Image Steganography Using Integer Wavelet Transform and Singular Value Decomposition
Research Paper | Journal Paper
Vol.7 , Issue.3 , pp.967-971, Mar-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i3.967971
Abstract
In this paper; we propose an image steganography method using singular value decomposition (SVD) and integer wavelet transform (IWT). The secret message which is in the form of watermark is embedded into the cover image by transforming the cover image using IWT and SVD. The proposed method provides more robustness against image processing and geometric attacks such as JPEG compression, low pass filtering and addition of noise, scaling, rotation and histogram equalization.
Key-Words / Index Term
Integer Wavelet Transform (IWT); Singular Value Decomposition (SVD); PSNR(Peak Signal to Noise Ratio); Structural Similarity Index For Measuring Image quality (SSIM); Normalized Correlation(NC).
References
[1] Fridrich, J.:"Steganography in digital media: Principles, algorithms, and applications". Cambridge University Press (2010).
[2] Atawneh, S., Almomani, A., Sumari, P.: Steganography in digital images: common approaches and tools. IETE Technical Review 30(4), 344-358(2013).
[3] Cheddad, A., Condell, J., Curran, K., Kevitt, M.P. Digital image Steganography: survey and analysis of current methods. Signal Processing 90(3), 727-752(2010).
[4]Cox, J., Miller, M.L., Bloom, J.A., Fridrich, J., Kalker, T. “Digital Watermarking and Steganography”. Elsevier (2008).
[5] Katzenbeisser, S., Petitcolas, F.A.P.: “Information hiding techniques for Steganography and digital watermarking”. Artech House Inc., Norwood(2000).
[6]Johnson, N.F., Jajodia, S. “Exploring Steganography: seeing the unseen”. IEEE Computer 31(2), 26-34(1998).
[7]Westfield, A., Pfitzmann, A.“ Attacks on steganographic systems”. In: Lecture Notes in Computer Science, vol. 1768, pp. 61-75. Springer (2000).
[8] Singh, S., siddiqui, T.J.: “Transform domain techniques for image steganography. Information Security in Diverse Computing Environments”, 245-259. IGI global (2014).
[9] Maheshkar S(2017) Region-based hybrid medical image watermarking for secure telemedicine applications.
Multimed Tools Appl 76(3):3617-3647.
[10] G.Prashanti, K. Sandhyarani, “A New Approach for Data Hiding with LSB steganography”, Emerging ICT
For Bridging the Future – proceedings of the 49th Annual Convention of the Computer Society of India CSI, Springer 2015, pp. 423-430.
[11]D.Debnath, S.Deb, N.Kar, “An Advanced Image Encryption Standard Providing Dual Security: Encryption Using Hill Cipher and RGB Image Steganography”,IEEE International Conference on Computational Intelligence and Networks (CINE), Jan. 2015, pp.178-183.
[12]D.Baby, J.Thomas, G. Augustie, E. George, N.R. Michael, “A Novel DWT based Image Securing method using Steganography”, International Conference on Information and Communication Technologies (ICICT),Procedia Computer Science, April 2015, pp. 612-618.
[13] H.Yang, X. Sun and G. Sun, “A High-Capacity Image Data Hiding Scheme Using Adaptive LSB Substitution” ,Journal of Radio Engineering Vol. 18, No. 4, pp. 509-516,2009
[14] S. Channalli and A. Jadhav, “Steganography an Art of Hiding Data”, International Journal on Computer Science and Engineering (IJCSE), 2009 pp. 137-141.
[15] R. Eswaraiah, Sai Alekhya Edara, E. Sreenivasa Reddy , “ Color Image Watermarking Scheme using DWT and DCT Coefficients of R, G and B Color Components”International Journal of Computer Applications (0975 – 8887) Volume 50 – No.8, July 2012.
[16]R.Eswaraiah & E.Sreenivasa Reddy,“Robust Watermarking Method for Color Images Using DCT Coefficients of Watermark”Global Journal of Computer Science and Technology Graphics & Vision Volume 12 Issue 12 Version 1.0 Year 2012Online ISSN: 0975-4172 & Print ISSN: 0975-4350.
Citation
Shaik Shabina, Ravipati Iswarya Lakshmi, Shaik Arshia, Vemuri Harshitha, Rayachoti Eswariah, "Image Steganography Using Integer Wavelet Transform and Singular Value Decomposition," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.967-971, 2019.
Alleviation of DDOS Attacks to Achieve Data Asylum In Cloud Computing
Research Paper | Journal Paper
Vol.7 , Issue.3 , pp.972-975, Mar-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i3.972975
Abstract
Data Security and privacy become the critical issue as cloud computing is getting famous for web-based services in modern era. Because cloud computing is providing Data access to everyone, data security become critical issue that limit many cloud based applications. Major issue of security in cloud computing because users can access sensitive data. One of these security challenges in cloud computing, Distributed Denial of Service (DDOS) attack is the major security threat to Web-applications as well as cloud computing. Attackers try to compromise the security with heavy traffic from different resources. This research will observe the effects of Distributed Denial of Service (DDOS) attacks on cloud server moreover mitigation technique will be discussed to prevent DDOS attacks on the cloud.
Key-Words / Index Term
Cloud Computing, Data Security and privacy, Data mitigation technique, DDOS
References
[1]. Balobaid. A, Alawad. W and Aljasim. H. 2016. Study on the impact of DoS and DDoS attacks on cloud and mitigation techniques. 2016 International Conference on Computer, Analytical and Security Trends (CAST), Pune, India, 1 (1): 416-421.
[2]. Changes. V and Ramachandran. M 2016. Towards the acquisition of data security with the cloud computing framework. IEEE transactions on a service computer, 9 (1): 138-151.
