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Digital image watermarking (DIW)is the way toward embedding watermark into a digital image (DI) for validation and in this way protecting the DI from copyright encroachment. In this paper, a versatile undetectable watermarking plan is paper in dual watermarking process. It is expected that the simplicity with which advanced media can be replicated will prompt an expansion of copyright encroachment. The implement work calculation is depicted in subtle elements. The 2 transform and dual watermarking are useful in course in such an approach to misuse their appealing properties. In this method the wavelet coefficients of the cover image to embed the watermark. The four sub bands of wavelet coefficients can be used to watermark the image. The new DCT coefficients form the singular value decompositions triangular matrices. Then the inverse DCT transform is applied by the inverse DWT. Watermark embedded using this algorithm is highly imperceptible. This scheme is robust against all sorts of attacks. It has very high data hiding capacity.

Digital image Watermarking, Dual watermarking, Image processing, DWT;DCT;SVD

[1] Qi Han, Lei Han, Erfu Wang, Jie Yang, “Dual Watermarking for Image Tamper Detection and Self-recovery”. 2013 Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing. 978-0-7695-5120-3/13 $26.00 © 2013 IEEE
[2] Rakhee Lakhera1 , Alka Gulati2 , Shital Gupta, “A Novel Approach for Watermarking using Dual Watermarking Technique in Noisy Regions”. IJCEM International Journal of Computational Engineering & Management, ISSN (Online): 2230-7893, Vol. 15 Issue 5, September 2012
[3] Vagesh Porwal, Siddharth Gupta, “Digital Watermarking: A survey on image watermarking in Frequency Domain using Genetic Algorithm”. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 4 Issue 4, April 2015
[4] Dr. Rajukumar L Biradar, Manjunath R. Hudagi, Sachin A. Urabinahatti, “Dual Watermarking Techniques for Efficient Watermarks”. International Journal of Engineering Technology, Management and Applied Sciences www.ijetmas.com February 2016, Volume 4, Issue 2, ISSN 2349-4476
[5] VANDANA S INAMDAR1,∗ and PRITI P REGE2, “Dual watermarking technique with multiple biometric watermarks”. S¯adhan¯ a Vol. 39, Part 1, February 2014, pp. 3–26. c Indian Academy of Sciences
[6] Sasmita Mishra, Amitav Mahapatra,Pranati Mishra, “A Survey on Digital Watermarking Techniques”. (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 4 (3) , 2013, 451-456
[7] Mohamed RADOUANE, Rochdi MESSOUSSI, Raja TOUAHNI and Tarik BOUJIHA,” Robust Method of Digital Image Watermarking using SVD Transform on DWT Coefficients with Optimal Block”, 2014 IEEE.
[8] Zigang Chen, Lixiang Li, Haipeng Peng, Yuhong Liu, and Yixian Yang, “A Novel Digital Watermarking based on General Non-negative Matrix Factorization”. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TMM.2018.2794985, IEEE Transactions on Multimedia
[9] Ninny Mittal, Anand Singh Bisen, Rohit Gupta, “An Improved Digital Watermarking Technique Based on 5-DWT,FFT & SVD”. International Conference on Trends in Electronics and Informatics ICEI 2017. 978-1-5090-4257-9/17/$31.00 ©2017 IEEE
[10] Alifa D’Silva, Nayana Shenvi, “Data Security Using SVD Based Digital Watermarking Technique”. International Conference on Trends in Electronics and Informatics ICEI 2017, 978-1-5090-4257-9/17/$31.00 ©2017 IEEE
[11] Guang Hua, Guoan Bi, Yong Xiang, “Dual Channel Watermarking – A Filter Perspective”. 2016 International Conference on Progress in Informatics and Computing (PIC). 978-1-5090-3484-0/16/$31.00 ©2016 IEEE
[12] Jin-Xia Yang, Dan-Dan Niu, “A Novel Dual Watermarking Algorithm for Digital Audio”. 2017 17th IEEE International Conference on Communication Technology. 978-1-5090-3944-9/17/$31.00 ©2017 IEEE
[13] Qing Chen, Meng Xiong, “Dual Watermarking Based on Wavelet Transform for Data Protection in Smart Grid”. 2016 3rd International Conference on Information Science and Control Engineering. 978-1-5090-2534-3 /16 $31.00 © 2016 IEEE
[14] Jeebananda Panda, Indu Kumari, Nitish Goel, “Dual Segment Video Watermarking using Energy Efficient Technique”. 2016 1st India International Conference on Information Processing (IICIP). 978-1-4673-6984-8/16/$31.00 © 2016 IEEE
[15] Sawiya Kiatpapan and Toshiaki Kondo, “Sawiya Kiatpapan and Toshiaki Kondo”. 2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). 978-1-4799-7961-5/15/$31.00 c 2015 IEEE
[16] Jingchun Zhang, Yanxia Xu, Huijuan Ma, “Dual Watermarking Scheme Based on Dynamic Feature Set”. 2011 International Conference on Mechatronic Science, Electric Engineering and Computer August 19-22, 2011, Jilin, China. 978-1-61284-722-1/11/$26.00 ©2011 IEEE
[17] Daojing Li, Bo Zhang, “DWTC: A Dual Watermarking Scheme Based on Threshold Cryptography for Web Document”. 201O International Conforence on Computer Application and System Modeling (ICCASM 2010) 978-1-4244-7237-6/10/$26.00 ©201O IEEE

D.S. Somra, M. Gupta, "Enhanced Techniques using Dual watermarking for DWT, DCT and SVD," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.190-197, 2019.

In this paper, design and comparative analysis outline strategy for trans-multiplexer (TMUX) system for communication with different windowing techniques are analyzed. In this scheme four adjustable window techniques viz., Blackman window, Saramaki window, Kaiser Window and ultra-spherical windows with different modulation techniques are used for designing a multi-channel filter bank trans-multiplexer FB-TMUX. A comparative study of performance of these four windowing functions with different modulation techniques such as Cosine, sine, complex and Extended lapped transform (ELT) for the recommended roll-off factor (RF) and stop band attenuation (As) is designed for trans-multiplexer is presented. The objective functions of the design to reduce Inter-symbol-interference (ISI) and specifically inter-channel-interference (ICI).

