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Cloud computing is the recent area of research where many applications are used. It provides virtual resources and It is based on parallel computing, distributed computing and grid computing which is the extension of previous computing. In this paper, we have introduced the fundamental of cloud computing, brief introduction of task scheduling in cloud environment and its classification of task scheduling such as heuristic, energy efficient and hybrid scheduling. Discussed some performance metrics such as make span, resource cost scalability, reliability and resource utilization. Task scheduling is play very important role in the cloud environment because it’s provide efficient utilization of resources and also provide the user requirements. The major objective of any task scheduling problem is to minimize overall execution time and reduces the cost.

Cloud Computing, Task Scheduling, Static scheduling , Dynamic scheduling, efficiency

[1]. The NIST Definition of Cloud Computing by Peter Mell Timothy Grance NIST Special Publication 800-145, 2011.
[2]. Swapnil M Parikh” A Survey on Cloud Computing Resource Allocation Techniques”,IEEE 2013.
[3]. Raja Manish Singh, Sanchita Paul, and Abhishek Kumar. "Task Scheduling in Cloud Computing: Review." International Journal of Computer Science and Information Technologies ,Vol.No5.(6),pp.7940-7944, 2014.
[4] M.R.Garey and D.S. Johnsons,”Computer and intractability: a guide to the theory of NP-completeness”. New York:W.H. Freeman,1979.
[5] Mrs.S.Selvarani1; Dr.G.Sudha Sadhasivam, improved cost-based algorithm for task scheduling in Cloud computing ,IEEE 2010
[6] Bo Yang, Xiaofei Xu, Fengu Tan, Dong Ho,” A utilized based job scheduling algorithm for cloud computing considering reliability factor “, IEEE 2011
[7].Ashwani Kumar Yadav and Hardwari Lal Mandori,” Study of Task Scheduling Algorithms in the Cloud Computing Environment: A Review” International Journal of Computer Science and Information Technologies, Vol. 8 (4) , pp.462-468, 2017
[8].Huankai Chen, Frank Wang, Na Helian, Gbola Akanmu” User Priority Guided Min-Min Scheduling Algorithm for Cloud Computing”, IEEE 2013.
[9]. Anshul Ahluwalia and Vivek Sharma,” A Survey on Task Scheduling Techniques in Cloud Computing”, International Journal of Engineering Trends and Technology, Vol. 33(9), pp.448-452, March 2016.

Nidhi Rajak, Diwakar Shukla, "Task Scheduling in Cloud Computing Environment," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.513-515, 2018.

Diabetic retinopathy is the prime cause of vision loss in diabetic patients. It is caused by the damage of retinal blood vessels due to prolonged diabetes. This paper investigates on some image processing operations to extract blood vessels taking five feature set based on texture properties of images for the analysis of diabetic retinopathy. The proposed method stands out prominent in terms of specificity and accuracy.

