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Solar photovoltaic technology is considered as one of the pure and clean technologies to produce electricity , but seems unattractive towards the use of it as a complement of electricity due to its low efficiency and high initial cost. As a result of its low efficiency it is nearly impossible to exploit the maximum solar power coming out of array therefore at its highest energy conversion output it leads to the operational failure of the device. As the radiation and temperature has their effects on the maximum power point, it is likely impossible to provide power operation at optimum level during all radiation levels. From years research are carried out on this and numerous MPPT techniques are introduced, refined , enforced and implemented with a proper execution. Different research groups have advocated different research methods which consist a little literature, where correlation between varieties of MPPT techniques are executed in terms of reliability, time of response and efficiency of conversion. This inspection gives a brief comparison among the realization of different MPPT methods and results a new MPPT technique with improve capability than the extant ones. The analysis done in this paper is as follows: At first, a solar PV array model based on MATLAB is first modeled and examined for validation following the employment of different techniques of MPPT under varying temperature on this PV array and different conditions for the effectiveness study of the particular MPPT technique.

MPPT, Solar PV system, Conversion efficiency, Solar PV array, Irradiance, Fuzzy Logic

[1] Y. Dong, J. Ding, J. Huang, L. Xu and W. Dong, "Invsetigation of PV inverter MPPT efficiency test platform," International Conference on Renewable Power Generation (RPG 2015), Beijing, 2015, pp. 1-4.
[2] S. Veeraraghavan, M. Kumaravel, K. Vasudevan and A. Jhunjhunwala, "Experimental studies and performance evaluation of solar PV powered BLDC motor drive with an integrated MPPT in fan applications,"2014 IEEE 40th Photovoltaic Specialist Conference (PVSC), Denver, CO, 2014, pp. 3713-3718.
[3] K. L. Lian, J. H. Jhang and I. S. Tian, “A Maximum Power Point Tracking Method Based on Perturb-and-ObserveCombinedWith Particle Swarm Optimization”IEEE Journal of PhotovoltaicsYear: 2014, Volume: 4, Pages: 626 – 633, IEEE Journals & Magazines.
[4] S. Abdourraziq and R. El Bachtiri, "A perturb and observe method using fuzzy logic control for PV pumping system," Multimedia Computing and Systems (ICMCS), 2014 International Conference on, Marrakech, 2014, pp. 1608-1612.
[5] T. Saini, D. Raveendhra and P. Thakur, "Stability analysis of FPGA based perturb and observe method MPPT charge controller for solar PV system," Engineering and Systems (SCES), 2013 Students Conference on, Allahabad, 2013, pp. 1-5.
[6] R. M. Linus and P. Damodharan, "Maximum power point tracking method using a modified perturb and observe algorithm for grid connected wind energy conversion systems," in IET Renewable Power Generation, vol. 9, no. 6, pp. 682-689, 8 2015.
[7] D. V. N. Ananth, "Performance evaluation of solar photovoltaic system using maximum power tracking algorithm with batterybackup," Transmission and Distribution Conference and Exposition (T&D), 2012 IEEE PES, Orlando, FL, 2012, pp. 1-8.
[8] Vijayakumar, Madhiyan and VijayanSubramaniam , “ Extendedreference signal generating scheme for Integration of unified power quality conditioner system”Taylor and Francis, Electric PowerComponents And Systems, 43 (8-10): 914-927, 2015.
[9] A. K. Yadav and H. Malik, "Optimization of tilt angle for installation ofsolar photovoltaic system for six sites in India," Energy Economics andEnvironment (ICEEE), 2015 International Conference on, Noida, 2015,pp. 1-4.
[10] P. Salmeron and S. P. Litran , “ A control Strategy for Hybrid PowerFilter to compensate four wires three Phase System ,” Power Electronics , IEEE Transactions onVolume-25,no.7, pp.1923-1931, July2010 .
[11] A. Sulaiman, F. Inambao and G. Bright, "Development of solarhydrogen energy for mobile robots," Robotics and MechatronicsConference (RobMech), 2013 6th, Durban, 2013, pp. 14-19.
[12] L. Piegari and R. Rizzo, "Adaptive perturb and observe algorithm forphotovoltaic maximum power point tracking," in IET Renewable PowerGeneration, vol. 4, no. 4, pp. 317-328, July 2010

M. Madhavi, G. VeeraShankara Reddy, "DESIGN OF PV- CELLS AND MPPT BY USING ANFIS CONTROLLER," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.1-6, 2018.

Data mining is the process of extracting hidden patterns of data. Association rule mining is an important data mining task that finds an interesting association among a large set of a data item. Association rule hiding is one of the techniques of privacy-preserving data mining to protect the association rules generated by association rule mining. In this paper, proposed a new data distortion technique for hiding sensitive association rules. Algorithms based on this technique either hide a specific rule using data alteration technique or hide the rules depending on the sensitivity of the items to be hidden. The proposed technique uses the idea of representative rules to prune the rules first and then hides the sensitive rules.

Data mining, Association rule mining, Support , Confidence

[1] S.Kasthuri and T.Meyyappan, " Detection of Sensitive Items in Market Basket Database using Association Rule Mining for Privacy Preserving", In the Proceeding of 2015, international conference on Pattern Recognition, Informatics adn Mobile Engineering, Feb 21-22, pp.200-203, 2015.
[2] S. Choudhary and A. Upadhyay, " Hiding Sensitive Data Item Using Association Rule Mining", International Journal of Engineering Sciences & Management, Vol.6, Issue.1,pp.13-21, 2016.
[3] D.C. Kalariya, V.Shah and J.Vala, " Association Rule Hiding based on Heuristic Approach by Deleting Item at R.H.S side of Sensitive Rule", International Journal of Computere Application, Vol.122, No 8, pp.25-28, 2015.
[4] P.Madhave, M.Mane adn S. Patil," Data mining using Association rule based on APPIORI algorithm and improved approach with illustration", International Journal of Latest Trends in Engineering and Technology,Vol.3, Issue.2, pp.107-113, 2013.
[5] M.Shridhar, M. Parmar,"Survey on Association Rule Mining and Its Approaches", International Journal of Computer Science and Engineering, Vol.5, Issue.3, pp.129-135, 2017.
[6] R. Solanki, “Principle of Data Mining”, McGraw-Hill Publication, India, pp. 386-398, 1998.

