Pub Date : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703220
Bhagyalakshmi, A. Dogra
Mobile ad hoc network (MANET) is a self-organized and self-configurable infrastructure less network where the mobile nodes move arbitrarily. One of the major characteristic that differentiate mobile ad-hoc networks from other types of networks is the ability of the mobile nodes to receive and forward packets as a router. The focus of the work is to devise a strategy to control the flooding of control packets through the network in a way to improve the QoS parameters associated with MANETs. The proposed strategy tries to reduce the number of the intermediate nodes that participate in the route discovery process thereby, reducing the total number of control packets that are forwarded by the nodes in the network. This is achieved by controlling the route request (RREQ) broadcast storm using the node’s queue length. The source appends a random number with RREQ which is compared with the queue vacancy proportion at each intermediate node. The intermediate node relays the RREQ packet if the random number generated is less than the queue vacancy proportion. This reduces the number of congested nodes forwarding the RREQ packets thereby improving QoS parameters, preserving the energy and increasing the overall network lifetime. The proposed algorithm Q-AODV is advancement over AODV that tries to find a less congested route based on queue vacancy. The proposed algorithm QAODV improves average end to end delay, throughput and jitter, to some extent, as compared to AODV. The simulation has been carried out on Qualnet.
{"title":"Q-AODV: A Flood control Ad-Hoc on Demand Distance Vector Routing Protocol","authors":"Bhagyalakshmi, A. Dogra","doi":"10.1109/ICSCCC.2018.8703220","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703220","url":null,"abstract":"Mobile ad hoc network (MANET) is a self-organized and self-configurable infrastructure less network where the mobile nodes move arbitrarily. One of the major characteristic that differentiate mobile ad-hoc networks from other types of networks is the ability of the mobile nodes to receive and forward packets as a router. The focus of the work is to devise a strategy to control the flooding of control packets through the network in a way to improve the QoS parameters associated with MANETs. The proposed strategy tries to reduce the number of the intermediate nodes that participate in the route discovery process thereby, reducing the total number of control packets that are forwarded by the nodes in the network. This is achieved by controlling the route request (RREQ) broadcast storm using the node’s queue length. The source appends a random number with RREQ which is compared with the queue vacancy proportion at each intermediate node. The intermediate node relays the RREQ packet if the random number generated is less than the queue vacancy proportion. This reduces the number of congested nodes forwarding the RREQ packets thereby improving QoS parameters, preserving the energy and increasing the overall network lifetime. The proposed algorithm Q-AODV is advancement over AODV that tries to find a less congested route based on queue vacancy. The proposed algorithm QAODV improves average end to end delay, throughput and jitter, to some extent, as compared to AODV. The simulation has been carried out on Qualnet.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"233 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114598334","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 : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703280
K. Gupta, P. Chatur
Functional Magnetic Resonance imaging (fMRI) provides sequence of 3D images which contains large number of voxels as information. There are many statistical methods evolved in last few years to analyze this information. Main concern of all these techniques is huge dimensions of the data produced by these images. This paper proposes an efficient hybrid method for feature selection and classification. This method combine entropy based genetic algorithm (EGA) with Linear Collaborative Discriminant Regression Classification (LCDRC) to form feature based classification method. Entropy based genetic algorithm is applied to find maximum significance between the input and output and also it radically reduces the redundancy within the input features. Experiments’ using Star-Plus dataset to classify fMRI images shows that EGA-LCDRC reduces up to 60% features and produces 96.73% accuracy.
