Pub Date : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760994
A. Balamurugan, K. Karthikeyan, S. J. P. Gnanaraj, N. Muthukumaran
Voltage_e stability analysis of IEEE 145-bus_e system_e is gauged through CPF in this paper. The recitation learning of PV_E is carried out in IEEE 145-bus_e test system_e for the development of voltage_e stability through Power System_e MATPOWER. When a power device is going thru unexpected loading, its balance is affected. It needs reimbursement to improve the steadiness from the disorders. Here, the machine is analyzed by using CPF to enhance the steadiness. Various operating situations like with out PV_E and with PV_E (tuned by using Dragonfly algorithm) are used to evaluate the overall recital of the suggested system_e. The results show that the device with PV_E (tuned with the aid of Dragonfly) display top result than the device without PV_E.
{"title":"Stability Analysis of Voltage in IEEE 145 Bus System by CPF using Dragonfly Algorithm","authors":"A. Balamurugan, K. Karthikeyan, S. J. P. Gnanaraj, N. Muthukumaran","doi":"10.1109/ICSCDS53736.2022.9760994","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760994","url":null,"abstract":"Voltage_e stability analysis of IEEE 145-bus_e system_e is gauged through CPF in this paper. The recitation learning of PV_E is carried out in IEEE 145-bus_e test system_e for the development of voltage_e stability through Power System_e MATPOWER. When a power device is going thru unexpected loading, its balance is affected. It needs reimbursement to improve the steadiness from the disorders. Here, the machine is analyzed by using CPF to enhance the steadiness. Various operating situations like with out PV_E and with PV_E (tuned by using Dragonfly algorithm) are used to evaluate the overall recital of the suggested system_e. The results show that the device with PV_E (tuned with the aid of Dragonfly) display top result than the device without PV_E.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128921569","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 : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760812
S. K. Barik, Srikanta Mohapatra, Subhra Debdas
In this paper, a novel architecture, multi-neuron functional link artificial neural network (MNFLANN), has been proposed and its performance in predicting wind energy is compared with the other conventional network models, i.e. ANN, multi-layer perceptrons (MLP) and functional link artificial neural networks (FLANN). The name, i.e. MNFLANN is given as per its structure which consists of multiple neurons unlike the conventional FLANN that consists of only one neuron in the output layer. The real-time wind energy data of October month of recent three years from Sotavento wind farm located in Spain has been taken into consideration to evaluate the performance of MNFLANN. Results show that the mean absolute percentage error (MAPE) during testing is so less, i.e. -1.32% for MNFLANN, compared to other conventional architectures, i.e. -9.47% for ANN, - 8.44% for MLP and 15.19% for FLANN. The proposed MNFLANN architecture effectively handles the nonlinearity in input data compared to other conventional architectures due to its improved structure.
{"title":"Multi-Neuron Functional Link Artificial Neural Network: A Novel Architecture and its Performance for Wind Energy Prediction","authors":"S. K. Barik, Srikanta Mohapatra, Subhra Debdas","doi":"10.1109/ICSCDS53736.2022.9760812","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760812","url":null,"abstract":"In this paper, a novel architecture, multi-neuron functional link artificial neural network (MNFLANN), has been proposed and its performance in predicting wind energy is compared with the other conventional network models, i.e. ANN, multi-layer perceptrons (MLP) and functional link artificial neural networks (FLANN). The name, i.e. MNFLANN is given as per its structure which consists of multiple neurons unlike the conventional FLANN that consists of only one neuron in the output layer. The real-time wind energy data of October month of recent three years from Sotavento wind farm located in Spain has been taken into consideration to evaluate the performance of MNFLANN. Results show that the mean absolute percentage error (MAPE) during testing is so less, i.e. -1.32% for MNFLANN, compared to other conventional architectures, i.e. -9.47% for ANN, - 8.44% for MLP and 15.19% for FLANN. The proposed MNFLANN architecture effectively handles the nonlinearity in input data compared to other conventional architectures due to its improved structure.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128407430","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 : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760855
M. Karpagam, B. Shankar, M. Janaranjan, M. S. Jaganath, R. Harivarathan, G. Maniram
Augmented reality is an advanced image processing technique. Using this technique we are able to real time industrial as well as domestic issues. Here In this project we are going to created an augmented reality application which displays the real time sensor values. Using this project we can monitor any dangerous work space safely. While scanning the trigger image the sensor paced in the machine gets the required data and it is processed. Then the data is stored in the cloud that is created. Then the API is called and the data is displayed in the augmented reality. If there is any technical issues in the machine then there will be an indication while scanning the trigger image.