N. S. Mrudula Jyothi, A. Suraj Kumar, "Privacy Preservation for Association Rule Mining," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.7-11, 2018.

Cloud computing is an internet-based service which provides a platform based upon the internet for performing computations. In other words, it is the hiring of services from the service providers. But, since data has to be stored at the third party location, the data owners are always concerned about the security of their data. There are a number of security issues related to cloud computing. In this paper, a comprehensive review of various security challenges faced by the cloud has been presented. The major focus of this paper is the security of the data stored on the cloud. We have provided a detailed discussion on various cryptographic algorithms that have been proposed in recent years to ensure data security. These algorithms have been compared and analyzed based upon various security parameters and their pros and cons have been highlighted. This analysis opens up a new space that can be used to develop a new algorithm which can overcome the shortcomings of the existing algorithms and can enhance the security of the existing systems.

Cloud Computing, Security, Privacy, Encryption, Decryption

[1] Modi, D. Patel, B. Borisaniya, A. Patel, and M. Rajarajan, “A survey on security issues and solutions at different layers of Cloud computing”, The Journal of Supercomputing, Vol. 63, Issue. 2, pp. 561-592, 2013.
[2] National Institute of Standards and Technology, Information Technology Laboratory, “The NIST Definition of Cloud Computing,” https://www.nist.gov/sites/default/files/documents/itl/cloud/cloud-def-v15.pdf.
[3] T.V. Sathyanarayana, L.M.I. Sheela, “Data security in cloud computing”, in Proceedings of IEEE International Conference on Green Computing, Communication and Conservation of Energy (ICGCE), India, pp. 822-827, 2013.
[4] A.M. Khan, S. Ahmad, M. Haroon, “A Comparative Study of Trends in Security in Cloud Computing ”, in Proceedings of the 5th IEEE International Conference on Communication Systems and Network Technologies (CSNT), India,pp. 586-590, 2015.
[5] P. Sirohi, A. Agarwal, “Cloud computing data storage security framework relating to data integrity, privacy and trust”, in Proceedings of the Ist IEEE International Conference on Next Generation Computing Technologies (NGCT), India, pp. 115-118, 2015.
[6] Ennajjar, Y. Tabii, A. Benkaddour, “Security in cloud computing approaches and solutions” in Proceedings of the 3rd IEEE International Colloquium Information Science and Technology (CIST), Morocco, pp. 57-61, 2014.
[7] Cloud Security Alliance, “Cloud Adoption Practices & Priorities Survey Report”https://downloads.cloudsecurityalliance.org/initiatives/surveys/capp/Cloud_Adoption_Practices_Priorities_Survey_Final.pdf.
[8] J. Somorovsky,M. Heiderich, M. Jensen, J. Schwenk, N. Gruschka, L.Iacono, “All your clouds are belong to us: security analysis of cloud management interfaces,” in Proceedings of the 3rd ACM workshop CCW,pp.3–14, 2011.
[9] G. Zhaolong, S. Yamaguchi, B.B. Gupta, “Analysis of various security issues and challenges in cloud computing environment: a survey” in Handbook of research on modern cryptographic solutions for computer and cyber security. IGI Global Publisher., pp. 393-419, 2016.
[10] Cloud Security Alliance, “The Notorious Nine Cloud Computing Top Threats in 2013” https://downloads.cloudsecurityalliance.org/initiatives/top_threats/The_Notorious_Nine_Cloud_Computing_Top_Threats_in_2013.pdf.
[11] Cloud Security Alliance, “The Treacherous Twelve,”https://downloads.cloudsecurityalliance.org/assets/research/top-threats/treacherous-12-top-threats.pdf.
[12] D.A.B. Fernandes, L.F.B Soares, J.V. Gomes, M.M Freire, P.R. Inacio, “Security issues in cloud environments: a survey”, International Journal of Information Security, Vol. 13, Issue 2, pp. 113-170, 2014.
[13] P. Ora, P.R. Pal, “Data security and integrity in cloud computing based on RSA partial homomorphic and MD5 cryptography”, in Proceedings of IEEE International Conference on Computer, Communication and Control (CC4), India, pp. 1-6, 2015.
[14] N. Shimbre, P. Deshpande, “Enhancing Distributed Data Storage Security for Cloud Computing Using TPA and AES Algorithm”, in Proceedings of IEEE International Conference on Computing Communication Control and Automation (ICCUBEA), India, pp. 35-39, 2015.
[15] N.S. Kumar, G.R. Lakshmi, B. Balamurugan, “Enhanced Attribute Based Encryption for Cloud Computing”, Procedia Computer Science, Vol. 46, pp. 689-696, 2015.
[16] S.Y. Huang, C.I. Fan, Y.F. Tseng, “Enabled/disabled predicate encryption in clouds”, Future Generation Computer Systems, Vol. 62, pp. 148-160, 2016.
[17] S.H. Seo, M. Nabeel, X. Ding, “An efficient certificateless encryption for secure data sharing in public clouds”, IEEE Tansactions on Knowledge and Data Engineering, Vol. 26, Issue. 9, pp. 2107-2119, 2014.
[18] S. Ma, “Identity-based encryption with outsourced equality test in cloud computing”, Information Sciences, Vol. 328,pp. 389-402, 2016.
[19] Y. Lu, J. Li, “A pairing-free certificate-based proxy re-encryption scheme for secure data sharing in public clouds”, Future Generation Computer Systems, Vol. 62, pp. 140-147, 2016.
[20] J. Li, J. Li, X. Chen, Z. Liu, C. Jia, “Privacy-preserving data utilization in hybrid clouds”, Future Generation Computer Systems, Vol. 30, pp. 98-106, 2014.
[21] J. Li, Y.K. Li, X. Chen, P.P.C Lee, W. Lou, “A hybrid cloud approach for secure authorized deduplication”, IEEE Transactions on Parallel and Distributed Systems, Vol. 26, Issue. 5, pp. 1206-1216, 2015.
[22] Z.P. Jia, X. Tian, “A novel security private cloud solution based on eCryptfs”, in Proceedings of the 6th IEEE International Conference on Information Management, Innovation Management and Industrial Engineering (ICIII), China, pp. 38-41, 2013.
[23] Q. Huang,Y. Yang, M. Shen, “Secure and efficient data collaboration with hierarchical attribute-based encryption in cloud computing”, Future Generation Computer Systems,Vol.72, pp. 239-249, 2017.
[24] L. Wu, Y. Zhang, K.K.r. Choo, D. He, “Efficient and secure identity-based encryption scheme with equality test in cloud computing", Future Generation Computer Systems, Vol. 73, pp. 22-31, 2017.
[25] M. Usman, M.A. Jan, X. He, “Cryptography-based secure data storage and sharing using HEVC and public clouds”, Information Sciences, Vol. 387, pp. 90-102, 2017.
[26] L. Yibin, K. Gai, L. Qiu, M. Qiu, H. Zhao, “Intelligent cryptography approach for secure distributed big data storage in cloud computing”, Information Sciences, Vol. 387, pp. 103-115,2017.
[27] C.A. Dhote, “Homomorphic encryption for security of cloud data”, Procedia Computer Science, Vol. 79, pp. 175-181, 2016.
[28] Odelu, A.K. Das, Y.S. Rao, S. Kumari, M.K. Khan, K.K. Choo, “Pairing-based CP-ABE with constant-size ciphertexts and secret keys for cloud environment”, Computer Standards & Interfaces, Vol. 54, pp. 3-9, 2017.
[29] S.K. Pasupuleti, S. Ramalingam, R. Buyya, “An efficient and secure privacy-preserving approach for outsourced data of resource constrained mobile devices in cloud computing”, Journal of Network and Computer Application, Vol. 64, pp. 12-22, 2016.
[30] J. Zhang, Y. Yang, Y. Chen, J.Chen, Q. Zhang, “A general framework to design secure cloud storage protocol using homomorphic encryption scheme”, Computer Networks, Vol.129, pp.37-50, 2017.
[31] J. Li, X. Li, L. Wang, D. He, H.Ahmad, X. Niu, “Fuzzy encryption in cloud computation: efficient verifiable outsourced attribute-based encryption”, Soft Computing, Vol. 22, Issue 3, pp. 707-714, 2018.
[32] A. Sanghi, S. Chaudhary, M. Dave, “Enhance the Data Security in Cloud Computing by Text Steganography,”, Smart Trends in Systems, Security and Sustainability, ser. Lecture Notes in Networks and Systems, Singapore: Springer, Vol. 18, 2018.

