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In this paper, the aim is finding the accurate edge of the brain image. Edge detection is the most important task in medical applications. Edge detection is the boundary of the particular image. MRI brain analysis is used for visualizing, analyzing and measuring the brain parts. Thresholding is the basic tool for image segmentation. Thresholding generate the binary image from the grayscale image by using some threshold value. Segmentation is the process to assign the pixels in the image to two or more classes. Here, this paper threshold the MRI Brain image using Minimum Cross-Entropy Thresholding. The cross entropy is the computationally attracting algorithm and the cross entropy is formulated in pixel to pixel basis. Then the resulting thresholding image applied the Fuzzy interface system. The Fuzzy Interface System has the many rules. The thresholding image checks the each rule, then identify the edge. The experiment is using the MRI brain image.

Minimum Cross Entropy Thresholding, Fuzzy Edge detection, Fuzzy interface system, MRI head scans

[1]Rafael C.Gonzalez Richard E. Woods, “Digital Image Processing”.2nd ed., Beijing:Publishing House Of Electronics Industry, 2007.
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N.Senthilkumaran, C.Kirubakaran, N. Tamilmani, "Fuzzy Edge Detection Using Minimum Cross Entropy Thresholding for MRI Brain Image," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.271-274, 2018.

Due to lack of Electricity generation, managing customer demand for energy in India is a difficult task. In order to satisfy the customer demand, Renewable Energy can be introduced for each house to meet out the demand. This paper tries to predict the energy consumption of a house by considering 10 years of data. The Back Propagation neural network prediction method is used widely for this purpose because of its high plasticity and simple structure. The proposed work uses Feed-Forward BackPropagation and Elman BackPropagation Network to predict the demand for electricity consumption. In both two networks are computed were correlation coefficient values and Sum of Square Errors. The result obtained shows FeedForward BackPropagation Network gives better predicts of energy demand.

Energy Prediction, Energy, Electricity Consumption, Neural Network

[1] E.E. Enebeli, “Causality Analysis of Nigerian Electricity Consumption and Economic Growth”. Journal of Economics and Engineering, ISSN: 2078-0346, iss. 4, Dec. 2010.
[2] M. Sarlak, T. Ebrahim, and S.S. Karimi Madahi, “Enhancement the accuracy of daily and hourly short-time Load forecasting Using neural network,” Journal of Basic and Applied Scientific Research, vol. 2, iss. 1, pp. 247-255, 2012.
[3] V.O. Oladokun, A.T. Adebanjo, and O.E. Charles-Owaba, “Predicting students’ academic performance using artificial neural network:A case study of an engineering course,” Pacific Journal of Science and Technology, vol. 9, iss. 1, pp. 72-79, 2008.
[4] O. Folorunso, A.T. Akinwale, O.E. Asiribo, and T.A. Adeyemo, “Population prediction using artificial neural network,” African Journal of Mathematics and Computer Science Research, vol. 3, iss. 8, pp. 263-271, 2010.
[5] K. P. Amber, R. Ahmad, M. W. Aslam, A. Kousar, M. S. Khan, “Intelligent techniques for forecasting electricity consumption of buildings”, Energy, Volume 157, 15 August 2018, Pages 886-893.
[6] Kunlong Chen, Jiuchun Jiang, Fangdan Zheng, Kunjin Chen, “A novel data-driven approach for residential electricity consumption prediction based on ensemble learning”, Energy, Volume 150,1 May 2018, Pages 49-60.
[7] Jihui Yuan, Craig Farnham, Chikako Azuma, Kazuo Emura, “Predictive artificial neural network models to forecast the seasonal hourly electricity consumption for a University Campus”, Sustainable Cities and Society, Volume 42, October 2018, Pages 82-92.
[8] Aowabin Rahman, Vivek Srikumar, Amanda D. Smith, “Predicting Electricity consumption for commercial and residential buildings using deep recurrent neural networks”, Applied Energy, Volume 212, 15 February 2018, Pages 372-385.
[9] Juan Vilar,Germán Aneiros, Paula Rana, “ Prediction intervals for Electricity demand and price using functional data”, International Journal of Electrical Power & Energy Systems, Volume 96, March 2018, Pages 457-472.
[10] Sukumar Mishra, Vivek Kumar Singh, “ Monthly Energy Consumption Forecasting Based on Windowed Momentum Neural Network”, IFAC- PapersOnline, Volume 48, Issue 2015.
[11] Javeed Nizami, S. S. A. K., and A.Z. Al-Garni, “Forecasting electric energy consumption using neural networks,” Energy Policy, vol.23, iss. 12, pp. 1097–1104, Dec. 1995.
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[14] Gonzalez, P.A., and Zamarreno, J.M., “Prediction of hourly energy consumption in buildings based on a feedback artificial neuralnetwork,” Energy and Buildings, vol. 37, iss. 6, pp. 595 – 601, 2005.
[15] Radiša Z. Jovanović, Aleksandra A. Sretenovic, Branislav D. Zivkovic, “Ensemble of various neural networks for prediction of heating energy consumption”, Energy and Buildings, Volume 94, 1 May 2015, Pages 189-199.
[16] L. G. B. Ruiz, R. Rueda, M. P. Cuéllar, M. C. Pegalajar, “Energy consumption forecasting based on Elman neural networks with evolutive optimization”, Expert Systems with Applications, Volume 92, February 2018, Pages 380-389.

