Pub Date : 2018-10-01DOI: 10.1109/CCCS.2018.8586831
Abhishek Paudel, Brihat Ratna Bajracharya, Miran Ghimire, Nabin Bhattarai, D. S. Baral
Music is an integral part of our life. People listen to music everyday as per their taste and mood. With the advancement and increase in volume of digital content, the choice for people to listen to diverse type of music has also increased significantly. Thus, the necessity of delivering the most suited music to the listeners has been an interesting field of research in computer science. One of the important measures to deliver the best music to listeners could be their personality traits. In order to determine the personality traits of a person, social media like Facebook can be a useful platform where people express their views on different matters, share their opinions and thoughts. This paper first describes the use of Naive Bayes classifier to determine the standard Big Five Personality Traits of a person based on their status updates on Facebook profile using basic natural language processing techniques, and then proceeds to present the use of thus obtained information about personality traits to enhance the widely implemented user-to-user collaborative filtering techniques for music recommendation.
{"title":"Using Personality Traits Information from Social Media for Music Recommendation","authors":"Abhishek Paudel, Brihat Ratna Bajracharya, Miran Ghimire, Nabin Bhattarai, D. S. Baral","doi":"10.1109/CCCS.2018.8586831","DOIUrl":"https://doi.org/10.1109/CCCS.2018.8586831","url":null,"abstract":"Music is an integral part of our life. People listen to music everyday as per their taste and mood. With the advancement and increase in volume of digital content, the choice for people to listen to diverse type of music has also increased significantly. Thus, the necessity of delivering the most suited music to the listeners has been an interesting field of research in computer science. One of the important measures to deliver the best music to listeners could be their personality traits. In order to determine the personality traits of a person, social media like Facebook can be a useful platform where people express their views on different matters, share their opinions and thoughts. This paper first describes the use of Naive Bayes classifier to determine the standard Big Five Personality Traits of a person based on their status updates on Facebook profile using basic natural language processing techniques, and then proceeds to present the use of thus obtained information about personality traits to enhance the widely implemented user-to-user collaborative filtering techniques for music recommendation.","PeriodicalId":6570,"journal":{"name":"2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS)","volume":"61 1","pages":"116-121"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85760779","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 : 2018-10-01DOI: 10.1109/CCCS.2018.8586834
R. Regmi, Arun K. Timalsina
Increasing trade volume adds up various challenges and risks for customs to maintain balance between trade facilitation and strong border control. With limited resources and manpower, it’s quite difficult to have exhaustive physical examination of all import and export consignments. To balance control and facilitation Revised Kyoto Convention (RKC) and World Trade Organization (WTO) Trade Facilitation Agreement (TFA) have clearly stated about implementation of effective risk management system. In this paper, deep learning model was trained and tested to segregate high risk and low risk consignment on randomly selected 200,000 data from Nepal Customs of the year 2017. Model was tested using supervised learning utilizing inspection result provided by Nepal Customs. Deep learning has improved accuracy and seizure rate than that of decision Tree (DT) and Support Vector Machine (SVM). All three methods have achieved a better result than current rule based risk management system. ANN had achieved better result than DT and SVM, by achieving 81% of seizure rate under 9% inspection.
