iosr-JCE   Volume-18 ~ Issue-5 ~ Sep-Oct 2016

Abstract: The main intention is RTL Design Architecture and HDL Design Implementation of Zetta Bytes Memory ASIC SOC IP Core for Advanced Parallel Array Distributed Pipelined Array Computing /Cloud Computing / Super VLIW Computing/Cluster Computing Processors/very High Big Data Control Stations /Servers , Arrays of Data Servers, Interfacing for All Advanced Real Time Smart Computing Products-wireless , telecom , consumer, advanced processor and controller IP cores, Aerospace/Avionics ,Automotive..........

Keyword: Application Specific Integrated Circuit, FPGA – Field Programmable Gate Array, HDL – Hardware Description Language , ISE – Integrated Software Environment

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Abstract: The Design is mainly Intended for HDL Design for Exa Hertz Clock based 2e23-1 Exa Bits Per Second (Ebps) PRBS Design for Ultra High Speed Applications/Products the estimation of HIGH Speed in terms of Ebps(Exa Bits Per Second) Data Rate for 2e23-1 Tapped PRBS Design Pattern Sequence. The Exa Bits Per Second Data Baud rate Speed estimated by Synchronization with Exa Hertz Clock (250 Clock Cycle Periodic time for 1 Exa Hertz Clock frequency ) The Exa Hertz E.B.P.S 2e23-1PRBS is Designed by using LFSR Linear Feed Back Shift Register & XOR Gate with Specific Tapping Points(18 and 23).........

Keyword: CCITT – Consulting Committee for International Telegraph & Telecom , ITU – International Telecom Unit, LFSR-Linear Feedback Shift Register, PRBS-Pseudo Random Binary Sequence, RTL- Register Transfer Level, , VHDL- Very High Speed Integrated Circuit Hardware Description Language.

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Abstract: Cloud computing offers your business many benefits. It allows you to set up what is essentially a virtual office to give you the flexibility of connecting to your business anywhere, any time. With the growing number of web-enabled devices used in today's business environment (e.g. Smartphones, tablets), access to your data is even easier. There are many more benefits to moving your business to the cloud: Reduced IT costs, Scalability, Flexibility, etc. However, Cloud Computing presents an added level of risk because essential services are often outsourced to a third party, which makes it harder to maintain data security, privacy and support data and service availability. It refers to a broad set of policies, technologies, and controls deployed to protect data, applications, and the associated infrastructure of cloud computing

[1] Haghighat, M., Zonouz, S., and Abdel-Mottaleb, M. , CloudID: Trustworthy Cloud-based and Cross-Enterprise Biometric Identification. Expert Systems with Applications, 42(21), 79057916., 2015.
[2] Srinavasin and Madhan State-of-the-art cloud computing security taxonomies: a classification of security challenges in the present cloud computing environment ACM ICACCI 2012
[3] Florian Pfarr and Thomas Buckel Cloud Computing Web Security Journal, 2009-12-28. [4] M. Pop and S. L. Salzberg Bioinformatics challenges of new sequencing technology Trends Genetics, vol. 24, no. 3, pp. 142 149, 2008.
[5] A. Greenberg, A. J. Hamilton, D. A. Maltz, and P. Patel The cost of a cloud: Research problems in data center networks ACM SIGCOMM Comput. Commun. Rev., vol. 39, no. 1, pp. 68 73, 2008. [6] Q. Zhang, L. Cheng, and R. Boutaba Cloud computing: state-oftheart and research challenges J. Internet Services Appl., vol. 1, no. 1, pp. 718, 2010.

Abstract: L'univers carcéral des pays en voie de développement présente des conditions de détention précaires de manière générale. Dans la partie septentrionale du Cameroun, en particulier, la pesanteur de la coutume est une difficulté majeure pour la prise en charge sanitaire des femmes. Au regard de la précarité de leurs conditions de détention, les femmes voient leur santé se dégrader dans la prison. Ces lieux sont des milieux mal aérés, exigus et à forte densité d'occupation. Il en résulte une carence du régime alimentaire et un manque d'hygiène corporelle, à quoi s'ajoute une insuffisance du personnel de santé.........