{"title":"Cognitive State Classification using Genetic Algorithm based Linear Collaborative Discriminant Regression","authors":"K. Gupta, P. Chatur","doi":"10.1109/ICSCCC.2018.8703280","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703280","url":null,"abstract":"Functional Magnetic Resonance imaging (fMRI) provides sequence of 3D images which contains large number of voxels as information. There are many statistical methods evolved in last few years to analyze this information. Main concern of all these techniques is huge dimensions of the data produced by these images. This paper proposes an efficient hybrid method for feature selection and classification. This method combine entropy based genetic algorithm (EGA) with Linear Collaborative Discriminant Regression Classification (LCDRC) to form feature based classification method. Entropy based genetic algorithm is applied to find maximum significance between the input and output and also it radically reduces the redundancy within the input features. Experiments’ using Star-Plus dataset to classify fMRI images shows that EGA-LCDRC reduces up to 60% features and produces 96.73% accuracy.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"531 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116229144","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 : 2018-12-01DOI: 10.1109/icsccc.2018.8703274
{"title":"ICSCCC 2018 Table of Contents","authors":"","doi":"10.1109/icsccc.2018.8703274","DOIUrl":"https://doi.org/10.1109/icsccc.2018.8703274","url":null,"abstract":"","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114763498","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 : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703347
Neha Gigi, AmanPreet Kaur
Information mining is an assignment which is utilized to discover the concealed example or data to break down any subject. These days a ton of research is going on web mining i.e. to mine the web assets to discover the example or shrouded data. In our research the main aim is to perform the text mining over the real time data to predict the result of election that which party will win the state or national election held in India. In our work we get the data from twitter where the citizens of India give the opinion about the political parties and the analysis of these sentiments is done to conclude the result.
{"title":"Sentimental Analysis On Social Feeds to Predict the Elections","authors":"Neha Gigi, AmanPreet Kaur","doi":"10.1109/ICSCCC.2018.8703347","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703347","url":null,"abstract":"Information mining is an assignment which is utilized to discover the concealed example or data to break down any subject. These days a ton of research is going on web mining i.e. to mine the web assets to discover the example or shrouded data. In our research the main aim is to perform the text mining over the real time data to predict the result of election that which party will win the state or national election held in India. In our work we get the data from twitter where the citizens of India give the opinion about the political parties and the analysis of these sentiments is done to conclude the result.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114638843","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 : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703310
Tanuj Wala, N. Chand, A. Sharma
Recently wireless sensor network has become attractive field of research. A wireless sensor network (WSN) contains plenty of sensor nodes (SNs) that continuously monitor the physical phenomenon. The sensed data is cooperatively transferred through the network towards sink. In large wireless sensor network numerous nodes are deployed together to perform a task. The nodes have sensing, computing and communication features. Since the nodes are constrained in power, storage and computation, so it is necessary to deal with these issues efficiently. Therefore, a greater challenge is involved in saving energy and enhancing the lifespan of the network. Connectivity among the sensor nodes is another critical challenge in WSN due to limited communication range of sensor nodes. As a result, there arises the problem of network disconnection and huge energy consumption. The issues have been addressed in this paper and the proposal contains two phase scheme. In first phase, SGP (Spectral Graph Partitioning) technique is applied for the identification of disconnected portion of the network. Second phase is based on the improved EM (Expectation Maximization) clustering scheme for efficient cluster head and group head selection to diminish the energy consumption in the network.
{"title":"Group Head Selection in WSN using EM Algorithm","authors":"Tanuj Wala, N. Chand, A. Sharma","doi":"10.1109/ICSCCC.2018.8703310","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703310","url":null,"abstract":"Recently wireless sensor network has become attractive field of research. A wireless sensor network (WSN) contains plenty of sensor nodes (SNs) that continuously monitor the physical phenomenon. The sensed data is cooperatively transferred through the network towards sink. In large wireless sensor network numerous nodes are deployed together to perform a task. The nodes have sensing, computing and communication features. Since the nodes are constrained in power, storage and computation, so it is necessary to deal with these issues efficiently. Therefore, a greater challenge is involved in saving energy and enhancing the lifespan of the network. Connectivity among the sensor nodes is another critical challenge in WSN due to limited communication range of sensor nodes. As a result, there arises the problem of network disconnection and huge energy consumption. The issues have been addressed in this paper and the proposal contains two phase scheme. In first phase, SGP (Spectral Graph Partitioning) technique is applied for the identification of disconnected portion of the network. Second phase is based on the improved EM (Expectation Maximization) clustering scheme for efficient cluster head and group head selection to diminish the energy consumption in the network.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125268712","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 : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703370
Shubham Jain, Pardeep Singh
Sentiment Analysis and Opinion Mining have been of great interest to the researchers during recent years. It is the process of classifying the opinions or sentiments according to the polarity of the text into positive, neutral and negative. Most of the organizations and industries highly depend on data analytics for their planning and decisionmaking process. Opinion mining and sentiment analysis have great importance in our day-to-day decision making from purchasing products and services to making investments. In this survey, we briefly incorporated the approaches and techniques proposed by researchers in recent investigations along with the issue related to sentiment analysis and opinion mining.
