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Agriculture is the main source of income in India. India produces various crops such as jute, wheat, rice, sugarcane, cotton, mustard, pulses and many more. Today India acquires 2nd rank worldwide in farming production. In 16 century, 99% of world crop productions are produced by India, Now, it is only produces 23% of agriculture products. However, there are some critical factors that influence the agriculture such as in efficient use of fertilizers, Chemicals, Heavy Rainfall, Degradation/Increment in temperature, PH value, less components value present in soil. This paper provides a systematic analysis by employing data mining techniques. This paper includes some techniques of Supervised and Unsupervised learning methods. In Agriculture domain, multiple linear regressions (MLR), Support vector machine (SVM), K nearest neighbor (KNN), Density-based spatial clustering applications with noise (DBSCAN) are the most widely used data mining algorithm which aimed to solve the issues of the agriculture up to some extent.

Agriculture, Yield prediction, Data Mining, MLR, KNN, DBSCAN, SVM, Clustering and Classification

[1] D Ramesh, B Vishnu Vardhan, “Analysis of Crop Yield Prediction Using Data Mining Techniques”, International Journal of Advanced Research in Engineering and Technology.
[2] Nikita Gandhi, Leisa Armstrong, “Applying Data Mining Techniques to Predict Yield of Rice in Humid Subtropical Climatic Zone of India”.
[3] Monali Paul, Santosh K. Vishwakarma and Ashok Verma, “Analysis of Soil Behaviour and Prediction of Crop Yield using Data Mining Approach”, IEEE.
[4] Dr. S. Hari Ganesh, Mrs. Jayasudha, “Data Mining Technique to Predict Accuracy of the Soil Fertility”,International Journal of Computer Science and Mobile Computing.
[5] Nikita Gandhi, Leisa Armstrong, “Rice Crop Yield Forecasting of Tropical Wet and Dry Climatic Zone of India Using Data Mining Techniques", 2016 3rd International Conference, Computing for Sustainable Global Development, Pg: 357-363.
[6] Anitha, “A Predictive Modeling Approach for Improving Paddy Crop Productivity using Data Mining Techniques”, Turkish Journal of Electrical Engineering and Computer Science 2017, Pg: 4777-4787.
[7] G.Nasrin Fathima, R.Geetha, “Agriculture Crop Pattern Using Data Mining Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering, Issue 5,May 2014, Pg: 781-786
[8] Vinayak A. Bhardi, Prachi P. Abhyankar, Ravina S.Patil, Sonal S. Patade, Tejaswani U.Nate, Anaya M.Joshi, “Analysis and Prediction in Agricultural Data using Data Mining Techniques” IJRISE.
[9] Jharna Majumdar, Sneha Naraseeyappa and Shilpa Ankalaki, “Analysis of Agriculture Data using Data Mining Techniques”, Journal of Big data, Springer Open 2017, Pg: 3-15.
[10] Sally Jo Cunningham, Geoffrey Holmes, “Developing Innovative in Agriculture Using Data Mining Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering.
[11] Umid Kumar Dey, Abdullah Hasan Masud, Mohammed Nazim Uddin, “Rice Yield Prediction Model Using Data Mining Techniques”, International Conference on Electrical, Computer and Communication Engineering, February 16-18, 2017, Pg: 321-326.
[12] S. Pudumalar, E.Ramanujam, R. Harine Rajashree, C. Kavya, T. Kiruthika, J. Nisha, “Crop Recommendation System for Precision Agriculture”, 2016 IEEE EIGHT International Conference on Advanced Computing, Pg: 32-36. [13] Neetu Chahal, Anuradha, “A Study on Agricultural Image Processing along with Classification Model”, 2015 IEEE International Advance Computing Conference, , Pg: 942-947.
[14] Pallavi V. Jirapure, Prarthana A. Deshkar, “Qualitative Data Analysis Using Regression Method for Agricultural Data”, 2016 IEEE.
[15] Nikhil Sethi, Dr. Kanwal Garg, “Exploiting Data Mining Technique for Rainfall Prediction”, International Journal of Computer Science and Information Technologies, Vol. 5(3), 2014, Pg: 3982-3984.
[16] Pooja G. Mate, Kavita R. Singh, Anand Khobragade, “Feature Extraction Algorithm for Estimation of Agriculture Acreage from Remote Sensing Images”, 2016 IEEE, Pg: 1-5.
[17] Chowdari K.K, Dr. Girisha R, Dr. K C Gouda, “A Study of Rainfall Over India Using Data Mining”, 2015 IEEE Sponsored International Conference on Emerging Research in Electronics, Computer Science and Technology, Pg: 44-47.
[18] Kuljit Kaur, Kanwakpreet Singh Atwal, “Effect of Temperature and Rainfall on Paddy Yield using Data Mining”, 2017 Seventh International Conference on Cloud Computing, Data Science and Engineering, Pg: 508-511.
[19] Raorane A. A., Kulkarni R. V., “Data Mining: An Effective Tool for Yield Estimation in the Agriculture Sector”, International Journal of Emerging Trends and Technology in Computer Science, July- August 2012, pg.75-79.
[20] P. Hariharan, K.Arulanandham, “Design an Disease Predication Application Using Data Mining Techniques for Effective Query Processing Results”, Advances in Computational Sciences and Technology.
[21] N. Gandhi ,L.Armstrong, O.Petkar and A. Tripathy, “Rice Crop Yield Prediction in India Using Support Vector Machine”, In IEEE, 2016.
[22] T. Ranjeet and L. Armstrong, “An Artificial Neural Network for Predicting Crop Yield in Nepal”, Ninth Conference for Information Technology in Agriculture” ICT’s for future Economic and Sustainable Agricultural Systems”, Perth, Australia.
[23] S. Jabjone and S. Wannasang, “Decision Support System Using Aritifical Neural Network to Predict Rice Production in Phimai Thailand”, IJSCM.
[24] Mehmed Kantardzic, “Data-Mining Concepts”, Edition:1, Copyright year: 2011.
[25] Mrs. Bharati, M. Ramageri “Data Mining Techniques and Applications”, IJCSE
[26] Fuzail Misarwala, KausarMukadam, and Kiran Bhowmick, “Applications of Data Mining in Fraud Detection”, IJCSE.

