In the recent era, everybody are dealing with the digital data. In such scenario individual one heavily depend on credit card. Therefore, the demand of online transactions and usage of e-commerce sites are rising at the rapid rate. The online payments are the main cause of increasing crime rate heavily. Hence, it is the huge challenge for banks and IT professional to identify and resolve such a critical problems. This critical issue can be tackle with the help of machine learning. This articles mainly emphasis on various data mining algorithms such as like C4.5, CART algorithms, J48, Naïve Bayes algorithm, EM algorithm, Apriori algorithm, SVM and so on and also inform the accuracy and precision of the result. The machine learning finds the genuine and non-genuine transition using learning pattern matching and classification technique. The machine learning also normalized the data, identify the anomalies in transaction and provide appropriate results.
{"title":"Analysis on Credit Card Fraud Detection and Prevention using Data Mining and Machine Learning Techniques","authors":"Puninder Kaur, Avinash Sharma, J. Chahal, Taruna Sharma, Vidhu Kiran Sharma","doi":"10.1109/iccica52458.2021.9697172","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697172","url":null,"abstract":"In the recent era, everybody are dealing with the digital data. In such scenario individual one heavily depend on credit card. Therefore, the demand of online transactions and usage of e-commerce sites are rising at the rapid rate. The online payments are the main cause of increasing crime rate heavily. Hence, it is the huge challenge for banks and IT professional to identify and resolve such a critical problems. This critical issue can be tackle with the help of machine learning. This articles mainly emphasis on various data mining algorithms such as like C4.5, CART algorithms, J48, Naïve Bayes algorithm, EM algorithm, Apriori algorithm, SVM and so on and also inform the accuracy and precision of the result. The machine learning finds the genuine and non-genuine transition using learning pattern matching and classification technique. The machine learning also normalized the data, identify the anomalies in transaction and provide appropriate results.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117130378","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697186
K. Gupta, Deepali Gupta, Sahil Gupta, R. Singla, Raman Gupta
The technological advancements capturing the modern era is taking place as a consequence of researches being made incessantly despite of global boundaries across the globe. The contributions of education industry in creating a simple life via automation tools is envisaged to be a boom. Research is considered to be the most prominent gear to drive the professional career of an educationist. The measure of research quality is number of times an article has been cited. This generates a need to evaluate mechanisms that can lead to increased citation count. Hence, in first phase of this research, survey has been conducted to expound on important factors that can contribute in greater citation count. The second phase of this research presents analysis in terms of precedence of contributing factors.
{"title":"A Framework to increase your Citation Count: A Partial Least Square Approach","authors":"K. Gupta, Deepali Gupta, Sahil Gupta, R. Singla, Raman Gupta","doi":"10.1109/iccica52458.2021.9697186","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697186","url":null,"abstract":"The technological advancements capturing the modern era is taking place as a consequence of researches being made incessantly despite of global boundaries across the globe. The contributions of education industry in creating a simple life via automation tools is envisaged to be a boom. Research is considered to be the most prominent gear to drive the professional career of an educationist. The measure of research quality is number of times an article has been cited. This generates a need to evaluate mechanisms that can lead to increased citation count. Hence, in first phase of this research, survey has been conducted to expound on important factors that can contribute in greater citation count. The second phase of this research presents analysis in terms of precedence of contributing factors.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126613246","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697259
{"title":"2021 - International Conference on Computational Intelligence and Computing Applications (ICCICA) [Title page]","authors":"","doi":"10.1109/iccica52458.2021.9697259","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697259","url":null,"abstract":"","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115255001","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697253
S. Kediya, Sanjiv Kumar
Retailing is defined as selling the goods or services to the customer in order to derive the profit. The most important aspect in retailing is customer satisfaction and deriving profit through customer satisfaction. Internet of Things (IoT) system enables companies with advantages of value creation and value proposition, which in turn strengthens the bond with their customers. This research paper explores various factors, which are critical to the success of the adoption of IoT by Indian Retail Industry. Some of the factors, which have been identified, are emerging technologies, business processes, need for data security, crucial competitive advantage and some more. The infrastructural issues are important as it affects the critical factor such as efficient and timely delivery of the products and services. Further, the paper explores the challenges involved in adoption of IOT by Indian Retail Industry.
