ijdmta iir - Academia.edu (original) (raw)

Papers by ijdmta iir

Research paper thumbnail of A Comparative study of Lexicon based and Machine learning based classifications in Sentiment analysis

Integrated Intelligent Research, 2012

Internet and WWW are growing a huge amount of data from the users entered the text that is relate... more Internet and WWW are growing a huge amount of data from the users entered the text that is related to the product review, opinions, attitudes, and any other services. This information is processing and analyzing various tools to be used. The NLP and Information retrieval methods are used to analyze the information and understanding these process information. The main problem of Sentiment analysis is classified into two categories that are Positive opinion or negative opinion. This paper focus to compare the lexical based sentiment classification and Machine learning based sentiment classification methods. Various algorithms and approaches and features are classified in this paper. Different methods are taken to solve the sentiment analysis problems.

Research paper thumbnail of A Survey on the Online Shopping Customer Review Data using Association Rule Mining

Integrated Intelligent Research, 2012

Revealing complex associations between entities is of vast significance for business optimization... more 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.

Research paper thumbnail of Bidirectional Recurrent Neural Network Language Model: Cross Entropy Churn Metrics for Defect Prediction Modeling

Integrated Intelligent Research, 2012

Software Defect Prediction (SDP) plays an active area in many research domain of Software Quality... more 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.

Research paper thumbnail of Detecting the Abnormal SQL Query Using Hybrid SVM Classification Technique in Web Application

Integrated Intelligent Research, 2012

Detecting SQL injection attacks (SQLIAs) is ending up progressively significant in database-drive... more 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 SVM's 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 SVM's assistance just in ordering significant information focuses lying close choice limit; staying less urgent datapoints are grouped by quick univariate nodes.

Research paper thumbnail of An Art of Web Services using Web Mining and Semantic Web Techniques

Integrated Intelligent Research, 2012

Today's web browsers serve as an easy access to numerous sources of text and multimedia data. Mor... more Today's 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.

Research paper thumbnail of Text Mining Techniques in Data Mining - Review

Integrated Intelligent Research, 2012

Discovering Knowledge in Databases and Extract Patterns and Knowledge in erroneous data is Data M... more Discovering Knowledge in Databases and Extract Patterns and Knowledge in erroneous data is Data Mining. The quality of text details is extracted by text mining using Statistical Methods. Relevance, Novelty, Interestingness decides the accuracy of Text Mining. Categorization, clustering, entity extraction and sentiment analysis are used for text mining. Natural language processing, analytical methods related techniques, and algorithms are implemented.

Research paper thumbnail of Widespread Review of Vehicular Ad Hoc Network

Integrated Intelligent Research, 2012

In this paper, main objective is collecting the information from road side traffic and share the ... more 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.

Research paper thumbnail of Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks

Integrated Intelligent Research, 2012

Data mining is the computer based process of analyzing huge sets of data and then extracting the ... more Data mining is the computer based process of analyzing huge sets of data and then extracting the meaning of the data. Data mining tools predict future trends, allowing business to make positive, knowledge-driven decisions. The huge amounts of data generated by traditional methods for prediction of heart disease are too complex and voluminous to be processed and analyzed. Data mining provides the technologies to transform these huge sets of data into useful information for decision making. Data mining techniques takes less time for the prediction of the disease with more accuracy. In this paper we survey different papers in which one or more algorithms of data mining used for the prediction of heart disease. The prediction by using data mining algorithm gives efficient results. Applying data mining techniques to heart disease treatment data can provide as reliable performance as that achieved in prediction and diagnosing of heart disease.

Research paper thumbnail of Enhanced Hand Gesture Recognition In Augmented Reality Using Genetic Algorithm

Integrated Intelligent Research, 2012

Hand Gesture recognition is technology which interpret human gestures using various algorithms.In... more 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(HG) recognition system for augmented reality using Genetic Algorithm (GA) and Artificial neural network algorithm (ANN) 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.

