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Regular-frequent patterns are an important type of regularities that exist in transactional, time-series and any other types of databases. A frequent pattern can be said regular-frequent if it appears at a regular interval given by the user specified threshold in the transactional database. The regularity calculation for every candidate pattern is a computationally expensive process, especially when there exist long patterns. Currently the FP-growth algorithm is one of the most popular and fastest approaches to mining periodic frequent item sets. Therefore, in this paper we introduce a novel concept of mining regular-frequent patterns (RFP) in transactional databases. We introduce two mining techniques based on transaction number and also based on products or itemsets on the vertical data format. The efficiency is achieved by eliminating aperiodic or irregular patterns during execution based on suboptimal solutions. Our tree based structure helps to captures the database contents in highly compact manner. Our experimental results are highly efficient and scalable as well as improve the overall response time.

Frequent patterns, regular patterns, transactional databases, vertical data format

[1] R. Agrawal, T. Imielinski, A.N. Swami “Mining Association Rules Between Sets of Items in Large Databases”, International Conference on Management of Data(ACM SIGMOD), pp. 207–216, 1993.
[2] R.U. Kiran, P.K. Reddy “Towards efficient mining of periodic frequent patterns in transactional databases”, DEXA, pp. 194-208, 2010.
[3] J. Han, J. Pei, Y.Yin “Mining Frequent Patterns without Candidate Generation”, International Conference on Management of Data(ACM SIGMOD), pp. 1–12, 2000.
[4] K. Amphawan, P. Lenca, A. Surarerks “Mining top-k periodicfrequent pattern from transactional databases without support threshold”, Advances in Information Technology, pp. 18–29, 2009.
[5] J.N. Venkatesh, R.U. Kiran, K, Reddy, M. Kitsuregawa “Discovering Periodic-Frequent Patterns in Transactional Databases Using All-Confidence and Periodic-All-Confidence”, Springer International Publishing,Switzerland, pp. 55-70, 2016.
[6] S.K. Tanbeer, C.F. Ahmed, B.-S. Jeong, Y.-K. Lee, “Discovering periodic frequent patterns in transactional databases” Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) , Springer, Heidelberg, PAKDD, LNCS, Vol. 5476, pp. 242–253,2009.
[7] J. Pei, J. Han “Constrained Frequent Pattern Mining: A Pattern-Growth View*”, SIGKDD Explorations, Vol.4, Issue.1, pp.31-39.
[8] R.U. Kiran, M. Kitsuregawa, P.K. Reddy, “Efficient Discovery of Periodic-Frequent Patterns in Very Large Databases” Journal of Systems and Software, 2015.
[9] A. Surana, R.U. Kiran, P.K. Reddy “An efficient approach to mine periodic frequent patterns in transactional databases” Cao, L., Huang, J.Z., Bailey, J., Koh, Y.S., Luo, J. (eds.) PAKDD Workshops 2011, Springer, Heidelberg (2012), LNCS, Vol.7104, pp.254–266, 2012.
[10] V.M. Nofong “Discovering Productive Periodic Frequent Patterns in Transactional Databases”, Springer-Verlag Berlin Heidelberg, 2016.
[11] R.U. Kiran, M. Kitsuregawa “Discovering quasi-periodic-frequent patterns in transactional databases” Bhatnagar V, Srinivasa S (eds) BDA 2013, LNCS, Springer International Publishing, Heidelberg, pp97–115, 2013.
[12] V. Kumar, V. Kumari “Incremental mining for regular frequent patterns in vertical format” International Journal of Engineering Technology, Vol.5, Issue.2, pp.1506–1511, 2013.
[13] M.J. Zaki “Parallel and distributed association mining: A survey” IEEE concurrency, pp. 14-25, 1999.
[14] T. Hu, S.Y. Sung, H. Xiong, Q. Fu “Discovery of Maximum Length Frequent Itemsets” Information Sciences, pp.69–87, 2008.
[15] N.Sethi, P.Sharma “Mining Frequent Pattern from lLarge Dynamic Database Using Compacting Data Sets” International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.3, pp.31–34, 2013.

S. Rana, "Analysis of Regular-Frequent Patterns in Large Transactional Databases," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1-5, 2018.

The image enhancement is the most prominent topic among researchers to introduce more amendments in this domain. The image enhancement covers a large number of techniques, mechanisms and ways to enhance the image. The contrast enhancement or to improve the brightness of the image is one of the way to improve the quality of the image. This study develops a novel approach for image contrast enhancement by considering HSI color model to extract the Hue, Saturation and Intensity of the image. Then the fuzzy inference system is applied to improve the intensity of the image pixels. The simulation is done by considering a set of four different images. The performance of the proposed work is evaluated in the terms of Detail Variance and Background Variance. The proposed work is compared with the traditional GLE (Global Local Image Enhancement), Enhanced AHE (Adaptive Histogram Equalization) and Original Image. The simulation results delineates that the proposed work performs outstanding in comparison to the tradition GLE, Enhanced AHE and original image.

Image Enhancement, Contrast Enhancement, Color Model, HIS Model, Fuzzy Inference Model

[1]. Anureet Kaur et al, “Region of Interest based Contrast Enhancement Techniques for CT images”, 2016 Second International Conference on Computational Intelligence & Communication Technology, Pp. 60-63, 2016.
[2]. Akshay Girdhar, Savita Gupta and Jaskaran Bhullar, “Region Based Adaptive Contrast Enhancement of Medical Ultrasound Images”, Computational Intelligence & Communication Technology (CICT), 2015 IEEE International Conference on, February 2015.
[3]. A. Djerouni, H. Hamada, and N. Berrached, “MR imaging contrast enhancement and segmentation using fuzzy clustering”, International Journal of Computer Science Issues, Vol. 8, No 2, Pp. 392-401, July 2011.
[4]. Makandar, A., & Halalli, B, “Image enhancement techniques using highpass and lowpass filters”, International Journal of Computer Applications, Vol 109, Issue 14, 2015.
[5]. Chin Yeow Wong, Shilong Liu, San Chi Liu, Md Arifur Rahman, Stephen Ching-Feng Lin, Guannan Jiang, Ngaiming Kwok and Haiyan Shi, “Image contrast enhancement using histogram equalization with maximum intensity coverage”, Journal of Modern Optics, Vol. 63, No. 16, Pp. 1618-1629, March 2016.
[6]. Cao, G., Huang, L., Tian, H., Huang, X., Wang, Y., & Zhi, R., “Contrast enhancement of brightness-distorted images by improved adaptive gamma correction”. Computers & Electrical Engineering, 2017.
[7]. Navdeep Kanwal, Akshay Girdhar and Savita Gupta, “Region Based Adaptive Contrast Enhancement of Medical X-Ray Images”, Bioinformatics and Biomedical Engineering, (iCBBE) 2011 5th International Conference on, May 2011.
[8]. Namita Naik,, “Low Contrast Image Enhancement using Wavelet Transform based Algorithms: A Literature Review” International Journal of Engineering and Technical Research (IJETR) ISSN: 2321-0869, Vol.-3, No.6, Pp 123-128, June 2015.
[9]. Ritika and Sandeep Kaur, “Contrast Enhancement Techniques for Images– A Visual Analysis”, International Journal of Computer Applications, Vol. 64, No. 1, Pp. 20-25, February 2013.
[10]. Kaur, R., & Kaur, S. “Comparison of contrast enhancement techniques for a medical image”. In Emerging Devices and Smart Systems (ICEDSS), IEEE, Pp 155-159, 2016.
[11]. Vijay A. Kotkar and Sanjay S. Gharde, “Review Of Various Image Contrast Enhancement Techniques”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 2, No. 7, Pp. 2786-2793, July 2013 .
[12]. Gopi.P.C, Sharmila.R, Indhumathi.T and Savitha.S, “An Intelligent New Age Method of Image Compression and Enhancement with Denoising for Bio-Medical Application”, IJSRCSE, Vol 1, Issue 4, Pp 12-16, 2013.

