Agriculture is the major source for the largest population in India to earn money and carry out their livelihood. Precision agriculture is already adopted in other countries, but we still need to involve IoT and cloud computing technologies for better production of crops. At present the climate differs in many areas around India due to various factors from human activities such as air pollution, deforestation, sewage and from natural changes such as distance of sea, wind direction, proximity to the equator. As per the changes in the climate, a farmer needs to predict which crop should be cultivated at which time. The dataset stores the details of the crop which should satisfy the requirements such as maximum and minimum temperature, maximum and minimum rainfall, soil type and location..............

Keywords: AWS, IoT, Raspberry Pi, DHT11, MQTT, Amazon Quick Sight

[1]. Q. Jing, A. V. Vasilakos, J. Wan, J. Lu, and D. Qiu, "Security of the internet of things: perspectives and challenges," WirelessNetworks, vol. 20, no. 8, pp. 2481–2501, 2014.
[2]. C.-W. Tsai, C.-F. Lai, and A. V. Vasilakos, "Future internet of things: open issues and challenges," Wireless Networks, vol. 20, no. 8, pp. 2201–2217, 2014. [3]. ABI Research. 9 May 2013
[3]. Gartner. 10 November 2015. Retrieved 21 April 2016
[4]. Davies, Nicola. "How the Internet of Things will enable 'smart buildings'". Extreme Tech.
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The term big data arose under the volatile increase of global data as a technology that is able to store and process big and varied volumes of data, providing both enterprises and science with deep insights over its clients/experiments. Although big data solves much of our current problems it still presents some gaps and issues that raise concern and need improvement. Big data is the term for any collection of datasets so large and complex that it becomes difficult to process using traditional data processing applications. The challenges include analysis, capture, search, sharing, storage, transfer, visualization, and privacy violations. Big data is a set of techniques and technologies that require.............

Keywords: Big Data, Hadoop, Map Reduce.

[1]. Yuri Demchenko ―The Big Data Architecture Framework (BDAF)‖ Outcome of the Brainstorming Session at the University of Amsterdam 17 July 2013.
[2]. Amogh Pramod Kulkarni, Mahesh Khandewal, ―Survey on Hadoop and Introduction to YARN‖, International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 5, May 2014).
[3]. Sagiroglu, S.Sinanc, D.,‖Big Data: A Review‖,2013, 20-24.
[4]. Ms. Vibhavari Chavan, Prof. Rajesh. N. Phursule, ―Survey Paper On Big Data‖ International Journal of Computer Science and Information Technologies, Vol. 5 (6), 2014.
[5]. Margaret Rouse, April 2010―unstructured data‖.

Web mining is application of data mining is very wide. Although web mining may have some difficult structure, long training time ,and difficult to understandable representation of results, web mining have acceptance ability for noisy data and high accuracy and are preferable in data mining. In this paper the data mining based on web mining is researched in detail, and the key technology and ways to achieve the data mining based on web mining networks are also researched.

Keywords: Data mining, web mining, web personalization, data mining process, implementation.

[1]. J. Srivastva, P. Desikan, and V. Kumar, Web mining – Concepts, Application and Research direction, pp. 51, 2009.
[2]. Preeti Chopra and Md. Ataullah. 2013. A Survey on improving thee Efficiency of different Web Structure Mining Algorithms.
[3]. Kleinberg, J.M., Authoritative sources in a hyperlinked environment. In Proceedings of ACM-SIAM Symposium on Discrete Algorithms, 1998, pages 668-677 – 1998.
[4]. Han, J., Kamber, M. Kamber. ―Data mining: concepts and techniques‖. Morgan Kaufmann Publishers, 2000
[5]. G. Srivastava, K. Sharma, V. Kumar," Web Mining: Today and Tomorrow", in the Proceedings of 2011 3rd International Conference on Electronics Computer Technology (ICECT), pp.399-403, April 2011.