U. Singh, K. Garg, "A Review Technique of Data Mining in the Agronomy field," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.788-791, 2018.

Soft computing exploits the biological processes to simplify scientific and technical problems. Correspondingly, soft computing is employed in Frequency Selective Surface designing. In this particular endeavor a Back Propagation Algorithm trained Artificial Neural Network is reported for the designing of single layer Frequency Selective Surface. The prime aspiration was to ascertain the Resonant Frequency and the Band Width of a crossed dipole Frequency Selective Surface. In due course of action and to attain maximized throughput latterly a hybrid Particle Swarm Optimization trained Artificial Neural Network Algorithm is formulated. The empirical study confirmed that Hybrid Particle Swarm Optimization trained Artificial Neural Network is amply efficient and effective for global and fast local searching procedures. Afterward a comparative analysis of Hybrid Particle Swarm Optimization and Back Propagation Algorithm is contemplated.

ANN, FSS, BPA, HPNNA, PSO

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[5]. Woo Cheol Choi, Ki Joon Kim, Young Joong Yoon, “Design of FSS Unit-cell Integrated in Water Bolus for Microwave Biomedical Application” Proceedings of iWEM2014, Sapporo, Japan, 2014.
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[9]. Mahuya Panda and Partha Pratim Sarkar, “Prediction Of Periodicity Of FSS Structure Using Particle Swarm Optimization”, I-manager’s Journal on Electronics Engineering, vol. 7 no. 3 ,pp25-31, 2017.
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Mahuya Panda, Partha Pratim Sarkar, "Hpnna Based Fss Designing: A Case Study," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.792-796, 2018.

The Rough Set Theory is used for incomplete or imperfect knowledge by using the lower and upper approximation. To perusal the data set using Rough Set Theory, many softwares are available. The present paper deals about the softwares which are predominantly utilized for Rough Set Theory in exploring the information system.