[3]. Li. Y, Gai. K., Qiu. L., Qiu. M and Zhao. H. 2017. Intelligent cryptographic approaches to deploy large data storage in cloud computers. Information Science, 387 (1): 103-115.
[4]. Manogaran. G, Thota. C and Kumar. M 2016. MetaCloudDataStorage Architecture for Great Data Security in Cloud Computing. Computer Science Procedures, 87 (1): 128-133.
[5]. Osanaiye. Oh, Choo. R, and Dlodlo M. 2016. Distributed Discontinuing Service (DDoS) in the Cloud: Cloud Vision Overview and DDoS Mitigation Framework. Computer Networks and Applications Journal, 67 (1): 147-165.
[6]. Osanaiye. O. 2015. Title: IP Spoofing Detection to Prevent DDoS Attacks in Cloud Computing. 2015 International Conference on Intelligence in Next Generation Network, Paris, 1 (1): 139-141.
[7]. Sabahi. F., 2011. Threats and cloud computer security responses. IEEE International Conference on Software and Communications Network, Xi`an, 1 (1): 245-249.
[8]. Sow. A, Earring. R and Radzik. T. 2016. Detection of known and unknown DDoS attacks via artificial neural networks. Neurocomputing, 172 (1): 385-393.
[9]. Shameli-Sendi. A, Pourzandi. M, Fekih-Ahmed. M and Cheriet. M. 2015. The reduced taxonomy of Denial of Service Blocking brings closer to the cloud computer. Computer Networks and Applications Journal, 58 (1): 165-179.
[10]. Somani. G, Gaur. M, Sanghi. D, Conti. M and Buyya. R. 2017. Resizing services for fast DDoS reduction in cloud computing environments. Annale van Telecommunicatie, 72 (5): 237-252.
[11]. Thapngam. T, Yu. S, Zhou. W and Makki. S. 2014. Service Disclaimer Tracking (DDoS) is exposed by traffic pattern analysis. Peer-to-peer Networks and Applications, 7 (4): 346-358.
[12]. Xiao. P, Li. Z, Qi. H, Qu. W and Yu. H. 2016. DDoS Detection Effective With Bloom Filter In SDN. 2016 IEEE Trustcom / BigDataSE / ISPA, Tianjin, China, 1 (1): 1-6.
[13]. Yu. S, Tian. Y, Guo. S and Wu. D. 2014. Can we defeat DDoS attacks in the cloud? IEEE transactions on parallel and distributed systems, 25 (9): 2245-2254.
[14]. Yang. L, Zhang. T, Song. J, Wang. J and Chen. P. 2012. DDoS attack defense for cloud computers. 2012 International Conference on Computer Science and Automation Engineering (CSAE), Zhangjiajie, China, 1 (1): 626-629.
Citation
Ashok Koujalagi, "Alleviation of DDOS Attacks to Achieve Data Asylum In Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.972-975, 2019.
A Study of Cryptographic Algorithms and its analysis on Data Security during Transmission
Survey Paper | Journal Paper
Vol.7 , Issue.3 , pp.976-980, Mar-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i3.976980
Abstract
The cryptographic cipher is the terminology used for securing the web user data over the network. It is closely related to the discipline of cryptology and cryptanalysis. Using mathematical equations, the cryptography make sure the data when transferred over network is not altered. The important things for the study of mathematical equations on data security, it is almost not possible to break the encryption algorithm without knowing the correct key value. This paper focuses on the study of cryptographic algorithms and its functioning over the data transmission. The throughput is calculated for encryption algorithms during encrypting of text data in different time period and it is found that the Diffie-Hellman Key Exchange Algorithm shows better performance as compared to other studied algorithms.
Key-Words / Index Term
Cryptographic algorithm, Cipher, Diffie-Hellman Key Exchange Algorithm
References
[1] Chia Long Wu, Chen HaoHu,“Computational Complexity Theoretical Analyses on Cryptographic Algorithms for Computer Security Application”, Innovations in Bio-Inspired computing and Applications(IBICA), 2018, pp. 307 – 311.
[2] Qing Liu, Yunfei Li, Lin Hao, “On the Design and Implementation of an Efficient RSA Variant”, Advanced Computer Theory and Engineering (ICACTE), 2017, pp.533-536.
[3] Mandal, B.K., Bhattacharyya, Bandyopadhyay S.K., “Designing and Performance Analysis of a Proposed Symmetric Cryptography Algorithm”, Communication Systems and Network Technologies (CSNT), 2017, pp. 453 – 461.
[4] Wang, Suli, Liu, Ganlai, “File encryption and decryption system based on RSA algorithm”, Computational and Information Sciences (ICCIS), 2016, pp. 797 – 800.
[5] Da Silva, J.C.L, "Factoring Semi primes and Possible Implications for RSA”, Electrical and Electronics Engineers in Israel (IEEEI), 2016, pp.182–183.
[6] Geethavani, B., Prasad, E.V. Roopa, R. “A new approach for secure data transfer in audio signals using DWT”, Sept 2016, pp. 1-6.
[7] Nagar, S.A., Alshamma, S., “High speed implementation of RSA algorithm with modified keys exchange", Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), pp. 639 – 642, 2016.
[8] Chong Fu, Zhi-liang Zhu, “An Efficient Implementation of RSA Digital Signature", Wireless Communications, Networking and Mobile Computing, Oct. 2016, pp.1-4.
[9] DiaaSalama, HatemAbdual Kader, MohiyHadhoud “Studying the Effects of Most Common Encryption Algorithms, International Arab Journal of e-Technology”, Vol. 2, No. 1, January 2017
[10] Turki Al-Somani,Khalid Al-Zamil, “Performance Evaluation of Three Encryption/Decryption Algorithms on the SunOS and Linux Operating Systems”, vol. 4, issue – 6, 2015, pp. 34-45
[11] Hongwei Si, YoulinCai, Zhimei Cheng, “An Improved RSA Signature Algorithm Based on Complex Numeric Operation Function", Challenges in Environmental Science and Computer Engineering (CESCE), 2016, pp.397–400.
