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International Journal of Data Mining Techniques and Applications最新文献

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Diagnosis and Prognosis of Oral Cancer using classification algorithm with Data Mining Techniques 基于数据挖掘技术的口腔癌分类算法的诊断与预后分析
International Journal of Data Mining Techniques and Applications Pub Date : 2021-12-06 DOI: 10.20894/ijdmta.102.010.002.005
Nikolaevich Ms, Khalil Ms
{"title":"Diagnosis and Prognosis of Oral Cancer using classification algorithm with Data Mining Techniques","authors":"Nikolaevich Ms, Khalil Ms","doi":"10.20894/ijdmta.102.010.002.005","DOIUrl":"https://doi.org/10.20894/ijdmta.102.010.002.005","url":null,"abstract":"Data mining is the process of researching data from different view points and condensing it into useful information. There are several types of algorithms in data mining such as Classification algorithms, Regression, Segmentation algorithms, association algorithms, sequence analysis algorithms, etc.,. The classification algorithm can be used to bifurcate the data set from the given data set and foretell one or more discrete variables, based on the other attributes in the dataset. Our method of creating new algorithm GA+ID3 easily identifies oral cancer data set from the given data set. The genetic based ID3 classification algorithm diagnosis and prognosis of oral cancer data set is identified by this paper.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114804733","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
Predicting Risk factor of Dengue using FP Growth Mining Technique 利用FP生长挖掘技术预测登革热危险因素
International Journal of Data Mining Techniques and Applications Pub Date : 2021-06-06 DOI: 10.20894/ijdmta.102.010.001.004
Slim Ms
{"title":"Predicting Risk factor of Dengue using FP Growth Mining Technique","authors":"Slim Ms","doi":"10.20894/ijdmta.102.010.001.004","DOIUrl":"https://doi.org/10.20894/ijdmta.102.010.001.004","url":null,"abstract":"Dengue is a debilitating malady which is caused by female mosquitoes chomp of Aedes mosquitoes. It is regularly found in hot areas. The dengue maladies mostly caused in 4 serotypes . A dengue malady grasp from gentle febrile malady to serious hemorrhagic fever. Anticipating the connection between serotypes of dengue and age of the people will help the biotechnologists and bioinformaticians to advance one stage to find solutions for dengue. Information Mining is a champion among the most completely and animating zones of research with the inspiration driving finding critical data from tremendous information accumulations . In Medical endeavors, Data Mining gives numerous purposes of enthusiasm, for instance, the area of the coercion in medicinal scope, sickness gauge, and availability of the helpful answer for the patients at bring down cost, acknowledgment of purposes behind disorders and recognizing verification of helpful treatment strategies. It is furthermore supportive to predict the risky diseases like Dengue fever, Cancer, Diabetes et cetera. In this Research work to reduce the death rate, the risk factors of the dengue are predicted using Association rule Mining","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132674169","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: 0
Application of Utility Mining using Frequent Itemset and Association Rules: A Survey 频繁项集和关联规则在效用挖掘中的应用综述
International Journal of Data Mining Techniques and Applications Pub Date : 2021-06-06 DOI: 10.20894/ijdmta.102.010.001.005
T.Indhumathy Ms, T.Velmurugan Mr
{"title":"Application of Utility Mining using Frequent Itemset and Association Rules: A Survey","authors":"T.Indhumathy Ms, T.Velmurugan Mr","doi":"10.20894/ijdmta.102.010.001.005","DOIUrl":"https://doi.org/10.20894/ijdmta.102.010.001.005","url":null,"abstract":"Mining on data reveals patterns that provide useful information for analysis, decision making and forecasting in various domains. Association Rule Mining (ARM) identifies patterns on itemsets which are either frequent or have interesting relationship amongst them based on strong rules and conceptually form a basis for Frequent Itemset mining (FIM) problems. FIM extracts binary values from transaction databases to identify frequently bought items but provides insufficient information for identifying infrequent items that generate maximum profit. So a latter problem, High utility itemsets (HUI) mining was developed to focus on the itemsets that generate huge profit to the business. Even though HUI is related to Business Intelligence, its application extends to Web Server Logs, Biological Gene Databases, Network Traffic Measurements and many other fields. This paper presents a survey on the algorithms from different aspects and perspectives based on Utility mining, Frequent Itemset generation and Association Rule Mining","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125996863","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: 0
Impacts of Evaluation Methods on Classification Algorithm s Accuracy 评价方法对分类算法准确率的影响
International Journal of Data Mining Techniques and Applications Pub Date : 2021-06-06 DOI: 10.20894/ijdmta.102.010.001.003
Kozachenko Mr
