iosr-JCE   Volume-18 ~ Issue-4 ~ July-August 2016

Abstract: Mobile ad-hoc networks are a specific kind of wireless networks that can be quickly deployed without pre- existing infrastructures. Even though there are various studies on cluster based security in MANET but there is a clear absence of a comprehensive performance study that provides a unified platform for comparing such techniques. Thus in order to provide a better understanding of cluster based security in MANET a brief survey is needed. Hence to the point, outline of the key concept, performance metrics used, as well as the advantages and drawbacks of each discussed mechanism are given..............

Keyword:............

[1]. Sandeep Kr. Agarwal, Amit Garg and K. V. Arya, "Security Issues & Clustering Based Solutions in Mobile Ad-hoc Networks - A Survey," Journal of International Academy of Physical Sciences, Vol. 16, pp. 295-308, 2012
[2]. Avinash Jethi and Seema, "Cluster Based Security Architecture in Wireless Ad-Hoc Networks: An Overview," Journal of Global Research in Computer Science, Vol. 2, 2011
[3]. Chandra Prakash, Kunal Jain, and Priyanka Tripathi, "A Comparative Study of Intrusion Detection System forWireless Sensor Network," International Journal of Advance Foundation and Research in Computer (IJAFRC), Vol. 1, 2014
[4]. J. Godwin Ponsam and R.Srinivasan, "A Survey on MANET Security Challenges, Attacks and its Countermeasures," International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Vol. 3, 2014
[5]. K. Gomathi, B. Parvathavarthini, "An Exclusive Survey on Cluster Based Key Management Techniques in MANET," International Journal of Engineering Development and Research, 2014

Abstract: Micro-blog has been an important social network platform in recent years. With the advances in information influence and propagation ability, the date of micro-blog has attracted the attention of scholars of all kinds. More and more people are interested in studying microblogging from various aspects, such as user motivation, information propagation, text analysis, user network structure. This paper is about the data analysis of tencent microblogging user. Then get the data information about user's friends and audiences from the database. The simulation results show that fans distribution of user is power-law and aver- length is smaller, which indicate that user are of local network properties...............

Keyword: Micro-blog；Data analysis；User；Fans

[1]. Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. What is Twitter, a Social Network or a News Media? International World Wide Web Conference Committee(IW3C2), ACM,2010.
[2]. M. Cha, A. Mislove, and K. P. Gummadi. A measurement-driven analysis of information propagation in the Flick social network. In Proc. of the 18th international conference on World Wide Web. ACM, 2009.
[3]. D. Zhao and M. B. Rosson. How and why people twitter: the role that micro-blogging plays in informal communication at work. In Proceedings of theACM 2009 international conference on Supporting group work. ACM, 2009.
[4]. M. E. J. Newman and J. Park. Why social networks are different from other types of networks. Phys. Rev. E, 68(3):036122, Sep 2003.
[5]. J. Weng, E.-P. Lim, J. Jiang, and Q. He. Twitterrank: finding topic-sensitive influential twitterers. In Proc. of the third ACM international conference on Web search and data mining. ACM, 2010.

Abstract: In this paper, a robust phase-based features extracted by binarization technique for ancient document images is proposed. Three feature maps derived from the phase information of an input document image constitute the core of this binarization model. These features are the maximum moment of phase congruency covariance, a locally weighted mean phase angle, and a phase preserved denoised image. The proposed model consists of three standard steps: 1) preprocessing; 2) main binarization; and 3) postprocessing..............

Keyword: Component; formatting; style; styling; insert (key words)

[1]. R. F. Moghaddam and M. Cheriet, "A multi-scale framework for adaptive binarization of degraded document images," Pattern Recognit., vol. 43, no. 6, pp. 2186–2198, 2010.
[2]. B. Su, S. Lu, and C. L. Tan, "Robust document image binarization technique for degraded document images," IEEE Trans. Image Process., vol. 22, no. 4, pp. 1408–1417, Apr. 2013.
[3]. K. Ntirogiannis, B. Gatos, and I. Pratikakis, "A combined approach for the binarization of handwritten document images," Pattern Recognit. Lett., vol. 35, pp. 3–15, Jan. 2014.
[4]. M. Valizadeh and E. Kabir, "Binarization of degraded document image based on feature space partitioning and classification," Int. J. Document Anal. Recognit., vol. 15, no. 1, pp. 57–69, 2010.
[5]. S. Lu, B. Su, and C. Tan, "Document image binarization using back- ground estimation and stroke edges," Int. J. Document Anal. Recognit., vol. 13, no. 4, pp. 303–314, 2010.