[12] Wenxue Tan,Wang Xiping, Jinju Xi, Meisen Pan, “A mechanism of quantitating the security strength of RSA key”, Electronic Commerce and Security (ISECS), 2015, pp. 357 – 361.
[13] Dhakar, R.S. Gupta, A.K. Sharma, P., “Modified RSA Encryption Algorithm (MREA)”, Advanced Computing & Communication Technologies (ACCT), 2015, pp. 426–429.
[14] Abdel-Karim Al Tamimi, "Performance Analysis of Data Encryption Algorithms", International Conference on Information and Communication Technologies, 2015, pp. 219-232.
[15] Li Dongjiang,Wang Yandan, Chen Hong, “The research on key generation in RSA public- key cryptosystem”, 2016, pp. 578–580.
Citation
Sachin Pandey, Rajendra Gupta, Pratima Gautam, "A Study of Cryptographic Algorithms and its analysis on Data Security during Transmission," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.976-980, 2019.
Architecture and Scheduling Algorithms for WFaaS in the Cloud
Research Paper | Journal Paper
Vol.7 , Issue.3 , pp.981-986, Mar-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i3.981986
Abstract
Cloud computing is one of the promising domains that has gained popularity in the recent years. It offers utility-oriented IT services to the users worldwide over the internet. In cloud, service providers manage and provide resources to users as per they use. Software or hardware can be used as per requirement; there is no need to buy them. Key role in cloud computing systems is managing different tasks. WFaaS provides a way to compose multiple software services/packages based on certain logic within a workflow service. In workflows completion of whole task applications require various sub-tasks which are executed in a particular manner. It facilitates a service and management environment for flexible application integration via workflows. In this paper some Workflow scheduling algorithms are also included, which are the most important part of cloud computing for workflow. This review paper describes cloud computing, basics of workflows and scheduling, some scheduling algorithms used in workflow management, factors considered by these algorithms, type of algorithm and tool used.
Key-Words / Index Term
Cloud computing, Public, Private, Hybrid, IaaS, PaaS, SaaS, WFaaS, Scheduling algorithm
References
[1] S.M. Hashemi, A.Kh. Bardsiri, “Cloud computing vs. grid computing,” ARPN journal of systems and software, vol. 2, No 5, pp. 188-194, May 2012.
[2] H. Alhakami, H. Aldabbas, T. Alwada, "Comparison between cloud and grid computing : review paper," International journal on cloud computing: services and architecture (IJCCSA), vol. 2, No. 4, pp. 1-21, August 2012.
[3] http://aws.amazon.com/ec2.
[4] Agarwal N. “Role of Cloud Computing in Development of Smart City” in International Journal of Science, Technology and Engineering (IJSTE), ISSN (online): 2349-784X, 228-232, 2017.
[5] Agarwal N. “Database Management on Clouds through NoSQL” in Aishwarya Research Review, ISSN 2249–2097, 55-63, 2015.
[6] Agarwal N. “Advantages and Uses of Cloud Computing in Business” published in Aishwarya Research Communication, ISSN 0975-3613, Vol.5, 37-41,2013.
[7] M. Shiraz, A. Gani, R. H. Khokhar, R. Buyya, "A review on distributed application processing frameworks in smart mobile devices for mobile cloud computing," IEEE communications surveys & tutorials, vol. 15, no. 3, pp. 1294-1313, 2013.
[8] R. Sakellariou, H. Zhao, “A hybrid heuristic for DAG scheduling on heterogeneous systems”.
[9] Niu S, Zhai J, Ma X, Tang X, Chen W. Cost-effective cloud HPC resource provisioning by building semi-elastic virtual clusters. Proceedings of SC13: International Conference for High Performance Computing, Networking, Storage and Analysis; ACM; 2013. Article No. 56.
[10] Carrington L, Snavely A, Wolter N. A performance prediction framework for scientific applications. Future Generation Computer Systems. 2006;22(3):336–346.
[11] Zhao Y, Fei X, Raicu I, Lu S. Opportunities and Challenges in Running Scientific Workflows on the Cloud. Proceedings of IEEE International Conference on Cyber-enabled distributed computing and knowledge discovery (CyberC); 2011. pp. 455–462.
[12] 18th international parallel and distributed processing symposium, 2004.
[13] Radulescu, A. Gemund, “Fast and effective task scheduling in heterogeneous systems,” Proceedings of the 9th heterogeneous computing workshop (HCW 2000), pp. 229-238, 2000.
[14] Y. K. Kwok, I. Ahmad, “Dynamic critical-path scheduling: an effective technique for allocating task graphs to multiprocessors,” IEEE transactions on parallel and distributed systems, vol. 7, no. 5, pp. 506-521, May 1996.
[15] G.C. Sih, E.A. Lee, “A compile-time scheduling heuristic for interconnection-constrained heterogeneous processor architectures,” IEEE transactions on parallel and distributed systems, vol. 4, no. 2, pp. 175-187, February 1993.
[16] H. Zhao, R. Sakellarious, “Scheduling multiple DAGs onto heterogeneous systems,” IEEE 20th international parallel and distributed processing symposium,2006.
[17] Z. Yu, W. Shi, “A planner-guided scheduling strategy for multiple workflow applications,” international conference on parallel processing - IEEE workshop, pp. 1-8, 2008.
[18] S. Pandey, L. Wu, S. Mayura Guru, R. Buyya, “A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments,” 24th IEEE international conference on advanced information networking and applications, PP 400-407, 2010.
