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The fundamental target of advanced education foundations is to give quality instruction to its understudies. One approach to accomplish most elevated amount of value in advanced education framework is by finding learning for expectation with respect to enrolment of understudies in a specific course, distance of conventional classroom showing model, discovery of out of line implies utilized as a part of online examination, identification of irregular qualities in the outcome sheets of the understudies, forecast about understudies` execution et cetera. The information is covered up among the instructive data set and it is extractable through data mining procedures. Show paper is intended to legitimize the capacities of data mining strategies in setting of advanced education by offering a data mining model for advanced education framework in the college. In this examination, the order undertaking is utilized to assess understudy`s execution and as there are numerous methodologies that are utilized for data characterization, the choice tree strategy is utilized here. By this assignment we extricate learning that portrays understudies` execution in end semester examination. It helps prior in recognizing the dropouts and understudies who require exceptional consideration and enable the educator to give proper prompting/guiding.

Educational Data Mining (EDM); Classification; Knowledge Discovery in Database (KDD); ID3 Algorithm

[1] Yongjian Fan, Yanguang Shen, Jianying Mai, “Study of the Model of E-commerce Personalized Recommendation System Based on Data Mining”, Electronic Commerce and Security, PP. 647 – 651, 2008.
[2] Xuejun Yang, Hongchun Zeng, Yonghong Huang, “ARTMAP-Based Data Mining Approach and Its Application to Library Book Recommendation”, Intelligent Ubiquitous Computing and Education, PP. 26 – 2, 2009.
[3] J.Sheela Jasmine, “Application of Fuzzy Logic in Neural Network Using Data Mining Techniques: A Survey”, International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.333-341, 2016.
[4] Lavannya Bhatia, S. S. Prasad, “Building a Distributed Generic Recommender Using Scalable Data Mining Library”, Computational Intelligence & Communication Technology (CICT), PP. 98 – 10, 2015.
[5] Liu Guo-rong, Zhang Xi-zheng, “Collaborative Filtering Based Recommendation System for Product Bundling”, 2006 International Conference on Management Science and Engineering, PP. 251 – 254, 2006.
[6] Hiral Y. Modi, Meera Narvekar, “Supporting Domain Analysis through Mining and Recommending Features from Online Product Listings”, IEEE Transactions on Software Engineering, Vol. 39, No. 12, PP. 1736 – 1752, 2013.
[7] Vincent Chan, Tony Hao Wu, Granfham Pang, “Decision support and data mining for direct consumer-to-consumer trading, Internet Technology and Secured Transactions (ICITST), PP. 205 – 208, 2014.
[8] Anand Shanker Tewari, Tasif Sultan Ansari, Asim Gopal Barman, “Opinion based book recommendation using Naive Bayes classifier”, Contemporary Computing and Informatics (IC3I), PP. 139 – 144, 2014.
[9] Silvana Aciar, Debbie Zhang, Simeon Simoff, John Debenham, “Informed Recommender: Basing Recommendations on Consumer Product Reviews”, IEEE Intelligent Systems, Vol. 22, No. 3 PP. 39 – 47, 2007.
[10] Suhasini Parvatikar, Bharti Joshi, “Online book recommendation system by using collaborative filtering and association mining”, IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), PP. 1 – 4, 2015.
[11] Kumari Priyanka, Anand Shanker Tewari, Asim Gopal Barman, “Personalised book recommendation system based on opinion mining technique”, Communication Technologies (GCCT), 2015 Global Conference, PP. 285 – 289, 2015.
[12] Shahab Saquib Sohail, Jamshed Siddiqui, Rashid Ali, “Ordered ranked weighted aggregation based book recommendation technique: A link mining approach”, Hybrid Intelligent Systems (HIS), 2014 14th International Conference, PP. 309 – 314, 2014.

V. Maniraj, "Excavating Educational Statistics to Investigate Scholars Performance," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.461-467, 2018.

