum scarcity issue is being addressed by using dynamic spectrum sharing of cognitive radio. Primary user detection is the primary task in cognitive radio. Modulation Classification can be applied as the vital principle in this article to identify the particular primary user radio type. A novel method to classify the modulation type has been suggested in this paper by using the neural network algorithm. The entire system is implemented using a Software Defined Radio platform (NIUSRP). The system will classify the type of modulation and which is used to detect the presence of the primary user for the cognitive radio application. In this paper, three different modulation type has been implemented in USRP SDR in real-time. The primary purpose of implementing the modulation classification algorithm is to identify the existence of a signal at a given location at a given frequency band at the given time.
{"title":"Modulation Classifier for Primary User Detection","authors":"P. Vijayakumar, Ditipriya Gorai, Nitin Chauhan, Ujjawal Kant","doi":"10.1109/ICCSP48568.2020.9182089","DOIUrl":"https://doi.org/10.1109/ICCSP48568.2020.9182089","url":null,"abstract":"Spectrum scarcity issue is being addressed by using dynamic spectrum sharing of cognitive radio. Primary user detection is the primary task in cognitive radio. Modulation Classification can be applied as the vital principle in this article to identify the particular primary user radio type. A novel method to classify the modulation type has been suggested in this paper by using the neural network algorithm. The entire system is implemented using a Software Defined Radio platform (NIUSRP). The system will classify the type of modulation and which is used to detect the presence of the primary user for the cognitive radio application. In this paper, three different modulation type has been implemented in USRP SDR in real-time. The primary purpose of implementing the modulation classification algorithm is to identify the existence of a signal at a given location at a given frequency band at the given time.","PeriodicalId":321133,"journal":{"name":"2020 International Conference on Communication and Signal Processing (ICCSP)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116713662","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 : 2020-07-01DOI: 10.1109/ICCSP48568.2020.9182237
K. Satish, A. Lalitesh, K. Bhargavi, M.Sishir Prem, T. Anjali
All over the world Drowsiness has been the significant cause of horrible accidents which is causing deaths and fatalities injuries. Day by Day fatal injuries numbers are increasing globally. From the past many years, researchers have concluded drivers with a lack of sleep and more tiredness which causes drowsiness of the driver. this paper shows a new experimental model is designed for detecting drowsiness of driver is presented to reduce accidents caused by this problem which increases transport safety. In this work, two ways are used to detect the drowsiness of a person effectively. First Driver face is captured and eye retina detection and facial feature extraction are done and blinking values are calculated then threshold values are set. Secondly, the Aurdino module is used which is integrated with elastomeric sensors for real-time calculation of driver hand pressure on the car steering wheel and the threshold value is set. The result from both methods is taken as input for taking the final decision and alerting the driver.
{"title":"Driver Drowsiness Detection","authors":"K. Satish, A. Lalitesh, K. Bhargavi, M.Sishir Prem, T. Anjali","doi":"10.1109/ICCSP48568.2020.9182237","DOIUrl":"https://doi.org/10.1109/ICCSP48568.2020.9182237","url":null,"abstract":"All over the world Drowsiness has been the significant cause of horrible accidents which is causing deaths and fatalities injuries. Day by Day fatal injuries numbers are increasing globally. From the past many years, researchers have concluded drivers with a lack of sleep and more tiredness which causes drowsiness of the driver. this paper shows a new experimental model is designed for detecting drowsiness of driver is presented to reduce accidents caused by this problem which increases transport safety. In this work, two ways are used to detect the drowsiness of a person effectively. First Driver face is captured and eye retina detection and facial feature extraction are done and blinking values are calculated then threshold values are set. Secondly, the Aurdino module is used which is integrated with elastomeric sensors for real-time calculation of driver hand pressure on the car steering wheel and the threshold value is set. The result from both methods is taken as input for taking the final decision and alerting the driver.","PeriodicalId":321133,"journal":{"name":"2020 International Conference on Communication and Signal Processing (ICCSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116737373","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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