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Owing to the past few decades, Face recognition came in to one of the most lively research areas in computer vision and pattern recognition. Human face recognition is indeed a challenging task, especially under the illumination and pose variations. It is challenging as well as attractive for its usefulness in the area of crime detection and identity verification. This paper compares the different face recognition techniques like feature extraction techniques like geometry-based feature extraction, appearance based techniques and template based feature extraction. With the increase in the number of proposed algorithms and techniques the survey and evaluation of these algorithms and techniques becomes more vital to provide a boost to the research activities. Henceforth, the primary aim of this paper is to provide a critical summary and working paradigm of the existing human face recognition techniques which in turn will be greatly useful for the researchers to compute the problems in hand.

Face Recognition System, Face Detection, Holistic approach, Feature-based approach, Face Segmentation

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[9] Divesh N. Agrawal, Deepak Kapgate, “Face Recogniion Using PCA Technique”, IJCSE Vol. 2, Issue 10, Oct 2014.
[10] SD. Kasute, M. Kolhekar, “ROI Based Medical Image Compression”, IJSRNSC, Vol. 5, Issue 1, April 2017.
[11] Abdulkarim I. Abughfa, Ahmed B. Elmadani, “Offline Signature Verification based on Image Processing and Hu Momet”, IJSRNSC, Vol. 4, Issue 5, Oct 2016.

D. Venkat Ravi Kumar, K. Raja Sravan Kumar, "A Philosophical Review on Different Face Recognition Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.929-933, 2018.

In digital world, there is a drastic increase of the websites that encouraged users to give their reviews on products, services, policies. This task of different data gathering and analysis of review is known as Opinion Mining. It analyses the text written in a natural language and classify them as positive or negative based on the human’s sentiments, emotions, opinions expressed on any product. Nowadays user reviews and comments are very important for further evaluating and making decision for new products or policies. This gave the chance to spammers to spread malicious reviews with a target to misguide users. Spam is the unwanted similar content flooded on the internet. There is a need to detect spam efficiently. This work focused on training words and finding out whether further sentences are spam or not spam to improve accuracy. This paper discuss and implements naive bayes classifier to detect spam reviews.

Opinion mining, naive bayes, spam

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[4] Nidhi R. Sharma , Prof. Vidya D. Chitre, "Opinion Mining, Analysis and its Challenges" International Journal of Innovations & Advancement in Computer Science IJIACS ISSN 2347 – 8616 Volume 3, Issue 1 April 2014.
[5] Ayesha Rashid, Naveed Anwer, Dr. Muddaser Iqbal, Dr. Muhammad Sher, "A Survey Paper:Areas, Techniques and Challenges of Opinion Mining" International Journal of Computer Science Issues, Vol. 10, Issue 6, No 2, November 2013.
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[13] Anchal, Abhilash Sharma, "SMS Spam Detection Using Neural Network Classifier" International Journal of Advanced Research in Computer Science and Software Engineering Research Paper, Volume 4, Issue 6, June 2014.
[14] Behrouz Minaei-Bidgoli, Saeedeh Sadat Sadidpour, Hossein Shirazi, Nurfadhlina Mohd Sharef, Mohammad Ebrahim Sanjaghi, "Context-Sensitive Opinion Mining using Polarity Patterns" International Journal of Advanced Computer Science and Applications, Vol. 7, No. 9, 2016.
[15] Nidhi Mishra and C K Jha, "Classification of Opinion Mining Techniques" International Journal of Computer Applications 56 (13):1-6, October 2012, Published by Foundation of Computer Science, New York, USA.
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[19] Haseena Rahmath P, "Opinion Mining and Sentiment Analysis - Challenges and Applications" International Journal of Application or Innovation in Engineering & Management (IJAIEM). Volume 3, Issue 5, May 2014.
[20]https://en.wikipedia.org/wiki/Naive_Bayes_classifier
[21] Nikhila Zalpuri, Meena Arora, "An Efficient Model for S.M.S Security and SPAM Detection: A Review", International Journal of Computer Sciences and Engineering, volume - 3, Issue - 12,Dec2015.
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Pooja, Komal Kumar Bhatia, "Spam Detection using Naive Bayes Classifier," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.934-938, 2018.