Mehzabeen Kaur, Baljit Singh Khehra, "An improved image enhancement approach with HSI color Fuzzy decision modelling," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.6-13, 2018.

Reputation is considered as the significant reliability index for measuring the degree of participation rendered by each mobile node towards co-operation. Reputation decreases drastically under the influence of byzantine nodes as they intentionally drop maximum number of packets. Byzantine nodes decrease packet delivery rate and throughput of the network in spite of consuming network resources. The existing Bayesian conditional probability based mitigation approaches fail to utilize the benefits of conditional probabilistic distributions like Poisson and Hyper-exponential distribution for detection. This paper presents an Integrated Poisson and Hyper-exponential Bayesian Probabilistic Factor-based Mitigation Mechanism (IPHBFMM) for investigating the influence of byzantine nodes and mitigate them for ensuring better routing process. IPHBFMM is potential in combining two independent variables for discriminating co-operative nodes from byzantine nodes based on past and present behaviour. Simulation results proved that the energy consumptions and communication overhead of IPHBFMM is excellently minimized by 26% and 34% compared to the existing Bayesian probability-oriented techniques considered for analysis.

Byzantine nodes, Poisson Factor, Hyper-Exponential distribution, Bayesian Probability. Co-operative Packet Forwarding Factor, Packet Forwarding Normalization Factor

[1] Buttyan, L and Hubaux, J-P, (2003) ‘Stimulating Coperation in Self–organizing Mobile Ad hoc Networks’, MONET Journal of Mobile Computing and Networking, Vol. 8, No. 1, pp. 579-592.
[2] Marti, S, Gulli, T.J, Lai, K and Baker, M, (2000) ‘Mitigating routing misbehaviour in mobile ad hoc networks Mobile Computing and Networking’, in Proc., 6th ACM Annual International Conference on Mobile Computing and Network (ACM-MobiCom), Boston, USA, Vol. 1, No. 1, pp. 255-265.
[3] Pusphalatha, M, Revathy, V, Rama Rao, P, (2009), ‘Trust based Energy aware reliable reactive protocol in mobile ad hoc networks’, World Academy of Science, Engineering and Technology, vol. 3, no. 27, pp. 335-338.
[4] Rizvi, S and Elleithy, M, (2009) ‘A new scheme for minimizing malicious behavior of mobile nodes in Mobile Ad Hoc Networks’, Internation Journal of computer Science and Information Security, Vol.. 3, No. 1, pp. 25-34.
[5] Michiardi, P and Molva, R, (2002) ‘CORE: a collaborative reputation mechanism to enforce node cooperation in mobile ad hoc networks’, in Proc., 6th IFIP Conf. on Security, Communications and Multimedia, Protoroz, Solvenia, vol. 228, no. 1, pp. 107-121.
[6] S. Goswami and S. Das, (2014),”A probabilistic approach to detect selfish node in MANET," International Journal of Computer Applications, vol. 3, no. 1, pp.23-26.
[7] B. Wang, S. Soltani, J. K. Shapiro, and P. T. Tan, Local detection of selfish routing behavior in ad hoc networks," in Proceedings of International Symposium on Parallel Architectures, Algorithms and Networks, vol. 2, no. 3, pp 23-34, December 2005.
[8] Buchegger, S and Boudec, J-Y, (2002), ‘Performance Analysis of the CONFIDANT protocol: Cooperation of Nodes – Fairness in Distributed Ad-hoc Networks’, in Proc., 3rd ACM International Symposium on Mobile ad hoc Networking and Computing (MobiHoc ’02), New York, USA, Vol. 1, No. 1, pp. 226-236.
[9] Kargl, F, Klenk, A,.Schlott, S and Weber, M, (2004), ‘Advanced Detection of selfish or Malicious Nodes in Ad hoc Networks’, in Proc., First European Workshop on Security in Ad-Hoc and Sensor Network (ESAS 2004), Heidelberg, Germany, Vol. 1, No. 1, pp. 255-263.
[10] B. Kailkhura, Y. S. Han, S. Brahma and P. K. Varshney, (2015) "Asymptotic Analysis of Distributed Bayesian Detection with Byzantine Data," in IEEE Signal Processing Letters, vol. 22, no. 5, pp. 608-612.
[11] C. Hortelano, T. Calafate, J. C. Cano, M. de Leoni, P. Manzoni, and M. Mecella,(2010), “Black-hole attacks in p2p mobile networks discovered through Bayesian Filters," Proceedings of OTM Workshops, vol. 2, no. 6, pp. 543-552.
[12] B.G. Chun, K. Chaudhuri, H. Wee, M. Barreno, C. H. Papadimitriou, and J. Kubiatowicz, Sel_sh caching in distributed systems: A game-theoretic analysis," in Proceedings of the 23th Annual ACM Symposium on Principles of Distributed Computing, vol. 7, no. 4, pp. 21-30, November 2004.
[13] B. Wang, S. Soltani, J. K. Shapiro, and P. T. Tan, “Local detection of selfish routing behavior in ad hoc networks," in Proceedings of International Symposium on Parallel Architectures, Algorithms and Networks, vol. 2, no.3, pp 23-34, December 2005.
[14] Chen, T.M, Varatharajan, V, (2009), ‘Dempster-Shafer Theory for Intrusion Detection in Ad Hoc Networks’, IEEE Internet Computing, vol. 3, no. 1, pp 234-241.
[15] Sengathir, J., & Manoharan, R. (2015). Exponential Reliability Coefficient based Reputation Mechanism for isolating selfish nodes in MANETs. Egyptian Informatics Journal, 16(2), 231-241.
[16] N. M. Webb, J. Richard, Shavelson, and E. H. Haertel, (2006), “Reliability Coefficients and Generalizability Theory”, Handbook of Statistics. Elsevier press, vol. 26.
[17] Steutel, F. W, and Harn, V. K, (2004).” Infinite divisibility of probability distributions on the real line”. MarcerDekkar, 2004.
[18] Annapourna, P Patil, Rajani Kanth, Bathey Sharanya,Dinesh Kumar, M.P, Malavika, J, (2011) ‘Design of Energy Efficient Routing protocols for MANETs’, International Journal of Computer Science Issues, vol. 8, no. 1, pp. 215-220.
[19] Hernandez-Orallo, Manuel, D, Serraty, Juan-Carlos Cano, Calafate, T and Manzoni’s, (2012) ‘Improving Selfish Node Detection in MANETs Using a collaborative Watchdog’, IEEE Communication Letters, Vol. 16, No.5, pp.
[20] Sengathir, J., & Manoharan, R. (2015). A futuristic trust coefficient-based semi-Markov prediction model for mitigating selfish nodes in MANETs. EURASIP Journal on Wireless Communications and Networking, 2015(1).