Web Service is a collection of software components which provide the services over the web. Web services are playing a key role in a wide range of modern business applications. The nature of loosely coupled connections and open accessibility may cause several security issues. Recently, a number of new standards and protocols have been introduced. When deploying a web service, security is one of the major issue that need to be addressed. In this paper, we discuss the possible threats to web services and recommend preventive measures.

WSDL, SOAP, REST, HTTPS, X.509

[1] Aruna.S, “Security in Web Services- Issues and Challenges,” International Journal of Engineering Research & Technology (IJERT) Vol. 5 Issue 09, September-2016.
[2] Lee, S., Jo, J. Y., & Kim, Y. (2015, June). Method for secure RESTful web service. In Computer and Information Science (ICIS), 2015 IEEE/ACIS 14th International Conference on (pp. 77-81). IEEE.
[3] Noor A. Altaani, Ameera S. Jaradat, “Security Analysis and Testing in Service Oriented Architecture,” International Journal of Scientific & Engineering Research, Volume 3, Issue 2, 2012.
[4] Balasubramanian, N., & Ruba, A. (2012, August). Security: a major threat for web services. In Advanced Communication Control and Computing Technologies (ICACCCT), 2012 IEEE International Conference on (pp. 104-109).
[5] Masood, A. (2013, November). Cyber security for service ori- ented architectures in a Web 2.0 world: An overview of SOA vulnerabilities in financial services. In Technologies for Home- land Security (HST), 2013 IEEE International Conference on (pp. 1-6). IEEE.
[6] Saravanaguru, R. A., Abraham, G., Ventakasubramanian, K., & Borasia, K. (2013).Securing Web Services Using XML Signa- ture and XML encryption. arXiv preprint arXiv:1303.0910.
[7] Hassan Reza, and Washington Helps, “Toward Security Analysis of Service Oriented Software Architecture,” Proceedings of the 2011 International Conference on Software Engineering Research and Practice, Vol. II, 2011.

Satyam Akunuri, Subbarao Perugu, Rajendra Prasad B, "A Survey on Security Issues in Web Services," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.468-470, 2018.

The human behaviour which considers six basic emotions which are happiness, sadness, anger, fear, surprise & disgust. It becomes important to detect emotional state of a person which will be induced by workload, background noise, physical environmental factors (e.g. G-force) & fatigue. Broadly, stress identification becomes a scientific challenge to analyze a human being interaction with environment Speech of human beings is the reflection of the state of mind. Proper evaluation of these speech signals into stress types is necessary in order to ensure that the person is in a healthy state of mind. In this paper we will get to know how speech Identifiers are trained and how we can enhance the basic recognition procedures by exploiting a pre-processor by use of pattern classification into different level of stress types. In this work we propose a MLP classifier for speech stress classification algorithm, with sophisticated feature extraction techniques as Mel Frequency Cepstral Coefficients (MFCC). The MLP algorithm assists the system to learn the speech patterns in real time and self-train itself in order to improve the classification accuracy of the overall system. The proposed system is suitable for real time speech and is language and word independent.

MLP, MFCC, stress classification, feature Selection.