{"title":"Impacts of Evaluation Methods on Classification Algorithm s Accuracy","authors":"Kozachenko Mr","doi":"10.20894/ijdmta.102.010.001.003","DOIUrl":"https://doi.org/10.20894/ijdmta.102.010.001.003","url":null,"abstract":"Decision trees are one of the most powerful and commonly used supervised learning algorithms in the field of data mining. It is important that a decision tree perform accurately when employed on unseen data; therefore, evaluation methods are used to measure the predictive performance of a decision tree classifier. However, the predictive accuracy of a decision tree is also dependent on the evaluation method chosen since training and testing sets of decision tree models are selected according to the evaluation methods. The aim of this paper was to study and understand how using different evaluation methods might have an impact on decision tree accuracies when they are applied to different decision tree algorithms.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130462161","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: 0
Enhanced Hand Gesture Recognition In Augmented Reality Using Genetic Algorithm 基于遗传算法的增强现实手势识别
International Journal of Data Mining Techniques and Applications Pub Date : 2021-06-06 DOI: 10.20894/ijdmta.102.0010.001.001
M.Sivaneshwari Ms
{"title":"Enhanced Hand Gesture Recognition In Augmented Reality Using Genetic Algorithm","authors":"M.Sivaneshwari Ms","doi":"10.20894/ijdmta.102.0010.001.001","DOIUrl":"https://doi.org/10.20894/ijdmta.102.0010.001.001","url":null,"abstract":"Hand Gesture recognition is technology which interpret human gestures using various algorithms.Interpreting human hand gestures has various challenges and issues such as image noise,visibility and orientation. There are various kinds of computer based algorithms have been proposed in the literature to overcome these limitations and still needs improvement. Hence in this research work a new Hand Gesture recognition system for augmented reality using Genetic Algorithm and Artificial neural network algorithm has been proposed.This shows that the experiment is successful and gesture recognition system is vigorous against various changes that are made in illumination changes and background changes. Experimental results show that the extracted features are effective, robust, and can cover the entire feature space of the selected gestures. This method satisfactory performance when compared with convensional methods","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115771308","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: 0
Detecting the Abnormal SQL Query Using Hybrid SVM Classification Technique in Web Application Web应用中基于混合SVM分类技术的异常SQL查询检测
International Journal of Data Mining Techniques and Applications Pub Date : 2020-12-10 DOI: 10.20894/ijdmta.102.009.001.009
S. R, Suriakala M
{"title":"Detecting the Abnormal SQL Query Using Hybrid SVM Classification Technique in Web Application","authors":"S. R, Suriakala M","doi":"10.20894/ijdmta.102.009.001.009","DOIUrl":"https://doi.org/10.20894/ijdmta.102.009.001.009","url":null,"abstract":"Detecting SQL injection attacks (SQLIAs) is ending up progressively significant in database-driven sites. A large portion of the investigations on SQLIA detection have concentrated on the structured query language (SQL) structure at the application level. Yet, those methodologies unavoidably neglects to identify those attacks that utilization previously put away methodology and information inside the database framework. While most existing techniques tended to towards diminishing the quantity of support vectors, the proposed philosophy concentrated on decreasing the quantity of test datapoints that need SVMs assistance in getting grouped. The focal thought is to inexact the choice limit of SVM utilizing paired trees. The subsequent tree is a half and half tree as in it has both univariate and multivariate (SVM) nodes. The cross breed tree takes SVMs assistance just in ordering significant information focuses lying close choice limit staying less urgent datapoints are grouped by quick univariate nodes.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"216 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122385665","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: 0
An Art of Web Services using Web Mining and Semantic Web Techniques 使用Web挖掘和语义Web技术的Web服务艺术
International Journal of Data Mining Techniques and Applications Pub Date : 2020-12-10 DOI: 10.20894/ijdmta.102.009.001.008
S. R
{"title":"An Art of Web Services using Web Mining and Semantic Web Techniques","authors":"S. R","doi":"10.20894/ijdmta.102.009.001.008","DOIUrl":"https://doi.org/10.20894/ijdmta.102.009.001.008","url":null,"abstract":"Todays web browsers serve as an easy access to numerous sources of text and multimedia data. More than a billion pages are indexed by search engines, and finding the desired information is not an easiest task. Over the last decade, there is an explosive growth in the information available on the World Wide Web (WWW). The objective of this paper is to provide an outline of web mining, its various classifications, its subtasks, and to give a perspective to the research community about the potential of applying techniques to extract meaningful patterns. This paper also gives information in the area of web services, semantic web mining and comparison of traditional web applications and semantic web applications thereby providing the guidelines for future research in the area of web services using web mining and semantic web.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"211 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121028158","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: 0
Widespread Review of Vehicular Ad Hoc Network 车辆自组织网络的广泛回顾
International Journal of Data Mining Techniques and Applications Pub Date : 2020-12-10 DOI: 10.20894/ijdmta.102.009.001.006
