Pub Date : 2022-02-12DOI: 10.1109/AISP53593.2022.9760517
Kiran Chand Ravi, V. Slyusar, J. Kumar
5G communication systems ensure high data rate, low latency, network reliability, and energy efficiency and high throughput that require new and very efficient antenna designs. In this paper, we proposed a simple and very effective antenna with centre frequency 28GHz designed on an RF4 substrate of 1. 6mm thickness. The performance characteristics of the antenna-like reflection coefficient (Sll), voltage standing wave ratio (VSWR), radiation pattern and impedance have been investigated using HFSS. optimization techniques are applied to achieve significant results. A defective ground structure was chosen for obtaining proper impedance matching. The simulated results are satisfactory and the proposed antenna is a good candidate to operate in the millimetre wave frequency band that is 28GHz range for 5G application.
{"title":"SRR Loaded Wideband Antenna 5G Application","authors":"Kiran Chand Ravi, V. Slyusar, J. Kumar","doi":"10.1109/AISP53593.2022.9760517","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760517","url":null,"abstract":"5G communication systems ensure high data rate, low latency, network reliability, and energy efficiency and high throughput that require new and very efficient antenna designs. In this paper, we proposed a simple and very effective antenna with centre frequency 28GHz designed on an RF4 substrate of 1. 6mm thickness. The performance characteristics of the antenna-like reflection coefficient (Sll), voltage standing wave ratio (VSWR), radiation pattern and impedance have been investigated using HFSS. optimization techniques are applied to achieve significant results. A defective ground structure was chosen for obtaining proper impedance matching. The simulated results are satisfactory and the proposed antenna is a good candidate to operate in the millimetre wave frequency band that is 28GHz range for 5G application.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"49 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86012811","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760576
Amogh Sawant, Shahid Shaikh, Dharmesh Sharma
Artificial Intelligence (AI) is the way into the future. Many undertakings are currently overseen by an AI rather than a human; nonetheless, many tasks that are as yet overseen by people can be better done utilizing an AI. However, since the AI innovation isn’t cutting edge as yet, it is unimaginable for now. Thus, we desire to foster an AI prepared through computer games, and for it to master intricate and pragmatic abilities playing them. This is conceivable through the intellectual abilities needed to play computer games and their dynamic and tangled climate. Video games are exceptionally valuable since we can promptly investigate how the specialist performs by contrasting its score with different players. We can imagine video games as a microcosm of human capacity since they are so various and pervasive across human culture. In this way, they are extraordinarily significant to evaluate and demonstrate AI.
{"title":"Game AI using Reinforcement Learning","authors":"Amogh Sawant, Shahid Shaikh, Dharmesh Sharma","doi":"10.1109/AISP53593.2022.9760576","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760576","url":null,"abstract":"Artificial Intelligence (AI) is the way into the future. Many undertakings are currently overseen by an AI rather than a human; nonetheless, many tasks that are as yet overseen by people can be better done utilizing an AI. However, since the AI innovation isn’t cutting edge as yet, it is unimaginable for now. Thus, we desire to foster an AI prepared through computer games, and for it to master intricate and pragmatic abilities playing them. This is conceivable through the intellectual abilities needed to play computer games and their dynamic and tangled climate. Video games are exceptionally valuable since we can promptly investigate how the specialist performs by contrasting its score with different players. We can imagine video games as a microcosm of human capacity since they are so various and pervasive across human culture. In this way, they are extraordinarily significant to evaluate and demonstrate AI.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"95 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90622149","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760657
J. Karthiyayini, Arpita Chowdary Vantipalli, Darshana Sailu Tanti, K. Malvika Ravi, Krtin Kannan
This paper is propelled from the generally existing project which is undertaking under the smart water quality management, which addresses an IoT (Internet of things) based brilliant water quality observing (SWQM) framework which we call it AquaSwach that guides in proper estimation of water condition dependent on five actual parameters i.e., temperature, pH, electric conductivity and turbidity properties and water purity estimation each time you drink water. Six sensors relate to Arduino-Mega in discrete way to detect the water parameters. Extracted data from the sensors are transmitted to a desktop application developed in NET platform and compared with the WHO (World Health Organization) standard values. The system consist of several sensors is used to measuring physical and chemical parameters of the water. The parameters such as temperature, PH, turbidity, flow sensor of the water can be measured. The measured values from the sensors can be processed by the core controller. The Arduino mega model can be used as a core controller. Finally, the sensor data can be viewed on internet using WI-FI system. With the help of a wireless GSM (Global System for Mobile communication), the customer will be informed about the condition of the filter, and the service provider is immediately informed of replacing the filter.
