2016 International Conference on Data Mining and Advanced Computing (SAPIENCE)最新文献

{"title":"Performance measure of color and texture in visual content retrieval in RGB color space","authors":"P. Shimi, Vince Paul","doi":"10.1109/SAPIENCE.2016.7684147","DOIUrl":"https://doi.org/10.1109/SAPIENCE.2016.7684147","url":null,"abstract":"Feature extraction simplifies the amount of information needed to describe the properties of an image accurately. This paper measures the performance of a CBIR system based on texture feature against combination of both color and texture feature. A Gray Level Co-occurrence Matrix is calculated for computing the texture feature of an image. Using these textual parameters similar images are extracted from a data set. RGB color space is considered for color feature extraction. Global Color Histogram is generated and calculated color features are represented as one dimensional feature vector. Then we combined both color and texture features to retrieve similar images from the dataset. In both situations Euclidean distance is used to measure the similarity of two images. By this experiment it is found that the system which uses the combination of color and texture has better performance in retrieving similar images from the dataset.","PeriodicalId":340137,"journal":{"name":"2016 International Conference on Data Mining and Advanced Computing (SAPIENCE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129531815","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}

{"title":"Prediction of students performance using Educational Data Mining","authors":"Ms Tismy Devasia, Ms Vinushree T P, Mr Vinayak Hegde","doi":"10.1109/SAPIENCE.2016.7684167","DOIUrl":"https://doi.org/10.1109/SAPIENCE.2016.7684167","url":null,"abstract":"Data mining plays an important role in the business world and it helps to the educational institution to predict and make decisions related to the students' academic status. With a higher education, now a days dropping out of students' has been increasing, it affects not only the students' career but also on the reputation of the institute. The existing system is a system which maintains the student information in the form of numerical values and it just stores and retrieve the information what it contains. So the system has no intelligence to analyze the data. The proposed system is a web based application which makes use of the Naive Bayesian mining technique for the extraction of useful information. The experiment is conducted on 700 students' with 19 attributes in Amrita Vishwa Vidyapeetham, Mysuru. Result proves that Naive Bayesian algorithm provides more accuracy over other methods like Regression, Decision Tree, Neural networks etc., for comparison and prediction. The system aims at increasing the success graph of students using Naive Bayesian and the system which maintains all student admission details, course details, subject details, student marks details, attendance details, etc. It takes student's academic history as input and gives students' upcoming performances on the basis of semester.","PeriodicalId":340137,"journal":{"name":"2016 International Conference on Data Mining and Advanced Computing (SAPIENCE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128554811","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}

{"title":"A Review on Data Mining techniques and factors used in Educational Data Mining to predict student amelioration","authors":"M. Anoopkumar, A. M. J. Md Zubair Rahman","doi":"10.1109/SAPIENCE.2016.7684113","DOIUrl":"https://doi.org/10.1109/SAPIENCE.2016.7684113","url":null,"abstract":"Educational Data Mining (EDM) is an interdisciplinary ingenuous research area that handles the development of methods to explore data arising in a scholastic fields. Computational approaches used by EDM is to examine scholastic data in order to study educational questions. As a result, it provides intrinsic knowledge of teaching and learning process for effective education planning. This paper conducts a comprehensive study on the recent and relevant studies put through in this field to date. The study focuses on methods of analysing educational data to develop models for improving academic performances and improving institutional effectiveness. This paper accumulates and relegates literature, identifies consequential work and mediates it to computing educators and professional bodies. We identify research that gives well-fortified advise to amend edifying and invigorate the more impuissant segment students in the institution. The results of these studies give insight into techniques for ameliorating pedagogical process, presaging student performance, compare the precision of data mining algorithms, and demonstrate the maturity of open source implements.","PeriodicalId":340137,"journal":{"name":"2016 International Conference on Data Mining and Advanced Computing (SAPIENCE)","volume":"47 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130808582","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}