Understanding the contents of a document via a text summarized version of the document requires a shorter time than reading the entire document, so that the summary text becomes very important. summarization requires a lot of time and cost when the documents are numerous and long document. Therefore, automatic summarization required to overcome the problem of reading time and cost. The propose features selection are the cornerstone in the generation process of the text summary. The summary quality is sensitive for those features in terms of how the sentences are scored based on the used features. The automatic text categorization, an ideal task-specific summary can be narrowly defined as the subset of most-informative features selected specifically with the categorization performance in mind.............

Keywords: Text summarization, pre-processing, Feature Selection, Text Classification

[1] J. Salton and C. Buckley, "Term Weighting Approaches in Automatic Text Retrieval", Information Processing and Management, vol. 24, no.5, pp.513-323, 1988.
[2] D. Marcu, "From Discourse Structures to Text Summaries", Proc. of the ACL 97/EACL-97 Workshop on intelligent scalable Text Summarization, pp.82-88, Madrid, Spain, 1997.
[3] R. Barzilay and M. Elhadad, "Using Lexical Chains for Text Summarization", Proc. of the ACL Workshop on Intelligent Scalable Text summarization, pp. 10-17, Madrid, Spain, 1997.
[4] D.R. Radev, H. Jing, and M. Budzikowska, "Centroid-based Summarization of Multiple Documents: Sentence Extraction, Utilitybased Evaluation, and User Studies", Proc. of ANLP-NAACL Workshop on Summarization, pp. 21-30, Seattle, Washington, April, 2000.

Web service discovery has received the most focus in the research field practically. Developing an accurate web service that can meet the user demands is a sophisticated task that also takes a vast amount of time. The web service discovery process has to be carried out with accuracy and rapidity so that the performance is improved. In the already available research work, it is performed employing a technique known as Web Service Operations Discovery Algorithm (WSODA). The web services yields the same functionality but has adiverse feature. Quality had become a significant challenge, which was not taken into consideration in the available work. Moreover, the time taken for discovering the web services is more in the system available. This is solved in the newly introduced research approach by presenting the technique known as Accurate and Faster Web Service Discovery Model (AFWSDM).............

Keywords : web service discovery, accuracy, precision, interface, conceptual meaning, semantic extraction.

[1]. Q.Z. Sheng, X. Qiao, A.V. Vasilakos, C. Szabo, S. Bourne, X. Xu. "Web services composition: A decade's overview", Information Sciences, vol. 280, pp. 218-238, 2014.
[2]. J.K.Y. Lau, J.P. Bruno. "U.S. Patent No. 9,936,333", Washington, DC: U.S. Patent and Trademark Office, 2018.
[3]. S. Deng, H. Wu, D. Hu, J.L. Zhao. "Service selection for composition with QoS correlations", IEEE Transactions on Services Computing, vol. 9(2), pp. 291-303, 2016.
[4]. H.Y. Paik, A.L. Lemos, M.C. Barukh, B. Benatallah, A. Natarajan. "Web Services–SOAP and WSDL", Web Service Implementation and Composition Techniques, pp. 25-66, 2017.
[5]. M. Suchithra, M. Ramakrishnan. "A survey on different web service discovery techniques", Indian Journal of Science and Technology, vol. 8(15), 2015.

Information Literacy is a set of skills that is needed to discover, regain, examine and make use of the same. Today is world of Information Age where everyone needs to be information literate regardless of whatever positions they are in. Information literacy is the surest way of helping solve the problem of choosing the right information from the abundance of information from various media. This paper explains Information Literacy Skills among post graduate patrons particularly in women colleges affiliated to Bharathidasan University, Trichy during their courseware. This article also refers to the problems in incorporating IL across their curriculum.

Keywords: Information Literacy, Post Graduate, Libraries, Women, Education, Bharathidasan University, Literacy Skills

[1]. American Library Association. (2000). Information literacy competency standards for higher education.
[2]. Women in India. (2017, February 14). In Wikipedia, The Free Encyclopedia. Retrieved 11:28, February 22, 2017, from https://en.wikipedia.org/w/index.php?title=Women_in_India&oldid=765395970
[3]. Prachi Salve Oct, 04 2016 "Tamil Nadu, Kerala have the most number of women entrepreneurs and high female literacy" http://www.firstpost.com/business/tamil-nadu-kerala-have-the-most-number-of-women-entrepreneurs-and-high-female-literacy- 3033194.html
[4]. Rustagi, P. (2016). Gender development indicators: issues, debates and ranking of districts.