[1] Schuller, Bjorn, et al., “Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first Challenge”, Speech Communication 53.9, pp. 1062-1087, 2011.
[2] Anagnostopoulos, Christos-Nikolaos, Theodoros Iliou, and Ioannis Giannoukos, “Features and Classifiers for Emotion Recognition from Speech: A survey from 2000 to 2011”, Artificial Intelligence Review 43.2, pp.155-177, 2015.
[3] Dipti D. Joshi, M. B. Zalte, “Speech Emotion Recognition: A Review”, Journal of Electronics and Communication Engineering (IOSR-JECE) 4.4, pp.34-37, 2013.
[4] Ververidis, Dimitrios, and Constantine Kotropoulos, “Emotional Speech Recognition: Resources, Features, and Methods”, Speech Communication 48.9, pp.1162-1181, 2006.
[5] El Ayadi, Moataz, Mohamed S. Kamel, and Fakhri Karray, “Survey on Speech Emotion Recognition”, Features, classification schemes, and databases, Pattern Recognition 44.3 pp. 572-587,2011.
[6] Scherer, Klaus R., “Vocal Communication of Emotion: A review of research paradigms”, Speech communication 40.1, pp.227-256, 2003.
[7] Vogt, Thurid, Elisabeth Andre, and Johannes Wagner, “Automatic recognition of emotions from speech: a review of the literature and recommendations for practical realization, Affect and emotion in human-computer interaction”, Springer Berlin Heidelberg, pp. 75-91, 2008.
[8] Burkhardt, Felix, et al., “A Database of German Emotional Speech”, INTER-SPEECH, Lisbon, Portugal, vol. 5, pp.1-4, 2005.
[9] Kwon, Oh-Wook, et al, “Emotion Recognition by Speech Signals, INTER-SPEECH, pp.1-4, 2003.
[10] Campbell, N. “Recording and Storing of Speech Data”. In: Proceedings LREC, pp. 12-25, 2002.
[11] Cowie, R., Douglas-Cowie, E., Savvidou, S., McMahon, E., Sawey, M., Schroder, M. Feeltrace, “An Instrument for Recording Perceived Emotion in Real Time”, In: Proceedings of the ISCA Workshop on Speech and Emotion, pp.19-24, 2000.
[12] Devillers, L., Cowie, R., Martin, J.-C., Douglas-Cowie, E., Abrilian, S., McRorie, M.: “Real life emotions in French and English TV video clips: an integrated annotation protocol combining continuous and discrete approaches”, 5th International Conference on Language Resources and Evaluation LREC, Genoa, Italy.2006.
[13] Douglas-Cowie, E., Campbell, N., Cowie R.P. “Emotional speech: Towards a new generation of databases”. Speech communication 40(1–2), pp.33-60, 2003
[14] Douglas-Cowie, E., et al. “The description of naturally occurring emotional speech”. In: Proceedings of 15th International Congress of Phonetic Sciences, Barcelona, 2003.
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[17] C. Bouveyron, B. Hammer, T. Villmann, "Recent developments in clustering algorithms", Proc. ESANN 2012, pp. 447-458,2012.
[18] D-S. Modha, W. Scott Spangler, "Feature weighting in k-means clustering", Machine Learning”, vol. 52, pp. 217-237, 2003.
[19] R. Xu, D. Wunsch, "Survey of clustering algorithms", IEEE Transactions on Neural Networks, pp. 645-678, 2005
[20] BOGERT, B. P.; HEALY, M. J. R.; TURKEY, J. W. “The Quefrency Alanysis of Time Series for Echoes: Cepstrum, Pseudo Autocovariance, Cross-Cepstrum and Saphe Cracking”, Proceedings of the Symposium on Time Series Analysis, (M. Rosenblatt, Ed) Chapter 15, New York: Wiley, pp.209-243, 1963.

N.P. Dhole, S.N. Kale, "Multilayer Perceptron Classification in Stress Speech Identification," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.471-475, 2018.

Since last decade a huge amount of information is transferred over the internet on day to day basis. However, all the information is not relevant to each user and is also difficult to find the right content for the user as per his/her need. Recommender system works as a guide to find or suggest right items for users. A movie recommendation system is predicting or suggest a movie which user might like using his/her previous watch list or history. After Netflix prize competition many academician and researchers have shown interest to develop new and better filtering techniques for the movie recommendation. This paper studies the two most fundamental techniques: content-based and collaborative filtering methods of information retrieval and shows their application for movie recommendation with pros and cons. An experiment was carried out over MovieLens 100K dataset to show the implementation of discussed methods. The obtained results have shown that Item-Item based neighbourhood collaborative filtering method is better among implemented three techniques with 0.786 MAE and 0.985 RMSE values.