Keywords: Univers carcéral, féminin, prison, Méri, Maroua, Extrême-Nord.

[1]. Bessin, M. et Lechien, M-H. « L'intimité de soins en prison. Ethnologie française, » 2002, tome XXXVII, p.69-80
[2]. Bourrounet G. et Pujol J. 2007 « Le soin en milieu carcéral » Institut de formation de manipulateurs en Electroradiologie Médicale CHU de Montpellier 71p
[3]. Duflot P. « Santé en prison : un enjeu de santé publique ». In Revue Française des Affaires Sociales, 1997, n° 1 PP 131- 135.
[4]. Forzie S. Nkem, 2008, Textes de base régissant l'administration pénitentiaire au Cameroun in SOPECAM 73p.
[5]. Gentilini M. Problèmes sanitaires dans les prisess en change. Paris, Flammarion, 1997..

Abstract: Data mining is a method that is used to select the information from large datasets and it performs the principal task of data analysis. The Clustering is a technique that consist groups of data and elements into disjoined clusters of data. The same cluster data are related to similar cluster and different cluster data belong to different cluster. Clustering can be done different methods like partition, Hierarchical, density based etc. In this paper the problem with k-mean i.e. initialization is discussed and the possible solutions have been elaborated. The spherical k-means is the approach currently used for clustering which have provided best results till date..........

Keyword: Data mining, clustering, K-mean, Spherical K-mean, Methods of initializing, Spherical initialized.

[1] Fillippone, M. Camastra ,F. Masulli, F. Rovetta S, "A survey of kernel and spectral methods for clustering", Pattern Recognition, vol 41, pp. 176–190, (2008)
[2] Lei Gu1, "A Locality Sensitive K-Means Clustering Method Based on Genetic Algorithms" Springer-Verlag Berlin Heidelberg, Part II,vol 7929, pp. 114–119, ( 2013).
[3] Anwiti Jain Anand Rajavat Rupali Bhartiya," Design Analysis and Implementation of Modified K-Mean Algorithm for Large Data-set to Increase Scalability and Efficiency", Fourth International Conference on Computational Intelligence and Communication Networks, IEEE , pp. 627-631, (2012).
[4] Jyoti Yadav, Monika Sharma," A Review of K-mean Algorithm" International Journal of Engineering Trends and Technology" (IJETT) ,Vol 4, pp. 2972-2976, ( 2013).
[5] Nikita Jain1,Vishal Srivastava, "DATA MINING TECHNIQUES: A SURVEY PAPER", International Journal of Research in Engineering and Technology(IJRET), Vol 02 , pp. 116-119, (2013)

Abstract: This paper describes the concept of ad hoc networking and the security issues faced by giving its background and presenting some of the security challenges that are faced by the mobile ad hoc network. Ad hoc wireless communication between devices is defined as a scheme, often referred to as ad hoc networking, which allows devices to establish communication, anytime and anywhere without the aid of a central infrastructure. Before the incorporation of technologies of bluetooth..........

Keyword: Mobile Ad Hoc Network, Security, vulnerabilities.

[1] Rajneesh Narula, Rajneesh Narula, Kaushal, Anish Arora Security Issues of Routing Protocols in MANETs 2012
[2] Wenjia Li and Anupam Joshi Security Issues in Mobile Ad Hoc Networks – A Survey
[3] Karan Singh, R. S. Yadav, Ranvijay International Journal of Computer Science and Security, Volume (1): Issue (1) 52 A review paper on network security
[4] Yongguang Zhang and Wenke Lee, Security in Mobile Ad-Hoc Networks, in Book Ad Hoc Networks Technologies and Protocols (Chapter 9), Springer, 2005.
[5] Amitabh Mishra and Ketan M. Nadkarni, Security in Wireless Ad Hoc Networks, in Book. The Handbook of Ad Hoc Wireless Networks (Chapter 30), CRC Press LLC, 2003.

Abstract: Growing the size of attacks that listed during the passing of data between the sending and the receiving has really demand to get high robust method for transfer information in secure manner. Both of cryptography and steganography are familiar and more utilized techniques which process data for encrypt and conceal the secret message, respectively. Two techniques are participating in the same aims and security services like: confidentiality, integrity and availability for secret message from not allowed access..........

