Pub Date : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315766
Pronaya Bhattacharya, Akhilesh Ladha, Ashwani Kumar, A. Verma, Umesh Bodkhe
The current Telecom sector is highly competitive due to increased Mobile Number Portability (MNP) of users. The ease of MNP and plenty of switching options between Telecom providers, leads to rise in attrition, known as the churn behavior in customers. Customer is always in pursuit of better services at cheaper rates from service vendors. Thus, in this competitive Telecom market, the providers face a dual issue to retain loyal customers, as well as attract new potential customers by providing cheap data plans and free calling options. Thus, this unreasonable demand vs. supply rate to satisfy such customers effects the profitability of the company, which is a serious concern. Thus, to mitigate such fluctuations, termed as customer churn (CC) behavior, the paper a novel scheme BaYcP, that addresses the CC problem in two phases. In the first phase, based on customer data-sets, risk profiling score (RPS) is generated based on descision trees, and is compared to a threshold value. Then based on scores higher than threshold, an optimal prediction model is built based on bayesian classifier on appropriate selected features. The model is trained and validated to achieve and accuracy of 97.89% which outperforms other state-of-the art approaches.
{"title":"BaY cP: A novel Bayesian customer Churn prediction scheme for Telecom sector","authors":"Pronaya Bhattacharya, Akhilesh Ladha, Ashwani Kumar, A. Verma, Umesh Bodkhe","doi":"10.1109/PDGC50313.2020.9315766","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315766","url":null,"abstract":"The current Telecom sector is highly competitive due to increased Mobile Number Portability (MNP) of users. The ease of MNP and plenty of switching options between Telecom providers, leads to rise in attrition, known as the churn behavior in customers. Customer is always in pursuit of better services at cheaper rates from service vendors. Thus, in this competitive Telecom market, the providers face a dual issue to retain loyal customers, as well as attract new potential customers by providing cheap data plans and free calling options. Thus, this unreasonable demand vs. supply rate to satisfy such customers effects the profitability of the company, which is a serious concern. Thus, to mitigate such fluctuations, termed as customer churn (CC) behavior, the paper a novel scheme BaYcP, that addresses the CC problem in two phases. In the first phase, based on customer data-sets, risk profiling score (RPS) is generated based on descision trees, and is compared to a threshold value. Then based on scores higher than threshold, an optimal prediction model is built based on bayesian classifier on appropriate selected features. The model is trained and validated to achieve and accuracy of 97.89% which outperforms other state-of-the art approaches.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127662784","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315787
Anuj Chauhan, Vandana Bhatia
Nowadays the most trending and bookmark game is cricket in the whole world in which various types of activities occur like a No-ball, Wide Ball, Boundaries, etc. Here we detect a composite feature combining computer vision Algorithm along with camera view analysis. Many human errors occur in cricket matches because a wide ball or no ball creates very crucial situations and these decisions create very contradictorily during a match. Today technology is playing the most important role in the present world. So we decided that detect the various activities using computer vision techniques that occur during a cricket match like crucial catches, LBW, No ball, wide ball, etc. Here we will discuss activity detection using computer vision. Technology has various dimensions. Today the technology available is not computed the data. The technology has many different applications and magnitudes/aspect at which the software is achieving higher accuracy and greater results when the software is precisely performed. Implementation in any sport is much beneficial. Then Games such as Tennis, Baseball, Rugby, Soccer, Hockey, Cricket, Football, Kabaddi, etc. and single-player games like Chess, Badminton, Shooting, etc. are also being considered well thought out as honor to their countries.
