Pub Date : 2022-03-09DOI: 10.1109/ESCI53509.2022.9758322
P. Sinha, Radhika Singh, Rahul Roy, Puneeta Singh
“Autism is a neurological condition that decapitates patient of development in language and communication skills restraining him/her from any social interaction and develops rigid, ardent behavior.” To overcome this problem we have come up with an educational app that helps people to get educated via friendly UI while under surveillance of personal or assigned therapist. Features include fast and effective teaching in vocabulary, therapist can personalize their education methods to make model effective, analysis system that will track every move of patient and utilize local medical AI records to report patient's metric progress and pattern of impairment triggers. Audio to Visual and vice-versa. Pronounce words and check whether input correct or not. The app is open source mainly to educate the people who are suffering from autism. It will benefit patient to gain knowledge of communication. Also, they would get something that will accompany them all time, so that they won't feel lonely also ensuring anxiety risk in case of physical therapy methods. Requirements of the application is implemented by using Java Struts, JDBC, XHTML, CSS, JavaScript, and DOM.
{"title":"Education and Analysis of Autistic Patients Using Machine Learning","authors":"P. Sinha, Radhika Singh, Rahul Roy, Puneeta Singh","doi":"10.1109/ESCI53509.2022.9758322","DOIUrl":"https://doi.org/10.1109/ESCI53509.2022.9758322","url":null,"abstract":"“Autism is a neurological condition that decapitates patient of development in language and communication skills restraining him/her from any social interaction and develops rigid, ardent behavior.” To overcome this problem we have come up with an educational app that helps people to get educated via friendly UI while under surveillance of personal or assigned therapist. Features include fast and effective teaching in vocabulary, therapist can personalize their education methods to make model effective, analysis system that will track every move of patient and utilize local medical AI records to report patient's metric progress and pattern of impairment triggers. Audio to Visual and vice-versa. Pronounce words and check whether input correct or not. The app is open source mainly to educate the people who are suffering from autism. It will benefit patient to gain knowledge of communication. Also, they would get something that will accompany them all time, so that they won't feel lonely also ensuring anxiety risk in case of physical therapy methods. Requirements of the application is implemented by using Java Struts, JDBC, XHTML, CSS, JavaScript, and DOM.","PeriodicalId":436539,"journal":{"name":"2022 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124383707","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 : 2022-03-09DOI: 10.1109/ESCI53509.2022.9758247
Rajni Jindal, C. Kumar, Gaurav Jawla, Harshit Goyal
Coronavirus (COVID-19) had major impacts on the daily lives of people. Lock-downs, work from home situations, loss in jobs, market changes, and less communication, and interaction between people especially during the stressful Covid period have made them more vulnerable to mental health issues, depression, loneliness, etc. With Covid related healthcare being given priority, the mental health issues faced by the public that has been both directly and indirectly affected by it have been majorly left ignored. These issues need to be taken care of by people on individual level and by the government for better public health. Hence, in this paper we introduce the emerging technique of data mining into the Covid-19 linked mental health for predicting the susceptibility of the general public around the globe to mental health side effects as a result of covid and pandemic circumstances. We used the COVIDiSTRESS survey data containing 103825 instances of people across the globe to identify the people more susceptible to Covid related stress. Logistic regression, random forest, xgboost, AdaBoost, and gradient boosting classifier were applied to the processed data giving an accuracy of 88.12%, 88.89%, 88.73%, 88.60%, and 89.25% respectively. The Models predicted the people who are likely to face covid stress based on different independent factors like their demographic variables, trust of authorities, corona concerns etc. The stress factor was measured using PSS-10 variable included in the survey. The result showed that the model developed with Gradient Boosting Classifier is found to be the most efficient model with an accuracy of 89.25%. Our analysis also showed that females, divorced/widowed people and full-time employees were more prone to stress amongst others in the gender/marital status/employment category.