N. Saranya, B.S.E. Zoraida, "Predicting Energy Consumption of a House using Neural Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.275-277, 2018.

The current study presents the result of investigation being done on determining the influence of multipass welding on micro-hardness and charpy v-notch impact strength of AISI 1020 GMA weld joints. AISI 1020 alloy is commonly used in many industrial applications like manufacturing of spindles, gudgeon pins, light duty gears, ships, structures and many more. Furthermore, the literature survey reveals that thick plates are generally welded in multi passes of welding. In this study, the joints are fabricated in single pass, double pass and triple pass of welding with ER 70S-6 solid filler material. Besides this, welding parameters also influence the mechanical properties and metallurgical properties of weld material. The investigation outcomes show that the thermal gradients were established which affect the micro-hardness and impact toughness. Further, it reveals that due to multi layers of the filler material the heat input in case of triple layer welded joint is maximum which leads to maximum average value of hardness (302 VHN) in joint 3 and minimum of 230 VHN is reported for joint 1. Furthermore, the maximum energy absorption capacity comes out to be maximum (71Joules) for joint 3.

AISI 1020, GMAW process, Micro-Hardness, Charpy v-notch impact toughness, Multipass

[1]. D.M.M. Corona, J. W- Ngam, H. Jimenez, T.G Langdon, “Effects on hardness and microstructure of AISI 1020 low-carbon steel processed by high-pressure torsion”, Journal of materials research and technology, Vol.6, PP. 355-360, 2017.
[2]. C. K. Ogbunnaoffor, J.U. Odo, Nnuka, E.E, “The effects of welding current and electrode types on tensile properties of mild steel”, International journal of scientific & engineering research, Vol. 7, PP. 1120 – 1123, 2016.
[3]. S. Murugan, P.V. Kumar, B. Raj, M.S.C. Bose, “Temperature distribution during multipass welding of plates”, International journal of pressure vessels and piping, vol. 75, PP. 891-905, 1998.
[4]. L.O. Osoba, J. O. Okeniyi, B. I. Pogoson, O. A. Fasuba, “Effects of single pass and multipass welding on austenitic stainless steel corrosion in aggressive environments”, Journal of science, pp. 514-529, 2017.
[5].H.L. Saunders, “Gas metal arc welding guidelines, The Lincoln electric company, Edition – Third, USA, 1987.
[6]. I. S. Asibeluo, E Emifoniye, “Effect of Arc welding current on the mechanical properties of A36 carbon steel weld joints”, SSRG International journal of mechanical engineering, Vol 2, PP. 79-87, 2015.
[7]. A. Hooda, A. Dhingra, S. Sharma, “Optimization of MIG welding process Parameters to predict maximum yield strength in AISI 1040”, International journal of mechanical engineering & robotics research, Vol. 1, PP. 203-210, 2014.
[8]. J. Norish, “Advance welding processes, technologies and process control, Wood Head Publishing Limited, Cambridge, England, 2006.
[9]. N. Chhabra, N.S. Kalsi , D. Singh, “Effect of Shielding Gases on Micro Hardness of FE 410 (AISI 1024)
Steel Welded Joint in GMAW Process”, International Journal on Emerging Technologies, Vol. 5, PP. 8-13, 2013.
