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The aim of the technique is to minimize the data duplication in the web mining patterns during the time of web based search in large data mining applications. Although there is a long line of work on identifying duplicates in relational data, only a few solutions focus on duplicate detection in more complex hierarchical structures, like XML data. In this system present a novel method for XML duplicate detection, called XML Dup. XML Dup uses a Bayesian network to determine the probability of two XML elements being duplicates, considering not only the information within the elements, but also the way that information is structured. In addition, to improve the efficiency of the network evaluation, a novel pruning strategy, capable of significant gains over the un optimized version of the algorithm, is presented. Through experiments, we show that our algorithm is able to achieve high precision and recall scores in several data sets. XML Dup is also able to outperform another state-of-the-art duplicate detection solution, both in terms of efficiency and of effectiveness.

Duplicate Detection, Network Evaluation, Efficiency, Effectiveness

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J. Selvi, R. Gayathri, "An Efficient Duplicate Detection Algorithm Using Data Cleansing", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.277-280, 2019.

Customer churn prediction is a challenging target but a very necessary and essential in emerging service-oriented businesses. It is also one of the important issues in customer relationship management. To predict a customer there is a number of data mining techniques applied for churn prediction, this paper reviews some recent developments and compares them in terms of data pre-processing and prediction techniques.

Customer Churn, Customer Retention, Customer Relationship Management, Logistic regression, Linear regression, Knowledge discovery, Data mining

[1]. Xiaohua Hu, A Data Mining Approach for Retailing Bank Customer Attrition Analysis, Applied Intelligence 22, 47–60, 2005.
[2]. Vandana Ahuja, Yajulu Medury, Corporate blogs as tools for consumer segmentation-using cluster analysis for consumer profiling, Journal of Targeting, Measurement and Analysis for Marketing 19, 173 – 182, 2011.
[3]. David L. García, Àngela Nebot, Alfredo Vellido, Intelligent data analysis approaches to churn as a business problem: a survey, Knowl Inf Syst 51:719–774, 2017.
[4]. Nadeem Ahmad Naz, Umar Shoaib and M. Shahzad Sarfraz, A Review on Customer Churn Prediction Data Mining Modeling Techniques, Indian Journal of Science and Technology, Vol 11(27), 2018.
[5]. Aleksandar J. Petkovski, Biljana L. Risteska Stojkoska, Kire V. Trivodaliev, and Slobodan A. Kalajdziski, Analysis of Churn Prediction: A Case Study on Telecommunication Services in Macedonia, 24th Telecommunications forum TELFOR 2016 Serbia, Belgrade, November 22-23, 2016.
[6]. Praveen Asthana, A comparison of machine learning techniques forcustomer churn prediction, International Journal of Pure and Applied MathematicsVolume 119 No. 10, 1149-1169, 2018

S. Venkatesh, M. Jeyakarthic, "A Review of Customer Churn Prediction Related Issues Using Data Mining Methods", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.281-284, 2019.

Approximate Nearest Neighbour (ANN) search has become a popular approach for performing fast and efficient retrieval on very large-scale datasets in recent years, as the size and dimension of data grow continuously. In this paper, we propose a novel vector quantization method for ANN search which enables faster and more accurate retrieval on publicly available datasets. We define vector quantization as a multiple affine subspace learning problem and explore the quantization centroids on multiple affine subspaces. We propose an iterative approach to minimize the quantization error in order to create a novel quantization scheme, which outperforms the state-of-the-art algorithms. The computational cost of our method is also comparable to that of the competing methods.

Approximate Nearest Neighbour Search, Binary Codes, Large-Scale Retrieval, Subspace Clustering, Vector Quantization.

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A. Ramya, S. Sangeetha, "K-Subspaces Quantization for Approximate Nearest Neighbour Search", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.285-288, 2019.

Recommender systems have become more important in various domains for lessening the issue of information overload. Traditional Recommender Systems are Collaborative filtering method and Content based filtering method.However, these recommendation methods suffer from data sparsity and cold start problem. So this paper proposes an ANN based recommender system. Artificial Neural Network –Learning Vector Quantization (ANNLVQ) and Optimized Learning Vector Quantization (ANNOLVQ) algorithms are used todevelop a multi-categorical classification model that predicts the class of a rating in recommender systems. In this proposed research, the problem of predicting the rating as a multi-label classification problem is considered where each rating has treated a label. Book dataset used for this proposed research. ANN recommender systems accuracy compared with collaborative filtering method recommender system and ANN recommender systems predicts more accuracy than traditional collaborative filtering method.