Abstract: This paper presents a novel technique for reconstruction of High-resolution images from multiple Low-resolution images. A Multi-frame approach implements fusion of non-redundant information which was obtained from several low-resolution images of the same scene. Many papers have been published over last two decades proposing a variety of methods for enhancing resolution using the multi-frame approach. This paper reviews some of those proposed methods and addresses their various shortcomings. We propose two methods that are applicable to both gray-scale and colour images...............

Keyword:Deblurring, Image Restoration, Multiframe, Regularization, Resolution Enhancement, Bilateral Filter, Super Resolution, Total Variation, Wiener Deconvolution

[1]. S. Lertrattanapanich and N. K. Bose, High resolution image formation from low resolution frames using Delaunay triangulation, IEEE Trans. Image Processing, vol. 11, pp. 1427–1441, Dec. 2002.
[2]. M. Elad and Y. Hel-Or, A fast super-resolution reconstruction algorithm for pure translational motion and common space invariant blur, IEEE Trans. Image Processing, vol. 10, pp. 1187–1193, Aug. 2001.
[3]. M. Irani and S. Peleg (April 1991), Improving resolution by image registration, CVGIP: Graph. Models Image Process., vol. 53, pp. 231–239.
[4]. S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, Fast and robust multi-frame superresolution, IEEE Trans. Image Processing, vol. 13, pp. 1327–1344, Oct. 2004.
[5]. Y. Li and F. Santosa, A computational algorithm for minimizing total variation in image restoration, IEEE Trans. Image Processing, vol. 5, pp. 987–995, June 1996.

Abstract: In the era of science and technology, computer is the most spirited constituent in our today's health care sector with variety of facilities. The use of Artificial Intelligent (AI), Knowledge Management, is the vital components and demanding for knowledge and evidence based health care sector. Decision support System has the feasible to diagnosis the way professionals' delicacy patients for the better. In this paper, we efforts on the features of clinical decision support system for lower Back Pain (LBP) diagnosis..............

Keyword:Clinical decision support systems (CDSS), Lower back pain (LBP), Web-based decision support systems (WDSS).

[1]. Froseen SE, Corey AS, " Clinical Decision Support and Acute Low Back Pain: Evidence-Based Order Sets.," Journal of American Colleague of Radiology. 2012; 9(10):704-712.
[2]. Murphy DR and Hurwitz EL, "Application of a diagnosis-based clinical decision guide in patients with low back pain.," Chiropractic & Manual Therapies 2011 ; 19(26): 1-10
[3]. Schoen DC. Low Back Pain. Orthopaedic Nursing 2004; 23(2): 154-157.
[4]. Evan RS. Case studies in clinical decision support: LDS Hospital Experience. In: Greenes RA. (eds.) Clinical Decision Support : The Road Ahead. Oxford: Elsevier Inc ; 2007.p.143-168.
[5]. Lin L, Hu PJ, Olivia R, Sheng L. A decision support system for lower back pain diagnosis: Uncertainty management and clinical evaluations. Journal of Decision Support Systems 2006; 42 :1152-1169.
[6]. Wendy Oude Nijeweme – d'Hollosy1, Lex van Velsen1,2 , Ilse C.S. Swinkels3 and Hermie Hermens1, " Clinical Decision Support Systems for Primary Care: The Identification of Promising Application areas and an Initial Design of a CDSS for lower back pain," 17th International Symposium on Health Information Management Research - ISHIMR 2015

Abstract: The banking system relies greatly on the use of Automated Teller Machines, debit and credit cards as a vital element of its payment processing systems. The efficient functioning of payment processing systems allows transactions to be completed safely and on time thereby contributing to operational performance. Customer accounts are equally exposed to risk with respect to fraud. The major security risk identified is identity and access management of customer funds. This research sought to use classification techniques to implement a novel fraud detection solution to establish legitimacy of customer transactions..............