Sentiment analysis (SA) is an intellectual and extracting process of the user’s feelings and emotions. It is one of the promising fields of Natural Language Processing (NLP) such as text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study effective states and subjective information. Sentiment analysis is widely applied to a voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. In this paper, the latest algorithms of sentiment analysis applications are investigated and presented briefly. This paper also introduces a survey on the different techniques and challenges of sentiment analysis.

Sentiment Analysis, Opinion Mining, Product Review, Data Review

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[3]Wang, D, Zhu, S & Li, T, Sum View "A Web-based engine for summarizing product reviews and customer opinions”, Expert Systems with Applications, vol. 40, no. 1, pp. 27-33, 2013.
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[7] Hai, Z, Chang, K & Cong, G, “One seed to find them all: mining opinion features via association”, in Proceedings of the 21st ACM international conference on Information and knowledge management, pp. 255-264, 2012.
[8] Cambria, E, Scholler, B, Xia, Y & Havasi, C, “New avenues in opinion mining and sentiment analysis”, IEEE Intelligent Systems, no. 2, pp. 15-21, 2013.
[9] Cho, KS, Jung, NR & Kim, UM, “Using word map and score based weight in opinion mining with map reduce”, in Service-Oriented Computing and Applications (SOCA), 2010 IEEE International Conference on, pp. 1-4, 2010.
[10] Veeraselvi, S & Saranya, C,” Semantic orientation approach for sentiment classification”, in Green Computing Communication and Electrical Engineering (ICGCCEE), 2014 International Conference on, pp. 1-6, 2014
[11] Peñalver-Martinez, I, Garcia-Sanchez, F, Valencia-Garcia, R, Rodríguez-García, MÁ, Moreno, V, Fraga, A & Sánchez-Cervantes, JL, “Feature-based opinion mining through Ontology’s”, Expert Systems with Applications, vol. 41, no. 13, pp. 5995-6008, 2014.
[12] Angulakshmi, G & ManickaChezian, R, “An analysis on opinion mining: techniques and tools”, International Journal of Advanced Research in Computer Communication Engineering, vol. 3, no. 7, pp. 7483-7487, 2014.
[13] Ortigosa-Herna´ ndez Jonathan et al. “Approaching sentiment analysis by using semi-supervised learning of multi-dimensional classifiers”, Neuron computing, pp.98–115,2012.
[14] Kaufmann JM. JMaxAlign, “A Maximum Entropy Parallel Sentence Alignment Tool”, In the Proceedings of COLING’12: Demonstration Papers, Mumbai. pp. 277–88,2012.
[15] Ankush Sharma, Aakanksha, Assistant Professor, Department of C.S.E, Chandigarh University Gharuan, India, International journal of Advanced Research in Computer and Communication Engineering, “ A Comparative Study Of Sentiments Analysis Using Rule Based and Support Vector Machine ” volume 3,2014.
[16] Walaa Meddhat , Ahmed Hassan ,Hoda Korashy “Sentiment analysis algorithms and applications: A survey, Ain Sham University, Faculty of Engineering, Computer & Systems Department, Egypt 19 April 2014.
[17] Chin-Shrng Yang, Hsiao-Ping Shih, Department of Information Management, Yuan Ze University, ChangLi, Taiwan,” A Rule-Based Approach For Effective Sentiment Analysis” PACIS 2012.
[18] Yanfang, C., Pu, Z., Anping, X., “Sentiment analysis based on an expanded aspect and polarity-ambiguous word lexicon”, Int. J. Adv. Comput. Sci. Appl, Vol. 6 pp.2,2015.
[19] Duyu, T., Bing, Q., Ting, L., Qiuhui, S., “Emotion analysisplatform on Chinese microblog”, CoRR J,2014.
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[21] Svetlana, K., Xiaodan, Z., Saif, M.M., “Sentiment analysis ofshort informal texts”, J. Artif. Intell. Res. Vol.50,2014.
[22] Qingxi, P., Ming, Z., “Detecting spam review through sentiment analysis”, J. Software, Vol.9, pp.8,2014.
[23] Bing, X., Liang, Z., “Improving twitter sentiment analysis with topic-based mixture modeling and semi-supervised training”, In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Short Papers). Association for Computational Linguistics, Baltimore, Maryland, USA,2014.
[24] Robert, R., "Modelling and Representing Negation in Data-driven Machine Learning-based Sentiment Analysis”, ESSEM@ AI* IA,2013.
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[27] Christine, L., Florian, K., Antal, V.D.B., “The perfect solution for detecting sarcasm in tweets #not”, In Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment, and Social Media Analysis. Association for Computational Linguistics, Atlanta, Georgia, pp. 29–37,2013.
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[34] Duyu, T., Bing, Q., Ting, L., Qiuhui, S., “Emotion analysis platform on Chinese micro blog”, Co RR J, 2014.
[35] Svetlana, K., Xiaodan, Z., Saif, M.M., “Sentiment analysis ofshort informal texts” J. Artif. Intell. Res, Vol.50, 2014.
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Akanksha Mrinali, Sanjeev Kumar Sharma, "Sentimental Analysis: A Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.939-951, 2018.