[10]. V. Rathi, Hunny, “Analyzing the effect o parameters on SMWA process”, International journal of engineering research management & technology, Vol 4, PP. 16-19, 2015.
[11]. S. Murugan, S.K. Rai, P.V. Kumar, T. Jayakumar, B. Raj, M.S.C. Bose, “Temperature distribution and residual stresses due to multipass welding in type 304 stainless steel and low carbon steel welds pads”, International journal of pressure vessels and piping, vol. 78, PP. 307-317, 2001.
[12]. S.I. Talabi, O.B. Owolabi, J.A. Adebisi, T. Yahaya, “Effect of welding variables on mechanical properties of low carbon steel welded joint”, Advances in Production Engineering & Management, Vol. 9, PP. 181-186, 2014.
[13]. K.V.S. Kumar, S. Gejendhiran, M. Prasath, “Comparative Investigation of Mechanical Properties in GMAW/GTAW for Various Shielding Gas Compositions”, Materials and Manufacturing Processes, PP. 996-1003, 2014.
[14]. S. Nakhodchi, A. Shokuhfar, S.A. Iraj, “Thomas Brian G (2015), “Evolution of temperature distribution and Microstructure in multipass welded AISI 321 stainless steel plates wiith different Thicknesses”,Journal of Pressure Vessel Technology, Vol. 137, PP. 061405-2 – 061405-15, 2015.
[15]. I.A. Ibrahim, S.A. Mohamat, A. Amir, A.Ghalib, “The Effect of Gas Metal Arc Welding (GMAW) processes on different welding parameters, International Symposium on Robotics and Intelligent Sensors (IRIS 2012), Malasyia, PP. 355-360, 2012.
[16]. I. Gowrisankar , A. K. Bhaduri, Seetharaman, D. D. N. Verma, D. R. G. Achar,“ Effect of the Number of Passes on the Structure and Properties of Submerged Arc Welds of AISI Type 316L Stainless Steel”, welding research supplement, PP. 147-157., 1987.
[17]. T. Singh, A.S. Shahi, M. Kaur, “Experimental studies on the effect of multipass welding on the mechanical properties of AISI 304 stainless steel SMAW joints”, International Journal of Scientific & Engineering Research, Vol.4, PP. 951-960, 2013.

Satish Kumar Bhatti, Gautam Kocher, Mandeep Singh, "Influence of Number of Weld Passes on Micro-Hardness and Impact Toughness of Gas Metal Arc (GMA) welded AISI 1020 joints," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.278-285, 2018.

Cloud computing is associate coming paradigm that gives tremendous benefits in economical aspects, resembling reduced time to promote, versatile computing capabilities, and limitless computing power. To use the complete potential of cloud computing, knowledge is transferred, processed and hold on by external cloud suppliers. However, data owners are very skeptical to place their data outside their own control sphere. Cloud computing is a new development of grid, parallel, and distributed computing with visualization techniques. It is changing the IT industry in a prominent way. Cloud computing has grown due to its advantages like storage capacity, resources pooling and multi-tenancy. In the proposed system, data to be send to the cloud is encrypted in two steps which are transposition cipher and by encrypting dynamic chunks in lesser time. Proposed system is also evaluated on various parameters like encryption time and data migration time. When compared it is seen that the performance of the proposed system is better than that of existing parameters in terms of evaluating parameters.

