IOSR Journal of Computer Engineering (IOSR-JCE)

May. - Jun. 2017 Volume 19 - Issue 3

Version 1 Version 2 Version 3 Version 4 Version 5 Version 6

Paper Type : Research Paper
Title : Sap Data Service for E-Commerce
Country : India
Authors : Pooja Mulam || Ruchi Rautela
: 10.9790/0661-1903050105     logo

Abstract:Now days, everyone is busier with their work life. Therefore a person gets less time to go for shopping. A person gives more preference to online shopping. In the technical world, online shopping refers to E-Commerce. There are many online E-Commerce sites available over web which deal with millions of transactions in a day. These websites deal with huge amount of data which must be maintained for analysing the daily/monthly/yearly transactions, profits and statistics. Data can come from different sources therefore there is a need to refine the data which gives accurate data statistics and provide better report. Here working of data service makes a sense. In this paper we will discuss how SAP data service can be used for E-commerce.

Keyword: SAP Data Service, E-Commerce, ETL, Reports

[1] https://en.wikipedia.org/wiki/Trade
[2] http://www.investorwords.com/1637/e_commerce.html
[3] https://www.infoq.com/articles/narayanan-soa-data-services
[4] http://searchsap.techtarget.com/definition /SAP-Data-Service
[5] http://www.thenextview.nl/blog/what-is-sap-data-services-bods


Paper Type : Research Paper
Title : Hardware Implementation of a Real Time Image Compression
Country : Iraq
Authors : Muzhir Shaban Al-Ani
: 10.9790/0661-1903050613     logo

Abstract: Real time processing of image deals with applying all required operations within a range of time not exceed the acceptable time of human eyes. Real time processing does not realized on standalone computer, so it need special hardware. The big challenge of image processing work is the processing time, in which there are big amount of data (pixels) that must be processed at a specific time. The most important objective aspect of this work and any image processing algorithm (software or hardware) is how to implement it in an efficient way via the effective management of the scheduling of the processing jobs and time allocated for each job. This work try to avoid this problem by inserting a Raspberry Pi device which is working as microcomputer and has the ability to work with image, video, audio and data.........

Keywords: Image Compression, 2D-DWT, Real Time Processing and Image Hardware Implementation.

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vol. 1, pp. 205-220, April 1992.
[3]. Win-Bin Huang, Yuan-Jui Chang, Alvin W.Y. Su and Yau-Hwang Kuo, "VLSI Design of A DWT/Modified Efficient SPIHT
Based Image Codec", ICICS,-PCM 11-18 December Singapore 2003.

[4]. M. W. Marcellin, M. J. Gormish, A. Bilgin and M. Boliek, "An overview of JPEG-2000", Proc. of Data Compression Conference, Snowbird, Utah, pp. 523-541, Mar. 2000.
[5]. ISO/IEC 15444-1:2000(E), Information technology – JPEG-2000 Image Coding System – Part 1: Core Coding System


Paper Type : Research Paper
Title : SSL-QA: Analysis of Semi-Supervised Learning for Question-Answering
Country : India
Authors : Parth Patel || Jignesh Prajapati
: 10.9790/0661-1903051415     logo

Abstract: Open domain natural language question answering (QA) is a process of automatically finding answers to questions searching collections of text files. Question answering (QA) is a long-standing challenge in NLP, and the community has introduced several paradigms and datasets for the task over the past few years. These patterns differ from each other in the type of questions and answers and the size of the training data, from a few hundreds to millions of examples. Context-aware QA paradigm and two most notable types of supervisions are coarse sentence-level and fine-grained span-level. In this paper we analyse different intensive researches in semi-supervised learning for question-answering.