Research paper thumbnail of An Efficient Technique using Data Mining Algorithms for Predicting Disease -A Review

Integrated Intelligent Research, 2012

Data mining is a high volume of data for needful information. The best and most popular data mini... more Data mining is a high volume of data for needful information. The best and most popular data mining techniques are rule mining, clustering, classification and sequence pattern. Data mining has incredible guarantee for the health industry, empowering health frameworks to efficiently utilize data and research to find wasteful aspects and best practices to enhance mind and lessen costs. Some experts believe that the opportunity to improve healthcare and reduce costs at the same time may account for 30% of total health care spending. All in all, this might be a win-win circumstance. In any case, because of the multifaceted nature of healthcare and the moderate appropriation of innovation, our industry lingers behind different enterprises in actualizing powerful information investigation and extraction procedures. For detecting a disease number of tests should be required from the patient. But using data mining technique the number of test should be reduced. This reduced test plays an important role in time and performance. This exploration paper examines how data mining procedures are used for anticipating lung diseases.

Research paper thumbnail of Automated Detection of Diabetic Retinopathy using Medical Image Processing Techniques

Integrated Intelligent Research, 2012

Glaucoma is an eye disease that can result in blindness if it is not detected and treated in prop... more Glaucoma is an eye disease that can result in blindness if it is not detected and treated in proper time. Diabetic related eye diseases like Diabetic Retinopathy (DR), Diabetic Maculopathy (DM) are major cause of blindness. Early detection of diabetic diseases plays an important role to prevent blindness. In last few years there are several researches done in medical image processing and detection from the fund us images such as Optic disk (OD) and the retinal vessels done in the automated detection of Diabetic retinopathy (DR), Diabetic Maculopathy (DM). This research paper represents the methods which are used in the automated detection of diabetic retinopathy. The recent methods used to detect the factors like hemorrhages and Micro Aneurysms (MA) are also discussed in this paper.

Research paper thumbnail of A Survey of Various Weighted based Clustering Algorithm for MANET

Integrated Intelligent Research, 2012

__ A Mobile Ad hoc Network is a collection of independent nodes that communicate over relative ba... more __ A Mobile Ad hoc Network is a collection of independent nodes that communicate over relative bandwidth, power constrained in wireless links. The network topology may transform quickly and randomly due to mobility of nodes. Also, in MANET the decentralized network leads to perform the routing functionalities by nodes themselves such as route discovery, topology discovery and delivering messages from source to destination. Clustering provides the finest answer for huge and dense mobile adhoc networks with high mobility. It also lifts the capacity of network and diminishes the routing overhead in order to bring more efficient and effective routing in MANET. There are two mechanisms in every clustering algorithm, (i) cluster formation and (ii) cluster maintenance. In cluster formation, cluster heads are selected among the nodes to form the hierarchical network. Selecting appropriate cluster head is one of the main research issues. In cluster maintenance, a unique mechanism is needed so that the cluster head can maintain the topological information of the cluster in spite of the cluster structure changes every time due to mobility of nodes. Thispaper mainly focuses on the weight based clustering approaches in MANET. .

Research paper thumbnail of Predicting Risk factor of Dengue using FP- Growth Mining Technique

Integrated Intelligent Research, 2012

Dengue is a debilitating malady which is caused by female mosquitoes (chomp of Aedes mosquitoes).... more 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 (DENV-1, DENV-2, DENV-3 and DENV-4). 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.