[19] T. A. L. Genez, L. F. Bittencourt, E. R. M. Madeira, “Workflow scheduling for saas / paas cloud providers considering two SLA levels,” IEEE network operations and management symposium (NOMS): mini-conference, pp. 906-912, 2012.
[20] C. Lin, S. Lu, “Scheduling scientific workflows elastically for cloud computing,” IEEE 4th international conference on cloud computing, pp. 246-247, 2011.
[21] H. Zhong, K. Tao, X. Zhang, “An approach to optimized resource scheduling algorithm for open-source cloud systems,” Fifth annual china grid conference
[22] (IEEE), pp. 124-129, 2010.
[23] M. Xu, L. Cui, H. Wang, Y. Bi, “A multiple QoS constrained scheduling strategy of multiple workflows for cloud computing,” IEEE international symposium on parallel and distributed processing with applications, pp. 629-634, 2009.
[24] Verma, S. Kaushal, “Deadline and budget distribution based cost- time optimization workflow scheduling algorithm for cloud,” International conference on recent advances and future trends in information technology (iRAFIT 2012).
Citation
Neetu Agarwal, "Architecture and Scheduling Algorithms for WFaaS in the Cloud," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.981-986, 2019.
Conceptual Review of Deep Learning Methods for Automatic Image Caption Generation
Review Paper | Journal Paper
Vol.7 , Issue.3 , pp.987-991, Mar-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i3.987991
Abstract
Automatic generation of caption for given images is a complex AI task. It is a problem of generating textual description for a given input image. This involves both image understanding and natural language generation. This is a very dynamic field. A lot of work has been done and currently ongoing in this domain. The recent frontiers of the fields are based on deep learning based methods. The purpose of this article is to provide overview of deep learning based image captioning methods to readers. The readers will first get basic concepts which are used to in development of various methods. Then basic information on datasets is given. Then three existing work are discussed followed by very brief discussion on other works. Concisely, this article presents classification of existing approaches, popular datasets and some of existing models followed by brief discussion of other works. Initially, the topic is introduced and then broader classification of deep learning based methods is discussed. At last, brief discussions on some methods are done.
Key-Words / Index Term
Image Caption Generation, Deep Learning, Computer Vision
References
[1] K. Papineni, S. Roukos, T. Ward, and W. jing Zhu, “Bleu: a method for automatic evaluation of machine translation,” in proc. Association for Computational Linguistics, Stroudsburg, PA, USA, 2002, pp. 311–318, 2002.
[2] M. Denkowski and A. Lavie, “Meteor universal: Language specific translation evaluation for any target language,” in proc. EACL 2014 Workshop on Statistical Machine Translation, 2014, Baltimore, USA.
[3] R. Vedantam, C. L. Zitnick, and D. Parikh, “Cider: Consensus-based image description evaluation,”, in proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 4566-4575
[4] P. Shah, V. Bakrola, and S. Pati, “Image captioning using deep neural architectures,” in proc. International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), IEEE, Piscataway, NJ, mar 2017.
[5] M. Z. Hossain, F. Sohel, M. F. Shiratuddin, and H. Laga, “A comprehensive survey of deep learning for image captioning,” CoRR, vol. abs/1810.04020, 2018.
[6] S. Bai and S. An, “A survey on automatic image caption generation,” Neurocomputing, vol. 311, pp. 291–304, 2018.
[7] M. Hodosh, P. Young, and J. Hockenmaier, “Framing image description as a ranking task: Data, models and evaluation metrics,” Journal of Artificial Intelligence Research, vol. 47, pp. 853–899, aug 2013.
[8] B. A. Plummer, L. Wang, C. M. Cervantes, J. C. Caicedo, J. Hockenmaier, and S. Lazebnik, “Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models,” in 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, IEEE, dec 2015, pp. 2641-2649.
[9] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár and C. L. Zitnick, “Microsoft COCO: Common objects in context,” in Computer Vision – ECCV 2014, pp. 740–755, Springer International Publishing, 2014.
[10] O. Vinyals, A. Toshev, S. Bengio, and D. Erhan, “Show and tell: A neural image caption generator,” in proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Boston, MA, USA, jun 2015, pp. 3156-3164.
[11] X. Liu, Q. Xu, and N. Wang, “A survey on deep neural network-based image captioning,” The Visual Computer, jun 2018, pp. 1–26.
[12] K. Xu, J. L. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhutdinov, R. S. Zemel, and Y. Bengio, “Show, attend and tell: Neural image caption generation with visual attention,” in proc. 32Nd International Conference on Machine Learning - Volume 37, ICML’15, JMLR.org, 2015, pp. 2048–2057.
[13] J. Mao, W. Xu, Y. Yang, J. Wang, Z. Huang, and A. Yuille, “Deep captioning with multimodal recurrent neural networks (m-rnn),” eprint arXiv:1412.6632 [cs.CV], Jun 2015.
[14] D.-J. Kim, D. Yoo, B. Sim, and I. S. Kweon, “Sentence learning on deep convolutional networks for image caption generation,” in proc. 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), IEEE, Xi`an, China, aug 2016.
[15] P. Anderson, X. He, C. Buehler, D. Teney, M. Johnson, S. Gould, and L. Zhang, “Bottom-up and top-down attention for image captioning and VQA,” in proc. IEEE Conference on Computer Vision and Pattern Recognition, (CVPR) 2018, Salt Lake City, UT, USA, Jun, 2018, pp. 6077-6086.
[16] J. Lu, C. Xiong, D. Parikh, and R. Socher, “Knowing when to look: Adaptive attention via a visual sentinel for image captioning,” in proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Honolulu, HI, USA, jul 2017..
[17] M. Pedersoli, T. Lucas, C. Schmid, and J. Verbeek, “Areas of attention for image captioning,” in proc. IEEE International Conference on Computer Vision (ICCV), IEEE, Venice, Italy, oct 2017.