The Internet and mobile usage has increased largely over the last years in India. This causes the trends in online shopping also increasing tremendously. Many e-commerce companies nowadays are conducting analysis of customer’s attitude towards online shopping using various survey techniques to find out the factors influencing the online behaviors. This study emphasis on the behaviors of customers of Bhopal city towards online shopping applications in the market. So, the main objective is to analyses customer’s mindset towards online shopping. For this study an online survey is conducted through Google Form. This survey includes a list of well organized questionnaire and collects the responses through e-mail from 166 respondents of Bhopal City. From this study many interesting and useful information have been discovered which can obviously help to understand the customer’s online shopping attitude and behavior so that e-commerce companies could make better strategies for increasing their online market specially in Bhopal City.

Online Shopping, E-Commerce, E-Survey, Google Form

[1] Report published by India Brand Equity Foundation, URL-http://www.ibef.org/industry/
Ecommerce-presentation. 2017.
[2] M. Mahesh Kumar, Sobha.P.G, “a Study on Consumers’ Attitude towards Online Shopping”, International Conference on "Research avenues in Social Science” Organize by SNGC, Coimbatore- Volume-1, Issue-3, pp.265-276.2016
[3] M. Rifaya Meera, R. Padmaja and R. Mohammed Abubakkar Siddique,”Preference of Customers towards Online Shopping Applications”, Imperial Journal of Interdisciplinary Research (IJIR) - Vol-3, Issue-1, pp.577-582, 2017.
[4] Deepika Bhatia, “Challenges in online shopping (B2C) in India”, International Journal of Scientific & Engineering Research, Volume 4, Issue 4,pp.313-317,2013.
[5] Guo Jun, Noor Ismawati Jaafar, “ A Study on Consumers’ Attitude towards Online Shopping in China”, International Journal of Business and Social Science- Volume 2,Issue 22, pp.122-132,2011.
[6] D. T. Venkatakrishnan,” A Study Of Trends In B2c Online Buying In Coimbatore District”, International Journal of Interdisciplinary Research in Arts and Humanities (IJIRAH)- Volume 2, Issue 2, pp.88-91,2017.
[7] Prashant Singh,” Consumer’s Buying Behaviors towards Online Shopping”, National Monthly Refereed Journal of Research in Commerce & Management- Volume 3, pp.27-34,2014.
[8] Ashish Bhatt,” Consumer Attitude towards Online Shopping in Selected Regions of Gujarat”, Journal of Marketing Management- Volume 2, Issue 2, pp.29-56,2014.
[9] Deepa Bakshi, Vikas Saraf,” a Study of Demographic Factors of Customers in Online Shopping (Special Reference To Bhopal City, Madhya Pradesh, India “international journal of science technology and management-volume 5, Issue 11, pp.230-236, 2016.
[10] G.R.Shalini, K.S.HemaMalini,” A Study Of Online Shopping Consumer Behavior In Chennai “, International Journal of Engineering, Business and Enterprise Applications, Volume 11, Issue 2, pp. 153-157, 2015.
[11] Ishan Arora, Gagandeep Singh, Lokesh Kumar, “Online product review analysis for sentiments”, International journal of computer science and engineering, vol-6, issue-5, pp.1045-1048, may-2018.