{"title":"IOT based AquaSwach","authors":"J. Karthiyayini, Arpita Chowdary Vantipalli, Darshana Sailu Tanti, K. Malvika Ravi, Krtin Kannan","doi":"10.1109/AISP53593.2022.9760657","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760657","url":null,"abstract":"This paper is propelled from the generally existing project which is undertaking under the smart water quality management, which addresses an IoT (Internet of things) based brilliant water quality observing (SWQM) framework which we call it AquaSwach that guides in proper estimation of water condition dependent on five actual parameters i.e., temperature, pH, electric conductivity and turbidity properties and water purity estimation each time you drink water. Six sensors relate to Arduino-Mega in discrete way to detect the water parameters. Extracted data from the sensors are transmitted to a desktop application developed in NET platform and compared with the WHO (World Health Organization) standard values. The system consist of several sensors is used to measuring physical and chemical parameters of the water. The parameters such as temperature, PH, turbidity, flow sensor of the water can be measured. The measured values from the sensors can be processed by the core controller. The Arduino mega model can be used as a core controller. Finally, the sensor data can be viewed on internet using WI-FI system. With the help of a wireless GSM (Global System for Mobile communication), the customer will be informed about the condition of the filter, and the service provider is immediately informed of replacing the filter.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"60 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90726117","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760616
Harshit Harsh, Akhil Indraganti, S. Vanambathina, Bharat Siva Yaswanth Ramanam, V. S. Chandu, Hari Kishan Kondaveeti
Toned voice study is gaining importance due to advancement in the music industry. The breaking down of toned voice and its backtracking is similar to carrying images from the source domain to the target domain while preserving its content representation. For our case, the mixed voice prints were transformed into their constituent component. The drawback of U-Net convolutional architecture is that the learning rate may come down in the middle layers for deeper models, so there is some risk if the network learning is ignored in some cases where the abstract features are represented in those layers. In this work, we proclaim the methodology CGRUN for the task of singing voice division. It leads to a causal system that is naturally suitable for real-time processing applications. The speech processing application is the segregation of toned voices for voice mixing. Through software evaluation, this experiment confirms the use of CGRUN for toned voice separation. The technical term used for toned voice segregation and its backtracking is Music Information Retrieval (MIR).
{"title":"Convolutional GRU Networks based Singing Voice Separation","authors":"Harshit Harsh, Akhil Indraganti, S. Vanambathina, Bharat Siva Yaswanth Ramanam, V. S. Chandu, Hari Kishan Kondaveeti","doi":"10.1109/AISP53593.2022.9760616","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760616","url":null,"abstract":"Toned voice study is gaining importance due to advancement in the music industry. The breaking down of toned voice and its backtracking is similar to carrying images from the source domain to the target domain while preserving its content representation. For our case, the mixed voice prints were transformed into their constituent component. The drawback of U-Net convolutional architecture is that the learning rate may come down in the middle layers for deeper models, so there is some risk if the network learning is ignored in some cases where the abstract features are represented in those layers. In this work, we proclaim the methodology CGRUN for the task of singing voice division. It leads to a causal system that is naturally suitable for real-time processing applications. The speech processing application is the segregation of toned voices for voice mixing. Through software evaluation, this experiment confirms the use of CGRUN for toned voice separation. The technical term used for toned voice segregation and its backtracking is Music Information Retrieval (MIR).","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"1 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89218479","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760584
Pratikhya Raut, U. Nanda, D. Panda, H. Nguyen
Double gate junction-less tunnel field effect transistor (DGJL-TFET) is investigated in this paper. The presence of double gate enhances high control over the channel for current conduction and the performance analysis of various parameters like input and output characteristics have been carried out by varying its dielectric materials with different dielectric constant and changing the thickness of oxide material. The complete device simulation and analysis are made using TCAD simulator. The simulation results depicting that the dielectric materials with high dielectric constant yields good electrical characteristics and the oxide with the least thickness value helps in better current conduction with good Ion/Ioff ratio. So this device is a promising device for low power application. Also by using dielectric with high dielectric constant increases the ON current which makes the device more flexible in nature.