V.Vijayagopal, K.Prabu, "Integrated Poisson and Hyper-exponential Bayesian Probabilistic Factor-oriented Efficient Routing Mechanism for MANETs," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.14-21, 2018.

Today manipulation of digital images has become easy due to powerful computers, advanced photo-editing software packages and high resolution capturing devices. Verifying the integrity of photo without any kind of special watermark or any prior knowledge is a critical issue. Photograph tampering techniques like copy-paste, which is very easy and effective to use, can extend image forging. The original content of the picture is copied to the desired locations. The increasing image modification software can easily manipulate the digital photo without leaving any visible clue. It’s important to study these issues because tempered photographs can cause social chaos, criminal and non-public consequences. It’s very important and additionally tough to discover the digital photograph forgeries. The main focus of this paper is to detect picture replica circulate forgery which is depended on SIFT (scale invariant feature transform) descriptors, which are invariant to rotation, scaling etc. Clustering algorithm is used for clustering of key points in images. Results show that, in comparison of existing methods MROGH, SURF-PHA provides consistent precision, recall and F1 score about 98.86%, 99.40%, and 99.13% respectively for the provided dataset. Experimental results indicate that this method is a robust method in detecting the copy-move forgery quickly and withstands certain transformations.

Copy-move Image Forgery, Forgery Detection, Feature Extraction, key-points, SIFT, Clustering

[1] https://www.researchgate.net/figure/Example-of-copy-move-forgery-a-Original-image-b-Forged-image-duplicated-object-h_fig1_317495890.
[2] V. Christlein, C. Riess, J. Jordan, C. Riess, and E. Angelopoulou, “An evaluation of popular copy-move forgery detection approaches,” Information Forensics and Security, IEEE Transactions on, vol. 7, no. 6, pp. 1841–1854, 2012.
[3] O. M. Alqershi, B. E. Khoo, “Passive detection of copy-move forgery in digital images: State-of-the-art,” Forensic Science International, vol. 231, no. 1, pp. 284–95, 2013.
[4] M. Emam, Q. Han, and X. Niu, “PCET based copy-move forgery detection in images under geometric transforms,” Multimedia Tools and Applications, vol. 75, no. 18, pp. 11513-11527, 2016.
[5] J. C. Lee, “Copy-move image forgery detection based on Gabor magnitude,” Journal of Visual Communication and Image Representation, vol. 31, pp. 320-334, 2015.
[6] I. Amerini, L. Ballan, R. Caldelli, A. Del Bimbo, and G. Serra, “A siftbased forensic method for copy–move attack detection and transformation recovery,” Information Forensics and Security, IEEE Transactions on, vol. 6, no. 3, pp. 1099–1110, 2011.
[7] B. Shivakumar and L. D. S. S. Baboo, “Detection of region duplication forgery in digital images using surf,” IJCSI International Journal of Computer Science Issues, vol. 8, no. 4, pp. 199-205, 2011.
[8] H. Huang, W. Guo, and Y. Zhang, “Detection of copy-move forgery in digital images using sift algorithm,” in Computational Intelligence and Industrial Application, 2008. PACIIA’08. Pacific-Asia Workshop on, vol. 2. IEEE, 2008, pp. 272–276.
[9] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International journal of computer vision, vol. 60, no. 2, pp. 91–110, 2004.
[10] X. Pan and S. Lyu, “Detecting image region duplication using sift features,” in Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on. IEEE, 2010, pp. 1706–1709.
[11] Emam , Mahmoud, et al. "A robust detection algorithm for image Copy-Move forgery in smooth regions." Circuits, System and Simulation (ICCSS), 2017 International Conference on. IEEE , 2017.
[12] Yadav, Preeti, and Yogesh Rathore. "Detection of copy-move forgery of images using discrete wavelet transform." International Journal on Computer Science and Engineering4.4 (2012): 565.
[13] Jaberi, Maryam, et al. "Improving the detection and localization of duplicated regions in copy-move image forgery." Digital Signal Processing (DSP), 2013 18th International Conference on. IEEE, 2013.
[14] Y. Lu, Y. Wan, Clustering by sorting potential values (CSPV): A novel potential-based clustering method, Pattern Recognition, 45(9) (2012), pp. 3512–3522.

Pooja Devi, Suman Deswal, "Clustering Based Feature Extraction for Image Forgery Detection," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.22-27, 2018.

The most important aspiration of present study is to make a mathematical and simulation modeling for magnetic effect of blood within undersized artery. Power law fluid characterizes the non-Newtonian quality of blood. The dynamical functioning of the blood flow is affected by the occurrence of the magnetic effects. The problem is worked out with analytical procedures with facilitate of boundary conditions and consequences are put on show graphically for different flow uniqueness like pressure drop, blood velocity, shear stress, etc. For the justification of mathematical model, the computation outcomes are compared with consequences from published text. In this article blood flow uniqueness are calculated for a precise set of values of the diverse factors concerned in the model examination and presented graphically. Few obtained outcome indicate that the flow characteristics in converging region, diverging region, and nontapered region are efficiently influenced by the occurrence of magnetic electrically field and justify inclination of artery and magnetic area respectively.

Non-Newtonian flow, Artery, Magnetic field, Shear stress, Velocity.