[18] K. Fu, J. Jin, R. Cui, F. Sha, and C. Zhang, “Aligning where to see and what to tell: Image captioning with region-based attention and scene-specific contexts,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, pp. 2321–2334, dec 2017.
[19] A. Poghosyan and H. Sarukhanyan, “Short-term memory with read-only unit in neural image caption generator,” in proc. Computer Science and Information Technologies (CSIT), IEEE, Yerevan, Armenia, sep 2017.
[20] V. Mullachery and V. Motwani, “Image captioning,” arXiv:1805.09137 [cs.CV], may 2018.
Citation
S. H. Patel, N.M. Patel, D.G. Thakore, "Conceptual Review of Deep Learning Methods for Automatic Image Caption Generation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.987-991, 2019.
A Multiple Qos Parameter Based Web Service Composition Using Petri Net Verification
Research Paper | Journal Paper
Vol.7 , Issue.3 , pp.992-997, Mar-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i3.992997
Abstract
Automatic Web service composition aims at combining several existent web services to generate composite web service automatically that satisfies more complex user requests. The process of automatic web service composition comprised of pre-processing phase, service discovery and ranking phase and planning, verification and execution phase. A domain-ontology-based Particle Swarm Optimization (PSO) inspired Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) method was used in the pre-processing phase. It filtered the noisy data and clustered the web services. The matching and ranking problem is considered in the discovery and ranking phase where the web services were discovered by Improved Bipartite Graph (IBG) and the discovered web services were ranked by Quality of Service (QoS) based fuzzy ranking algorithm. A Petrinet based model was used for planning, verification and execution of web services. In this model, the web services can be directly mapped into the Petrinet. In this paper, the planning, verification and execution of web services is improved by including multiple QoS such as availability, reliability, time, cost, accuracy, accessibility, modifiability and security in Petrinet model. Based on the multiple QoS parameters, the Petrinet model verified the composition plan. It describes the static vision of a system and dynamic behavior of processes. The Petrinet with multi QoS models the internal operations of web services, interactions among them and the processes in all phases of automatic web service composition process.
Key-Words / Index Term
Automatic web service composition, Petrinet based model, Petrinet with multiple QoS, Verification of composite web service, optimal selection of composition plan
References
[1] Y. Cardinale, J. El Haddad, M. Manouvrier, M. Rukoz, “Web service composition based on petri nets: Review and contribution”, In International Workshop on Resource Discovery Springer, Berlin, Heidelberg, pp. 83-122, 2012.
[2] T. Agarwal, N. Sharma, “Efficient Load Balancing Using Restful Web Services in Cloud Computing: A Review”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 6, Issue. 3, pp. 67-70, 2018.
[3] M. Chen, T.H. Tan, J. Sun, Y. Liu, J. Pang, X. Li, “Verification of functional and non-functional requirements of web service composition”, In International Conference on Formal Engineering Methods Springer, Berlin, Heidelberg, pp. 313-328, 2013.
[4] S. Rawat, P. Chaturvedi, “Performance Analysis of QoS Parameters in OFDM Based Network”, International Journal of Scientific Research in Network Security and Communication, Vol. 5, Issue. 3, pp. 128-132, 2017.
[5] K.M. Sundaram, T. Parimalam, “PSO-inspired BIRCH and Improved Bipartite Graph for Automatic Web Service Composition”, International Journal of Applied Engineering Research, Vol. 12, Issue. 8, pp. 1765-1771, 2017.
[6] Q.M. Yu, W. Lan, D. M. Huang, “Fishery web service composition method based on ontology, Journal of Integrative Agriculture, Vol. 11, Issue. 5, pp. 792-799, 2012.
[7] B. Tian, Y. Gu, “Formal Modeling and Verification for Web Service Composition”, JSW, Vol. 8, Issue. 11, pp. 2733-2737, 2013.
[8] D. Wang, Y. Yang, Z. Mi, “A genetic-based approach to web service composition in geo-distributed cloud environment”, Computers & Electrical Engineering, Vol. 43, pp. 129-141, 2015.
[9] Z.Z. Liu, D.H. Chu, Z.P. Jia, J.Q. Shen, L. Wang, “Two-stage approach for reliable dynamic Web service composition”, Knowledge-Based Systems, Vol. 97, pp. 123-143, 2016.
[10] Y. Xia, X. Luo, J. Li, Q. Zhu, “A petri-net-based approach to reliability determination of ontology-based service compositions”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 43, Issue. 5, pp. 1240-1247, 2013.
[11] Q. Yu, L. Chen, B. Li, “Ant colony optimization applied to web service compositions in cloud computing”, Computers & Electrical Engineering, Vol. 41, pp. 18-27, 2015.
[12] Y. Lei, Z. Jiantao, W. Fengqi, G. Yongqiang, Y. Bo, “Web service composition based on reinforcement learning”, In 2015 IEEE International Conference on Web Services (ICWS), pp. 731-734, 2015.
[13] F. Chen, M. Li, H. Wu, “GACRM: A dynamic multi-Attribute decision making approach to large-Scale Web service composition”, Applied Soft Computing, Vol. 61, pp. 947-958, 2017.
Citation
T. Parimalam, K. Meenakshi Sundaram, "A Multiple Qos Parameter Based Web Service Composition Using Petri Net Verification," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.992-997, 2019.