{"title":"Process mining for project management","authors":"J. Joe, Yasha Ballal, Tanya Emmatty, S. Kulkarni","doi":"10.1109/SAPIENCE.2016.7684142","DOIUrl":"https://doi.org/10.1109/SAPIENCE.2016.7684142","url":null,"abstract":"Business process mining or process mining is the intersection between data mining and business process modelling that extracts business patterns from event logs. Event logs are freely available in any organization. Business logs are a potential source of useful information. By the various patterns that are present in the logs, a lot can be estimated about the type of procedures that should be incorporated into the organization for better performance. Event logs store information about time and event data of business processes. Process mining algorithms are used to mine business process models using event logs. Generating automated business models out of this could provide valuable insight to a firm eventually leading to customer satisfaction. Process Mining works by three phases: discovery, conformation and alteration. By using process mining, many kinds of information can be collected about the process, such as control-flow, performance, organizational information and decision patterns. A process model could be represented as Petri nets which is a formal graphical representation of the workflow diagram or it can be represented as Business Process Modelling Notation. This project aims to develop a user friendly platform which is capable of generating petri net like models by process mining. By using various process mining algorithms we will develop software which would mine the event logs of a particular firm. It would provide a data or workflow analysis scheme. This would optimize business process intelligence and thus provide alternative and superior work strategies. In this project, we are mainly targeting project management using process mining. There are many projects that are undertaken by an IT company that all follow the same procedure. The concept of business process mining can be used in order to improve the performance of a company by optimizing its Software Development Life Cycle. By feeding the previous logs of a similar project of the company, the software would give a flowgraph. This flowgraph can help to identify the sequence of the activities, roles in the organization as well as various efficiency parameters. The algorithm being used is the Heuristic Miner Algorithm for process mining.","PeriodicalId":340137,"journal":{"name":"2016 International Conference on Data Mining and Advanced Computing (SAPIENCE)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133692870","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}

{"title":"A study of cloud computing environments for High Performance applications","authors":"K. R. Sajay, S. Babu","doi":"10.1109/SAPIENCE.2016.7684127","DOIUrl":"https://doi.org/10.1109/SAPIENCE.2016.7684127","url":null,"abstract":"High performance applications requires high processing power to compute highly intensive and complex applications for research, engineering, medical and academic projects. In the traditional way, an organization will have to pay very high costs to run an HPC (High Performance computing) application. The organization has to purchase highly expensive hardware for running an HPC application and maintaining it afterwards. The HPC resources on the company premises may not satisfy all the demands of scientific application where resources may not suitable for corresponding requirements. Considering the case of SMEs (small and medium enterprises), an increasing demand is always challenging. Cloud computing is an on-demand, pay-as-you-go-model, that offers scalable computing resources, unlimited storage in an instantly available way. In this paper we included requirements of HPC applications in cloud, cluster based HPC applications, types of clusters, Google's HPC Cloud architecture, performance analysis of various HPC cloud vendors and four case studies of HPC applications in cloud.","PeriodicalId":340137,"journal":{"name":"2016 International Conference on Data Mining and Advanced Computing (SAPIENCE)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122123772","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}

{"title":"An implementation of efficient techniques for tree based mining in human social dynamics","authors":"Asmita Shejale, Vishal Gnagawane","doi":"10.1109/SAPIENCE.2016.7684138","DOIUrl":"https://doi.org/10.1109/SAPIENCE.2016.7684138","url":null,"abstract":"Meetings are an important communication and coordination activity of teams: status is discussed, new decisions are made, alternatives are considered, details are explained, information is presented, and new ideas are generated. As such, meetings contain a large amount of rich project information that is often not formally documented. Capturing all of this informal meeting information has been a topic of research in several communities over the past decade. In this work, data mining techniques are used to detect and analyze the frequent interaction patterns to discover various types of knowledge on human interactions. An interaction tree based pattern mining algorithms was proposed to analyze tree structures and extract interaction flow patterns for meetings. The work extends for tree based mining algorithm proposed for human interaction flow, where the human interaction flow in a discussion session is represented as a tree. Proposed system extends an interactive tree based pattern mining algorithm in two ways. First, it is proposed a mining method to extract frequent patterns of human interaction to support several categories of meeting. Second, it is explored modified embedded tree mining for hidden interaction pattern discovery. Modified Embedded sub tree mining is the generalization of induced sub trees, which not allow direct parent child branches, also considers ancestor-descendant branches. The experimental results show the discovered patterns can be utilized to evaluate a meeting discussion (debate) is efficient and compare the results of different algorithms of interaction flow.","PeriodicalId":340137,"journal":{"name":"2016 International Conference on Data Mining and Advanced Computing (SAPIENCE)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123182028","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}