[1] F. Abraham, M. Behr, M. Heinkenschloss, “Shape optimization in steady blood flow: a numerical investigation of non-Newtonian cause”, Computer Methods in Biomechanics & Biomedical Engineering, Vol.8, Issue.2, pp. 127–137, 2005.
[2] A. Kumar, G.C. Sharma, C.L.Varshney, “Performance modeling & investigation of blood flows in elastic artery”, Applied Mathematics & Mechanics, Vol.26, Issue.3, pp. 345-354, 2005.
[3] A. Kumar, “Finite Element Galerkin’s scheme for flow in blood vessels with magnetic effects”, International Journal of Applied Systemic Studies, Vol.2, Issue3, pp. 284-293, 2009.
[4] S. Chakravarty, K.L. Kelvin, A. Ikbal, P.K. Mazumdar, J. Wong, “Unsteady reaction of Non-Newtonian blood flow by a stenosed narrow vessel in electrically field”, Journal of Computational & Applied Mathematics, Vol.230, Issue.1, pp. 243–259, 2009.
[5] K. Das, G.C. Saha, “Arterial MHD Pulsatile flow of blood influnced by periodic body acceleration”, Bul. Soc. Math. Banja Luka, Vol.16, pp. 21-42, 2009.
[6] V.P. Rathod, S. Tanveer, “Pulsatile Flow of paired Stress Fluid through Porous system influnced by Periodic Body Acceleration & Electrically flow Field”, Bulletin of the Malaysian Mathematical Sciences Society, Vol.32, Issue.2, pp. 245–259, 2009.
[7] J. Singh, R. Rajbala, “Analytical result of 2-Dimensional Model of Blood Flow with Variable Viscosity through an Indented Artery due to LDL effect in the occurrence of Magnetic Field”, International Journal of the physical sciences, Vol.5, Issue.12, pp.1857-1868, 2010.
[8] K. Wong, “Modeling of Blood fluid flow resistance for an atherosclerotic artery with manifold stenosis & Poststenotic dilatations”, ANZIAM Journal, Vol.51, pp.66-82, 2010.
[9] A. Kumar, “Performance & analysis of blood flow through carotid artery”, International Journal of Engineering & Business Management, Vol.3, Issue.4, pp.1-6, 2011.
[10] A. Kumar, “Performance Model and investigation of Blood Flow in Small Vessels with Magnetic Effects”, IJE, Vol.25, Issue.2, pp.190-196, 2012.
[11] I. Eldesoky, “Slip Effects on the Unsteady MHD Pulsatile Blood Flow through Porous system in an Artery influnced by Body Acceleration”, H.P.C., Interernational Journal of Math and Mathematical Sciences, Article ID 860239, 26 pages, 2012.
[12] A. Kumar, “Performance Modeling & Mechanical Behavior of Blood Vessel in the survival of Magnetic Effects”, African Journal of Basic & Applied Sciences, Vol.5, Issue.3, pp.149-155, 2013.
[13] N. Srivastava, “Research article on “study of flow properties of the Blood through an Inclined Tapered Porous Artery system with Mild Stenosis Influenced by Inclined Magnetic Field”, H.P.C., Journal of Biophysics, Art. ID 797142, 9 pages, 2014.
[14] A. Aiman, T. Bourhan, “Simulations of Magnetohemodynamics in Stenosed Arteries in Diabetic or Anemic Models”, Computational & Mathematical Methods in Medicine, Art. ID 8123930, 13 pages, 2016.
[15] T. Blessy, K.S. Sumam, “Blood Flow in Human Arterial System-A Review”, ICETEST & Procedia Technology, Vol.24, pp.339–346, 2016.
[16] V. Mwanthi, E. Mwenda, K.K. Gachoka, “Velocity Profiles of Unsteady Blood Flow through an Inclined Circular Tube with Magnetic Field”, Journal of Advances in Mathematics and Computer Science, Vol.24,Issue.6, pp.1-10, 2017.

Sanjeev Kumar Sharma, Jyoti Singh Raghav, Anil Kumar, "Modeling of Blood Flow Through Artery With Magnetic Effects," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.28-36, 2018.

Hadoop Distributed File System (HDFS) is highly a fault-tolerant distributed file system associated with Hadoop framework. HDFS can handle a large amount of data known as big data. HDFS deals with data archival as well. Data archiving is a phenomenon which finds inactive data and moves it into a separate storage premise. Cloud-based storage facilitates it cost-effectively while Hadoop clusters provide the computational power required. However, protecting archived data is the main concern of the data owner point of view. Erasure Coding (EC) is a method which has the mechanism to regain lost data as well. Of late aHDFS was developed to have special data archival features with EC. The problem with it is that it needs similar the computational cost for data of different sizes. Towards this end, we proposed a methodology to overcome this problem. A model application has built to exhibit evidence of the idea. The empirical results revealed that the methodology presented improves the computational efficiency in rendering data archival services.

Hadoop, HDFS, Data archival system, Erasure codes

[1] D. Borthakur, “The hadoop distributed file system: Architecture and design, 2007,” Apache Software Foundation, 2012
[2] S. S. Miller, M. S. Shaalan, and L. E. Ross, “Correspondent-centric management email system uses message-correspondent relationship data table for automatically linking a single stored message with its correspondents,” Sep. 2 2003, uS Patent 6,615,241
[3] N. Madhusudhana Reddy, Dr. C. Nagaraju, Dr. A. AnandaRao, “Toward Secure Computations in Distributed Programming Frameworks: Finding Rogue nodes through Hadoop logs”, JATIT (Journal of Theoritical and Applied Information Technology),ISSN No: 1992-8645, Vol95, No 23, December 2017, Page Nos: 6398-6409
[4] O. Khan, R. C. Burns, J. S. Plank, W. Pierce, and C. Huang, “Rethinking erasure codes for cloud file systems: minimizing i/o for recovery and degraded reads.” in FAST, 2012, p. 20
[5] R. T. Kaushik and K. Nahrstedt, “T: a data-centric cooling energy costs reduction approach for big data analytics cloud,” in Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis. IEEE Computer Society Press, 2012, p. 52
[6] R. Gupta, H. Gupta, U. Nambiar, and M. Mohania, “Efficiently querying archived data using hadoop,” in Proceedings of the 19th ACM international conference on Information and knowledge management. ACM, 2010, pp. 1301–1304.
[7] L. L. You, K. T. Pollack, and D. D. Long, “Deep store: An archival storage system architecture,” in Data Engineering, 2005. ICDE 2005. Proceedings. 21st International Conference on. IEEE, 2005, pp. 804–815.
[8] T. J. Schwarz, Q. Xin, E. L. Miller, D. D. Long, A. Hospodor, and S. Ng, “Disk scrubbing in large archival storage systems,” in Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, 2004.(MASCOTS 2004). Proceedings. The IEEE Computer Society’s 12th Annual International Symposium on. IEEE, 2004, pp. 409–418
[9] Z. Ren, J. Wan, W. Shi, X. Xu, and M. Zhou, “Workload analysis, implications, and optimization on a production hadoop cluster: A case study on taobao,” Services Computing, IEEE Transactions on, vol. 7, no. 2, pp. 307–321, 2014
[10] I. S. Reed and G. Solomon, “Polynomial codes over certain finite fields,” Journal of the society for industrial and applied mathematics, vol. 8, no. 2, pp. 300–304, 1960.
[11] M. Ovsiannikov, S. Rus, D. Reeves, P. Sutter, S. Rao, and J. Kelly, “The quantcast file system,” Proceedings of the VLDB Endowment, vol. 6, no. 11, pp. 1092–1101, 2013.
[12] J. Huang, Y. Wang, X. Qin, X. Liang, S. Yin, and C. Xie, “Exploiting pipelined encoding process to boost erasure-coded data archival,” Parallel and Distributed Systems, IEEE Transactions on, vol. 26, no. 11, pp. 2984–2996, 2015
[13] S. Quinlan and S. Dorward, “Venti: A new approach to archival storage.” in FAST, vol. 2, 2002, pp. 89–101.
[14] M. Sathiamoorthy, M. Asteris, D. Papailiopoulos, A. G. Dimakis, R. Vadali, S. Chen, and D. Borthakur, “Xoring elephants: Novel erasure codes for big data,” vol. 6, no. 5, pp. 325–336, 2013.
[15] J. Wang, P. Shang, and J. Yin, “Draw: A new data-grouping-aware data placement scheme for data intensive applications with interest locality,” in Cloud Computing for Data-Intensive Applications. Springer, 2014, pp. 149–174
[16] L. Pamies-Juarez, F. Oggier, and A. Datta, “Decentralized erasure coding for efficient data archival in distributed storage systems,” in Distributed Computing and Networking. Springer, 2013, pp. 42–56.
[17] J. C. Chan, Q. Ding, P. P. Lee, and H. H. Chan, “Parity logging with reserved space: Towards efficient updates and recovery in erasurecoded clustered storage,” in Proceedings of the 12th USENIX Conference on File and Storage Technologies (FAST 14), 2014, pp. 163–176.
[18] Sunita Choudhary; Preeti Narooka “Hugepage & Swappiness functions for optimization of the search graph algorithm using Hadoop framework”: 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC).
[19] Youngho Song; Young-Sung Shin; Miyoung Jang; Jae-Woo Chang “Design and implementation of HDFS data encryption scheme using ARIA algorithm on Hadoop” : 2017 IEEE International Conference on Big Data and Smart Computing (BigComp)
[20] Yasser Altowim; SharadMehrotra “Parallel Progressive Approach to Entity Resolution Using MapReduce”: 2017 IEEE 33rd International Conference on Data Engineering (ICDE).
[21] Yuanqi Chen, Yi Zhou, Shubbhi Taneja, Xiao Qin, Senior Member, IEEE, Jianzhong Huang, “aHDFS: An Erasure-Coded Data Archival Systemfor Hadoop Clusters”, IEEE Transactions on Parallel and Distributed Systems, 2017