{"title":"Performance Analysis of Double Gate Junctionless TFET with respect to different high-k materials and oxide thickness","authors":"Pratikhya Raut, U. Nanda, D. Panda, H. Nguyen","doi":"10.1109/AISP53593.2022.9760584","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760584","url":null,"abstract":"Double gate junction-less tunnel field effect transistor (DGJL-TFET) is investigated in this paper. The presence of double gate enhances high control over the channel for current conduction and the performance analysis of various parameters like input and output characteristics have been carried out by varying its dielectric materials with different dielectric constant and changing the thickness of oxide material. The complete device simulation and analysis are made using TCAD simulator. The simulation results depicting that the dielectric materials with high dielectric constant yields good electrical characteristics and the oxide with the least thickness value helps in better current conduction with good Ion/Ioff ratio. So this device is a promising device for low power application. Also by using dielectric with high dielectric constant increases the ON current which makes the device more flexible in nature.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"136 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89757028","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760586
D. Monisha, N. Nelson
Lungs being an important organ in the respiratory system, it is prone to many chronic diseases involving tumor cells. These lung tumors are treatable, if diagnosed at early stage. Among lung tumors, the non-small cell category is irresponsive even for chemotherapy treatment when diagnosed at later stage. This work concentrates on improving the diagnosis of non-small tumor cells at early stage through image processing techniques. The CT image of lungs is used for discriminating the tumor cells from healthy non-tumor cells. Upon using computer aided image processing techniques, the level of accuracy in assessing the tumor cells can be improved. Initially, the noise present in the CT image is removed using Wiener filter by improving the signal to noise ratio. The vascular structures in the image are removed and possible tumor cells are segmented from other healthy cells using region growing technique. After extracting the features, the Support Vector Machine and Naïve Bayesian techniques are used for classifying the tumor cells and healthy cells.
{"title":"Detection of lung tumor using SVM and Bayesian classification","authors":"D. Monisha, N. Nelson","doi":"10.1109/AISP53593.2022.9760586","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760586","url":null,"abstract":"Lungs being an important organ in the respiratory system, it is prone to many chronic diseases involving tumor cells. These lung tumors are treatable, if diagnosed at early stage. Among lung tumors, the non-small cell category is irresponsive even for chemotherapy treatment when diagnosed at later stage. This work concentrates on improving the diagnosis of non-small tumor cells at early stage through image processing techniques. The CT image of lungs is used for discriminating the tumor cells from healthy non-tumor cells. Upon using computer aided image processing techniques, the level of accuracy in assessing the tumor cells can be improved. Initially, the noise present in the CT image is removed using Wiener filter by improving the signal to noise ratio. The vascular structures in the image are removed and possible tumor cells are segmented from other healthy cells using region growing technique. After extracting the features, the Support Vector Machine and Naïve Bayesian techniques are used for classifying the tumor cells and healthy cells.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88318246","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}
This paper proposes a speech enhancement method for non-stationary Gaussian noise based on regularized non-negative matrix factorization (NMF). The magnitudes of speech and noise are implemented by a model based in iterative posterior NMF which are applied using prior distributions in transform domain. This is used since the sample distributions of the above are well suited to Weibull and Rayleigh densities well. For the accomplishment in time-varying noise environments, both the speech and noise bases of NMF are adapted simultaneously. With the usage of estimated speech presence probability, this paper proposes to adaptively estimate the statistics of these distributions. The method in this paper gives the best results for perceptual evaluation of speech quality (PESQ) and the signal-to-distortion ratio (SDR).
