Pub Date : 2022-12-16DOI: 10.1109/SMART55829.2022.10046908
Tajpal, Monika Abrol
Virtualized methods are gaining popularity nowadays. Cloud storage in some kind of a complex is also affected. The concept of this study is web grid connected capabilities and real environmental virtualized compute service. It emphasizes the congestion control mechanism as well. The suggested approach would use system dynamics to automatically migrate each computing infrastructure nodes and enhance its speed.
{"title":"Economic Order Quantity Method for a Virtual VM Distributed System","authors":"Tajpal, Monika Abrol","doi":"10.1109/SMART55829.2022.10046908","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10046908","url":null,"abstract":"Virtualized methods are gaining popularity nowadays. Cloud storage in some kind of a complex is also affected. The concept of this study is web grid connected capabilities and real environmental virtualized compute service. It emphasizes the congestion control mechanism as well. The suggested approach would use system dynamics to automatically migrate each computing infrastructure nodes and enhance its speed.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114357039","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10047300
Lokesh Pawar, Shruti Kuhar, Deepak Rawat, Aarav Sharma, R. Bajaj
Breast cancer is one of the pronounce cancer among females, following lung cancer despite constant efforts by developed countries. However, if the diagnosis is made in the early non-metastatic stage, it can be cured in 70- 80% of cases. Therefore, it is vitally important to detect cancer and predict the stage as accurately as possible. We proposed an optimal model to predict the chance of early breast cancer inheritance and to undergo further treatment as soon as possible. The features are trained using classification machine learning The performance of these traditional machine learning algorithms has the potential to improve. There is room for correction, so our aim is to optimize the prediction model to improve the performance. The results obtained with our optimized ensemble algorithm are quite satisfactory and improved with an accuracy of 83.07%.
{"title":"An Optimized Ensemble Model for Early Breast Cancer Prediction","authors":"Lokesh Pawar, Shruti Kuhar, Deepak Rawat, Aarav Sharma, R. Bajaj","doi":"10.1109/SMART55829.2022.10047300","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047300","url":null,"abstract":"Breast cancer is one of the pronounce cancer among females, following lung cancer despite constant efforts by developed countries. However, if the diagnosis is made in the early non-metastatic stage, it can be cured in 70- 80% of cases. Therefore, it is vitally important to detect cancer and predict the stage as accurately as possible. We proposed an optimal model to predict the chance of early breast cancer inheritance and to undergo further treatment as soon as possible. The features are trained using classification machine learning The performance of these traditional machine learning algorithms has the potential to improve. There is room for correction, so our aim is to optimize the prediction model to improve the performance. The results obtained with our optimized ensemble algorithm are quite satisfactory and improved with an accuracy of 83.07%.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114819889","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10047785
Vineet Kumar Chauhan, Trilok Singh, Abhishek Dixit, Raushan Kumar Singh, Pradeep Kumar Singh, J. P. Singh
In today's digital age, attendance systems utilizing facial recognition are essential in schools, universities, companies, etc. One feature of the human body that might help identify a person is the face. Using a camera, we initially built a database of various people's faces for this project, and the recognizer algorithm would use this information. The system will be prepared to take attendance on its own after the database has been established. A cross-check will be performed between the recently acquired face image and the database. The attendance will be recorded and the data immediately saved into the excel sheet if the same face's image is found in the database. This attendance-based system using Face Recognition will be developed using PCA (Principal Component Analysis) and CNN (Convolutional Neural Network).