M. R, P. T.
{"title":"Widespread Review of Vehicular Ad Hoc Network","authors":"M. R, P. T.","doi":"10.20894/ijdmta.102.009.001.006","DOIUrl":"https://doi.org/10.20894/ijdmta.102.009.001.006","url":null,"abstract":"In this paper, main objective is collecting the information from road side traffic and share the collected information. The Identity based Batch verification (IBV) scheme is one such scheme, which makes VANET more secure and efficient maintaining privacy through anonymity and reduction of verification time of messages by verifying the min batch, are the ideas of this scheme. This paper highlights the security issues of the current IBV scheme and introduces the concept of the random change of anonymous identity with time as well as location, to prevent the security attack and to maintain the privacy. In this scheme, performances are evaluated in terms of delay and transmission overhead.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121603915","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: 0
Bidirectional Recurrent Neural Network Language Model: Cross Entropy Churn Metrics for Defect Prediction Modeling 双向递归神经网络语言模型:用于缺陷预测建模的交叉熵流失度量
International Journal of Data Mining Techniques and Applications Pub Date : 2020-12-10 DOI: 10.20894/ijdmta.102.009.001.010
N. R, K. S
{"title":"Bidirectional Recurrent Neural Network Language Model: Cross Entropy Churn Metrics for Defect Prediction Modeling","authors":"N. R, K. S","doi":"10.20894/ijdmta.102.009.001.010","DOIUrl":"https://doi.org/10.20894/ijdmta.102.009.001.010","url":null,"abstract":"Software Defect Prediction (SDP) plays an active area in many research domain of Software Quality of Assurance (SQA). Many existing research studies are based on software traditional metric sets and defect prediction models are built in machine language to detect the bug for limited source code line. Inspired by the above existing system. In this paper, defect prediction is focused on predicting defects in source code. The objective of this thesis is to improve the software quality for accurate defect prediction is source code for file level. So, that it helps the developer to find the bug and fix the issue, to make better use of a resource which reduces the test effort, minimize the cost and improve the quality of software. A new approach is introduced to improve the prediction performance of Bidirectional RNNLM in Deep Neural Network. To build the defect prediction model a defect learner framework is proposed and first it need to build a Neural Language Model. Using this Language Model it helps to learn to deep semantic features in source code and it train & test the model. Based on language model it combined with software traditional metric sets to measure the code and find the defect. The probability of language model and metric set Cross-Entropy with Abstract Syntax Tree (CE-AST) metric is used to evaluate the defect proneness and set as a metric label. For classification the metric label K-NN classifier is used. BPTT algorithm for learning RNN will provide additional improvement, it improves the predictions performance to find the dynamic error.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126699191","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: 1
A Survey on the Online Shopping Customer Review Data using Association Rule Mining 基于关联规则挖掘的网上购物顾客评论数据研究
International Journal of Data Mining Techniques and Applications Pub Date : 2020-12-10 DOI: 10.20894/ijdmta.102.009.001.011
B.Hemalatha Assistant Professor
{"title":"A Survey on the Online Shopping Customer Review Data using Association Rule Mining","authors":"B.Hemalatha Assistant Professor","doi":"10.20894/ijdmta.102.009.001.011","DOIUrl":"https://doi.org/10.20894/ijdmta.102.009.001.011","url":null,"abstract":"Revealing complex associations between entities is of vast significance for business optimization, prediction and decision making. Such associations include not only co-occurrence-based explicit relations but also non co-occurrence-based implicit ones. Associative rule mining (ARM) is used to study these implicit and explicit relationships. Online shopping customer review (OSCR) data has become a major information resource for consumers and has extremely important implications for a wide range of management activities. Consumer reviews examine the bond between service quality and customer purchase behaviour in online shopping context. Apriori is a key algorithm for mining frequent item sets for Boolean association rules. To develop the efficiency of the level-wise generation of frequent itemsets in online customer shopping customer review data, Apriori property is used to reduce the search space .The detection of interesting patterns in this collection of data can guide to important marketing and management strategic decisions. In this survey paper, some of the research work carried out on customer online shopping data is discussed. Also, the use of Apriori algorithm for the same type of data set is analyzed.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133511164","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: 0
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