{"title":"Augmented Reality based Monitoring System","authors":"M. Karpagam, B. Shankar, M. Janaranjan, M. S. Jaganath, R. Harivarathan, G. Maniram","doi":"10.1109/ICSCDS53736.2022.9760855","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760855","url":null,"abstract":"Augmented reality is an advanced image processing technique. Using this technique we are able to real time industrial as well as domestic issues. Here In this project we are going to created an augmented reality application which displays the real time sensor values. Using this project we can monitor any dangerous work space safely. While scanning the trigger image the sensor paced in the machine gets the required data and it is processed. Then the data is stored in the cloud that is created. Then the API is called and the data is displayed in the augmented reality. If there is any technical issues in the machine then there will be an indication while scanning the trigger image.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129762838","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 : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760873
H. Liao
Analysis on the integrated development of the traditional information and rural tourism based on remote sensing image data analysis is conducted in this paper. Texture reflects the spatial variation characteristics of pixel gray level, and is a pattern that is regularly arranged in the entire image or a certain area in the image. Using traditional methods for the remote sensing image feature extraction can not avoid the defect of large deviation of feature segmentation results caused by broken cloud clutter, hence, the wavelet analysis is combined. Further, the integrated development of traditional information and rural tourism is selected as the application scenario. Through different sets of the simulations, the efficiency is shown.
{"title":"Analysis on the Integrated Development of Traditional Information and Rural Tourism based on Remote Sensing Image Data Analysis","authors":"H. Liao","doi":"10.1109/ICSCDS53736.2022.9760873","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760873","url":null,"abstract":"Analysis on the integrated development of the traditional information and rural tourism based on remote sensing image data analysis is conducted in this paper. Texture reflects the spatial variation characteristics of pixel gray level, and is a pattern that is regularly arranged in the entire image or a certain area in the image. Using traditional methods for the remote sensing image feature extraction can not avoid the defect of large deviation of feature segmentation results caused by broken cloud clutter, hence, the wavelet analysis is combined. Further, the integrated development of traditional information and rural tourism is selected as the application scenario. Through different sets of the simulations, the efficiency is shown.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130112421","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 : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760993
B. R, S. Deepajothi, Prabaharan G, Daniya T, P. Karthikeyan, V. S
The enormous development of information sent through the IoT devices to end-user devices has expanded the significance of creating intrusion detection systems. Intrusion detection system plays a vital role in the smart home, smart city, agriculture, and business organizations. The intruder crate attack and send the data through the IoT sensor device to attack the IoT environment. There is numerous deep learning model is developed and deployed in the IoT environment to detect the intrusion's activity in the IoT environment. This survey paper explores the deep supervised learning model, deep unsupervised learning model, and data set used in the IoT environment for the intrusions detection system. Finally, the open research problem in the intrusion detection system in the IoT environment is presented.
{"title":"Survey on Intrusions Detection System using Deep learning in IoT Environment","authors":"B. R, S. Deepajothi, Prabaharan G, Daniya T, P. Karthikeyan, V. S","doi":"10.1109/ICSCDS53736.2022.9760993","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760993","url":null,"abstract":"The enormous development of information sent through the IoT devices to end-user devices has expanded the significance of creating intrusion detection systems. Intrusion detection system plays a vital role in the smart home, smart city, agriculture, and business organizations. The intruder crate attack and send the data through the IoT sensor device to attack the IoT environment. There is numerous deep learning model is developed and deployed in the IoT environment to detect the intrusion's activity in the IoT environment. This survey paper explores the deep supervised learning model, deep unsupervised learning model, and data set used in the IoT environment for the intrusions detection system. Finally, the open research problem in the intrusion detection system in the IoT environment is presented.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130373271","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 : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9761007
Jebakumar D Immanuel, Harish M Ragavan, Priscilla G Rani, K. Niveditaa, G. Manikandan
The main cause of disability and suicide is depression, which contributes most to global disability. Face-to-face interviews are typically used by psychologists to diagnose depressed individuals. The use of social media as a means of expressing one's mood has grown in recent years. A person's polarity influences how their emotions and opinions are analysed in Sentiment Analysis (SA). There is an implicit or explicit expression of sentiment in the text. Numerous studies on mental depression found that tweets created by users with major mental disturbances are used for depression detection. To aid the process of depression detection, this research study leverages social media (Twitter) data to forecast depressed users and estimate their depression intensity. LSTMs that are lexicon-enhanced are generally recommended. A lexicon-enhanced, deep learning-based LS TM model was proposed.