Cloud Computing, Data Slicing, Data Encryption, Cloud Migration, Cloud Security

[1] G. Kumar, V. Laxmi, "An Approach for Securing Data on Cloud Using Data Slicing and Cryptography", World wide Journal of Multidisciplinary Research and Development, pp. 371-375, 2017.
[2] K. Ullah and M. N. A. Khan,"Security and Privacy Issues in Cloud Computing Environment: A Survey Paper", International Journal of Grid and Distributed Computing Vol.7, No.2, pp. 89-98 ,2014.
[3] R. P. Padhy, M. R. Patra,S. C. Satapathy, "Cloud Computing: Security Issues and Research Challenges", IRACST - International Journal of Computer Science and Information Technology & Security (IJCSITS) ,Vol. 1, No. 2, 2011.
[4] S. K. and R. H. Goudar, "Cloud Computing – Research Issues, Challenges, Architecture, Platforms and Applications: A Survey", International Journal of Future Computer and Communication, Vol. 1, No. 4,2012.
[5] Hasan Omar Al-Sakran,"ACCESSING SECURED DATA IN CLOUD COMPUTING ENVIRONMENT",International Journal of Network Security & Its Applications , Vol.7, No.1, 2015
[6] R. Sumithra & Sujni Paul,"A survey paper on cloud computing security and outsourcing data mining in cloud platform",International Journal of Knowledge Management & e-Learning, Vol. 3, No. 1, pp. 43-48 2011.
[7] M. Ahmed and M. A. Hossain, "CLOUD COMPUTING AND SECURITY ISSUES IN THE CLOUD", International Journal of Network Security & Its Applications (IJNSA), Vol.6, No.1, 2014.
[8] S. Pearson and A. Benameur,"Privacy, Security and Trust Issues Arising from Cloud Computing", 2nd IEEE International Conference on Cloud Computing Technology and Science
[9] S. Y. Koy, K. Jeony, R. Morales,"The HybrEx Model for Confidentiality and Privacy in Cloud Computing",2011
[10] A. Goel, S. Goel, "Security Issues in Cloud Computing", International Journal of Application or Innovation in Engineering & Management (IJAIEM),Vol. 1, Issue 4, 2012
[11] C. Patel, S. S. Chauhan, B. Patel, "A Data Security Framework for Mobile Cloud Computing", International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, Issue 2,2015.
[12] S. Y. Hashemi, P. S. Hesarlo, "Security, Privacy and Trust Challenges in Cloud Computing and Solutions",I.J. Computer Network and Information Security, 8, 34-40,2014.
[13] R. K. Kalluri, Dr. C. V. Guru Rao, "Addressing the Security, Privacy and Trust Challenges of Cloud Computing",(IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (5) , 6094-6097, 2014.
[14] A. A. Soofi, M. I. Khan, "A Review on Data Security in Cloud Computing", International Journal of Computer Applications (0975 – 8887) Volume 94 – No 5,2014
[15] Jayalakshmi S, H. kunder, "A Review Paper on RASP Data Perturbation for Confidential and Efficient Queries in the Cloud", INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN ELECTRICAL, ELECTRONICS, INSTRUMENTATION AND CONTROL ENGINEERING, Vol. 3, Special Issue 1, 2015
[16] Miss. R. Begum,Mr. R.N. Kumar and Mr. V. Kishore,"Data Confidentiality Scalability and Accountability (DCSA) in Cloud Computing", International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2, Issue 11, 2012.
[17] L. V. Singh, A. V. Bole, "Security Issues of Cloud Computing- A Survey", IJARCSMS, Volume 3, Issue 1, 2015.

Rajpreet Kaur, Paramjeet Singh, Shaveta Rani , "Migrated Encrypted Data in Cloud using Data Slicing Approach," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.286-290, 2018.