Manreet Sohal, Sandeep Sharma, "Comparative Analysis of Different Cryptographic Mechanisms of Data Security and Privacy in Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.12-24, 2018.

In this paper, we have proposed the fuzzy hyper-line segment neural network (FHLSNN) by using association rule mining(FHLARM). Regression tree is used for pattern recognition. We have used supervised learning neural network classifier for classification of fuzzy sets. The FHLARM make the pattern classification with the help of hyper-line segments. It has two endpoints and corresponding member-ship function. The proposed model is evaluated by using iris, wine and solar mine datasets. For extraction of rules, we have used association rule mining. It gives the better classification accuracy results on various datasets as compared to previous methods. Regression tree maintains a hierarchy of extracting rules.

Fuzzy sets, Neural Network, Supervised and unsupervised methods, Pattern classification, FMM

[1] L.A .Zadeh, ”Fuzzy Sets”, Information and Control 8, 338-353 (1965).
[2] Simpson, Patrick K. "Fuzzy min-max neural networks. I. Classification." IEEE transactions on neural networks 3.5 (1992): 776-786.
[3] Simpson, Patrick K. "Fuzzy min-max neural networks-part 2: Clustering." IEEE Transactions on Fuzzy systems 1.1 (1993): 32.
[4] Bellman, R., R. Kalaba, and L. A. Zadeh, RAND Memorandum RM-4307-PR,“Abstraction and Pattern Classification” (1964).
[5] Bellman, Richard, Robert Kalaba, and L. Zadeh. "Abstraction and pattern classification." Fuzzy Sets, Fuzzy Logic, And Fuzzy Systems: Selected Papers by Lotfi A Zadeh. 1996. 44-50.
[6] Kulkarni, U. V., T. R. Sontakke, and G. D. Randale, "Fuzzy hyperline segment neural network for rotation invariant handwritten character recognition." Neural Networks, 2001. Proceedings. IJCNN`01. International Joint Conference on. Vol. 4. IEEE, 2001.
[7] Bezdek, James C., “Fuzzy models and algorithms for pattern recognition and image processing”. Vol. 4. Springer Science & Business Media, 1999.
[8] Anderberg, Michael R. “Cluster analysis for applications”, No. OAS-TR-73-9. Office of the Assistant for Study Support Kirtland AFB N MEX, 1973.
[9] Baruah, Arati B., and Russell C. Welti. "Adaptive resonance theory and the classical leader algorithm." Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on. Vol. 2. IEEE, 1991.
[10] Kulkarni, U. V., D. D. Doye, and T. R. Sontakke. "General fuzzy hypersphere neural network." Neural Networks, 2002. IJCNN`02. Proceedings of the 2002 International Joint Conference on. Vol. 3. IEEE, 2002.
[11] Nandedkar Abhijeet V., and Prabir K. Biswas. "A fuzzy min-max neural network classifier with compensatory neuron architecture." IEEE transactions on neural networks 18.1 (2007): 42-54.
[12] Gabrys, Bogdan, and Andrzej Bargiela. "General fuzzy min-max neural network for clustering and classification." IEEE transactions on neural networks 11.3 (2000): 769-783.
[13] Kulkarni U. V., T. R. Sontakke, and G. D. Randale. "Fuzzy hyperline segment neural network for rotation invariant handwritten character recognition." Neural Networks, 2001. Proceedings. IJCNN`01. International Joint Conference on. Vol. 4. IEEE, 2001.
[14] Patil P. M., U. V. Kulkarni, and T. R. Sontakke. "General fuzzy hyperline segment neural network." Systems, Man and Cybernetics, 2002 IEEE International Conference on. Vol. 4. IEEE, 2002.
[15] Sonule Preetee M., and Balaji S. Shetty. "An enhanced fuzzy min–max neural network with ant colony optimization based-rule-extractor for decision making." Neurocomputing 239 (2017): 204-213.
[16] Lakshmi, K. S., and G. Santhosh Kumar. "Association rule extraction from medical transcripts of diabetic patients." Applications of Digital Information and Web Technologies (ICADIWT), 2014 Fifth International Conference on the. IEEE, 2014.
[17] Sato, Makoto, and Hiroshi Tsukimoto. "Rule extraction from neural networks via decision tree induction." Neural Networks, 2001. Proceedings. IJCNN`01. International Joint Conference on. Vol. 3. IEEE, 2001.
[18] Jagielska lona. "Linguistic rule extraction from neural networks for descriptive data mining." Knowledge-Based Intelligent Electronic Systems, 1998. Proceedings KES`98. 1998 Second International Conference on. Vol. 2. IEEE, 1998.