Content-Based Filtering, Collaborative Filtering, Movie Recommendation

[1] G. Adomavicius and a Tuzhilin, “Toward the Next Generation of Recommender Systems: a Survey of the State of the Art and Possible Extensions,” IEEE Trans. Knowl. Data Eng., vol. 17, no. 6, pp. 734–749, 2005.
[2] G. Linden, B. Smith, and J. York, “Amazon.com recommendations: Item-to-item collaborative filtering,” IEEE Internet Comput., vol. 7, no. 1, pp. 76–80, 2003.
[3] B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, “Item-based collaborative filtering recommendation algorithms,” Proc. tenth Int. Conf. World Wide Web - WWW ’01, pp. 285–295, 2001.
[4] J. Zhang, Y. Lin, M. Lin, and J. Liu, “An effective collaborative filtering algorithm based on user preference clustering,” Appl. Intell., vol. 45, no. 2, pp. 230–240, 2016.
[5] B. M. Sarwar, G. Karypis, J. A. Konstan, and J. T. Riedl, Application of Dimensionality Reduction in Recommender System - A Case Study, vol. 1625. 2000.
[6] C. C. Aggarwal, “Content-Based Recommender Systems,” in Recommender Systems, 2016, pp. 139–166.
[7] F. Cacheda, V. Carneiro, D. Fernández, and V. Formoso, “Comparison of collaborative filtering algorithms,” ACM Trans. Web, vol. 5, no. 1, pp. 1–33, 2011.
[8] J. Bobadilla, F. Ortega, and A. Hernando, “A collaborative filtering similarity measure based on singularities,” Inf. Process. Manag., vol. 48, no. 2, pp. 204–217, 2012.
[9] F. O. Isinkaye, Y. O. Folajimi, and B. A. Ojokoh, “Recommendation systems: Principles, methods and evaluation,” Egypt. Informatics J., vol. 16, no. 3, pp. 261–273, 2015.
[10] X. Su and T. M. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques,” Adv. Artif. Intell., vol. 2009, no. Section 3, pp. 1–19, 2009.
[11] F. M. Harper and J. A. Konstan, “The MovieLens Datasets: History and Context,” ACM Trans. Interact. Intell. Syst., vol. 5, no. 4, p. 19:1--19:19, 2015.

S.K. Raghuwanshi, R.K. Pateriya, "Movie Recommendation System: Content-Based and Collaborative Filtering," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.476-481, 2018.

Data mining is one of the most essential steps of Knowledge Discovery process that is required to extract interesting patterns from enormous size of data. In this paper, we have used the BCG coverage data i.e. Percentage of live births who received Bacillus Calmette Guerin (BCG) a vaccine against tuberculosis and forecast the BCG coverage percentage for the next five years based on historical yearly data of BCG coverage in India by using the exponential smoothing technique of forecasting. Exponential Smoothing is a well-liked forecast technique that uses weighted values of previous series observations to predict the immediate future for time series data. The aim of this paper is to study the exponential smoothing method of time series for forecasting purpose.