Mathematical Modeling of EDCH Clustering Algorithm for WSNs
Research Paper | Journal Paper
Vol.7 , Issue.3 , pp.998-1005, Mar-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i3.9981005
Abstract
In WSN, the energy consumed by each sensor node of the network influences the lifetime of the networks, more than the utilization of energy increases more than the lifetime of the networks decreases, this is why the enhance of the lifetime of the networks requires a strategy or protocol which reduces the power utilization of the transmission or reception of data by the nodes. In the recent years to a great extent research has been done to maximize a life time of network node. The hierarchical protocols (Cluster based-approach) have been developed in order to decrease the network traffic toward the BS (Base Station) and therefore extend the network lifetime. The number of clusters and also distribution of CH (Cluster Heads) are necessary for energy efficiency and adaptability of clustering approaches. EDCH (Effective Distance Cluster Head) is a novel energy-efficient clustering algorithm proposed recently for WSN (wireless sensor networks) to extend network lifetime by uniformly distributing of CHs (Cluster Heads) across the network. In this paper, we propose an mathematical method to model the energy utilization of the EDCH (Effective Distance Cluster Head) algorithm. The results of our extensive simulation study prove a reasonable accuracy of the proposed mathematical model to predict the energy utilization under different operational conditions. Here proposed mathematical model reveals a number of implications about the effects of different parameters on the energy utilization pattern of the EDCH (Effective Distance Cluster Head) clustering algorithm.
Key-Words / Index Term
WSN, Clustering, Energy Efficiency, EDCH, Mathematical Model
References
[1] F. Akyildiz, S. Weilian, Y. Sankarasubramaniam, and E. Cayirci, “A Survey on Sensor Networks,” IEEE Communications Magazine, vol. 40, no. 8, pp. 102–114, 2002.
[2] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-Efficient Communication Protocol for Wireless Microsensor Networks,” in 33rd Annual Hawaii International Conference on System Sciences, 2000.
[3] M. Mathew and N. Weng, “Quality of Information and Energy Efficiency Optimization for Sensor Networks via Adaptive Sensing and Transmit-ting,” IEEE Sensors Journal, vol. 14, pp. 341–348, February 2014.
[4] G. Anastasi, A. Falchi, A. Passarella, M. Conti, and E. Gregori, “Performance Measurements of Motes Sensor Networks,” in 7th ACM International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM), (Venice, Italy), pp. 174–181, ACM, 2004
[5] Karl, H., & Willig, A. (2005). Protocols and architectures for wireless sensor networks. Hoboken, NJ: Wiley.
[6] Rajkumar, Dr H G Chandrakanth, Dr D G Anand, and Dr T John Peter.” Research Challenges and Characteristic Features in Wireless Sensor Networks”, in Int. J. Advanced Networking and Applications, Volume: 09 Issue: 01 Pages: 3321-3328 (2017) ISSN: 0975-0290.
[7] M. M. Zanjireh, A. Shahrabi, and H. Larijani, “ANCH: A New Clus-tering Algorithm for Wireless Sensor Networks,” in 27th International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 450–455, IEEE, 2013.
[8] Chan, H., & Perrig, A. (2004). ACE: An emergent algorithm for highly uniform cluster formation. In Wireless Sensor Networks. Lecture Notes in Computer Science, Vol. 2920, pp. 154–171.
[9] Low, C. P., Fang, C., Ng, J. M., & Ang, Y. H. (2008). Efficient load-balanced clustering algorithms for wireless sensor networks. Computer Communications, 31(4), 750–759.
[10] Manjeshwar, A., & Agrawal, D. P. (2002). APTEEN: A hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks. In Proceedings International Parallel and Distributed Processing Symposium, IPDPS 2002 (pp. 195–202).
[11] Gu, Y., Wu, Q., & Rao, N. S. V. (2010). Optimizing cluster heads for energy efficiency in large-scale heterogeneous wireless sensor networks. International Journal of Distributed Sensor Networks. doi:10.1155/2010/961591.3328 (2017) ISSN: 0975-0290.
[12] Tarannum, S., Srividya, S., Asha, D. S., Padmini, R., Nalini, L., Venugopal, K. R., et al. (2008). Dynamic hierarchical communication paradigm for Wireless Sensor Networks: A centralized, energy efficient approach. In 11th IEEE Singapore International Conference on Communication Systems, Singapore (pp. 959–963).
[13] Ci, S., Guizani, M., & Sharif, H. (2007). Adaptive clustering in wireless sensor networks by mining sensor energy data. Computer Communications, 30(14–15), 2968–2975.
[14] Huang, Y. F., Luo, W. H., Sum, J., Chang, L. H., Chang, C. W., & Chen, R. C. (2007). Lifetime Performance of an energy efficient clustering algorithm for cluster-based wireless sensor networks. In Frontiers of High Performance Computing and Networking ISPA 2007 Workshops. Lecture Notes in Computer Science, Vol. 4743, pp. 455–464.
[15] Zhang, M., Gong, C., & Lu, Y. (2008). An novel dynamic clustering algorithm based on geographical location for wireless sensor networks. In 2008 International Symposium on Information Science and Engineering (ISISE), Piscataway, NJ, USA (pp. 565–568).
[16] W. Heinzelman, A. Chandrakasan, H. Balakrishnan. "Energy-Efficient communication protocol for wireless microsensor network", Proc. of the Hawaii International Conference on System Sciences, IEEE Computer Society, Washington. DC USA,Jan 2000, pp.3005-3014
[17] Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366–379.
[18] Manjeshwar, A., & Agrawal, D. P. (2001). TEEN: a routing protocol for enhanced efficiency in wireless sensor networks. In Proceedings of the 1st International Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing, San Francisco, CA (pp. 2009-2015).
[19] Ye, M., Li, C., Chen, G., & Wu, J. (2005). EECS: An energy efficient clustering scheme in wireless sensor networks. In 24th IEEE International Performance, Computing, and Communications Conference, IPCCC 2005 (pp. 535–540).
[20] Kamimura, J., Wakamiya, N., & Murata, M. (2006). A distributed clustering method for energy-efficient data gathering in sensor networks. International Journal of Wireless and Mobile Computing, 1(2), 113–120.
