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2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC)最新文献

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Analysis of change in coordinate system on clustering 聚类时坐标系变化的分析
2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC) Pub Date : 2016-03-10 DOI: 10.1109/ICCTAC.2016.7567339
Manish Joshi, Yogita S. Patil
{"title":"Analysis of change in coordinate system on clustering","authors":"Manish Joshi, Yogita S. Patil","doi":"10.1109/ICCTAC.2016.7567339","DOIUrl":"https://doi.org/10.1109/ICCTAC.2016.7567339","url":null,"abstract":"Clustering is one of the data mining techniques used in a knowledge discovery process. It is assumed that a good representation of data points may yield good clustering results [6]. This paper discusses the effect of the coordinate system on the clustering. In this paper, we propose a density based clustering approach to group objects represented using Polar coordinate system. The experiment is carried out on different datasets. To evaluate the goodness of cluster result we have used internal and external validity measures. In most of the cases, data points representation using conventional Cartesian coordinate system results in better clustering performance as compared to clustering obtained from same data represented using Polar coordinate system. However, it is observed that for certain Data Sets our proposed density based approach on Polar coordinates clustering results outperform conventional approach. Hence, we can conclude that an appropriate representation of data points may yield more appropriate clustering results.","PeriodicalId":367688,"journal":{"name":"2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125660828","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}
引用次数: 4
A short review on data security and privacy issues in cloud computing 云计算中的数据安全和隐私问题综述
2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC) Pub Date : 2016-03-10 DOI: 10.1109/ICCTAC.2016.7567341
Arjun U, Vinay S
{"title":"A short review on data security and privacy issues in cloud computing","authors":"Arjun U, Vinay S","doi":"10.1109/ICCTAC.2016.7567341","DOIUrl":"https://doi.org/10.1109/ICCTAC.2016.7567341","url":null,"abstract":"Cloud computing has gained substantial research interest, owing to its vast range of services. The major issues in cloud computing are its security and privacy. The term security has multiple facets such as confidentiality, availability and integrity. A perfect security solution must ensure all the security parameters effectively. This paper focuses on the security of data alone. This paper intends to review various security and privacy related issues and the possible solutions in literature. The challenges associated with the security and privacy related issues are also discussed.","PeriodicalId":367688,"journal":{"name":"2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133192006","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}
引用次数: 26
Enhanced AES cryptosystem by using genetic algorithm and neural network in S-box 基于S-box的遗传算法和神经网络对AES密码系统进行了改进
2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC) Pub Date : 2016-03-10 DOI: 10.1109/ICCTAC.2016.7567340
K. Kalaiselvi, Anand Kumar
{"title":"Enhanced AES cryptosystem by using genetic algorithm and neural network in S-box","authors":"K. Kalaiselvi, Anand Kumar","doi":"10.1109/ICCTAC.2016.7567340","DOIUrl":"https://doi.org/10.1109/ICCTAC.2016.7567340","url":null,"abstract":"Cryptography based on block ciphers use Key-dependent ciphers for encryption and decryption. The efficiency of these systems depends on the security and the speed of the algorithm. The encryption process needs to be adaptive and dynamic in order to face any cryptanalytic attacks. Increasing the complexity of the algorithm is one way to prevent the attacks. The introduced complexity increases the execution time of the algorithm which leads to timing attacks. This paper attempts to propose two enhanced AES cryptosystem by employing Genetic algorithm (GA) in SPboxes and modification of AES by implementing nonlinear neural network (NN) in SP network to increase the security against timing attack and reduce the computational time of the proposed system. Both GA and NN are used in key expansion and key distribution of the AES algorithm.","PeriodicalId":367688,"journal":{"name":"2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132015437","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}
引用次数: 21
An efficient technique for author name disambiguation 一种有效的作者姓名消歧技术
2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC) Pub Date : 2016-03-10 DOI: 10.1109/ICCTAC.2016.7567344
Rima Hazra, Anomitra Saha, Shubhra Baran Deb, Debasis Mitra