Rose2, Rosetta, Rough Set Exploration System, Weka, Rough Sets

[1]. Z. Pawlak, Rough Sets, Institute of Theoretical and Applied Polish Academy of Sciences.
[2]. R. Silvia and L.-T. Germano, “Rough Set Theory – Fundamental Concepts, Principals, Data Extraction, and Applications”.
[3]. ROSE- Software Implementation of the Rough Set Theory: B. Predki, S R. lowinski, J. Stefanowski, Springer- verlag Berlin Heideberg, 1998.
[4]. IDSS: Laboratory of Intelligent Decision Support System, R. Słowiński, Poznań University of Technology.
[5]. Komorowski`s BioInformatics Lab, Komorowski
[6]. The Rosetta Software System: Ohrn S., Komorowski J., Skowron A., Synak P.
[7]. RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories, by L. S.Riza, A. Janusz, D. Slezak, C. Cornells, F. Herrera, J.M. Benitez, C. Bergmeir and S. Stawicki.

[8]. A New Version of Rough Set Exploration System: Bazan J. G., Szczuka M. S., Wroblewski J., Spriinger- verlag Berlin Heideberg, 2002.
[9]. Group of Logic, Institute of Mathematics, Warsaw University and the Group of Computer Science, Institute of Mathematics , University of Rzeszów, Poland.
[10]. WEKA: The Wailato Environment for Knowledge Anlysis: S.R. Garnar, Department of computer Science, University of Wailato.
[11]. A survey of Software packages Used for rough set Analysis, Z. Abbas, S.M. A. Burner, Journal of Computer and Communication, 2016, 4, 10 – 18.

P. Mehta, P. Jain, "Software Related To Rough Set Theory," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.797-799, 2018.

This is a kind of technology is applicable to all type of farm land cultivation. This technology observe actual status of the land and its environment. Based on the observed data it enrich or improve the farmland cultivation. In this system its a combinations of IOT, Big data analytics, wireless sensor network technologies. In Indian scenario all the fertile land are cultivated into an manual farm. So, the yield of the land should be improved because all data`s are observed from sensor as well as traditional knowledge. The proposed system observe the data from environment, soil, water and fertilizer. After the collection of information it will be analyzed. Based on the analysis we take a decision for cultivating the crop. It also take the traditional knowledge for analyzing the cultivation. The proposed system is working in the form of integrated environment. It work like an automated system. In the proposed system consist of the various sensor which are fixed into the fertile land. So, this sensor give the nature of the farmland. The proposed system improve the agriculture cultivation. We can apply this system into an all type of farmland. Whether the land is arid or water rich environment, it can be possible to give the good result.

IOT, Sensor, Cloud Computing, Wireless Sensor Network

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[2]. Annual Report 2017-2018 by Department of agriculture cooperation and Farmers welfare,India.
[3]. S.R.Prathiba,Anupama Hongal,M.P.Jyothi “IOT Based Monitoring System in Smart Agriculture”.
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B. Anandakumar, "Integrated Automated Agricultural Operations Using Multiple Technologies," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.800-803, 2018.

In Today’s digital generation the growth of IT industries gradually increase with the volume of data which is generated by the most of applications. To handle such a large amount of data Relational Database are not sufficient. No SQL database is an emerging alternative to the relational database which provides high scalability, performance and high availability. The paper, discuss about different types of NoSQL database with its opportunities and applications.

NoSQL Database , Relational Database

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Jyoti Kharade, Anil Rama Kale, Dhanaji S. Kharade, "NOSQL Database: Opportunities and Applications," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.804-807, 2018.

Digital video tampering is an act of malicious modification of video content. This could be done to hide or cover an object or to alter the meaning conveyed by the digital video. The research performed is summarized in this paper by analyzing various inter frame forgery detection approaches for digital video, proposed so far, highlighting the strengths and weaknesses of each approach discussed. All approaches proposed so far are making use of P-frames for forgery detection. Comparison of P-frames and B-frames has been performed in terms of complexity and accuracy of algorithms developed using each of them. All the way through the research performed, authors tried to access the worth of B-frames in digital video forgery detection.