[1]. Asli Celikyilmaz, Marcus Thint, Zhiheng Huang, A Graph-based Semi-Supervised Learning for Question-Answering. Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP- 2009.
[2]. Jong-Hoon Oh, Kentaro Torisawa, Chikara Hashimoto, Ryu Iida, Masahiro Tanaka, Julien Kloetzer, A Semi-Supervised Learning Approach to Why-Question Answering. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence-2016.
[3]. Zhilin Yang, Junjie Hu, Ruslan Salakhutdinov, William W. Cohen, Semi-Supervised QA with Generative Domain-Adaptive Nets, arXiv -2017.
[4]. Jinxiu Chen, Donghong Ji, C. Lim Tan, and Zhengyu Niu. 2006. Relation extraction using label propaga-tion based semi-supervised learning. In Proceedings of the ACL-2006.
[5]. Charles L.A. Clarke, Gordon V. Cormack, R. Thomas Lynam, and Egidio L. Terra. 2006. Question an-swering by passage selection. In In: Advances in open domain question answering, Strzalkowski, and Harabagiu (Eds.), pages 259–283. Springer.


Paper Type : Research Paper
Title : Effect of PUs Mobility in CRAHNs using an Activity and Mobility Based Routing Protocol
Country : Egypt
Authors : Faisal Awad Mahgoub || Hussein A. Elsayed || Salwa El Ramly || IEEE Senior
: 10.9790/0661-1903051622    logo

Abstract: Cognitive Radio (CR) is capable to identify the unused spectrum in order to allow Cognitive users (CUs) to occupy it without interfering the primary users (PUs). Routing in Cognitive Radio Ad-Hoc Networks (CRAHNs) is a very challenging task due to diversity in the available channels. In this paper, Mobility and Activity Based Routing Protocol (MABRP), is proposed. In the proposed protocol, CUs discover next hops based on the collected spectrum and mobility information. In addition, using cooperative communication mechanisms to reveal new routing opportunities, enhance route qualities, and enable true coexistence of primary and secondary networks is investigated. The performance of MABRP and Cognitive Ad-hoc On-demand Distance Vector (CAODV) are evaluated on the basis of packet........

Keywords:Cognitive Radio, CRAHNs, CAODV, MABRP, Routing Protocols

[1]. Haykin, S.: Cognitive Radio: Brain-Empowered Wireless Communications. IEEE Journal on Selected Areas in Communications, 23(2), 2005, 201–220.
[2]. IF Akyildiz, WY Lee, MC Vuran, M Shantidev, "Next generation/ dynamic spectrum access/cognitive radio wireless networks: a survey". Computer Networks, 50(13), 2006, 2127-2159.
[3]. Mitola, J.: Cognitive Radio Architecture Evolution. Proceedings of the IEEE, 97(4), 2009, 626–641.
[4]. Akyildiz, I.F., Lee, W.-Y., Chowdhury, K.R.: CRAHNs: Cognitive Radio Ad Hoc Networks, Ad Hoc Networks, 7(5), 2009, 810–836.
[5]. Cesana, M., Cuomo, F., Ekici, E.: Routing in Cognitive Radio Networks: Challenges and Solutions. Ad Hoc Networks, 9(3), 2011, 228–248.


Paper Type : Research Paper
Title : Data Integrity Verification in Cloud Computing
Country : India.
Authors : Gaurav Gupta || Prof.(Dr.) Naveen Hemrajani || Ajay Kumar
: 10.9790/0661-1903052327     logo

Abstract: Cloud computing is recognized as a hottest technology which has a significant impact on IT field in the nearby future. Cloud computing is an Internet based computing. It provides the services to the organizations like storage, applications and servers. Cloud computing is on demand and pay per use service. That means customers pay providers based on usage. Data Integrity is the major issue in cloud computing. To provide the integrity various methods have been proposed by the researchers. In this paper we will proposed a model which will provide the data integrity using Elgamal Algorithm and SHA-2 Algorithm. The Proposed model shows that the data which is uploaded on cloud is secured if the hash key matches with the local keys which are stored on the system.