{"title":"Predicting Susceptibility to Covid Stress Using Data Mining","authors":"Rajni Jindal, C. Kumar, Gaurav Jawla, Harshit Goyal","doi":"10.1109/ESCI53509.2022.9758247","DOIUrl":"https://doi.org/10.1109/ESCI53509.2022.9758247","url":null,"abstract":"Coronavirus (COVID-19) had major impacts on the daily lives of people. Lock-downs, work from home situations, loss in jobs, market changes, and less communication, and interaction between people especially during the stressful Covid period have made them more vulnerable to mental health issues, depression, loneliness, etc. With Covid related healthcare being given priority, the mental health issues faced by the public that has been both directly and indirectly affected by it have been majorly left ignored. These issues need to be taken care of by people on individual level and by the government for better public health. Hence, in this paper we introduce the emerging technique of data mining into the Covid-19 linked mental health for predicting the susceptibility of the general public around the globe to mental health side effects as a result of covid and pandemic circumstances. We used the COVIDiSTRESS survey data containing 103825 instances of people across the globe to identify the people more susceptible to Covid related stress. Logistic regression, random forest, xgboost, AdaBoost, and gradient boosting classifier were applied to the processed data giving an accuracy of 88.12%, 88.89%, 88.73%, 88.60%, and 89.25% respectively. The Models predicted the people who are likely to face covid stress based on different independent factors like their demographic variables, trust of authorities, corona concerns etc. The stress factor was measured using PSS-10 variable included in the survey. The result showed that the model developed with Gradient Boosting Classifier is found to be the most efficient model with an accuracy of 89.25%. Our analysis also showed that females, divorced/widowed people and full-time employees were more prone to stress amongst others in the gender/marital status/employment category.","PeriodicalId":436539,"journal":{"name":"2022 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130530440","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 : 2022-03-09DOI: 10.1109/esci53509.2022.9758192
{"title":"Best Wishes Received for the 4th IEEE International Conference on Emerging Smart Computing and Informatics (ESCI-2022)","authors":"","doi":"10.1109/esci53509.2022.9758192","DOIUrl":"https://doi.org/10.1109/esci53509.2022.9758192","url":null,"abstract":"","PeriodicalId":436539,"journal":{"name":"2022 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"22 19","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131840102","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 : 2022-03-09DOI: 10.1109/ESCI53509.2022.9758215
Naga Venkata Sai Prakash Nagulapati, Sudharsan Reddy Venati, Vishal Chandran, Subramani R
There are a lot of challenges associated with - autonomous driving, and one such challenge is pedestrian detection and tracking, especially in this complex world, multiple people are involved in complicated and cluttered backgrounds. In this paper a novel method is proposed to detect, track and predict pedestrians based on Histograms of Oriented Gradients (HOG) algorithm and the Camshift algorithm respectively. These two algorithms run on top of the Kalman filtering framework. The Kalman filter is used as a tracker for precisely localizing and tracking the pedestrians. Then triangle similarity is used to calculate distance.
{"title":"Pedestrian Detection and Tracking Through Kalman Filtering","authors":"Naga Venkata Sai Prakash Nagulapati, Sudharsan Reddy Venati, Vishal Chandran, Subramani R","doi":"10.1109/ESCI53509.2022.9758215","DOIUrl":"https://doi.org/10.1109/ESCI53509.2022.9758215","url":null,"abstract":"There are a lot of challenges associated with - autonomous driving, and one such challenge is pedestrian detection and tracking, especially in this complex world, multiple people are involved in complicated and cluttered backgrounds. In this paper a novel method is proposed to detect, track and predict pedestrians based on Histograms of Oriented Gradients (HOG) algorithm and the Camshift algorithm respectively. These two algorithms run on top of the Kalman filtering framework. The Kalman filter is used as a tracker for precisely localizing and tracking the pedestrians. Then triangle similarity is used to calculate distance.","PeriodicalId":436539,"journal":{"name":"2022 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128462726","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 : 2022-03-09DOI: 10.1109/ESCI53509.2022.9758214
Priyanka Shahane
Campus placement is an activity of participating, identifying and hiring young talent for internships and entry level positions. Reputation and yearly admissions of the institute invariably depend upon the placements provided by the institute to the students. Therefore, most of the institutions, assiduously, try to boost their placement department in order to improve their organization on a full scale. Any assistance during this specific space can have a good impact on the institute's capability to position it's students. In this study, the target is to analyze student's placement data of last year and use it to determine the probability of campus placement of the present students. For this we have experimented with four different machine learning algorithms i.e. Logistic Regression, Decision Tree, K Nearest Neighbours and Random Forest.