Multirate, Filter Bank Transmultiplexer, Filter Bank, Extended lapped transform, ultraspherical window

[1]. Harris, F. J. (2004). Multirate signal processing for communication systems. Prentice Hall PTR.
[2]. Sohn, S. W., Lim, Y. B., Yun, J. J., Choi, H., & Bae, H. D. (2012). A filter bank and a self-tuning adaptive filter for the harmonic and interharmonic estimation in power signals. IEEE Transactions on Instrumentation and Measurement, 61(1), 64-73.
[3]. Taskovski, D., & Koleva, L. (2012). Measurement of harmonics in power systems using near perfect reconstruction filter banks. IEEE Transactions on Power Delivery, 27(2), 1025-1026.
[4]. Le, T. K. (2012, July). Automated method for scoring breast tissue microarray spots using quadrature mirror filters and support vector machines. In 2012 15th International Conference on Information Fusion (pp. 1868-1875). IEEE.
[5]. Chandran, S. (2003). Scheme for subband adaptive beamforming array implementation using quadrature mirror filter banks. Electronics Letters, 39(12), 891-892.
[6]. Chan, S. C., Pun, C. K., & Ho, K. L. (2004). New design and realization techniques for a class of perfect reconstruction two-channel FIR filterbanks and wavelets bases. IEEE Transactions on Signal Processing, 52(7), 2135-2141.
[7]. Afonso, V. X., Tompkins, W. J., Nguyen, T. Q., & Luo, S. (1999). ECG beat detection using filter banks. IEEE transactions on biomedical engineering, 46(2), 192-202.
[8]. Chiang, H. T., Phoong, S. M., & Lin, Y. P. (2007). Design of nonuniform filter bank transceivers for frequency selective channels. EURASIP Journal on Applied Signal Processing, 2007(1), 142-142.
[9]. Kumar, A., Pooja, R., & Singh, G. K. (2016). An efficient closed-form design method for nearly perfect reconstruction of non-uniform filter bank. ISA transactions, 61, 167-178.
[10]. Zhao, Y., & Swamy, M. N. S. (2013). The analysis and design of two-dimensional nearly-orthogonal symmetric wavelet filter banks. Multidimensional Systems and Signal Processing, 24(1), 199-218.
[11]. Dolecek, G. J. (2009). Design of wideband CIC compensator filter for a digital IF receiver. Digital Signal Processing, 19(5), 827-837.
[12]. Vaidyanathan, P. P. (1993). Multirate systems and filter banks. Pearson Education India.
[13]. Vishwakarma, A., Kumar, A., & Singh, G. K. (2016). Transmultiplexer filter bank systems: a research overview. International Journal of Signal and Imaging Systems Engineering, 9(3), 146-155.
[14]. Saxena, C., Murarka, P. D., & Gupta, H. K. Noise Reduction in ECG Signals Using Notch Filter.
[15]. Zhou, J., Wang, C., Li, X., & Zhou, X. (2015). Fast approximate hash table using extended counting Bloom filter. IJCSE, 11(4), 380-390.
[16]. Leelavathi, M. V., & SahanaDevi, K. J. (2016). Efficient Deep Learning for Big Data: A Review. IJCSE, 4(3), 30-35.
[17]. Lavania, K. K., & Kumar, R. (2012). Image enhancement using filtering techniques. International Journal on Computer Science and Engineering, 4(1), 14.
[18]. Mian, A. S., Bennamoun, M., & Owens, R. (2009). U.S. Patent Application No. 12/017,643.
[19]. Creusere, C. D., & Mitra, S. K. (1995). A simple method for designing high-quality prototype filters for M-band pseudo QMF banks. IEEE Transactions on Signal Processing, 43(4), 1005-1007.
[20]. kumar Soni, R., Jain, A., & Saxena, R. (2013). A design of IFIR prototype filter for Cosine Modulated Filterbank and Transmultiplexer. AEU-international Journal of Electronics and Communications, 67(2), 130-135.
[21]. Soni, R. K., Jain, A., & Saxena, R. (2010). An improved and simplified design of pseudo-transmultiplexer using Blackman window family. Digital Signal Processing, 20(3), 743-749.
[22]. Martin, P., Cruz-Roldán, F., & Saramaki, S. (2004, May). A new window for the design of cosine-modulated multirate systems. In 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No. 04CH37512) (Vol. 3, pp. III-529). IEEE.
[23]. Vishwakarma, A., Kumar, A., & Singh, G. K. (2015). A prototype filter design for cosine modulated transmultiplexer using weighted constrained least squares technique. AEU-International Journal of Electronics and Communications, 69(6), 915-922.
[24]. Kumar, A., Singh, G. K., & Kuldeep, B. (2011). An improved and simplified approach for designing cosine modulated filter bank using window technique. Journal of Mathematical Modelling and Algorithms, 10(3), 213-226.
[25]. Kumar, A., & Kuldeep, B. (2012). Design of M-channel cosine modulated filter bank using modified Exponential window. Journal of the Franklin Institute, 349(3), 1304-1315.
[26]. Datar, A., Jain, A., & Sharma, P. C. (2013). Design and performance analysis of adjustable window functions based cosine modulated filter banks. Digital Signal Processing, 23(1), 412-417.
[27]. Kumar, A., Singh, G. K., & Anand, R. S. (2011). A simple design method for the cosine-modulated filter banks using weighted constrained least square technique. Journal of the Franklin Institute, 348(4), 606-621.
[28]. Bergen, S. W., & Antoniou, A. (2007). An efficient closed-form design method for cosine-modulated filter banks using window functions. Signal Processing, 87(5), 811-823.
[29]. Kumar, A., Singh, G. K., & Anand, R. S. (2011). An improved closed form design method for the cosine modulated filter banks using windowing technique. Applied Soft Computing, 11(3), 3209-3217.
[30]. Hameed, K. A., & Elias, E. (2005, December). Extended lapped transforms with linear phase basis functions and perfect reconstruction. In 2005 12th IEEE International Conference on Electronics, Circuits and Systems (pp. 1-4). IEEE.
[31]. Alhava, J., Viholainen, A., & Renfors, M. (2003, May). Efficient implementation of complex exponentially-modulated filter banks. In Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS`03. (Vol. 4, pp. IV-IV). IEEE.
[32]. Cruz-Roldán, F., Amo-López, P., Maldonado-Bascón, S., & Lawson, S. S. (2002). An efficient and simple method for designing prototype filters for cosine-modulated pseudo-QMF banks. IEEE signal processing letters, 9(1), 29-31.

Zaffer Iqbal Mir, Javiad Ahmad Sheikh, Mehboob ul Amin, G.M. Bhat, "Design of Filter Bank Transmultiplexer System for Communication Using Different Modulation Technique," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.198-202, 2019.