{"title":"Image-Based Attendance System using Facial Recognition","authors":"Vineet Kumar Chauhan, Trilok Singh, Abhishek Dixit, Raushan Kumar Singh, Pradeep Kumar Singh, J. P. Singh","doi":"10.1109/SMART55829.2022.10047785","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047785","url":null,"abstract":"In today's digital age, attendance systems utilizing facial recognition are essential in schools, universities, companies, etc. One feature of the human body that might help identify a person is the face. Using a camera, we initially built a database of various people's faces for this project, and the recognizer algorithm would use this information. The system will be prepared to take attendance on its own after the database has been established. A cross-check will be performed between the recently acquired face image and the database. The attendance will be recorded and the data immediately saved into the excel sheet if the same face's image is found in the database. This attendance-based system using Face Recognition will be developed using PCA (Principal Component Analysis) and CNN (Convolutional Neural Network).","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115110861","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10047509
Satyajit Panigrahi, Sharmila Subudhi, S. Ninoria
Neural Networks have dominated the sphere of Machine Learning and computerized decision trends for the past decade. The most straightforward neural architecture holds the key to some of humanity's most complex and vexing problems. When this concept of mimicking the human brain in digital or machine interpretation was first materialized in the late 1940s, the analysts were crippled by the technological reach of their time. But slowly, the advent of faster computational prowess and memory extensions paved the way for the intuitive backpropagation process in 1975, which was the first robust training procedure globally accepted. It becomes the fundamental requisite of almost all technological interactions we experience every day. Understanding the reflective activities, of an Artificial Neural Network is the first step toward more profound innovations and discoveries in machine learning. This paper specifically attempts to give an insight on various types of Neural Networks. Pros and cons of each Neural Network is summarized including their performance analysis in several application areas.
{"title":"A Comprehensive Performance Analysis on Artificial Neural Networks","authors":"Satyajit Panigrahi, Sharmila Subudhi, S. Ninoria","doi":"10.1109/SMART55829.2022.10047509","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047509","url":null,"abstract":"Neural Networks have dominated the sphere of Machine Learning and computerized decision trends for the past decade. The most straightforward neural architecture holds the key to some of humanity's most complex and vexing problems. When this concept of mimicking the human brain in digital or machine interpretation was first materialized in the late 1940s, the analysts were crippled by the technological reach of their time. But slowly, the advent of faster computational prowess and memory extensions paved the way for the intuitive backpropagation process in 1975, which was the first robust training procedure globally accepted. It becomes the fundamental requisite of almost all technological interactions we experience every day. Understanding the reflective activities, of an Artificial Neural Network is the first step toward more profound innovations and discoveries in machine learning. This paper specifically attempts to give an insight on various types of Neural Networks. Pros and cons of each Neural Network is summarized including their performance analysis in several application areas.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117237618","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10047741
Rama Mishra, K. Joshi, Durgaprasad Gangodkar
By successfully implementing data exchange, phone computation (MEC) plays a vital role in allowing a variety of service applications. However, the distinctive features of MEC also cause issues with privacy and data security which hinders the growth of MEC. A potential solution to ensure the security and authenticity of data exchange is blockchain. However, due to the changing nature of channel capacity and limited bandwidth, integrating cryptocurrency into the MEC system is a difficult task. In this paper, we use an asymmetric learning technique to propose a highly secure exchange mechanism for the bitcoin MEC system. First, an architecture for safe data exchange in the MEC system that is enabled by blockchain is provided. Then, based on the system resources on hand and the users' expectations for privacy, we provide a customizable secrecy technique. In order to enhance system performance while minimising MEC system energy consumption and increasing blockchain network throughput, a safe data sharing optimization problem is then developed in the blockchain-enabled MEC system. In particular, an asynchronous learning strategy is used to address the posed issue. Comparing our suggested safe data sharing technique to various well-known reference techniques in terms of typical performance, energy consumption, and reward, the numerical results indicate that it is better.
