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The financial organizations such as banks assess credit worthiness of borrowers before providing new loans. Certain assessments are done on the basis of ‘probability of default’ for the potential borrower which is based on credit scoring of customer. Banks are having customer`s portfolios that are likely to go through current crisis without much difficulty, but at the same time it is difficult to determine the risks arising due to various factors. Most of the features in customer databases have little predictive effect on the credit worthiness of the customer. So, determining features that affects the credit worthiness is another important task before knowledge discovery. The predictive models, based on Ensemble Methods (EM): a ML paradigm under Data Mining (DM), have the capability to determine the relevant features and customer’s credit worthiness in efficient manner. In this paper, a good number of prediction models proposed in the literature are surveyed. To deal with major challenges like data incompleteness, noise and its vastness while building predictive models, this work proposes a predictive framework (classifier) for detection of Credit Worthiness. It employs a Supervised Attribute (Feature) selection method to select the worth of subset, filters and an Enhanced Ensemble Method based on Boosting for precise convergence. The proposed Enhanced Ensemble based classifier is implemented in Weka 3.8.2. Later in this work, to know the performance of proposed model, evaluation and comparison against several model is presented. The proposed framework selects minimum number of attributes and is good in terms of False Positive, Recall, True Positive and Prediction Rate than original base classifiers. Also, the model is good for applying on large datasets due the benefit of ensembles.

Artificial Intelligence, Boosting, Classification, Credit Risk, Credit Worthiness, Ensemble Methods, Machine Learning, Predictive Models, Weka

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[3] Stuti Nathaniel, Anand Motwani, Arpit Saxena, "Cloud based Predictive Model for Detection of ‘Chronic Kidney Disease’ Risk", International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.185-188, 2018.
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[9] C.R.Durga devi, Dr.R.Manicka chezian, “A Relative Evaluation of the Performance of Ensemble Learning in Credit Scoring”, IEEE International Conference on Advances in Computer Applications (ICACA), 978-1-5090-3770-4/16, 2016
[10] Zhang, “Z., Research of credit risk of commercial bank`s personal loan based on CHAID decision tree”, Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), 2011.
[11] http://www.cs.waikato.ac.nz/~ml/weka/index.html
[12] Weka, University of Waikato, Hamilton, New Zealand.
[13] Anand Motwani, Goldi Bajaj, Sushila Mohane, "Predictive Modelling for Credit Risk Detection using Ensemble Method", International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.863-867, 2018.
[14] Ling Kock Sheng, and Teh Ying Wah, “A comparative study of data mining techniques in predicting consumers’ credit card risk in banks”, African Journal of Business Management Vol. 5 (20), pp. 8307-8312, 16 September, 2011
[15] http://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients

M. Singh, G. K. Dixit, "Modeling Customer’s Credit Worthiness using Enhanced Ensemble Model," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1466-1470, 2018.

Using Machine Learning (ML) and data analytics in the banking organizations is more than a trend and has become essential to keep up with the market competition and reduce credit risks. In recent years, Customer’s Credit Worthiness is becoming more crucial for financial organizations. In past many credit risk models, that are actually statistical tools, are used to infer the future probabilities of customers to become default. At the same time with the massive increase in the volume, variety and velocity of data generated through various banking and business transactions pose a great computational and storage challenge for data analysis and intelligence tasks. To address the challenges for intelligence tasks Cloud Computing (CC) paradigm is evolved. The data and computation can be distributed to any CC environment with minimal effort nowadays. Also, CC paradigm turned out to be valuable alternatives to speed-up ML platforms. This paper aims to build and assess the performance of the 03 machine learning models, for prediction of credit card payment defaulter, over Microsoft Azure Machine Learning Platform. Finally a predictive analytics framework for classifying and predicting payment default by credit holder is proposed. For developing and testing the model a large, real and recent dataset of credit card, obtained from UCI repository, is used. The key focus of the work is on detection of Credit Worthiness which is defined as the ‘probability of default’ on the loan or credit from financial organizations like banks. The efficacy of model is demonstrated, on the basis of prediction accuracy and other metrics, against benchmark classifiers. Proposed work also demonstrates the use of Microsoft Azure cloud which is one of the foremost cloud environments for ML. The results attained by the proposed model are promising and the obtained results have potential to direct the future research work in domain.

Artificial Intelligence, Predictive Modelling, Cognitive Computing, Computer Vision, Credit Risk, Credit Worthiness, Data Classification, Financial Organization, Machine Learning, Microsoft Azure, Cloud Computing

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[22] https://aws.amazon.com/aml/
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[27] http://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients

A. Motwani, P. Chaurasiya, G. Bajaj, "Predicting Credit Worthiness of Bank Customer with Machine Learning over Cloud," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1471-1477, 2018.