Pattern matching is a technique to find the given pattern over the text within a database. Different types of algorithms are used to find the desired pattern over a text. For easy retrieval of DNA sequences of various diseases which are stored in large databases and comparison happens in sequence analysis. By using DNA pattern matching if it is found that a particular sequence occurs again and again and by counting the no of occurrence to find the existence and intensity of a disease. Compression is a technique for reducing the quantity of data used to represent any content without excessively reducing the quality of the data(ie. image, video etc.). Data compression is the process of encoding information using fewer bits than an uncoded representation is also making a use of specific encoding schemes. This procedure also reduces the number of bits required to store over the disk. For large amount of data, compression is a technique that makes storing easier. Different techniques are used for data compression. In this paper to find out a particular pattern in the given compressed DNA sequence using Brute-force, Boyer-Moor and KMP string matching algorithm and also measure performance of those algorithms more efficiently. Those algorithms are executed over the compressed DNA sequence to measure their performances and to avoid wasteful comparison. Two different techniques, ¼ th compression and Huffman compression is used for compressing the DNA sequences and compared which one is better among these two different techniques for pattern matching.

Bioinformatics, DNA Pattern Matching, Brute-force, Boyer-Moor, KMP, ¼ th compression, Huffman coding

[1] S.T. Klein, D. Shapira, “A New Compression Method for Compressed Matching”,IEEE conference on Data Compression (DCC), August, 2002.
[2] E.S.de Moura,G.Navarro,N.Ziviani and R.Baeza-Yates,“Direct pattern matching on compressed text”, In Proc. 5th International Symp. on String Processing and Information Retrieval, IEEE Computer Society, pp. 90-95,1998.
[3] Raju Bhukya, DVLN Somayajulu, “Exact Multiple Pattern Matching Algorithm using DNA Sequence and Pattern Pair”,International Journal of Computer Applications (IJCA),Vol. 17 No.8,pp: 32-38, March 2011.
[4] G.Navarro,T.Kida,M.Takeda,A.Shinohara,S.Arikawa, “Faster Approximate String Matching over Compressed Text”,IEEE conference on Data Compression (DCC),August, 2002.
[5] Mamta Sharma,”Compression Using Huffman Coding”,International Journal of Computer Science and Network Security(IJCSNS), VOL.10 No.5, May 2010.
[6] Priya jain,Shikha Pandey, “Comparative Study on Text Pattern Matching for Heterogeneous System”, International Journal of Computer Science & Engineering Technology (IJCSET), Vol. 3 No. 11 , pp: 537-543, Nov 2012.
[7] S.RAJESH,S.PRATHIMA,Dr.L.S.S.REDDY,“Unusual Pattern Detection in DNA Database Using KMP Algorithm”, International Journal of Computer Applications, Vo;.1, No. 22, pp: 1-5, 2010.
[8] Panwei Cao, Suping Wu, “Parallel Research on KMP Algorithm”, IEEE International Conference on Consumer Electronics, Communications and Networks (CECNet), May, 2011.
[9] Lei Chen, Shiyong Lu, Jeffrey Ram, “Compressed Pattern Matching in DNA Sequences”, Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference (CSB 2004).

G. Dutta, A. Mukherjee, "A Novel Approaches to Study Different DNA Pattern Matching Algorithms over Two Compression Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.291-295, 2018.

Popularly used recommendation for product is very important, also creating association among them serves with best recommendation. In this work FP Growth is used which calculates scores for final recommendation. Different approaches are used for dealing with different keywords to suggest recommendation. Efficient recommendation can be provided by creating relationships between associated books. Recommendation system recommend items and products depending on user`s interest and preferences. The proposed recommendation system for books is based on calculating scores and frequency by using opinion mining and sentimental analysis.

Opinion Mining, FP Growth, Recommendation System, Sentimental Analysis.