Keywords: cloud computing; data integrity; pay per use model

[1] Sarvan Kumar, R. and A. saxena.2011 Data integrity proofs in cloud storage in communication system and network, third international conference.
[2] Subhashini S. and V. Kavitha. 2011. A survey on security issue in service delivery models of cloud computing, journal of network and computer application.
[3] Nepal S.2011. data integrity as a service in cloud computing, IEEE.
[4] S. Mahdi Shariati, Abouzarjomehri, M. Hossein Ahmadzadegan.2015.Challenges and security issues in cloud computing from two perspectives: Data security and privacy protection. 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), pp. 1078 - 1082.
[5] A. Dhamija and V. Dhaka. 2015. A novel cryptographic and steganographic approach for secure cloud data migration. International Conference Green Computing and Internet of Things (ICGCIoT), pp. 346–351.


Paper Type : Research Paper
Title : An Investigation into Ipredation in Cyberspace towards Developing a Framework for Preventing Ipredators' Attacks
Country : Kenya.
Authors : Ambrose Kinyanjui Muchonjo || Prof. Gregory Wanyembi || Dr. Cyrus Makori
: 10.9790/0661-1903052836     logo

Abstract: The information age society is becoming highly dependent on technological advancements and in particular software platform and devices that can access the cyberspace. The way people acquire, access and share data, files and, information have significantly revolutionized peoples‟ social-technical interactions shaping their lifestyle and activities in regard to both the physical space and cyberspace. Ubiquitous computing for instance tends to offer digital society great, exciting, powerful features and capabilities that enable reliable and productive encounters within convenient environments. In ubiquitous environments however, there is high possibility of users‟ data, files and other users‟ digital assets being exposed to high risk of disclosure and tampering by iPredators. Such disclosure and tampering of users‟ sensitive data and information potentially exposes the targeted persons in society to many risks in regard to their digital and environmental security and privacy. In event of a successful iPredator‟s attacks on targets...............

Keywords: iPredator, iPredation, cyberspace, attacks prevention

[1]. Aftab,P. Wired Safety (2012) Wired Safety Resources on cyberbullying, cyberstalking, sexting and Internet safety. Retrieved June 28th 2015 from http://www.wiredsafety.org and http://www.aftab.com
[2]. Alexander K. (2012). National Cyber Security Framework Manual. Pretoria: Van Schaik Publishers.
[3]. BusinessDailyAfrica (2013). businessdailyafrica.com. Retrieved March 16th, 2016, from www.businessdailyafrica.com/Corporate-News: http://www.businessdailyafrica.com/Corporate-News/Experts-fault-Kenya-s-cyber-security-after-18-month-test/-/539550/2083724/-/61begg/-/index.html
[4]. Georgia Tech Information Security Centre, (2008). Emerging Cyber Threats Gibson, W. (1984). Neuromancer New York, NY: HarperCollins.
[5]. Gentleman, J. (2015, November 4). www.nytimes.com. Retrieved March 15, 2016, from nytimes.com: http://www.nytimes.com/2015/11/05/world/africa/kenya-government-corruption.html?_r=0.


Paper Type : Research Paper
Title : Local Differential Excitation Binary Co-occurrence Pattern (LDEBCoP): A New Descriptor for Texture and Bio-Medical Image Retrieval
Country : India
Authors : G V Satya Kumar || P G Krishna Mohan
: 10.9790/0661-1903053748     logo

Abstract: This paper presents a novel pattern based feature descriptor named as Local Differential Excitation Binary Co- occurrence Pattern (LDEBCoP) for texture and biomedical image retrieval. The proposed method exploits the local structure information using differential excitation. Further, to produce more compact local binary patterns the adjacent neighbourhood pixel pairs are considered in the computation of differential excitation. In the proposed method, the co-occurrence of pixel pairs in local binary map have been observed using gray level co-occurrence matrix(GLCM) in different directions and distances for better feature representation. Previous methods have utilized histogram to obtain the frequency information of local pattern map but co- occurrence of pixel pairs is more robust than frequency of patternss...............

Keywords:Differential excitation, Local binary pattern, Image retrieval, GLCM, Pattern recognition.