[19] Juvonen, Antti, and Tuomo Sipola. "Combining conjunctive rule extraction with diffusion maps for network intrusion detection." International Symposium on Computers and Communications. IEEE, 2013.
[20] E.J. Fortuny, Enric Junque, and David Martens. "Active learning-based pedagogical rule extraction." IEEE transactions on neural networks and learning systems 26.11 (2015): 2664-2677.
[21] Al Iqbal, Ridwan. "Eclectic extraction of propositional rules from neural networks." Computer and Information Technology (ICCIT), 2011 14th International Conference on. IEEE, 2011.
[22] Duch Wlodzislaw, Rafal Adamczak, and Krzysztof Grabczewski. "A new methodology of extraction, optimization and application of crisp and fuzzy logical rules." IEEE Transactions on Neural Networks 12.2 (2001): 277-306.
[23] Agarwal Rakesh, and Ramakrishnan Srikant. "Fast algorithms for mining association rules." Proc. of the 20th VLDB Conference. 1994.
[24] Park Jong Soo, Ming-Syan Chen, and Philip S. Yu. An effective hash-based algorithm for mining association rules. Vol. 24. No. 2. ACM, 1995.
[25] https://in.mathworks.com/matlabcentral/fileexchange/42541-association-rules.
[26] Kumbhare, Trupti A., and Santosh V. Chobe. "An overview of association rule mining algorithms." International Journal of Computer Science and Information Technologies 5.1 (2014): 927-930.

B. S. Shetty, U. V. Kulkarni, Preetee M. Sonule, Manisha N. Shinde, "Fuzzy Hyper-line Segment Neural Network by using Association Rule Mining," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.25-31, 2018.

This paper describes the behavior of support vector machine based classification for varying sizes of heap-grain samples. Different grains like cow peas, green gram, ground nut, green peas, jowar, red gram, soya and toor dal are considered for the study. The color and texture features are used as input to the SVM classifier. The recognition accuracy is observed for specific size training and mixed size training methods. The recognition accuracy is found to be 100% for the test samples with which the classifier is trained and decreased when training and testing samples are different. The work finds application in automatic recognition and classification of food grains by the service robots in the real world.

Classification, feature extraction, grain samples, support vector machine

[1] Yuyong Cui, Zhiyuan Zeng and Bitao Fu (2008), Hyperspectral Image Classification Based on Compound Kernels of Support Vector Machine , Proceedings of the 8th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, China page. 263-269.
[2] Qing Song, , Wenjie Hu, and Wenfang Xie (2002) ,Robust Support Vector Machine With Bullet Hole Image Classification, IEEE Transactions on Systems Man and Cybernetics-Part C:Applications and Reviews, Vol.32. No.4, page.440-448
[3] Li, Jing; Allinson, Nigel; Tao, Dacheng and Li, Xuelong (2006). Multitraining support vector machine for image retrieval, , Vol.15, No,11, page 3597- 3601.
[4] Evgeniy Gabrilovich, Shaul Markovitch, (2004), Text Categorization with Many redundant Features: Using Aggressive Feature Selection to Make SVMs Competitive with C4.5 Proceedings of the 21st International Conference on Machine Learning.
[5] Subhransu Maji Alexander C. Berg Jitendra Malik, (2008), Classification using Intersection Kernel Support Vector Machines is efficient, IEEE Conference on Computer Vision and Pattern Recognition.
[6] Bhaskar Mehta Saurabh Nangia (2008), Detecting Image Spam using Visual Features and Near Duplicate Detection, WWW 2008.
[7] Amit David, Boaz Lerner (2005), Support vector machine-based image classification for genetic syndrome diagnosis, Pattern Recognition Letters, 2Vol.6, page. 1029–1038.
[8] Reda A. El-Khoribi, (2008) Support Vector Machine Training of HMT Models for Multispectral Image Classification, International Journal of Computer Science and Network Security, Vol.8, page 9.
[9] Yasemin Altun, Ioannis Tsochantaridis, Thomas Hofmann, (2003), Hidden Markov Support Vector Machines, Proceedings of the Twentieth International Conference on Machine Learning.
[10] Corinna Cortes and V. Vapnik, (1995) Support-Vector Networks, Machine Learning, Vol.20, page 273-297
[11] M. Aizerman, E. Braverman, and L. Rozonoer (1964).Theoretical foundations of the potential function method in pattern recognition learning, Automation and Remote Control, Vol.25, page 821-837.