{"title":"Blockchain-Enabled Secure Data Sharing Scheme in Wireless Communication","authors":"Rama Mishra, K. Joshi, Durgaprasad Gangodkar","doi":"10.1109/SMART55829.2022.10047741","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047741","url":null,"abstract":"By successfully implementing data exchange, phone computation (MEC) plays a vital role in allowing a variety of service applications. However, the distinctive features of MEC also cause issues with privacy and data security which hinders the growth of MEC. A potential solution to ensure the security and authenticity of data exchange is blockchain. However, due to the changing nature of channel capacity and limited bandwidth, integrating cryptocurrency into the MEC system is a difficult task. In this paper, we use an asymmetric learning technique to propose a highly secure exchange mechanism for the bitcoin MEC system. First, an architecture for safe data exchange in the MEC system that is enabled by blockchain is provided. Then, based on the system resources on hand and the users' expectations for privacy, we provide a customizable secrecy technique. In order to enhance system performance while minimising MEC system energy consumption and increasing blockchain network throughput, a safe data sharing optimization problem is then developed in the blockchain-enabled MEC system. In particular, an asynchronous learning strategy is used to address the posed issue. Comparing our suggested safe data sharing technique to various well-known reference techniques in terms of typical performance, energy consumption, and reward, the numerical results indicate that it is better.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123393779","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10046716
N. Kumar, D. K. Sinha
Following the car and keeping an eye on its pace thanks to our technology. The vehicles' maximum speed may be set herein. In certain situations, in the event of a collision, our application will communicate the travellers and the position of the site to the cops, doctor, and close relatives. As an idea, if the detour route is 60 kilometre, so when user surpasses the road rules, it will record the data stored in the database. We may modify the speeding limit's value utilising our webpage. If the vehicle was taken we may be able to find it. When either of the competing cars' illumination is too high while employing network communication, one car lights will instantly dim. We are able to prevent tragedies as a result.
{"title":"Manual LED Adjustment Vintage Airspeed Monitoring without Microcontroller","authors":"N. Kumar, D. K. Sinha","doi":"10.1109/SMART55829.2022.10046716","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10046716","url":null,"abstract":"Following the car and keeping an eye on its pace thanks to our technology. The vehicles' maximum speed may be set herein. In certain situations, in the event of a collision, our application will communicate the travellers and the position of the site to the cops, doctor, and close relatives. As an idea, if the detour route is 60 kilometre, so when user surpasses the road rules, it will record the data stored in the database. We may modify the speeding limit's value utilising our webpage. If the vehicle was taken we may be able to find it. When either of the competing cars' illumination is too high while employing network communication, one car lights will instantly dim. We are able to prevent tragedies as a result.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124080270","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10047150
Amit Jain, Shruti Rani
Machine Learning is one of the highly recognized research areas nowadays. Different algorithms are used widely across several domains to implement the concepts. In this paper, discussion has been done in relation to machine learning along with its types, application areas [1].
{"title":"Machine Learning and its Applications: A Study","authors":"Amit Jain, Shruti Rani","doi":"10.1109/SMART55829.2022.10047150","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047150","url":null,"abstract":"Machine Learning is one of the highly recognized research areas nowadays. Different algorithms are used widely across several domains to implement the concepts. In this paper, discussion has been done in relation to machine learning along with its types, application areas [1].","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"182 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124747765","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10047119
Gulista Khan, K. Jain, Neha Anand, Wajid Ali
Accuracy of any machine learning model plays a crucial role as the prediction needs to be accurate, to prevent any discrepancy. This paper is concisely providing a way, a solution, a review on the solution of how we can improve the accuracy of the classifiers so that we get approximately accurate results. The best suited way is to apply Genetic Algorithm (GA) along with the classifiers. To analyze this approach, we will use various classifiers like Decision Tree, KNN, SVM, Gradient Boosting etc. Our main aim is to analyze the results obtained by the classifiers, firstly without GA and then with GA and observe will GA was able to improve the accuracy or not.