Keyword: modified Fatha, AES, Steganography, cryptography

[1]. Mohammad S., M. Hassan Shirali-Shahreza,An Improved Version of Persian/Arabic Text Steganography Using "La" Word, Proceedings of IEEE 2008 6th National Conference on Telecommunication Technologies and IEEE 2008 2nd Malaysia Conference on Photonics, 26-27 August 2008, Putrajaya, Malaysia.
[2]. Ammar Odeh,"Robust text steganography algorithms for secure data communications", PHD thesis, UNIVERSITY OF BRIDGEPORT, May 2015.
[3]. Bharti P., and Soni R., "A New Approach of Data Hiding in Images using Cryptography and Steganography", International Journalof Computer Applications, Vol.58, No.18, pp.1-5, 2012.
[4]. Mohammed Aabed, Sameh Awaideh, Abdul-Rahman Elshafei,Adnan Gutub, "Arabic Diacritics Based Steganography", IEEE International Conference on Signal Processing and Communications (ICSPC 2007), Pages 756-759, Dubai, UAE, 24-27 November 2007.
[5]. Jibran A. M., Kamran K., and Hameedullah K., "Evaluation of Steganography for Urdu /Arabic Text.", Journal of Theoretical and Applied Information Technology (JATIT), Vol.4, No.3, pp.232–237, (2008).

Abstract: Among the various biometrics technologies, iris recognition is the most reliable and accurate biometric identification system available. In this paper a novel framework for recognizing and identifying iris is been proposed. Iroimages are pre-processed to remove noise using median filtering on different image planes separately. GLCM and Gabor wavelet texture features have been used to identify the Iris and classification tree approach is used to classify them...............

[1]. S. Prabhakar, S. Pankanti, and A. K. Jain, ―Biometric recognition: Security and privacy concerns‖, IEEE Security and Privacy, vol. 1, No. 2, pp. 33-42, 2003.
[2]. Ravi Das, ―Retinal recognition Biometric technology in practice‖, Keesing Journal of Documents & Identity, vol. 22, pp. 11-14, 2007.
[3]. Charles Boncelet (2005).‖Image Noise Models‖. In Alan C. Bovik. Handbook of Image and Video Processing.
[4]. Priyanka Shrivastava, Uday Pratap Singh, ―Noise Removal using First Order Neighborhood Mean Filter‖, 978-1-4799-3064-7/14/$31. 00, 2014 IEEE.
[5]. Fu Bin, Xiong XinyanI, Sun Guozhen, ―An efficient Mean Filter Algorithm‖,, Proceedings of the 2011 IEEEIICME International Conference on Complex Medical Engineering May 22 - 25, Harbin, China.

Abstract: We are living in a world full of data. Every passing second, large data is generated by Social Media, E-Commerce, Stock Exchange and many other platforms. Now-a-days, microblogging sites are used for manypurposes, such as communication, trend detection in marketing and business products, sentiment analysis, election prediction, education and much more, which has changed the public perspective of personalization and socialization. Twitter, is one of the major sources of data which generates more than 500 million of tweets per day. Every user usually shows his feeling or emotion about the topics of his interest.........

Keyword: Talend, sentiment analysis, lexicon, AFFIN, sentiwordnet

[1]. https://www.cs.uic.edu/~liub/FBS/SentimentAnalysis-and-OpinionMining.pdfBing Liu. Sentiment Analysis and Opinion Mining, Morgan & Claypool Publishers, May 2012.BLiu -Synthesis lectures on human language technologies, 2012 - morganclaypool.com
[2]. About sentimental analysis is available on the link- https://en.wikipedia.org/wiki/Sentiment_analysis [3]. Ravi, Kumar, and Vadlamani Ravi. "A survey on opinion mining and sentiment analysis: tasks, approaches and applications." Knowledge-Based Systems 89 (2015): 14-46.
[4]. Talend: About Talend. https://www.talend.com/about-us.
[5]. Turney, Peter D.Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews." In Proceedings of the 40th annual meeting on association for computational linguistics, pp. 417-424. Association for Computational Linguistics, 2002.