Artificial Neural Network, collaborative filtering, Learning Vector Quantization, Book Recommendation, Recommender systems.

[1]. Alharthi, H., Inkpen, D., & Szpakowicz, S. A survey of book recommender systems. Journal of Intelligent Information Systems, 51(1), 139-160, (2018).
[2]. Jakomin, M., Curk, T., & Bosnić, Z. Generating inter-dependent data streams for recommender systems. Simulation Modelling Practice and Theory, 88, 1-16,(2018).
[3]. Scholz, M., Dorner, V., Schryen, G., & Benlian, A. A configuration-based recommender system for supporting e-commerce decisions. European Journal of Operational Research, 259(1), 205-215, (2017).
[4]. Lin, K. P., Shen, C. Y., Chang, T. L., & Chang, T. M. A Consumer Review-Driven Recommender Service for Web E-Commerce. In Service-Oriented Computing and Applications (SOCA), 2017 IEEE 10th International Conference on (pp. 206-210), 2017.
[5]. Smith, B., & Linden, G. Two decades of recommender systems at Amazon. com. Ieee internet computing, 21(3), 12-18, (2017).
[6]. Zhang, F., Lee, V. E., Jin, R., Garg, S., Choo, K. K. R., Maasberg, M., ... & Cheng, C. Privacy-aware smart city: A case study in collaborative filtering recommender systems. Journal of Parallel and Distributed Computing, (2018).
[7]. Kaur, H., Kumar, N., & Batra, S. An efficient multi-party scheme for privacy-preserving collaborative filtering for healthcare recommender system. Future Generation Computer Systems, (2018).
[8]. Nilashi, M., Ibrahim, O., & Bagherifard, K. A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Systems with Applications, 92, 507-520, (2018).
[9]. Yang, B., Lei, Y., Liu, J., & Li, W. Social collaborative filtering by the trust. IEEE transactions on pattern analysis and machine intelligence, 39(8), 1633-1647, (2017).
[10]. Liu, Y., Wang, S., Khan, M. S., & He, J. A novel deep hybrid recommender system based on auto-encoder with neural collaborative filtering. Big Data Mining and Analytics, 1(3), 211-221, (2018).
[11]. Li, Y., Wang, D., He, H., Jiao, L., & Xue, Y. Mining intrinsic information by matrix factorization-based approaches for collaborative filtering in recommender systems — Neurocomputing, 249, 48-63, (2017).
[12]. Zafari, F., & Moser, I. Modeling socially-influenced conditional preferences over feature values in recommender systems based on factorized collaborative filtering. Expert Systems with Applications, 87, 98-117, (2017).
[13]. Bampis, C. G., Rusu, C., Hajj, H., & Bovik, A. C. Robust Matrix Factorization for Collaborative Filtering in Recommender Systems. In Asilomar Conf. on Signals, Systems, and Computers, (2017).
[14]. Mazze, A. (2017). Recommender system using ANN. Neural Networks & Machine Learning, 1(1), 3-3.
[15]. Lee, D. H., & Brusilovsky, P.Improving personalized recommendations using community membership information. Information Processing & Management, 53(5), 1201-1214, (2017).
[16]. Suglia, A., Greco, C., Musto, C., de Gemmis, M., Lops, P., & Semeraro, G. A deep architecture for content-based recommendations exploiting recurrent neural networks. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (pp. 202-211). ACM, (2017).
[17]. Devooght, R., & Bersini, H. Long and short-term recommendations with recurrent neural networks. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (pp. 13-21). ACM, (2017).
[18]. Yi, B., Shen, X., Zhang, Z., Shu, J., & Liu, H. Expanded autoencoder recommendation framework and its application in movie recommendation. In Software, Knowledge, Information Management & Applications (SKIMA), 2016 10th International Conference on(pp. 298-303). IEEE, (2016).
[19]. Veugen, T., & Erkin, Z. Content-based recommendations with approximate integer division. In Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on (pp. 1802-1806), (2015).
[20]. Shen, X., Yi, B., Zhang, Z., Shu, J., & Liu, H. Automatic Recommendation Technology for Learning Resources with Convolutional Neural Network. In Educational Technology (ISET), 2016 International Symposium on (pp. 30-34), (2016).
[21]. Chen, L., & Wang, F. Explaining recommendations based on feature sentiments in product reviews. In Proceedings of the 22nd International Conference on Intelligent User Interfaces (pp. 17-28). ACM, (2017).
[22]. Paradarami, T. K., Bastian, N. D., & Wightman, J. L. A hybrid recommender system using artificial neural networks. Expert Systems with Applications, 83, 300-313, (2017).
[23]. Tewari, A. S., & Barman, A. G. Sequencing of items in personalized recommendations using multiple recommendation techniques. Expert Systems with Applications, 97, 70-82, (2018).