Vishwanath S. Kamatar, Rajesh Yakkundimath, Girish Saunshi, "Behavior of SVM based classification for varying sizes of heap-grain images," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.32-42, 2018.

Soil is a precious resource of the environment to survival and humanistic welfare .From the mixture of minerals, organic matter and living organism components soil is formed. Soil Physical, Chemical and Biological characteristics plays a very significant role in agricultural field. Accelerated low cost and predictable assessment of soil quality under agricultural management is necessary to accomplish convenient observation of the effects of various management practices on soil conditions to avoid soil degradation and ensure feasible soil productivity and also soil security. The objective of this study is to find soil Physical and Chemical parameter contents from top surface (0-20cm) of agricultural soil in Vaijapur taluka. We measure raw spectral reflectance of all soil samples .and also use 1st and 2nd Derivatives techniques for pre-processing spectral data. Mostly soil texture of Vaijapur taluka comes under the clay loam. In this study we predicting soil properties with particular sensitive band such as soil water(1400 nm, 1900 nm, 2200 nm),pH (1477nm,1932nm and 2200 nm),Sand(1900 nm),Silt(2000 nm),Clay(2200 nm) and for Soil Organic Matter(750-1000nm) for better crop production in Vaijapur taluka using Field Spec4 Spectroradiometer between 350-2500 nm wavelength. It is a rapid, non-destructive, and cost-effective and time consuming tool for evaluating the soil parameters. According to R-square and RMSE values the PLSR model gives better results to achieve these objectives and here we also suggest crops which is suitable for soil in Vaijapur taluka.

Spectroradiometer, VNIR-SWIR, PLSR

[1] Ramdas D. Gore, Reena H. Chaudhari, and Bharti W. Gawali,” Creation of Soil Spectral Library for Marathwada Region”, Cloud Publications, ISSN 2320 - 0243,27 June 2016
[2]Francesca Garfagnoli,Gianluca Martelloni,”Two GUIs based analysis tool for Sepctroradiometer data pre-processing”,Springer,2013
[3] Stuart ,” Infrared spectroscopy:fundamentals and applications”,John wiley& Sons:Chichester,England,2004
[4] Pratibha R.Bhise,Sonali B.Kulkarni,”Review on analysis and classification techniques applied on soil characteristics data classification “,IJETTCS,vol .06 ,Issue.01,ISSN-2278-6856,Jan-Feb 2017.
[5] D. Curcioa, G. Ciraolob, F. D’Asaroa, M. Minacapilli,” Prediction of soil texture distributions using VNIR-SWIR reflectance spectroscopy”,ELSEVIER,2013
[6]Supannika Potithep,Reiichiro Ishii,Rikie Suzuki,”The potential of Normalized Difference Soil Index (NDSI)for soil water content estimation in Mongolia,”JAMSTEC,Japan
[7]Ting He.Jing Wang,Zongjian Lin ,Ye Cheng,”Spectral Features of soil organic matter”,Geospatial Information Science,ISSN:1009-5020,2009
[8]Prediction of soil texture distributions using VNIR-SWIRREflectance Spectroscopy”,ELSEVIER,2013
[9] Sandeep V. Gaikwad, Karbhari V. Kale, Sonali B. Kulkarni, Amarsinh B. Varpe and Ganesh N Pathare,” Agricultural Drought Severity Assessment using Remotely Sensed Data: A Review ”, International Journal of Advanced Remote Sensing and GIS, 2015, Volume 4, Issue 1, pp. 1195-1203, ISSN 2320 – 0243
[10]Amol.D.Vibhute,K.V.Kale,RajeshD.Dumal,S.C.Mehrotra,”Soil type classification and mapping using Hyperspectral Remote Sensing Data”,IEEE,2015
[11] Bhise Pratibha .R ,Kulkarni Sonali .B ,”Remote Sensing and Data Mining techniques applied on soil characteristics data classification”,IOSR,Vol.02,ISSN-2278-0661,pp-83-91.
[12] Pratibha R.Bhise,Sonali B.Kulkarni,”Review on analysis and classification techniques applied on soil characteristics data classification “,IJETTCS,vol .06 ,Issue.01,ISSN-2278-6856,Jan-Feb 2017.
[13] Tarin Paz-Kagan , Moshe Shachak , Eli Zaady , Arnon Karnieli ,” A spectral soil quality index (SSQI) for characterizing soil function in areas of changed land use”,Elsevier,2014
[14] Haiqing Yang, Boyan Kuang, Abdul M. Mouazen”, Affect of Different Preprocessing Methods on Principal Component Analysis for Soil Classification”,IEEE,2011
[15] Ashwini Dilip Padmanabhi, Saima Ansari, Dr. R. R. Deshmukh,” Hyperspectral analysis of soil total nitrogen using ASD Filedspec 4”, IJARCS, ISSN No. 0976-5697,July 2017
[16]https://stats.idre.ucla.edu/wpcontent/uploads/2016/02/pls.pdf
[17] R.A. Viscarra Rossel T, D.J.J. Walvoort,” Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties”,Geoderma -Elsevier, 59–75,9 March 2005.
[18]Hakan Arslan,Mehmet Tasan,”Predicting field capacity,wilting point and the other physical properties of soils using hyperspectral reflectance spectroscopy :two different statistical approaches”,Springer,2014
[19] Snehal N.Kulkarni,Dr. Ratnadeep R. Deshmukh,” Monitoring Carbon, Nitrogen, Phosphor and Water Contents of Agricultural Soil by Reflectance Spectroscopy using ASD Fieldspec Spectroradiometer”, IJESC,November 2016
[20] A.Gholizadeh , M. S. M. Amin , L. Borůvka , M. M. Saberioon,” Models for Paddy Soil Physical Properties Estimation Using Visible and Near Infrared Reflectance Spectroscopy”, Research Gate, Journal of Applied Spectroscopy 81(3):534-540 • April 2014
[21] A. Cambou, R. Cardinael, Ernest Kouakoua, Manon Villeneuve, C. Durand,Bernard Barth_es,”Prediction of soil organic carbon stock using visible and near infrared reactance spectroscopy (VNIRS) in the field”,HAL,2015
[22] Offer Rozenstein,Tarin Paz-Kagan, Christoph Salbach, and Arnon Karnieli,” Comparing the Effect of Preprocessing Transformations on Methods of Land-Use Classification Derived From Spectral Soil Measurements”,IEEE, 1939-1404 ,2015
[23] Dan Shiley,” 283-7 Measurement of Soil Mineralogy Using Near-Infrared Reflectance Spectroscopy”, ASD Inc., Boulder,2013

