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Clustering temporal data compiled from cancer registries is a crucial problem faced by many data analyst owing to the elevated high dimensionality, weight value calculation, multi view data and multifaceted temporal correlation. This research work reveals a hypothetical effect of Temporal Clustering (TC) in various domains on cancer genome risk estimates by introducing data mining clustering algorithms. For the first time, cancer genome datasets samples were made available for the complete genome sequences consisting of point mutations and structural alternations for a huge number of cancer types which allows the variation of cancer subtypes in an exceptional excellent global analysis. In this work, TC algorithm is presented to the allocation of several time- series into a set of non-overlapping parts that fit in to k temporal clusters. The paper presents a group of clustering communal framework for multi view data, TW-K-means and an automated two-level variable clustering algorithm that can be used to calculate the weights for views and person variables. A new ATBCWCE structure is projected to improve the risk estimates in cancer genome.

Temporal data clustering, weighted consensus function, multi view learning, k-means

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[17]. Verdoolaege G. and Y. Rosseel,” Activation Detection In Event-Related FMRI Through Clustering Of wavelet Distributions”, Proceedings of 2010 IEEE 17th International Conference on Image Processing, September 26-29, 2010, Hong Kong, pp 4393-4395.
[18]. Fern X. and C. Brodley, “Solving Cluster Ensemble Problem by Bipartite Graph Partitioning,” Proc. Int’l Conf. Machine Learning, pp. 36-43, 2004
[19]. Gionis A., H. Mannila, and P. Tsaparas, “Clustering Aggregation,” ACM Trans. Knowledge Discovery from Data, vol. 1, no. 1, article no. 4, Mar. 2007.
[20]. Singh V., L. Mukerjee, J. Peng, and J. Xu, “Ensemble Clustering Using Semidefinite Programming,” Advances in Neural Information Processing Systems, pp. 1353-1360, 2007.
[21]. Topchy A., M. Law, A. Jain, and A. Fred, “Analysis of Consensus Partition in Cluster Ensemble,” Proc. IEEE Int’l Conf. Data Mining, pp. 225-232, 2004.
[22]. Chaudhuri K., S. Kakade, K. Livescu, and K. Sridharan, “Multiview Clustering via Canonical Correlation Analysis,” Proc. 26th Ann. Int’l Conf. Machine Learning, pp. 129-136, 2009
[23]. Tsai C.-Y. and C.-C. Chiu, “Developing a Feature Weight Self-Adjustment Mechanism for a k-Means Clustering Algorithm,” Computational Statistics and Data Analysis, vol. 52, no. 10, pp. 4658- 4672, 2008.
[24]. Deng Z., K. Choi, F. Chung, and S. Wang, “Enhanced Soft Subspace Clustering Integrating Within-Cluster and Between- Cluster Information,” Pattern Recognition, vol. 43, no. 3, pp. 767- 781, 2010.
[25]. Parvin H, Minaei-Bidgoli B, Parvin S, Alinejad H (2012b) A New Classifier ensemble methodology based on subspace learning. J Exp Theor Artif Intell. doi:10.1080/0952813X.2012.715683
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Sathishkumar. K, V. Thiagarasu, E. Balamurugan, David Otto Arthur , "Clustering Mutual Outline for Multi Assessment Temporal Data and cancer Data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.71-79, 2018.

Security was a big deal for a long time. With the viruses, malware and ransomware are another problems seen by the practitioner. This paper shows working of ransomware with its evolution and general characteristics of few popular ransomware. In evolution part, the paper provides s study from first ransomware to current days. The study shows the light on various kind of infection performed by a ransomware including data infection and infected machine. Different attackers made choices to the target various attack; the paper provides a sight on various target types of the ransomware. This paper trying to demonstrate few ransomware attacks case studies to show the problem created by various ransomware as an example. After an attack, what a victim should do after infection is also discussed at the end of the paper. How people can save their system and what are the safety measures to save the system from ransomware, are also discussed by the researcher. At the end of the paper, the researcher points out few steps to save systems and data.