M. Mounica, A. Ananda Rao, P. Radhika Raju, "PABR Algorithm for Improving The Data Archival Performance of aHDFS," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.37-42, 2018.

The term education data mining deals with extracting knowledge out of academic database which can be used for providing suitable patterns to education managers, teachers, and students. Education is a progressing field and students need to put in extra efforts to keep the right move towards learning. This paper presents on approach to study the student data and implementing various data mining classification algorithms. Thus, finding out the best algorithm, that can help in evaluating the final grade of a student and finding the best fit for identification of possible results beforehand, so that appropriate interventions can be planned. For our research we collected the data from a reputed higher education institute related to a set of students pertaining to their current and previous academic records. The data were filtered, cleaned, and processed for training different data mining models to define classifications based on different criteria. This method may be considered useful in finding out the students who are at the state of high risk in a very early stage, thus allowing the educationists to provide the appropriate advice to learners in a timely manner.

Education Data Mining, academic intervention, Data Classification, pattern identification

[1] C. Tekin, J. Braun, and M. van der Schaar, “etutor: Online learning for personalized education,” in Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2015.
[2] Y. Meier, J. Xu, O.Atan and M.V. D. Schaar” Personalized Grade Prediction: A Data Mining Approach” 2015 IEEE International Conference on Data Mining.
[3] K. H. Rashan, Anushka Peiris, “Data Mining Applications in the Education Sector”, MSIT, Carnegie Mellon University, retrieved on 28/01/2011
[4] A. Dutt, M. A. Ismail, T. Herawan “A Systematic Review on Educational Data Mining” 2169-3536 (c) 2016 IEEE.
[5] S. K. Yadav, S. Pal “ Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification” World of Computer Science and Information Technology Journal (WCSIT) ISSN: 2221-0741 Vol. 2, No. 2, 51-56, 2012.
[6] I. Shingari , D.Kumar” A Survey On Various Aspects Of Education Data Mining In Predicting Student Performance” JASC, June , 2018
[7] A. Auysha1, A. Jayachandran” Online Ensemble Learning of Data Streams with Gradually Evolved” International Journal of Scientific Research in Computer Science, Engineering and Information Technology , Vol 2, Issue 2.
[8] Himanshi, Komal Kumar Bhatia” Prediction Model for Under-Graduating Student’s Salary Using Data Mining Techniques” April, 2018
[9] A.Deepa , E. Chandra Blessie” Input Analysis for Accreditation Prediction in Higher Education Sector by Using Gradient Boosting Algorithm” June, 2018
[10] V. Kumar “Data Mining with R Learning with Case Studies” Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
[11] G. P. eiro, S.Perelman, J. P. Guerschman, J. M. Paruelo “How to evaluate models: Observed vs. predicted or predicted vs. observed?” Elsevier Ecological Modelling • September 2008

Isha Shingari, Dinesh Kumar, "Predicting Student Performance Using Classification Data Mining Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.43-48, 2018.

Detection and localization of active and passive targets using sensor arrays play an important role in the field of array signal processing. In this paper the problem of estimating the direction of arrivals of broad banded linear and quadratic chirp sources using both nested and co-prime arrays is addressed. Traditional uniform arrays can only detect N-1 number of sources with N physical sensors using high resolution beam formers like MUSIC. However the nested and co-prime arrays can detect more number of sources than the number of sensors by exploiting the difference co-array structure based on the correlation of the observations. Difference co-array is the distinct sensor locations obtained by taking all possible pairwise differences of sensor locations in the original array. As the chirp signal, commonly used in both radar and sonar systems is better processed in the fractional Fourier domain, the detection is done using fractional Fourier transform (FrFT). But as the traditional FrFT is limited to the analysis of linear chirps, detection using modified FrFT is found to be the apt choice for quadratic chirps. Subsequently, the direction of arrival estimation is achieved using subspace methods which include MUSIC and minimum-norm in the proposed work. The effectiveness of the algorithm is validated through different signals including real data obtained from a practical sonar array. It is seen from the computer simulations that nested-MUSIC combination has better resolution and accuracy than all other combinations.

Direction of arrival estimation, Fractional Fourier transform, Chirp sources, Nested array, Coprime array