[1] P Devika, R C Jisha and G P Sajeev, “A Novel Approach for Book Recommendation Systems,” Published in International Conference on Computational Intelligence and Computing Research, 2016 IEEE.
[2] A.K. Singh, A. Kumar, and A. K. Maurya, “Association rule mining for web usage data to improve websites,” in Advances in Engineering and Technology Research (ICAETR), 2014 International Conference on. IEEE, 2014, pp. 1–6.
[3] S. Rao and P. Gupta, “Implementing improved algorithm over apriori data mining association rule algorithm 1,” 2012.
[4] O. R. Za ́ıane, “Building a recommender agent for e-learning systems,” in Computers in Education, 2002. Proceedings. International Conference on. IEEE, 2002, pp. 55–59.
[5] R. Agrawal, R. Srikant et al., “Fast algorithms for mining association rules,” in Proc. 20th int. conf. very large data bases, VLDB, vol. 1215, 1994, pp. 487–499.
[6] P. Nagarnaik and A. Thomas, “Survey on recommendation system methods,” in Electronics and Communication Systems (ICECS), 2015 2nd International Conference on. IEEE, 2015, pp. 1496–1501.
[7] J. Yang, Z. Li, W. Xiang, and L. Xiao, “An improved apriori algorithm based on features,” in Computational Intelligence and Security (CIS), 2013 9th International Conference on. IEEE, 2013, pp. 125–128.
[8] Y. W. Lo and V. Potdar, “A review of opinion mining and sentiment classification framework in social networks,” in 2009 3rd IEEE International Conference on Digital Ecosystems and Technologies. Ieee, 2009, pp. 396–401.
[9] P. Jomsri, “Book recommendation system for digital library based on user profiles by using association rule,” in Innovative Computing Technology (INTECH), 2014 Fourth International Conference on. IEEE, 2014, pp. 130–134.
[10] M. Al-Maolegi and B. Arkok, “An improved apriori algorithm for association rules,” arXiv preprint arXiv:1403.3948, 2014.
[11] International Journal of Scientific Research in Computer Sciences and Engineering (ISSN: 2320-7639).

Jaya Chauhan, Pragya Shukla, Nilima Karankar, "Book Recommendation System using Multiple User Opinion," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.296-301, 2018.

Cloud computing is an emerging technology the way that organizations manage their data, owing to its attractive features such as robustness, low cost, and ubiquitous nature. However, privacy concerns arise whenever sensitive data is out-sourced to the cloud where the data is processed and stored. The fact that users no longer have physical possession of the outsourced data makes it a formidable task to achieve the data confidentiality and integrity. As the data, in most cases encrypted, have to be not only stored, but also processed in clouds, the cryptography-based data confidentiality and integrity protection approaches are not adequate to satisfy the security requirements. Proxy re-encryption serves as a promising solution to secure the data sharing in the cloud computing. It enables a data owner to encrypt shared data in cloud under its own public key, which is further transformed by a semi-trusted cloud server into an encryption intended for the legitimate recipient for access control. To achieve a flexible and fine-grained access control on the outsourced data in cloud environment, in this paper we design a functional encryption system.

Cloud Computing Component, Privacy-Preserving methods, Privacy-Preserving Algorithms