Ransomware, Security, attack, Cryptowall, Cryptolock, Wannacry.

[1]. R. Richardson and M. Nort, Ransomware: Evolution, Mitigation and Prevention”, International Management Review, Vol. 13, No. 1 2017.
[2]. An Osterman Research, “Best Practices for Dealing With Phishing and Ransomware SPON”, White Paper Published September 2016.
[3]. C.Beek and A. Furtak, “Targeted ransomware No Longer a Future Threat: Analysis of a targeted and manual ransomware campaign”, Advanced Threat Research, Intel security, feb2016.
[4]. J. Wyke and A. Ajjan, “The Current State of Ransomware”, A SophosLabs technical paper December 2015.
[5]. WannaCry Response, Metasys, Johnson Control, June 2017.
[6]. WannaCry Rensomware Analysis, White paper, May 2017, Stream scan.
[7]. B. N. Giri, N. Jyoti, Mc. A. AVERT, AVAR 2006, Auckland.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.169.5881&rep=rep1&type=pdf
[8]. P. Bihola, R. Sheth, “Emerging Threats of 2017: Ransomware, IOC’s”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Volume 2 | Issue 6, pp.-996-1003.
[9]. P.H. Rughani, Data recovery from ransomware affectedandroid phone using forensic tools, vo.-5, issue-8, International Journal of Computer science and Engineering, pp. 67-70.

A.K. Maurya, N. Kumar, A. Agrawal, R. A. Khan, "Ransomware: Evolution, Target and Safety Measures," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.80-85, 2018.

Since, motorcycles are affordable and a daily mode of transport there has been a rapid increase in motorcycle accidents, due to the fact that most of the motorcyclists do not wear a helmet which has made it an ever-present danger every day to travel by motorcycle. In last couple of years Government has made it a punishable offense to ride a motorcycle without helmet. The existing video surveillance based system is effective but it requires significant human assistance whose efficiency decreases with time and human biasing also comes into picture. So, automation of this process is highly desirable. In this paper, we propose an approach for automatic detection of motorcyclists without helmet using surveillance videos in real-time .The proposed approach first detects the motorcycle from the surveillance videos using background subtraction. Then it classifies between helmet and non-helmet using 1st order and 2nd order derivative edge detection algorithm and neural network. After detection if the motorcyclists are found without helmet then it will trace the vehicle number plate of the motorcyclists using (OCR) Optical Character Recognition and Neural Network and a copy of challan will be generated and will be send via SMS to the respective traffic rule violator.

Helmet Detection, license number plate detection, Derivative edge detection Algorithm, Neural Network, (OCR) Optical Character Recognition

