Pub Date : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936351
G. Gomathy, P. Kalaiselvi, Dharani Selvaraj, D. Dhinakaran, A. P, D. Arul Kumar
Improper waste disposal causes hazard to human health and environmental pollution while causing a desire for a successful and significant series of waste materials. However, due to outdated or ineffective waste management techniques, most garbage cans placed in towns may be seen to be overflowing. Therefore, a real-time remote tracking device is required to inform the appropriate authority of the volume of trash in boxes so that it can be immediately cleared. The enhancement and evaluation of an IoT self-powered, simple-to-connect alternative to monitor the level of overflowing trash cans from a valued tracking station are provided in this work. Bin Level Monitoring Units (BLMU), the last sensor nodes of the developed IoT device, can be installed in each garbage can where the unfilled stage is desired to be observed. Every BLMU measures how empty each trash can is and communicates that information to a wi-fi access point unit (WAPU). Each local device is connected to a central Internet of Things device that is installed in each region using LoRa devices, which offer longer communication distances. This facilitates the connection of several devices to a network and allows accessibility to the IoT module. Consequently, this technique makes it simpler to keep an eye on the garbage can in real time.
{"title":"Automatic Waste Management based on IoT using a Wireless Sensor Network","authors":"G. Gomathy, P. Kalaiselvi, Dharani Selvaraj, D. Dhinakaran, A. P, D. Arul Kumar","doi":"10.1109/ICECAA55415.2022.9936351","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936351","url":null,"abstract":"Improper waste disposal causes hazard to human health and environmental pollution while causing a desire for a successful and significant series of waste materials. However, due to outdated or ineffective waste management techniques, most garbage cans placed in towns may be seen to be overflowing. Therefore, a real-time remote tracking device is required to inform the appropriate authority of the volume of trash in boxes so that it can be immediately cleared. The enhancement and evaluation of an IoT self-powered, simple-to-connect alternative to monitor the level of overflowing trash cans from a valued tracking station are provided in this work. Bin Level Monitoring Units (BLMU), the last sensor nodes of the developed IoT device, can be installed in each garbage can where the unfilled stage is desired to be observed. Every BLMU measures how empty each trash can is and communicates that information to a wi-fi access point unit (WAPU). Each local device is connected to a central Internet of Things device that is installed in each region using LoRa devices, which offer longer communication distances. This facilitates the connection of several devices to a network and allows accessibility to the IoT module. Consequently, this technique makes it simpler to keep an eye on the garbage can in real time.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132204844","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936098
Ashish Nagila, Skanda Mg, Fxe Dwin Deepak, Raj Kumar, P. Nandankar, Hemavathi, S. M
The article discusses the PV nursed energy effective, ultra-fast, high power, high gain DC-DC converter for EV charging with MPPT through the Hybrid Simplified Firefly and Neighborhood Attraction firefly (HSFNA) algorithm. The Single-Ended Primary Inductor Converter (SEPIC) is used because of its efficient MPPT operation with ultra-high gain with high efficiency and easy control system. The continuous input current, high current handling capability,and DC voltage with good quality power are required for charging the EV battery. Though there are numerous isolated dual bridge unidirectional converters available for EV charging, the high current demand for EV batteries cannot be met. The proposed converter provides higher current charging for the battery on demand by looking into the various control parameters. An ideal PV module is assumed to study the operation of the proposed converter, and an additional HSFNA algorithm supports the global maximum power point under various operating conditions like partial shading. The simulation of the proposed converter iscarried out and the results arediscussed.