[1] M.A. Richards, “Fundamentals of Radar Signal Processing”, McGraw-Hill Publication, India, 2005.
[2] S. Zhou, P. Willett, “Submarine location estimation via a network of detection only sensors”, IEEE Trans. Signal Process., Vol. 55, Issue 6, pp. 3104 - 3115, 2007.
[3] H. Krim, M. Viberg, “Two decades of array signal processing research: The parametric approach”, IEEE Signal Process. Mag., Vol. 13, pp. 67-94, 1996.
[4] D.H. Johnson, D.E. Dudgeon, “Array Signal Processing Concepts and Techniques”, Prentice-Hall, USA, 1993.
[5] J. Capon, “High-resolution frequency-wavenumber spectrum analysis”, In the Proceedings of the IEEE, USA, pp. 1408-1418, 1969.
[6] R. Schmidt, “Multiple emitter location and signal parameter estimation”, IEEE Trans. on Antennas and Propagation, Vol. 34, Issue 3, pp. 276-280, 1986.
[7] B.D. Rao, K.V.S. Hari, “Performance Analysis of Root-Music”, IEEE Trans. on Acoust., Speech and Signal Process., Vol. 37, Issue 12, pp. 1939-1949, 1989.
[8] H.L. Van Trees, “Detection, Estimation, and Modulation Theory- Part IV, Optimum Array Processing”, Wiley, USA, 2002.
[9] R.Roy, T.Kailath, “Estimation of signal parameters via rotational invariance techniques,” IEEE Trans. Acoust., Speech, Signal Process., Vol. 37, Issue 7, pp. 984-995, 1989.
[10] J.R. Klauder, A.C. Price, S. Darlington, W.J. Albersheim, “The theory and design of chirp radars”, Bell Syst. Tech. Jour., Vol. 39, Issue 4, pp. 745-808, 1960.
[11] P.H. Leong, T.D. Abhayapala, T.A. Lamahewa, “Multiple target localization using wideband echo chirp signals”, IEEE Trans. Signal Process., Vol.61, Issue 16, pp. 4077-4089, 2013.
[12] I.S. Yetik, A. Nehorai, “Beamforming using the fractional Fourier transform”, IEEE Trans. Signal Process., Vol. 51, pp. 1663-1668, 2003.
[13] A.A. Winder, “Sonar system technology”, IEEE Trans. Sonics Ultrason., Vol. 22, Issue 5, pp. 291-332, 1975.
[14] M. Wax, T.J. Shan, T. Kailath, “Spatio temporal spectral analysis by eigenstructure methods”, IEEE Trans. on Acoust., Speech and Signal Process., Vol. 32, Issue 4, pp. 817-827, 1984.
[15] G. Su, M. Morf, “The signal subspace approach for multiple wideband emitter location”, IEEE Trans. on Acoust., Speech and Signal Process., Vol. 31, Issue 6, pp.1502-1522, 1983.
[16] H. Wang, M. Kaveh, “Coherent signal-subspace processing for the detection and estimation of angles of arrival of multiple wide-band sources”, IEEE Trans. Acoust., Speech, Signal Process., Vol. 33, Issue 4, pp. 823-831, 1985.
[17] M. A. Doron, A. J. Weiss, “On focusing matrices for wide-band array processing”, IEEE Trans. on Signal Process., Vol. 40, Issue 6, pp. 1295-1302, 1992.
[18] E.D. Claudio, R. Parisi, “WAVES: weighted average of signal subspaces for robust wideband direction finding”, IEEE Trans. on Signal Process., Vol. 49, Issue 10, pp. 2179-2191, 2001.
[19] Piya Pal, P.P. Vaidyanathan, “A novel autofocusing approach for estimating directions of arrival of wideband signals”, In the Proceedings of the 43rd Asilomar Conference on Signals, Systems and Computers, USA, pp. 1663-1667, 2009.
[20] R. Jacob, T. Thomas, A. Unnikrishnan, “Applications of fractional Fourier transform in sonar signal processing”, IETE Jour. Res. Vol. 55, pp. 16-27, 2009.
[21] S. Sahay, D. Pande, V. Gadre, P. Sohani, “A novel generalized time-frequency transform inspired by the fractional Fourier transform for higher order chirps”, In the Proceedings of the International Conference on Signal Processing (SPCOM), INDIA, pp. 1-5, 2012.
[22] S. Sahay, T. Meghasyam, R.K. Roy, G. Pooniwala, S. Chilamkurthy, V. Gadre, “Parameter estimation of linear and quadratic chirps by employing the fractional Fourier transform and a generalized time frequency transform”, Sadhana, Vol. 40, Issue 4, pp. 1049-1075, 2015.
[23] Z. K. Peng, G. Meng, Z. Q. Lang, F. L. Chu, W. M. Zhang, Y. Yang, “Polynomial chirplet transform with application to instantaneous frequency estimation”, IEEE Trans. Meas. and Instrum., Vol. 60, Issue 9, pp. 3222-3229, 2011.
[24] S. Mann, S. Haykin, “The chirplet transform: Physical considerations”, IEEE Trans. Signal Process., Vol. 43, pp. 2745-2761, 1995.
[25] P. Pal, P. Vaidyanathan, “Nested arrays: A novel approach to array processing with enhanced degrees of freedom”, IEEE Trans. Signal Process., Vol. 58, Issue 8, pp. 4167-4181, 2010.
[26] P. Vaidyanathan, P. Pal, “Sparse sensing with co-prime samplers and arrays”, IEEE Trans. Signal Process., Vol. 59, Issue 2, pp. 573-586, 2011.
[27] Namias, “The fractional order Fourier transform and its application to quantum mechanics”, J. Inst. Math. Appln., Vol. 25, pp. 241-265, 1980.
[28] H.M. Ozaktas, O. Arkan, M.A. Kutay, G. Bozdagi, “Digital computation of the fractional Fourier transform”, IEEE Trans. Signal Process., Vol. 44, Issue 9, pp. 2141-2150, 1996.
[29] H. M. Ozaktas, M. A. Kutay, Z. Zalevsky, “The fractional Fourier transform with applications in optics and signal processing”, Wiley, USA, 2000.
[30] Y. Cui, K. Liu, J. Wang, “Direction of arrival estimation of coherent wideband LFM signals in multipath environment”, In the Proceedings of the International Conf. on Signal Process., USA, pp. 58-61, 2010.
[31] X. Jin, T. Zhang, J. Bai, D. Zhao, “DoA estimation of coherent wideband LFM signals based on fractional Fourier transform and virtual array”, In the Proceedings of the IEEE CISP, USA, pp. 4380-4384, 2010.
[32] C. Capus and K. Brown, “Short-time fractional Fourier methods for the time-frequency representation of chirp signals”, J. Acoust. Soc. Amer., Vol. 113, Issue 6, pp. 3253-3263, 2003.
[33] C. Capus, Y.Rzhanov, L.Linnett, “Analysis of multiple linear chirp signals”, In the Proceedings of the IEE Symposium on Time-Frequency Analysis and Applns., London, pp. 41-47, 2000.
[34] C.L. Liu and P.P. Vaidyanathan, “Remarks on the spatial smoothing step in coarray MUSIC”, IEEE Signal Process. Lett., Vol. 22, Issue 9, pp. 1438–1442, 2015.
[35] G. Sreekumar, Leena Mary and A. Unnikrishnan, “Beam-forming of broad-band QFM sigals using generalised time-frequency transform”, Elseiver Signal Process., submitted.
[36] S. Bardhan, D. Rajapan, S. Zacharia, M. Eldhose, P.M. Rajeshwari, D.S. Sreedev, C. Kannan , S. Jacob and M.A. Atmanand, “Detection of buried objects using active sonar”, IEEE-UT, 2015.
[37] S. Bardhan and S. Jacob, “Experimental observation of DoA estimation algorithms in a tank environment for sonar application”, In the Proceedings of the SYMPOL, INDIA, 2015.

G. Sreekumar, Leena Mary, A. Unnikrishnan, "Doa Estimation of Broad-Banded Linear and Quadratic Chirps Using Nested and Co-Prime Arrays," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.49-57, 2018.