Data Mining, BCG, Time Series data, Exponential Smoothing

[1] Manaswini Pradhan, “Data Mining & Health Care: Techniques of Application,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 2, no. 12, pp.7445-7455, Dec. 2014.
[2] V. Priyavadana, A. Sivashankari and R. Senthil Kumar, “A Comparative Study of Data Mining Applications in Diagnosing Diseases,” International Research Journal of Engineering and Technology, vol. 2, no. 7, pp. 1046-1053, Oct. 2015.
[3] Bharati M. Ramageri, “Data Mining Techniques and Applications,” Indian Journal of Computer Science and Engineering, vol. 1, no. 4, pp. 301-305.
[4] Sourabh Shastri, Anand Sharma and Vibhakar Mansotra, “Predicting Pilgrimage in Numbers to Shri Mata Vaishno Devi, Katra, J&K using Time Series Analysis,” International Journal of Emerging Research in Management & Technology, vol. 4, no. 10, pp. 102-106, Oct. 2015.
[5] Mohitulameen Ahmed Mustafi, “Factor Influencing of Child Immunization in Bangladesh,” International Journal of Mathematics and Statistics Studies, vol. 1, no. 3, pp. 55-65, Sept. 2013.
[6] The NHP India website. [Online]. Available: http://www.nhp.gov.in/
[7] Sourabh Shastri, Anand Sharma and Prof. Vibhakar Mansotra, “Child Immunization Coverage - A Critical Review,” IOSR Journal of Computer Engineering, vol. 18, no. 5, pp. 48-53, Oct. 2016.
[8] Michael Favorov et. al., “Comparative Tuberculosis (TB) Prevention Effectiveness in Children of Bacillus Calmette-Guerin (BCG) Vaccines from Different Sources, Kazakhstan,” PLoS One, vol. 7, no. 3, pp. 1-8, Mar. 2012.
[9] Oli, A.N. et. al., “Potency and immunogenicity of bacillus calmette Guerin (BCG) vaccines used in routine immunization programme in South-East, Nigeria,” African Journal of Pharmacy and Pharmacology, vol. 8, no. 47, pp. 1186-1191, Dec. 2014.
[10] Nagabhushanam D. et. al., “Prediction of Tuberculosis using Data Mining Techniques on Indian Patient’s Data,” International Journal of Computer Science and Technology, vol. 4, no. spl-4, pp. 262-265, Oct-Dec. 2013.
[11] The TBFACTS website. [Online]. Available:
http://www.tbfacts.org/
[12] The UNICEF Global Databases website. [Online]. Available: http://www.data.unicef.org/
[13] Sourabh Shastri, Anand Sharma and Vibhakar Mansotra, “A Model for Forecasting Tourists Arrival in J&K, India,” International Journal of Computer Applications, vol. 129, no. 15, pp. 32-36, Nov. 2015.
[14] Eva Ostertagova and Oskar Ostertag, “The Simple Exponential Smoothing Model,” in Proc MMaMS, 2011, p.380.
[15] Anand Sharma, Sourabh Shastri and Vibhakar Mansotra, “Forecasting Public Healthcare Services in Jammu & Kashmir Using Time Series Data Mining,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 5, no. 12, pp. 570-575, Dec. 2015.

Sourabh Shastri, Amardeep Sharma, Vibhakar Mansotra, Anand Sharma, Arun Singh Bhadwal, Monika Kumari, "A Study on Exponential Smoothing Method for Forecasting," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.482-485, 2018.

Speech Recognition is ability to translate a dictation or spoken words to text format. In the field of electronics and computers, speech has not been used much more due to the complexity and different types of sounds and speech signals. However, with traditional methods, processes and algorithms, we can simply process the speech signals and identify the text. This paper presents an efficient speech recognition system based on discrete curvelet transform (DCT) and Artificial Neural Network (ANN) methods to enhance the identification rate. This research article comprised in two distinct phases, a feature extractor and a recognizer is presented. In Feature Extraction phase, Curvelet transform extract the features called curvelets from the given input speech signal and elements of these signals which support in gaining higher recognition rates. For feature matching, Artificial Neural Networks is used as classifiers. The performance evaluation has been demonstrated in terms of accurate recognition rate, maximum noise power of interfering sounds, miss rates, hit rates, and false alarm rate. The accurate classification rate was 98.3 % for the sample speech signals. Performance comparisons with similar studies found in the related literature indicated that our proposed ANN structures yield satisfactory results and improve the recognition rates.