Sanjeev Gour, "A Study on Customer’s Mindset towards Online Shopping: (With Special Reference to Bhopal City)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.952-956, 2018.

People suffering from Lung cancer are commonly found throughout the world. There are many people who die of this fatal ailment every year. Even though there are many reasons for the occurrence of this disease, it is difficult to say if a person is suffering from lung cancer, unless we test for it. This is costly. It would be highly useful if we could identify significant symptoms in a person that could help to predict the possibility of occurrence of this disease in them. With this objective in mind, we have used a dataset consisting of values of 16 significant symptoms in people who were diagnosed and tested for lung cancer. Based on knowledge of various factors leading to lung cancer and the general symptoms in people suffering from lung cancer, we have chosen these biomarkers. Statistical techniques were used to study and analyze the occurrence of each symptom / factor in the sample population with and without lung cancer, to arrive at a set of predominant symptoms in people with lung cancer. The dataset was also tested for significance of factors using a data mining tool. The dataset was classified using various decision tree algorithms and the results were compared. Decision rules were also generated using Apriori Analysis. This was used to build three data models that help to predict the occurrence of lung cancer among patients with significant symptoms. A maximum accuracy of prediction of 93% was achieved. This was found to be better than prediction through decision tree classifiers.

Data Mining, Classification, Prediction, Lung Cancer, Apriori Analysis

[1]. M.Venkat Dass, M.A.Rasheed, M.M.Ali, “Classification of lung cancer subtypes by data mining technique”, Proceedings of the 2014 International Conference on Control, Instrumentation, Energy and Communication, pages 558-562, ISBN: 978-1-4799-2044-0, Jan 31 2014
[2]. Juliet Rani Rajan, C.Chilambu Chelvan, “A survey on mining techniques for early lung cancer diagnosis”, Proceedings of the 2013 International Conference on Gren Computing, Communication and Conservation of Energy, ISBN: 978-1-4673-6126-2, 12-14 Dec 2013
[3]. Ahmed K. Emran AA., Jesmin T, Mukti RF, Rahman MZ, Ahmed . “Early detection of lung cancer risk using data mining”, Asian Pacific Journal of Cancer Prevention, Issue 14(1), Pages 595-598, 2013.
[4]. Ada, Rajneet Kaur, “A Study of Detection of Lung Cancer using Data Mining Classification Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol 3(3), ISSN 2277 128X, Pages 131-134, Mar 2013
[5]. Haofan Yang, Yi-Phing Phoebe Chen, “Data Mining in lung cancer pathologic staging diagnosis: Correlation between clinical and pathology information”, Expert Systems with Applications, Vol 42(15), Pages 6168-6176, Sep 2015
[6]. Dr.P.Thangaraju, G.Barkavi, “Lung cancer early diagnosis using some data mining classification techniques: A Survey”, CompuSoft- An International Journal of Advanced Computer Technology, ISSN 2320-0790, Vol 3(6), June 2014
[7]. Kawsar Ahmed, Tasnuba Jesmin, et al., “An early Detection of Lung Cancer Risk Using Data mining”, Proceedings of the Bangladesh Society For Biochemistry and molecular Biology Conference 2013, 12-13 Jan 2013
[8]. Guoxin Tang, “Data mining and Analysis of lung cancer data”, Ph.D Thesis, Dept. Of Mathematics, University of Louisville, USA https://doi.org/10.18297/etd/1418
[9]. V.Krishnaiah, Dr G. Narasimha, Dr.N.Subhash Chandra, “Diagnosis of Lung Cancer Prediction System using Data Mining Classification Techniques”, International Journal of Computer Science and Information Technologies, Vol 4(1), Pages 39-45, ISSN 0975-9646, 2013
[10]. Suresh H. Moolgavkar, E.Georg Luebeck, Daniel Krewski and Jan M.Zielinski, “Radon, Cigarette Smoke, and Lung Cancer” A Re-analysis of the Colorado Plateau Uranium Miners’ Data”, Journal of Epidemiology, ISSN 1044 3983, Vol 4(3),pages 204-217, May 1993
[11]. M. Shukla, A. K. Malviya, "Analysis and Comparison of Classification Algorithms for Student Placement Prediction", International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.69-81, 2018.
[12]. Ramisetti Uma Maheswari, R Raja Sekhar, "Pruning and Ranking Based Classifier for Efficient Detection of Android Malware", International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.201-205, 2018.