{"title":"Accuracy Improvement of Classifiers Using Genetic Algorithm","authors":"Gulista Khan, K. Jain, Neha Anand, Wajid Ali","doi":"10.1109/SMART55829.2022.10047119","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047119","url":null,"abstract":"Accuracy of any machine learning model plays a crucial role as the prediction needs to be accurate, to prevent any discrepancy. This paper is concisely providing a way, a solution, a review on the solution of how we can improve the accuracy of the classifiers so that we get approximately accurate results. The best suited way is to apply Genetic Algorithm (GA) along with the classifiers. To analyze this approach, we will use various classifiers like Decision Tree, KNN, SVM, Gradient Boosting etc. Our main aim is to analyze the results obtained by the classifiers, firstly without GA and then with GA and observe will GA was able to improve the accuracy or not.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124852453","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10047360
Nishank Jain, A. Chaudhary, Anil Kumar
The COVID-19 pandemic has caused a huge decline in money usage, with everything turning online these days. It has contributed to an increase in contactless payments that was unimaginable before. A credit card is the most extensively used method of payment, and it is becoming increasingly digital as the number of daily electronic transactions increases, making it more vulnerable to fraud. Credit card firms have suffered losses because of widespread card fraud. The most common worry is the recognition of credit card fraud. As a result, organizations are looking toward advanced device understanding technologies since they can handle a lot of data and spot irregularities that humans would miss. The development of effective To stop these losses, fraud detection algorithms are essential. An increasing number of these algorithms rely on cutting-edge computer methods that can assist fraud investigators. However, the appearance of the full-proof Fraud Detection System demands the use of high performing algorithms that are both exact and sturdy enough to handle massive amounts of data. The algorithm is run using open-source software using R statistical programming. This project tries to provide options by studying several fraud detection systems and highlighting their strengths and limitations.
{"title":"Credit Card Fraud Detection using Machine Learning Techniques","authors":"Nishank Jain, A. Chaudhary, Anil Kumar","doi":"10.1109/SMART55829.2022.10047360","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10047360","url":null,"abstract":"The COVID-19 pandemic has caused a huge decline in money usage, with everything turning online these days. It has contributed to an increase in contactless payments that was unimaginable before. A credit card is the most extensively used method of payment, and it is becoming increasingly digital as the number of daily electronic transactions increases, making it more vulnerable to fraud. Credit card firms have suffered losses because of widespread card fraud. The most common worry is the recognition of credit card fraud. As a result, organizations are looking toward advanced device understanding technologies since they can handle a lot of data and spot irregularities that humans would miss. The development of effective To stop these losses, fraud detection algorithms are essential. An increasing number of these algorithms rely on cutting-edge computer methods that can assist fraud investigators. However, the appearance of the full-proof Fraud Detection System demands the use of high performing algorithms that are both exact and sturdy enough to handle massive amounts of data. The algorithm is run using open-source software using R statistical programming. This project tries to provide options by studying several fraud detection systems and highlighting their strengths and limitations.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127263442","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 : 2022-12-16DOI: 10.1109/SMART55829.2022.10046810
Lokesh Pawar, Rohit Bajaj, Gaurav Bathla, R. K. Sidhu, Deepak Rawat
Routing strategies are the vital part of route management in a network oriented setup. These adjustments launch such type of speed in to the network that the latency almost disappears. Few adjustments are already discussed by several authors, but the idea behind this article is something related to the hybrid algorithm where Global and partial adjustment strategies are intelligently combined at the peak time or at an emergency situation. We propose an algorithm for hybrid routing adjustments in software defined networks.
{"title":"Hybrid Approach for Load Balancing in Software Defined Networks","authors":"Lokesh Pawar, Rohit Bajaj, Gaurav Bathla, R. K. Sidhu, Deepak Rawat","doi":"10.1109/SMART55829.2022.10046810","DOIUrl":"https://doi.org/10.1109/SMART55829.2022.10046810","url":null,"abstract":"Routing strategies are the vital part of route management in a network oriented setup. These adjustments launch such type of speed in to the network that the latency almost disappears. Few adjustments are already discussed by several authors, but the idea behind this article is something related to the hybrid algorithm where Global and partial adjustment strategies are intelligently combined at the peak time or at an emergency situation. We propose an algorithm for hybrid routing adjustments in software defined networks.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132778456","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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