Speech Recognition, Curvelet Transform, Feature Extraction, Artificial Neural Network

[1] B. Suksiri and M. Fukumoto," computer and information science", springer, Kochi University of Technology (KUT), Kami City, Japan, pp 15-26, 2016.
[2] http://recognize-speech.com/preprocessing.
[3] Thiang and S. Wijoyo," Speech Recognition Using Linear Predictive Coding and Artificial Neural Network for Controlling Movement of Mobile Robot", International Conference on Information and Electronics Engineering IPCSIT, Singapore, PP 121-131 , 2011.
[4] C. Kuriana, K. Balakrishnan, “Development & evaluation of different acoustic models for Malayalam continuous speech recognition”, International Conference on Communication Technology and System Design,cochin , pp.1081-1088, 2011.
[5] Md. A. Ali , M. Hossain and Md. N. Bhuiyan," Automatic Speech Recognition Technique for Bangla Words", International Journal of Advanced Science and Technology, Vol. 50, 2013.
[6] V. Ca and V. Radhab," Speaker Independent Isolated Speech Recognition System for Tamil Language using HMM", International Conference on Communication Technology and System Design, Coimbatore, PP.1097 – 1102, 2012.
[7] P. Mishra and P. K. Mishra," A Study of various speech features and classifiers used in speaker identification", IJERT, vol. 5, issue 2, 2016.
[8] A. Choudhary, R.S. Chauhan and Gautam Gupta, “Automatic Speech Recognition System for Isolated & Connected Words of Hindi Language By Using Hidden Markov Model Toolkit (HTK)”, in Proceedings of International Conference on Emerging Trends in Engineering and Technology,vol. 4, issue 6, pp.244– 252,2012.
[9] P. Saini, P. Kaur and M. Dua," Hindi Automatic Speech Recognition Using HTK", International Journal of Engineering Trends and Technology (IJETT) – Vol. 4, Issue 6- June 2013.
[10] M. Moneykumar and E. Sherly," malayalam word identification for speech recognition system", An International Journal of Engineering Sciences, Special Issue iDravadian , Vol. 15, 2014.
[11] J. S. Pokhariya and S. Mathur, “Sanskrit Speech Recognition using Hidden Markov Model Toolkit”, International Journal of Engineering Research & Technology (IJERT),Vol.3, Issue 10, pp.93-98, 2014.
[12] G. Nijhawan and Dr. M.K. Soni, “Real Time Speaker Recognition System for Hindi Words”, International Journal of Information Engineering and Electronic Business, Vol. 6, pp. 35-40, 2014,.

Nidamanuru Srinivasa Rao, Chinta Anuradha, SV Naga Sreenivasu, "An Efficient Implementation of Speech Recognition based on Curvelet Transform and Artificial Neural Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.486-492, 2018.

In today’s world searching information over the internet is one of the difficult task. Specialized tools called search engines plays an important role to retrieve information from world wide web. There are many of search engines available today but retrieving meaningful information is difficult. However to overcome this problem in search engines to retrieve meaningful information intelligently, semantic web technologies are playing a major role. In this research work we present a study on the search engine generations and the role of search engines in intelligent web search technologies.

Search engine, intelligent search, and semantic web

[1]http://compnetworking.about.com/cs/worldwideweb/g/bldef_www.htm
[2]http://searchcrm.techtarget.com/definition/World-Wide-Web
[3] http://en.wikipedia.org/wiki/World_Wide_Web
[4] http://www.freelancer.com/jobs/Web-Search/
[5]http://en.wikibooks.org/wiki/PlanoTse_Handbook_for_Job_Search_Automation/What_is_ web_search%3F
[6] http://en.wikipedia.org/wiki/Web_search_engine
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[8] http://www.webopedia.com/TERM/S/search_engine.html
[9]http://www.pcmag.com/encyclopedia/term/54339/web-search-engines
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[11]http://www.zeald.com/Blog/x_post/types-of-search-engines.html
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[13] Berners-Lee, T., Hendler, J. and Lassila, O. “The Semantic Web”, Scientific American, May 2001.
[14] Deborah L. McGuinness. “Ontologies Come of Age”. In Dieter Fensel, J im Hendler, Henry Lieberman, and Wolfgang Wahlster, editors. Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential. MIT Press, 2002.
[15] G.Madhu1, Dr.A.Govardhan and Dr.T.V.Rajinikanth, “Intelligent Semantic Web Search Engines: A Brief Survey”, International journal of Web & Semantic Technology (IJWesT) Vol.2, No.1, January 2011.
[16] Andrei Broder, “Taxonomy of Web Search”

