Pub Date : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234471
G. Mukherjee, Arpitam Chatterjee, B. Tudu
Medicinal plants are getting increasingly popular across the world for their ability to cure different diseases including chronic ones. The chemical compositions present in those plant leaves are main contributors for the healing characteristics. The potential of using such plants also depends on the maturity of the medicinal plant under use. The leaves with appropriate maturity can cause better healing potential. This paper presents a computer vision based approach towards identification of medicinal leaves namely Kalmegh and Tulsi against the different maturity levels. The morphological features from the processed images of leaves with different maturity levels are extracted in this work. The feature sets are subjected to Principal Component Analysis (PCA) based identification and separability measures for identification purpose. The results show that the presented morphological feature based maturity identification can be a promising method.
{"title":"Morphological feature based maturity level identification of Kalmegh and Tulsi leaves","authors":"G. Mukherjee, Arpitam Chatterjee, B. Tudu","doi":"10.1109/ICRCICN.2017.8234471","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234471","url":null,"abstract":"Medicinal plants are getting increasingly popular across the world for their ability to cure different diseases including chronic ones. The chemical compositions present in those plant leaves are main contributors for the healing characteristics. The potential of using such plants also depends on the maturity of the medicinal plant under use. The leaves with appropriate maturity can cause better healing potential. This paper presents a computer vision based approach towards identification of medicinal leaves namely Kalmegh and Tulsi against the different maturity levels. The morphological features from the processed images of leaves with different maturity levels are extracted in this work. The feature sets are subjected to Principal Component Analysis (PCA) based identification and separability measures for identification purpose. The results show that the presented morphological feature based maturity identification can be a promising method.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115678509","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 : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234500
Sushila Maheshkar, Bhavishya Mathur, R. Roushan, Ajay Kumar Mallick
With the rapid increase in data and its complexity of configuration and deployment bring a new challenge to the research community. In this scenario, automatic Hadoop cluster deployment and management tool provide a new horizon which plays a vital role in resource and packet management in distributed environment. This paper describes a low-cost Automatic Hadoop Cluster Deployment and Management tool supported with a Web Application. The application is capable of detecting machines in the network as well as setting up the cluster with the results being provided to the user on a Web Interface from where the user could manage the entire cluster intuitively to gain a better user experience. The web application helps the user to control the management of the cluster along with access of the remote systems whenever necessary.
{"title":"Automatic hadoop cluster deployment and management tool","authors":"Sushila Maheshkar, Bhavishya Mathur, R. Roushan, Ajay Kumar Mallick","doi":"10.1109/ICRCICN.2017.8234500","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234500","url":null,"abstract":"With the rapid increase in data and its complexity of configuration and deployment bring a new challenge to the research community. In this scenario, automatic Hadoop cluster deployment and management tool provide a new horizon which plays a vital role in resource and packet management in distributed environment. This paper describes a low-cost Automatic Hadoop Cluster Deployment and Management tool supported with a Web Application. The application is capable of detecting machines in the network as well as setting up the cluster with the results being provided to the user on a Web Interface from where the user could manage the entire cluster intuitively to gain a better user experience. The web application helps the user to control the management of the cluster along with access of the remote systems whenever necessary.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127132692","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 : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234518
S. Mazumdar, Rohit Choudhary, A. Swetapadma
This work proposes an ANN based method for fetal heart rate monitoring. Various measurements are taken and given as input to the ANN based classifier to detect fetal health such as ‘Normal’, ‘Suspect’ and ‘Pathologic’. All the design and simulation works are carried out with MATLAB software. ANN based classifier is trained with data from various recordings of cardiotocography. After the network is trained it is tested with various test cases. Performance of the network is checked in terms of percentage accuracy. The proposed method is found to be 99.9% accurate in detecting the fetal health. Hence the proposed ANN based method can be used effectively for fetal health monitoring.