{"title":"Campus Placements Prediction & Analysis using Machine Learning","authors":"Priyanka Shahane","doi":"10.1109/ESCI53509.2022.9758214","DOIUrl":"https://doi.org/10.1109/ESCI53509.2022.9758214","url":null,"abstract":"Campus placement is an activity of participating, identifying and hiring young talent for internships and entry level positions. Reputation and yearly admissions of the institute invariably depend upon the placements provided by the institute to the students. Therefore, most of the institutions, assiduously, try to boost their placement department in order to improve their organization on a full scale. Any assistance during this specific space can have a good impact on the institute's capability to position it's students. In this study, the target is to analyze student's placement data of last year and use it to determine the probability of campus placement of the present students. For this we have experimented with four different machine learning algorithms i.e. Logistic Regression, Decision Tree, K Nearest Neighbours and Random Forest.","PeriodicalId":436539,"journal":{"name":"2022 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129367952","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 : 2022-03-09DOI: 10.1109/ESCI53509.2022.9758229
Shivani Wadhwa, D. Gupta, Bhavna Sareen, Ruchi Kawatra
With the increase in Internet of Things (IoT) devices, data produced by these devices is also increasing. For its trusted use, security of the IoT data is very important. Nowadays, blockchain is a contemporary technology which is used in various fields for providing security. But the mining task of the blockchain is very computation intensive. So, there exist dependency on resource providers like near-by devices, edge network for providing resources to the miners for the task of computations. In our proposed framework, bidding of the resources is done by the miners from near-by devices and edge network. Reverse stackelberg game and auction mechanism are used to reach optimality of the decisions. Nash equilibrium is also achieved. Less storage is consumed by few blocks of the blockchain network as meta data is stored in some blocks. Experimental evaluation is done to compute average delay and net profit.
{"title":"Optimal Resource Allocation by Reverse Stackelberg Game Approach in Blockchain","authors":"Shivani Wadhwa, D. Gupta, Bhavna Sareen, Ruchi Kawatra","doi":"10.1109/ESCI53509.2022.9758229","DOIUrl":"https://doi.org/10.1109/ESCI53509.2022.9758229","url":null,"abstract":"With the increase in Internet of Things (IoT) devices, data produced by these devices is also increasing. For its trusted use, security of the IoT data is very important. Nowadays, blockchain is a contemporary technology which is used in various fields for providing security. But the mining task of the blockchain is very computation intensive. So, there exist dependency on resource providers like near-by devices, edge network for providing resources to the miners for the task of computations. In our proposed framework, bidding of the resources is done by the miners from near-by devices and edge network. Reverse stackelberg game and auction mechanism are used to reach optimality of the decisions. Nash equilibrium is also achieved. Less storage is consumed by few blocks of the blockchain network as meta data is stored in some blocks. Experimental evaluation is done to compute average delay and net profit.","PeriodicalId":436539,"journal":{"name":"2022 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115955534","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 : 2022-03-09DOI: 10.1109/ESCI53509.2022.9758276
K. Dev, S. Patra, S. Rout, Sibananda Behera, Biswajit Sahoo, Rabindra Kumar Barik
The volume of data is continuously expanding as a result of the quick rise of the Industrial Internet of Things (IIoT), social media, digitization, as well as wireless communication in numerous sectors. With the help of fog computing, cloud computing is an emerging technique for handling and analyzing enormous amounts of data storage. Fog Computing is a set of methods for improving the quality of services (QoS) offered to consumers via cloud computing, which is becoming increasingly overburdened as a result of enormous data flows. All of the data is being sent to the cloud, and retrieving it from there causes a lot of latency and necessitates a lot of network capacity. The fog nodes are seen as a heterogeneous multi-VM system with a finite queue in which the VMs are shared by many client requests. When the system's request queue length exceeds a threshold value Nj (j = 1,2,….r-1), the (j + 1)thVM begins processing the requests and continues until the waiting buffer length again reduced to the same level. The recursive technique is used to obtain the steady-state queueing size distribution, which takes into account Markovian arrival with service time. We derived several system properties and studied the fog system's performance based on the client requests and the queue length.