Semantic matching is a kind of ontology matching technique that depends on linguistics info encoded in light weight ontologies to establish nodes that square measure semantically connected. Ontologies matching are associate operator that identifies those nodes within the two structures that semantically correspond to at least one another. Matching concept is assessed into two classes like Syntax and linguistics Structures. Syntax matching concept is mainly focuses on syntax supported to the acceptable compiler. Linguistics is the main accustomed resolve the given word victimization logical analysis. The main objective of this proposed work is to determine the probability of semantic word used in the e-content which is retrieved from the given document. These techniques used to stem and trim the word from the given document and classify based on the knowledge such as Factual, Procedural and Conceptual. These Classified words are reconstructed into tree structures, used to calculate the probability of outcome and evidence. These effective and effusive techniques mainly to reduce the time, memory utilization and efficiency based on the proposed SAS (Semi-Automated Semantic) algorithm.

Semantic, Syntax, ontologies, Structures, stem

[1] Ranjna Jain, Neelam Duhan, A.K.Sharma,"Comparative Study on Ontology Management Approaches in Semantic Web",IJCSE Vol 6, Issue 1, PP 132-140. -Jan 2018.
[2] Muqeem Ahmed,"Semantic Based Intelligent Information Retrieval through Data mining and Ontology", IJCSE VOL- 5, ISSUE - 10,PP 210-217,Oct-2017.
[3] S. Banerjee,"A Semantic Web Based Ontology in the Financial Domain", International Journal of Computer and Information Engineering, Vol:7, No:6,PP 807-810, 2013.
[4] H. Srimathi, "Semantic Web based Personalized eLearning", International Journal of Applied Information Systems (IJAIS), Volume 2– No.1, PP 11-16, May 2012.
[5] Vasani Krunal A, "CONTENT EVOCATION USING WEB SCRAPING AND SEMANTIC ILLUSTRATION",IOSR Journal of Computer Engineering (IOSR-JCE),Volume 16, Issue 3, Ver. IX , PP 54-60,May-Jun. 2014.
[6] Hasida, K. "Semantic Authoring and Semantic Computing". Sakurai, A. et al. (Eds.): JSAI 2003/2004, LNAI 3609, PP 137–149.
[7] Diana Man, "Ontologies in Computer Science, Didactica Mathematica", Vol. 31, No 1, pp. 43–46,2013.
[8] YassineGargouri,"Ontology Maintenance using Textual Analysis, Systematic-cybernetics and informatics",Vol-1,Number 5,pp-63-68,2016.
[9] Arun K.Pujari,, ―Data mining techniques‖, Universities Press (India) Pvt. Ltd. 2001.International Journal of Management, Technology And Engineering, Volume 8, Issue IX, SEPTEMBER-2018.
[10] Kristina M Doing-Harris, "Automated Concept, and Relationship Extraction from the Semi- Automated Ontology Management (SEAM) System", International Journal Biomedical Semantics, April 2015.
[11] D. Elangovan, “Semi-Automated Semantic Matched Concept Extraction Model for E-Content Development”, International Journal of Applied Engineering Research, November 5, 2016.

D.Elangovan, K.Nirmala, "Semantic Matching Concept Using Semi-Automated Semantic Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.203-206, 2019.

Because of a phenomenal increment in the quantity of computing assets in various associations, compelling jobs scheduling algorithms are required for proficient asset use. Job scheduling in considered as NP difficult issue in parallel and disseminated registering situations, for example, group, matrix and mists. Meta-heuristics, for example, Genetic Algorithms, Ant Colony Optimization, Artificial Bee Colony, Cuckoo Search, Firefly Algorithm, Bat Algorithm and so on are utilized by researchers to get close ideal answers for work scheduling issues. These meta-heuristic algorithms are utilized to plan distinctive sorts of jobs, for example, BSP, Workflow and DAG, Independent undertakings and Bag-of-Tasks. This paper is an endeavor to give exhaustive review of prominent nature-enlivened meta-heuristic procedures which are utilized to plan distinctive classifications of jobs to accomplish certain execution targets.

ACO, BAT, Cuckoo, genetic algorithm

[1] F. Sabahi, “Cloud Computing Security Threats and Responses,” pp. 245–249, 2011.
[2] D. Boru, D. Kliazovich, F. Granelli, P. Bouvry, and A. Y. Zomaya, “Energy-efficient data replication in cloud computing datacenters,” Cluster Comput., vol. 18, no. 1, pp. 385–402, 2015.
[3] N. M. Dhanya and G. Kousalya, “Adaptive and Secure Application Partitioning for Of fl oading in Mobile Cloud Computing,” vol. 1, pp. 45–53, 2015.
[4] and M. R. L. Zhang, Z. Zheng, “BFTCloud: A byzantine fault tolerance framework for voluntary-resource cloud computing,” Cloud Comput. (CLOUD), 2011 IEEE Int. Conf., pp. 444–451, 2011.
[5] S. Pandey, L. Wu, S. M. Guru, and R. Buyya, “A Particle Swarm Optimization-based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments,” 2010.
[6] A. Kaur, “A Review on Various Job Scheduling Algorithms,” vol. 13, no. 3, pp. 359–367, 2017.
[7] S. Id, W. Count, and C. E. R. Count, “A GA Based Job Scheduling strategy for Computational Grid by Krishan Veer,” pp. 29–34, 2015.
[8] R. Baldoni, J. M. Hélary, A. Mostefaoui, and M. Raynal, “On modeling consistent checkpoints and the domino effect in distributed systems,” Rapp. Rech. Natl. Rech. En Inform. En Autom., 1995.
[9] X. Zhou and L. Y. He, “A virtualized hybrid distributed file system,” Proc. - 2013 Int. Conf. Cyber-Enabled Distrib. Comput. Knowl. Discov. CyberC 2013, pp. 202–205, 2013.
[10] M. Arioua, Y. Assari, I. Ez-zazi, and A. Oualkadi, “Multi-hop cluster based routing approach for wireless sensor networks,” Procedia - Procedia Comput. Sci., vol. 83, no. Ant, pp. 584–591, 2016.
[11] S. P. Dandamudi, “Parallel Job Scheduling on Multicluster Computing Systems,” 2003.
[12] P. Switalski and F. Seredynski, “Scheduling parallel batch jobs in grids with evolutionary metaheuristics,” J. Sched., vol. 18, no. 4, pp. 345–357, 2014.
[13] “Content Checked For Plagiarism : al lS eo oo lS.”
[14] S. Tahilyani, “A New Genetic Algorithm Based Lane-By-Pass Approach for Smooth Traffic Flow on Road Networks,” vol. 1, no. 3, pp. 32–36, 2012.
[15] C. R. Reeves, “A genetic algorithm for flowshop sequencing,” Comput. Oper. Res., vol. 22, no. 1, pp. 5–13, Jan. 1995.
[16] W. Wen, C. Wang, D. Wu, and Y. Xie, “An ACO-Based Scheduling Strategy on Load Balancing in Cloud Computing Environment,” 2015.
[17] B. Li, W. Xu, S. Member, and H. Zhang, “PAPR Reduction for Hybrid ACO-OFDM Aided IM / DD Optical Wireless Vehicular Communications,” vol. 9545, no. c, 2017.
[18] A. Hossein and G. X. Yang, “Bat algorithm for constrained optimization tasks,” pp. 1239–1255, 2013.
[19] S. Mirjalili and S. Mohammad, “Binary bat algorithm,” 2013.
[20] J. Xie, Y. Zhou, and H. Chen, “A novel bat algorithm based on differential operator and Levy flights trajectory,” Comput. Intell. Neurosci., vol. 2013, 2013.
[21] I. Engineering, “OPTIMAL POWER FLOW USING CUCKOO OPTIMIZATION ALGORITHM,” pp. 4213–4218, 2013.
[22] N. Optimisation, “A comprehensive review of cuckoo search : variants and hybrids Iztok Fister Jr .*, Dušan Fister and Iztok Fister,” vol. 4, no. 4, 2013.
[23] K. C. Udaiyakumar and M. Chandrasekaran, “Application of Firefly Algorithm in Job Shop Scheduling Problem for Minimization of Makespan,” Procedia Eng., vol. 97, pp. 1798–1807, 2014.
[24] B. Jiang, J. Wu, X. Zhu, and D. Hu, “Priority-Based Live Migration of Virtual Machine,” pp. 376–385, 2013.
[25] M. R. Abid, K. Kaddouri, K. Smith, M. Idriss, E. Ouadghiri, and M. Gerndt, “Virtual Machines ’ Load-Balancing in Inter-Clouds,” pp. 5–12, 2016.
[26] X. Y. S. Deb, “Cuckoo search : recent advances and applications,” 2013.
[27] X. Cui, B. Mills, T. Znati, and R. Melhem, “Shadow replication: An energy-aware, fault-tolerant computational model for green cloud computing,” Energies, vol. 7, no. 8, pp. 5151–5176, 2014.
[28] A. Thomas, G. Krishnalal, and J. R. V P, “Credit Based Scheduling Algorithm in Cloud Computing Environment,” Procedia - Procedia Comput. Sci., vol. 46, no. Icict 2014, pp. 913–920, 2015.
[29] N. Kord and H. Haghighi, “An Energy-Efficient Approach for Virtual Machine Placement in Cloud Based Data Centers,” pp. 44–49, 2013.
[30] J. Li, C. Pu, Y. Chen, V. Talwar, and D. Milojicic, “Improving Preemptive Scheduling with Application-Transparent Checkpointing in Shared Clusters,” pp. 222–234.