{"title":"An efficient technique for author name disambiguation","authors":"Rima Hazra, Anomitra Saha, Shubhra Baran Deb, Debasis Mitra","doi":"10.1109/ICCTAC.2016.7567344","DOIUrl":"https://doi.org/10.1109/ICCTAC.2016.7567344","url":null,"abstract":"Scholarly digital libraries have become an important source of bibliographic records for scientific communities. Author name search is one of the most common query exercised in digital libraries. The name ambiguity problem in the context of author search in digital libraries, arising from multiple authors sharing the same name, poses many challenges. A number of name disambiguation methods have been proposed in the literature so far. A variety of bibliographic attributes have been considered in these methods. However, hardly any effort has been made to assess the potential contribution of these attributes. We, for the first time, evaluate the potential strength and/or weaknesses of these attributes by a rigorous course of experiments on a large data set. We also explore the potential utility of some attributes from different perspective. A close look reveals that most of the earlier work require one or more attributes which are difficult to obtain in practical applications. Based on this empirical study, we identify three very common and easy to access attributes and propose a two-step hierarchical clustering technique to solve name ambiguity using these attributes only. Experimental results on data set extracted from a popular digital library show that the proposed method achieves significantly high level of accuracy (> 90%) for most of the instances.","PeriodicalId":367688,"journal":{"name":"2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC)","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116346780","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}
引用次数: 4
A survey on detection and classification of rice plant diseases 水稻病害检测与分类研究进展
2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC) Pub Date : 2016-03-10 DOI: 10.1109/ICCTAC.2016.7567333
Jitesh P. Shah, H. Prajapati, V. Dabhi
{"title":"A survey on detection and classification of rice plant diseases","authors":"Jitesh P. Shah, H. Prajapati, V. Dabhi","doi":"10.1109/ICCTAC.2016.7567333","DOIUrl":"https://doi.org/10.1109/ICCTAC.2016.7567333","url":null,"abstract":"Identifying disease from the images of the plant is one of the interesting research areas in computer and agriculture field. This paper presents a survey of different image processing and machine-learning techniques used in the identification of rice plant diseases based on images of disease infected rice plants. This paper presents not only survey of various techniques but also concisely discusses important concepts of image processing and machine learning applied to plant disease detection and classification. We carry out detailed study of 19 papers, covering the work on rice plant diseases and other different plants and fruits, and present a survey of these papers based on important criteria. These criteria include size of image dataset, no. of classes(diseases), preprocessing, segmentation techniques, types of classifiers, accuracy of classifiers etc. We utilize our survey and study to propose and design our work on detection and classification of rice plant diseases.","PeriodicalId":367688,"journal":{"name":"2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117317680","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}
引用次数: 111
Computation offloading frameworks in mobile cloud computing : a survey 移动云计算中的计算卸载框架综述
2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC) Pub Date : 2016-03-10 DOI: 10.1109/ICCTAC.2016.7567332
Shantanu V. Deshmukh, Rinku Shah
{"title":"Computation offloading frameworks in mobile cloud computing : a survey","authors":"Shantanu V. Deshmukh, Rinku Shah","doi":"10.1109/ICCTAC.2016.7567332","DOIUrl":"https://doi.org/10.1109/ICCTAC.2016.7567332","url":null,"abstract":"Mobile devices are characterized by limited and non-scalable resources such as low processing power, insufficient primary memory and short battery life. Resource intensive applications such as augmented reality, artificial intelligence, artificial vision, object tracking, image processing and natural language processing are becoming popular. These applications are unable to execute effectively on resource constraint mobile devices. In the last few years considerable research has been done on Mobile Cloud Computing to extend the capabilities of mobile devices. Various models and frameworks have been proposed to offload resource intensive components of applications to cloud for efficient execution. This paper surveys recent computation offloading frameworks to determine and analyze the factors which affect the wide acceptance of these frameworks.","PeriodicalId":367688,"journal":{"name":"2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126915426","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}
引用次数: 28
Effective route planning in road networks using multi constraint routing algorithm 基于多约束路由算法的路网有效路径规划
2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC) Pub Date : 2016-03-10 DOI: 10.1109/ICCTAC.2016.7567336
P. Jayasheelan, Assistant Professor, F. Mary, Magdalene Jane