{"title":"Risk Management in customs using Deep Neural Network","authors":"R. Regmi, Arun K. Timalsina","doi":"10.1109/CCCS.2018.8586834","DOIUrl":"https://doi.org/10.1109/CCCS.2018.8586834","url":null,"abstract":"Increasing trade volume adds up various challenges and risks for customs to maintain balance between trade facilitation and strong border control. With limited resources and manpower, it’s quite difficult to have exhaustive physical examination of all import and export consignments. To balance control and facilitation Revised Kyoto Convention (RKC) and World Trade Organization (WTO) Trade Facilitation Agreement (TFA) have clearly stated about implementation of effective risk management system. In this paper, deep learning model was trained and tested to segregate high risk and low risk consignment on randomly selected 200,000 data from Nepal Customs of the year 2017. Model was tested using supervised learning utilizing inspection result provided by Nepal Customs. Deep learning has improved accuracy and seizure rate than that of decision Tree (DT) and Support Vector Machine (SVM). All three methods have achieved a better result than current rule based risk management system. ANN had achieved better result than DT and SVM, by achieving 81% of seizure rate under 9% inspection.","PeriodicalId":6570,"journal":{"name":"2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS)","volume":"24 1","pages":"133-137"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87208521","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 : 2018-10-01DOI: 10.1109/CCCS.2018.8586833
M. Nasreen, Madhooshri Iyer, E. P. Jayakumar, T. Bindiya
In today’s world, security threats for vehicles are on a constant rise and hence the necessity for ensuring safety for automobiles is of prime importance. Over speeding in accident prone areas is a major cause of road accidents. Accident rates are higher in some areas like school zones, hilly areas, highways, slippery terrains etc. It is in this context that Intelligent Transport Systems is an important and developing field. In this work, the conventional networks GSM and GPS have been used along with sensors positioned in the vehicle. Parking issues are another serious issue which needs attention. This is highly challenging because of the fact that a typical modern automobile doesn’t contain any systems in place to make parking easy. Thus, the objective of this work is to create a vehicular tracking system to ensure the safety of the vehicle and an efficient Automatic Parking system wherein parallel parking is done autonomously and efficiently.
{"title":"Automobile Safety and Automatic Parking System using Sensors and Conventional Wireless Networks","authors":"M. Nasreen, Madhooshri Iyer, E. P. Jayakumar, T. Bindiya","doi":"10.1109/CCCS.2018.8586833","DOIUrl":"https://doi.org/10.1109/CCCS.2018.8586833","url":null,"abstract":"In today’s world, security threats for vehicles are on a constant rise and hence the necessity for ensuring safety for automobiles is of prime importance. Over speeding in accident prone areas is a major cause of road accidents. Accident rates are higher in some areas like school zones, hilly areas, highways, slippery terrains etc. It is in this context that Intelligent Transport Systems is an important and developing field. In this work, the conventional networks GSM and GPS have been used along with sensors positioned in the vehicle. Parking issues are another serious issue which needs attention. This is highly challenging because of the fact that a typical modern automobile doesn’t contain any systems in place to make parking easy. Thus, the objective of this work is to create a vehicular tracking system to ensure the safety of the vehicle and an efficient Automatic Parking system wherein parallel parking is done autonomously and efficiently.","PeriodicalId":6570,"journal":{"name":"2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS)","volume":"42 1","pages":"51-55"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74630855","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 : 2018-10-01DOI: 10.1109/CCCS.2018.8586821
Lyla B. Das, A. C., John K. Sunny
The largest financial market in the world is the Forex (Foreign Currency Exchange) market, by virtue of the highest volume of trading that takes place on a daily basis. Being able to read underlying market patterns and making smart choices amidst the turbulent and organic Forex marketplace is the first step to decision making. Traders and investors harvest profitable returns from Forex market by buying and selling when the exchange rates are respectively low and high. Indicator analyses can be used to locate the ideal times to convert back and forth between currencies. These Indicator analyses themselves involve parameters, which are usually chosen manually from experience, which usually are not the optimal choices. The parameters can be optimized from historic data using software, but this is computationally intensive and time consuming. In this paper, we propose a method to speed up the optimization of indicator parameters, using CUDA parallel processing API of NVIDIA GPUs (Graphical Processing Units) as opposed to the classic CPU based sequential approach. While it seemed logical to incorporate several high-end processors (CPUs) in order to harness more computing power, we aim at demonstrating that a GPU based implementation, based on suitably written kernels and threads, has the potential to be scaled for industrial use.