{"title":"An Analysis of Factors Affecting IoT Adoption by Indian Retail Industry","authors":"S. Kediya, Sanjiv Kumar","doi":"10.1109/iccica52458.2021.9697253","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697253","url":null,"abstract":"Retailing is defined as selling the goods or services to the customer in order to derive the profit. The most important aspect in retailing is customer satisfaction and deriving profit through customer satisfaction. Internet of Things (IoT) system enables companies with advantages of value creation and value proposition, which in turn strengthens the bond with their customers. This research paper explores various factors, which are critical to the success of the adoption of IoT by Indian Retail Industry. Some of the factors, which have been identified, are emerging technologies, business processes, need for data security, crucial competitive advantage and some more. The infrastructural issues are important as it affects the critical factor such as efficient and timely delivery of the products and services. Further, the paper explores the challenges involved in adoption of IOT by Indian Retail Industry.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122665164","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697204
U. D. Dixit, M. Shirdhonkar
Document image retrieval is an interesting and popular area of research that has been evolved into many stages. The current trends show that it is still an emerging area due to the availability of document scanner app with mobiles and handheld devices. The objective of this paper is to provide an insight into document retrieval, classification, a general architecture, related issues and new opportunities for research in the domain.
{"title":"Document Image Retrieval: Issues and Future Directions","authors":"U. D. Dixit, M. Shirdhonkar","doi":"10.1109/iccica52458.2021.9697204","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697204","url":null,"abstract":"Document image retrieval is an interesting and popular area of research that has been evolved into many stages. The current trends show that it is still an emerging area due to the availability of document scanner app with mobiles and handheld devices. The objective of this paper is to provide an insight into document retrieval, classification, a general architecture, related issues and new opportunities for research in the domain.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132600291","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697263
S. Nimkar, M. Khanapurkar
Since the last decade, the Internet of Things (IoT) is one of the most talked technology by Academicians & Industries due to its wide variety of applications. IoT has evolved a lot from M2M (Machine to Machine) Communication, WSN (Wireless Sensor Networks) CPS (Cyber-Physical System) to the latest Cloud, Fog & Edge Computing. Enabling the Smart Cities is one of the most promising applications of the Internet of Things as it affects the many human lives directly. Smart city promises Health & Environment monitoring, Smart Transportation, Water Quality monitoring, School & Apartment Building Monitoring Energy Management etc. these all together will facilitate the Municipal, District & State Authorities in Enforcement of Schemes & Laws so, it is termed as Smart City Governance or IoTaaSG (IoT as a Service to Governance). Due to huge data generated from billions of sensors implemented for different applications, it becomes a tedious task to manage such large data & get desired information out of it. In this paper, we try to focus on dedicated scalable architecture for IoTaaSG using Edge Computing.