Chinu, Ramil Gupta, Ekta, "WEB SEARCHING AN ART," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.493-496, 2018.

Cloud service provides everything as a service over the Internet or Intranet. Provisioning and allocation of virtual resource over the network requests based on used demand (pay-as-you-go). Big Data, which has large set of data that are so voluminous and complex that traditional method is not enough to process the data, Hadoop MapReduce framework is used to process the large set of data in a distributed manner. Efficient slave nodes selection is difficult to setup Hadoop cluster in cloud environment which led to more cost. We have proposed an algorithm called Particle Swarm Optimization(PSO) that determines the optimal number of nodes in the Hadoop cluster utilizes based on the data sets which provides efficient job execution on minimal set of DataNodes in cloud environment.

Hadoop, MapReduce, Virtualization, PSO,YARN, HDFS

[1]. Selvaprabhu, “Fragile data Storing in public cloud for hospital administration”2017 14th, VOL 5, NO14, “IEEE International Conference on Services Computing”.
[2]. .AniketMalatpure,”Testing Private Cloud Reliability Using a Public CloudValidation SaaS”,2017,IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW).
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[4]. Mukhtaj Khan, Yong Jin, Maozhen Li, Yang Xiang, and Changjun Jiang, “Hadoop Performance Modeling for Job Estimation and Resource Provisioning”, FEBRUARY 2016, NO.2, VOL. 27, “IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS”.
[5]. Tzu-Chi Huang, Kuo-Chih Chu, Yu-Ruei Rao, “Smart Intermediate Data Transfer for MapReduce on Cloud Computing”, Volume 1 Issue 4, 2011, “International Conference on Cloud Computing and Big Data”.
[6]. R.Thangaselvi, Ananthbabu, Aruna, Jagadeesh,” Improving the efficiency ofMapReduce scheduling algorithm in Hadoop”, Number 1, Volume 19, “IEEE ComputerSociety”.
[7]. J. Dean and S. Ghemawat, “MapReduce: Simplified data process-ing on large clusters,” in Proc. 6th Symp. Operating Syst. Des. Imple-mentation, 2004, p. 10.
[8]. G. Ananthanarayanan, S. Kandula, A. Greenberg, I. Stoica, Y. Lu, B. Saha, and E. Harris, “Reining in the outliers in Map-Reduce clusters using Mantri,” in Proc. 9th USENIX Conf. Operating Syst. Des. Implementation, 2010.
[9]. Dr. (Mrs.) Ananthi Sheshasaayee ,“ A Theoretical Framework for Cloud Resource Provisioning using MapReduce Technique “PG and Research Department of Computer Science&Application.
[10]. K. Kambatla, A. Pathak, and H. Pucha, “Towards optimizing Hadoop provisioning in the cloud,” in Proc. Conf. Hot Topics Cloud Comput., 2009.
[11]. Amol C. Adamuthe,”Solving Resource Provisioning in Cloud using GAs andPSO”,Dept. of CSE, RIT, Rajaramnagar-Islampur, MS, India ,2013 Nirma University International Conference on Engineering (NUiCONE).
[12]. Balaji Palanisamy, “Cost-Effective Resource Provisioning for MapReduce in a Cloud”, IEEETRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 26, NO 5, MAY 2015.
[13]. Bramantyo Adrian, “Analysis of K-means Algorithm For VM Allocation in Cloud Computing”, Yogyakarta, Indonesia, International Conference on Data and Software Engineering, 2015.
[14]. Clayton Maciel Costa, “Service Response Time Measurement Model of Service Level Agreements in Cloud environment”, IEEE International Conference on Smart City, SocialCom together with DataCom, 2015.
[15]. Yongmei WEI, “A cost-effective and reliable cloud storage“, Nanyang Polytechnic Singapore, IEEE International Conference on Cloud Computing, 2014.
[16]. P.Varalakshmi, Maheshwari.K, “Cost-Optimized Resource Provisioning in Cloud“, Department of Information Technology, Anna University-MIT, International Conference on Recent Trends in Information Technology (ICRTIT), 2013.
[17]. Mahesh B. Nagpure,”An Efficient Dynamic Resource Allocation Strategy for VM Environment in Cloud”, Student M. tech 2nd year, Dept of Computer Science &Engineering, 2015 International Conference on Pervasive Computing (ICPC).
[18]. Vivek Rajani, “A VM Allocation Strategy for Cluster of Open Host in Cloud Environment”, Research Scholar ITSNS- GTU PG SCHOOL, 2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT).
[19]. Berl, A., Gelenbe,“Energy-Efficient Cloud Computing”, The Computer Journal August 19 2009.
[20]. Vidhyasagar. B. S., S. Aravinda Krishnan, D. Manikkannan, and J. Arunnehru. "An Implementation and Performance Monitoring of Virtual Machines using Ganglia in Eucalyptus Private Cloud." International Journal on Computer Science and Engineering (IJCSE), 2017