diabetic retinopathy, sensitivity, specificity, accuracy, fundus image

[1]. G. B. Kande, P. V. Subbaiah, T. S. Savithri, "Feature extraction in digital fundus images,” Journal of Medical and Biological Engineering, vol. 29, No. 3, 2009.
[2]. N. Amrutkar, Y. Bandgar, S. Chitalkar, S.L. Tade, “Retinal blood vessel segmentation algorithm for diabetic retinopathy and abnormality detection using image subtraction,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol.2, Issue 4, April 2012.
[3]. D.S.S. Raja and S. Vasuki, “Automatic detection of blood vessels in retinal images for diabetic retinopathy diagnosis,” Computational and Mathematical Methods in Medicine, Vol 2015, article id 419279, 2015.
[4]. C. Nayak, L. Kaur, “Retinal blood vessel segmentation for diabetic retinopathy using multilayered thresholding,” International Journal of Science and Research, Vol 4, Issue 6, ISSN-2319-7064, June 2015.
[5]. M.B. Wankhade, Dr. A.A. Gurjar, “Analysis of disease using retinal blood vessels detection”, International Journal of Engineering and Computer Science, Vol. 5, Issue 12, Pg. 19644-19647, ISSN-2319-7242, December 2016.
[6]. F. Shami, H. Seyedarabi, and A. Aghagolzadeh, “Better detection of retinal abnormalities by accurate detection of blood vessels in retina,” in Proceedings of the 22nd Iranian Conference on Electrical Engineering (ICEE’14), pp. 1493–1496,Tehran, Iran, May 2014.
[7]. Y. Hou, “Automatic segmentation of retinal blood vessels based on improved multiscale line detection,” Journal of Computing Science and Engineering, vol. 8, no. 2, pp. 119–128, 2014.
[8]. D. Marın, A. Aquino, M. E. Geg´undez-Arias, and J. M. Bravo, “A new supervised method for blood vessel segmentation in retinal images by using gray-level andmoment invariants-based features,” IEEE Transactions on Medical Imaging, vol. 30, no. 1, pp. 146–158, 2011.
[9]. X. Yin, B.W-H. Ng, J. He, Y. Zhang and D. Abbott, “Accurate image analysis of the retina using hessian matrix and binarisation of thresholded entropy with application of texture mapping,” PLoS ONE, vol. 9, no. 4, Article ID e95943, 2014.
[10]. M. M. Fraz, P. Remagnino, A. Hoppe, and S. A. Barman, “Retinal image analysis aimed at extraction of vascular structure using linear discriminant classifier,” in Proceedings of the International Conference on Computer Medical Applications (ICCMA’13), pp. 1–6, Sousse, Tunisia, January 2013.
[11]. U. T. V. Nguyen, A. Bhuiyan, L. A. F. Park, and K. Ramamohanarao, “An effective retinal blood vessel segmentation method using multi-scale line detection,” Pattern Recognition, vol. 46, no. 3, pp. 703–715, 2013.
[12]. D. Kayal and S. Banerjee, “A new dynamic thresholding based technique for detection of hard exudates in digital retinal fundus image,” in Proceedings of the 1st International Conference on Signal Processing and Integrated Networks (SPIN ’14), pp. 141–144, February 2014.

[13]. P. M. Rokade and R. R. Manza, “Automatic detection of hard exudates in retinal images using haar wavelet transform,” International Journal of Application or Innovation in Engineering & Management, vol. 4, no. 5, pp. 402–410, 2015.
[14]. R.C. Gonzalez, R.E. Woods, “Digital Image Processing,” 2nd edition, PHI, New Delhi, India.
[15]. S. Annadurai, R. Shanmugalakshmi, “Fundamentals of Digital Image Processing” Pearson, 2011.
[16]. M. M. Fraz, P. Remagnino, A. Hoppe et al., “An ensemble classification-based approach applied to retinal blood vessel segmentation,” IEEE Transactions on Biomedical Engineering, vol. 59, no. 9, pp. 2538–2548, 2012.
[17]. R. V. Kalviainen and H. Uusitalo, “DIARETDB1 diabetic retinopathy database and evaluation protocol,” in Proceedings of the 11th Conference on Medical Image Understanding and Analysis (MIUA ’07), Warwick, UK, September 2007.
[18]. M. Niemeijer, B. van Ginneken, M. J. Cree et al., “Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs,” IEEE Transactions on Medical Imaging, vol. 29, no.1, pp. 185–195, 2009.
[19]. A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Transactions on Medical Imaging, vol. 19, no. 3, pp. 203–210, 2000.
[20]. P. Umorya, R. Singh, “A Comparative Based Review on Image Segmentation of Medical Image and its Technique,” International Journal of Scientific Research in Computer Sciences and Engineering, Vol. 5, Issue. 2, pp. 71-76, April, 2017.
[21]. A. Samant, S. Kadge, “Classification of a Retinal Disease based on Different Supervised Learning Techniques,” International Journal of Scientific Research in Network Security and Communication, Vol. 5, Issue. 3, pp. 9-13, June, 2017.

Abhinandan Kalita, "Analysis of Diabetic Retinopathy Based on Texture Properties of Retinal Images," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.516-522, 2018.