Research paper thumbnail of An Analysis of Road Accidental Data Using Clustering and Itemset Mining Algorithms

Integrated Intelligent Research, 2012

Road accidental detection is one of the emerging issue in recent days, which has been focused by ... more Road accidental detection is one of the emerging issue in recent days, which has been focused by many researchers. Road accident is the major cause for unnatural death, and desirability, which is unpredictable. So, many existing works aimed to develop some prediction approaches for analyzing the real time dataset and predicting the accidental rate for future. But, it limits with the drawbacks like inefficient prediction, reduced accuracy, and increased time consumption. Thus, this paper aims to propose a new prediction model by implementing various data mining techniques. It includes the stages of preprocessing, clustering, and itemset mining. Initially, the dataset obtained from the UCI repository is preprocessed by eliminating the irrelevant attributes and filling the missing values. Then, the density based clustering technique is implemented to group the filtered data into a cluster. After that, the rules are formed based on the support and confidence values for predicting the future. Finally, the frequent items are mined by the use of Apriori algorithm. In experiments, the performance results of the proposed system is validated and evaluated by using various measures such as accuracy, precision, recall, and time consumption.

Research paper thumbnail of An Investigation into Brain Tumor Segmentation Techniques

Integrated Intelligent Research, 2012

A tumor is an anomalous mass in the brain which can be cancerous. Such anomalous growth within th... more A tumor is an anomalous mass in the brain which can be cancerous. Such anomalous growth within this restricted space or inside the covering skull can cause problems. Detecting brain tumors from images of medical modalities like CT scan or MRI involves segmentation (Division into parts) for analysis and can be a challenging task. Accurate segmentation of brain images is very essential for proper diagnosis of tumor and non-tumor areas for clinical analysis. This paper details on segmentation algorithms for brain images, advantages, disadvantages and a comparison of the algorithms.

Research paper thumbnail of Impacts of Evaluation Methods on Classification Algorithm's Accuracy

Integrated Intelligent Research, 2012

Decision trees are one of the most powerful and commonly used supervised learning algorithms in t... more 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.

Research paper thumbnail of An Innovative System for Classifying Cervical Cancer Using Features Based ANFIS Classifier

Integrated Intelligent Research, 2012

Cervical Cancer (CC) is one among the most vulnerable and exceedingly affected diseases among lad... more Cervical Cancer (CC) is one among the most vulnerable and exceedingly affected diseases among ladies around the globe. Usually, cells develop and divide to deliver more cells just when the body needs them. The proposed model exhibits cost-sensitive classifiers that have three primary stages; the principal stage is preprocessing the original data to set it up for classification model which is developed based on Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier. To enhance the classifying accuracy for determining the cervical cancer threat, we are considering Whale Optimization (WO) to select optimal features like both genetic and natural components. The motivation behind data mining is to discover some data which isn't specifically obvious or retrievable by perusing data or executing simple inquiries to the data. A precise and convenient prediction could maintain a strategic distance from any future issue at a specific level.

Research paper thumbnail of Application of Utility Mining using Frequent Itemset and Association Rules: A Survey

Integrated Intelligent Research, 2012

Mining on data reveals patterns that provide useful information for analysis, decision making and... more 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.

Research paper thumbnail of Cyber Physical Service Attacks in Positive Security using Data Mining

Integrated Intelligent Research, 2012

Crisis administration is progressively reliant on systems for data social affair, coordination an... more Crisis administration is progressively reliant on systems for data social affair, coordination and Physical framework control, and therefore is progressively defenseless against arrange disappointments. digital assault could cause such system disappointments deliberately, to obstruct crafted by people on call and boost the effect of a Physical crisis trust that they can be utilized to encourage cooperation between EM professionals and scientists of various orders, from data security and control frameworks to a Physical dissent of administration assault (PDoS) in which IoT gadgets flood the Physical transfer speed of a CPS. In this paper, we evaluate the populace based hazard to a gathering of IoT gadgets focused by malware for a PDoS assault. There are two fundamental process in view of the security concern. 1) Defenders can bound botnet movement and 2) enacting a base level of security has just a restricted impact, while boosting dynamic resistance can diminish botnet action discretionarily. Peculiarity discovery for the base client, SNMP, and) and find underlying drivers utilizing a mix of gathered shrewdness and instinct. Troubleshooting systems is just getting to be harder as systems are getting greater (current server farms may contain 10 000 switches, a grounds system may serve 50 000 clients, a 100-Gb/s whole deal connection may convey 100 000 streams) and are getting more confounded (with more than 6000 RFCs, switch programming depends on a large number of lines of source code, and system chips regularly contain billions of doors). It is a little ponder that system engineers have been marked bosses of multifaceted nature. Think about two cases. Case 1 Suppose a switch with a broken line card begins dropping parcels quietly. Alice, who controls 100 switches, gets a ticket from a few miserable clients griping about network of the system DoS assault model to allude by interruption distinguished conceivable strides to moderate the recognized DoS assaults, and assess the appropriateness of these answers for teleoperated hazard control. The more extensive objective of our paper is to bring issues to light, and increment comprehension of developing digital security dangers against teleoperated security Risk frameworks.