Regression testing is needed to ensure the correct behavior of software after change. For the process of automation and selection of test cases, a number of meta-heuristic techniques have been used in literature. In this paper, bat algorithm, cuckoo search and multi-objective binary genetic algorithms have been discussed. The proposed multi-objective binary genetic algorithm is evaluated against test functions and its performance is analyzed in comparison to existing algorithms i.e. bat and cuckoo search algorithm. For this, we have considered factors such as fault coverage and execution time. The related dataset is extracted from benchmark repository named flex object which originates from SIR. Results indicate that multi-objective performs better than bat and cuckoo search algorithm.

Software Testing, Regression Testing, Bat Algorithm, Cuckoo Search Algorithm, Software Maintenance

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S. Raheja, R. Singh, "Test case selection using multi-objective Evolutionary Algorithms," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1478-1484, 2018.

Data mining is the exploration and analysis of large data sets, in order to discover meaningful patterns and rules. Fuzzy Logic can be integrated with Data Mining techniques to incorporate human type reasoning in pattern discovery. In this paper we are using Fuzzy Data Mining techniques in a database created from a survey pertaining to Financial Inclusion. The popular Data Mining techniques like clustering and association rule mining are used in a fuzzy framework at various stages of the analysis. Starting from the survey database this paper proceeds through all the steps of Data Mining like pre-processing, attribute selection, segmenting quantitative values, clustering and finally reaching at natural fuzzy association rules. Financial Inclusion is one of the key areas where economists and governments try to concentrate for the eradication of poverty. With this analysis we clearly reach at a conclusion that Education and introduction to Information Technology plays the most significant role in Financial Inclusion

Financial inclusion, Fuzzy Data Mining, Fuzzy Logic, Fuzzy Clustering

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Binu Thomas, "The Role of Technology and Education in Financial Inclusion – A Data Mining Analysis in a Fuzzy Framework," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1485-1497, 2018.

Data is recognized as an important source for knowledge generation. Sometime user may aware about requirement but sometime may not. Recommender systems are software or technical facilities to provide items suggestions or predict customer preferences by using prior user information. Recommendations can help to increase sales and improve user satisfaction. Music Recommendation system can help to explore relative music based on user preference or internal similarity. A hybrid recommender system is usually developed through the combination of multiple recommendation techniques to boost the quality of recommendations. This paper uses content-based filtering with K-mean clustering algorithm for music recommendation system which provides effective and relevant content to be suggested.

Recommendation system; Content-based filtering; K-mean; Data Mining

[1]. Paulo Chiliguano, Gyorgy Fazekas “Hybrid Music Recommender Using Content-based And Social Information” published in IEEE ICASSP, 2016 pp 2618-2622
[2]. Milind Mathur, Ayush Kesarwani , “Selective Unsupervised Feature Learning with Convolutional Neural Network (S-CNN)” published in NCNHIT 2013
[3]. B Amini, R Ibrahim, MS Othman, MA Nematbakhsh . Expert Systems with Applications 42 (2), 913-928, 2015 . 14, 2015. Discovering the impact of knowledge in recommender systems: A comparative study International Journal of Computer Applications (0975–8887) 23 (4)‏.
[4]. Jyotsna Chanda, “An Improved Web Page Recommendation System Using Partitioning and Web Usage Mining” Proceedings of the International Conference on Intelligent Processing, Security and Advanced Communication. Article No. 80.
[5]. International Journal of Scientific Research in Computer Sciences and Engineering (ISSN: 2320-7639) .

Karishma Mandloi, Amit Mittal, "Hybrid Music Recommendation System Using Content-based Filtering and K-Mean Clustering Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1498-1501, 2018.

Billions of data related to the user queries is stored in several web Pages and it is growing each day. Many times, Query result is not satisfied to the user. Sometimes web pages display irrelevant data or insufficient data to the user. This type of problem is solved by using page ranking algorithm. Page ranking is based on the user queries. Page rank algorithm is massively used for ranking the web pages in order of most relevant in search engines World-Wide-Web. Page Rank work as main role in the process of web mining. Based on user query a rank list is associated with the listed web pages by any search engine. Therefore the web pages display higher Page ranks are listed in the top rank that helps the user to get a most relevant and useful information in minimum possible time. In this page ranking algorithm we can display both link and the content at a time. Many algorithms are used for page ranking such as Google page rank algorithm, Hyperlink-Induced topic search(HITS) algorithm etc., Using this algorithm we can easily eliminate the problem of older outdated web pages from our rank list. Page rank does not change only by the user click because most visited web pages are not useful or satisfied to the user. So we find the most visited web pages as well as total times spend by the user on the particular web pages. Based on current trend the particular web pages ranking is updated regularly.