[24]. Liu, D. R., Chen, K. Y., Chou, Y. C., & Lee, J. H. Online recommendations based on a dynamic adjustment of recommendation lists. Knowledge-Based Systems, (2018).
[25]. de Campos, L. M., Fernández-Luna, J. M., Huete, J. F., & Redondo-Expósito, L. Positive unlabeled learning for building recommender systems in a parliamentary setting. Information Sciences, 433, 221-232, (2018).
[26]. Lee, S. J., Xu, Z., Li, T., & Yang, Y. A novel bagging C4. 5 algorithm based on wrapper feature selection for supporting wise clinical decision making. Journal of biomedical informatics, 78, 144-155, (2018).
[27]. Li, T., Fu, K., Choi, M., Liu, X., & Chen, Y. Toward Robust and Efficient Training of Generative Adversarial Networks with Bayesian Approximation. In the Approximation Theory and Machine Learning Conference, (2018).
[28]. Liu, Y., Wang, S., Khan, M. S., & He, J. A novel deep hybrid recommender system based on auto-encoder with neural collaborative filtering. Big Data Mining and Analytics, 1(3), 211-221, (2018).
[29]. Li, Y., Wang, D., He, H., Jiao, L., & Xue, Y. Mining intrinsic information by matrix factorization-based approaches for collaborative filtering in recommender systems — Neurocomputing, 249, 48-63, (2017).
[30]. Zafari, F., & Moser, I. Modeling socially-influenced conditional preferences over feature values in recommender systems based on factorized collaborative filtering. Expert Systems with Applications, 87, 98-117, (2017).
[31]. Bampis, C. G., Rusu, C., Hajj, H., & Bovik, A. C. Robust Matrix Factorization for Collaborative Filtering in Recommender Systems. In Asilomar Conf. on Signals, Systems, and Computers, (2017).
[32]. Mazze, A. (2017). Recommender system using ANN. Neural Networks & Machine Learning, 1(1), 3-3.
[33]. Lee, D. H., & Brusilovsky, P.Improving personalized recommendations using community membership information. Information Processing & Management, 53(5), 1201-1214, (2017).
[34]. Suglia, A., Greco, C., Musto, C., de Gemmis, M., Lops, P., & Semeraro, G. A deep architecture for content-based recommendations exploiting recurrent neural networks. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (pp. 202-211). ACM, (2017).
[35]. Devooght, R., & Bersini, H. Long and short-term recommendations with recurrent neural networks. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (pp. 13-21). ACM, (2017).Yi, B., Shen, X., Zhang, Z., Shu, J., & Liu, H. Expanded autoencoder recommendation framework and its application in movie recommendation. In Software, Knowledge, Information Management & Applications (SKIMA), 2016 10th International Conference on(pp. 298-303). IEEE, (2016).
[36]. Veugen, T., & Erkin, Z. Content-based recommendations with approximate integer division. In Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on (pp. 1802-1806), (2015).
[37]. Shen, X., Yi, B., Zhang, Z., Shu, J., & Liu, H. Automatic Recommendation Technology for Learning Resources with Convolutional Neural Network. In Educational Technology (ISET), 2016 International Symposium on (pp. 30-34), (2016).
[38]. Chen, L., & Wang, F. Explaining recommendations based on feature sentiments in product reviews. In Proceedings of the 22nd International Conference on Intelligent User Interfaces (pp. 17-28). ACM, (2017).
[39]. Paradarami, T. K., Bastian, N. D., & Wightman, J. L. A hybrid recommender system using artificial neural networks. Expert Systems with Applications, 83, 300-313, (2017).
[40]. Tewari, A. S., & Barman, A. G. Sequencing of items in personalized recommendations using multiple recommendation techniques. Expert Systems with Applications, 97, 70-82, (2018).
[41]. de Campos, L. M., Fernández-Luna, J. M., Huete, J. F., & Redondo-Expósito, L. Positive unlabeled learning for building recommender systems in a parliamentary setting. Information Sciences, 433, 221-232, (2018).
[42]. Lee, S. J., Xu, Z., Li, T., & Yang, Y. A novel bagging C4. 5 algorithm based on wrapper feature selection for supporting wise clinical decision making. Journal of biomedical informatics, 78, 144-155, (2018).
[43]. Li, T., Fu, K., Choi, M., Liu, X., & Chen, Y. Toward Robust and Efficient Training of Generative Adversarial Networks with Bayesian Approximation. In the Approximation Theory and Machine Learning Conference, (2018).
[44]. Li, T., Fu, K., Choi, M., Liu, X., & Chen, Y. Toward Robust and Efficient Training of Generative Adversarial Networks with Bayesian Approximation. In the Approximation Theory and Machine Learning Conference, (2018).
[45]. Belka, A., Fischer, M., Pohlmann, A., Beer, M., & Höper, D. (LVQ-KNN: Composition-based DNA/RNA binning of short nucleotide sequences utilizing a prototype-based k-nearest neighbor approach. Virus research, 258, 55-63, (2018)