Hardeep Kaur, Anil Kumar, "Optimized Scheduling Procedure for Enhancing Resource Utilization in Hetrogeneous Cloud Enviornment," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.207-215, 2019.

This paper presents a method for segmenting the web users based on their web access patterns. History of web pages visited by users includes informations like access sequences of web users and number of visits of web pages and reveals interest of users in particular pages. The web users’ access patterns can be segmented to group the users with similar interests. In this work, a simple, count based technique is used for preprocessing web access data so as to convert it into a database with fixed number of attributes. A novel approach based on fuzzy clustering principles for unsupervised clustering is extended to identify the number of web user groups based on their access patterns. This method starts by assuming that all the data points are initial clusters. Pairs of similar clusters are then merged based on fuzzy membership values. This paper also compare the cluster count obtained with this approach with the cluster count obtained with Cohonen’s unsupervised clustering algorithm. The tools available with IBM SPSS Modeler 14.1 are used to benchmark the quality of cluster estimation.

Fuzzy Clustering, Web access pattern, Fuzzy Logic, k-means clustering, c-means clustering

[1] W.H. Inmon, “The Data Warehouse and Data Mining”, ACM Commn , 1996, 39:.49-50.
[2] Berkhin P. (2006) A Survey of Clustering Data Mining Techniques. In: Kogan J., Nicholas C., Teboulle M. (eds) Grouping Multidimensional Data. Springer, Berlin, Heidelberg
[3] J Han, M Kamber, Data Mining Concepts and Techniques, Elsevier, 2003.
[4] R. Cooley, B. Mobasher, J. Srivastava: Data Preparation for Mining World Wide Web Browsing Patterns, Journal of Knowledge and Information Systems, 1999, 1(1).
[5] G. Raju, A. Singh, Th. Shanta Kumar, Binu Thomas, Integration of Fuzzy Logic in Data Mining: A comparative Case Study, Proc. of International Conf. on Mathematics and Computer Science, Loyola College, Chennai, 2008, pp.128-136,
[6] I.V. Cadez, C. Meek, Visualization of Navigation Patterns on a Web Site Using Model Based Clustering, citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.163.1042.
[7] Ajith Abraham, Business Intelligence from Web Usage Mining, Journal of Information & Knowledge Management, 2003, 2(4).
[8] D. Cosic , S. Loncaric, New Methods for Cluster Selection in Unsupervised Fuzzy Clustering, citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.51.2642.
[9] X. Xiong, K.L.Chan, K.L. Tan, Similarity-driven cluster merging method for unsupervised fuzzy clustering, Proceedings of the 20th conference on Uncertainty in artificial intelligence AUAI Press Arlington, Virginia, United States, 2004.
[10] J.Srivastava, R.Cooley, M. Deshpande, and P.-N. Tan. Web Usage Mining: Discover and Applications of Usage Patterns from Web Data. In ACM SIGKDD Explorations, 2000, 1(2) , pp 12-23.
[11] J. Pei, J. Han, B. Mortazavi, H. Zhu: Mining Access Patterns Efficiently from Web Logs. Proceedings of the 4th PAKDD, Kyoto, Japan, 2000, pp.396-407.
[12] E. Cox, Fuzzy Modeling and Genetic Algorithms for Data Mining And Exploration, Elsevier, 2005.
[13] Sankar K. Pal, P. Mitra, Data Mining in Soft Computing Framework: A Survey, IEEE transactions on neural networks, 2002, 13(1).
[14] Binu Thomas, Raju G., and Sonam Wangmo, A Modified Fuzzy C-Means Algorithm for Natural Data Exploration, www.waset.org/journals/waset/v49/v49-88.pdf.
[15] B. Thomas and G. Raju, A Fuzzy Threshold Based Modified Clustering Algorithm for Natural Data Exploration, Lecture Notes in Computer Science, 2010, 6122, pp. 167-172.
[16] J. Pei, J. Han, B. Mortazavi-asl, and H. Zhu. Mining Access Pattern Efficiently from Web Logs in Proc. 2000 Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD`00), Kyoto, Japan, April 2000
[17] M. Spiliopoulou, L. C. Faulstich, K. Winkler, A Data Miner Analyzing the Navigational Behaviour of Web Users in Proc. of the Workshop on Machine Learning in User Modelling of the ACAI`99 Int. Conf., Creta, Greece, July 1999
[18] R.Cooley, B. Mobasher and J. Srivastava. Web Mining: Information and Pattern Discovery on the World Wide Web in Proceedings of the 9th IEEE International Conference on Tools with Artificial Intelligence (ICTAI`97), November 1997.
[19] B. Mobasher, R.Cooley, and J. Srivastava. Automatic Personalization Based on Web Usage Mining in Communication of ACM, August, 2000 (Volume 43 , Issue 8)
[20] T. Joachims, D. Freitag, T. Mitchell WebWatcher: A Tour Guide for the World Wide Web in IJCAI97 -- Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, pages 770--775, Nagoya, Japan.
[21] N.Sujatha, K. Prakash, "An Efficient and Scalable Auto Recommender System Based on Users Behavior", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.6, pp.35-40, 2018.
[22] D.S. Rajput, R.S. Thakur, G.S. Thakur, "Clustering approach based on Efficient Coverage with Minimum Weight for Document Data", International Journal of Computer Sciences and Engineering, Vol.1, Issue.1, pp.6-13, 2013.
[23] http://kdd.ics.uci.edu/databases/msnbc/