{"title":"Systematic Survey on Sentiment Analysis","authors":"Shubham Jain, Pardeep Singh","doi":"10.1109/ICSCCC.2018.8703370","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703370","url":null,"abstract":"Sentiment Analysis and Opinion Mining have been of great interest to the researchers during recent years. It is the process of classifying the opinions or sentiments according to the polarity of the text into positive, neutral and negative. Most of the organizations and industries highly depend on data analytics for their planning and decisionmaking process. Opinion mining and sentiment analysis have great importance in our day-to-day decision making from purchasing products and services to making investments. In this survey, we briefly incorporated the approaches and techniques proposed by researchers in recent investigations along with the issue related to sentiment analysis and opinion mining.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128037050","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}
In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic. The paper focuses on the use of Regression and LSTM based Machine learning to predict stock values. Factors considered are open, close, low, high and volume.
{"title":"Stock Market Prediction Using Machine Learning","authors":"I. Parmar, Navanshu Agarwal, Sheirsh Saxena, Ridam Arora, Shikhin Gupta, Himanshu Dhiman, Lokesh Chouhan","doi":"10.1109/ICSCCC.2018.8703332","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703332","url":null,"abstract":"In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic. The paper focuses on the use of Regression and LSTM based Machine learning to predict stock values. Factors considered are open, close, low, high and volume.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"36 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124508531","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 : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703200
Rajib Ghosh, Shaktideo Kumar, Prabhat Kumar
In this article, an attempt has been made to develop a system for classification of online handwritten text and non-text data from within a single online handwritten document in the most popular Indic script-Devanagari. As per our knowledge, no recognized work exists for handwritten text and non-text document classification in online mode in any Indic script. To develop this system an elliptical region-wise feature extraction approach has been proposed in this article. In this approach, each online stroke information of text and non-text documents is divided into smaller elliptical regions by constructing several concentric ellipses around the stroke. Each elliptical region is further divided into several sub-regions before extracting various structural and directional features of stroke portions from each sub region. These features are then studied in Hidden Markov Model (HMM) based classification platform. The efficiency of the present system has been measured on a self-generated dataset and it has provided promising result.
{"title":"Classification in Devanagari Script using Elliptical Region-wise Features","authors":"Rajib Ghosh, Shaktideo Kumar, Prabhat Kumar","doi":"10.1109/ICSCCC.2018.8703200","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703200","url":null,"abstract":"In this article, an attempt has been made to develop a system for classification of online handwritten text and non-text data from within a single online handwritten document in the most popular Indic script-Devanagari. As per our knowledge, no recognized work exists for handwritten text and non-text document classification in online mode in any Indic script. To develop this system an elliptical region-wise feature extraction approach has been proposed in this article. In this approach, each online stroke information of text and non-text documents is divided into smaller elliptical regions by constructing several concentric ellipses around the stroke. Each elliptical region is further divided into several sub-regions before extracting various structural and directional features of stroke portions from each sub region. These features are then studied in Hidden Markov Model (HMM) based classification platform. The efficiency of the present system has been measured on a self-generated dataset and it has provided promising result.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123722964","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 : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703273
Ojus Thomas Lee, Rajat Porwal, S. D. M. Kumar, P. Chandran
In today’s world, cloud storage systems built on distributed technology, are being used to store, manage and access massive amount of data in real time. The data replication based storage method is used by the storage service providers, to ensure fault tolerance although simple, results in storage overhead. With erasure code based storage systems, additional storage requirements to ensure fault tolerance can be reduced, while ensuring reliability equivalent to data replication. Several schemes of erasure coding existing today, however performance evaluation of such schemes through real distributed storage systems, is costly and time consuming. A feasible alternative solution for the problem is the use of simulators. In this research paper, we present a framework that simulates the behavior of an erasure code based storage system. This framework is implemented as an extension to CloudSim, thus making it a platform capable of performance evaluation of the erasure coding schemes. The simulator developed can measure the encoding, decoding delays, transmission delays and congestion. The simulator also has provisions for creating virtual storage nodes, fail the nodes and restore them as part of the testing of the erasure coded storage system.