[1] Wang J, Zhao Y et al, “Providing Privacy Preserving in cloud computing”, International Conference on Test and Measurement, vol 2(2009), 213–216.
[2] Greveler U, Justus b et al., “A Privacy Preserving System for Cloud Computing”, 11th IEEE International Conference on Computer and Information Technology, 648–653(2011).
[3] G. Ateniese, K. Benson, and S. Hohenberger, “KeyPrivate Proxy Re-Encryption”, Proc. Topics in Cryptology, 2009, pp. 279-294.
[4] Assad Abbas and Samee U. Khan, Senior Member, IEEE,
“A Review on the State-of-the-Art Privacy-Preserving Approaches in the e-Health Clouds”, IEEE journal of biomedical and health informatics, vol. 18, no. 4, july 2014.
[5] Zhiguang Qin , Hu Xiong, Shikun Wu, and Jennifer Batamuliza, “A Survey of Proxy Re-Encryption for Secure Data Sharing in Cloud Computing”, JOURNAL OF L ATEX CLASS FILES, VOL. 13, NO. 9, SEPTEMBER 2014.
[6] P. Angin et al., “An entity-centric approach for privacy and identity management in cloud computing,” in Reliable Distributed Systems, 201029th IEEE Symposium on, New Delhi, IEEE. 2010, pp. 177–183.
[7] Zhou M, Mu Y et al, ”Privacy-Preserved Access Control 3. for Cloud Computing”, International Joint Conference of IEEE TrustCom-11/IEEE ICESS- 11/FCST-11, 83–90(2011).
[8] Mu, Y., Varadharajan, V., and Nguyen, K. Q. (1999) “Delegated Decryption”, Proceedings of the 7th IMA International Conference on Cryptography and Coding, Cirencester, UK, pp.258-269.
[9] Amazon Elastic Computer Cloud (EC2). http://aws.amazon.com/ec2/
[10] Amazon Simple Storage Service (S3). http://aws.amazon.com/s3/
[11] RuWei Huang, Si Yu, Wei Zhuang, XiaoLin Gui, Design of Privacy-Preserving Cloud Storage Framework, 2010 Ninth International Conference on Grid and Cloud Computing
[12] Yu, S., Wang, C., Ren, K., Lou W, “Achieving secure, scalable, and fine-granied data access control in cloud computing”, INFOCOM’10: 29th IEEE international conference on computer communications, San Diego, CA, USA, pp, 534-542(2010).
[13] W. Sharon Inbarani , G. Shenbagamoorthy, C. Kumar Charlie Paul, “Proxy Re-encryption Schemes for Data Storage Security in Cloud- A Survey”, International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 1, January- 2013 ISSN: 2278-0181.
[14] Nagaraju.P,Nagamalleswara rao .N.,” A Detailed Study of Security Aspects in Cloud Computing”, International Journal of Emerging Technologies in Engineering Research (IJETER),Volume 4, Issue 6, June (2016).

Nagaraju. P, Nagamalleswara Rao.N, Vinod Kumar .Ch.R, "A Privacy Preserving cloud Storage Framework by using Server Re-encryption Mechanism (SRM)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.302-309, 2018.

In Modern World, an exponential growth of web services is been observed over the Internet, This offers a big challenge for the optimal selection of the best web service among a group of web services with similar functionalities. The selection process must be made in order to determine which relevant Web services would satisfy a user`s needs. In this paper, a methodology for web service selection combines prefiltering followed by skyline selection. The K-Means clustering technique act as a prefiltering step for grouping similar web services based on the similar quality of service and filter out unrelated web services. From the set of filtered web services, Skyline technique will obtain a set of non-dominant web services and select the skyline services as the best candidate services. This Approach will reduce the searching space and increase the performance of the service selection. More precisely, we propose a model for the selection of Web services based on nonfunctional criteria of QoS. In order to show the feasibility performance study of the proposed architecture, the result of the test shows improvement in the web service selection.