Rajimol A, "Clustering of Web Access Patterns for Segmenting Web Users Using a Fuzzy Based Cluster Estimation Method," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.216-222, 2019.

Wireless sensor network is an important communication resource in wireless area. It enable to recognize many resources and file data such as temperature, humidity and many other dynamic functional data which needed observation. In many remote areas where the different terminology adaption is difficult while dealing with opposite situations wireless sensor network help in establish a proper communication between them. A proper network and communication also get disadvantage of intruder and anomaly within the network. WSN deals with attack resistance and finding such node which participate in such activity. Many algorithm used for finding such selfish attack nodes and preventing data from them. In this paper the proposed algorithm shown is proposed for the selfish node detection and prevention. The approach is performed using NS-2 simulation tool with dynamic node number selection model. The observe outcome while running the script observe high performance over existing scenario.

Wireless Sensor Networks (WSN), MANET (Mobile AD-Hoc Networks), Selfish node, network failure, data packet transmission

[1] Pushpendu Kar, Student Member, IEEE and Sudip Misra, Senior Member, IEEE, Reliable and Efficient Data Acquisition in Wireless Sensor Networks in the Presence of Transfaulty Nodes, 1932-4537 (c) 2015 IEEE.
[2] Abdul Razaque, M. Abdulghafour, Meer Jaro Khan, Detection of Selfish Attack over Wireless Body Area Networks, 2017 IEEE Conference on Open Systems (ICOS), November 13-14, 2017, Miri, Sarawak, Malaysia.
[3] JaydipSen, and Kaustav Goswami2, an Algorithm for Detection of Selfish Nodes in Wireless Mesh Networks, Oct. 28-30, 2009.
[4] Naveen Kumar Gupta, **Ashish Kumar Sharma, Abhishek Gupta, Selfish Behaviour Prevention and Detection in Mobile Ad-Hoc Network Using Intrusion Prevention System (IPS), Volume-1 Issue-2, September 2012.
[5] SonaTaheri, Musa Mammadov , Learning The Naive Bayes Classifier With Optimization Models, Vol. 23, No. 4, 787–795 DOI: 10.2478/amcs-2013-0059.
[6] Peng Jiang, A New Method for Node Fault Detection in Wireless Sensor Networks, 24 February 2009.
[7] AbolfazlAkbari, NedaBeikmahdavi, Ali khosrozadeh, OmidPanah, A Survey Cluster-Based and Cellular Approach to Fault Detection and Recovery in Wireless Sensor Networks, Journal 8 (1): 76-85, 2010.
[8] Meenakshi Panda, Pabitra Mohan Khilar, Distributed Soft Fault Detection Algorithm in Wireless Sensor Networks using Statistical Test, 2012 2nd IEEE International Conference on Parallel, Distributed and Grid Computing.
[9] Amol Shende1, Prof. Vikrant Chole2, A Review on Improving Packet Analysis in Wireless Sensor Network using Bit Rate Classifier, Vol.5 Issue.6, June- 2016, pg. 102-107.
[10] Charles E. Perkins, Ad-hoc On-Demand Distance Vector Routing.
[11] G. Vigna, S. Gwalani, K. Srinivasan, E.M. Belding-Royer, and R.A. Kemmerer, “An intrusion detection tool for AODV-based ad hoc wireless networks,” in Proc of Annual Comp. Sec. Appl. Conf (ACSAC) 2004, pp. 16-27.
[12] A. Pirzada and C. McDonald, “Establishing trust in pure ad hoc networks,” in Proceedings of the 27th Australian Conference on Computer Science, 2004, pp. 181-199.
[13] M. Conti, E. Gregori, and G. Maselli, “Reliable and efficient forwarding in MANETs,” Ad Hoc Networks Journal, Vol 4, No 3, 2006, pp. 398-415.
[14] L. Santhanam et al, “Distributed self-policing architecture for fostering node cooperation in wireless mesh networks,” in Proc. of PWC 2006, Vol 4217, pp. 147-158. [15] D. Johnson, C. Perkins, J. Arkko, Mobility Support in IPv6, RFC 3775, June 2004
[16] V. Devarapalli, R. Wakikawa, A. Petrescu, P. Thubert, Network Mobility (NEMO) Basic Support Protocol, RFC 3963, January 2005
[17] T. Ernst, H-Y. Lach, Network Mobility Support Terminology, draftietf-nemo-terminology-04.txt, October, 2005
[18] A.Vani ,Detection and Elimination of Wormhole Attacks in a MANET ,Int. J. Sci. Res. in Computer Science and Engineering Vol-5(5), Oct 2017,
[19] Dipali Koshtiand Supriya Kamoji ,Comparative study of Techniques used for Detection of Selfish Nodes in Mobile Ad hoc Networks IJSCE Volume-1, Issue-4, September 2011
[20] Neenavath Veeraiah, Dr.B.T.Krishna Selfish ,Node Detection IDSM Based Approach Using Individual Master Cluster Node in Proceedings of the Second International Conference on Inventive Systems and Control (ICISC 2018) IEEE

Priya Mishra, Ompal Singh, Abhishek Bhatt , "A Trust Election based Mechanism for finding selfish node and preventing them from Attack," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.223-229, 2019.

