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Author

V. Pandiyaraju

Other affiliations: College of Engineering, Guindy
Bio: V. Pandiyaraju is an academic researcher from Anna University. The author has contributed to research in topics: Routing protocol & Water conservation. The author has an hindex of 3, co-authored 5 publications receiving 38 citations. Previous affiliations of V. Pandiyaraju include College of Engineering, Guindy.

Papers
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Journal ArticleDOI
TL;DR: A new approach is proposed to infer user interests based on their queries and fast profile logs and to provide relevant information to users based on personalization to provide most accurate and relevant contents to the users when compared with other related work.
Abstract: Predicting user interest based on their browsing pattern is useful in relevant information retrieval. In such a scenario, queries must be unambiguous and precise. For a broad-topic and ambiguous query, different users may with different interests may search for information from the internet. The inference and analysis of user search goals using rules will be helpful to enhance the relevancy and user experience. A major deficiency of generic search system is that they have static model which is to be applied for all the users and hence are not adaptable to individual users. User interest is important when performing clustering so that it is possible to enhance the personalization. In this paper, a new approach is proposed to infer user interests based on their queries and fast profile logs and to provide relevant information to users based on personalization. For this purpose, a framework is designed to analyze different user profiles and interests while query processing including relevance analysis. Implicit Feedback sessions are also constructed from user profiles based on mouse and button clicks made in their current and past queries. In addition, browsing behaviors of users are analyzed using rules and also using the feedback sessions. Temporary documents are generated in this work for representing the feedback sessions effectively. Finally, personalization is made based on browsing behavior and relevant information is provided to the users. From the experiments conducted in this work, it is observed that the proposed model provide most accurate and relevant contents to the users when compared with other related work.

28 citations

Journal ArticleDOI
TL;DR: A new intelligent routing protocol uses fuzzy rules and the protocol is called as Terrain based Routing using Fuzzy rules for precision agriculture and the fuzzy inference system developed in this work has been used to take decisions for routing.
Abstract: Many agricultural activities can be highly enhanced by using sensor networks and data mining techniques. One of these activities is the regulation of the quantity of water in cultivated fields. Moreover, wireless sensor network have become a more emerging technology in precision agriculture during the recent years. The important issue in the design of wireless sensor networks is the utilization of energy and to enhance the lifetime of the sensor nodes. In this paper, a new intelligent routing protocol has been proposed to improve the network lifetime and to provide energy efficiency in the routing process which is used to provide data to the irrigation system. This novel intelligent energy efficient routing protocol uses fuzzy rules and the protocol is called as Terrain based Routing using Fuzzy rules for precision agriculture. The fuzzy inference system developed in this work has been used to take decisions for routing. The system has been implemented and compared with two routing algorithms called Region Based Routing and Equalized Cluster Head Election Routing Protocol. The experimental results show that the proposed algorithm performs better than the other existing algorithms.

26 citations

Proceedings ArticleDOI
01 Dec 2014
TL;DR: A new Non-NaiveBayesian algorithm is proposed in this project work which improves the performance of the Naive Bayesian algorithm by using Error Distribution functions.
Abstract: Agri-mining is a recent trend that helps the farmer through an Information Technology domain that is needed to improve the crop yield system In this paper, create an effective classifier For that purpose, Soybean dataset is used for analysis and is pre-processed for improving the accuracy of the classifier Then the pre-processed input is given to the Non-Naive classifier which performs classification by using the Error Distribution method in the Kernel space A new Non-Naive Bayesian algorithm is proposed in this project work which improves the performance of the Naive Bayesian algorithm by using Error Distribution functions The result is compared with Naive Bayesian algorithm using the performance measures such that the ROC and Confusion Matrix Then the performance of the proposed model is proved to be better than the Naive Bayesian algorithm

3 citations

01 Jan 2015
TL;DR: A digital signature based authentication scheme with key management where Key Distribution Centers are decentralized in manner and a Key Policy Attribute based encryption scheme along with asymmetric key cryptosystem is used for encrypting the data that is stored in the cloud.
Abstract: Privacy, Security and Access Control are some of the challenges while sharing of physical resources among un trusted tenants. An anonymous authentication in cloud ensures that cloud users remain anonymous while getting duly authenticated. We propose a digital signature based authentication scheme with key management where Key Distribution Centers are decentralized in manner. A Key Policy Attribute based encryption scheme along with asymmetric key cryptosystem is used for encrypting the data that is stored in the cloud. To provide access policy Extended Attributed Control Markup Language (XACML) is used. Moreover, data stored in cloud is vulnerable to losses or corruption in order to overcome this, an automatic retrieval mechanism is used.

1 citations


Cited by
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Journal ArticleDOI
TL;DR: A new fuzzy logic-based product recommendation system which dynamically predicts the most relevant products to the customers in online shopping according to the users’ current interests and uses ontology alignment for making decisions that are more accurate and predict dynamically based on the search context.

48 citations

Journal ArticleDOI
TL;DR: A novel recommendation system which provides suitable contents by refining the final frequent item patterns evolving from frequent pattern mining technique and then classifying the final contents using fuzzy logic into three levels is proposed.
Abstract: A relevant and suitable content recommendation is an important and challenging task in e-learning. Relevant terms are retrieved in a recommender system that should also cope with varying user preferences over time. This paper proposes a novel recommendation system which provides suitable contents by refining the final frequent item patterns evolving from frequent pattern mining technique and then classifying the final contents using fuzzy logic into three levels. This is achieved by generating frequent item patterns after consolidating the user interest changes with an extended error margin quotient. Moreover, fuzzy rules are used in this work to enable the rule mining constraints for accommodating all types of learners while applying rules on the pattern tables. This method aims at mining the data stream preferences into equal-sized windows and caters to the varying user interest ratings over time. Experiments prove its efficiency and accuracy over existing conventional methods.

42 citations

Journal ArticleDOI
TL;DR: It is revealed from the results that RSM has optimized the test procedures and trials needed for the proportioning of SCC so as to maximize the slump flow and compressive strength effectively than DOE and IREMSVM model have conformed.
Abstract: This paper elucidates a data predicting model using an intelligent rule-based enhanced multiclass support vector machine and fuzzy rules (IREMSVM-FR) while optimizing the test practices and trials needed for the proportioning of self-compacting concrete (SCC) using response surface methodology (RSM). The SCC requires a wide range of material content, and hence, more numbers of investigations were typically essential to select a suitable mixture to get the required properties of SCC. Taguchi’s methodology with an L18 array and three-level factor was used to reduce the number of the experiment. Four regulating elements, i.e., cement, fly ash, water powder ratio and superplasticizer, were used. Two results such as slump flow in the fresh state and the compressive strength in the hardened state at 28 days were assessed. Optimizations of the results were set by using RSM. The reactions of material parameters examined to optimize the fresh and hardened properties such as slump flow and compressive strength of SCC. The full quadratic equation of a model can be used to assess the influence of constituent materials on the properties of SCC. Moreover, these 28-days observation records are considered as SCC dataset. For predicting the properties of SCC, an existing intelligent classification algorithm IREMSVM-FR has been used. In which cement (kg), fly ash (kg), water powder ratio (W/P) and superplasticizer (l/m3) were taken as sources of data, whereas slump flow and compressive strength were the responses. It is revealed from the results that RSM has optimized the test procedures and trials needed for the proportioning of SCC so as to maximize the slump flow and compressive strength effectively than DOE and IREMSVM model have conformed.

35 citations