{"title":"Cricket Activity Detection Using Computer Vision","authors":"Anuj Chauhan, Vandana Bhatia","doi":"10.1109/PDGC50313.2020.9315787","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315787","url":null,"abstract":"Nowadays the most trending and bookmark game is cricket in the whole world in which various types of activities occur like a No-ball, Wide Ball, Boundaries, etc. Here we detect a composite feature combining computer vision Algorithm along with camera view analysis. Many human errors occur in cricket matches because a wide ball or no ball creates very crucial situations and these decisions create very contradictorily during a match. Today technology is playing the most important role in the present world. So we decided that detect the various activities using computer vision techniques that occur during a cricket match like crucial catches, LBW, No ball, wide ball, etc. Here we will discuss activity detection using computer vision. Technology has various dimensions. Today the technology available is not computed the data. The technology has many different applications and magnitudes/aspect at which the software is achieving higher accuracy and greater results when the software is precisely performed. Implementation in any sport is much beneficial. Then Games such as Tennis, Baseball, Rugby, Soccer, Hockey, Cricket, Football, Kabaddi, etc. and single-player games like Chess, Badminton, Shooting, etc. are also being considered well thought out as honor to their countries.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128104540","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315801
Ramya Balabhadrapathruni, Suman De
In recent times, one of the most used scenarios in many industry domains is enhancing the bids or tenders made by suppliers. In this paper, we will be analyzing one such use case for studying the effects of mixed feature selection to optimize the Learning model. The use case is to target and build a predictive clustering model in such a way that the scheduler receives the suggestions based on the most optimal options. There are few feature selection, enhancement, and scaling methodologies which this paper aims to explore with real-time data. Based on the analysis, the most important feature derived would be used to predict the optimal suggestion. The results will then be compared to understand the shortfalls and strong points of this new approach based on the accuracy of prediction. A clustering model will not just help reduce the hours of manual effort put into selecting the right source but will also provide an authentic and optimal option for a scheduler's consideration.
{"title":"A Study on Analysing the impact of Feature Selection on Predictive Machine Learning Algorithms","authors":"Ramya Balabhadrapathruni, Suman De","doi":"10.1109/PDGC50313.2020.9315801","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315801","url":null,"abstract":"In recent times, one of the most used scenarios in many industry domains is enhancing the bids or tenders made by suppliers. In this paper, we will be analyzing one such use case for studying the effects of mixed feature selection to optimize the Learning model. The use case is to target and build a predictive clustering model in such a way that the scheduler receives the suggestions based on the most optimal options. There are few feature selection, enhancement, and scaling methodologies which this paper aims to explore with real-time data. Based on the analysis, the most important feature derived would be used to predict the optimal suggestion. The results will then be compared to understand the shortfalls and strong points of this new approach based on the accuracy of prediction. A clustering model will not just help reduce the hours of manual effort put into selecting the right source but will also provide an authentic and optimal option for a scheduler's consideration.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115724153","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315327
Kumar Satyajeet, Kavita Pandey
Today with the ever-growing demand of the internet and every second the transition to new technology, in-vehicle system also requires up-gradation. In this study, finding optimal positioning of roadside in vehicular Ad hoc Network (VANET) has been explored using Artificial Intelligence, as it is transforming every domain to a new level. Machine Learning can help us in predicting the optimal position of Roadside unit using the volume of vehicles and via verifying the longitude and latitude of the traffic vehicle. Various clustering techniques K-Means, Mean_Shift, Density-Based Spatial clustering of Application with Noise, Expectation_Maximization clustering (GMM) and Agglomerative_Hierarchical clustering has been applied on vehicle data consisting of longitude, latitude and volume of the taxi. Data was collected from NYC taxi (New York) from January 2016 to June 2016. Our results shows that machine learning provide excellent results in terms of position predictions.