Vidhyasagar B S, Ajithkumar M, Shaik Sajid, Syed Khadeer , Rahul P, J. Arunnehru, "Cost Effective PSO Model for MapReduce in Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.497-501, 2018.

Ad-hoc networks are mostly used in each and every field of our daily life. There are so many circumstances in wireless ad-hoc networks, on which performance of networks depends. To achieve a better network performance, it is mandatory to identify the network circumstances and appropriate routing protocols. Routing protocols plays a vital role a routing process in ad-hoc networks. AODV (Ad-hoc on demand Distance Vector), DSR (Dynamic Source Routing), and DSDV (Destination-Sequenced Distance-Vector) are the well familiar routing protocols which are mostly used in mobile ad-hoc networks. In this paper, we analysed these routing protocols by considering several performance metrics like throughput, end-to-end delay, normalized routing load, received packets at various speeds and pause times.

Pause time, speed, AODV, average throughput, PDR, E2E delay, normalized routing load

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[2] Kavita Bhatnagar: Performance Analysis of Routing Protocols in MANET: International Refereed Journal of Engineering and Science (IRJES), vol. 06, no. 10, 2017, pp. 16–19.
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Pushpender Sarao, P. Sindhu, V. Navakishor, "Analysis of Routing Protocols based on Network parameters in WANET," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.8-13, 2018.

Machine learning is a field of artificial intelligence in which computers learn from experience. The field of machine learning is a famous research area in computer science. Machine learning applications are helpful in various domains of computer science, chemical sciences, spatial technology, bioinformatics, agriculture, digital forensics and more. Machine learning algorithms are useful in the fields of pattern recognition, pattern classification, text classification, SMS classification, computer vision, mobile learning and more. In the present work performance assessment of three machine learning algorithms namely logistic regression, random forest and naïve bayes with three feature selection methods viz. correlation based, Information based and gain ratio is conducted on a mobile device. The above-mentioned machine learning algorithms along with feature selection methods are assessed for the performance metrics of accuracy, precision, F- Measure, recall and Receiver Operating Characteristics.

Machine Learning; Logistic Regression; Naïve Bayes; Random Forest; Gain Ratio, Information Gain

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A. Thakur, "Performance Assessment of Machine Learning Algorithms with Feature Selection Methods," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.502-505, 2018.