Abstract: Harvesting data and investing in analytics have almost become a norm of the organization. Successful organizations are achieving business advantages by analysing massive amount of data that is generated during their business practices. This unconventional enormous size data is referred as Big Data. Big data has received significant attention in recent years. Although, Big Data Analytics has become the need of the day, several businesses are still lagging..........

Keyword: Big data, Big Data Analytics, Business Analytics, Business Intelligence

[1]. J. P. Dijcks, Oracle: Big data for the enterprise, Oracle White Paper, 2012.
[2]. Big Data Survey Research Brief, Sas White Paper,2013.
[3]. M. Schroeck, R. Shockley, J. Smart, D. Romero-Morales, and P. Tufano, Analytics: the real-world use of big data: How innovative enterprises extract value from uncertain data, Executive Report, IBM Institute for Business Value and Said Business School at the University of Oxford, 2012.
[4]. Turn Big Data into Big Value, A Practical Strategy, Intel White Paper, 2013.

[5]. JIANG, Jing. Information Extraction from Text. (2012). Mining Text Data. Research Collection School Of Information Systems.

Abstract: Data is accumulating from almost all aspects of our everyday lives that it becomes huge and multi-structured and has hidden useful information. The challenges with Big Data include capture, curation, storage, search, sharing, transfer, analysis, and visualization. Big Data provides materials for mining hidden patterns to support innovation mostly by data mining. The interaction research with Big Data support methods for innovation is rare at present. Knowledge discovered by data mining is novel and quantitative. However, it still lacks a uniform knowledge management model to support the innovation process effectively. The fact shows that since there emergence, big data techniques are changing very fast..........

Keyword: Map Reduce, Real time analysis, Information Security.

[1]. A Navint Partners White Paper, ―Why is BIG Data Important?‖ May 2012, http://www.navint.com/images/Big.Data.pdf.
[2]. http://www.informatioweek.com/software/business-intelligence/sas-gets.hip-to-hadoop-forbigdata/ 240009035? / pgno.2.
[3]. Chen Mingqi, Jiang He. USA Information Network Security New Strategy Analysis in Big Data [J]. Information Network Security. 2012(8):32—35
[4]. Narayanan A, Shmatikov V. How to break anonymity of the Netflix prize dataset. ArXiv Computer Science e-prints, 2006, arXiv:cs/0610105: 1-10
[5]. G. Caruana, Maozhen Li, Man Qi, A mapreduce based parallel svm for large scale spam filtering, in: Fuzzy Systems and Knowledge Discovery, FSKD, 2011, pp. 2659–2662. [6] Alfons Kemper, Thomas Neumann, Hyper: a hybrid oltp&olap main memory database system based on virtual memory snapshots, in: ICDE, 2011, pp. 195–206.

Abstract: In Opportunistic networks, if a mobile finds that the content of its interest is available in other mobiles, it will send a message to request for the content and download it. The term subscriber denotes a node which requests for the content and the term publisher denotes a node which has the content. There can be two types of caches in a mobile node, namely private and public caches. Private cache stores the data of its own interest where as public cache stores the data of other nodes' interests. While a subscriber node requests another node for a particular content that is in the communication range for which it is missing, thereafter it forwards the request to other neighbouring nodes..........

Keyword: Opportunistic Networks (Oppnets), Caching, Publisher/Subscriber, Public key Cryptography, Digital signatures, Pseudonyms, Digital certificates

[1]. William Stallings. Wireless Communications and Networks. Prentice Hall, 2005.
[2]. Ericsson. Mobile Data Traffic Surpasses Voice. Press releases. 2010, http:// www.ericsson. com/thecompany/ press /releases/ 2010/03/1396928.
[3]. Krishna Nadiminti, Marcos Dias de Assunção, and Rajkumar Buyya. Distributed Systems and Recent Innovations: Challenges and Benefits. Department of Computer Science and Software Engineering, The University of Melbourne, Australia, 2006.
[4]. Rajendra V. Boppana, and Satyadeva P Konduru. An adaptive distance vector routing algorithm for mobile, ad hoc networks. In Proceedings of IEEE INFOCOM, Anchorage, AK, USA, 2001.
[5]. Charles E. Perkins and Elizabeth M. Royer. Ad-hoc On-Demand Distance Vector Routing. Second IEEE Workshop on Mobile Computer Systems and Applications, New Orleans, LA , USA , 1999.