在互联网需求不断增长、新技术日新月异的今天，车载系统也需要升级换代。在本研究中，利用人工智能探索了在车载自组织网络(VANET)中寻找最优路边定位，因为它正在将每个领域都转变到一个新的水平。机器学习可以帮助我们利用车辆的数量，并通过验证交通车辆的经纬度来预测路边单元的最佳位置。将K-Means、Mean_Shift、基于密度的带噪声空间聚类、Expectation_Maximization聚类(GMM)和Agglomerative_Hierarchical聚类等聚类技术应用于出租车的经纬度和体积数据。数据收集自2016年1月至2016年6月的NYC taxi (New York)。我们的研究结果表明，机器学习在位置预测方面提供了出色的结果。
{"title":"Comparative Analysis of Clustering Techniques for Deployment of Roadside Units","authors":"Kumar Satyajeet, Kavita Pandey","doi":"10.1109/PDGC50313.2020.9315327","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315327","url":null,"abstract":"Today with the ever-growing demand of the internet and every second the transition to new technology, in-vehicle system also requires up-gradation. In this study, finding optimal positioning of roadside in vehicular Ad hoc Network (VANET) has been explored using Artificial Intelligence, as it is transforming every domain to a new level. Machine Learning can help us in predicting the optimal position of Roadside unit using the volume of vehicles and via verifying the longitude and latitude of the traffic vehicle. Various clustering techniques K-Means, Mean_Shift, Density-Based Spatial clustering of Application with Noise, Expectation_Maximization clustering (GMM) and Agglomerative_Hierarchical clustering has been applied on vehicle data consisting of longitude, latitude and volume of the taxi. Data was collected from NYC taxi (New York) from January 2016 to June 2016. Our results shows that machine learning provide excellent results in terms of position predictions.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134504401","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315827
Ekta Dixit, Vandana Jindal
Presently, the sensor network is an active region of interest due to various applications. The assistance and identification of the harmful objects are assisted by the generation of the environmental monitoring schemes in emerging technology. Air Pollution is the main problem that affects living creatures. In this paper, the research on the use of WSN in air pollution monitoring has been done. The main focus of the research has been done on the idea of the detection of air pollution and related methods that helped in the detection of air pollution. Moreover, the architecture of the wireless air pollution monitoring system has been described along with the interrelated components. Also, an energy-efficient routing protocol in the wireless air pollution monitoring system has been discussed. Additionally, the comparative analysis of heterogeneous and homogeneous protocol for improving the network lifetime of WSN has been done. However, energy efficiency is the maj or restraint of the restricted lifespan of WSN. Consequently, the main goal of the current research is to find the solution to decrease the energy consumption issue and a way to improve the network lifetime of both the protocols.
{"title":"Survey on Recent Cluster Originated Energy Efficiency Routing Protocols For Air Pollution Monitoring Using WSN","authors":"Ekta Dixit, Vandana Jindal","doi":"10.1109/PDGC50313.2020.9315827","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315827","url":null,"abstract":"Presently, the sensor network is an active region of interest due to various applications. The assistance and identification of the harmful objects are assisted by the generation of the environmental monitoring schemes in emerging technology. Air Pollution is the main problem that affects living creatures. In this paper, the research on the use of WSN in air pollution monitoring has been done. The main focus of the research has been done on the idea of the detection of air pollution and related methods that helped in the detection of air pollution. Moreover, the architecture of the wireless air pollution monitoring system has been described along with the interrelated components. Also, an energy-efficient routing protocol in the wireless air pollution monitoring system has been discussed. Additionally, the comparative analysis of heterogeneous and homogeneous protocol for improving the network lifetime of WSN has been done. However, energy efficiency is the maj or restraint of the restricted lifespan of WSN. Consequently, the main goal of the current research is to find the solution to decrease the energy consumption issue and a way to improve the network lifetime of both the protocols.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121606429","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315849
Nikhil Saini, Jeet Rabari, Mamta C. Padole, Vaibhav Solanki
Load balancing is the process of improving the performance of the system by sharing of workload among the processors. The workload of a machine means the total processing time it requires to execute all the tasks assigned to it. Load balancing is one of the important factors to heighten the working performance of the cloud service provider. The benefits of distributing the workload include increased resource utilization ratio which further leads to enhancing the overall performance thereby achieving maximum client satisfaction. In this paper, we are demonstrating the use of the singleton model for load balancing.