{"title":"Optimizing VM Allocation with Queue Dependent Requests in fog Network","authors":"K. Dev, S. Patra, S. Rout, Sibananda Behera, Biswajit Sahoo, Rabindra Kumar Barik","doi":"10.1109/ESCI53509.2022.9758276","DOIUrl":"https://doi.org/10.1109/ESCI53509.2022.9758276","url":null,"abstract":"The volume of data is continuously expanding as a result of the quick rise of the Industrial Internet of Things (IIoT), social media, digitization, as well as wireless communication in numerous sectors. With the help of fog computing, cloud computing is an emerging technique for handling and analyzing enormous amounts of data storage. Fog Computing is a set of methods for improving the quality of services (QoS) offered to consumers via cloud computing, which is becoming increasingly overburdened as a result of enormous data flows. All of the data is being sent to the cloud, and retrieving it from there causes a lot of latency and necessitates a lot of network capacity. The fog nodes are seen as a heterogeneous multi-VM system with a finite queue in which the VMs are shared by many client requests. When the system's request queue length exceeds a threshold value Nj (j = 1,2,….r-1), the (j + 1)thVM begins processing the requests and continues until the waiting buffer length again reduced to the same level. The recursive technique is used to obtain the steady-state queueing size distribution, which takes into account Markovian arrival with service time. We derived several system properties and studied the fog system's performance based on the client requests and the queue length.","PeriodicalId":436539,"journal":{"name":"2022 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124344290","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 : 2022-03-09DOI: 10.1109/ESCI53509.2022.9758195
Monika Sethi, S. Ahuja, Sehajpreet Singh, Jyoti Snehi, Mukesh Chawla
Alzheimer's disease (AD) is a prevalent psychological disorder. The economic cost of treating for AD patients is expected to increase. Therefore in the last few years, research on AD diagnostic has laid great emphasis on computer-aided methods. The significance of developing an artificial intelligent diagnostic technique towards accurate and early AD classification seems essential. Deep-learning models hold significant benefits over machine learning approaches as these techniques do not require any kind of feature engineering. Moreover, T1-weighted Magnetic Resonance Imaging (MRI) is the neuroimaging data modality which is widely practiced for such a purpose. In some cases, the most significant barrier to integrating DL models into pre-existing applications is a lack of adequate data architecture. Changing medical information is usually hard to communicate, examine, and interpret. Transfer learning (TL) allows designers to use a combination of models in order to fine-tune a specified solution to a target problem. Transferring knowledge across two separate models could lead a generally a more reliable and precise model. In this work, researchers utilized an EfficientNet TL model already trained on ImageNet dataset to categorise subjects as AD vs. Cognitive Normal (CN) based on MRI scans of the brain. The dataset for this study was acquired from Alzheimer Disease Neuroimaging Initiative (ADNI). The performance parameters such as accuracy, AUC were used to evaluate the model. The proposed model on ADNI dataset achieved an accuracy level of 91.36% and AUC as 83% in comparison to other existing transfer learning models.
{"title":"An Intelligent Framework for Alzheimer's disease Classification Using EfficientNet Transfer Learning Model","authors":"Monika Sethi, S. Ahuja, Sehajpreet Singh, Jyoti Snehi, Mukesh Chawla","doi":"10.1109/ESCI53509.2022.9758195","DOIUrl":"https://doi.org/10.1109/ESCI53509.2022.9758195","url":null,"abstract":"Alzheimer's disease (AD) is a prevalent psychological disorder. The economic cost of treating for AD patients is expected to increase. Therefore in the last few years, research on AD diagnostic has laid great emphasis on computer-aided methods. The significance of developing an artificial intelligent diagnostic technique towards accurate and early AD classification seems essential. Deep-learning models hold significant benefits over machine learning approaches as these techniques do not require any kind of feature engineering. Moreover, T1-weighted Magnetic Resonance Imaging (MRI) is the neuroimaging data modality which is widely practiced for such a purpose. In some cases, the most significant barrier to integrating DL models into pre-existing applications is a lack of adequate data architecture. Changing medical information is usually hard to communicate, examine, and interpret. Transfer learning (TL) allows designers to use a combination of models in order to fine-tune a specified solution to a target problem. Transferring knowledge across two separate models could lead a generally a more reliable and precise model. In this work, researchers utilized an EfficientNet TL model already trained on ImageNet dataset to categorise subjects as AD vs. Cognitive Normal (CN) based on MRI scans of the brain. The dataset for this study was acquired from Alzheimer Disease Neuroimaging Initiative (ADNI). The performance parameters such as accuracy, AUC were used to evaluate the model. The proposed model on ADNI dataset achieved an accuracy level of 91.36% and AUC as 83% in comparison to other existing transfer learning models.","PeriodicalId":436539,"journal":{"name":"2022 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124369533","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 : 2022-03-09DOI: 10.1109/ESCI53509.2022.9758236
Kamurthi Ravi Teja, Shakti Raj Chopra, Rahul Sharma
This paper discusses the unprecedented sixth-generation (6G) technology and the issues of future Cloud services. Malware attacks are perpetuated in the wireless network world and have been encountered for two decades. So, we authors pointed out those attacks and presented them inside the paper. We also built the mathematical three compartmental modelling framework (MTCMF) to calculate the maximum number of Infectious devices and servers of the computing systems. Nevertheless, future cloud services will face some issues on storage, security, and energy consumption. So, we proposed solutions to fix those issues. And to reduce the long-distance communication, a standard decentralized cloud service setup is needed to offer services to the nearest networked areas. This implies to deploy Edge Cloud Data Centers (ECDC). The Main cloud center and ECDC are always in sync and intertwined with each other. So, we presented the future cloud set-up ideology too. To make this setup possible, we proposed the best QAM modulation technique in this paper. By 2030, all sectors like hospitals, industries, education, cities, homes, Internet of Things (loT) devices, and beyond, will be connected to the 6G cloud services. Meaning, the entire world will be cloudified in the future.