{"title":"An innovative method for fetal health monitoring based on artificial neural network using cardiotocography measurements","authors":"S. Mazumdar, Rohit Choudhary, A. Swetapadma","doi":"10.1109/ICRCICN.2017.8234518","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234518","url":null,"abstract":"This work proposes an ANN based method for fetal heart rate monitoring. Various measurements are taken and given as input to the ANN based classifier to detect fetal health such as ‘Normal’, ‘Suspect’ and ‘Pathologic’. All the design and simulation works are carried out with MATLAB software. ANN based classifier is trained with data from various recordings of cardiotocography. After the network is trained it is tested with various test cases. Performance of the network is checked in terms of percentage accuracy. The proposed method is found to be 99.9% accurate in detecting the fetal health. Hence the proposed ANN based method can be used effectively for fetal health monitoring.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122330225","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 : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234502
Asit Barman, P. Dutta
In this paper, we propose a novel framework for expression recognition by using salient landmarks induced shape signature. Detection of effective landmarks is achieved by appearance based models. A grid is formed using the landmark points and accordingly several triangles within the grid on the basis of a nose landmark reference point are formed. Normalized shape signature is derived from grid. Stability index is calculated from shape signature which is also exploited as significant feature to recognize the facial expressions. Statistical measures such as range, moment, skewness, kurtosis and entropy are used to supplement the feature set. This enhanced feature set is fed into Multilayer Perceptron (MLP) and Nonlinear AutoRegressive with eXogenous (NARX) to differentiate the expressions into different categories. We investigated our proposed system on Cohn-Kanade (CK+), JAFFE, MMI and MUG benchmark databases to conduct and validate our experiment and established its performance superiority over other existing competitors.
{"title":"Facial expression recognition using shape signature feature","authors":"Asit Barman, P. Dutta","doi":"10.1109/ICRCICN.2017.8234502","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234502","url":null,"abstract":"In this paper, we propose a novel framework for expression recognition by using salient landmarks induced shape signature. Detection of effective landmarks is achieved by appearance based models. A grid is formed using the landmark points and accordingly several triangles within the grid on the basis of a nose landmark reference point are formed. Normalized shape signature is derived from grid. Stability index is calculated from shape signature which is also exploited as significant feature to recognize the facial expressions. Statistical measures such as range, moment, skewness, kurtosis and entropy are used to supplement the feature set. This enhanced feature set is fed into Multilayer Perceptron (MLP) and Nonlinear AutoRegressive with eXogenous (NARX) to differentiate the expressions into different categories. We investigated our proposed system on Cohn-Kanade (CK+), JAFFE, MMI and MUG benchmark databases to conduct and validate our experiment and established its performance superiority over other existing competitors.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122912764","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 : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234507
Sandip Joardar, Dwaipayan Sen, Diparnab Sen, Arnab Sanyal, A. Chatterjee
This paper presents a Pose Invariant Face Recognition algorithm for pose-variance in face databases, which is one of the toughest challenges of any face recognition based biometrics, using a novel feature extraction technique. The feature extraction of the raw images is based upon a novel patch-wise self-similarity measure within an image. The algorithm has been tested upon a Far-infrared (FIR) imaging based Face database called the JU-FIR-F1: FIR Face Database that has been developed in the Electrical Instrumentation and Measurement Laboratory, Electrical Engineering Department, Jadavpur University, Kolkata, India. The results obtained through extensive experimentation clearly demonstrate the superiority of the proposed method over the existing algorithms.
{"title":"Pose invariant thermal face recognition using patch-wise self-similarity features","authors":"Sandip Joardar, Dwaipayan Sen, Diparnab Sen, Arnab Sanyal, A. Chatterjee","doi":"10.1109/ICRCICN.2017.8234507","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234507","url":null,"abstract":"This paper presents a Pose Invariant Face Recognition algorithm for pose-variance in face databases, which is one of the toughest challenges of any face recognition based biometrics, using a novel feature extraction technique. The feature extraction of the raw images is based upon a novel patch-wise self-similarity measure within an image. The algorithm has been tested upon a Far-infrared (FIR) imaging based Face database called the JU-FIR-F1: FIR Face Database that has been developed in the Electrical Instrumentation and Measurement Laboratory, Electrical Engineering Department, Jadavpur University, Kolkata, India. The results obtained through extensive experimentation clearly demonstrate the superiority of the proposed method over the existing algorithms.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126971511","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 : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234499
J. Datta, Indrajit Pan, S. Bhattacharyya
Cloud services are gaining popularity with times. Service level agreement (SLA) serves a basic understanding between the clients and cloud service providers (CSP). Ensuring secured and adequate service is a basic need of the customers. In this work a set of compliance parameters for cloud service level agreement is identified. A generic rule base is designed to empower the process with an automated model. Finally a Turing model has been developed for automated monitoring and assessment of service level agreement between cloud service provider and cloud client. This generic model is suitable for different cloud deployment models and attuned with WS-agreement set.
