Pub Date : 2019-05-31DOI: 10.34218/IJCET.10.3.2019.004
K. Dubey, G. Shrivastava
{"title":"FORESTALLING GROWTH RATE IN TYPE II DIABETIC PATIENTS USING DATA MINING AND ARTIFICIAL NEURAL NETWORKS: AN INTENSE SURVEY","authors":"K. Dubey, G. Shrivastava","doi":"10.34218/IJCET.10.3.2019.004","DOIUrl":"https://doi.org/10.34218/IJCET.10.3.2019.004","url":null,"abstract":"","PeriodicalId":38492,"journal":{"name":"International Journal of Computer Aided Engineering and Technology","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78556239","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 : 2019-05-31DOI: 10.34218/IJCET.10.3.2019.002
P. Nataraja, B. Ramesh
{"title":"MACHINE LEARNING ALGORITHMS FOR HETEROGENEOUS DATA: A COMPARATIVE STUDY","authors":"P. Nataraja, B. Ramesh","doi":"10.34218/IJCET.10.3.2019.002","DOIUrl":"https://doi.org/10.34218/IJCET.10.3.2019.002","url":null,"abstract":"","PeriodicalId":38492,"journal":{"name":"International Journal of Computer Aided Engineering and Technology","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74876150","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 : 2019-05-31DOI: 10.34218/IJCET.10.3.2019.001
S. Sriram, R. Govindarajan
{"title":"PERMUTATION LABELING OF JOINS OF KITE GRAPH","authors":"S. Sriram, R. Govindarajan","doi":"10.34218/IJCET.10.3.2019.001","DOIUrl":"https://doi.org/10.34218/IJCET.10.3.2019.001","url":null,"abstract":"","PeriodicalId":38492,"journal":{"name":"International Journal of Computer Aided Engineering and Technology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90214788","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 : 2019-05-31DOI: 10.34218/IJCET.10.3.2019.018
Kshitij Tripathi
The classification of data is an important field of data mining comes under supervised learning. In this approach classifier is trained on the pre-categorized data thereafter tested on unseen part called test data to evaluate it. The other related field clustering comes under unsupervised learning is used for categorizing data into different clusters or assigning labels to them which are previously unknown. In this article the classification of data is done and we are using artificial neural networks (ANN) for pre-processing i.e. removing noisy instances through novel clustering technique and then classifying pre-processed data through ANN. Both are exhaustive approaches. The data set used in this article is PIMA Indian diabetes data set available on UCI repository.
{"title":"DIABETES CLASSIFICATION AND PREDICTION USING ARTIFICIAL NEURAL NETWORK","authors":"Kshitij Tripathi","doi":"10.34218/IJCET.10.3.2019.018","DOIUrl":"https://doi.org/10.34218/IJCET.10.3.2019.018","url":null,"abstract":"The classification of data is an important field of data mining comes under supervised learning. In this approach classifier is trained on the pre-categorized data thereafter tested on unseen part called test data to evaluate it. The other related field clustering comes under unsupervised learning is used for categorizing data into different clusters or assigning labels to them which are previously unknown. In this article the classification of data is done and we are using artificial neural networks (ANN) for pre-processing i.e. removing noisy instances through novel clustering technique and then classifying pre-processed data through ANN. Both are exhaustive approaches. The data set used in this article is PIMA Indian diabetes data set available on UCI repository.","PeriodicalId":38492,"journal":{"name":"International Journal of Computer Aided Engineering and Technology","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81482535","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 : 2019-04-30DOI: 10.34218/ijcet.10.2.2019.020
Bindiya, Sandeep Sharma
{"title":"IMPROVED PRE-COPY APPROACH FOR A SECURITY BASED LIVE VIRTUAL MACHINE MIGRATION IN CLOUD COMPUTING","authors":"Bindiya, Sandeep Sharma","doi":"10.34218/ijcet.10.2.2019.020","DOIUrl":"https://doi.org/10.34218/ijcet.10.2.2019.020","url":null,"abstract":"","PeriodicalId":38492,"journal":{"name":"International Journal of Computer Aided Engineering and Technology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85018579","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 : 2019-04-30DOI: 10.34218/ijcet.10.2.2019.015
Y. N. Reddy, M. Nagendra
Mobile Ad Hoc Networks (MANETs) are the wireless networks which can be deployed instantly without requiring any fixed wired infrastructure. MANETs are specifically very much useful in military, commercial and civilian applications. Since infrastructure less MANETs have dynamic topology and battery powered mobile nodes, it is a challenging task to provide secure data transmission between any pair of nodes in MANET. Multipath on Demand Routing is one possible solution to provide security in MANET. This paper proposes a new method (SDNMR) of providing secure communication by integrating trust based mechanism with multipath on demand routing approaches in MANETs. The simulation analysis of proposed method reveals the facts that the method provides significant security to the data compared to previous related work.