[1] Ahonen, T., Hadid, A., and Pietikäinen, M., 2004, "Face recognition with local binary patterns", Computer Vision-ECCV 2004( Springer)., pp. 469–481.
[2] Ning,J., Zhang,D., and Wu,C.,2009 , "Robust object tracking using joint color texture histogram", J. Pattern Recognit. Artif. Intell.,23(7), pp. 1245–1263.
[3] Pietikäinen,M.,and Mäenpää,T.,2005, Texture analysis with local binary patterns, The Handbook of Pattern Recognition and Computer Vision, World Scientific Publishing, Singapore, pp. 197–216.
[4] Fernandez,A., Ghita, O., Gonzalez, E., Bianconi,F., and Whelan, P.F.,2011, "Evaluation of robustness against rotation of LBP, CCR and ILBP features in granite texture classification", Trans.on Mach. Graph. Vision., 22(6), pp. 913–926.
[5] Pietikäinen,M., Hadid, A.,Zhao,G., and Ahonen, T.,2011, Computer vision using local binary patterns, Computational Imaging and Vision(springer),London. doi:10.1007/978-0-85729-748-8.


Paper Type : Research Paper
Title : Predicting Student Performance Based On Clustering And Classification
Country : India
Authors : Snehal Bhogan || Kedar Sawant || Purva Naik || Rubana Shaikh || Odelia Diukar || Saylee Dessai
: 10.9790/0661-1903054952     logo

Abstract: In today's world the education field is growing, developing widely and becoming one of the most crucial industries. The data available in the educational field can be studied using educational data mining so that the unseen knowledge can be obtained from it. In this paper, various data mining approaches like Clustering, classification and regression our used to predict the students' performance in examination in advance, so that necessary measures can be taken to improvise on their performance to score better marks. A hybrid approach of Enhanced K-strange points clustering algorithm and Naïve Bayes classification algorithm is presented implemented and compared it with existing hybrid approach which is K-means clustering algorithm and Decision tree. Finally, to predict student performance, multiple linear regression is used..............

Keywords: classification, clustering, data mining, student prediction, regression

[1] Md. Hedayetul Islam Shovon and Mahfuza Haque, An Approach of improving Student's Academic performance by using k-means clustering algorithm and decision tree, (IJASC) International Journal of Advanced Computer Science and Applications Vol.3, No. 8, 2012.
[2] Thaddeus Matundura Ogwoka, Wilson Cheruiyot and George Okeyo, A model for predicting student's Academic Performance using a Hybrid of k-means and decision tree algorithm, International Journal of Computer Applications Technology and Research, Vol.4, Issue 9, 693-697, 2015, ISSN:2319-8656.
[3] M. Durairaj and C. Vijitha, Educational Data mining for Prediction of Student Performance Using Clustering Algorithm, International Journal of Computer Science and Information Technologies, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol5(4), 2014, 5987-5991, ISSN:0975-9646.
[4] Terence Johnson and Dr. Santosh Kumar Singh, "Enhanced K- Strange Points Clustering Algorithm", 2015 International Conference on Emerging Information Technology and Engineering Solutions.
Books:
[5] Jiawei Han and Micheline Kamber, Data Mining - Concepts and Techniques, Second Edition, Original ISBN: 978-1-55860-901-3, Indian Reprint ISBN: 978-81-3120535-8


Paper Type : Research Paper
Title : Securing Sensitive Digital Data Techniques
Country : Bulgaria
Authors : Tony Karavasilev || Svetoslav Enkov
: 10.9790/0661-1903055366     logo

Abstract: This paper identifies the correct and wrong approaches of securing digital data. It also proposes further ways of increasing the overall security of data transfer and storage. The variety of data can be encrypted either for comparison only purposes or for further active reuse and update, which implies the need of having a different approach when processing the data flow. Several effective models and techniques of encrypting sensitive digital information are presented in this paper, that excludes all reviewed bad practices.

Keywords: cryptography, security, secure, data, compression, hash functions, encryption, pseudo-random number generators, hash collisions, rainbow tables, salt, HMAC, PBKDF2, SHA-2, SHA-3, AES, RSA, SSL.

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