Semantic web, since its inception is approved for providing contexts to the search strings applicable to a given domain. Various frameworks or models based on semantic technologies utilizing semantic enhanced annotations and reasoning are recognized to deliver more relevant outputs. Thus, Semantic Web based recommenders are required for enriched recommendations in this age of information overload on the web. Contextual data may be used not only to represent domain objects and the user preferences in a more precise and refined way but also to apply better matching procedures with the aid of semantic similarity measures. Also, the presently used content-based recommendation techniques and collaborative filtering ones may certainly benefit from the introduction of explicit domain knowledge to produce recommendations using logical inferences applicable in that domain. Both recommender systems and semantic web complement each other and may aid in their progress mutually. In the last decade, there has been some research work done utilizing the semantic web technologies for aiding recommender systems, which play a significant role towards the goal of semantic web. In this paper, first, recommender systems (RS) have been discussed along with key research concerns, benefits and issues being explored and revisited. Second, scope and literature survey has been presented in the track of how semantic web technologies have contributed to enhancements of RS. Third, the role of various semantic web technologies has been explored and discussed for enhancement of present recommender systems. Fourth, useful inferences of the work done are tabulated along with the key discussions.

Semantic aided recommender systems, ontology, semantic web technologies, recommendation issues. Linked open dataset

[1] Beierle, Felix, Akiko Aizawa, and Joeran Beel. "Exploring Choice Overload in Related-Article Recommendations in Digital Libraries." Cornell University Library, 2017.
[2] Middleton, Stuart E. "Capturing knowledge of user preferences with recommender systems." Ph.D. diss., University of Southampton, 2003.
[3] Ahmad Khan, Aatif & Kumar Malik, Sanjay. “A semi search algorithm towards semantic search using domain ontologies.” International Journal of Autonomic Computing, 2017.
[4] Berners-Lee, T., Hendler, J. and Lassila, O. "The Semantic Web." Scientific American, pp. 19-37, 2001.
[5] Jaglan, Gaurav and Kumar Malik, Sanjay. “LOD: Linking and querying framework for shared data on Web”, International Conference on Cloud Computing, Data Science & Engineering, 2018.
[6] Allemang, Dean, and Jim Hendler. "Semantic Web for the Working Ontologist.” Morgan Kaufman Publishers, pp. 21-25, 2008.
[7] Bizer, Christian, Tom Heath, Kingsley Idehen, and Tim Berners-Lee. "Linked data on the web (LDOW2008)" In Proceedings of the 17th international conference on World Wide Web, ACM, pp. 1265-1266, 2008.
[8] Horrocks, Ian, Bijan Parsia, Peter Patel-Schneider, and James Hendler. "Semantic web architecture: Stack or two towers?." In International Workshop on Principles and Practice of Semantic Web Reasoning,. Springer, Berlin, Heidelberg, pp. 37-41, 2005.
[9] Hanani, Uri, Bracha Shapira, and Peretz Shoval. "Information filtering: Overview of issues, research and systems." User modeling and user-adapted interaction 11.3, pp. 203-259, 2001.
[10] Major, C. H., & Savin-Baden, M. “An introduction to qualitative research synthesis: Managing the information explosion in social science research” 2010.
[12] Gupta, A., Lamba, H., & Kumaraguru, P., “Analyzing fake content on Twitter” In eCrime Researchers Summit (eCRS), IEEE, pp. 1-12, 2013.
[11]Zahoor, S. Addressing Cold Start Problem in Recommendation Systems with Collaborative filtering and Reverse Collaborative Filtering, In International Journal of Computer Sciences and Engineering pp. 211–214, 2018.
[13] Ricci, Francesco, Lior Rokach, and Bracha Shapira. "Recommender systems: introduction and challenges." Recommender systems handbook. Springer, Boston, MA, pp. 1-34, 2015.
[14] McAuley, J., Pandey, R., & Leskovec, J., “Inferring networks of substitutable and complementary products” In Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 785-794, 2015.
[15] Aggarwal, Charu C. “Recommender systems", Springer International Publishing, 2016
[16] Yeung, Chi Ho. "Do recommender systems benefit users? a modeling approach", Journal of Statistical Mechanics: Theory and Experiment, 2016.
[17] Choudhary, V., & Zhang, Z. J., “Recommender systems and consumer product search” Tech. report, Working Paper, 2016.