Research paper thumbnail of Detecting the Product Rating Aspects from Textual Customer Reviews Using Data Mining Techniques with LS-SVM

Integrated Intelligent Research, 2012

Individuals, organizations, companies are using facebook media for sharing information and utiliz... more Individuals, organizations, companies are using facebook media for sharing information and utilize it to improve their marketing strategy. Likes, share and followers provide the opinions of the products or service which turns to an encouragement for the development of that companies or organizations. Using the number of likes and shares of a particular product, its popularity can be easily predicted. It is very helpful for the companies to assess their performance and also to campaign their product in the society. It will be useful not only for companies but also for the customers. It also highlights the importance of the product reviews which gains more attention by the product buyers to decide whether to buy the intended product or not based on their various aspects of the product. For example, monitor, processor speed, memory, etc are considered before buying a PC. Theis sentiment analysis of product reviews will provide nearly accurate statistics regarding a product, providing an ease to the customers for analyzing the product and zero down his/her search for an online product. In this research, a study is conducted to analyze the popularity level of website in social networking based on the posts shared on facebook to enhance the marketing strategies. Recently, data mining tools are used to identify the product rating from the customer reviews. In this paper, page ranking algorithm is used to predict product rating from online reviews.

Research paper thumbnail of A Comparative study of Lexicon based and Machine learning based classifications in Sentiment analysis

Integrated Intelligent Research, 2012

Internet and WWW are growing a huge amount of data from the users entered the text that is relate... more Internet and WWW are growing a huge amount of data from the users entered the text that is related to the product review, opinions, attitudes, and any other services. This information is processing and analyzing various tools to be used. The NLP and Information retrieval methods are used to analyze the information and understanding these process information. The main problem of Sentiment analysis is classified into two categories that are Positive opinion or negative opinion. This paper focus to compare the lexical based sentiment classification and Machine learning based sentiment classification methods. Various algorithms and approaches and features are classified in this paper. Different methods are taken to solve the sentiment analysis problems.

Research paper thumbnail of A Survey on the Online Shopping Customer Review Data using Association Rule Mining

Integrated Intelligent Research, 2012

Revealing complex associations between entities is of vast significance for business optimization... more 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.

Research paper thumbnail of Bidirectional Recurrent Neural Network Language Model: Cross Entropy Churn Metrics for Defect Prediction Modeling

Integrated Intelligent Research, 2012

Software Defect Prediction (SDP) plays an active area in many research domain of Software Quality... more 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.

Research paper thumbnail of Detecting the Abnormal SQL Query Using Hybrid SVM Classification Technique in Web Application

Integrated Intelligent Research, 2012

Detecting SQL injection attacks (SQLIAs) is ending up progressively significant in database-drive... more 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 SVM's 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 SVM's assistance just in ordering significant information focuses lying close choice limit; staying less urgent datapoints are grouped by quick univariate nodes.

Research paper thumbnail of An Art of Web Services using Web Mining and Semantic Web Techniques

Integrated Intelligent Research, 2012

Today's web browsers serve as an easy access to numerous sources of text and multimedia data. Mor... more Today's 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.