Data mining, Hyperlink-Induced Topic Search [HITS] algorithm, Page Rank Algorithm.

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V.Banu Priya, T.Meyyapan, SM Thamarai,, "Page Ranking Algorithm for Ranking Web Pages," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1502-1505, 2018.

Traditional data mining techniques predict frequent itemsets without considering the temporal data. Due to this, efficiency of the frequent itemsets mining is not upto the mark on the temporal data. A new extended apriori algorithm proposed in this research work handles the time interval while identifying the frequent itemsets. The main objective of this research work is to identify patternset in periodic intervals from the temporal data sets. Datasets from UCI data repository is subjected to this proposed method. Experimental results are tabulated and plotted. The results show improvement over the traditional apriori algorithm.

Data mining, Apriori Algorithm, Frequent Itemsets, Temporal Data.

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B. Sowndarya, T. Meyyappan, SM Thamarai, "A Novel Methodology for Mining Frequent Itemsets from Temporal Dataset," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1506-1511, 2018.

As of late, we have seen a twist of audit sites. It displays an incredible chance to share our perspectives for different items we buy. Be that as it may, we confront the data over-burdening issue. Instructions to mine significant data from surveys to comprehend a client`s inclinations and make an exact proposal is vital. Customary recommender frameworks (RS) think about a few components, for example, client`s buy records, item classification, and geographic area. In this work, we propose a conclusion based rating expectation technique (RPS) to enhance forecast exactness in recommender frameworks. Initially, we propose a social client nostalgic estimation approach and ascertain every client`s notion on things/items. Besides, we consider a client`s own wistful properties as well as mull over relational nostalgic impact. At that point, we think about item notoriety, which can be surmised by the wistful appropriations of a client set that mirror clients` thorough assessment. Finally, we intertwine three components client supposition likeness, relational wistful impact, and thing`s notoriety closeness into our recommender framework to make a precise rating forecast. We direct an execution assessment of the three nostalgic factors on a genuine dataset gathered from Yelp.

Meta-Data, Rating prediction, yelp

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Arshiya Begum, Ruksar Fatima, "Predicting Rating from Textual Reviews," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1512-1516, 2018.

Palm Print can be useful to understand human behavior. Palm prints are the mirrors to our inborn talents and potentials, talent and likings. If it is not recognized appropriately and well in time, they may remain shadowed all through a person’s life. Then it follows a life full of dislike and frustrations of underperformance at work or dissatisfaction of occupation. There are some characteristics of child on his/her born time with above average intelligence in some specific areas. For extracting, fingerprint patterns and correlating them with their behavior and personality type. The application works based on intelligence palmistry. The images of human palm form input to the system. Then, system applies digital image processing and analysis techniques on input images to identify certain features in the image. By using knowledge base of intelligence palmistry, it analyzes certain features in image and predicts behavior traits

Intelligence Palmistry, Knowledge Base, Human Palm, Behavioral traits

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[2] D. M. Shah “Decision Support system for Image Analysis” in journal of Advanced Research in Computer Engineering, 1(1-2) January-December 2007, pp 51-56.
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N.H. Desai, D. B. Shah, "Analysis on Human Behavior Traits Using Intelligence Palmistry," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1517-1520, 2018.

Cloud computing is now a most popular technology of the present generation. Energy efficiency is big aspect to think as the big data center is consuming a lot of energy to run and to serve their customers. Energy efficient algorithm and techniques are required to reduce the carbon emissions. In this paper we have worked for consolidation of Virtual Machine(VM) by detecting over-utilized hosts by using Pattern matching and reduced number of migrations by taking a new approach of Mode Absolute Deviation. It analyzes the historical data of CPU usages to search the usage pattern of CPU and finds the dynamic thresholds values for migration of virtual machine. The work has been carried out in CloudSim and the results in our work has been better than previous work[1] and we are able to save energy and reduce the number of migrations by using our proposed method.

Energy Efficient, host overloading, VM Consolidation, VM Migration, Mode, Cloud Computing

[1] O. Sharma and H. Saini, “VM Consolidation for Cloud Data Center Using Median Based Threshold Approach,” Procedia Comput. Sci., vol. 89, pp. 27–33, 2016.
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N. Kumar, R. Kumar, "Energy Efficient Host Overloading Detection Algorithm in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1521-1525, 2018.