Although Convolutional Neural Networks performed better in object detection, CNNs does not care about spatial relationships existing in an image. In this paper, we try describe "capsule network based object detection" model COD based on the VGG16 model (as a base network), which presents a substantial result in many sections of object detection over Convolution Neural Network based model by achieving the problem of spatial relationships. We used matrix capsules and dynamic EM routing to classify object from different viewpoints. The whole model is grounded on "dynamic routing between capsules", which is suggested by Geoffrey E Hinton. Both proposed theories use capsules that maps feature properties of an object as information for detecting that object which is extracted by capsules and Dynamic routing groups the capsules of lower level into parent level capsules by an iterative dynamic routing process. We train and test our model on Pascal VOC 2007 and dataset. We implement this in python using Keras (Tensorflow as backend) and train our model in Google cloud compute engine. COD achieves an accuracy of 67.3 mAP on Pascal VOC-2007 dataset and performing a comparable performance with Fast R-CNN.

Object Detection, CNN, Capsule Networks, VGG16

[1] S. Ren, K. He, R. Girshick and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.
[2] M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn and A. Zisserman, "The Pascal Visual Object Classes Challenge: A Retrospective," International Journal of Computer Vision, 2014.
[3] Jia Deng, Wei Dong, R. Socher, Li-Jia Li, Kai Li and Li Fei-Fei, "ImageNet: A large-scale hierarchical image database," IEEE Conference on Computer Vision and Pattern Recognition, 2009.
[4] J. Dai, Y. Li, K. He and J. Sun, "R-FCN: Object Detection via Region-based Fully Convolutional Networks," 2016.
[5] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu and A. C. Berg, "SSD: Single Shot MultiBox Detector" IEEE European Conference on Computer Vision, 2015.
[6] J. Redmon, S. Divvala, R. Girshick and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," 2015.
[7] Ross Girshick, "Fast RCNN" IEEE International Conference on Computer Vision, 2015.
[8] R. Girshick, J. Donahue, T. Darrell and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2014.
[9] S. Sabour, N. Frosst and G. E. Hinton, "Dynamic Routing Between Capsules," 2017.
[10] G. E. Hinton, A. Krizhevsky and S. D. Wang, "Transforming Auto-encoders," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011.
[11] D. Wang and Q. Liu, “An Optimization View on Dynamic Routing between Capsules" Workshop track in International Conference on Learning Representations 2018.
[12] E. Xi, S. Bing and Y. Jin, "Capsule Network Performance on Complex Data," 2017.
[13] A. Jaiswal, W. AbdAlmageed and P. Natarajan, "CapsuleGAN: Generative Adversarial Capsule Network," 2018.
[14] A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto and H. Adam, "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" 2017.
[15] J. R. R. Uijlings, K. E. A. Van De Sande, T. Gevers and A. W. M. Smeulders, "Selective Search for Object Recognition" International Journal of Computer Vision 2013.
[16] J. Long, E. Shelhamer and T. Darrell, "Fully convolutional networks for semantic segmentation," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015.
[17] K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," 2015.
[18] L. Zhu and H. Yuan, "Spatial Relationship for Object Recognition" International Conference on Learning Representations 2015.
[19] Hoiem, D., Chodpathumwan, Y., Dai, “Diagnosing error in object detectors”, IEEE European Conference on Computer Vision, 2012.

Amit Baghel, Swati Dwivedi, "Capsule-Networks: Towards Object-Detection Capsule Object-Detector (COD)," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.230-236, 2019.

The process used to collect the student’s feedback is manual and takes more time to complete its analysis and report generation. As technology is changing at fast rate, maximum numbers of android application are available for educational purpose. In recent years the android technology with web services has brought many drastic changes in the mobile application development field. In this paper we proposed a system which provides a simple interface for collection and analysis of student’s feedback. It can be used by educational institutes or colleges to maintain the records of student’s feedback. Valuing and asking for feedback has recognized benefits for both faculty and students. For faculty to develop and improve teaching skills. Using this application, students can fill their feedback through any android based mobile. Once they submit it, their feedback will be analyzed quickly and feedback report can be generated within very short span of time.

Students Feedback System, Adndorid, Online Feedback

[1] Rajvee Patel, Omkar Agrawal, Yash Gangani, Ashish Vishwakarma, “College Feedback System”, International Research Journal of Engineering and Technology, Volume: 05 Issue: 01, pp. 1351 – 1353, 2018
[2] Phani Rama Prasad, Chella Sailatha, Gangapratima V, Harika D, Harika V, “COLLEGE STUDENT FEEDBACK SYSTEM”, International Journal For Technological Research In Engineering, Volume 4, Issue 9, pp. 1686 – 1688, 2017
[3] Nikhil H.M, Varada Sunitkumar, Shruti S Basapur, R. Vinil Shah, “Design and Implementation of Student Feedback System at Education System”, International Journal of Engineering Research in Computer Science and Engineering, Vol 5, Issue 4, pp. 563 – 565, 2018
[4] Sivasankari S, Srimathi. P. S., Ramya S, Dr. G. Fathima, “Online Feedback System for Educational Institutions for Better Evaluation of Faculty’s Performance Using Semantic Web (SW) Technology”, International Journal of Innovative Research in Science, Engineering and Technology, Volume 5, Special Issue 2, pp. 275 – 279, 2016
[5] Divyansh Shrivastava, Shubham Kesarwani, Amol K. Kadam, Aarushi Chhibber, Naveenkumar, Jayakumar, “Online Student Feedback Analysis System with Sentiment Analysis”, International Journal of Innovative Research in Science Engineering and Technology, Vol. 6, Issue 5, pp. 8445 – 8451, 2017

A. B. Shinde, V. L. Karade, S. S. Sutar, "Android Based Student Feedback System for Improved Teaching Learning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.237-243, 2019.

In this paper wireless power transmission (WPT) technique has been projected for transmitting the power in the electrical system.Transmission of electrical energy from source to load for a distance without any conducting wires or cables is called as wireless power transmission. In this paper renewable energy (electrical power is generated from solar panel) has been used as a source for the proposed technique. Then the generated electrical power is fed to DC-DC converter, hence the voltage is increased. After that the increased voltage has been fed to class E power amplifier. The output of class E amplifier i.e., 90V, 13.56 MHz is transmitted through transmitter to the receiver. One of the Near-field wireless power transfer techniques is opted i.e., (Magnetic resonance coupling). Magnetic Resonance coupling technique has been utilised for the transmission of power between transmitter and receiver. Then the Power from receiver has been fed to the rectifier circuit & converted into DC voltage around 75.6 V from that it has been given to DC load.