[21] Suhas K. Pawar, Abhishek R. Tawde, ArchanaPokharkar, PriyaPanjwani, Prof.SuhasPatil “A Survey of Cluster formation Protocols in Wireless Sensor Networks”;2014 p. 40-49
[22] O. Younis and S. Fahmy. Heed: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. Mobile Computing,
[23] C. Li, M. Ye, G. Chen, and J. Wu. An energy-efficient unequal clustering mechanism for wireless sensor networks. In Mobile Adhoc and Sensor Systems Conference, 2005. IEEE International Conference on, 2005.
[24] Q. Zhang, R. H. Jacobsen, and T. S. Toftegaard. Bio-inspired lowcomplexity clustering in large-scale dense wireless sensor networks. In Global Communications Conference (GLOBECOM), 2012 IEEE, pages
[25] A. Al Islam, C. S. Hyder, H. Kabir, and M. Naznin. Finding the optimal percentage of cluster heads from a new and complete mathematical model on leach. Wireless Sensor Network, 2(2):129–140, 2010.
[26] I. Beretta, F. Rincon, N. Khaled, P. R. Grassi, V. Rana, and D. Atienza. Design exploration of energy-performance trade-offs for wireless sensor networks. In Design Automation Conference (DAC), 2012 49th ACM/EDAC/IEEE, pages 1043–1048, 2012.
[27] J. Gupchup, A. Terzis, R. Burns, and A. Szalay. Model-based event detection in wireless sensor networks. In Workshop on Data Sharing and Interoperability on the World-Wide Sensor Web (DSI), 2008.
[28] S. Bandyopadhyay and E. J. Coyle. An energy efficient hierarchical clustering algorithm for wireless sensor networks. In INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications. IEEE Societies, volume 3, pages 1713–1723, 2003.
[29] J. C. Choi and C. W. Lee. Energy modeling for the cluster-based sensor networks. In Computer and Information Technology, 2006. CIT ’06. The Sixth IEEE International Conference on, pages 218–218, 2006.
[30] S. Foss and S. Zuyev. On a certain segment process with voronoi clustering. 1993
Citation
Rajkumar, H. G. Chandrakanth, "Mathematical Modeling of EDCH Clustering Algorithm for WSNs," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.998-1005, 2019.
A Versatile Graph Matching Algorithm and Its Application to Scheme Matching
Research Paper | Journal Paper
Vol.7 , Issue.3 , pp.1006-1007, Mar-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i3.10061007
Abstract
Coordinating additives of information patterns or two records occasions assumes a key activity in statistics warehousing, e-commercial enterprise, or then again even biochemical packages. In this paper we gift a coordinating calculation depending on a fixpoint calculation that is usable crosswise over diverse conditions. The calculation takes charts (diagrams, indexes, or different records systems) as statistics, and provides as yield a mapping among relating hubs of the charts. Contingent upon the coordinating goal, a subset of the mapping is picked utilizing channels. After our calculation runs, we count on that a human should check and if critical modify the consequences. In truth, we examine the `precision` of the calculation by tallying the quantity of required changes. We directed a customer consider, wherein our exactness metric turned into applied to gauge the paintings price range that the clients ought to get by using the use of our calculation to get an underlying coordinating. At final, we delineate how our coordinating calculation is dispatched as one of some extraordinary nation administrators in an actualized testbed for overseeing records models and mappings.
Key-Words / Index Term
Supergraph Search, Graph Database, Query Optimization
References
[1] P. A. Bernstein, T. Bergstraesser, J. Carlson, S. Pal, P. Sanders, and D. Shutt.Microsoft Repository Version 2 and the Open Information Model.Information Systems, pages 71–98, 1999.
[2] P. A. Bernstein, A. Halevy, and R. Pottinger.A Vision for Management of Complex Models. SIGMOD Record, pages 55–63, 2000.
[3] S. Brin and L. Page.The Anatomy of a Large-Scale Hypertextual Web Search Engine.In Proc. WWW7 Conf. Computer Networks, 1998.
[4] S. S. Chawathe and H. Garcia-Molina.Meaningful Change Detection in Structured Data. In Proc. SIGMOD’97, pages 26–37, 1997.
[5] W. W. Cohen. Integration of Heterogeneous Databases Without Common Domains Using Queries Based on Textual Similarity. In Proc. SIGMOD 1998, pages 201–212, 1998.
[6] A. Doan, P. Domingos, and A. Halevy.Reconciling Schemas of Disparate Data Sources: A Machine-Learning Approach.In Proc. SIGMOD 2001, 2001.
[7] XML Document Object Model (DOM), W3C Recommendation. http://www.w3.org/TR/REC-DOMLevel-1/, Oct. 1998.
[8] D. Gusfield and R. Irving. The Stable Marriage Problem: Structure and Algorithms. MIT Press, Cambridge, MA, 1989.
[9] M. Kanehisa. Post-Genome Informatics.Oxford University Press, 2000.
[10] O. Lassila and R. Swick.Resource Description Framework (RDF) Model and Syntax Specification. http://www.w3.org/TR/REC-rdf-syntax/, 1998.
[11] W.-S. Li and C. Clifton. SEMINT: A tool for identifying attribute correspondences in heterogeneous databases using neural networks. Trans. on Data &Knowledge Engineering, pages 49–84, 2000.
[12] L. Lov`asz and M. Plummer.Matching Theory. NorthHolland, Amsterdam, 1986. [13] J. Madhavan, P. A. Bernstein, and E. Rahm.Generic Schema Matching with Cupid.In Proc. 27th VLDB Conf., Sept. 2001.
[14] S. Melnik, H. Garcia-Molina, and E. Rahm. Similarity Flooding: A Versatile Graph Matching Algorithm. Extended Technical Report, http://dbpubs.stanford.edu/pub/2001-25, 2001.