{"title":"Effective route planning in road networks using multi constraint routing algorithm","authors":"P. Jayasheelan, Assistant Professor, F. Mary, Magdalene Jane","doi":"10.1109/ICCTAC.2016.7567336","DOIUrl":"https://doi.org/10.1109/ICCTAC.2016.7567336","url":null,"abstract":"Today, applications that take the congestions caused by traffic jams into account already exist. During rush hour, it is common that multiple congestions occur on the road network. If the number of traffic jams is too big, the radio stations that transmit TMC messages to the end user often only report the longest traffic jams. Those applications use the Traffic Message Channel (TMC) technique to calculate a route around the congestion whenever the application receives a message about a traffic jam being present. The study for finding the optimal shortest path on graphs with nonnegative weights has taken many forms. The weights of a road network are usually either the length of the arcs or the time it takes to traverse the arc from one vertex to another. The latter depending on the length of the arc and the speed a vehicle is allowed to travel. The majority of route planning applications use the shortest path as a synonym for the fastest path. The Multi Constraint algorithm uses three arrays to calculate the shortest path. The first one contains the neighbors of each node. The second array stores the number of neighbors each node has, and the third contains the link weights. In order for the algorithm to work, the road network has to have all these arrays available.","PeriodicalId":367688,"journal":{"name":"2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125265636","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}
引用次数: 3
Prediction of mutual fund net asset value using low complexity feedback neural network 基于低复杂度反馈神经网络的共同基金净资产价值预测
2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC) Pub Date : 2016-03-10 DOI: 10.1109/ICCTAC.2016.7567345
C. Anish, B. Majhi
{"title":"Prediction of mutual fund net asset value using low complexity feedback neural network","authors":"C. Anish, B. Majhi","doi":"10.1109/ICCTAC.2016.7567345","DOIUrl":"https://doi.org/10.1109/ICCTAC.2016.7567345","url":null,"abstract":"A neural network family is commonly used for improving financial forecasting accuracy. This paper proposes a feedback functional link artificial neural network (FFLANN) for the prediction of net asset value (NAV) of Indian Mutual funds which incorporates fewer computational load and fast forecasting capability. It is clear from the root mean square error (RMSE) and mean absolute percentage error (MAPE) that the proposed model shows superior performance than the MLANN and FLANN.","PeriodicalId":367688,"journal":{"name":"2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123412213","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}
引用次数: 7
A survey on network security tools for open source 关于开源网络安全工具的调查
2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC) Pub Date : 2016-03-10 DOI: 10.1109/ICCTAC.2016.7567330
Nabanita Mandal, Sonali Jadhav
{"title":"A survey on network security tools for open source","authors":"Nabanita Mandal, Sonali Jadhav","doi":"10.1109/ICCTAC.2016.7567330","DOIUrl":"https://doi.org/10.1109/ICCTAC.2016.7567330","url":null,"abstract":"Providing security to the network in an open source has become a major challenge nowadays. Data transmitted through the network is not considered to be safe. Various threats like sniffing, spoofing, phishing exist. This paper presents an overview of various network threats and attacks to the network. There are many tools in the open source which are developed as a counter measure to these attacks. Tools like nmap, tcpdump, firewall, wireshark has been discussed in this paper.","PeriodicalId":367688,"journal":{"name":"2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127399288","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}
引用次数: 16
Hybrid classification model of correlation-based feature selection and support vector machine 基于相关性特征选择和支持向量机的混合分类模型
2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC) Pub Date : 2016-03-10 DOI: 10.1109/ICCTAC.2016.7567338
V. K. Dubey, A. Saxena
{"title":"Hybrid classification model of correlation-based feature selection and support vector machine","authors":"V. K. Dubey, A. Saxena","doi":"10.1109/ICCTAC.2016.7567338","DOIUrl":"https://doi.org/10.1109/ICCTAC.2016.7567338","url":null,"abstract":"In this paper, we propose a hybrid classification model, which has correlation based filter feature selection algorithm and support vector machine as a classifier. In this method, features are ordered according to their Absolute correlation value with respect to the class attribute. Then top K Features are selected from ordered list of features to form a reduced dataset. The classification accuracy is measured using SVM classifiers with and without extending features of the reduced dataset. This proposed classifier model is applied to five high-dimensional binary class datasets. It is observed that the proposed method yields higher classification accuracies in the case of three out of five high dimensional datasets with a reasonably small number of features.","PeriodicalId":367688,"journal":{"name":"2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127968958","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}
引用次数: 9
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