{"title":"Load Balancing in Heterogeneous Distributed Systems Using Singleton Model","authors":"Nikhil Saini, Jeet Rabari, Mamta C. Padole, Vaibhav Solanki","doi":"10.1109/PDGC50313.2020.9315849","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315849","url":null,"abstract":"Load balancing is the process of improving the performance of the system by sharing of workload among the processors. The workload of a machine means the total processing time it requires to execute all the tasks assigned to it. Load balancing is one of the important factors to heighten the working performance of the cloud service provider. The benefits of distributing the workload include increased resource utilization ratio which further leads to enhancing the overall performance thereby achieving maximum client satisfaction. In this paper, we are demonstrating the use of the singleton model for load balancing.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115322754","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315807
R. Mittal, Varun Malik, Vikram Singh, Jaiteg Singh, Amandeep Kaur
Web mining is an important approach to retrieve and analyse the information from web server log data. In the internet-driven information age, a lot of data is present on the web in many ways and analysing such data using the web mining methods cam result in some novel insights. Such data can be extracted from the server log files and can be preprocessed to be used for various web mining functionalities. In this paper authors used the data from web server log files, preprocessed it and then applied various classification algorithms such as Naïve bayes,KNN,decision tree,random forest and analysed the results. The best approach was then chosen to further improve the performance of the classifier by integrating it with genetic algorithm. In this context, a hybrid approach, namely RFGA was used integrating Random forest and genetic algorithm on the dataset and the results of different machine learning classifiers were compared with RFGA in terms of the predictive accuracy.
{"title":"Integrating Genetic Algorithm with Random Forest for Improving the Classification Performance of Web Log Data","authors":"R. Mittal, Varun Malik, Vikram Singh, Jaiteg Singh, Amandeep Kaur","doi":"10.1109/PDGC50313.2020.9315807","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315807","url":null,"abstract":"Web mining is an important approach to retrieve and analyse the information from web server log data. In the internet-driven information age, a lot of data is present on the web in many ways and analysing such data using the web mining methods cam result in some novel insights. Such data can be extracted from the server log files and can be preprocessed to be used for various web mining functionalities. In this paper authors used the data from web server log files, preprocessed it and then applied various classification algorithms such as Naïve bayes,KNN,decision tree,random forest and analysed the results. The best approach was then chosen to further improve the performance of the classifier by integrating it with genetic algorithm. In this context, a hybrid approach, namely RFGA was used integrating Random forest and genetic algorithm on the dataset and the results of different machine learning classifiers were compared with RFGA in terms of the predictive accuracy.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"47 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123479816","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315743
Pranab Sharma
Image segmentation is the method of partitioning, or segmenting, different parts of the image in such a way that all segments are disjoint and each has similar elements. This process has wide applications in the field of medicine and photography industry. There are many ways in which image segmentation can be performed, from which K-Means clustering algorithm is well renowned due to its simplicity and effectiveness to perform the task. In this paper, an improved variant of K-Means Clustering algorithm is presented. The algorithm rests on applying partial contrast stretching, eliminating randomness in choosing the initial cluster centres for K-means algorithm, and removing the unwanted noise from median filters to obtain a high-quality image output.
{"title":"Advanced Image Segmentation Technique using Improved K Means Clustering Algorithm with Pixel Potential","authors":"Pranab Sharma","doi":"10.1109/PDGC50313.2020.9315743","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315743","url":null,"abstract":"Image segmentation is the method of partitioning, or segmenting, different parts of the image in such a way that all segments are disjoint and each has similar elements. This process has wide applications in the field of medicine and photography industry. There are many ways in which image segmentation can be performed, from which K-Means clustering algorithm is well renowned due to its simplicity and effectiveness to perform the task. In this paper, an improved variant of K-Means Clustering algorithm is presented. The algorithm rests on applying partial contrast stretching, eliminating randomness in choosing the initial cluster centres for K-means algorithm, and removing the unwanted noise from median filters to obtain a high-quality image output.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128581283","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315780
S. Kaur, Nidhi Bhatla
Image forgery detection is the area of research in the field of biometric and forensics. Digital pictures are the resource of data. In the present world of technology, image processing software tools have developed to generate and modify digital images from one location to another. With the current technology, it is simple to establish image forgery by addition and subtraction of the components from the pictures that lead to image interfering. Copy-move image forgery is created by copying and pasting the element in a similar image. Hence, copy-move forgery has become an area of research in the image forensic unit. Various methods have been implemented to detect digital image forgery. Some issues still required to resolve like time complexity, fake, and blurred image. In existing research, the block and feature-based approach used to remove a forged area from the image using SIFT and RANSAC algorithm. The forgery dataset of the 80 pictures collected to achieve accuracy of up to 95%. In the research work, the PBFOA method has been implemented to optimize and extract the features using the component analysis method. FCM is used for image segmentation in the input image. PBFOA is based on an optimization process to select valuable features based on the calculation of the fitness function. In this method, two steps are used to re-verify the instance, features (i) Slower and faster condition. BFOA steps are described in detail in this research paper. Initial steps, Spread the feature set in the whole system. In the rapid condition selected and to eliminate the valuable features one at a time, then reproduction phase is implemented with the help of the fitness function to recover the feature values and detect the forgery information in the uploaded image. The simulation setup using MATLAB 2016a version and improve the accuracy rate and image quality parameter. Performance analysis depends on the proposed metrics FAR, FRR, ACC, Precision, Recall, and compared with the existing methods.