{"title":"Ultra-Fast Charging E-Vehicle Batteries from PV using DC-DC Converter","authors":"Ashish Nagila, Skanda Mg, Fxe Dwin Deepak, Raj Kumar, P. Nandankar, Hemavathi, S. M","doi":"10.1109/ICECAA55415.2022.9936098","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936098","url":null,"abstract":"The article discusses the PV nursed energy effective, ultra-fast, high power, high gain DC-DC converter for EV charging with MPPT through the Hybrid Simplified Firefly and Neighborhood Attraction firefly (HSFNA) algorithm. The Single-Ended Primary Inductor Converter (SEPIC) is used because of its efficient MPPT operation with ultra-high gain with high efficiency and easy control system. The continuous input current, high current handling capability,and DC voltage with good quality power are required for charging the EV battery. Though there are numerous isolated dual bridge unidirectional converters available for EV charging, the high current demand for EV batteries cannot be met. The proposed converter provides higher current charging for the battery on demand by looking into the various control parameters. An ideal PV module is assumed to study the operation of the proposed converter, and an additional HSFNA algorithm supports the global maximum power point under various operating conditions like partial shading. The simulation of the proposed converter iscarried out and the results arediscussed.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133401833","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936110
R. Sankarganesh, A. Govindarasu
In recent decades, the electric vehicles play an enormous role for green house system. An electrical driven system was replaced by the combustion engine. But the EVCS electric vehicle charging system has number of challenges. In this research, a novel technique is proposed and implementedfor estimating BatteryResidual Capacity or BRC in electric vehicles or EVs. Modelling the battery of electric vehicles using Modified adaptive Neuro-fuzzy inference system is the major implication of the method in discussion. The most workable open engines would be Switched Reluctance Motor (SRM) for the sake of EV applications. Nearby the available battery bank, a photovoltaic or PV board has been placed in order to build driving miles electric vehicles. So as to regulate the vitality stream into as well as out of PV board, battery just as SRMdrive, Modified Adaptive Neuro Fuzzy Inference controller (MANFIS) installed tri-port converter has been anticipated here. The different Electric Vehicle battery working profiles that are explored incorporate consistent current release just as arbitrary current release also driving cycles of standard Electric Vehicle. On comparing the contrasting residual battery capacity and the genuine residual battery capacity, the exactness as well as viability of suggested demonstrating strategy could be accessed. In the event of some charging of battery directly from the PV board then a multiple region charging regulator strategy would be used enemy practical utilization of essentialness. A MANFIS enabled innovation with tri port iscreated in MATLAB-SIMULINK condition. The outcomes are ended up being effective in delivering diminished symphonious contortion. It also has the ability for improving advertise for EVs in the adjacent future.
{"title":"Modified Adaptive Neuro Fuzzy Controller Modeling for Controlled Plug-In Hybrid Electric Vehicle for Battery Residual Capacity","authors":"R. Sankarganesh, A. Govindarasu","doi":"10.1109/ICECAA55415.2022.9936110","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936110","url":null,"abstract":"In recent decades, the electric vehicles play an enormous role for green house system. An electrical driven system was replaced by the combustion engine. But the EVCS electric vehicle charging system has number of challenges. In this research, a novel technique is proposed and implementedfor estimating BatteryResidual Capacity or BRC in electric vehicles or EVs. Modelling the battery of electric vehicles using Modified adaptive Neuro-fuzzy inference system is the major implication of the method in discussion. The most workable open engines would be Switched Reluctance Motor (SRM) for the sake of EV applications. Nearby the available battery bank, a photovoltaic or PV board has been placed in order to build driving miles electric vehicles. So as to regulate the vitality stream into as well as out of PV board, battery just as SRMdrive, Modified Adaptive Neuro Fuzzy Inference controller (MANFIS) installed tri-port converter has been anticipated here. The different Electric Vehicle battery working profiles that are explored incorporate consistent current release just as arbitrary current release also driving cycles of standard Electric Vehicle. On comparing the contrasting residual battery capacity and the genuine residual battery capacity, the exactness as well as viability of suggested demonstrating strategy could be accessed. In the event of some charging of battery directly from the PV board then a multiple region charging regulator strategy would be used enemy practical utilization of essentialness. A MANFIS enabled innovation with tri port iscreated in MATLAB-SIMULINK condition. The outcomes are ended up being effective in delivering diminished symphonious contortion. It also has the ability for improving advertise for EVs in the adjacent future.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132781219","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936053
Ascharya Soni, Anuraag Khare, P. S. Darshan Balaji, Sachin Verma, K. P. Asha Rani, S. Gowrishankar
It is crucial to comprehend how insect pest populations affect the subsequent yield or harvest since the ultimate goal of agriculture is to provide a sustained economic production of crop products. Using pesticides is the simplest technique to manage the pest infestation. However, using pesticides improperly or in excess can harm both people and animals as well as the plants. Machine learning algorithms and image processing techniques are widely used in agricultural research, and they have significant potential, particularly in plant protection, which ultimately leads to crop management. This paper highlights the detection of pests and their control measures. A smartphone camera will capture photographs of the pests through a mobile app built using the Flutter framework. The images are then analyzed in the app using various transfer learning based models for available pest identification kaggle dataset. The flutter framework offers the ability to monitor targets in real-time on a mobile device and aids in visualizing the detected pest by integrating augmented reality on to the app.