[18] Di Noia, Tommaso, Iván Cantador, and Vito Claudio Ostuni. "Linked open data-enabled recommender systems: ESWC 2014 challenge on the book recommendation" Semantic Web Evaluation Challenge. Springer, Cham, 2014.
[19] Chen, Li, Guanliang Chen, and Feng Wang. "Recommender systems based on user reviews: the state of the art" User Modeling and User-Adapted Interaction 25, no. 2, pp. 99-154, 2015.
[20] Bobadilla, Jesús, Fernando Ortega, Antonio Hernando, and Abraham Gutiérrez. "Recommender systems survey." Knowledge-based systems 46, pp. 109-132, 2013.
[21] Resnick, Paul, and Hal R. Varian. "Recommender systems." Communications of the ACM 40, no. 3, pp. 56-58, 1997.
[22] Espín, Vanesa, María V. Hurtado, and Manuel Noguera. "Nutrition for Elder Care: a nutritional semantic recommender system for the elderly." Expert Systems 33(2), pp. 201-210, 2016.
[23] Lu, Wei, Fu-lai Chung, Kunfeng Lai, and Liang Zhang. "Recommender system based on scarce information mining." Neural Networks 93, pp. 256-266, 2017.
[24] Lu, W., Chung, F. L., Lai, K., & Zhang, L. “Recommender system based on scarce information mining”. Neural Networks, 93, pp. 256-266, 2017.
[25] Ekstrand, Michael D., Mucun Tian, Ion Madrazo Azpiazu, Jennifer D. Ekstrand, Oghenemaro Anuyah, David McNeill, and Maria Soledad Pera. "All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness." In Conference on Fairness, Accountability, and Transparency, pp. 172-186, 2018.
[26] Alharthi, H., & Inkpen, D., “Content-based recommender system enriched with wordnet synsets” In International Conference on Intelligent Text Processing and Computational Linguistics, Springer, Cham, pp. 295-308, 2015.
[27] Dzyabura, D., & Hauser, J. R., “Recommending Products When Consumers Learn Their Preferences”, 2016.
[28] Knijnenburg, B. P., & Berkovsky, S., “Privacy for Recommender Systems: Tutorial Abstract” In Proceedings of the Eleventh ACM Conference on Recommender Systems ACM, pp. 394-395, 2017.
[29] Sir, M., Bradac, Z., & Fiedler, P., ”Ontology versus Database” IFAC-PapersOnLine, 48(4),pp. 220-225, 2015.
[30] Noy, N. F., & McGuinness, D. L., “Ontology Development 101: A Guide to Creating Your First Ontology-what is an ontology and why we need it?”, 2016.
[31] Forbes, David E., et al. "Ontology Engineering." Ontology Engineering Applications in Healthcare and Workforce Management Systems. Springer, Cham, pp. 27-40, 2018.
[32] Stutzman, Fred; Gross, Ralph; and Acquisti, Alessandro "Silent Listeners: The Evolution of Privacy and Disclosure on Facebook" Journal of Privacy and Confidentiality: Vol. 4 : Iss. 2, Article 2, 2013.
[33] Bernstein, Abraham, James Hendler, and Natalya Noy. "A new look at the semantic web." Communications of the ACM 59, no. 9, pp. 35-37, 2016.
[34] Rivera, Luis Cabrera, et al. "Semantic Recommender System for Touristic Context Based on Linked Data." Information Fusion and Geographic Information Systems (IF&GIS`2015). Springer, Cham, pp. 77-89, 2015.
[35] Peska, Ladislav, and Peter Vojtas. "Hybrid recommending exploiting multiple DBpedia language editions." Semantic Web Evaluation Challenge. Springer, Cham, 2014.
[36] Aguilar, Jose, Priscila Valdiviezo-Díaz, and Guido Riofrio. "A general framework for intelligent recommender systems." Applied computing and informatics 13.2, pp. 147-160, 2017.
[37] Fraihat, Salam, and Qusai Shambour. "A framework of semantic recommender system for e-learning." Journal of Software 10(3), pp. 317-330, 2015.
[38] Nadee, Wanvimol. "Modelling user profiles for recommender systems." PhD diss., Queensland University of Technology, 2016.
[39] Gunes, Ihsan, Cihan Kaleli, Alper Bilge, and Huseyin Polat. "Shilling attacks against recommender systems: a comprehensive survey." Artificial Intelligence Review 42, no. 4 pp. 767-799, 2014.
[40] Garcia Esparza, S., O’Mahony, M.P., Smyth, B., “Effective product recommendation using the real-time web.” In: Bramer, M., Petridis, M., Hopgood, A. (eds.) Proceedings of the 30th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, UK, Springer, pp. 5–18, 2010.
[41] Zuva, Tranos, Sunday O. Ojo, Seleman Ngwira, and Keneilwe Zuva. "A survey of recommender systems techniques, challenges and evaluation metrics" International Journal of Emerging Technology and Advanced Engineering 2, no. 11, pp. 382-386, 2012.
[42] Shokeen, J. On Measuring the Role of Social Networks in Project Recommendation, In International Journal of Computer Sciences and Engineering. pp. 215–219. 2018.

