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Subhash K. Shinde

Bio: Subhash K. Shinde is an academic researcher from Lokmanya Tilak College of Engineering. The author has contributed to research in topics: Sentiment analysis & Scheduling (computing). The author has an hindex of 8, co-authored 28 publications receiving 315 citations. Previous affiliations of Subhash K. Shinde include University of Mumbai & Bharati Vidyapeeth University.

Papers
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Journal ArticleDOI
TL;DR: The divide-and-conquer approach improves the proposed system, as is proven experimentally through comparison with the existing BATS and improved differential evolution algorithm (IDEA) frameworks when turnaround time and response time are used as performance metrics.
Abstract: Cloud computing is required by modern technology. Task scheduling and resource allocation are important aspects of cloud computing. This paper proposes a heuristic approach that combines the modified analytic hierarchy process (MAHP), bandwidth aware divisible scheduling (BATS) + BAR optimization, longest expected processing time preemption (LEPT), and divide-and-conquer methods to perform task scheduling and resource allocation. In this approach, each task is processed before its actual allocation to cloud resources using a MAHP process. The resources are allocated using the combined BATS + BAR optimization method, which considers the bandwidth and load of the cloud resources as constraints. In addition, the proposed system preempts resource intensive tasks using LEPT preemption. The divide-and-conquer approach improves the proposed system, as is proven experimentally through comparison with the existing BATS and improved differential evolution algorithm (IDEA) frameworks when turnaround time and response time are used as performance metrics.

156 citations

Journal ArticleDOI
TL;DR: The results obtained empirically demonstrate that the proposed CBBCHPRS performs superiorly and alleviates problems such as cold-start, first-rater and sparsity.
Abstract: In recent years, there is overload of products information on world wide web. A personalized recommendation is an enabling mechanism to overcome information overload occurred when shopping in an Internet marketplace. This paper proposes a novel centering-bunching based clustering (CBBC) algorithm which is used for hybrid personalized recommender system (CBBCHPRS). The proposed system works in two phases. In the first phase, opinions from the users are collected in the form of user-item rating matrix. They are clustered offline using CBBC into predetermined number clusters and stored in a database for future recommendation. In the second phase, the recommendations are generated online for active user using similarity measures by choosing the clusters with good quality rating. This helps to get further effectiveness and quality of recommendations for the active users. The experimental results using Iris dataset show that the proposed CBBC performs better than K-means and new K-medodis algorithms. The performance of CBBCHPRS is evaluated using Jester database available on website of California University, Berkeley and compared with ants recommender system (ARS). The results obtained empirically demonstrate that the proposed CBBCHPRS performs superiorly and alleviates problems such as cold-start, first-rater and sparsity.

94 citations

Proceedings ArticleDOI
20 Dec 2008
TL;DR: The architecture of on line recommendation in Web usage mining (OLRWMS) is proposed for enhancing accuracy of classification by interaction between classifications, evaluation, and current user activates and user profile in online phase of this architecture.
Abstract: The Internet is one of the fastest growing areas of intelligence gathering. During their navigation Web users leave many records of their activity. This huge amount of data can be a useful source of knowledge. Sophisticated mining processes are needed for this knowledge to be extracted, understood and used. Web Usage Mining (WUM) systems are specifically designed to carry out this task by analyzing the data representing usage data about a particular Web Site. WUM can model user behavior and, therefore, to forecast their future movements. Online prediction is one Web usage mining application. However, the accuracy of the prediction and classification in the current architecture of predicting userspsila future requests systems can not still satisfy users especially in huge Web sites. To provide online prediction efficiently, we develop architecture for online recommendation for predicting in Web Usage Mining System .In this paper we propose architecture of on line recommendation in Web usage mining (OLRWMS) for enhancing accuracy of classification by interaction between classifications, evaluation, and current user activates and user profile in online phase of this architecture.

30 citations

Journal ArticleDOI
TL;DR: A fast k-medoids clustering algorithm which is used for Hybrid Personalized Recommender System (FKMHPRS) and results obtained empirically demonstrate that the proposed FK MHPRS performs superiorly.
Abstract: Recommender systems attempt to predict items in which a user might be interested, given some information about the user's and items' profiles. This paper proposes a fast k-medoids clustering algorithm which is used for Hybrid Personalized Recommender System (FKMHPRS). The proposed system works in two phases. In the first phase, opinions from the users are collected in the form of user-item rating matrix. They are clustered offline using fast k-medoids into predetermined number clusters and stored in a database for future recommendation. In the second phase, clusters are used as the neighborhoods, the prediction rating for the active users on items are computed by either weighted sum or simple weighted average. This helps to get more effective and quality recommendations for the active users. The experimental results using Iris dataset show that the proposed fast k-medoids performs better than k-medoids and k-mean algorithms. The performance of FKMHPRS is evaluated using Jester database available on website of California University, Berkeley and compared with web personalized recommender system (WPRS). The results obtained empirically demonstrate that the proposed FKMHPRS performs superiorly.