{"title":"Performance evaluation of classifiers for spam detection with benchmark datasets","authors":"Bindu V Research Scholar, Ciza Thomas","doi":"10.1109/SAPIENCE.2016.7684121","DOIUrl":"https://doi.org/10.1109/SAPIENCE.2016.7684121","url":null,"abstract":"Detection of unwanted, unsolicited mails called spam from email is an interesting area of research. Researchers with the help of machine learning algorithms normally find the best classifier that distinguishes a spam from a benign mail called ham. It is necessary to evaluate the performance of any new spam classifier using standard data sets. The public corpora of email data sets that are available has certain special characteristics that reflects the time of compilation, the number of users considered and the general subject of the messages. This paper describes a comprehensive study on the performance evaluation of various machine learning algorithms using two benchmark data sets. The evaluations clearly demonstrate the superior performance of the tree classifiers and ensemble based classifiers with trees as basic classifier. Both the tree classifier and the ensemble classifier were performing with accuracy greater than 96% and mean absolute error less than 0.05%.","PeriodicalId":340137,"journal":{"name":"2016 International Conference on Data Mining and Advanced Computing (SAPIENCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133229404","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}

{"title":"Improving image retrieval precision using combination of circular reranking and time-based reranking","authors":"Sani Sadiq, K. J. Helen","doi":"10.1109/SAPIENCE.2016.7684110","DOIUrl":"https://doi.org/10.1109/SAPIENCE.2016.7684110","url":null,"abstract":"Search reranking is regarded as a common way to boost image retrieval precision. The problem is not simple especially when there are multiple features to be considered for search, which often happens in image retrieval. This paper proposes the combination of Circular reranking and Time-based reranking methods for improving the precision of image retrieval. Circular reranking utilises multiple features of an image, usually textual and visual descriptions, for reranking. Initially, it will conduct multiple runs of random walk for obtaining initial search results. Secondly, two features of an image are exchanged for better mutual reinforcement which makes multiple keyword search possible. Lastly, reranked results are attained through exchanging the ranking scores among different features in a cyclic manner. Time-based reranking is based on the count of Time, View and Download, of an image. Time count is the time duration between opening and closing of an image. View and Download counts are the total number of views and downloads respectively for an image. In our approach, Time-based reranking is performed on the Circular reranked list for improving precision, appropriately combining features of both reranking methods, while retrieving images during search.","PeriodicalId":340137,"journal":{"name":"2016 International Conference on Data Mining and Advanced Computing (SAPIENCE)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125674387","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}

{"title":"Implementation of RETE algorithm using course finder system","authors":"M. Pallavi, P. Vaisakh, N. P. Reshna","doi":"10.1109/SAPIENCE.2016.7684165","DOIUrl":"https://doi.org/10.1109/SAPIENCE.2016.7684165","url":null,"abstract":"The RETE algorithm is an efficiently organized pattern matching algorithm for implementing production rule systems, used to determine which of the production rules should fire based on its data store. This paper presents how RETE algorithm can be used to improve the efficiency of expert system recommendation. The `COURSE FINDER' is an expert undergraduate course recommendation system. This system aims to assist undergraduate students who wish to join Amrita School of Arts and Sciences, Mysore. This expert system enables students to select suitable courses based on their skill sets without needing to consult an advisor. This system works on Rule based mechanism and RETE algorithm. The result was expected, where most of the undergraduate students who tested the system, were satisfied.","PeriodicalId":340137,"journal":{"name":"2016 International Conference on Data Mining and Advanced Computing (SAPIENCE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126625299","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}

{"title":"Reactive power control by using capacitors and implement the method of PSO","authors":"M. Suneetha, R. S. Rao, B. Subramanyam","doi":"10.1109/SAPIENCE.2016.7684137","DOIUrl":"https://doi.org/10.1109/SAPIENCE.2016.7684137","url":null,"abstract":"Reactive power control is a very essential and necessary strategy to maintain the safe and reliable operation of power systems. There are different methods available for optimization of reactive power. In spite of the advantages of power electronic devices, placement of the capacitors still remains technically viable and an economically affordable option for reactive power control. In this paper we have proposed a method of reactive power control by optimum sizing of capacitor with the help of particle swarm optimization. In this work we have presented a method to reduce the reactive and active power losses by having a coordinated and constrained optimization approach. To validate the proposed approach an IEEE 30 bus, test bus considered. At tool in the form of Graphical User Interface is also presented as part of this work.","PeriodicalId":340137,"journal":{"name":"2016 International Conference on Data Mining and Advanced Computing (SAPIENCE)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116152843","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}