{"title":"Edge Computing for IoT: A Use Case in Smart City Governance","authors":"S. Nimkar, M. Khanapurkar","doi":"10.1109/iccica52458.2021.9697263","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697263","url":null,"abstract":"Since the last decade, the Internet of Things (IoT) is one of the most talked technology by Academicians & Industries due to its wide variety of applications. IoT has evolved a lot from M2M (Machine to Machine) Communication, WSN (Wireless Sensor Networks) CPS (Cyber-Physical System) to the latest Cloud, Fog & Edge Computing. Enabling the Smart Cities is one of the most promising applications of the Internet of Things as it affects the many human lives directly. Smart city promises Health & Environment monitoring, Smart Transportation, Water Quality monitoring, School & Apartment Building Monitoring Energy Management etc. these all together will facilitate the Municipal, District & State Authorities in Enforcement of Schemes & Laws so, it is termed as Smart City Governance or IoTaaSG (IoT as a Service to Governance). Due to huge data generated from billions of sensors implemented for different applications, it becomes a tedious task to manage such large data & get desired information out of it. In this paper, we try to focus on dedicated scalable architecture for IoTaaSG using Edge Computing.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"862 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133352322","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697152
Ubio Obu, Gopal Sarkarkar, Yash Ambekar
Vertical farms have become increasingly popular in today’s society. Its popularity owes to the increasing relevance of vertical farms as a panacea for the effect of desertification and urbanization as proposed by experts and researchers. Urbanization is increasing annually, as at the middle of year 2020 according to statistica.com the rate of urbanization was at 56 percent, and it has been estimated that by the year 2050 about 70-90% of global population will live in cities. The tripod effect of urbanization, desertification and climate change has made vertical farms a convenient alternative. While vertical farm is helping to solve this problem, the interface of computers with vertical farms has exponentially increased the efficiency, it has helped to create convenient environments which can be controlled, as such facilitating and all-around production of food crops all through the year despite changing environmental conditions. In this paper, we are taking a step further to see how computer vision can help in this process. So far IoT has been used to monitor the farm extrinsic factors, and get relevant data, the problem with that method is that only the external factors are being monitored, in this paper we will be exploring how computer vision can monitor intrinsic factors, but beyond that, we will also explore how computer vision and machine learning methods could be used together with IoT for the control of vertical farms as well to create favorable conditions for the planting of vertical farms.
{"title":"Computer Vision for Monitor and Control of Vertical Farms Using Machine Learning Methods","authors":"Ubio Obu, Gopal Sarkarkar, Yash Ambekar","doi":"10.1109/iccica52458.2021.9697152","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697152","url":null,"abstract":"Vertical farms have become increasingly popular in today’s society. Its popularity owes to the increasing relevance of vertical farms as a panacea for the effect of desertification and urbanization as proposed by experts and researchers. Urbanization is increasing annually, as at the middle of year 2020 according to statistica.com the rate of urbanization was at 56 percent, and it has been estimated that by the year 2050 about 70-90% of global population will live in cities. The tripod effect of urbanization, desertification and climate change has made vertical farms a convenient alternative. While vertical farm is helping to solve this problem, the interface of computers with vertical farms has exponentially increased the efficiency, it has helped to create convenient environments which can be controlled, as such facilitating and all-around production of food crops all through the year despite changing environmental conditions. In this paper, we are taking a step further to see how computer vision can help in this process. So far IoT has been used to monitor the farm extrinsic factors, and get relevant data, the problem with that method is that only the external factors are being monitored, in this paper we will be exploring how computer vision can monitor intrinsic factors, but beyond that, we will also explore how computer vision and machine learning methods could be used together with IoT for the control of vertical farms as well to create favorable conditions for the planting of vertical farms.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132016132","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697258
Swapna Choudhary, S. Dorle
Vehicular ad-hoc networks (VANETs) are one of the most stochastic networks in terms of node movement patterns. Due to the high speed of vehicles, nodes form temporary clusters and shift between clusters rapidly, which limits the usable computational complexity for quality of service (QoS) and security enhancements. Hence, VANETs are one of the most insecure networks and are prone to various attacks like Masquerading, Distributed Denial of Service (DDoS) etc. Various algorithms have been proposed to safeguard VANETs against these attacks, which vary concerning security and QoS performance. These algorithms include linear rule-checking models, software-defined network (SDN) rules, blockchain-based models, etc. Due to such a wide variety of model availability, it becomes difficult for VANET designers to select the most optimum security framework for the network deployment. To reduce the complexity of this selection, the paper reviews statistically investigate a wide variety of modern VANET-based security models. These models are compared in terms of security, computational complexity, application and cost of deployment, etc. which will assist network designers to select the most optimum models for their application. Moreover, the paper also recommends various improvements that can be applied to the reviewed models, to further optimize their performance.