{"title":"SECURE DATA TRANSMISSION THROUGH NODE-DISJOINT ON DEMAND MULTIPATH ROUTING IN MANETS","authors":"Y. N. Reddy, M. Nagendra","doi":"10.34218/ijcet.10.2.2019.015","DOIUrl":"https://doi.org/10.34218/ijcet.10.2.2019.015","url":null,"abstract":"Mobile Ad Hoc Networks (MANETs) are the wireless networks which can be deployed instantly without requiring any fixed wired infrastructure. MANETs are specifically very much useful in military, commercial and civilian applications. Since infrastructure less MANETs have dynamic topology and battery powered mobile nodes, it is a challenging task to provide secure data transmission between any pair of nodes in MANET. Multipath on Demand Routing is one possible solution to provide security in MANET. This paper proposes a new method (SDNMR) of providing secure communication by integrating trust based mechanism with multipath on demand routing approaches in MANETs. The simulation analysis of proposed method reveals the facts that the method provides significant security to the data compared to previous related work.","PeriodicalId":38492,"journal":{"name":"International Journal of Computer Aided Engineering and Technology","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83256940","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 : 2019-04-30DOI: 10.34218/ijcet.10.2.2019.017
P. K. Sinha, S. Ahluwalia, Deepanshu Gupta
{"title":"8 BIT SINGLE CYCLE PROCESSOR","authors":"P. K. Sinha, S. Ahluwalia, Deepanshu Gupta","doi":"10.34218/ijcet.10.2.2019.017","DOIUrl":"https://doi.org/10.34218/ijcet.10.2.2019.017","url":null,"abstract":"","PeriodicalId":38492,"journal":{"name":"International Journal of Computer Aided Engineering and Technology","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73367809","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 : 2019-04-30DOI: 10.34218/ijcet.10.2.2019.016
R. Sridevi, P. Dinadayalan, S. B. Britto
Self-Organizing Maps are widely used unsupervised neural network architecture to discover group of structures in a dataset. Feature Selection plays a major role in Machine Learning. “An Appropriate Feature Classification Model using Kohonen Network (AFCM)” is based on Recurrent Neural Network approach for feature selection which clusters relevant and irrelevant features from the dataset present in cloud environment. The proposed model not only clusters relevant and irrelevant features but also refine the clustering process by minimizing the errors and irrelevant features. The AFCM consists of Feature Selection Organizer and Convergence SOM. In the Feature Selection Organizer, features are clusters into Relevant and Irrelevant Feature classes. The Convergence SOM helps to improve the prediction accuracy in the Relevant Feature set and to reduce the irrelevant features. The efficiency of the proposed model is extensively tested upon real world medical datasets. The experimental result on standard medical dataset shows that the AFCM is better than the Traditional models.
{"title":"AN APPROPRIATE FEATURE CLASSIFICATION MODEL USING KOHONEN NETWORK ","authors":"R. Sridevi, P. Dinadayalan, S. B. Britto","doi":"10.34218/ijcet.10.2.2019.016","DOIUrl":"https://doi.org/10.34218/ijcet.10.2.2019.016","url":null,"abstract":"Self-Organizing Maps are widely used unsupervised neural network architecture to discover group of structures in a dataset. Feature Selection plays a major role in Machine Learning. “An Appropriate Feature Classification Model using Kohonen Network (AFCM)” is based on Recurrent Neural Network approach for feature selection which clusters relevant and irrelevant features from the dataset present in cloud environment. The proposed model not only clusters relevant and irrelevant features but also refine the clustering process by minimizing the errors and irrelevant features. The AFCM consists of Feature Selection Organizer and Convergence SOM. In the Feature Selection Organizer, features are clusters into Relevant and Irrelevant Feature classes. The Convergence SOM helps to improve the prediction accuracy in the Relevant Feature set and to reduce the irrelevant features. The efficiency of the proposed model is extensively tested upon real world medical datasets. The experimental result on standard medical dataset shows that the AFCM is better than the Traditional models.","PeriodicalId":38492,"journal":{"name":"International Journal of Computer Aided Engineering and Technology","volume":"115 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86575821","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 : 2019-04-30DOI: 10.34218/ijcet.10.2.2019.021
K. Thyagarajan, T. B. Reddy
{"title":"MULTI-LEVEL ENERGY EFFICIENT IMPROVED UNEQUAL CLUSTERING IN WIRELESS SENSOR NETWORKS ","authors":"K. Thyagarajan, T. B. Reddy","doi":"10.34218/ijcet.10.2.2019.021","DOIUrl":"https://doi.org/10.34218/ijcet.10.2.2019.021","url":null,"abstract":"","PeriodicalId":38492,"journal":{"name":"International Journal of Computer Aided Engineering and Technology","volume":"2547 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86584700","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 : 2019-04-30DOI: 10.34218/ijcet.10.2.2019.018
R. Prasad, Gurram Veera Raghavaiah
{"title":"A NEW PARADIGM OF SECURITY MODEL FOR TREASURY INFORMATION SYSTEM -- E-GOVERNANCE","authors":"R. Prasad, Gurram Veera Raghavaiah","doi":"10.34218/ijcet.10.2.2019.018","DOIUrl":"https://doi.org/10.34218/ijcet.10.2.2019.018","url":null,"abstract":"","PeriodicalId":38492,"journal":{"name":"International Journal of Computer Aided Engineering and Technology","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84888750","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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