{"title":"Big Data Forex Analysis using GPU Computing","authors":"Lyla B. Das, A. C., John K. Sunny","doi":"10.1109/CCCS.2018.8586821","DOIUrl":"https://doi.org/10.1109/CCCS.2018.8586821","url":null,"abstract":"The largest financial market in the world is the Forex (Foreign Currency Exchange) market, by virtue of the highest volume of trading that takes place on a daily basis. Being able to read underlying market patterns and making smart choices amidst the turbulent and organic Forex marketplace is the first step to decision making. Traders and investors harvest profitable returns from Forex market by buying and selling when the exchange rates are respectively low and high. Indicator analyses can be used to locate the ideal times to convert back and forth between currencies. These Indicator analyses themselves involve parameters, which are usually chosen manually from experience, which usually are not the optimal choices. The parameters can be optimized from historic data using software, but this is computationally intensive and time consuming. In this paper, we propose a method to speed up the optimization of indicator parameters, using CUDA parallel processing API of NVIDIA GPUs (Graphical Processing Units) as opposed to the classic CPU based sequential approach. While it seemed logical to incorporate several high-end processors (CPUs) in order to harness more computing power, we aim at demonstrating that a GPU based implementation, based on suitably written kernels and threads, has the potential to be scaled for industrial use.","PeriodicalId":6570,"journal":{"name":"2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS)","volume":"104 1","pages":"14-19"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87721847","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 : 2018-10-01DOI: 10.1109/CCCS.2018.8586812
T. Zaidi, Nitya Nand Dwivedi
VoIP transfer voice over networks such as LAN. This technology is growing rapidly due to support of existing network infrastructure at low cost. Various simulations have been done and it is observed that by increasing the VoIP client, packet length and traffic arrival rate the performance of step network affected. In the current work packet dropped, packet received, voice traffic sent and end-to-end delay is estimated for various queuing disciplines like PQ, FIFO and WFQ. It is depicted that queuing disciplines effects the applications performance and utilization of resources.
{"title":"Voice Packet Performance Estimation through Step Network Using OPNET","authors":"T. Zaidi, Nitya Nand Dwivedi","doi":"10.1109/CCCS.2018.8586812","DOIUrl":"https://doi.org/10.1109/CCCS.2018.8586812","url":null,"abstract":"VoIP transfer voice over networks such as LAN. This technology is growing rapidly due to support of existing network infrastructure at low cost. Various simulations have been done and it is observed that by increasing the VoIP client, packet length and traffic arrival rate the performance of step network affected. In the current work packet dropped, packet received, voice traffic sent and end-to-end delay is estimated for various queuing disciplines like PQ, FIFO and WFQ. It is depicted that queuing disciplines effects the applications performance and utilization of resources.","PeriodicalId":6570,"journal":{"name":"2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS)","volume":"27 1","pages":"156-160"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79680377","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 : 2018-10-01DOI: 10.1109/CCCS.2018.8586804
Mounesh Marali, S. Sudarsan
Challenge of graceful migration through upgrades and updates is a daunting task for process control systems. Issues confronted include aging hardware and software, as well as shortage of process experts with knowledge of vintage control systems. We offer techniques and solutions for graceful evolution to current generation control system. Our approach covers entire system including HMI, Controller, I/O and Field Interface Layers. We showcase stepwise implementation approach while addressing the concerns. We also provide cost-benefit analysis and pre-requisites for choosing specific evolution path.