{"title":"Empirical investigation of VANET-based security models from a statistical perspective","authors":"Swapna Choudhary, S. Dorle","doi":"10.1109/iccica52458.2021.9697258","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697258","url":null,"abstract":"Vehicular ad-hoc networks (VANETs) are one of the most stochastic networks in terms of node movement patterns. Due to the high speed of vehicles, nodes form temporary clusters and shift between clusters rapidly, which limits the usable computational complexity for quality of service (QoS) and security enhancements. Hence, VANETs are one of the most insecure networks and are prone to various attacks like Masquerading, Distributed Denial of Service (DDoS) etc. Various algorithms have been proposed to safeguard VANETs against these attacks, which vary concerning security and QoS performance. These algorithms include linear rule-checking models, software-defined network (SDN) rules, blockchain-based models, etc. Due to such a wide variety of model availability, it becomes difficult for VANET designers to select the most optimum security framework for the network deployment. To reduce the complexity of this selection, the paper reviews statistically investigate a wide variety of modern VANET-based security models. These models are compared in terms of security, computational complexity, application and cost of deployment, etc. which will assist network designers to select the most optimum models for their application. Moreover, the paper also recommends various improvements that can be applied to the reviewed models, to further optimize their performance.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132038904","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697203
S. Dewalkar, S. Nangrani
Batteries especially lithium-ion are better choice as an energy storage option in electric vehicles due to its features as long life, low self-discharge, and economical. To ensure the reliable and better operational efficiency of battery-powered electric vehicles, an accurate assessment of the state-of-charge (SoC) is essential. So the proposed model has a coulomb counting method model and neural network model through which the SoC is estimated and compared. The traditional way of the SoC estimation that is the coulomb counting method gives the SoC having errors as compared to the neural network method. After comparing the results obtained we can clearly derive that neural network model gives fewer errors. Traditional method coulomb counting method is having more errors than the neural network model.
{"title":"State of Charge Estimation System for Electric Vehicle Batteries using ANN","authors":"S. Dewalkar, S. Nangrani","doi":"10.1109/iccica52458.2021.9697203","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697203","url":null,"abstract":"Batteries especially lithium-ion are better choice as an energy storage option in electric vehicles due to its features as long life, low self-discharge, and economical. To ensure the reliable and better operational efficiency of battery-powered electric vehicles, an accurate assessment of the state-of-charge (SoC) is essential. So the proposed model has a coulomb counting method model and neural network model through which the SoC is estimated and compared. The traditional way of the SoC estimation that is the coulomb counting method gives the SoC having errors as compared to the neural network method. After comparing the results obtained we can clearly derive that neural network model gives fewer errors. Traditional method coulomb counting method is having more errors than the neural network model.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131066480","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697234
Bismin V. Sherif, P. Salini
A mobile ad hoc network, abbreviately called as MANET, is an infrastructure-less network of mobile nodes connected wirelessly in a self-configuring, self-organizing manner. Because of the fact that MANETs can be effectively used in crucial applications like disaster relief operations and rescue operations, it has gained the attention of research community. But due to on-the-fly characteristics, MANETs are highly vulnerable to security attacks. Since the presence of attacker nodes degrades the performance of MANET, special considerations are required to enhance the performance of the network by developing more sophisticated techniques to detect and eliminate these attacks from the network. This paper focuses on the study of existing traditional and machine learning approaches which helps in identifying the presence of malicious nodes in MANET.
{"title":"Effective and Prominent Approaches for Malicious Node Detection in MANET","authors":"Bismin V. Sherif, P. Salini","doi":"10.1109/iccica52458.2021.9697234","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697234","url":null,"abstract":"A mobile ad hoc network, abbreviately called as MANET, is an infrastructure-less network of mobile nodes connected wirelessly in a self-configuring, self-organizing manner. Because of the fact that MANETs can be effectively used in crucial applications like disaster relief operations and rescue operations, it has gained the attention of research community. But due to on-the-fly characteristics, MANETs are highly vulnerable to security attacks. Since the presence of attacker nodes degrades the performance of MANET, special considerations are required to enhance the performance of the network by developing more sophisticated techniques to detect and eliminate these attacks from the network. This paper focuses on the study of existing traditional and machine learning approaches which helps in identifying the presence of malicious nodes in MANET.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134088298","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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