[1] Maya Sharma , "use your head - use a helmet", Team NDTV article , Dec 12 , 2016.
[2] Anisha Bhatia ,"Wearing helmets - A choice between life and death", Team NDTV article , Dec 9 , 2016.
[3] C.Y.Wen, S.H.Chiu, J.J.Liaw, and C.P. Lu, "The safety helmet detection for ATM`s surveillance system via the modified hough transform" in IEEE 37th Annual International Carnahan Conference on Security Technology , 2003 , pp- 364-369.
[4] C.C. Chiu, M.Y. Ku, and H. -T. Chen, "Motorcycle detection and tracking system with occlusion segmentation,” in Proceeding of International Workshop on Image Analysis for Multimedia Interactive Services , Santorini , Greece, 6-8 June 2007, pp. 32-32.
[5] J. Chiverton , "Helmet presence classification with motorcycle detection and tracking," IET Intelligence Transport Systems (ITS) , Volume 6 , no. 3 , pp. 259-269, 2012.
[6] R. V. Silva , T. Aires , and V. Rodrigo , " Helmet Detection on Motorcyclists using image descriptors and classifiers", in Proceeding of Graphics , Patterns an Images ( SIBGRAPI) , Rio de Janeiro ,Brazil , 27-30 August 2014 , pp. 141-148.
[7] K. Dahiya, D. Singh and C.K .Mohan, "Automatic detection of bike riders without helmet using surveillance videos in real - time", in Proceeding of International Joint Conference Neural Networks (IJCNN), Vancouver, Canada , 24-2 July 2016,pp.3046-3051.
[8] Pathasu Doughmala, Katanyoo Klubsuwan, "Half and Full Helmet Detection in Thailand using Haar Like Feature and Circle Hough Transform on Image Processing" in Proceeding of IEEE International Conference on Computer and Information Technology, Thailand, Bangkok , pg. 611-614,2016.
[9] Hanit Karwal , Akshay Girdhar , “Vehicle Number Plate Detection System for Indian Vehicles”, in Proceeding of IEEE International Conference on Computational Intelligence and Communication Technology pg. 8-12,2015.
[10] Norizam Sulaiman , "Development of Automatic Vehicle Plate Detection System " in Proceeding of IEEE 3rd International Conference on System Engineering and Technology , 17-20 Aug .2013.
[11] Sahil Shaikh , Bornika Lahiri, Gopi Bhatt, Nirav Raja, “A novel approach for Automatic Number Plate Recognition”, in Proceeding of International Conference on Intelligent Systems and Signal Processing ( ISSP), pg. 375-380, 2013.
[12] C. Shyang-Lih, C.Li-shen, C.Yun-Chung, C. Sei- Wan. “Automatic License Plate Recognition,” IEEE Transaction on Intelligent Transportation Systems, vol. 5, no.1, pp. 42-53, 2004.
[13] P. Cika, “Vehicle license plate detection and recognition using symbol analysis,” Proceedings of the 34th International Conference on Telecommunications and Signal processing,” 2011, pp. 589- 592.
[14] Arkadiusz Pawlik, “High performance automatic number plate recognition in video streams”, Image Processing Theory, Tools and Applications, 2012.
[15] Rahim Panahi, “Accurate Detection and Recognition of Dirty Vehicle Plate Numbers for High- Speed Applications", IEEE transactions on intelligent transportation systems, vol. 18, no. 4,pg. 767-779, April 2017.
[16] D. Singh , C. Vishnu , and C.K Mohan , "Visual Big data analytics for traffic monitoring in smart city ," in proc. IEEE Conf . Machine Learning and Application (ICMLA) , Anaheim , California , 18-20 December 2016.

Sneha A. Ghonge, Jignyasa B. Sanghavi, "Smart Surveillance System for Automatic Detection of License Plate Number of Motorcyclists without Helmet," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.86-89, 2018.

Raindrops stick to a window glass can considerably destroy the visibility of a scene. Detecting and removing raindrops will benefit in many computer image applications. We presented hybrid approach to detect and remove raindrops and then restore the image back ground in a single image. Proposed framework is based on K-means clustering and hybrid Otsu plus median filter for the efficient retrieval of Rain droplets from the single image. The k-means clustering is used which results in highest correct clustering rate and Hough transform is used to detect and remove the raindrops from single image. We present a complete review on different approach for raindrop detection and removal. The proposed system will gives better results than available approaches for raindrop detection and removal. Results analysis will be performed on the basis of the efficiency and accuracy of the proposed method.

Image Clustering, Hough transforms Otsu filter, Median filter, Image segmentation, K-means Algorithm