Web Service Selection, Quality of Service (QoS), K-Means Clustering, Skyline

Ronak R Kayastha, JwalantBaria, “A survey on Web Service Selection and Ranking Methods”, International Journal of Computer Applications 117(16):5-8, May 2015.
[2]. LalitPurohit, Sandeep Kumar, “Exploring K-Means Clustering and skyline for Web Service Selection”, 2016 11th international conference on industrial and information systems(ICIIS).
[3]. WalidSerrai ,AbdelkrimAbdelli , Lynda Mokdad , YoucefHammal, “An Efficient Approach for Web Service Selection”, ISCC 2016, PEDISWESA.
[4]. NarimanAmmar, Zaki Malik, BrahimMedjahed, Mohammed Alodib, “K-Anonymity Based Approach For Privacy-Preserving Web Service Selection”, 2015 IEEE International Conference on Web Services.
[5]. Banage T. G. S. Kumara, Incheon Paik, T. H. A. S. Siriweera,Koswatte R. C. Koswatte, “QoS Aware Service Clustering to Bootstrap the Web Service Selection”, 2017 IEEE 14th International Conference on Services Computing.
[6]. Zainab Aljazzaf, “Bootstrapping quality of Web Services”, in Journal of King Saud University Computer and Information Sciences (2015) pages 27, 323–333, 2015
[7]. HoucineBelouaar ,OkbaKazar, KhaledRezeg, “Web service selection based on TOPSIS algorithm”, 2017 International Conference on Mathematics and information Technology, Adrar, Algeria – December 4 - 5, 2017.
[8]. Ronak R Kayastha, JwalantBaria, “A survey on Web Service Selection and Ranking Methods”, International Journal of Computer Applications 117(16):5-8, May 2015.
[9]. Y. Xia, P. Chen, L. Bao, M. Wang, J. Yang, “A qos-aware web service selection algorithm based on clustering in Web Services”, (ICWS), 2011 IEEE International Conference on, July 2011, pp. 428– 435.
[10]. K. Zheng, H. Xiong, Y. Cui, J. Chen, L. Han, “User clustering based web service discovery”, 2012 Sixth International Conference on Internet Computing for Science and Engineering, April 2012, pp. 276–279.
[11]. A. Ouadah, K. Benouaret, A. Hadjali, F. Nader, “Combining skyline and multi-criteria decision methods to enhance web services selection,” inProgrammingandSystems(ISPS),201512thInternational Symposium on, April 2015, pp. 1–8.
[12]. Jian Wu, Liang Chen, Tingting Liang,” Selecting Dynamic Skyline Services for QoS-based Service Composition”, College of computer science and technology, Zhejiang University, Hangzhou, China.
[13]. Yue Wang, You Song, Mingyang Liang,” A Skyline-based Efficient Web Service Selection Method Supporting Frequent Requests “proceedings of the 2016 IEEE 20th international conference on computer supported cooperative work in design.
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Shahidul Islam, S.Britto Ramesh Kumar, Ab Rashid Dar, "An Approach for an Effective Web Service Selection using Filtering and Skyline Method," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.310-316, 2018.

The devastating effect of vector-borne diseases is well-known in tropical and sub-tropical regions across the globe. Mosquitos serve as vectors to spread diseases such as dengue and malaria which cause wide-spread desolation on a yearly basis. By carrying contagious pathogens, they infect millions of people across various countries, putting the health of many at risk and causing numerous fatalities. Measures of prevention and control prove to be widely unsuccessful due to the lack of a targeted approach. This results in the sub-optimal allocation of over-stretched healthcare and financial resources. Moreover, traditional control methods prove to be hazardous when used excessively and indiscriminately. They are recommended to be used only where needed. By virtue of the factors discussed, there arises a need for a scientific method to predict areas with high susceptibility and a system designed to scale the model to larger geographical areas.

Human Mosquito Contact Rate, Infected Class, SIR Model, Susceptible Class

[1] Ram Singh, Naveen Sharma, "Computational Modelling and Analysis of Transmission dynamics of Zika Virus Based on Treatment", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 1, pp.111-116, March-April.2018
[2] Oloko-oba Mustapha O., Badru R. A, Popoola O. S., Alaga T. A., Ogunyemi S. A., Samson S. A., "Assessment of Filling Station in Ilorin, Kwara State, Nigeria Using Geospatial Techniques", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 1, Issue 2, pp.69-74, September-October.2016

Ayush Sachdeva, "V2P (Vector to People) Disease Prediction : A Differential Equation Approach," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.317-321, 2018.

Fused Medical image of different modalities produces more explanatory image compared to the input images considered separately. This is useful for the medical practitioners for better treatment planning for the patient. In this paper we have experimented with various mathematical transforms to fuse Computed Tomography (CT) and Magnetic Resonance imaging (MRI) scans of lumber spine. CT images mainly depict more information related to bones of the scanned body part whereas MR images provide the details of soft tissues more clearly. CT and MR images have been aligned / registered with each other to achieve better fusion output. Ten cases have been considered for generating the image datasets for experiments. All the fused results are compared using four quantitative quality assessment parameters: entropy, standard deviation, fusion factor and fusion symmetry and also by qualitative way. Quantitative and qualitative assessment indicates that fused images generated by fast walsh hadamard transform carry symmetrically good amount of information from both images and of good contrast. These images can be used for better patient treatment planning by medical practitioners.

Medical image processing; image fusion, image transforms, CT, MR

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B.N. Palkar, D. Mishra, "Fusion of CT and MR scans of lumbar spine using discrete image transforms," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.322-330, 2018.