N.Vijayalakshmi, J.PolleyAmilya, "Application of Knowledge Engineering for Prediction of Lung Cancer," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.957-960, 2018.

Mental stress is becoming a threat to people’s health now a days. With the rapid pace of life, more and more people are feeling stressed. It is not easy to detect users stress in an early time to protect user. With the fame of web-based social networking, individuals are used it for sharing their day by day activities and interacting with friends, via web-based networking media stages, making it possible to use online social network data for stress detection. Facebook application contains different posts which shows different emotions. Conventional neural network(CNN) is used for topic extraction. Using Support Vector Method(SVM) we can classified users are in stress or not.After classification users are in stress or not ,k-nearest neighbours algorithm (KNN) is used for recommendation of hospital on a map.This system is proposed for users healthy mental state.

Stress Detection, Factor Graph Model, Microblog, Social media, Healthcare, Social Interaction

[1] Yuan Zhang, Jie Tang, Jimeng Sun, Yiran Chen, and JinghaiRao.”Moodcast: Emotion prediction via dynamic continuous factor graph model” 2016 IEEE International Conference on Data Mining
[2] Liqiang Nie, Yi-Liang Zhao, Mohammad Akbari, JialieShen, and Tat-Seng Chua.”Bridging the vocabulary gap between health seekers and healthcare knowledge” 2013
[3] Frank R. Kschischang, Senior Member ”Factor Graphs and the Sum-Product Algorithm” ,IEEE TRANSACTIONS 2015
[4] Xiao jun Chang, Yi Yang1, Alexander G. Hauptmann, Eric P. Xing and Yao-Liang Yu “Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection Proceedings” of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015)
[5] Jennifer Golbeck, Cristina Robles, Michon Edmondson, and Karen Turner.”Predicting personality from twitter”. In Passat/socialcom 2011, Privacy, Security, Risk and Trust, pages 149–156, 2011
[6] Sepandar D. Kamvar. “We feel fine and searching the emotional web”. In In Proceedings of WSDM, pages 117–126, 2011
[7] Dan C Ciresan, Ueli Meier, Jonathan Masci, Luca Maria Gambardella,furgenSchmidhuber.”Flexible,high performance convolutional neural networks for image classification”.In Proceedings of International Joint Conference on Artificial Intelligence, pages 1237–1242, 2011.
[8] Chi Wang, Jie Tang, Jimeng Sun, and Jiawei Han “Dynamic social influence analysis through time-dependent factor graphs”IEEE 2011
[9] AndreyBogomolov, Bruno Lepri, MichelaFerron, Fabio Pianesi, and Alex Pentland.”Daily stress recognition from mobile phone data, weather conditions and individual traits.” In ACM International Conference on Multimedia, pages 477–486, 2014.
[10] H. Lin, J. Jia, Q. Guo, Y. Xue, J. Huang, L. Cai, and L. Feng. “Psychological stress detection from cross-media microblog data using deep sparse neural network”.In proceedings of IEEE International Conference on Multimedia & Expo, 2014.
[11] Lexing Xie and Xuming He. “Picture tags and world knowledge: learning tag relations from visual semantic sources” 2013
[12] QuanGuo, JiaJia, GuangyaoShen, Lei Zhang, LianhongCai, and Zhang Yi. “Learning robust uniform features for cross-media social data by using cross autoencoders. “Knowledge Based System,102:64– 75, 2016

K. R. Bhokare, N. M. More, "USING SOCIAL INTERACTIONS ON SOCIAL NETWORKS DETECTING USERS IN STRESS," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.961-965, 2018.