{"title":"Graceful Reincarnation of Legacy Industrial Control Systems","authors":"Mounesh Marali, S. Sudarsan","doi":"10.1109/CCCS.2018.8586804","DOIUrl":"https://doi.org/10.1109/CCCS.2018.8586804","url":null,"abstract":"Challenge of graceful migration through upgrades and updates is a daunting task for process control systems. Issues confronted include aging hardware and software, as well as shortage of process experts with knowledge of vintage control systems. We offer techniques and solutions for graceful evolution to current generation control system. Our approach covers entire system including HMI, Controller, I/O and Field Interface Layers. We showcase stepwise implementation approach while addressing the concerns. We also provide cost-benefit analysis and pre-requisites for choosing specific evolution path.","PeriodicalId":6570,"journal":{"name":"2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS)","volume":"1 1","pages":"224-229"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89169476","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 : 2018-10-01DOI: 10.1109/CCCS.2018.8586811
Nishtha Hooda, S. Bawa, P. Rana
Many crucial applications of wireless sensor networks rely radically on routing protocols for an efficient data delivery. This paper presents a case study of scrutinizing the use-fulness of hybridization of machine learning classifiers in order to develop a Multi-Criteria Topsis based Ensemble (MCTOPE) framework. Technique for Order of preferences by similarity to Ideal Solution (TOPSIS), a multi-criteria assessment algorithm is employed to optimize the built ensemble learner for the prediction of an optimal reactive routing protocol for a wireless sensor network (WSN). The performance of the framework is first validated using six different machine learning datasets, and then the proposed method is implemented as a web application using R script and Python Django web framework. After experimenting with more than thousand combinations of training samples and ten base classifiers for the routing protocol prediction problem, MCTOPE framework builds an ensemble of support vector machine and neural network classifiers with an accuracy of 99.6%, which is far better, when it is compared with the performance of state-of-the-art classifiers. With the appearance of tremendous growth of machine learning classifiers in plenty of applications, an automatic ensemble building machine learning technique helps in minimizing the risk of obtaining poor results from a single classifier system, and will play a big part for efficient predictions in the future.
{"title":"MCTOPE Ensemble Machine Learning Framework: A Case Study of Routing Protocol Prediction","authors":"Nishtha Hooda, S. Bawa, P. Rana","doi":"10.1109/CCCS.2018.8586811","DOIUrl":"https://doi.org/10.1109/CCCS.2018.8586811","url":null,"abstract":"Many crucial applications of wireless sensor networks rely radically on routing protocols for an efficient data delivery. This paper presents a case study of scrutinizing the use-fulness of hybridization of machine learning classifiers in order to develop a Multi-Criteria Topsis based Ensemble (MCTOPE) framework. Technique for Order of preferences by similarity to Ideal Solution (TOPSIS), a multi-criteria assessment algorithm is employed to optimize the built ensemble learner for the prediction of an optimal reactive routing protocol for a wireless sensor network (WSN). The performance of the framework is first validated using six different machine learning datasets, and then the proposed method is implemented as a web application using R script and Python Django web framework. After experimenting with more than thousand combinations of training samples and ten base classifiers for the routing protocol prediction problem, MCTOPE framework builds an ensemble of support vector machine and neural network classifiers with an accuracy of 99.6%, which is far better, when it is compared with the performance of state-of-the-art classifiers. With the appearance of tremendous growth of machine learning classifiers in plenty of applications, an automatic ensemble building machine learning technique helps in minimizing the risk of obtaining poor results from a single classifier system, and will play a big part for efficient predictions in the future.","PeriodicalId":6570,"journal":{"name":"2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS)","volume":"5 1","pages":"92-99"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84764765","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 : 2018-10-01DOI: 10.1109/CCCS.2018.8586809
Rojina Deuja, Rozy Karna, Ramesh Kusatha
The scientific exploration of data for educational research is often referred to as Educational Data Mining (EDM). EDM concentrates upon devising methods for evaluating data coming from educational settings to understand students and the locale in which they study. This paper, in particular, encompasses those students who are currently pursuing their higher education. In spite of a substantial inclination of students towards getting a degree, the success rate is remarkably low. Numerous studies have been conducted, seeking to develop methodologies that identify students who are at risk of unsatisfactory performance. In our approach, we explore multiple factors that have been theoretically assumed to affect the performance of students in college and use neural networks to predict their grades. We also introduce the scientific assessment of course difficulty prior to using it as a measure for a students’ performance in that course. The model can, therefore, be utilized to identify students who are most likely to perform under par and assist them in achieving better grades.