[1] M.Ramesh Kanthan, Dr.S.Naganandini Sujatha, ”Rain drop Detection and Removal using K-Means Clustering”, 2015 IEEE International Conference on Computational Intelligence and Computing Research, 978-1-4799-7849-6/15,IEEE ,2015.
[2] Shaodi You, Robby T. Tan, Rei Kawakami, Yasuhiro Mukaigawa, Katsushi Ikeuchi,” Adherent Raindrop Modelling, Detection and Removal in Video”, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 38, No. 9, September 2016
[3] Dereck D Webster, “Automatic Rain Drop Detection for Improved Sensing in Automotive Computer Vision Applications”, M.Sc. Thesis., Cranfield University, 2014.
[4] Martin Roser “Video-based raindrop detection for improved image registration” ICCV 2009.
[5] R. G. Willson, M. Maimone, A. Johnson, and L. Scherr, “An optical model for image artifacts produced by dust particles on lenses,” Pasadena, CA, USA: Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2005.
[6] C. Zhou and S. Lin, “Removal of image artifacts due to sensor dust,” in Proc. IEEE Conf. Computes. Vis. Pattern Recog., 2007, pp. 1–8.
[7] H.Kurihata,T.Takahashi,I.Ide,Y.Mekada,H.Murase,Y.Tamatsu, and T. Miyahara, “Rainy weather recognition from in-vehicle camera images for driver assistance,” in Proc. IEEE Intell. Vehicles Symp, 2005, pp.205–210.
[8] D. Eigen, D. Krishnan, and R. Fergus, “Restoring an image taken through a window covered with dirt or rain,” in Proc. IEEE Int. Conf. compute. Vis., 2013, pp. 633–640.
[9] Montero-Martinez, G., A. B. Kostinski, R. A. Shaw, and F. Garcia-Garcia (2009), do all raindrops fall at terminal speed? Geophys .Res. Lett. 36, L11818, doi:10.1029/2008GL037111.
[10] E. Villermaux and B. Bossa, “Single-drop fragmentation determines size distribution of raindrops,” Nature Physics, vol. 5, no. 9, pp. 697–702, 2009.
[11] C. Liu, J. Yuen, and A. Torralba, “Sift flow: Dense correspondence across scenes and its applications,” IEEE Trans. Pattern Anal. Machine Intell, vol. 33, no. 5, pp. 978–994, May 2011.
[12] D.Y .Huang .T.-W. Linand W.-C. Hu, ”Automatic Multilevel Thresholding Based On Two-Stage Otsu`s Method With Cluster Determination By Valley Estimation”, International Journal of Innovative Computing, Information and Control, ICIC International, vol.7, no.10, 2011, pp. 5631-5644.
[13] N. Otsu, “A threshold selection method from gray-level histogram,” IEEE Transactions on System Man Cybernetics, vol. SMC-9, no.1, 1979, pp. 62-66.
[14] W. X.-ke and Z. Z.-qiang, “An Application of Multi-Thresholding Ostu Algorithm on Chromatic Image”, Computer Application, vol.26, 2006, pp.14-15.
[15] Shaodi You, Robby T. Tan, Rei Kawakami el al, “Raindrop Detection and Removal from Long Range Trajectories” CVPR 2014.
[16] Shaodi You, Robby T. Tan , “Adherent Raindrop Detection and Removal in Video”, CVPR 2013

S.P. Kullarkar, S.V. Jain , "Review on Rain drop detection and removal using k-means clustering and Hough transformation," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.90-94, 2018.

Cancer is one of most dangerous diseases and the incidence rate of cancer in India is increasing every year. Today, number of tools and techniques are available to analyze large cancer dataset. Data mining tools are most frequently used to identify patterns in cancer patients and in cancer diagnosis or detection. Data mining is now widely used in health care industry as it has a great capability to extract hidden patterns from the large past medical record of cancer patients. This study uses Data Mining techniques called Classification and Clustering to classify and compare the incidence rates of TCR (Tobacco Related Cancer) and Non-TCR (Non-Tobacco Related Cancer) in two areas of Bhopal city and extracted some useful and interesting fact from the incidence rates data of cancer patients. The incidence rate data of cancer were obtained during past 40 years from Bhopal-population-based cancer registries (PBCR) of two areas partitioned after Gas tragedy of Bhopal city. This study can be helpful to the medical analysts as a decision support system. The study was done using WEKA Tool.