In this paper, we present an overview of our research in real time Neutrosophic logic based intrusion detection systems (IDSs). We focus on issues related to deploying a data mining-based IDS in a real time environment Information security has become a critical issue with the rapid development of business and other transaction systems over the internet. One of the toughest disputes in IDS is uncertainty handling. IDS offer a new challenge in handling uncertainty when normal and the abnormal behaviors in networked computers are hard to predict as the boundaries cannot be well defined. In this paper we have introduced a genetically evolving approach for IDS using Neutrosophic Logic classifier which is a generalization of the fuzzy logic, intuitionistic logic, and the three-valued logics that use an indeterminate value..............

Keywords - Indeterministic, uncertainty, Neutrosophic, intrusion, genetic algorithm

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[4]. J. Cannady, "Artificial neural networks for misuse detection," in Proceedings of the 1998 National Information Systems Security Conference, pp. 443–456, Arlington, VA, USA, 1998.
[5]. A. A. Aburomman and M. B. I. Reaz, "A novel weighted support vector machines multiclass classifier based on differential evolution for intrusion detection systems," Information Sciences, vol. 414, pp. 225–246, 2017.

Face recognition has been a fast growing, challenging and interesting area in real time applications. A large number of face recognition algorithms have been developed in last decades. In this paper an attempt is made to review a wide range of methods used for face recognition comprehensively. This paper deals with the topic of face recognition techniques using digital image processing. Face recognition has always been a very challenging task for the researches. On the one hand, its applications may be very useful for personal verification and recognition.

Keywords- Karhunen-Loève, eigenfaces, eigen values, line edge maps

[1]. L.C. Jain, "Intelligent biometric techniques in fingerprint and face recognition". Boca Raton: CRC Press, 1999.
[2]. Aurélio Campilho, Mohamed Kamel, "Image analysis and recognition : international conference, ICIAR 2004, Porto, Portugal, September 29-October 1, 2004". Berlin ; New York : Springer, c2004.
[3]. Yongsheng Gao; Leung, M.K.H., "Face recognition using line edge map". Pattern Analysis and Machine Intelligence, IEEE Transactions on , Volume: 24 Issue: 6 , June 2002, Page(s): 764 -779.
[4]. M.A. Turk, A.P. Pentland, "Face Recognition Using Eigenfaces". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3-6 June 1991, Maui, Hawaii, USA, pp. 586-591.
[5]. Pentland, A.; Choudhury, T. , "Face recognition for smart environments ". Computer, Volume: 33 Issue: 2 , Feb. 2000, Page(s): 50 -55.

Face Recognition is an challenging area in research. Many Researchers found innovative ideas in this area. This paper deals with the role of face recognition in machine language.

Keywords- Facebook, machine learning, face detection

[1]. Mattew Turk and Alex Pentland," Eigenfaces for Recognition," SPIE Vol.1192 IRCVVIn (i989), 22- 32.
[2]. Kirby and Sirovich, 1990. Application of Karhunen- Loeve procedure for the characterization of human faces. IEEE Trans. pattern analysis and machine intelligence, 12:103-108.
[3]. Turk, M.A. and A.L. Pentland, 1991. Face recognition using Eigen faces. Proc. IEEE computer society Conf Computer Vision and pattern recognition, pp: 586-591.
[4]. Kyungim Baek, Bruce A. Draper, J. Ross Beveridge, Kai She, "PCA vs. ICA: A Comparison on the FERET Data Set", Proceedings of the 6th Joint Conference on Information Science (JCIS), 2002, pp. 824-827.
[5]. T. Chen, W.Yin, X.-S. Zhou, D. Comaniciu, T. S. Huang, Total Variation Models for Variable Lighting Face Recognition and Uneven Background Correction", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28(9), 2006, pp.1519- 1524.