{"title":"TSLA: Turing based service level agreement assessment model over diverse cloud deployments","authors":"J. Datta, Indrajit Pan, S. Bhattacharyya","doi":"10.1109/ICRCICN.2017.8234499","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234499","url":null,"abstract":"Cloud services are gaining popularity with times. Service level agreement (SLA) serves a basic understanding between the clients and cloud service providers (CSP). Ensuring secured and adequate service is a basic need of the customers. In this work a set of compliance parameters for cloud service level agreement is identified. A generic rule base is designed to empower the process with an automated model. Finally a Turing model has been developed for automated monitoring and assessment of service level agreement between cloud service provider and cloud client. This generic model is suitable for different cloud deployment models and attuned with WS-agreement set.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"41 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132389583","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 : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234506
Priyojit Das, Sujay Saha
Form the time of its invention, microarray technology is continuously growing and has been taking major role in biological research. This technology generates huge amount of gene expression data for biological analysis. Parallel computation methods are required to find functional associations from this large amount of biological data. An unsupervised machine learning technique, clustering algorithm groups similar genes based on entire conditions. But normal clustering methods cannot find different cellular processes from gene expression data because a biological activity can start functioning in the presence of some specific conditions. So, biclustering techniques are used instead of normal clustering. Biclustering basically identifies a set of genes that are co-expressed for some specific experimental conditions. Here we introduce an improved shuffled frog leaping algorithm(SFLA) based approach to find biclusters. SFLA is a hybrid of evolutionary memetic algorithm and collective intelligence based particle swarm optimization algorithm. Also It has faster convergence speed. By applying the proposed algorithm on yeast (Saccharomyces cerevisiae) cell cycle dataset, large number of biologically significant biclusters are obtained, which are verified by gene ontology database, compared to other existing algorithms. Also the biclusters have small MSR value and large size.
{"title":"A novel SFLA based method for gene expression biclustering","authors":"Priyojit Das, Sujay Saha","doi":"10.1109/ICRCICN.2017.8234506","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234506","url":null,"abstract":"Form the time of its invention, microarray technology is continuously growing and has been taking major role in biological research. This technology generates huge amount of gene expression data for biological analysis. Parallel computation methods are required to find functional associations from this large amount of biological data. An unsupervised machine learning technique, clustering algorithm groups similar genes based on entire conditions. But normal clustering methods cannot find different cellular processes from gene expression data because a biological activity can start functioning in the presence of some specific conditions. So, biclustering techniques are used instead of normal clustering. Biclustering basically identifies a set of genes that are co-expressed for some specific experimental conditions. Here we introduce an improved shuffled frog leaping algorithm(SFLA) based approach to find biclusters. SFLA is a hybrid of evolutionary memetic algorithm and collective intelligence based particle swarm optimization algorithm. Also It has faster convergence speed. By applying the proposed algorithm on yeast (Saccharomyces cerevisiae) cell cycle dataset, large number of biologically significant biclusters are obtained, which are verified by gene ontology database, compared to other existing algorithms. Also the biclusters have small MSR value and large size.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"118 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131914065","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 : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234498
Anirbit Sengupta, D. Sengupta, Abhijit Das
A MANET or Mobile Ad-Hoc Network is decentralized in nature and it is basically a collection of heterogeneous mobile nodes which are autonomous and can communicate among themselves over the wireless link. In this infrastructure-less network, all the nodes dynamically route the packets by themselves with help of some protocols for sending or receiving the packet information. The ZRP or Zone Routing Protocol for MANET is a hybrid kind of routing protocol which uses the advantages of both proactive and reactive route discovery mechanisms while communicating locally within a cluster or communicating to a different cluster respectively. In our work we have modified the ZRP in three different approaches. We have extensively simulated and compared the performance of traditional ZRP and our Modified ZRP which coined as EZRP using OPNET network simulator. The results indicate that Modified methods perform better than the traditional
{"title":"Designing an enhanced ZRP algorithm for MANET and simulation using OPNET","authors":"Anirbit Sengupta, D. Sengupta, Abhijit Das","doi":"10.1109/ICRCICN.2017.8234498","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234498","url":null,"abstract":"A MANET or Mobile Ad-Hoc Network is decentralized in nature and it is basically a collection of heterogeneous mobile nodes which are autonomous and can communicate among themselves over the wireless link. In this infrastructure-less network, all the nodes dynamically route the packets by themselves with help of some protocols for sending or receiving the packet information. The ZRP or Zone Routing Protocol for MANET is a hybrid kind of routing protocol which uses the advantages of both proactive and reactive route discovery mechanisms while communicating locally within a cluster or communicating to a different cluster respectively. In our work we have modified the ZRP in three different approaches. We have extensively simulated and compared the performance of traditional ZRP and our Modified ZRP which coined as EZRP using OPNET network simulator. The results indicate that Modified methods perform better than the traditional","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115231858","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 : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234476