{"title":"Data-Driven Predictive Analysis of Student Performance In College Using Neural Networks","authors":"Rojina Deuja, Rozy Karna, Ramesh Kusatha","doi":"10.1109/CCCS.2018.8586809","DOIUrl":"https://doi.org/10.1109/CCCS.2018.8586809","url":null,"abstract":"The scientific exploration of data for educational research is often referred to as Educational Data Mining (EDM). EDM concentrates upon devising methods for evaluating data coming from educational settings to understand students and the locale in which they study. This paper, in particular, encompasses those students who are currently pursuing their higher education. In spite of a substantial inclination of students towards getting a degree, the success rate is remarkably low. Numerous studies have been conducted, seeking to develop methodologies that identify students who are at risk of unsatisfactory performance. In our approach, we explore multiple factors that have been theoretically assumed to affect the performance of students in college and use neural networks to predict their grades. We also introduce the scientific assessment of course difficulty prior to using it as a measure for a students’ performance in that course. The model can, therefore, be utilized to identify students who are most likely to perform under par and assist them in achieving better grades.","PeriodicalId":6570,"journal":{"name":"2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS)","volume":"18 1","pages":"77-81"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82427163","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 : 2018-10-01DOI: 10.1109/CCCS.2018.8586814
Yogesh Kothyari, Ajit Singh
The workflow preparation complications contain quality of service. The task-reserve mapping sustaining workflow solicitations necessitate extraordinary computational power and often involve a large amount of data transmission from one place to another. Furthermore, due to dependency exist on among tasks schedulers must be brought forth according to given preference constraints. Cloud computing is a new business-oriented platform service that facilitates an infinite number of services by providing heterogeneous and virtualized resources to users based on a pay-as-you-go model. The distinctive quality of service (QoS) is market-oriented and conventional approach for facing new challenges like autonomy, on-demand payment and unprecedented openness. This paper presents multi-objective workflow scheduling algorithm (MWSA) which optimally run the workflow execution process for minimization of total cost and makespan. This algorithm uses the concept of an adaptive elite-based particle-swarm-optimization (PSO) for implementing task-resource mapping. A comparative study of presented algorithm is also made with some existing algorithms.
{"title":"An Account Of Multi-Neutral Workflow Procedure For Cloud Situation","authors":"Yogesh Kothyari, Ajit Singh","doi":"10.1109/CCCS.2018.8586814","DOIUrl":"https://doi.org/10.1109/CCCS.2018.8586814","url":null,"abstract":"The workflow preparation complications contain quality of service. The task-reserve mapping sustaining workflow solicitations necessitate extraordinary computational power and often involve a large amount of data transmission from one place to another. Furthermore, due to dependency exist on among tasks schedulers must be brought forth according to given preference constraints. Cloud computing is a new business-oriented platform service that facilitates an infinite number of services by providing heterogeneous and virtualized resources to users based on a pay-as-you-go model. The distinctive quality of service (QoS) is market-oriented and conventional approach for facing new challenges like autonomy, on-demand payment and unprecedented openness. This paper presents multi-objective workflow scheduling algorithm (MWSA) which optimally run the workflow execution process for minimization of total cost and makespan. This algorithm uses the concept of an adaptive elite-based particle-swarm-optimization (PSO) for implementing task-resource mapping. A comparative study of presented algorithm is also made with some existing algorithms.","PeriodicalId":6570,"journal":{"name":"2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS)","volume":"32 1","pages":"206-209"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76528772","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 : 2018-10-01DOI: 10.1109/cccs.2018.8586837
{"title":"ICCCS 2018 Organizing Committee","authors":"","doi":"10.1109/cccs.2018.8586837","DOIUrl":"https://doi.org/10.1109/cccs.2018.8586837","url":null,"abstract":"","PeriodicalId":6570,"journal":{"name":"2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS)","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90666277","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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