Nowadays Collaborative Filtering (CF) is a generally accepted recommendation and prediction algorithm based on other related attributes in which users can express their opinions on their products by rating them. CF algorithm is used to collect the existing user ratings and to predict ratings on unknown items for an individual user, and recommends to the users the items which are maximum predicted ratings. Multidirectional similarity learning is proposed Collaborative Filtering method in that, Principle Component Analysis is used to predict asymmetric rating prediction for multiple attributes similarity. Feature Reduction is applied to reduce the feature size after feature selection Process and it can be implemented using Singular Value Decomposition. After the product resemblance relation is learned..............

Keywords- CF, MCDA,MDSL, HOSVD

[1]. Yi Cai; Ho-Fung Leung; Qing Li; Huaqing Min; Jie Tang; Juanzi Li Based Collaborative Filtering Recommendation " , IEEE Transactions on Knowledge and Data Engineering ,2014, Volume: 26, Issue: 3 Pages: 766 – 779
[2]. G. Adomavicius and A. Tuzhilin, "Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734-749, Jun. 2005.
[3]. J. Figueria, S. Greco, and M.Ehrgott, Multiple Criteria Decision Analysis: State of the Art Surveys, Springer, 2005.
[4]. Sung Young Jung; Jeong-Hee Hong; Taek-Soo Kim "A statistical model for user preference Knowledge and Data Engineering, IEEE Transactions on Year: 2005, Volume:17, Issue: 6 Pages: 834 – 843.
[5]. Jun Wang, Arjen P. de Vries, Marcel J.T. Reinders "Unifying User based and Itembased Collaborative Filtering Approaches by Similarity Fusions" Proceeding in SIGR 2006 ,29th Annual International ACM conference on research and development in information retreieval pp-501-508.

The purpose of this paper is to introduce a new type of fuzzy generalized homeomorphisms namely fgspr-homeomorphism and fgspr*-homeomorphism in fuzzy topological spaces and study some of their properties.

Keywords- fgspr-homeomorphism and fgspr*-homeomorphism.

[1]. K. K Azad, On fuzzy semi continuity, fuzzy almost continuity and fuzzy weakly continuity, J.Math.Anal.Appl., vol. 82(1), pp. 14 – 32, 1981.
[2]. C. L. Chang, Fuzzy Topological Spaces, J.Math.Anal.Appl., vol. 24, pp. 182 – 190, 1968.
[3]. M. Ferraro and D. H. Foster, Differentiation of Fuzzy Continuous Mappings on Fuzzy Topological Vector Spaces, J.Math.Anal.Appl., vol. 121, pp. 589 – 601, 1987.
[4]. M. Thiruchelvi and Gnanambal Ilango, Fuzzy Generalized Semi Preregular Continuous Functions in Fuzzy Topological Spaces, International Journal of Pure and Applied Mathematics, vol. 106(6), pp. 75 – 83, 2016.
[5]. M. Thiruchelvi and Gnanambal Ilango, Fuzzy Generalized Semi Preregular Closed Sets in Fuzzy Topological Spaces, International Journal of Humanities and Social Science Invention, vol. 6(9), pp. 63 – 71, 2017.

A new type of fuzzy compact space and fuzzy compact function namely fgspr compact space and fgspr compact function with the concept of fgspr-open set are introduced. Some characterizations on their properties are obtained.

Keywords: - fgspr compact space and fgspr compact function

[1]. K. K Azad, On fuzzy semi continuity, Fuzzy Almost Continuity and Fuzzy Weakly Continuity, J.Math.Anal.Appl. 82(1), 1981, 14- 32.
[2]. C. L. Chang, Fuzzy Topological Spaces, J.Math.Anal.Appl. 24, 1968, 182-190.
[3]. H. Kareem, Fuzzy Proper Mapping, Journal of Babylon University, Pure and Applied Sciences, 22 (2), 2014, 588-606.
[4]. Saleem Yaseen Mageed and Nadir George Mansour, Fuzzy Semi Pre Compact Space on Fuzzy Topological Spaces, Iraq Academic Scientific Journal, 2017, 393-410.
[5]. M. Thiruchelvi and Gnanambal Ilango, Fuzzy Generalized Semi Preregular Continuous Functions in Fuzzy Topological Spaces, International Journal of Pure and Applied Mathematics, 106(6), 2016, 75-83.