Research paper thumbnail of Text Mining Techniques in Data Mining - Review

Integrated Intelligent Research, 2012

Discovering Knowledge in Databases and Extract Patterns and Knowledge in erroneous data is Data M... more Discovering Knowledge in Databases and Extract Patterns and Knowledge in erroneous data is Data Mining. The quality of text details is extracted by text mining using Statistical Methods. Relevance, Novelty, Interestingness decides the accuracy of Text Mining. Categorization, clustering, entity extraction and sentiment analysis are used for text mining. Natural language processing, analytical methods related techniques, and algorithms are implemented.

Research paper thumbnail of Widespread Review of Vehicular Ad Hoc Network

Integrated Intelligent Research, 2012

In this paper, main objective is collecting the information from road side traffic and share the ... more 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.

Research paper thumbnail of Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks

Integrated Intelligent Research, 2012

Data mining is the computer based process of analyzing huge sets of data and then extracting the ... more Data mining is the computer based process of analyzing huge sets of data and then extracting the meaning of the data. Data mining tools predict future trends, allowing business to make positive, knowledge-driven decisions. The huge amounts of data generated by traditional methods for prediction of heart disease are too complex and voluminous to be processed and analyzed. Data mining provides the technologies to transform these huge sets of data into useful information for decision making. Data mining techniques takes less time for the prediction of the disease with more accuracy. In this paper we survey different papers in which one or more algorithms of data mining used for the prediction of heart disease. The prediction by using data mining algorithm gives efficient results. Applying data mining techniques to heart disease treatment data can provide as reliable performance as that achieved in prediction and diagnosing of heart disease.

Research paper thumbnail of Enhanced Hand Gesture Recognition In Augmented Reality Using Genetic Algorithm

Integrated Intelligent Research, 2012

Hand Gesture recognition is technology which interpret human gestures using various algorithms.In... more 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(HG) recognition system for augmented reality using Genetic Algorithm (GA) and Artificial neural network algorithm (ANN) 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.

Research paper thumbnail of An Efficient Technique using Data Mining Algorithms for Predicting Disease -A Review

Integrated Intelligent Research, 2012

Data mining is a high volume of data for needful information. The best and most popular data mini... more Data mining is a high volume of data for needful information. The best and most popular data mining techniques are rule mining, clustering, classification and sequence pattern. Data mining has incredible guarantee for the health industry, empowering health frameworks to efficiently utilize data and research to find wasteful aspects and best practices to enhance mind and lessen costs. Some experts believe that the opportunity to improve healthcare and reduce costs at the same time may account for 30% of total health care spending. All in all, this might be a win-win circumstance. In any case, because of the multifaceted nature of healthcare and the moderate appropriation of innovation, our industry lingers behind different enterprises in actualizing powerful information investigation and extraction procedures. For detecting a disease number of tests should be required from the patient. But using data mining technique the number of test should be reduced. This reduced test plays an important role in time and performance. This exploration paper examines how data mining procedures are used for anticipating lung diseases.

Research paper thumbnail of Automated Detection of Diabetic Retinopathy using Medical Image Processing Techniques

Integrated Intelligent Research, 2012

Glaucoma is an eye disease that can result in blindness if it is not detected and treated in prop... more Glaucoma is an eye disease that can result in blindness if it is not detected and treated in proper time. Diabetic related eye diseases like Diabetic Retinopathy (DR), Diabetic Maculopathy (DM) are major cause of blindness. Early detection of diabetic diseases plays an important role to prevent blindness. In last few years there are several researches done in medical image processing and detection from the fund us images such as Optic disk (OD) and the retinal vessels done in the automated detection of Diabetic retinopathy (DR), Diabetic Maculopathy (DM). This research paper represents the methods which are used in the automated detection of diabetic retinopathy. The recent methods used to detect the factors like hemorrhages and Micro Aneurysms (MA) are also discussed in this paper.