Nimish Ronghe, Sayali S. Nakashe, A. Pawar, S. Bobde
Facial analysis in videos and images has been a relatively tough task for machine learning models. Recent use of deep learning approaches has demonstrated substantial improvement in results and reliability and can be used for problems such as face recognition, emotion recognition and emotion reaction prediction. In the case of emotion reaction, relevant information of emotions in individual frames often must be aggregated over a variable length sequence of frames and speech signal to produce an appreciable prediction. Emotion reaction prediction is a subset of sequence analysis task and heavily relies on dynamic temporal and spectral features. Convolution neural networks (CNNs) have been extensively used for emotion recognition problems and have produced reliable results. However, they lack the ability to extract time-series information from a sequence of inputs and cannot model an emotion transaction. Recurrent neural networks (RNNs) are being used profoundly due to their ability to yield impressive results on a variety of tasks in the field of sequence analysis. In this work, we propose a system for emotion recognition and reaction prediction in videos. The primary focus is experimental analysis of a hybrid CNN-RNN architecture for emotion transaction analysis that can recognize the emotion in a frame in a video and predict its appropriate reaction.
{"title":"Emotion recognition and reaction prediction in videos","authors":"Nimish Ronghe, Sayali S. Nakashe, A. Pawar, S. Bobde","doi":"10.1109/ICRCICN.2017.8234476","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234476","url":null,"abstract":"Facial analysis in videos and images has been a relatively tough task for machine learning models. Recent use of deep learning approaches has demonstrated substantial improvement in results and reliability and can be used for problems such as face recognition, emotion recognition and emotion reaction prediction. In the case of emotion reaction, relevant information of emotions in individual frames often must be aggregated over a variable length sequence of frames and speech signal to produce an appreciable prediction. Emotion reaction prediction is a subset of sequence analysis task and heavily relies on dynamic temporal and spectral features. Convolution neural networks (CNNs) have been extensively used for emotion recognition problems and have produced reliable results. However, they lack the ability to extract time-series information from a sequence of inputs and cannot model an emotion transaction. Recurrent neural networks (RNNs) are being used profoundly due to their ability to yield impressive results on a variety of tasks in the field of sequence analysis. In this work, we propose a system for emotion recognition and reaction prediction in videos. The primary focus is experimental analysis of a hybrid CNN-RNN architecture for emotion transaction analysis that can recognize the emotion in a frame in a video and predict its appropriate reaction.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115685772","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 : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234534
M. Kar, M. Mandal, Debashis Nandi
In the modern digital era, the storage of digital data in multi-users systems and transmission over the internet is prime important in the society. The cryptographic technique is one of the possible solutions for protecting digital information for unauthorized uses. In this technique, the encryption algorithm should be simple to design and implementation. In the proposed article, a novel color image encryption algorithm is presented employing a 4D Lorenz system. The encryption technique is very sensitive to the input 256 bits key and input plane image. Initially, the color image is split into three different channels R, G, and B. Next each channel is encrypted separately by employing diffusion and confusion mechanism. Finally, all three channels R, G, and B are combined to produce the required encrypted image. Various security tests have been conducted to prove the validity of the presented encryption technique. The encryption time was considerably less as compared to other reported works.
{"title":"RGB image encryption using hyper chaotic system","authors":"M. Kar, M. Mandal, Debashis Nandi","doi":"10.1109/ICRCICN.2017.8234534","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234534","url":null,"abstract":"In the modern digital era, the storage of digital data in multi-users systems and transmission over the internet is prime important in the society. The cryptographic technique is one of the possible solutions for protecting digital information for unauthorized uses. In this technique, the encryption algorithm should be simple to design and implementation. In the proposed article, a novel color image encryption algorithm is presented employing a 4D Lorenz system. The encryption technique is very sensitive to the input 256 bits key and input plane image. Initially, the color image is split into three different channels R, G, and B. Next each channel is encrypted separately by employing diffusion and confusion mechanism. Finally, all three channels R, G, and B are combined to produce the required encrypted image. Various security tests have been conducted to prove the validity of the presented encryption technique. The encryption time was considerably less as compared to other reported works.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114378114","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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