{"title":"Weibull Prior based Single Channel Speech Enhancement using Iterative Posterior NMF","authors":"S. Vanambathina, Vaishnavi Anumola, Ponnapalli Tejasree, Nandeesh Kumar, Rama Prakash Reddy Ch","doi":"10.1109/AISP53593.2022.9760648","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760648","url":null,"abstract":"This paper proposes a speech enhancement method for non-stationary Gaussian noise based on regularized non-negative matrix factorization (NMF). The magnitudes of speech and noise are implemented by a model based in iterative posterior NMF which are applied using prior distributions in transform domain. This is used since the sample distributions of the above are well suited to Weibull and Rayleigh densities well. For the accomplishment in time-varying noise environments, both the speech and noise bases of NMF are adapted simultaneously. With the usage of estimated speech presence probability, this paper proposes to adaptively estimate the statistics of these distributions. The method in this paper gives the best results for perceptual evaluation of speech quality (PESQ) and the signal-to-distortion ratio (SDR).","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"17 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86892737","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760655
I. Berrada, Fatimazahra Barramou, O. B. Alami
Every day, each bank around the world has to analyze many credit applications from its customers and prospects, individuals, professionals, or companies. Banks develop their rating system based on different parameters but most of them do not take benefit of the tremendous set of Big Data available and gathered continuously. To extract valuable information, Big Data analysis (BDA) and artificial intelligence (AI) lead to interesting applications for the banking industry such as segmentation, customized service, customer relationship management, fraud detection, credit risk assessment, and in all back, middle, and front office missions. This article presents the benefit of artificial intelligence for credit risk assessment. A state of art for the actual research advance is discussed concerning this specific item. To handle this review, we first focused on the keywords to capture and analyze the available articles of experts. We limited the period from 2016 to 2021 to skim the recent advances. Researchers have explored different methods with feature selection, classification, and prediction. Algorithms of Data mining, machine learning (supervised and unsupervised), and deep learning (artificial neural networks) are very different and tackle various aspects to be explored. With these advances, banks can become smart and propose a better and quicker service while preserving themselves from losses due to credit defaulters. Support vector machine, Catboost, decision tree, and logistic regression have delivered interesting results according to the studied researches.
{"title":"A review of Artificial Intelligence approach for credit risk assessment","authors":"I. Berrada, Fatimazahra Barramou, O. B. Alami","doi":"10.1109/AISP53593.2022.9760655","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760655","url":null,"abstract":"Every day, each bank around the world has to analyze many credit applications from its customers and prospects, individuals, professionals, or companies. Banks develop their rating system based on different parameters but most of them do not take benefit of the tremendous set of Big Data available and gathered continuously. To extract valuable information, Big Data analysis (BDA) and artificial intelligence (AI) lead to interesting applications for the banking industry such as segmentation, customized service, customer relationship management, fraud detection, credit risk assessment, and in all back, middle, and front office missions. This article presents the benefit of artificial intelligence for credit risk assessment. A state of art for the actual research advance is discussed concerning this specific item. To handle this review, we first focused on the keywords to capture and analyze the available articles of experts. We limited the period from 2016 to 2021 to skim the recent advances. Researchers have explored different methods with feature selection, classification, and prediction. Algorithms of Data mining, machine learning (supervised and unsupervised), and deep learning (artificial neural networks) are very different and tackle various aspects to be explored. With these advances, banks can become smart and propose a better and quicker service while preserving themselves from losses due to credit defaulters. Support vector machine, Catboost, decision tree, and logistic regression have delivered interesting results according to the studied researches.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"1987 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82260305","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760527
S. Sriraman, R. Manjunathan, Nethraa Sivakumar, S. Pooja, Nikhil Viswanath
In this paper, we analyze the performance of graph convolutional networks (GCNs) in predicting COVID-19 incidence in states and union territories (UTs) in India as a semisupervised learning task. By training the model with data from a small number of states whose incidence is known, we analyze the accuracy in predicting incidence levels in the remaining states and UTs in India. We explore the effect of pre-existing factors such as foreign visitor count, senior citizen population and population density of states in predicting spread. To show the robustness of this model, we introduce a novel method to choose states for training that reduces bias through random sampling in five regions that cover India’s geography. We show that GCNs, on average, produce a 9% improvement in accuracy over the best performing non-graph-based model and discuss if the results are feasible for use in a real-world scenario.