P.R. Bhise, S.B. Kulkarni, "Evaluation of Soil Physical/Chemical Parameters for Agriculture Production in Vaijapur Taluka Using VNIR-SWIR Reflectance Spectroscopy," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.43-48, 2018.

Recently Educational data mining has gained the attention of the researcher in the research industry and also in the society because of the availability of a large amount of data. There is a need for turning such data into useful information and knowledge. At present, there is a lack of well defined diagnostic algorithm to predict the reason for student absenteeism. It is critical to identify the most significant attributes in a dataset using the traditional statistical methods. This paper focuses on overcoming the difficulties involved in analyzing the student dataset by using Machine Learning Techniques. For mining purpose, Data pre-processing is done on the dataset which is a collection of questionnaire gathered from students in a semi-rural institution. Multilayered Back Propagation Algorithm was used to construct the neural network with weights and bias by applying a transfer function in the dataset. The highly influencing attributes having high weights and bias from the dataset was chosen and a Neural Network was constructed. This knowledge is used to identify the reason for the leave taken by the students and helps the management and staff members to improve the performance of the student.

Educational Data Mining, Artificial Neural Networks, Multilayered Back Propagation Algorithm

[1]. Gurpreet Kaur, Manreet Sohal, “IOT Survey: The Phase Changer in Healthcare Industry”, International Journal of Scientific Research in Network Security and Communication, Vol-6, Issue-2, pp.34-39, 2016.
[2]. L.K. Ojha1 , L.K. Tiwary, R. Sharma,” Information Communication Technology Integration in Education”, International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.3, pp.14-15, June-2016.
[3]. Shubham Billus, Shivam Billus, Rishab Behl,” Weather Prediction through Sliding Window Algorithm and Deep Learning”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.5, pp.01-05, October -2018.
[4]. Kaur, P., Singh, M., &Josan, G. S. “Classification and prediction based data mining algorithms to predict slow learners in the education sector”. Procedia Computer Science, Vol57, pp.500-508, 2015.
[5]. Reale, C., Gavin, K., Librić, L., &Jurić-Kaćunić, D. “Automatic classification of fine-grained soils using CPT measurements and Artificial Neural Networks”. Advanced Engineering Informatics, Vol 36, pp.207-215, 2018.
[6]. Wong, W. T., & Hsu, S. H. “Application of SVM and ANN for image retrieval”. European Journal of Operational Research, Vol173, Issue 3, pp.938-950, 2006.
[7]. Jia, W., Zhao, D., & Ding, L. “An optimized RBF neural network algorithm based on partial least squares and genetic algorithm for classification of small sample”, Applied Soft Computing, Vol48, pp.373-384, 2016.
[8]. Bondarenko, A., Aleksejeva, L., Jumutc, V., &Borisov, A. “Classification Tree Extraction from Trained Artificial Neural Networks”, Procedia Computer Science, Vol104, pp.556-563, 2017.
[9]. Gallego, A. J., Calvo-Zaragoza, J., Valero-Mas, J. J., & Rico-Juan, J. R.“Clustering-based k-nearest neighbor classification for large-scale data with neural codes representation”. Pattern Recognition, Vol74, pp.531-543, 2018.
[10]. Rizk, Y., Hajj, N., Mitri, N., &Awad, M. “Deep Belief Networks and Cortical Algorithms: A Comparative Study for Supervised Classification”, Applied Computing and Informatics, 2018.
[11]. Ougiaroglou, S., Diamantaras, K. I., &Evangelidis, G.“Exploring the effect of data reduction on Neural Network and Support Vector Machine classification”, Neurocomputing, 2018.
[12]. Zhang, J., Williams, S. O., & Wang, H. “Intelligent computing system based on pattern recognition and data mining algorithms”, Sustainable Computing: Informatics and Systems, 2017.

S. Muthkumaran, P. Geetha, E. Ramaraj, "A Multilayered Back Propagation Algorithm to Predict Significant Attributes of UG Pursuing Students Absenteeism at Rural Educational Institution," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.49-53, 2018.

For a Deregulated system we assume all Pool participants use a price curve, rather than a cost curve, to exchange the power. Participants think about market prices for which they can maximize their profit, while Pool coordinators try to maximize the system-wide benefits. Using constrained economic dispatch, Pool benefits will be maximized when all participants trade THE power at marginal cost, as participants try to maximize their own benefits, they may either decrease their bids in order to retail more power or increase the price in order to make more profit.

Deregulated system, Pool coordinators Formatting, payoff matrix, UI rate, marginal cost