S. Prasanna Priya, M. Karthikeyan, "A Novel Recommender System based on Artificial Neural Network Learning Vector Quantization Classification Approach", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.289-299, 2019.

This project “Web Network Statistics generator and analyzer” has been developed to provide the better security mechanism for intranet services and reporting. There are a range of functions and reports that provide extensive information on complete process of official mail management and file blocker for different set of users those who are logged into this system. It also handles website status and produce a modification guides. This web tracking system is developing by using the PHP as front end with SQLSERVER as back end. The project consists of various modules such as admin, user, registration, web upload, web user monitoring, file transfer watcher, Email blocker. It verifies the hidden images and verifies links & URL’s Connections. It includes the secured gateway system for the entire server system and transactions performing on the process based actions with perspective to the web product development. This system has a range of functions and reports that provide extensive information on all website status and produce a modification guides. This system is platform independent and can be used by any kind of learner. It is designed with robust and easy to implement in any network places. It is highly scalable and provides the feasible updates to make this project efficient and handle by any kind of user.

web network, statistics, generator, gateway, file blocker

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[2]. Beginning PHP, MySQL and Apache. Wrox Press Ltd. Paperback- 1 June, 2003.
[3]. Making Use of PHP by Appu.John Wiley & Sons Inc. Paperback- 24 July, 2002
[4]. PHP and MySQL Web Development by Luke Welling, Laura Thomson.Sams. Paperback- 30 March, 2001.
[5]. PHP Bible by Converse.John Wiley & Sons Inc. Paperback- 4 October, 2002.

B. Sivaranjani, M. Priyadharshini, "Web Network Statistics Generator and Analyzer", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.300-303, 2019.

The nodes in a wireless network to sleep periodically can save energy, it also incurs higher latency and lower throughput. We consider the problem of designing optimal sleep schedules in wireless networks, and show that finding sleep schedules that can minimize the latency over a given subset of source-destination pairs is NP-hard. We proposed green-wave sleep-scheduling (GWSS)—inspired by synchronized traffic lights—for scheduling sleep-wake slots and routing data on duty-cycling wireless adhoc networks. We also derive a latency lower bound given by d + O(1/p) for any sleep schedule with a required active rate (i.e., the fraction of active slots of each node) p, and the shortest path length d. We offer a novel solution to optimal sleep scheduling using green-wave sleep scheduling (GWSS), inspired by coordinated traffic lights, which is shown to meet our latency lower bound (hence is latency-optimal) for topologies such as the line, grid, ring, torus and tree networks, under light traffic. For high traffic loads, we propose non-interfering GWSS, which can achieve the maximum throughput scaling law given by T(n,p) = ¿(p/¿n) bits/sec on a grid network of size n, with a latency scaling law D(n,p) = O(¿n) + O(1/p).

Wireless Network, Sleep Scheduling, Greenwave, High Latency, Low Throughput

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K.R. Devi, K. Harini Priya, "Green Wave Sleep Scheduling Algorithm in Wireless Network", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.304-306, 2019.

Many websites offering Location Based Services (LBS) provide a k NN search interface that returns the top-k nearest-neighbor objects (e.g., nearest restaurants) for a given query location. This paper addresses the problem of crawling all objects efficiently from an LBS website, through the public k NN web search interface it provides. Specifically, we develop crawling algorithm for 2D and higher-dimensional spaces, respectively, and demonstrate through theoretical analysis that the overhead of our algorithms can be bounded by a function of the number of dimensions and the number of crawled objects, regardless of the underlying distributions of the objects. We also extend the algorithms to leverage scenarios where certain auxiliary information about the underlying data distribution, e.g., the population density of an area which is often positively correlated with the density of LBS objects, is available. Extensive experiments on real-world datasets demonstrate the superiority of our algorithms over the state-of-the-art competitors in the literature.