G. Jaglan, S.K. Malik, "Blending Semantic Web with Recommender Systems," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.523-531, 2018.

Cloud computing is an Internet-based computing pattern through which shared resources are provided to devices on demand. In order to provide safe and secure operation propose a hierarchical attribute based access control scheme by extending cipher text-policy attribute-based encryption (CP-ABE) with a hierarchical structure of multi authorities and exploiting attribute-based signature (ABS). The proposed scheme not only achieves scalability due to its hierarchical structure, but also inherits fine-grained access control with authentication in supporting write privilege on outsourced data in cloud computing. In addition, we decouple the task of policy management from security enforcement by using the extensible access control markup language (XACML) framework. Extensive analysis shows that our scheme is both efficient and scalable in dealing with access control for out- sourced data in cloud computing.

CP-ABE,ABS,XACML

[1] https://apprenda.com/library/cloud/introduction-to-cloud-computing/
[2] https://www.hindawi.com/journals/scn/2017/2713595/
[3] https://eprint.iacr.org/2014/612.pdf
[4] R. Nallakumar, N. Sengottaiyan, S. Nithya, “A Survey of Task Scheduling Methods in Cloud Computing”, International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.9-13, 2014. https://www.hindawi.com/journals/scn/2017/2713595/
[5] S. Roschke, et aI., "Intrusion Detection in the Cloud," presented at the Eighth IEEE International Conference on Dependable, AutonomIc and Secure Computing, Chengdu, China, 2009.
[6] B.P. Rimal, Choi Eunmi, I. Lumb, “A Taxonomy and Survey of Cloud Computing Systems”, Intl. Joint Conference on INC, IMS and IDC, 2009, pp. 44-51,Seoul, Aug, 2009. DOI: 10.1109/NCM.2009.218
[7] B. R. Kandukuri, R. Paturi V, A. Rakshit, “Cloud Security Issues”, In Proceedings of IEEE International Conference on Services Computing, pp. 517-520, 2009. K. Elissa, “Title of paper if known,” unpublished.
[8] Cloud Computing. Wikipdia. Available at http://en.wikipedia.org/wiki/Cloud_computing
[9] Cong Wang, Qian Wang, KuiRen, and Wenjing Lou, “Ensuring Data Storage Security in Cloud Computing,” 17th International workshop on Quality of Service, USA, pp.1-9, July 13-15, 2009, ISBN: 978-1-4244-3875-4
[10] C. Weinhardt, A. Anandasivam, B. Blau, and J. Stosser.”Business Models in the Service World.”IT Professional, vol. 11, pp. 28-33, 2009.
[11] B. Patel, S. Patel, “Various Load Balancing Algorithms in cloud computing”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol¬.1, Issue.¬2 pp.23-29, 2015.

R. Manimegalai, .A. Priyadharshini, "Privacy Preserving Public Auditing with Data Storage Security in Cloud Computing : An Overview," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.532-534, 2018.