The aim of the research paper is to study the impact of virtual reality on effective learning. Furthermore, it analyse the virtual reality education providers and the learners of a motor skill training institute to study the satisfaction and performance. The data is gathered through structured questionnaire from the VR training providers and learners from a private training institute. The performance comparison shows the improvement due to the presence of virtual reality. This research examines the opinion of VR learners with the analysis of feedback questionnaire. Spearman correlation coefficient is used to find the relationships between the user’s performances in the VR environment. Research shows the advantages of virtual reality on learning. It shows the awareness level and the infrastructure availability of the training. The learners and the skill training providers favour the application of Virtual reality tool for effective learning. Even though the learners have favourable opinion regarding the VR application, the infrastructure is not adequate to meet the needs of the learners. Apart from the satisfaction among the learners, VR reduces the visualization complexity in the learning process. The presence of 3D Virtual environment develops more satisfaction among the learners. The result shows that due to the presence of virtual environment the learner’s attention, perception and memory develops. This research outcome can be used in the classroom set-ups of other courses. Cloud software sharing is one of the possible to improve the current virtual reality applied learning environment.

Virtual reality, Simulation, Learners’ Satisfaction, Motor skills, Awareness, Opinion and Performance

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B. Senthil Kumar, Jaya Prakash D, "A Study on The Relationships Between The Virtual Reality & Learning," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.966-969, 2018.

Vehicular systems has turned into a major research area because of its highlights and applications. An efficient traffic management with proper road safety and its standardization is needed to develop a smart transportation system. To achieve the smart transportation system the vehicles on the road requires are required more correspondence computing features and expanded sensing power about other vehicles travelling in the same road. There are various arrangements were proposed to address the difficulties and issues of vehicular systems. Vehicular Cloud Computing (VCC) is one of the arrangements, that remarkably affects movement administration and road safety by right away utilizing vehicular resources. Consequently, a few advanced Intelligent Transportation Systems (ITS) techniques were utilized by using VCC. In this technique, conveyance resources concerned computing power, storage, and web property which will be shared between drivers. They are connecting with customers through web. VCC based ITS idea is a vital society impact that desires traffic management and road safety.

Vehicular Cloud Computing, Transportation Systems, Cloud Computing, Wireless Network

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[20] Priyan Malarvizhi Kumar, S. Lokesh, R. Varatharajan, Gokulnath Chandra Babu, P. Parthasarathy, Cloud and IoT based disease prediction and diagnosis system for healthcare using Fuzzy neural classifier, Future Generation Computer Systems, . https://doi.org/10.1016/j.future.2018.04.036, 2018
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M. Ramya Devi, S. Lokesh, B. Manikandan, T.Senthilkumar, "Vehicular Cloud Computing Based Intelligent Transportation System for Traffic Management and Road Safety," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.970-975, 2018.

Rank aggregation is the issue of producing an `Icon sensus" ranking for a given arrangement of rankings. At the point when connected to the web, this discovers applications in meta-searching, spam fighting and word association methods. Rank aggregation can be thought of as the unsupervised analog to regression, in which the objective is to locate an aggregate ranking that limits the separation to every one of the positioned records in the info set. Rank aggregation has likewise been proposed as an effective method for closest neighbor positioning of categorical data, and gives a robust way to deal with the issue of consolidating the conclusion of specialists with various scoring schemes, as are basic in ensemble methods. In ranking aggregation, the objective is to outline a gathering of rankings over an arrangement of choices by a single (consensus) positioning. This issue has been the subject of a good arrangement of consideration in different fields: beginning from races in elections decision hypothesis.