[1] Bhanu Bhushan, (Power Grid Corporation of India Limited) and Anjan Roy, and P. Pentayya (Western Regional Load Despatch Centre), “The Indian Medicine “ IEEE PES General Meet, 2004
[2] Bhanu Bhushan, (CERC, India), “The Indian Medicine” CIGRE 2005, 21, rue d’Artois, F-75008, PARIS.
[3] Bhanu Bhushan “ABC of ABT - A Primer On Availability Tariff ” June 2005
[4] Sushilkumar Soonee, S.Ramesh Narasimhan and Vivek Pandey, “Significance of Unscheduled Interchange Mechanism in the Indian Electricity Supply Industry” ICPSODR-2006, Department of Electrical Engineering, ITBHU.
[5] Ashkan Kian and Ali Keyhani, “Stochastic Price Modelling of Electricity in Deregulated Energy Market” IEEE, 34th Hawaii International Conference on System Science-2001 - 0-7695-0981-9/01 2001
[6] Javier Contreras and Tomás Gómez,” Auction Design in Day-Ahead Electricity Markets (Republished) “IEEE Transactions, Power Systems, Vol. 16,pp. 88-96, August 2001.
[7] Claudia P. Rodriguez and George J. Anders , “Bidding Strategy Design for Different Types of Electric Power Market Participants” IEEE Trans, Power Systems, Vol. 19, pp.964-971, May 2004
[8] N.Vaitheeswaran and R.Balasubramanian, “Stochastic Model for Optimal Declaration of Day Ahead Station Availability in Power Pools in India,” IEEE Trans, power system, vol.20, pp.701-708, June 2006.
[9] R.W.Ferrero, S.M.Shahidehpour and V.C.Ramesh” Transaction Analysis In Deregulated Power Systems Using Game Theory”. IEEE Transactions on Power Systems, Vol. 12, No. 3, August 1997.
[10] Vasileios P. Gountis and Anastasios G. Bakirtzis, “Bidding Strategies for Electricity Producers in a Competitive Electricity Marketplace” IEEE Transactions On Power Systems, Vol. 19, pp.356-365, February 2004.
[11] R.M.Holmukbea, Ms.Yogini Pawar", R.S.DesaiC, T.S.Hasarmanid “Availability Based Tariff and Its Impact on Different Industry Players-A, Review” Pune, India.
[12] A Game Theory Approach to Demand Side Management in Smart Grids Nadine Hajj and Mariette Awad American University of Beirut, Beirut, Lebanon {njh05,mariette.awad}@aub.edu.lb Springer International Publishing Switzerland 2015 D. Filev et al. (eds.), Intelligent Systems’2014, Advances in Intelligent Systems and Computing 323, DOI: 10.1007/978-3-319-11310-4_70
[13] TRANSACTION ANALYSIS IN DEREGULATED POWER SYSTEMS USING GAME THEORY IEEE Transactions on Power Systems, Vol. 12, No. 3, August 1997 by R.W. Ferrero * & S.M. Shahidehpour & V.C. Ramesh

A.I. Modi, T.V. RABARI, "Game Theory Approch on The Decision Making Process for Defining Obtainable Prices At Generator Side in A Deregulated Environment," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.54-56, 2018.

MANET dynamic routing protocols mainly used in reactive based routing in dynamic scenario and topology is unpredictable and uncertain in these cases, normal routing is not sufficient so we are changed QoS metrics to maintain end to end stable routing in the behavior of routing protocols. We are taken the QoS metrics used in AODV, TORA and DSR dynamic routing protocols and compare and analyzed in different node stupidities.

MANET, Routing Protocols, QoS metric parameters

[1] AODV, "Ad-hoc On-demand Distance Vector Routing", RFC 3561.
[2] Saad M. Adam*, Rosilah Hassan “Delay aware Reactive Routing Protocols for QoS in MANETs: a Review”, Journal of Applied Research and Technology, Vol.11,December 2013.
[3] N. H. Vaidya, "Mobile ad hoc networks: routing, MAC and transport issues," Tutorial on Mobile Ad Hoc Networks, http://www. crhc. uiuc. edu/nhv, University of Illinois at Urbana-Champaign, USA, 2004.
[4] J. Broch, D. B. Johnson, and D. A. Maltz, "The dynamic source routing protocol for mobile ad hoc networks,” Internet-Draft, draft-ietf-manet-dsr-00. txt1998.
[5] Vijaya Bhaskar.ch , Dr. D.S.R.Murthy , “ A reliable Routing Approach in Mobile AdHoc Network based on genetic Algoriths” International Journal of Research in Computer and Communication Technology, (IJRCCT) ISSN(o) 2278-5841, ISSN(P) 2320-5156, www.IJRCCT.org, Vol 2, Issue 10, October- 2013.
[6] J. Abdullah, "QoS Routing Solutions for Mobile Ad Hoc Network," Mobile Ad-Hoc Networks: Protocol Design, pp. 417-454, 2011.Ismail, R. Hassan, A. Patel, and R. Razali, "A study of routing protocol for topology configuration management in mobile ad hoc network," 2009, pp. 412-417.
[7] Maninder Singh and Anjali, “Performance Analysis of Proactive, Reactive and Hybrid MANET Routing Protocols on IEEE 802.11 Standard”, International Journal of Computer Applications, Volume 54– No.12, PP. 1-8, September 2012.
[8] Karanveer Singh and Naveen Goyal, “PERFORMANCE ANALYSIS OF MANETS ROUTING PROTOCOL USING OPNET”, International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) Volume 5, Issue 4,pp. 815-819, April 2016.
[9] C. E. Perkins and E. M. Royer, “The ad hoc on-demand distance vector protocol,” in Ad hoc Networking, Addison-Wesley, pp. 173–219, 2000.
[10] J. Srilakshmi, S.S.S.N. Usha Devi N., "Secure and
Efficient Multipath Routing Using Overlay Nodes",
International Journal of Scientific Research in Computer
Science and Engineering, Vol.6, Issue.5, pp.16-19,
2018.
[11] Annlin Jeba S.V., Gnana King D.R., "Combining Trust
with Authentication Information for Routing in Wireless
Sensor Networks", International Journal of Scientific
Research in Network Security and Communication,
Vol.6, Issue.5, pp.38-46, 2018.

Vijaya Bhaskar Ch, D.S.R. Murthy, "Qos Metrics for end to end Stable Routing in MANET," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.57-61, 2018.