Slant is a common artefact inwhich handwritten word takes the form of slope and handwriting recognition system becomes less accurate. For this reason slant estimation and correction is a standard step in handwriting recognition systems for processing written text after skew detection and correction. If Skew correction and Slant correction are done successfully then recognition part will be more prominent. Handwritten characters of natural handwriting are usually italicized due to mechanism of handwriting and personality. In this paper the slant of the Bengali online handwritten words has been estimated and has been corrected. In slant estimation and correction, we have used Projection profile histogram method to detect core region or busy zone of the handwritten words and we defined the estimated head line (matra) and estimated base line of the words. Then we have detected almost vertical (considering a threshold value of angle 45 degree) straight lines which meet or closer to meet the head line (matra) and baseline of the core region present inside the words. After detecting those vertical straight lines we have calculated angle of slant of each vertical straight lines separately and calculated average slant angle. Then rotate all the pixels into the particular slant angle to do slant correction considering the base as fixed point. We have tested this proposed technique on 2655 Bengali handwritten words and have achieved an outstanding result of 96 percentage of accuracy.

Online Handwriting, Head Line, Base Line, Core Region, Virtical Lines, Slant

[1] Mazumdar, Bijaychandra,“The history of the Bengali language” (Repr. [d. Ausg.] Calcutta, 1920. ed.). New Delhi: Asian Educational Services. p. 57. ISBN 8120614526, 2000.
[2] Fitrianingsih, Sarifuddin Madenda, Suryarini Widodo, Rodiah, “Slant Correction and Detection for Offline Cursive Handwriting using 2D Affine Transform”, International Journal of Engineering Research & Technology (IJERT), Vol. 5 Issue 08, August-2016.
[3] A. Papandreou, B. Gatos, “Slant estimation and core-region detection for handwritten Latin words”, Pattern Recognition Letters, Vol. 35 (2014) , pp- 16–22.
[4] Ch. N. Manisha, Y.K. Sundara Krishna, E. Sreenivasa Reddy, “Slant Correction for Offline Handwritten Telugu Isolated Characters and Cursive Words”, International Journal of Applied Engineering Research, Volume 11, Number 4 (2016) pp 2755-2760.
[5] Alceu de S. Britto Jr, Robert Sabourin, Edouard Lethelier, Flávio Bortolozzi and Ching Y. Suen. (1999). “Slant normalization of handwritten numeral strings”. [Online] Available: www.etsmtl.ca/ETS/media/ImagesETS/Labo/LIVIA/Publications/1999/BrittoISKDM.pdf.
[6] Ali, M. A., and Jumari, K. B., “Base-Area Detection and Slant Correction Techniques Applied for Arabic Handwritten Characters Recognition Systems”, Int. Conf. on Artificial Intelligence and Pattern Recognition, Orlando, USA. 2009. pp. 133-138.

G.Mandal, T. Biswas, "Slant Estimation and Correction for Online Handwritten Bengali Words," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.535-539, 2018.

Providing different scenarios of service is the prime bedrock of Cloud computing. Cloud computing offer various services to its associated users. The service offered by cloud depends up on the nature of service subscribed by users associated with it. Storage as-a-service is one of the offered services provided by cloud via internet to its clients. Huge data resides in cloud storage management. To ensure security, cloud shall guarantee that our information is protected in all odds and be accessible whenever need arises. In circumstances like Fire, Flood, Tremors, Technical Snags, Equipment Failure and Coincidental deletion, our data access may be lost. To keep up the effectiveness of the outsourced data there need to be timely implementation of Data Backup involving Recovery Services. This paper provides the comprehensive investigation of different backup methods utilized for Cloud Computing with respect to technical snags and provide the gateways to access shortcomings in cloud backup aspect.