{"title":"A Burgeoning 6G Technology and its Cloud Services","authors":"Kamurthi Ravi Teja, Shakti Raj Chopra, Rahul Sharma","doi":"10.1109/ESCI53509.2022.9758236","DOIUrl":"https://doi.org/10.1109/ESCI53509.2022.9758236","url":null,"abstract":"This paper discusses the unprecedented sixth-generation (6G) technology and the issues of future Cloud services. Malware attacks are perpetuated in the wireless network world and have been encountered for two decades. So, we authors pointed out those attacks and presented them inside the paper. We also built the mathematical three compartmental modelling framework (MTCMF) to calculate the maximum number of Infectious devices and servers of the computing systems. Nevertheless, future cloud services will face some issues on storage, security, and energy consumption. So, we proposed solutions to fix those issues. And to reduce the long-distance communication, a standard decentralized cloud service setup is needed to offer services to the nearest networked areas. This implies to deploy Edge Cloud Data Centers (ECDC). The Main cloud center and ECDC are always in sync and intertwined with each other. So, we presented the future cloud set-up ideology too. To make this setup possible, we proposed the best QAM modulation technique in this paper. By 2030, all sectors like hospitals, industries, education, cities, homes, Internet of Things (loT) devices, and beyond, will be connected to the 6G cloud services. Meaning, the entire world will be cloudified in the future.","PeriodicalId":436539,"journal":{"name":"2022 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132890160","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 : 2022-03-09DOI: 10.1109/ESCI53509.2022.9758315
V. Kamble, M. Dale
Children are the most important part of society. Every parent is concerned about their health and security. The children's age group of 0 to 5 years is extremely vulnerable. New security options need to be found for the children in this age group. Biometric recognition using their footprint will be an emerging trend for children. This research uses footprint crease pattern of children for recognition. The crease pattern on footprints is extracted for the features. The database of 48 children is collected from preschools and neighborhoods. These images are preprocessed and enhanced. The Transfer learning approach of deep learning is used to compare the proposed method of identification of children. Different deep learning algorithms VGG16, VGG19, ResNet50, AlexNet are used. The proposed method is a fine tuned, customized AlexNet model. The comparison of parameters used is done for all algorithms. Proposed model reduces the number of parameters by 1,69,30,688 with the accuracy of 98 %.
{"title":"Deep Learning for Biometric Recognition of Children using Footprints","authors":"V. Kamble, M. Dale","doi":"10.1109/ESCI53509.2022.9758315","DOIUrl":"https://doi.org/10.1109/ESCI53509.2022.9758315","url":null,"abstract":"Children are the most important part of society. Every parent is concerned about their health and security. The children's age group of 0 to 5 years is extremely vulnerable. New security options need to be found for the children in this age group. Biometric recognition using their footprint will be an emerging trend for children. This research uses footprint crease pattern of children for recognition. The crease pattern on footprints is extracted for the features. The database of 48 children is collected from preschools and neighborhoods. These images are preprocessed and enhanced. The Transfer learning approach of deep learning is used to compare the proposed method of identification of children. Different deep learning algorithms VGG16, VGG19, ResNet50, AlexNet are used. The proposed method is a fine tuned, customized AlexNet model. The comparison of parameters used is done for all algorithms. Proposed model reduces the number of parameters by 1,69,30,688 with the accuracy of 98 %.","PeriodicalId":436539,"journal":{"name":"2022 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116003057","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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