{"title":"Forgery Detection For High-Resolution Digital Images Using FCM And PBFOAAlgorithm","authors":"S. Kaur, Nidhi Bhatla","doi":"10.1109/PDGC50313.2020.9315780","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315780","url":null,"abstract":"Image forgery detection is the area of research in the field of biometric and forensics. Digital pictures are the resource of data. In the present world of technology, image processing software tools have developed to generate and modify digital images from one location to another. With the current technology, it is simple to establish image forgery by addition and subtraction of the components from the pictures that lead to image interfering. Copy-move image forgery is created by copying and pasting the element in a similar image. Hence, copy-move forgery has become an area of research in the image forensic unit. Various methods have been implemented to detect digital image forgery. Some issues still required to resolve like time complexity, fake, and blurred image. In existing research, the block and feature-based approach used to remove a forged area from the image using SIFT and RANSAC algorithm. The forgery dataset of the 80 pictures collected to achieve accuracy of up to 95%. In the research work, the PBFOA method has been implemented to optimize and extract the features using the component analysis method. FCM is used for image segmentation in the input image. PBFOA is based on an optimization process to select valuable features based on the calculation of the fitness function. In this method, two steps are used to re-verify the instance, features (i) Slower and faster condition. BFOA steps are described in detail in this research paper. Initial steps, Spread the feature set in the whole system. In the rapid condition selected and to eliminate the valuable features one at a time, then reproduction phase is implemented with the help of the fitness function to recover the feature values and detect the forgery information in the uploaded image. The simulation setup using MATLAB 2016a version and improve the accuracy rate and image quality parameter. Performance analysis depends on the proposed metrics FAR, FRR, ACC, Precision, Recall, and compared with the existing methods.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130639518","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315751
M. Khanna, Harmaninder Jit Singh Sidhu, R. Bansal
Industry4.0 was originated in the Germany who defines major technological changes in manufacturing and laid down certain protocols for worldwide competitiveness of German industry. As the new era of ‘smart’ factory is about to begin, in which computers are connected with robotics remotely and use machine learning programs that can control the automatic machines with ease. In this paper, the basic inspiration of industry4.0 will be shared. The analysis of the effectiveness of Government of India's ‘Make in India’ initiative on manufacturing industry is assceesd. In the end, India's competitiveness in automotive industry and India readiness as preferred investment destination by all major automobiles giants will be discussed. And further some of the Government of India's initiative to boost up Auto Sector is also discussed.
{"title":"Industry 4.0: A Study of India's Readiness as Preferred Investment Destination in Automotive and Auto Component Industry","authors":"M. Khanna, Harmaninder Jit Singh Sidhu, R. Bansal","doi":"10.1109/PDGC50313.2020.9315751","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315751","url":null,"abstract":"Industry4.0 was originated in the Germany who defines major technological changes in manufacturing and laid down certain protocols for worldwide competitiveness of German industry. As the new era of ‘smart’ factory is about to begin, in which computers are connected with robotics remotely and use machine learning programs that can control the automatic machines with ease. In this paper, the basic inspiration of industry4.0 will be shared. The analysis of the effectiveness of Government of India's ‘Make in India’ initiative on manufacturing industry is assceesd. In the end, India's competitiveness in automotive industry and India readiness as preferred investment destination by all major automobiles giants will be discussed. And further some of the Government of India's initiative to boost up Auto Sector is also discussed.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129683753","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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