Data Mining, Classification, Clustering, WEKA, Tobacco Related Cancer

[1] N. Kumar, S. Khatri, ”Implementing WEKA for medical data classification and early disease prediction-computational intelligence and communication Technology, 10 feb-2017.
[2] S. Asthana, R.S.Patil, S.Labani, ” Tobacco‑related cancers in India: A review of incidence reported from population‑based cancerregistries”, Indian Journal of Medical and Paediatric Oncology. Volume 37, Issue 3, pp-152-157, Jul-Sep 2016.
[3] S.Gupta, D.Kumar, A.Sharma, ”Data Mining Classification Techniques Applied For Breast Cancer Diagnosis And Prognosis”, Indian Journal of Computer Science and Engineering (IJCSE ) Vol. 2 No. 2,pp.-188-195, Apr-May 2011.
[4] M. Durairaj, V. Ranjani,.” Data Mining Applications In Healthcare Sector: A Study”, International Journal Of Scientific & Technology Research ,Volume 2, Issue 10, pp.-29-35, October 2013.
[5] O.Niakšu, O. Kurasova, “ Data Mining Applications in Healthcare: Research vs Practice”, Data Mining Application in Healthcare Research vs Practice, pp.-58-70, 2014.
[6] S. Gour, “Developing decision model by mining historical price data of Infosys for stock market prediction IJCSE-2347-2693,Vol.4-Issue-10,pp-92-97, 2016.
[7] S. L. Ting, C. C. Shum, S. K. Kwok, A. H. C. Tsang, W. B. Lee, “ Data Mining in Biomedicine: Current Applications and Further Directions for Research- scientific Research”, J. Software Engineering & Applications, 2, pp.- 150-159, 2009.
[8] P.Ramachandran, N. Girja, T.Bhuvaneswari, “Early Detection and Prevention of Cancer using Data Mining Techniques”, International Journal of Computer Applications (0975 – 8887) Volume 97– No.13, pp.-48-53, July 2014.
[9] A. Joseph, S. David,” Applications of Machine Learning in Cancer Prediction and Prognosis”, Departments of Biological Science and Computing Science, University of Alberta Edmonton, AB, Canada, Cancer Informatics, pp.- 59-77, 2006.
[10] Y.K. Anupama, S.Amutha,R.Babu ,” Survey on Data Mining Techniques for Diagnosis and Prognosis of Breast Cancer” International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169, Volume: 5, Issu-2, pp.- 33 – 37, Feb-2017.
[11] M. Shabaz, S.Faruq,M.Shaeen, S.A. Masood, ”Cancer Diagnosis Using Data Mining Technology” Life Science Journal, vol-9, issue-1, pp-308-313, September 2012.

Sanjeev Gour , "Classifying the Incidence Rates of Cancers Using Data Mining Techniques (Perspective to Gas Leakage Accident of Bhopal City)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.95-100, 2018.

Bitcoin - a new virtual currency may change the world as it is the first currency which is not tied to any bank or government agency. It is also the first global currency that uses cryptography allowing users to utilize money anonymously and having full control over its saving and expenditure. Since its inception in 2009, it is still in its infancy but is gaining popularity with each passing day and might replace existing currencies in near future. Bitcoin being open source acts as a revolutionary currency for e-procurement. The aim of this paper is to explore the concept behind exchanging and trading, introduces the concept of Bitcoin and illustrates the process of Bitcoin mining.