Technical Snags, Central Repository, Remote Repository, Parity Cloud Service, Seed Block

[1] Shubhashis Sengupta and K.M. Annervaz “Multi-site data distribution for disaster recovery—A planning framework,” ELSEVIER, 2014.
[2] Yashodha Sambrani and Dr. Rajashekarappa “Efficient Data Backup Mechanism for Cloud Computing,” International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, Issue 7, 2016.
[3] Rodrigo S. Coutoa, Stefano Secci, Miguel Elias M. Campista and Luís Henrique M.K. Costa “Server placement with shared backups for disaster-resilient clouds,” ELSEVIER, 2015.
[4] Mayuri Tidke, Vijayshree Jadhav, Sonali Parab, Shubhrata Patil and Y. K. Patil “Seed Block Algorithm: A New Approach for Data Back-up and Recovery in Cloud Computing,” International Journal of Engineering Science and Computing, Vol. 6, no. 4, 2016.doi: 10.4010/2016.941.
[5] Shilpi U. Vishwakarma and Praveen D. Soni “Cloud Mirroring: A Technique of Data Recovery,” International Journal of Current Engineering and Technology, Vol 5, no. 2, 2015.
[6] Praveen S. Challagidad, Ambika S. Dalawai and Mahantesh N. Birje “Efficient and Reliable Data Recovery Technique in Cloud Computing,” Internet of Things and Cloud Computing, Vol. 5, No. 5-1, 2017, pp. 13-18. doi: 10.11648/j.iotcc.s.2017050501.13
[7] Megha Rani Raigonda1 and Tahseen Fatima2 “A Cloud Based Automatic Recovery and Backup System with Video Compression,” International Journal of Engineering and Computer Science, Vol 5, no. 9, 2016.
[8] Chi-won Song, Sungmin Park, Dong-wook Kim, Sooyong Kang, 2011, “Parity Cloud Service: A Privacy-Protected Personal Data Recovery Service,” International Joint
[9] Tanay Kulkarni, Sumit Memane, Onkar Nene and Krupali Dhaygude “INTELLIGENT CLOUD SECURITY BACK-UP SYSTEM,” International Journal of Technical Research and Applications, Vol. 3, no. 2, 2015, PP. 241-245
[10] Yoichiro Ueno, Noriharu Miyaho, Shuichi Suzuki and Kazuo Ichihara “Performance Evaluation of a Disaster Recovery System and Practical Network Applications in Cloud Computing Envionments,” International Journal on Advances in Networks and Services, vol 4 no 1 & 2, 2

Danish Nazir and Mir Aman Sheheryar, "Data Backup and Recovery Methods in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.540-544, 2018.

Basalt Fiber-Reinforced Polymer (BFRP) bars are rising as an important choice as inside reinforcement for concrete structures, especially when erosion protection or electromagnetic straightforwardness is looked for. BFRP has been effectively utilizing as a part of the fortifying of the segments and beams. It additionally been executed as reinforcement in the beam and got the satisfactory flexural quality which is progressively the steel Basalt composite bars are made by using basalt filaments and a pitch epoxy folio. They are non-destructive, comprise of 80% strands and have a tensile quality three times that of the steel bar typically utilized as a part of building development. Wherever consumption issues exist, basalt fiber composite bars can possibly supplant steel in reinforced concrete. Contrasted with FRP bar as they are utilized for little sum in building industry the basalt bar are less expensive and have better strength under extraordinary conditions. It is presumed that basalt bars are of awesome enthusiasm for the building business and can be utilized for instance in connect decks, seaward structure and in component structures.