{"title":"Graph Convolutional Networks for Predicting State-wise Pandemic Incidence in India","authors":"S. Sriraman, R. Manjunathan, Nethraa Sivakumar, S. Pooja, Nikhil Viswanath","doi":"10.1109/AISP53593.2022.9760527","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760527","url":null,"abstract":"In this paper, we analyze the performance of graph convolutional networks (GCNs) in predicting COVID-19 incidence in states and union territories (UTs) in India as a semisupervised learning task. By training the model with data from a small number of states whose incidence is known, we analyze the accuracy in predicting incidence levels in the remaining states and UTs in India. We explore the effect of pre-existing factors such as foreign visitor count, senior citizen population and population density of states in predicting spread. To show the robustness of this model, we introduce a novel method to choose states for training that reduces bias through random sampling in five regions that cover India’s geography. We show that GCNs, on average, produce a 9% improvement in accuracy over the best performing non-graph-based model and discuss if the results are feasible for use in a real-world scenario.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"44 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82881861","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760667
Ch Madhurya, Ajith Jubilson E, Goutham N
In last quarter of 2019, Corona Virus Disease (COVID-19), has flared up globally due to which many organizations and institutions are suffering and practically they are going to be closed if the current scenario does not change. COVID-19 is an transmissible disease causes due to Serious Acute Respiratory Syndrome Corona Virus-2 (SARS-CoV-2), which spreads from small liquid particles released from mouth or nose of an infected person. With this virus, anyone can get sick and become seriously ill or even die at any age. The best way to protect our self and others is by wearing a properly fitted facemask, washing hands regularly or frequently rubbing your hands by using an alcohol-based sanitizer and the way is to get vaccinated when ones turn comes. The proposed study uses Convolutional Neural Networks (CNNs) which is a technique of deep learning is used for classification by processing images. This study uses deep learning techniques for identifying if the person is with proper facemask or with no facemask from live video streams. For training the model the dataset is collected kaggle repository which contains 2000 images and attained an accuracy of 98.2% while training the model. The created system is put into action with the help of openCV, python and mobileV2 architecture v2 for recognizing the persons who are wearing and not wearing the facemasks.
{"title":"Facemask Detection using Convolutional Neural Networks (CNN)","authors":"Ch Madhurya, Ajith Jubilson E, Goutham N","doi":"10.1109/AISP53593.2022.9760667","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760667","url":null,"abstract":"In last quarter of 2019, Corona Virus Disease (COVID-19), has flared up globally due to which many organizations and institutions are suffering and practically they are going to be closed if the current scenario does not change. COVID-19 is an transmissible disease causes due to Serious Acute Respiratory Syndrome Corona Virus-2 (SARS-CoV-2), which spreads from small liquid particles released from mouth or nose of an infected person. With this virus, anyone can get sick and become seriously ill or even die at any age. The best way to protect our self and others is by wearing a properly fitted facemask, washing hands regularly or frequently rubbing your hands by using an alcohol-based sanitizer and the way is to get vaccinated when ones turn comes. The proposed study uses Convolutional Neural Networks (CNNs) which is a technique of deep learning is used for classification by processing images. This study uses deep learning techniques for identifying if the person is with proper facemask or with no facemask from live video streams. For training the model the dataset is collected kaggle repository which contains 2000 images and attained an accuracy of 98.2% while training the model. The created system is put into action with the help of openCV, python and mobileV2 architecture v2 for recognizing the persons who are wearing and not wearing the facemasks.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"31 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80504470","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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