{"title":"ECSim-2: A Performance Evaluator for Erasure Code based Storage Systems","authors":"Ojus Thomas Lee, Rajat Porwal, S. D. M. Kumar, P. Chandran","doi":"10.1109/ICSCCC.2018.8703273","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703273","url":null,"abstract":"In today’s world, cloud storage systems built on distributed technology, are being used to store, manage and access massive amount of data in real time. The data replication based storage method is used by the storage service providers, to ensure fault tolerance although simple, results in storage overhead. With erasure code based storage systems, additional storage requirements to ensure fault tolerance can be reduced, while ensuring reliability equivalent to data replication. Several schemes of erasure coding existing today, however performance evaluation of such schemes through real distributed storage systems, is costly and time consuming. A feasible alternative solution for the problem is the use of simulators. In this research paper, we present a framework that simulates the behavior of an erasure code based storage system. This framework is implemented as an extension to CloudSim, thus making it a platform capable of performance evaluation of the erasure coding schemes. The simulator developed can measure the encoding, decoding delays, transmission delays and congestion. The simulator also has provisions for creating virtual storage nodes, fail the nodes and restore them as part of the testing of the erasure coded storage system.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121571456","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 : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703278
Kavleen Kour, Jaspreet Kour, Parminder Singh
The Internet of Things abbreviated as IoT is a concept that refers to physical objects which can gather and share information. The aim of developing this concept is to develop a real time platform to communicate efficiently, smartly and quickly as compared to a system depending on human intervention. The smart objects exchange and consume data and finally analyze and manage it. It is a broad and widespread concept which has many smart applications which create better life experiences in terms of health, safety, security, business etc. In this paper we discuss the building blocks of IoT followed by its smart applications. It covers Smart city as one of its important application along with a brief study of smart city in India and abroad.
物联网(Internet of Things，简称IoT)是一个概念，指的是能够收集和共享信息的物理对象。开发这一概念的目的是开发一个实时平台，与依赖于人为干预的系统相比，可以高效、智能和快速地进行通信。智能对象交换和消费数据，并最终对其进行分析和管理。这是一个广泛而广泛的概念，它有许多智能应用，可以在健康，安全，保安，商业等方面创造更好的生活体验。在本文中，我们讨论了物联网的构建模块及其智能应用。它涵盖了智慧城市作为其重要应用之一，并简要介绍了印度和国外对智慧城市的研究。
{"title":"Smart Applications of Internet of Things","authors":"Kavleen Kour, Jaspreet Kour, Parminder Singh","doi":"10.1109/ICSCCC.2018.8703278","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703278","url":null,"abstract":"The Internet of Things abbreviated as IoT is a concept that refers to physical objects which can gather and share information. The aim of developing this concept is to develop a real time platform to communicate efficiently, smartly and quickly as compared to a system depending on human intervention. The smart objects exchange and consume data and finally analyze and manage it. It is a broad and widespread concept which has many smart applications which create better life experiences in terms of health, safety, security, business etc. In this paper we discuss the building blocks of IoT followed by its smart applications. It covers Smart city as one of its important application along with a brief study of smart city in India and abroad.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"47 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120975741","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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