Knn, Hidden Objects, Location Based Services, Crawling, Density

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P. Krithika, G. Sowmiya, "Crawling Hidden Objects with KNN Queries", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.307-309, 2019.

Posting online reviews and rating their satisfaction purchased products has become an increasingly popular way to share the information for anonymous candidates who has interest in purchasing the product. In addition, people leave their interests and near-future purchasing plan on the web such as search history and search query volume. From this phenomenon, the prediction of sales performance is possible in many products by mining the data sets which are left on the web by consumers’ online activities. In this paper, we focused on the movie ticket sales which word-of-mouth effect is prominent, and our goal is to forecast the sales performance of the near-weekend using box-office data and external factors such as online reviews, star ratings and search volume. For this work, we gather 1.7 million online reviews and movie ratings, and we also gather the daily search volume of movies’ title for past three years. Using machine learning techniques and linear modeling, we develop a model for high-accuracy predicting of ticket sales on near-future. We also analyze a relationship between ticket sales performance on weekends and box-office data, online reviews, star ratings, and search volume. Through this work, we support to decide the ideal number of screens for a given weekend, thus it contributes to a substantial increase in the rate of profit on movie markets.

Box-Office Data, Online Reviews, Star Ratings, Ticket Sales

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M. Keerthana, J. Rabacca Cinthiya, "Predicting Ticket Sales Using Web-Based External Factors and Box-Office Data", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.310-312, 2019.

This system has been successfully developed and hosted in the different server configurations and undergoes different kind of tests in the environment of multi user systems any number of user can enjoy the videos with multi access. It has enough functionality beyond web TV to any home entertainment system. It has a quick access to the thousands of video files available in various categories. It provides a high quality performance for the videos to be watched out by the viewers. When multiple video sources are live-encoded and transmitted over a common wireless network, each stream needs to adapt its encoding parameters to wireless channel fluctuations, so as to avoid congesting the network. We present a stochastic system model for analyzing multi-user congestion control for live video coding and streaming over a wireless network. Variations in video content complexities and wireless channel conditions are modeled as independent Markov processes, which jointly determine the bottleneck queue size of each stream. Interaction among multiple users is captured by a simple model of random traffic contention. Using the model, we investigate two distributed congestion control policies: the approach based on stochastic dynamic programming (SDP) and a greedy heuristic. Compared to fixed-quality coding with no congestion control, performance gains in the range of 0.5-1.3 dB in average video quality are reported for both schemes from simulation results under various video and channel characteristics.

Broadcasting, Multiuser, Congestion Control, Fixed Quality, Channel

[1] TutunJuhana, On the design of FM broadcasting remote monitoring system, Proceeding of 2015 1st International Conference on Wireless and Telematics, ICWT 2015
[2] http://www.audemat.com/
[3] http://www.devabroadcast.com/
[4] http://davicom.com
[5] butt - broadcast using this tool, diaksesmelalui http://www.danielnoethen.de/
[6] Icecast, diaksesmelalui http://icecast.org/

A. Sangeetha, M. Tamilarasi, "Web Broadcasting and Multi User System", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.313-315, 2019.

Appraisal is an expert estimate of the value of something. Staff appraisal can be tricky. If you get this wrong, you will negatively impact your staff. This intranet web application will provide the feasible solution to boost the production of each of your staffs, regardless of their current achievement level. An effective staff appraisal system ought to validate by the students of the department. This aspect of the performance appraisal process will begin anew unless the employee is scheduled to participate in the Supervisory Performance Appraisal process or the process is warranted as a result of the Performance Growth Plan, or the staff’s job description has changed significantly. The evaluation cycle outlined below provides the employer with the opportunity to assess and evaluate the performance of the teacher on the district-adopted teacher performance evaluation criteria. Throughout the course of the evaluation cycle, strengths and areas of growth will be identified and communicated to staff. The assessment tool used for staff contains criteria like specific behaviours, knowledge, presentation skills that pertain to all staff. It is assumed that all staffs of the college are professional and, as such, will perform duties with integrity, and maintain a positive, vigilant attitude toward student physical safety and emotional well being.

Appraisal System, Supervisory Performance, Svm, College Staff, Evaluation Tool

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N. Brindha, P. Dhivya, "Appraisal Evaluation Tool for College Staff", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.316-319, 2019.