[15] R. J. Miller, L. M. Haas, and M. A. Hernandez.Schema Mapping as Query Discovery.In Proc. VLDB 2000, 2000.
[16] R. Motwani and P. Raghavan.Randomized Algorithms.Cambridge University Press, 1995.
[17] Y. Papakonstantinou, H. Garc´ıa-Molina, and J. Widom. Object Exchange Across Heterogeneous Information Sources. In Proc. of the 11th IEEE Int. Conf. on Data Engineering (ICDE), pages 251–260, Taipe, Taiwan, Mar. 1995.
[18] E. Rahm and P. A. Bernstein.On Matching Schemas Automatically.Technical Report MSR-TR-2001-17, http://www.research.microsoft.com/pubs/, Feb. 2001.
Citation
J. Pradeep Kumar, V. Sree Haritha, J. Sai Balaji Reddy, "A Versatile Graph Matching Algorithm and Its Application to Scheme Matching," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1006-1007, 2019.
Prediction of Women’s Diabetic Disorder Using R Tool
Research Paper | Journal Paper
Vol.7 , Issue.3 , pp.1008-1011, Mar-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i3.10081011
Abstract
This Project is Modern Medicine generates a great deal of information which is deserted into the medical dataset. The proper analysis of such information may reveal some interesting facts, which may otherwise be hidden or go dissipate data analytics is one such field which tries to extract some interesting facts from a huge dataset. In this project, an attempt is made to analyze the diabetic dataset and drive some interesting facts from it which can be a prediction model. Random forest algorithm builds in multiple decision trees and merges them to get a more accurate and stable prediction. A huge medical dataset accessible in different data repositories used in the real world application. This aim of this project is to give a detailed version predictive models from base to state-of-art, describing predictive models, steps to develop a predictive model for determining diabetic disorder.
Key-Words / Index Term
Diabetic disorder, Classification, Prediction, And Random Forest
References
[1] Meherwar Fatima1, Maruf Pasha2, “Survey of Machine Learning Algorithms for Disease Diagnostic” , Journal of Intelligent Learning Systems and Applications, 2017, 9, 1-16.
[2] Sadhana, Savitha Shetty, “Analysis of Diabetic Data Set Using Hive and R ", International Journal of Emerging Technology and Advanced Engineering, 2014.
[3] Dr. Saravana Kumar, Eswari, Sampath & Lavanya," Predictive Methodology for Diabetic Data Analysis in Big Data", ELSEVIER, 2015.
[4] Rahul Joshi, Minyechil Alehegn, "Analysis and prediction of diabetes diseases using machine learning algorithm: Ensemble approach" International Research Journal of Engineering and Technology (IRJET) , 2017.
[5] Quan Zou, Kaiyang Qu , Yamei Luo , Dehui Yin, Ying Ju, and Hua Tang," Predicting Diabetes Mellitus With Machine Learning Techniques” Frontiers in Genetics,2018.
[6] Abdullah A. Aljumah, Mohammed Gulam Ahmad, Mohammad Khubeb Siddiqui, "Application of data mining: Diabetes health care in young and old patients" in Journal of King Saud University – Computer and Information Sciences (2013).
[7] Allen Daniel Sunny1, Sajal Kulshreshtha2, Satyam Singh3, Srinabh4, Mr. Mohan Ba5, Dr. Sarojadevi H.6,” Disease Diagnosis System By Exploring Machine Learning Algorithms”, International Journal of Innovations in Engineering and Technology (IJIET), Volume 10 Issue 2 May 2018
[8] http://www.patient.co.uk/doctor/diabetes-mellitus
[9] http://www.idf.org/diabetesatlas/introduction
[10] https://data.world/data-society/pima-indians-diabetes- database/workspace/project-summary
Citation
G. Kanimozhi, S. Nalini, "Prediction of Women’s Diabetic Disorder Using R Tool," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1008-1011, 2019.
Location Aware Routing Schemes for Mobile Adhoc Networks
Research Paper | Journal Paper
Vol.7 , Issue.3 , pp.1012-1014, Mar-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i3.10121014
Abstract
One of the promising wireless network that is based on anytime, anywhere access is the mobile ad hoc network (MANET). A MANET consists of a set of mobile hosts without any support of other devices such as base stations. It is attractive since it can be quickly setup and operated by batteries only. Some critical issues are required to be handled carefully while implementing MANETs in reality. Routing is one of the most critical issues in MANETs. As MANETs allow nodes to be mobile, to change their positions during communication, it may generate issues like route failures and network partitions. The conventional routing schemes are not appropriate in such scenarios. Some advance routing algorithms, such as AODV, DSR, DSDV are proposed which has improved performance significantly. By location awareness, we mean that a host is capable of knowing its current physical location in the three-dimensional world. This paper explores some of the most successful location aware routing schemes.
Key-Words / Index Term
Gedir, Gps, Gpsr, Gra, Lalr, Manet
References
[1] Hardik K Molia, Rashmi Agrawal , “Exploring the Challenges in MANETs”, International Journal of Advanced Networking & Applications 2014.
[2] D. Forsberg et al., Distributing Mobility Agents Hierarchically Under Frequent Location Updates, Sixth IEEE International Workshop on Mobile Multimedia Communications, 1999.
[3] A. Fasbenderetal., Variable and Scalable Security: Protection of Location Information in Mobile IP, Mobile Technology for the Human Race, IEEE 46th Vehicular Technology Conference, 1996.
[4] W. Chen, N . Jain, and S. Singh, ANMP: ad hoc network management protocol, IEEE Journal On Selected Areas In Communications, 17(8), 1999.
Citation
Viranchee Dave, H.B. Bhadka, "Location Aware Routing Schemes for Mobile Adhoc Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1012-1014, 2019.