{"title":"Pest Identification and Control using Deep Learning and Augmented Reality","authors":"Ascharya Soni, Anuraag Khare, P. S. Darshan Balaji, Sachin Verma, K. P. Asha Rani, S. Gowrishankar","doi":"10.1109/ICECAA55415.2022.9936053","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936053","url":null,"abstract":"It is crucial to comprehend how insect pest populations affect the subsequent yield or harvest since the ultimate goal of agriculture is to provide a sustained economic production of crop products. Using pesticides is the simplest technique to manage the pest infestation. However, using pesticides improperly or in excess can harm both people and animals as well as the plants. Machine learning algorithms and image processing techniques are widely used in agricultural research, and they have significant potential, particularly in plant protection, which ultimately leads to crop management. This paper highlights the detection of pests and their control measures. A smartphone camera will capture photographs of the pests through a mobile app built using the Flutter framework. The images are then analyzed in the app using various transfer learning based models for available pest identification kaggle dataset. The flutter framework offers the ability to monitor targets in real-time on a mobile device and aids in visualizing the detected pest by integrating augmented reality on to the app.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114074938","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936239
S. Sivakumar, D. Jayaram, S. V, V. Avasthi, R. Dhanalakshmi, S. S. Kumar
More than 500,000 humans go to emergency rooms every year for kidney stone problems. One out of each ten humans will broaden a kidney stone sooner or later in their lives. In India, kidney stones are one of the most common diseases which can be fatal if not treated properly. It can be caused by various parameters making it even more difficult to treat. When kidney stones are discovered in their early stages, they are much easier to treat than when they are discovered later on. To help this purpose, this study aims the development a website that is capable of predicting the presence of kidney stones using an image that was uploaded by the user itself. This website serves as a preliminary screening tool for the detection of kidney stones. This website is backed up by the algorithm which is proven to be the best in the prediction of kidney stones after a comparison between two different algorithms. These algorithms are trained and tested using the dataset which was obtained from Kaggle. This dataset is preprocessed to ensure the best performance of the classifier models. The performance of both the models is then compared and it is found that theSupport Vector Machine (SVM) algorithm is better than the Logistic Regression (LR) algorithm. The website is also integrated with the cloud using the AWS platform. This ensures the presence of an eternal space that supports the website when the number of users of the website increases.
{"title":"Deployment of Disease Prediction Model in AWS Cloud","authors":"S. Sivakumar, D. Jayaram, S. V, V. Avasthi, R. Dhanalakshmi, S. S. Kumar","doi":"10.1109/ICECAA55415.2022.9936239","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936239","url":null,"abstract":"More than 500,000 humans go to emergency rooms every year for kidney stone problems. One out of each ten humans will broaden a kidney stone sooner or later in their lives. In India, kidney stones are one of the most common diseases which can be fatal if not treated properly. It can be caused by various parameters making it even more difficult to treat. When kidney stones are discovered in their early stages, they are much easier to treat than when they are discovered later on. To help this purpose, this study aims the development a website that is capable of predicting the presence of kidney stones using an image that was uploaded by the user itself. This website serves as a preliminary screening tool for the detection of kidney stones. This website is backed up by the algorithm which is proven to be the best in the prediction of kidney stones after a comparison between two different algorithms. These algorithms are trained and tested using the dataset which was obtained from Kaggle. This dataset is preprocessed to ensure the best performance of the classifier models. The performance of both the models is then compared and it is found that theSupport Vector Machine (SVM) algorithm is better than the Logistic Regression (LR) algorithm. The website is also integrated with the cloud using the AWS platform. This ensures the presence of an eternal space that supports the website when the number of users of the website increases.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121951350","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936567
G. Murthy, V. Iswarya, K. R. Sri, K. Harshitha, Ch. Prasanth
Spasmodic dysphonia, a rare voice disorder is detected in the current work using Random Forest frame work. Voice pathology is related to the vocal tract area affecting the quality of speech. Numerous voice pathologies have been over the years of them are unnoticed as the symptoms are not significant. Even the symptoms are known the nature of the disorder is difficult to identify due to the over lapping nature of the symptoms. The existing algorithms for voice pathology detection are capable of classifying between normal and affected subjects, while the nature of the disorder has been considered in the proposed algorithm. Computational complexity has been reduced due to the incorporation of finite significant energy features estimated over non overlapping frames. Classification of accuracy of 93.5 has been seen with a population of 100 trees.