{"title":"AI to Detect Social Media users Depression Polarity Score","authors":"Jebakumar D Immanuel, Harish M Ragavan, Priscilla G Rani, K. Niveditaa, G. Manikandan","doi":"10.1109/ICSCDS53736.2022.9761007","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9761007","url":null,"abstract":"The main cause of disability and suicide is depression, which contributes most to global disability. Face-to-face interviews are typically used by psychologists to diagnose depressed individuals. The use of social media as a means of expressing one's mood has grown in recent years. A person's polarity influences how their emotions and opinions are analysed in Sentiment Analysis (SA). There is an implicit or explicit expression of sentiment in the text. Numerous studies on mental depression found that tweets created by users with major mental disturbances are used for depression detection. To aid the process of depression detection, this research study leverages social media (Twitter) data to forecast depressed users and estimate their depression intensity. LSTMs that are lexicon-enhanced are generally recommended. A lexicon-enhanced, deep learning-based LS TM model was proposed.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128690444","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 : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9761029
A. Sivasangari, Sivakumar, suji helen, S. Deepa, Vignesh, Suja
In a variety of medical diagnostic applications, Automatic Defect Detection in clinical imaging has turned into the developing field. Computerized discovery of cancer in MRI which gives the data about the aberrant tissues which is essential for the diagnosis. The traditional technique for Abnormalities detection in Brain is human investigation. This strategy is illogical because of the vast volume of data and the imperfection. Henceforth, trusted and programmed algorithms are preferred to prevent the passing pace of human. In this way, Automated tumor discovery techniques are created as it would save the specialist (radiologist) time and acquire the perfectness. Because of the complexities and diversity of malignancies, MRI brain tumour identification is a difficult task. Machine learning approaches are employed to get over the limitations of traditional classifiers in detecting malignancies in brain scans in this study. MRI scans can be utilised to successfully identify sick cells from healthy ones using machine learning and image classifiers. Convolutional neural network algorithm has been used for classification.
{"title":"Detection of Abnormalities in Brain using Machine Learning in Medical Image Analysis","authors":"A. Sivasangari, Sivakumar, suji helen, S. Deepa, Vignesh, Suja","doi":"10.1109/ICSCDS53736.2022.9761029","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9761029","url":null,"abstract":"In a variety of medical diagnostic applications, Automatic Defect Detection in clinical imaging has turned into the developing field. Computerized discovery of cancer in MRI which gives the data about the aberrant tissues which is essential for the diagnosis. The traditional technique for Abnormalities detection in Brain is human investigation. This strategy is illogical because of the vast volume of data and the imperfection. Henceforth, trusted and programmed algorithms are preferred to prevent the passing pace of human. In this way, Automated tumor discovery techniques are created as it would save the specialist (radiologist) time and acquire the perfectness. Because of the complexities and diversity of malignancies, MRI brain tumour identification is a difficult task. Machine learning approaches are employed to get over the limitations of traditional classifiers in detecting malignancies in brain scans in this study. MRI scans can be utilised to successfully identify sick cells from healthy ones using machine learning and image classifiers. Convolutional neural network algorithm has been used for classification.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117064591","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 : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9761018
Xiyan Ji
This paper studies the monitoring system of toxic substance pollution in the production of chemical plants based on big data technology. In order to realize the monitoring of harmful gases in the chemical production process, a data collector is formed with ATmega16 single-chip microcomputer as the core, and a harmful gas intelligent monitoring system is formed through Ethernet. The requirements for clear captured images, sensitive pan/tilt control, the main control room of the explosion-proof monitoring system is built in the chemical industry. The main control software of the digital hard disk video host realizes the monitoring and control of the cameras at each monitoring point. It can also transmit the company's various video signals through broadband or ADSL networks. Pass it on to the management of the company to achieve an active role in the safe production and operation of chemical companies and increase by 12.3%.