Rank Aggregation, Particle Swarm Optimization, Genetic Algorithm, Robust Rank Aggregation

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[2] D. Sculley, ”Rank Aggregation for Similar Items”, Work performed at Yahoo!, Inc., in Spring of 2006, pp.1-12.
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[4] Lili Yana, ZhanjiGuia, WencaiDub,QingjuGuo, ”An Improved PageRank Method based on Genetic Algorithm for Web Search”, Procedia Engineering 15 ,2011, pp. 2983 – 2987.
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[6] GattacaLv, ”An Analysis of Rank Aggregation Algorithms”, arXiv:1402.5259v5 [cs.DS] 4 May 2014, pp.1-12.
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[8] Maunendra Sankar Desarkar, SudeshnaSarkar,Pabitra Mitra, ”Preference relations based unsupervised rankaggregation for metasearch”,Expert Systems With Applications 49 ,2016,pp.86–98.
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[12] Ronaldo C. Prati, "Combining feature ranking algorithms through rank aggregation", The 2012 International Joint Conference on Neural Networks (IJCNN).
[13] J. M. Maestre, Hideaki Ishii, "Node Aggregation for Enhancing PageRank", IEEE Access ( Volume: 5 ).
[14] Randall Wald, "An extensive comparison of feature ranking aggregation techniques in bioinformatics", 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI).
[15] Samuel Oliveira, "Evolutionary rank aggregation for recommender systems", 2016 IEEE Congress on Evolutionary Computation (CEC).

Shabnam Parveen, R. K.Chauhan, "Page Rank Aggregation Methods: A Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.976-980, 2018.

We have entered in the era of Big Data. Through better analysis of the large volumes of data that are becoming available, there is the potential for making faster advances in many scientific disciplines and improving the profitability and success of many enterprises. However, attacks against data are also increasing. We need to protect the data we collect and store, securely transfer data over the network and keep output derived from these data confidential. In this paper, we discuss about various challenges big data technology face today and tools to overcome those challenges.

Big Data, Security, Hadoop Technology

[1] Sameer Walker Affiliated with, Madhu Siddalingaiah “Motivation for Big Data-Pro Apache Hadoop” http://link.springer.com/chapter/10.1007/978-1-4302-48644_1#page-2
[2] Jonathan Stuart Ward and Adam Barker-“Undefined By Data: A Survey of Big Data Definitions” School of Computer Science-University of St Andrews, UK{jonthan.stuart.ward, adam.barker}@standrews.ac.ukhttp://arxiv.org/pdf/1309.5821v1.pdf
[3] BIGDATA ANALYTICS - 5th QUARTER BY PHILIP RUSSOM -TDWI research.
http://www.tableau.com/sites/default/files/whitepapers/tdwi _bpreport_q411_big_data_analytics_tableau.pdf.
[4] Alvaro A. Cárdenas, Pratyusa K. Manadhata, Sreeranga “Big Data Analytics for Security”Posted by P. Rajanhttp://www.infoq.com/articles/bigdata-analytics-for-security.
[5] data electronically available at http://www.umuc.edu/cybersecurity/about/cybersecurityasics.cfmumuc[71]http://whatis.techtarget.com/definition/cybersecurity
[6] data electronically available at http://whatis.techtarget.com/glossary/Security-Threats-anduntermeasures
[7] data electronically available at teradata-http://www.teradata.com/Cyber-Security- http://bigdata.teradata.com/US/Success-Stories/Innovationsand-Insights/
[8] Securing Big Data - Part 1-Posted by Steve Jones at Tuesday, January 06, 2015
[9]data electronically available at http://service-architecture.blogspot.com/2015/01/securingbig-data-part-2-understanding.html.
[10] unstructured data in big data environment- data electronically available at http://www.dummies.com/how-to/content/unstructureddata-in-a-big-data-environment.html
[11] data electronically available at http://ictactjournals.in/paper/IJSC_Paper_6_pp_1035_1049. pdf

Dinesh Singh, Dayanand, Arushi Arya, "Security Challenges in Big Data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.981-985, 2018.