{"title":"A Novel Algorithm for Detecting Spasmodic Dysphonia Voice Pathology using Random Forest Frame Work","authors":"G. Murthy, V. Iswarya, K. R. Sri, K. Harshitha, Ch. Prasanth","doi":"10.1109/ICECAA55415.2022.9936567","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936567","url":null,"abstract":"Spasmodic dysphonia, a rare voice disorder is detected in the current work using Random Forest frame work. Voice pathology is related to the vocal tract area affecting the quality of speech. Numerous voice pathologies have been over the years of them are unnoticed as the symptoms are not significant. Even the symptoms are known the nature of the disorder is difficult to identify due to the over lapping nature of the symptoms. The existing algorithms for voice pathology detection are capable of classifying between normal and affected subjects, while the nature of the disorder has been considered in the proposed algorithm. Computational complexity has been reduced due to the incorporation of finite significant energy features estimated over non overlapping frames. Classification of accuracy of 93.5 has been seen with a population of 100 trees.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121107984","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936278
Arpit Arora, Mohana Mohana
In the product development and management area, .NET is critical. The sequential development of versions of .NET describes the importance and continuous feedback of customers about their experience. There are several architectural and functional differences of .NET evolution to its cross-platform version i.e., .NET core and above. Prominence of .NET in the improvement of development sector is evident. Quantum of open-source projects all over the globe and place of C# among the five most well-known programming languages are two pointers. Its ubiquity is simply going to develop, particularly now that the most recent emphasis (.NET 5) has changed business by presenting the idea of general programming advancement. .NET help for programming improvement isn’t restricted to the numerous programming dialects can utilize. .NET likewise advances utilization of a few prescribed procedures while allowing to utilize the methodology like to construct our application. .NET framework was the underlying kind of .NET. It gives engineer a bunch of APIs for most widely recognized programming needs and connects with basic working framework. It runs just on Windows, and its lifecycle is reaching a conclusion at this moment, after the arrival of .NET 5. Numerous executions emerged from that point forward, so the .NET name made ambiguities. .NET 5 means to make concrete the underlying vision of a widespread improvement stage.
在产品开发和管理领域，. net至关重要。. net版本的连续开发描述了客户对其体验的重要性和持续反馈。从。net演进到跨平台版本，即。net核心及以上版本，在架构和功能上存在一些差异。. net在改进开发领域中的突出作用是显而易见的。全球开源项目的数量和c#在五大最知名编程语言中的地位是两个指针。它的无处不在只会继续发展，特别是现在，最近的重点是。.NET通过提出通用编程改进的概念改变了业务。.NET对编程改进的帮助不仅限于可以使用的众多编程方言。.NET还促进了一些指定过程的使用，同时允许使用类似于构建应用程序的方法。NET框架是。NET的基础。它为工程师提供了一堆api，以满足最广泛认可的编程需求，并与基本的工作框架相连接。它只在Windows上运行，在。net 5到来之后，它的生命周期在这一刻即将结束。从那以后出现了大量的执行，所以。net的名字变得模棱两可，. net 5意味着使广泛改进阶段的潜在愿景具体化。
{"title":"Architectural and Functional Differences in Dot Net Solutions","authors":"Arpit Arora, Mohana Mohana","doi":"10.1109/ICECAA55415.2022.9936278","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936278","url":null,"abstract":"In the product development and management area, .NET is critical. The sequential development of versions of .NET describes the importance and continuous feedback of customers about their experience. There are several architectural and functional differences of .NET evolution to its cross-platform version i.e., .NET core and above. Prominence of .NET in the improvement of development sector is evident. Quantum of open-source projects all over the globe and place of C# among the five most well-known programming languages are two pointers. Its ubiquity is simply going to develop, particularly now that the most recent emphasis (.NET 5) has changed business by presenting the idea of general programming advancement. .NET help for programming improvement isn’t restricted to the numerous programming dialects can utilize. .NET likewise advances utilization of a few prescribed procedures while allowing to utilize the methodology like to construct our application. .NET framework was the underlying kind of .NET. It gives engineer a bunch of APIs for most widely recognized programming needs and connects with basic working framework. It runs just on Windows, and its lifecycle is reaching a conclusion at this moment, after the arrival of .NET 5. Numerous executions emerged from that point forward, so the .NET name made ambiguities. .NET 5 means to make concrete the underlying vision of a widespread improvement stage.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121090705","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936128
K. SuganyaDevi, V. Nandhalal, Satheeshkumar Palanisamy, S. Dhanasekaran
Vehicle Ad-hoc Network (VANET) offers advancements in conjunction with different protection strategies and places the driver in a comfortable environment while driving. It is able to provide traffic and safety information to other vehicles. The management of smart cities has become more dependent on VANETs, and numerous improvement strategies have been developed to ensure user privacy, security, and safety. However, the researchers continue to face significant difficulties due to security issues. This study examined recent research strategies that aim to strengthen security through a variety of approaches. In the proposed work, the performance of the secured data dissemination between cars is evaluated in three different traffic model situations using the Modified TESLA broadcast authentication method.