Online Handwritten Signature verification plays a significant role in the field of administrative, banking, business sector, etc. Therefore, an accurate signature verification system is required in order to provide an identification of an individual. A new Online Handwritten Signature verification is proposed based on a Multirate Support Vector Machine (MSVM) and for verification the SUSIG database is used. The input database is obtained from the pressure sensitive tablet, removal of noise and resizing is done through fourth order wavelet and discrete cosine transform. Further, the functional feature such as standard deviation, skewness etc. are extracted and processed to MSVM for generation of threshold value between genuine and sample signature. The obtained result is more sensitive, specific and accurate. The Equal Error rate (EER) of 0.33 is obtained, so that the proposed system shows competitive performance with the other existing approaches.

Online Handwritten Signature Verification,(OHSV), Multirate Support Vector Model (MSVM), Discrete, Wavelet Transform (DWT), Discrete Cosine Transformation (DCT), Feature Extraction, Forgery, Threshold value

[1] Nitin Tiwari, "An Overview and Analysis Based on Biometric Framework Technique and Fingerprint Biometric Technology", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.69-74, 2017
[2] Abughfa, A.B. Elmadani, "Offline Signature Verification Based on Image Processing and Hu Moment", International Journal of Scientific Research in Network Security and Communication, Vol.4, Issue.5, pp.1-7, 2016.
[3] EdigaLingappa, Geetavani.B, JambulaHareesha , "Online Signature Verification using Dynamic Properties", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.33-38, 2017.
[4] Kashi R, Hu J, Nelson W L and Turin W “Hidden Markov Model Approach to On-Line Handwritten Signature Verification”, Conf. Document Analysis and Recognition 4: pp.253-257, 1997.
[5] Alister K and Yanikoglu B, identity Authentication Using Improved On-Line Signature Verification Method. Pattern Recognition Letters 26: pp. 2400-2408,2005.
[6] Fierrez, J, Garcia J O, Ramos D and Rodriguez J G “HMM based on-line signature verification: Feature extraction and signature modelling
‘,. Pattern Recognition Letters. 28: pp.2325–2334 2007.
[7] Kholmatov A and Yanikoglu B “Identity authentication using improved Online Handwritten Signature verification method”, Pattern Recognition. Letter 26: pp. 2400–2408, 2005.
[8] Kour J, Hanmandlu, M and Ansar, A Q “Online Handwritten Signature verification using GA-SVM”, International Conference on Image Information Processing (ICIIP 2011), pp. 1 – 4, 2011.
[9] Durrani M Y, Khan S and Khalid S, “VerSig: a new approach for Online Signature verification”, Cluster Computing, pp. 1-11, 2017
[10 Ibrahim M T, Kyan M and Guan L “On-line signature verification using global features”, Canadian conference on Electrical and Computer Engineering, (CCECE`09), pp. 682-685, 2009.
[11] Song X, Xia X. and Luan F “Online Handwritten Signature verification based on stable features extracted dynamically”, IEEE Transactions on Systems, Man and Cybernetics: Systems 47: pp.2663-2676, 2017.
[12] Guru D S and Prakash H N “Online Handwritten Signature verification and recognition: An approach based on symbolic representation”, IEEE transactions on pattern analysis and machine intelligence 31: pp.1059 – 1073, 2009.
[13] Jain A K, Griess F D and Connell S D “On-line signature verification”, Pattern Recognition 35: pp. 2963– 2972, 2002.
[14] Zalasiński M “New algorithm for on-line signature verification using characteristic global features”, In: Information Systems Architecture and Technology: Proceedings of 36th International Conference, pp. 137- 146, 2016.
[15] Khalil M I, Moustafa, M and Abbas H M “Enhanced DTW based on-line signature verification”, In: Conf. Image Processing (ICIP) 16: pp. 2713-2716,2009.
[16] Vaseghi B, and Hashemi, S “Online Handwritten Signature Verification Using Vector Quantization and Hidden Markov Model”, IOSR Journal of Electronics and Communication Engineering (IOSR- JECE)10: pp.48-53,2015.
[17] Draouhard J P, Sabourin R and Godbout M “A neural network approach to Off-line Signature verification using directional PDF”, Pattern Recognition 29: pp.415-424, 1996.
[18] Salvador and Chan P “Toward accurate dynamic time warping in linear time and space”, Intelligent Data Analysis 11: pp. 61-71,2007.
[19] Liu Y, Yang, Z, and Yang, L “Online Handwritten Signature verification based on DCT and sparse representation”, IEEE transactions on cybernetics 45: pp.2498-2511,2015.
[20] García M L, Lara R R, Hurtado O M, Navarro E C “Embedded system for biometric Online Handwritten Signature verification”, IEEE Transactions on industrial informatics 10: pp.491- 501,2014.
[21] Fischer A, Diaz M, Plamondon R.,and Ferrer M A “Robust score normalization for DTW-based on- line signature verification”. Int. conf. Document Analysis and Recognition (ICDAR), pp.241- 245,2015.
[22] Zalasiński M, Cpałka K and Andersson E R. “An idea of the dynamic signature verification based on a hybrid approach”, In: International Conference on Artificial Intelligence and Soft Computing, pp.232-246, 2016.
[23] Aguilar J F, Krawczyk S, Garcia J O and Jain A K, “Fusion of local and regional approaches for on- line signature verification”, In: Proc. Int. Conf. Adv. Biometric Recognition Syst. (IWBRS), pp. 188–196, 2005.
[24] Rehman, A U, Rehman S, Babar Z H, Qadeer M K and Seelro F A “Offline Signature Recognition and Verification System Using Artificial Neural Network”, University of Sindh Journal of Information and Communication Technology 2: pp.73-80, 2018.
[25] Iranmanesh, V, Ahmad S M S, Adnan W A W, Yussof S, Arigbabu O A, and Malallah, F L, “Online Handwritten signature verification using neural network classifier based on principal component analysis”, The Scientific World Journal, pp.1-8, 2014.
[26] Diaz M, Fischer A, Ferrer M A, and Plamondon R. “Dynamic signature verification system based on one-real signature”, IEEE transactions on cybernetics 48: pp.228-239,2018.
[27] Manjunatha K S, Manjunath S, Guru D S, and Somashekara M T “Online Signature verification based on writer dependent features and classifiers”, Pattern Recognition Letters 80: pp.129-136, 2016.
[28] Baraki P and Ramaswamy V “Biometric Authentication of a Person Using Mouse Gesture Dynamics: An Innovative Approach”, In: International Proceedings on Advances in Soft Computing, Intelligent Systems and Application 7: pp. 331-344,2018.
[29] Rohilla S and Sharma A “SVM Based Online Signature Verification Technique Using Reference Feature Vector”, Proceedings of the National Academy of Sciences, India Section A: Physical Sciences 87: pp.125-136,2017.
[30] Roy D, Chowdhury A, Sihnaray A and Ghose A “Novel Handwritten signature verification system based on shadow sensing”, In: IEEE SENSORS, pp. 1-3,2017.
[31] Serdouk Y, Nemmour H and Chibani Y “Handwritten signature verification using the quad-tree histogram of templates and a Support Vector-based artificial immune classification”, Image and Vision Computing 66: pp. 26-35,2017.
[32] Tran V Q, Chew M T, Demidenko S, Kuang Y C, and Ooi M “Simple signature verification sub- system for identity recognition”, In: IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), pp. 94-98,2017.
[33] Kiran G V, Kunte R S R and Samuel S “On-line signature verification system using probabilistic feature modelling”, In: Sixth International Symposium on Signal Processing and its Applications, pp.355- 358,2001.
[34] Khalid M, Mokayed H, Yuso, R, and Ono O 2009 Online Handwritten Signature verification with neural networks classifier and fuzzy inference. In:Third Asia International Conference Modelling & Simulation (AMS`09), pp. 236-241.
[35] Reza A G, Lim H and Alam M J “An Efficient Online Handwritten Signature Verification Scheme Using Dynamic Programming of String Matching”, In: International Conference on Hybrid Information Technology 6935: pp. 590–597,2011.
[36] Gruber C, Gruber T, Krinninger S and Sick B “Online Handwritten Signature verification with support vector machines based on LCSS kernel function”, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 40: pp. 1088-1100,2010.