Abstract: Cloud computing has provided a platform to its users where they are charged on the basis of usage of the cloud resources; this is known as "pay-as-you-use". Today, Cloud computing is the most hyped technology arenas and it has became one of the rapidly rising sections of IT. It has permitted us to measure our servers in better and availability to provide the services to greater number of the end users. . In cloud environment it is very difficult to detect and filter the attack packets because everything is virtualized there. Issues of protecting the cloud from the attackers and hackers cannot be underestimated. EDOS attacks are the cloud specific attacks and such attack causes the financial loss to the end users. The cloud service model automatically balances the resources according to their request of the consumers.........

Keyword: Cloud Computing; EDOS-Shield; EDoS; Mitigation; Security; SLA; Pattern Recognition

[1]. Al-Haidari, F., M. Sqalli, and K. Salah. 2015 "Evaluation of the Impact of EDoS Attacks Against Cloud Computing Services." Arabian Journal for Science and Engineering, 40(3): 773-785.
[2]. Al-Haidari, F., Sqalli, M.H. and Salah, K., 2012, June. Enhanced edos-shield for mitigating edos attacks originating from spoofed ip addresses. In Trust, Security and Privacy in Computing and Communications (TrustCom), 2012 IEEE 11th International Conference on : 1167-1174. IEEE.
[3]. Bakshi, A. and Yogesh, B., 2010, February. "Securing cloud from ddos attacks using intrusion detection system in virtual machine." In Communication Software and Networks, ICCSN'10. Second International Conference on : 260-264, IEEE.
[4]. Baig, Zubair A., Sait S.M. and Binbeshr F. 2016. "Controlled Access to Cloud Resources for Mitigating Economic Denial of Sustainability (EDoS) Attacks." Computer Networks. 97: 31-47
[5]. Baig, Zubair A., and Binbeshr Farid 2013. "Controlled Virtual Resource Access to Mitigate Economic Denial of Sustainability (EDoS) Atta.

Abstract: The retinal images are widely used in the diagnosis and treatment of various eye diseases such as diabetic retinopathy, cataract and glaucoma. Normally retinal images are classified manually by clinicians trained in time and process resources. Segmentation imaging of the eye retina provides an important role in the calculation the shape and size of the optic disc and the anterior segment and the abnormal growth of any geometry in the eye region. It automatically and precisely calculates the values of location, position and contour area and a structural part of the image required by ophthalmologists. When there is an elevated intra ocular pressure from the normal condition, the subject is affected by glaucoma and in this condition the retinal nerve fiber layer and the optic disc are affected and this leads to progressive loss of vision.........

Keyword: Diabetic retinopathy, Glaucoma, Segmentation, Cup to disc ratio

[1]. M Ozaki, Y. Adachi, Y. Iwahori, and N. Ishii, Application of fuzzy theory to writer recognition of Chinese characters, International .Diabetic Retinopathy‖, U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES National Institutes of Health National Eye Institute NIH Publication No: 06-2171Revised 9/03
[2]. .Thomas Walter, Jean-Claude Klein, Pascale Massin, and Ali Erginay, ―A Contribution of Image Processing to the Diagnosis of Diabetic Retinopathy—Detection of Exudates in Color Fundus Images of the Human Retina‖,
[3]. IEEE Transactions On Medical Imaging, Vol. 21, No. 10, October 2002. IEEE Trans. Biomedical Engg, Vol. 51, No. 2, Feb 2004.
[4]. A Novel Approach to Diagnose Diabetes Based on the Fractal Characteristics of Retinal Images‖, Shu-Chen Cheng and Yueh-Min Huang,IEEE Transactions On Information Technology In Biomedicine, Vol. 7, No. 3, September 2003.
[5]. Automated Feature Extraction in Color Retinal Images by a Model Based Approach‖, Huiqi Li and OpasChutatape, IEEE Transactions On Biomedical Engineering, Vol. 51, No. 2, February 2004.