Web services have become the primary source for constructing software system over Internet. The quality of whole system greatly dependents on the QoS of single Web service, so QoS information is an important indicator for service selection. In reality, QoS of some Web services may be unavailable for users. How to predicate the missing QoS value of Web service through fully using the existing information is a difficult problem. This paper attempts to settle this difficulty by fusing Pearson similarity and Slope One methods for QoS prediction. In this paper, the Pearson similarity is adopted between two services as the weight of their deviation. Meanwhile, some strategies like weight adjustment and SPC-based smoothing are also utilized for reducing prediction error. In order to evaluate the validity of the proposed algorithm, comparative experiments are performed on the real-world data set. The result shows that the proposed algorithm exhibits better prediction precision than both basic Slope One and the well-known WsRec algorithm in most cases. Meanwhile, the new approach has the strong ability of reducing the impact of noise data.

Web services, QoS prediction, Slope One, similarity, collaborative filtering

[1] M. P. Papazoglou and D. Georgakopoulos, (2003). Serive-Oriented Computing, Communcications of the ACM, Vol. 46, No. 10, ACM Press, pp. 25–65.
[2] D. A. Menasce, (2002). QoS issues in Web services, IEEE Internet Computing, Vol. 6, No. 6, IEEE CS Press, pp. 72–75.
[3] J. B. Schafer, J. Konstan, and J. Riedi, (1999). Recommender Systems in E-Commerce, Proc. of the 1st ACM Conference on Electronic Commerce (EC’09), ACM Press, Denver, CO, USA, pp. 158–166.
[4] L. Shao, J. Zhang, Yong Wei, and et al., (2007). Personalized QoS
Prediction forWeb Services via Collaborative Filtering, Proc. of the IEEE International Conference on Web Services (ICWS’07), IEEE CS Press, Salt Lake City, Utah, USA, pp. 439–446.
[5] Z. Zheng, H. Ma, M. R. Lyu, and I. King, (2011). QoS-Aware Web Service Recommendation by Collaborative Filtering, IEEE Trans. on Services Computing, Vol.4, No. 2, IEEE CS Press, pp. 140– 152.
[6] Q. Xie, K. Wu, J. Xu, and et al., (2010). Person- alized Context-Aware QoS Prediction for Web Services Based on Collaborative Filtering, Proc. of the 6th International Conference on Advanced Data Min- ing and Applications (ADMA’10), Part II, Springer-Verlag Berlin, Chongqing, China, pp. 368–375.
[7] Y. Jiang, J. Liu, M. Tang, and X. Liu, (2011). An Effective Web Service Recommendation Method based on Personalized Collaborative Filtering, Proc. of IEEE International Conference on Web Services (ICWS’11), IEEE CS Press, Washington, DC, USA, pp. 211–218.
[8] A. K. Menon, K. P. Chitrapura, S. Garg, and et al., (2011). Response Prediction using Collaborative Filtering with Hierarchies and Side-information, Proc. of the 17th ACM SIGKDD International Con- ference on Knowledge Discovery and Data Mining (KDD’11), ACM Press, San Diego, CA, USA, pp.141–149.
[9] D. Lemire and A. Maclachlan, (2005). Slope One Pre- dictors for Online Rating-Based Collaborative Filtering, Proc. of the 2005 SIAM International Data Min ing Conference (SDM’05), Newport Beach, Califor- nia, USA, pp. 1–5.
[10] X. Su and T. M. Khoshgoftaar, (2009). A Survey of Collaborative Filtering Techniques, Advances in Ar- tificial Intelligence, Hindawi Publishing Corporation, pp. 1–19.
[11] Linyuan Lü, Matéš Medo, Chi Ho Yeung, and et al., (2012). Recommender Systems, Physics Reports, Vol. 519, No. 1, Elsevier B. V., pp. 1–49.
[12] S. Vucetic and Z. Obradovic, (2000). A Regression- based Approach for Scaling-up Personalized Recom- mender Systems, Proc. of the ACM WebKDD Workshop (WebKDD’00), Boston, MA, USA, pp. 1– 9.
[13] B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl, (2001). Item-based Collaborative Filtering Recom mender Algorithms, Proc. of the 10th International Conference on World Wide Web (WWW’01), ACM Press, Hong Kong, China, pp. 285–295.
[14] J. Oakland, (2003). Statistical Process Control, the 5th Revised Edition, Butterworth-Heinemann Ltd.
[15] H. Sun, Z. Zheng, J. Chen, and M. R. Lyu, (2011). NRCF: A Novel Collaborative Filtering Method for Service Recommendation, Proc. of IEEE International Conference on Web Services (ICWS’11), IEEE CS Press, Washington, DC, USA, pp. 702–703.
[16] Y. Shi, K. Zhang, B. Liu, and L. Cui, (2011). A New QoS Prediction Approach Based on User Clustering and Regression Algorithms, Proc. of IEEE International Conference on Web Services (ICWS’11), IEEE CS Press, Washington, DC, USA, pp. 726–727.
[17] J. Li, L. Sun, and J. Wang, (2012). A Slope One Collaborative Filtering Recommendation Algorithm Using Uncertain Neighbors Optimizing, Proc. of WAIM 2011 International Workshops, Springer- Verlag Berlin, Wuhan, China, pp. 160–166.
[18] P. Wang and H. W.Ye, (2009). A Personalized Recommendation Algorithm Combining Slope One Scheme and User Based Collaborative Filtering, Proc. of International Conference on Industrial and Information Systems (IIS’09), IEEE CS Press, Haikou, China, pp. 152–154.
[19] D. J. Zhang, (2009). An Item-based Collaborative Filtering Recommendation Algorithm Using Slope One Scheme Smoothing, Proc. of the Second International Symposium on Electronic
Commerce and Security (ISECS’09), Vol. 2, IEEE CS Press, Nanchang, China, pp. 215–217.
[20] X. Li, F. Zhou, and X. Yang, (2011). Research on Trust Prediction Model for Selecting Web Services based on Multiple Decision Factors, International Journal of Software Engineering and Knowledge En- gineering, Vol. 21, No. 8, World Scientific Publishing Company, pp. 1075–1096.

G. Vadivelou, E. Ilavarasan, "Fusion of Pearson Similarity and Slope One Methods for QoS Prediction for Web Services," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.62-68, 2018.