{"title":"Data Security and Safety Services using Modified Timed Efficient Stream Loss-Tolerant Authentication in Diverse Models of VANET","authors":"K. SuganyaDevi, V. Nandhalal, Satheeshkumar Palanisamy, S. Dhanasekaran","doi":"10.1109/ICECAA55415.2022.9936128","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936128","url":null,"abstract":"Vehicle Ad-hoc Network (VANET) offers advancements in conjunction with different protection strategies and places the driver in a comfortable environment while driving. It is able to provide traffic and safety information to other vehicles. The management of smart cities has become more dependent on VANETs, and numerous improvement strategies have been developed to ensure user privacy, security, and safety. However, the researchers continue to face significant difficulties due to security issues. This study examined recent research strategies that aim to strengthen security through a variety of approaches. In the proposed work, the performance of the secured data dissemination between cars is evaluated in three different traffic model situations using the Modified TESLA broadcast authentication method.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"269 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116621319","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936488
M. Krishna, J. Praveenchandar
The study aims to identify the frauds committed using a payment card such as credit cards, debit cards, and also an experiment is performed to find the best suitable algorithm among Random forest and Logistic Regression. Materials and Methods: To stop the fraud detections using Random forest (N=10) and Logistic regression (N=10) with supervised learning that gives insights from the previous data. Results: The precision of the random forest is 76.29% compared with Logistic regression with accuracy of 74.65% with statistical significance value p=0.03 (p<0.05) using Independent sample t test. Conclusion: This results proved that Random forest was significantly better for Fraud detection than Logistic regression within the study’s limits.
{"title":"Comparative Analysis of Credit Card Fraud Detection using Logistic regression with Random Forest towards an Increase in Accuracy of Prediction","authors":"M. Krishna, J. Praveenchandar","doi":"10.1109/ICECAA55415.2022.9936488","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936488","url":null,"abstract":"The study aims to identify the frauds committed using a payment card such as credit cards, debit cards, and also an experiment is performed to find the best suitable algorithm among Random forest and Logistic Regression. Materials and Methods: To stop the fraud detections using Random forest (N=10) and Logistic regression (N=10) with supervised learning that gives insights from the previous data. Results: The precision of the random forest is 76.29% compared with Logistic regression with accuracy of 74.65% with statistical significance value p=0.03 (p<0.05) using Independent sample t test. Conclusion: This results proved that Random forest was significantly better for Fraud detection than Logistic regression within the study’s limits.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114662402","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936466
M. Mohan, Anuradha Patil, S. Mohana, P. Subhashini, Sumit Kushwaha, S. M. Pandian
Denoising magnetic resonance images are traditionally done individually, introducing undesired artefacts like blurring. To solve this issue, this paper offers a unique adaptive interpolation architecture that simultaneously allows for image data augmentation, noise removal, and detail augmentation. The multi-tier kernel (MTK) algorithm adjusts the extrapolation weights based on mathematical features in magnetic resonance (MR) data. The MTK weight matrix is then adaptively sharpened, and a Rician bias corrective is used to reduce Rician noise and improve small details. After processing, the noise eliminates the bias produced by the asymmetric sources. The adaptive MTK, in this way, extends the zero ordering estimating methodology to higher regression power facilitating edge maintenance and detail restoration. Investigation outcomes using genuine and MR images (with/without noise) evidenced that the algorithm delivered good restoration outcomes than conventional deep-learning-based approaches but with fewer requirements and calculation burden.
{"title":"Multi-Tier Kernel for Disease Prediction using Texture Analysis with MR Images","authors":"M. Mohan, Anuradha Patil, S. Mohana, P. Subhashini, Sumit Kushwaha, S. M. Pandian","doi":"10.1109/ICECAA55415.2022.9936466","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936466","url":null,"abstract":"Denoising magnetic resonance images are traditionally done individually, introducing undesired artefacts like blurring. To solve this issue, this paper offers a unique adaptive interpolation architecture that simultaneously allows for image data augmentation, noise removal, and detail augmentation. The multi-tier kernel (MTK) algorithm adjusts the extrapolation weights based on mathematical features in magnetic resonance (MR) data. The MTK weight matrix is then adaptively sharpened, and a Rician bias corrective is used to reduce Rician noise and improve small details. After processing, the noise eliminates the bias produced by the asymmetric sources. The adaptive MTK, in this way, extends the zero ordering estimating methodology to higher regression power facilitating edge maintenance and detail restoration. Investigation outcomes using genuine and MR images (with/without noise) evidenced that the algorithm delivered good restoration outcomes than conventional deep-learning-based approaches but with fewer requirements and calculation burden.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115371422","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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