{"title":"Environmental Intelligent Monitoring System based on the Pollution of Toxic Substances in Chemical Production under the Background of Big Data","authors":"Xiyan Ji","doi":"10.1109/ICSCDS53736.2022.9761018","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9761018","url":null,"abstract":"This paper studies the monitoring system of toxic substance pollution in the production of chemical plants based on big data technology. In order to realize the monitoring of harmful gases in the chemical production process, a data collector is formed with ATmega16 single-chip microcomputer as the core, and a harmful gas intelligent monitoring system is formed through Ethernet. The requirements for clear captured images, sensitive pan/tilt control, the main control room of the explosion-proof monitoring system is built in the chemical industry. The main control software of the digital hard disk video host realizes the monitoring and control of the cameras at each monitoring point. It can also transmit the company's various video signals through broadband or ADSL networks. Pass it on to the management of the company to achieve an active role in the safe production and operation of chemical companies and increase by 12.3%.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122869812","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 : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760831
R. Jaswanthi, E. Amruthatulasi, Ch. Bhavyasree, Ashutosh Satapathy
Calories play an essential role in health aspects that lead to diseases like coronary heart disease, liver disease, cancer, and cholesterol. A study from 2020 reported that globally, overweight adults outnumber underweight individuals by more than 1.9 billion, while obese adults outnumber underweight ones by 650 million. Statistics from India show that abdominal obesity is the most significant risk factor, and it varies from 16.9% to 36.3%. Deep learning is an advanced image processing technology that solves problems and ensures food challenges because deeper networks have a better ability to process many features in an image. In our study, we propose a hybrid framework to predict the calorie content of food items on a plate. This includes three main parts: segmentation to segment the food from the image, image classification for classifying the food items, and calculating the calories present in those food items. A generative adversarial network is used for the segmentation, while a convolutional neural network is used for the classification and calorie estimation. The above models trained on the food images from the UNIMIB 2016 dataset have correctly recognized and estimated the calories of a food item with an accuracy of 95.21%.
{"title":"A Hybrid Network Based on GAN and CNN for Food Segmentation and Calorie Estimation","authors":"R. Jaswanthi, E. Amruthatulasi, Ch. Bhavyasree, Ashutosh Satapathy","doi":"10.1109/ICSCDS53736.2022.9760831","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760831","url":null,"abstract":"Calories play an essential role in health aspects that lead to diseases like coronary heart disease, liver disease, cancer, and cholesterol. A study from 2020 reported that globally, overweight adults outnumber underweight individuals by more than 1.9 billion, while obese adults outnumber underweight ones by 650 million. Statistics from India show that abdominal obesity is the most significant risk factor, and it varies from 16.9% to 36.3%. Deep learning is an advanced image processing technology that solves problems and ensures food challenges because deeper networks have a better ability to process many features in an image. In our study, we propose a hybrid framework to predict the calorie content of food items on a plate. This includes three main parts: segmentation to segment the food from the image, image classification for classifying the food items, and calculating the calories present in those food items. A generative adversarial network is used for the segmentation, while a convolutional neural network is used for the classification and calorie estimation. The above models trained on the food images from the UNIMIB 2016 dataset have correctly recognized and estimated the calories of a food item with an accuracy of 95.21%.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127355939","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 : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760973
Navneeth C Krishnan, Ashish Eapen Varghese, Viswajith Sankar, Achyuth Jm, A. Ravikumar, Jisha John
The face of a person is his uniqueness or identity. Along with textual data like names and identification numbers, the physical features of one's face are also a very efficient way to identify and preserve individuality. This feature improved accuracy in identifying and distinguishing between specific individuals when utilized with other identification labels. This paper aims to provide a conferencing platform and attendance marking with the help of facial recognition. The traditional method of calling names to mark attendance causes various issues in online classes. The inclusion of facial recognition and other monitoring methods ensures a more accurate and efficient way for attendance marking. In this work, Computer Vision techniques for video monitoring purposes, login tracking, and other features for better and more efficient utility.
{"title":"Smart Meet — Facial Recognition-based Conferencing Platform","authors":"Navneeth C Krishnan, Ashish Eapen Varghese, Viswajith Sankar, Achyuth Jm, A. Ravikumar, Jisha John","doi":"10.1109/ICSCDS53736.2022.9760973","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760973","url":null,"abstract":"The face of a person is his uniqueness or identity. Along with textual data like names and identification numbers, the physical features of one's face are also a very efficient way to identify and preserve individuality. This feature improved accuracy in identifying and distinguishing between specific individuals when utilized with other identification labels. This paper aims to provide a conferencing platform and attendance marking with the help of facial recognition. The traditional method of calling names to mark attendance causes various issues in online classes. The inclusion of facial recognition and other monitoring methods ensures a more accurate and efficient way for attendance marking. In this work, Computer Vision techniques for video monitoring purposes, login tracking, and other features for better and more efficient utility.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113973813","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：