Bitcoin, Mining, Money, Virtual Currency

[1] Nakamoto.S ,"Bitcoin: A peer-to-peer electronic cash system." ,2008.
[2] https://www.coindesk.com/peter-thiel-bitcoin-like-reserve-form-money/
[3] https://www.forbes.com/sites/johnmauldin/2014/12/01/is-bitcoin-the-future/#2f38eb4b2ceb
[4] Ali.S,Clarke.D and McCorry. P,"Bitcoin: Perils of an Unregulated Global P2P Currency",New Castle University Computer Sciences,No. CS-TR-1470,2005.
[5] Khan.QR, Butt. MA, Asger M, Zaman M, “Integrity Model based Intrusion Detection System: A Practical Approach” International Journal of Computer Applications.Vol. 115(10), 2015.
[6] Razeef.M, Butt. MA, and Zaman. M, "Review of Predictive Analytic Modeling Techniques."
[7] Mehraj.M, Butt.MA and Zaman.M, “Automatic Speech Recognition Approach For Diverse Voice Commands”, International Journal. Vol.8(9).2017.
[8] Firdous. A., Pawar.N., Butt. M.A. and Zaman.M., “Character Recognition: A Signature Approach” ,International Journal of Advanced Research in Computer Science,Vol. 8(5), 2017.
[9] Hassan .M., Butt. M.A. and Zaman.M “Logistic Regression Versus Neural Networks: The Best Accuracy in Prediction of Diabetes Disease.”; Asian Journal of Computer Science and Technology, Vol.6(2), pp.33-42. 2017.
[10] Firdous, A., Pawar, N., Butt, M.A. and Zaman, M.,”Character Recognition: A Signature Approach.”, International Journal of Advanced Research in Computer Science, 8(5). 2017.
[11] https://99bitcoins.com/bill-gates-bitcoin/
[12] https://bitcointrader.org.au/testimonial/eric-schmidt-ceo-of-google/
[13] Nayak, Deveeshree, and. Butt, M.A "Empowering cloud security through sla." International Journal of Global Research in Computer Science (UGC Approved Journal) ,pp.30-33, 2017.
[14] Butt. M.A et al. "Threat Mitigation Strategy in Information System using Intrusion Detection System: A General Classification Reviews." International Journal of Computer Applications,Vol. 115(10 )2015.
Diana.K "An Analysis Of Anonymity In Bitcoin Using P2P Network Traffic." 2013.

S.M. Nasti, S.J.Nasti, R.Bashir, M.A. Butt, "Bitcoin:Surveying First Revolutionary Cryptographic Virtual Currency," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.101-103, 2018.

In the Big data world securing the sensitive data become more complex and time consuming process. In the big data sharing of sensitive, it exacerbates the threat of sensitive data falling into the un-authorized. To combat this sensitive data threat, enterprises turn to cryptosystem. In the cryptosystem encryption is the process of encoding sensitive data so that only authorized or privileged parties can decrypt and read the sensitive data applying this methodology in application level we provide complete security on the sensitive data.

Cryptography – Policy – Data Encryption - Privileged User – Enhanced Schmidt Samoa

[1] http://www.sas.com/en_us/insights/big-data/what-is-big-data.html
[2] https://globalecco.org/big-data-insider-threats-and-international-intelligence-sharing
[3] "Sensitive Information" (definition) Aug. 23, 1996. Retrieved Feb. 9 2013.
[4] "DEPARTMENT OF INDUSTRY: PERSONAL INFORMATION PROTECTION AND ELECTRONIC DOCUMENTS ACT" Canada Gazette, Apr. 03 2002. Retrieved Feb. 9 2013.
[5] http://motherboard.vice.com/read/even-tor-cant-save-small-time-hackers
[6] https://www.qubole.com/blog/big-data/hadoop-security-issues/
[7] https://securosis.com/assets/library/reports/Securing_Hadoop_Final_V2.pdf
[8] https://securosis.com/blog/securing-hadoop-architectural-security-issues
[9] http://www.bmc.com/blogs/big-data-security-issues-challenges-for-2016/
[10] https://en.wikipedia.org/wiki/Health_Insurance_Portability_and_Accountability_Act
[11] http://searchdatamanagement.techtarget.com/definition/HIPAA
[12] http://blog.vormetric.com/2015/06/23/locking-down-data-full-disk-encryption-vs-file-level-encryption/
[13] Performance analysis of Jordan Totient RSA (JkRSA) and NTRU, International Journal of Scientific & Engineering Research, Volume 5, Issue 3, March-2014 1099 ISSN 2229-5518
[14] https://www.vormetric.com/data-security-solutions/use-cases/privileged-user

Narayana Galla, Padmavathamma Mokkala, "An Proposal of Securing the Data at Application Level using Enhanced Schmidt Samoa in Big Data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.104-107, 2018.