Research paper thumbnail of A Survey of Various Weighted based Clustering Algorithm for MANET

Integrated Intelligent Research, 2012

__ A Mobile Ad hoc Network is a collection of independent nodes that communicate over relative ba... more __ A Mobile Ad hoc Network is a collection of independent nodes that communicate over relative bandwidth, power constrained in wireless links. The network topology may transform quickly and randomly due to mobility of nodes. Also, in MANET the decentralized network leads to perform the routing functionalities by nodes themselves such as route discovery, topology discovery and delivering messages from source to destination. Clustering provides the finest answer for huge and dense mobile adhoc networks with high mobility. It also lifts the capacity of network and diminishes the routing overhead in order to bring more efficient and effective routing in MANET. There are two mechanisms in every clustering algorithm, (i) cluster formation and (ii) cluster maintenance. In cluster formation, cluster heads are selected among the nodes to form the hierarchical network. Selecting appropriate cluster head is one of the main research issues. In cluster maintenance, a unique mechanism is needed so that the cluster head can maintain the topological information of the cluster in spite of the cluster structure changes every time due to mobility of nodes. Thispaper mainly focuses on the weight based clustering approaches in MANET. .

Research paper thumbnail of Predicting Risk factor of Dengue using FP- Growth Mining Technique

Integrated Intelligent Research, 2012

Dengue is a debilitating malady which is caused by female mosquitoes (chomp of Aedes mosquitoes).... more 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 (DENV-1, DENV-2, DENV-3 and DENV-4). 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.

Research paper thumbnail of An Analysis of Road Accidental Data Using Clustering and Itemset Mining Algorithms

Integrated Intelligent Research, 2012

Road accidental detection is one of the emerging issue in recent days, which has been focused by ... more Road accidental detection is one of the emerging issue in recent days, which has been focused by many researchers. Road accident is the major cause for unnatural death, and desirability, which is unpredictable. So, many existing works aimed to develop some prediction approaches for analyzing the real time dataset and predicting the accidental rate for future. But, it limits with the drawbacks like inefficient prediction, reduced accuracy, and increased time consumption. Thus, this paper aims to propose a new prediction model by implementing various data mining techniques. It includes the stages of preprocessing, clustering, and itemset mining. Initially, the dataset obtained from the UCI repository is preprocessed by eliminating the irrelevant attributes and filling the missing values. Then, the density based clustering technique is implemented to group the filtered data into a cluster. After that, the rules are formed based on the support and confidence values for predicting the future. Finally, the frequent items are mined by the use of Apriori algorithm. In experiments, the performance results of the proposed system is validated and evaluated by using various measures such as accuracy, precision, recall, and time consumption.

Research paper thumbnail of An Investigation into Brain Tumor Segmentation Techniques

Integrated Intelligent Research, 2012

A tumor is an anomalous mass in the brain which can be cancerous. Such anomalous growth within th... more A tumor is an anomalous mass in the brain which can be cancerous. Such anomalous growth within this restricted space or inside the covering skull can cause problems. Detecting brain tumors from images of medical modalities like CT scan or MRI involves segmentation (Division into parts) for analysis and can be a challenging task. Accurate segmentation of brain images is very essential for proper diagnosis of tumor and non-tumor areas for clinical analysis. This paper details on segmentation algorithms for brain images, advantages, disadvantages and a comparison of the algorithms.

Research paper thumbnail of Impacts of Evaluation Methods on Classification Algorithm's Accuracy

Integrated Intelligent Research, 2012

Decision trees are one of the most powerful and commonly used supervised learning algorithms in t... more 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.