Prathibha MK, Basavaraj L, "Enhanced Approach on Online Handwritten Signature Verification through Multi rate SVM with Wavelet Transformation," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.58-65, 2018.

In this paper an attempt has been made to review on application of data mining techniques in the field of agriculture. India is the largest producer and consumer of sugar in the world and its most efficient crops in converting solar energy into chemical energy. Sugar-cane is an important commercial crop of the world. About 45 million sugarcane farmers, their dependents and a large agricultural force, constituting 7.5 percent of the rural population, are involved in sugar-cane cultivation, harvesting and ancillary activities. Sugar industries development is backbone to economic development of the nation. In India, Sugar industry is the second largest agro-based industry and it contributes significantly to the socio economic development of the nation. The major Sugar-cane crop growing states in India are Uttar Pradesh, Bihar, Assam, Haryana, Gujarat, Maharashtra, Karnataka and Tamil Nadu. This paper presents state of Karnataka datasets to predict less error rate on better productivity and yield using regression metrics.

Agriculture data, Data mining Techniques, Weka tool,Regression Model

[1] Ajaykumar ,pritee sharma,”Climate change and sugarcane Productivity in india : An Econometric Analysis”,Journal of social and development sciences vol5.No.2 pp-111-122,Jun 2015 .
[2] Camps-Valls G, Gomez-Chova L, Calpe-Maravilla J, Soria-Olivas E, Martin-Guerrero JD, Moreno J., 2003, "Support vector machines for crop classification using hyperspectral data", Lect Notes Comp Sci 2652: pp. 134–141.
[3] Kumar, A., Sharma, P. & Ambrammal, S. K. (2014). “Climatic Effects on Food Grain Productivity in India: A Crop Wise Analysis”, Journal of Studies in Dynamics and Change, 1(1), 38-48.
[4] Sellam,V, Poovammal, E., “Prediction of Crop Yield using Regression Analysis”, Indian Journal of Science and Technology, Vol. 9, issue.38, pp.1- 5, 2016.
[5] Sujatha, R., Isakki, P., “A study on crop yield forecasting using classification techniques”, International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE), pp.1-4, 2016.
[6] Ankalaki, S., Chandra, N., Majumdar, J., “Applying Data Mining Approach and Regression Model to Forecast Annual Yield of Major Crops in Different District of Karnataka”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, issue 2, pp.25-29, 2016.
[7] Kushwaha, A.K., Sweta Bhattachrya, “Crop yield prediction using Agro Algorithm in Hadoop”, International Journal of Computer Science and Information Technology & Security (IJCSITS), Vol. 5, issue.2, pp.271-274, 2015.
[8] Rub, G., “Data Mining of Agricultural Yield Data: A Comparison of Regression Models”, 9th Industrial Conference, Vol.5633, pp.24-37, 2009..
[9] Raorane, A.A., Kulkarni R.V., “Data Mining: An effective tool for yield estimation in the agricultural sector”, International Journal of Emerging Trends & Technology in Computer Science(IJETTCS), Vol. 1, issue 2, pp.75-79, 2012.
[10] Nagarajan,”Sustainable farming practices in sugarcane cultivation”, Kisan world, A journal of agriculture and Rural Development, Vol 40, Jan 2013, pp. 28 – 31.
[11] Nazir A,Jariko,G.A. and Junejo,M.A. (2013) “Factor Affecting Sugarcane Production in Pakistan”Munich Personal RePEc Archive.http://mpra.ub.Uni-menchen.de/50359/.
[12] Dlamini,S Rugambisa,J.I.Masuku,M.B.and Belete,A.(2010),”Technical Efficiency of the small scale sugarcane faramers in Swaziland: A case study of Vuvulane and Big Bed Farmers”, African Journal of Agricultural Research,Vol.5(9).935-940.
[13] Gupta, S., Sen, P. & Srinivasan, S. (2012). “Impact of Climate Change on Indian Economy: Evidence from Food Grain Yields”, Centre for Development Economics Working Paper 218, Delhi.
[14] Srivastava, A. K. & Rai, M. K. (2012). “Sugarcane Production: Impacts of Climate Change and its Mitigation” ,Biodiversitas, 13(4), 214-227.
[15] Kumar, V., Sharma, Y. & Chauhan, S. (2011a). “Impact of Climate Change on the Growth and Production of Saccharum Offcinarum and Magnifera Indica” International Journal of Science Technology and Management, 2(1), 42-47.
[16] Geethalakshmi, V., Lakshmanan, A., Rajalakshmi, D., Jagannathan, R., Sridhar, G., Ramara, Bhuvaneswari, A. P., Gurusamy, K. L. & Anbhazhagan, R. (2011)”Climate Change Impact Assessment and Adaptation Strategies to Sustain Rice Production in Cauvery Basin of Tamil Nadu”, Current Science, 101(03). 342-347.
[17] Masters, G., Baker, P. & Flood, J. (2010)”Climate Change and Agricultural Commodities” CABI Working Paper, 02.
[18] Kalra, N., Chakraborty, D., Sharma, A., Rai, J., Monica, H. K., Subhash, C., Kumar, P., Ramesh, B. S., Barman, D., Mittal, R. B., Lal, M. & Sehgal, M. (2008).”Effect of Increasing Temperature on Yield of Some Winter Crops in Northwest India”, Current Science, 94(1), 82-88.
[19] Kapur, D., Khosla, R. & Mehta, P. B. (2009). “Climate Change: India’s Options”, Economic and Political Weekly, 36(31), 34-42.
[20] Sundar singh and Veeraputhiran,”Enhancing sugarcane productivity”, Kisanworld, A journal of Agriculture and Rural Development’, June 2000, pp. 18-19.
[21] D.Venkatesh,M.Venkateswars :”An overview of the indian sugar industry”, BIMS International Journal of social science research ISSN 2455-4839.

M. Mohanadevi, V. Vinodhini, "Accurate Error Prediction of Sugarcane Yield Using a Regression Model," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.66-71, 2018.