Wireless power transmission, Magnetic Resonant coupling, Class-E power amplifier

[1]. H. Hertz, Electric Waves: “Being Researches on the Propagation of Electric Action with Finite Velocity Through Space”. Dover Pub. Inc., 1962.
[2]. W. C. Brown, “The history of power transfer by radio waves,” IEEE Trans. Microwave Theory Techology., vol. MTT-32, no. 9, pp. 1230–1242, Sep. 1984.
[3]. L. R. Varshney, “Transporting information and energy simultaneously”, in Proc. IEEE ISIT’08, Toronto, Canada, Jul. 2008, pp. 1612–1616.
[4]. Md. Sazzad Hossain,” Renewable energy: Spaced based solar Power transmission” ,International Journal of Scientific & Engineering Research, Volume 3, Issue 11, November-2012 ISSN 2229-5518
[5]. Aristeidis Karalis, J.D.Joannopoulos, and Marin Soljačić, “Wireless Non-Radiative Energy Transfer”, Center for Materials Science and Engineering and Research Laboratory of Electronics,” MIT, 77 Massachusetts Avenue, Cambridge, MA 02139.
[6]. P. Deepika, N. Dharma Rao,T. Satwik Raman, V. Ganesh, “Magnetic Resonance Coupled Wireless Power Transfer with Class-E Power Amplifier from solar input” , International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882 Volume 6, Issue 3, March 2017.
[7]. Cheon S, Kim Y. H, Kang S. Y, Lee M. L, Lee J. M, Zyung T, “Circuit-Model-Based Analysis of a Wireless Energy-Transfer system via Coupled Magnetic Resonances.” IEEE Trans Ind Electron, vol. 58, No. 7, pp. 2906–2914, 2011.
[8]. Kiani M, Ghovanloo M, “The Circuit Theory Behind CoupledMode Magnetic Resonance-Based Wireless Power Transmission.” IEEE Trans Circuits Syst I, vol. 59, No. 9, pp. 2065–2074, 2012.
[9]. Bou E, Sedwick R, Alarcon E, “Maximizing Efficiency through Impedance Matching from a Circuit-Centric Model of Non-Radiative Resonant Wireless Power Transfer.” In: Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), pp. 29– 32, 2013.
[10]. Brown W. C, Eves E. E, “Beamed Microwave Power Transmission and its Application to Space,” IEEE Trans Micro Theory Tech, vol. 40, No. 6, pp. 1239–50, 1992.
[11]. McSpadden J. O, Mankins J. C, “Space Solar Power Programs and Microwave Wireless Power Transmission Technology”, IEEE Micro Mag, vol. 3, No. 4, pp. 46–57, 2002.
[12]. Yang Liu1,Jiaqi Fan, “Simulation study on Series Model of Wireless Power Transfer via Magnetic Resonance Coupling”, 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC), DOI: 10.1109/ITOEC.2017.8122409
[13]. Aravind L ,Usha P “Wireless Power Transmission Using Class E Power Amplifier From Solar Input”.International Journal of Engineering Research & Technology , Vol. 4 Issue 06, June 2015. ISSN: 2278-0181.
[14]. P.L.Chavan , Abhishek Kachare , Rohini Bhalawane , Priynka Jagtap , Ganesh Patil , Rahul Jadhav, “TESLA COIL WIRELESS POWER TRANSMISSION”, International Journal of Scientific Research Engineering & Technology (IJSRET), Volume 7, Issue 3, March 2018, ISSN 2278 – 0882.

SK.Abbas, P.Jyothi Swaroop, M. Swathi Priyanka, R.Dinesh, "Modelling of Wireless Power Transmission," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.244-248, 2019.

Nowadays, modern computing environment, having lots of challenges towards flexibility and processing capabilities. So Data Centers are required. Each data center provides physical wires by which enormous compute, network and storage resources are connected. Data centers are responsible for computation, space, network points and their effective and efficient operations. Therefore, enhancing the performance of the system likes total productivity, reliability and availability having the requirement to minimize the energy consumption of Data Centers. So, energy Consumption reductions are not only to enhance the system performance but also optimize the cost. Thus, an energy optimization is becoming a challenging task due to speedy growth in data and computing applications. In this paper we critically studied about the different energy based proposed methods and comprehensive survey along with a taxonomy of network topologies, either used in commercial data centers, or proposed by researchers

Cloud Computing, Energy Consumption, Energy Saving, DCN Topology, Energy Architecture