Research paper thumbnail of An Innovative System for Classifying Cervical Cancer Using Features Based ANFIS Classifier

Integrated Intelligent Research, 2012

Cervical Cancer (CC) is one among the most vulnerable and exceedingly affected diseases among lad... more Cervical Cancer (CC) is one among the most vulnerable and exceedingly affected diseases among ladies around the globe. Usually, cells develop and divide to deliver more cells just when the body needs them. The proposed model exhibits cost-sensitive classifiers that have three primary stages; the principal stage is preprocessing the original data to set it up for classification model which is developed based on Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier. To enhance the classifying accuracy for determining the cervical cancer threat, we are considering Whale Optimization (WO) to select optimal features like both genetic and natural components. The motivation behind data mining is to discover some data which isn't specifically obvious or retrievable by perusing data or executing simple inquiries to the data. A precise and convenient prediction could maintain a strategic distance from any future issue at a specific level.

Research paper thumbnail of Application of Utility Mining using Frequent Itemset and Association Rules: A Survey

Integrated Intelligent Research, 2012

Mining on data reveals patterns that provide useful information for analysis, decision making and... more 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.

Research paper thumbnail of Cyber Physical Service Attacks in Positive Security using Data Mining

Integrated Intelligent Research, 2012

Crisis administration is progressively reliant on systems for data social affair, coordination an... more Crisis administration is progressively reliant on systems for data social affair, coordination and Physical framework control, and therefore is progressively defenseless against arrange disappointments. digital assault could cause such system disappointments deliberately, to obstruct crafted by people on call and boost the effect of a Physical crisis trust that they can be utilized to encourage cooperation between EM professionals and scientists of various orders, from data security and control frameworks to a Physical dissent of administration assault (PDoS) in which IoT gadgets flood the Physical transfer speed of a CPS. In this paper, we evaluate the populace based hazard to a gathering of IoT gadgets focused by malware for a PDoS assault. There are two fundamental process in view of the security concern. 1) Defenders can bound botnet movement and 2) enacting a base level of security has just a restricted impact, while boosting dynamic resistance can diminish botnet action discretionarily. Peculiarity discovery for the base client, SNMP, and) and find underlying drivers utilizing a mix of gathered shrewdness and instinct. Troubleshooting systems is just getting to be harder as systems are getting greater (current server farms may contain 10 000 switches, a grounds system may serve 50 000 clients, a 100-Gb/s whole deal connection may convey 100 000 streams) and are getting more confounded (with more than 6000 RFCs, switch programming depends on a large number of lines of source code, and system chips regularly contain billions of doors). It is a little ponder that system engineers have been marked bosses of multifaceted nature. Think about two cases. Case 1 Suppose a switch with a broken line card begins dropping parcels quietly. Alice, who controls 100 switches, gets a ticket from a few miserable clients griping about network of the system DoS assault model to allude by interruption distinguished conceivable strides to moderate the recognized DoS assaults, and assess the appropriateness of these answers for teleoperated hazard control. The more extensive objective of our paper is to bring issues to light, and increment comprehension of developing digital security dangers against teleoperated security Risk frameworks.

Research paper thumbnail of Detecting the Product Rating Aspects from Textual Customer Reviews Using Data Mining Techniques with LS-SVM

Integrated Intelligent Research, 2012

Individuals, organizations, companies are using facebook media for sharing information and utiliz... more Individuals, organizations, companies are using facebook media for sharing information and utilize it to improve their marketing strategy. Likes, share and followers provide the opinions of the products or service which turns to an encouragement for the development of that companies or organizations. Using the number of likes and shares of a particular product, its popularity can be easily predicted. It is very helpful for the companies to assess their performance and also to campaign their product in the society. It will be useful not only for companies but also for the customers. It also highlights the importance of the product reviews which gains more attention by the product buyers to decide whether to buy the intended product or not based on their various aspects of the product. For example, monitor, processor speed, memory, etc are considered before buying a PC. Theis sentiment analysis of product reviews will provide nearly accurate statistics regarding a product, providing an ease to the customers for analyzing the product and zero down his/her search for an online product. In this research, a study is conducted to analyze the popularity level of website in social networking based on the posts shared on facebook to enhance the marketing strategies. Recently, data mining tools are used to identify the product rating from the customer reviews. In this paper, page ranking algorithm is used to predict product rating from online reviews.