Dynamic examination extracts vital data about software systems which are helpful in testing, troubleshooting and support exercises. Prevalent dynamic examination strategies combine either data on the estimation of the factors or data on relations between orders for techniques. GK-tail, for creating model that address the trade between program components and strategy orders. Therefore, these methodologies don`t catch the vital relations that exist on information esteem and conjuring succession. GK-tail broadens the k-tail algorithm to removing limited state automata from execution take after the example of limited state automata with parameters. GK-tail+, another way to deal with deducing monitored limited state machines from execution hints at question arranged projects. GK-tail+ is another arrangement of surmising criteria that speak profoundly component of the derivation procedure: It to a great extent lessens the deduction time of GK-tail while creating watched limited state machines with a practically identical level of review and specificity. Along these lines, GK-tail+ progresses the preliminary results of GK-tail by tending to all the three principle difficulties of taking in models of program conduct of execution follow. This paper displays the method and the consequences of some preparatory analyses that demonstrate the possibilities of the approach’s available.

Dynamic analysis, Behavioural models, Finite state machines, Verification

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K. Laxmi Pradeep, K. Madhavi, "Approaches for Efficient Learning Software Models: A Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.108-113, 2018.

This system mainly focus to increase the productivity of rice crops which is one of a critical problem that the farmers are facing. Using Php code this study helps to connect globally the farmers through online-Global Positioning System and know their productivity and problems that they face during production. Images of rice crops captured by the farmers with the very short duration of rice growth period is uploaded. The LAB values are determined for the captured image. Clustering and segmentation is done for the images to separate the foreground and background of the image. These values are compared with the pre-estimated Nitrogen(N) values that are obtained using (leaf color chart) LCC. Multi class support vector machine(MSVM) is used to procure the amount of Nitrogen values to be tacked on to make the crop lusher. Based on the N value status the amount of urea to be added is determined. When the farmer capture picture of the rice crops, the amount of N present in it will be displayed on the screen.

Production, Rice crops, ICT

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[3]Amandeep Singh, ManinderLal Singh. (2015), ‘Automated Color Prediction of Paddy Crop Leaf using Image Processing’, IEEE International Conference on Technological Innovations in ICT for Agriculture and Rural Development.
[4] Ali M.M, Ahmed Al-Ani, Derek Eamus& Daniel K. Y. Tan. (2016), ‘Leaf Nitrogen Determination Using Non-Destructive Techniques – A Review’, Journal of Plant Nutrition.
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Savithri. V., Anuradha. G., "Prognosis of Lush In Rice Crops and Nourishing Inadequacy by Exerting Multiclass SVM Through GPS," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.114-119, 2018.

Agriculture is the main sector of employment in India. Yet, it contributes only 13.7% to the total GDP of India. One of the major causes for the continuing downfall in agricultural trends is cultivation of crops that are not suitable with the environmental factors like soil and weather conditions. One way to solve this problem is to use the Recommendation System. It is the information filtering system forecasting the items that may be additional interest for user within a big set of items on the basis of user’s interests. This system uses the Collaborative filtering, which offer some recommendations to users on the basis of matches in behavioral and functional patterns of users and also shows similar fondness and behavioral patterns with those users. It also seeks to predict the suitability of an item for a given set of conditions. Such a recommendation system can provide suggestions for a crop that can be cultivated based on soil and weather conditions. The research focus on to build a recommendation system that can collect raw data for environmental factors like soil, weather parameters from experienced farmers, agricultural researchers and other stakeholders. The collected data then will be maintained whether this data is processed. Statistic data analysis and predictive modeling are applied in order to predict a suitable crop accordingly.

Recommendation System, Agriculture, Collaborative Filtering, Predictive Modeling

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[9] Ashwini A. Chirde, Umila K. Biradar , ”A Survey on Collaborative Filtering in Accordance with the Agricultural Application”, International Journal of Computer Applications (0975 – 8887), 2014
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K.Anji Reddy, R.Kiran Kumar, "Recommendation System: A Collaborative Model for Agriculture," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.120-123, 2018.