[1] A. Uchechukwu, K. Li, Y. Shen, “Energy Consumption in Cloud Computing Data Centers”, International Journal of Cloud Computing and Services Science, Vol.3, No.3, pp. 31-48, 2014
[2] J. Koomey, “Estimating Total Power Consumption by Server in the U.S and the World”, Lawrence Berkeley National Laboratory, Stanford University, pp. 1-31, 2007.
[3] J. Toress, “Green Computing, The next wave in computing”, Jordi Torres, In Ed. UPC Technical University of Catalonia, Barcelona, 2010.
[4] P. Kogge, “The Tops in Flops”, IEEE Spectrum, pp. 49-54, 2011.
[5] M. Al-Fares, A. Loukissas, A. Vahdat, “A scalable, commodity data center network architecture,” ACM SIGCOMM Computer Communication Review, Vol. 38, No. 4, pp. 63–74, 2008.
[6] J. Ren, Y. Zhang, N. Zhang, D. Zhang, and X. Shen, “Dynamic channel access to improve energy efficiency in cognitive radio sensor networks,” IEEE Trans. Wireless Commun., Vol. 15, No. 5, pp. 3143–3156, 2016.
[7] A. Uchechukwu, K. Li, Y. Shen, “Improving Cloud Computing Energy Efficiency”, IEEE Asia Pacific Cloud Computing Congress, 2012.
[8] C. Guo, H. Wu, K. Tan, L. Shi, Y. Zhang, S. Lu, “Dcell a scalable and fault-tolerant network structure for data centers”, ACM Sigcomm Computer Communication Review, Vol. 38, no. 4, pp. 75– 86, 2008.
[9] C. Guo, G. Lu, D. Li, H. Wu, X. Zhang, Y. Shi, C. Tian, Y. Zhang, and S. Lu, “Bcube. a high performance, server-centric network architecture for modular data centers”, ACM Sigcomm Computer Communication Review, vol. 39, no. 4, pp. 63–74, 2009.
[10] H. Wu, G. Lu, D. Li, C. Guo, Y. Zhang, “Mdcube. a high performance network structure for modular data center interconnection”, In the Proceedings of the 2009 ACM 5th International Conference on Emerging Networking Experiments and Technologies, Rome, Italy ,pp. 25–36, 2009.
[11] S. K. Abda, S. A. R. Al-Haddadb, F. Hashimb, A. B. H. J. Abdullahc, and S. Yussof, ‘‘An effective approach for managing power consumption in cloud computing infrastructure’’, J. Comput. Sci., vol. 21, pp. 349–360, 2017.
[12] L. Guo , G. Hu, Y. Dong, Y. L. Luo, Y. Zhu, “A Game Based Consolidation Method of Virtual Machines in Cloud Data Centers With Energy and Load Constraints”, IEEE Access, pp. 4664-4676, 2018.
[13] J. Xu, H. Wu, L. Chen, C. Shen, “Online Geographical Load Balancing for Mobile Edge Computing with Energy Harvesting”, IEEE Transaction, pp. 1-30, 2017.
[14] S. Mazumdar, M. Pranzo, “Power efficient server consolidation for cloud data center”, Elsevier, pp. 4-16, 2017.
[15] S. K. Abd, S.A.R Al-Haddad, F. Hashim, A.B.H.J.A. azizol, S.Yusso, “An effective approach for managing power consumption in cloud computing infrastructure”, Journal of Computational Science, pp. 1-33, 2016.
[16] T. Kaur, I. Chana, “Energy Efficiency Techniques in Cloud Computing: A Survey and Taxonomy”, ACM Computing Surveys, Vol. 48, No. 2, pp. 22.1-22.46, 2015.
[17] D. Kliazovich, P. Bouvry, F. Granelli, N. L. S. da Fonseca, “Energy consumption optimization in cloud data centers” John Wiley & Sons, Luxembourg, pp. 193-215, 2015.
[18] Y. Ding, X. Qin, L. Liu, and T. Wang, ‘‘Energy efficient scheduling of virtual machines in cloud with deadline constraint’’, Future Generat. Comput. Syst., Vol. 50, pp. 62–74, 2015.
[19] A. Hammadi, L. Mhamdi , “A survey on architectures and energy efficiency in data center networks”, Elsevier, p.p. 1- 21, 2014.
[20] C. M. Wu, R. S. Chang, and H. Y. Chan, ‘‘A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters,’’ Future Generat. Comput. Syst., Vol. 37, pp. 141–147, 2014.
[21] X. Z. Member, L. T. Yang, H. Chen, J. Wang, S. Yin, X. Liu, “ Real-Time Tasks Oriented Energy-Aware Scheduling in Virtualized Clouds’’, IEEE Transaction, pp. 1-14, 2014.
[22] C. Ghribi, M. Hadji, D. Zeghlache, “Energy efficient vm scheduling for cloud data centers”, IEEE/ACM 13th International Symposium on Cluster, Cloud, and Grid Computing, p.p 671-678, 2013.
[23] W. Chawarut, L. Woraphon, ‘‘Energy-aware and real-time service management in cloud computing,’’ In the Proceedings of the 2013 IEEE 10th International Conference on Electrical Engineering or Electronics, Computer, Telecommunications and Information Technology, Krabi, Thailand, pp. 1–5, 2013.
[24] E. Volk, A. Tenschert, M. Gienger, “Improving energy efficiency in data centers and federated cloud environments. comparison of CoolEmAll and Eco2Clouds approaches and metrics”, In the Proceedings of the IEEE 3rd International Conference on Cloud and Green Computing, pp.443-450, 2013.
[25] D. Boru, D. Kliazovich, F. Granelli, P. Bouvry, A.Y. Zomaya, “Energy-Efficient Data Replication in Cloud Computing Datacenters”, IEEE Globecom workshop on Cloud Computing Systems, Networks and Applications, pp. 446-451, 2013.
[26] T. V. do, C. Rotter, “Comparision of scheduling schemes for on demand Iaas request”, Elsevier, Volume 85, Issue 6, 2012.
[27] A. Beloglazov, J. Abawajy, R. Buyya, ‘‘Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing,’’ Future Generat. Comput. Syst., Vol. 28, No. 5, pp. 755–768, 2012.
[28] S. Chaisiri, B.S. Lee, D. Niyato, “Optimization of Resource Provisioning Cost in Cloud Computing”, IEEE transactions on services computing, Vol. 5, No. 2, p.p. 164-177, 2012.
[29] S. Mazumdar, M. Pranzo, ‘‘Power efficient server consolidation for cloud data center’’, Future Generat. Comput. Syst., Vol. 70, pp. 4–16, 2017.
[30] W. Zhu, Y. Zhuang, L. Zhang, ‘‘A three-dimensional virtual resource scheduling method for energy saving in cloud computing’’, Future Generat. Comput. Syst., Vol. 69, pp. 66–74, 2017.
[31] H. Duan, C. Chen, G. Min, Y. Wu, ‘‘Energy-aware scheduling of virtual machines in heterogeneous cloud computing systems’’, Future Generat. Comput. Syst., Vol. 74, pp. 142–150, 2017.
[32] L. Zhou, ‘‘On data-driven delay estimation for media cloud’’, IEEE Trans. Multimedia, Vol. 18, No. 5, pp. 905–915, 2016.
[33] R. Li, Q. Zheng, X. Li, and J. Wu, ‘‘A novel multi-objective optimization scheme for rebalancing virtual machine placement’’, In the Proceedings of the IEEE 9th International Conference on Cloud Comput., San Francisco, CA, USA, pp. 710–717, 2016.
[34] M. Nir, A. Matrawy, “Economic and Energy Considerations for Resource Augmentation in Mobile Cloud Computing”, IEEE Transactions on Cloud Computing, p.p 1-14, 2015.
[35] J. M. H. Elmirghani, T. Klein, K. Hinton, L. Nonde, A. Q. Lawey, T. E. H. El Gorashi, M. O. I. Musa, and X. Dong, “GreenTouch GreenMeter Core Network Energy-Efficiency Improvement Measures and Optimization”, Optical Society of America, Vol. 10, p.p. A250- A269, 2018.
[36] P. Ehsan, P. Massoud, “Minimizing data center cooling and server power costs”, In the Proceedings of the ACM/IEEE 4th International Conference on on Low Power Electronic and Design (ISLPED), pp. 145-150, 2009.
[37] V. Liu, D. Halperin, A. Krishnamurthy, T. Anderson, “F10 A fault-tolerant engineered network”, In Presented as part of the USENIX 10th Symposium on Networked Systems Design and Implementation (NSDI 13), pp. 399–412, 2013.
[38] J. Moore, J. Chase, P. Ranganathan, R. Sharma, “Making Scheduling ‘Cool’ Temperature-Aware Workload Placement in Data Centers” In the Proceedings of the USENIX Annual Technical Conference, Anaheim, CA, USA, pp. 10–15, 2005.

Ashish Kumar Pandey, Shish Ahmad, "Energy Optimization in Cloud Computing: A Review," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.249-256, 2019.