Basalt, Fibre, Polymer, Steel, Concrete, Fibre Cincrete, Reinforced Concrete

[1] K.Tamilselvan and Dr. N. Balasundaram “Hybrid Fiber Reinforced Concrete Beam”, (March 2017).
[2] Anil Ronad Et Al. “Basalt Fiber Reinforced Geopolymer Concrete”, (Aug 2016).
[3] Arul Raj C., Baskar K, “Steel Fibre and Nylon Fibre Reinforced Concrete Beam” (April 2017).
[4] Criganz.T .Vad “Basalt Fibre as a Reinforcement of Polymer Composites”, (Periodica Polytechnica, Mechanical Engineering Pg 49, (2010).
[5] Ramakrishan, V.Panchalan “A New Construction Material Non Corrosive Basalt Reinforced Concrete”, Special Publication 229,253-270
[6] Wiberg .A “Strengthening of Concrete Using Cementious Carbon Fibre Composites”, (Poyal Technology, Stockholm, Sweden)
[7] C.B Kukreja and S.Chawla “Flexuaral Characterstic of Steel Fibre Reinforced Concrete) the Indian Concrete Journal Pg 58, 2007.
[8] Bolishetti Chaitanya, S Sunil Pratap Reddy, Muchakurthi Karthik, “An Experimental Investigation on Effect of High Temperatures on Steel”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.156-163, 2017.
[9] M.C.Nataraja, “Fibre Reinforced Concrete-Behaviour Properties and Application Indian Concrete Journal, Pg 248.
[10] Rana, “Some Studies On Steel Fibre Reinforced Concrete”, International Journal of Concrete Technology and Advanced Engineering, Volume 3, Issue 1, January 2013.
[11] ACI Committee 544, “State Of The Art Report on Fiber Reinforced Concrete”, ACI Manual of Concrete of Practice, Part 5, Pg 20-23.
[12] Dr.Muhanad Shalall, “Strength and Elasticity of Steel Fiber Reinforced Concrete at High Temperatures Journal of Engineering Development Vol 11, Sep 2008.
[13] Milind V Mohod, “Performance of Steel Fiber Reinforced Concrete”, Indian Journal of Engineering and Science, Vol 1, Pg 1-4.
[14] Marek Ubanski “Investigation on Concrete Reinforced with Basalt Rebars as an Effective Alternative of Convential RC Beams” International Journal of Engineering, vol 2, Pg 237.
[15] Subramanian R.V, “Reinforcement of Polymers by Basalt Bar”, International Composite News, Pg 1-10.
[16] Maceij Szumigala, “Flexuaral Behaviour of Full Scale Basalt Frp Beams” Expermental and Numerical Studies Scientific and Technical Conference Material Problem in Civil Engineering, Pg 518-525.

P. Hareesh, S.Pravin Kumar, "Experimental Investigation Study on Flexural Behaviour of Basalt Fiber Reinforced Concrete Beam with Steel Fibers," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.545-549, 2018.

The aim of automating the task of generating flow control designs for oil and gas flow comes with the different dimension constraints and cost factors. The resulted designs should satisfy given dimension boundaries and pre- specified conditions. To make the module more efficient and automate we combine the machine learning and constraint satisfaction module which resulted in reduction of time complexity and how the accuracy gets maintained. The result shows how the separate module of machine learning and optimization module work and how the results get vary when we combine both modules. The constraint we want to optimize are size and cost of the design. The main factors we considered for measuring performance are time complexity and accuracy.

Constraint Satisfaction Problem, Constraint Optimization, Optimization Engine, Machine Learning

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H.A. Kore, S.B. Mane, A. Madkaikar, "Size and Cost Optimization of AutoCAD Oil and Gas Control Flow Designs Using Constraint Satisfaction Problem and Machine Learning," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.550-555, 2018.

The energy consumption within cloud increases as fault or failure encountered within cloud computing. This paper presents the analysis of mechanisms used to decrease the energy consumption and enhances fault tolerance degree. The mechanisms which are discussed include both proactive and reactive fault tolerance. This study presents the comparatives analysis of techniques used to ensure fault tolerance and parameters which are enhanced through the application of the techniques. The modification to the existing techniques is required which is concluded through this proposed survey. VM Migration strategies are followed in order to migrate the load on the fittest virtual machine. This happens only if fault appears within the virtual machine. To preserve the VMs against the faults parametric comparison of existing techniques is required. Parametric enhancement is critical in future work.

Cloud computing, VM migration, virtual machine, data centre

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Sunila, Kamaljit Kaur, "Fault Aware Energy Efficient Mechanisms in Cloud: A Comprehensive Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.556-568, 2018.

With the limitation of the power utilization in wireless device nodes. The creation is intended to recover the hindrance of capacity breakdown and lifetime of the sensor node. Each sensor node is associated with a memory and has less power requirement. The main aspiration of this paper is to make the node active and consume less energy for transmitting or receiving the data from the sensor node. The paper considers three approaches such as LEACH and PEGASIS algorithm.

Leader-node selection approach, Leach algorithm

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Akash Gupta, Rishi Kumar Srivastava, "A Review on Wireless Sensor Network Using LEACH Protocol for Improving Lifespan of Sensor Nodes," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.569-572, 2018.