27 citations


Cited by
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Journal ArticleDOI
TL;DR: An overview of recommender systems as well as collaborative filtering methods and algorithms is provided, which explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.
Abstract: Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

2,639 citations

Journal ArticleDOI
01 Jun 2015
TL;DR: This paper reviews up-to-date application developments of recommender systems, clusters their applications into eight main categories, and summarizes the related recommendation techniques used in each category.
Abstract: A recommender system aims to provide users with personalized online product or service recommendations to handle the increasing online information overload problem and improve customer relationship management. Various recommender system techniques have been proposed since the mid-1990s, and many sorts of recommender system software have been developed recently for a variety of applications. Researchers and managers recognize that recommender systems offer great opportunities and challenges for business, government, education, and other domains, with more recent successful developments of recommender systems for real-world applications becoming apparent. It is thus vital that a high quality, instructive review of current trends should be conducted, not only of the theoretical research results but more importantly of the practical developments in recommender systems. This paper therefore reviews up-to-date application developments of recommender systems, clusters their applications into eight main categories: e-government, e-business, e-commerce/e-shopping, e-library, e-learning, e-tourism, e-resource services and e-group activities, and summarizes the related recommendation techniques used in each category. It systematically examines the reported recommender systems through four dimensions: recommendation methods (such as CF), recommender systems software (such as BizSeeker), real-world application domains (such as e-business) and application platforms (such as mobile-based platforms). Some significant new topics are identified and listed as new directions. By providing a state-of-the-art knowledge, this survey will directly support researchers and practical professionals in their understanding of developments in recommender system applications. Research papers on various recommender system applications are summarized.The recommender systems are examined systematically through four dimensions.The recommender system applications are classified into eight categories.Related recommendation techniques in each category are identified.Several new recommendation techniques and application areas are uncovered.

1,177 citations

Journal ArticleDOI
TL;DR: A detailed investigation and analysis of various machine learning techniques have been carried out for finding the cause of problems associated with variousMachine learning techniques in detecting intrusive activities and future directions are provided for attack detection using machinelearning techniques.
Abstract: Intrusion detection is one of the important security problems in todays cyber world. A significant number of techniques have been developed which are based on machine learning approaches. However, they are not very successful in identifying all types of intrusions. In this paper, a detailed investigation and analysis of various machine learning techniques have been carried out for finding the cause of problems associated with various machine learning techniques in detecting intrusive activities. Attack classification and mapping of the attack features is provided corresponding to each attack. Issues which are related to detecting low-frequency attacks using network attack dataset are also discussed and viable methods are suggested for improvement. Machine learning techniques have been analyzed and compared in terms of their detection capability for detecting the various category of attacks. Limitations associated with each category of them are also discussed. Various data mining tools for machine learning have also been included in the paper. At the end, future directions are provided for attack detection using machine learning techniques.

398 citations

Journal ArticleDOI
TL;DR: This paper revise the user-based collaborative filtering (CF) technique, and proposes two recommendation approaches fusing usergenerated tags and social relations in a novel way that achieve more precise recommendations than the compared approaches.
Abstract: Recommender systems, which provide users with recommendations of content suited to their needs, have received great attention in today’s online business world. However, most recommendation approaches exploit only a single source of input data and suffer from the data sparsity problem and the cold start problem. To improve recommendation accuracy in this situation, additional sources of information, such as friend relationship and user-generated tags, should be incorporated in recommendation systems. In this paper, we revise the user-based collaborative filtering (CF) technique, and propose two recommendation approaches fusing usergenerated tags and social relations in a novel way. In order to evaluate the performance of our approaches, we compare experimental results with two baseline methods: user-based CF and user-based CF with weighted friendship similarity using the real datasets (Last.fm and Movielens). Our experimental results show that our methods get higher accuracy. We also verify our methods in cold-start settings, and our methods achieve more precise recommendations than the compared approaches. key words: recommender system, collaborative filtering, social tagging, social network

304 citations

Journal ArticleDOI
TL;DR: This research solves two main drawbacks of recommender systems, sparsity and scalability, using dimensionality reduction and ontology techniques, and uses ontology to improve the accuracy of recommendations in CF part.
Abstract: A new method is developed for recommender systemsThe recommender system is developed based on collaborative filteringScalability and sparsity issues in recommender systems are solvedMovieLens and Yahoo! Webscope R4 datasets are used for method evaluationThe method is effective in solving the sparsity and scalability problems in CF Improving the efficiency of methods has been a big challenge in recommender systems It has been also important to consider the trade-off between the accuracy and the computation time in recommending the items by the recommender systems as they need to produce the recommendations accurately and meanwhile in real-time In this regard, this research develops a new hybrid recommendation method based on Collaborative Filtering (CF) approaches Accordingly, in this research we solve two main drawbacks of recommender systems, sparsity and scalability, using dimensionality reduction and ontology techniques Then, we use ontology to improve the accuracy of recommendations in CF part In the CF part, we also use a dimensionality reduction technique, Singular Value Decomposition(SVD), to find the most similar items and users in each cluster of items and users which can significantly improve the scalability of the recommendation method We evaluate the method on two real-world datasets to show its effectiveness and compare the results with the results of methods in the literature The results showed that our method is effective in improving the sparsity and scalability problems in CF

273 citations