Video Forgery Detection, Group of Pictures (GOP), B-frames, Video tampering, Intra Frame, Predicted Frame, Bi-directional frames, High efficiency video coding

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[11]. Umesh Kumar Singh, Chanchala Joshi, Suyash Kumar Singh, "Zero day Attacks Defense Technique for Protecting System against Unknown Vulnerabilities", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.1, pp.13-18, 2017Su, Y., Zhang, J., Liu, J.: Exposing digital video forgery by detecting motion-compensated edge artifact. In: Proceedings of International Conference on Computational Intelligence and Software Engineering, Wuhan, China. Vol. 1, no. 4, pp. 11–13 (2009).
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V.Joshi, S. Jain, C. Bansal, "B-Frames: Efficiency Analysis for Digital Video Tampering Detection in Videos with Variable GOP Structure," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.808-815, 2018.

The web mining is a process which plays a important role in analyzing the web users behaviour. The web usage mining is a part of web mining has a major influence on web personalization. The aim of this study is the taking out necessary information from web access log files and applying data mining technique for analyzing the shopping behaviour of the customers through online.

Data Mining , E-Commerce , WebLog Analysis, Behavioural Patterns

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Siva Jyothi Barla, G.V. Gayathri, "Recommending Based on Analysis of User`s Behaviour in An E-Commerce Website," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.816-820, 2018.

A standout amongst the most difficult activities in data frameworks is separating data from unstructured writings, including medical archive classification. A classification calculation that arranges a medical record by examining its substance and classifying it under predefined themes from the Medical Subject Headings (MeSH). It gathered a corpus of 50 full-content diary articles (N=50) from MEDLINE, which were at that point ordered by specialists in light of MeSH. Utilizing natural language processing (NLP), the calculation orders the gathered articles under MeSH subject headings. It assessed the calculation`s result by estimating its accuracy and review of coming about subject headings from the calculation, contrasting outcomes with the real archives` subject headings. The calculation ordered the articles effectively under 45% to 60% of the genuine subject headings and got 40% to 53% of the aggregate subject headings rectify. This holds promising answers for the worldwide wellbeing field to file and arrange medical archives quickly.

MeSH, Natural Language Processing, MEDLINE, Classification

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R. Aravazhi, M. Chidambaram, "Improving Mediterm Classification in Medical Subject Headings (MeSH)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.821-825, 2018.

Digital watermarking is a powerfull tool to protect digital multimedia content from illegal distribution and unatherised acces.Thus it can be used in multimedia applications for videos and images.Here we are proposing a new watermarking scheme which is Histogram based and it can be used to provide high security, robustness.This watermarking is aimed for images and videos also. The shape of histogram of image is manipulated to for the efficient insertion of digital watermarks to the images.The movement of pixels from one region to other will remove the side effect of Gaussian filtering. The proposed system is highly robust as it makes changes to the histogram of the image. The use of secret key improves the security of the proposed system. Also the imperceptiblity of watermarking makes the scheme perfect.Here the three step scenario for watermarking makes a good protection system for digital contents.In video watermarking the video frames are seperated before inserting the watermarks.The digital watermarking can be used in applications like identifying content owners, Protection of video contents,filtering of digital contents,ownership communication,..etc

Digital watermarking,Histogram,Content owners

[1] S. Xiang, H. J. Kim, and J. Huang, Invariant image watermarking based on statistical features in the low-frequency domain, IEEE Trans. Circuits Syst. Video Technol., vol. 18, no. 6, pp. 777790, Jun. 2008.
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P. Afeefa, Ihsana Muhammed, "Application of A Video And Image Watermarking Based On Histogram Shape," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.826-827, 2018.

Big data, as the term implies is a collection of huge amount of data. This can be valuable information to any organization. This huge amount of data requires emerging new technologies and design that makes it easier to take out information. As today world is connected virtually through internet so daily data generated is very high like in the form of zettabytes. As complexity of data has increased, there is a difficulty in managing such data by using traditional computing resources. It’s necessary to secure big data environments from the infringement of confidential data. This paper presents introduction of big data and various security issues in this area.

Big data, Security, Privacy

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B. Duhan, D. Singh, "Big Data and its Security Issues," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.828-831, 2018.