Keyword: Classification, Bayesian Network, Automated Teller Machines, Data Mining.

[1]. Barros, C., Carvalho, A. , & Freitas, A. (2015). Automatic Design of Decision-Tree Induction Algorithms. New York, NY: Springer. [2]. Delen, D. (2014). Real-World Data Mining: Applied Business Analytics and Decision Making. New Jersey, NJ: . FT Press.

[3]. Kulis, B. (2008). Scalable Kernel Methods for Machine Learning. ProQuest.

[4]. Mittal,A. , & Kassim, A. (2007). Bayesian Network Technologies: Applications and Graphical Models: Applications and Graphical Models. New Tork, NY: Idea Group Inc.
[5]. Pun, J. (2011). Improving Credit Card Fraud Detection using a Meta-Learning Strategy. Published Thesis.Graduate Department of Chemical Engineering and Applied Chemistry. University of Toronto.

Abstract: The Quantum Cellular Automata (QCA) majority block gate, while more robust than the simple or classic version of the gate, is still affected by lithographic manufacturing errors that affect the polarization of each individual QCA and the block circuit. Analysis and correction of these errors has been done using Bayesian, Markovian, or neural network methodologies. The problem with learning an objective to minimize errors a priori is the inherent multiobjective nature of optimization in a network of QCA composing the majority gate. .............

Keyword: Reinforcement learning, Game theory, Quantum Cellular Automata, Pareto optimization, QCA gates.

[1]. P. D. Tougaw and C. S. Lent, Logical device implementation using quantum cellular automata, Journal of Applied Physics, 75, 1994, 1818-1825.
[2]. C.S. Lent and P.D. Tougaw, A device architecture for computing with quantum dots, Proc. of the IEEE, 85(4), 1997, 541-557.
[3]. M.T. Niemier, M.J. Kontz, P.M. Kogge, A design of and design tools for a novel quantum dot based microprocessor, Proc. of the 37th Design Automation Conference, Los Angeles, CA, 2000, 227-232.
[4]. W. Porod, Quantum-dot devices and quantum-dot cellular automata," International Journal of Bifurcation and Chaos, 7(10), 1997, 2199-2218.
[5]. G. L. Snider, A. O. Orlov, et. al., Quantum-dot cellular automata, Journal of Vacuum Science Technology A, 17(4), 1999, 1394-1398.

Abstract: An effective decision support system needs to deal with several heterogeneous data sources. Sometimes, the knowledge was explored without integration data sources are wrong. Also, Decision support system is weak in the reusability. To address these challenges, this paper proposes semantic enhancement on the gene expression model (genotype/phenotype system) as an Evolutionary Algorithm to be suitable for "communication decision support system" called Semantic Gene Expression Decision Support System (SGEDSS). The proposed method can solve several mining tasks as association rules and classification through a large volume of heterogeneous data.............

Keyword: Data Mining, Decision Support System, Evolutionary Algorithm, genotype/phenotype system, Ontology-based..

[1] E. Eman K., M. Elnahas, and M. GhanamFatma. Framework for using Ontology Base to Enhance Decision Support System.Framework 2.02 (2013).
[2] Srivastava, Kingshuk, P. S. V. S. Sridhar, and AnkitDehwal. Data Integration Challenges and Solutions: A Study. International Journal of Advanced Research in Computer Science and Software Engineering 2.7 (2012).

[3] Zaman, ErMajid, S. M. K. Quadri, and ErMuheet Ahmed Butt. Information Integration for Heterogeneous Data Sources. IOSR Journal of Engineering2.4 (2012): 640-643.
[4] Bizid, Imen, et al. Integration of heterogeneous spatial databases for disaster management. Advances in ConceptualModeling.Springer International Publishing, 2013.77-86.
[5] Buitelaar, Paul, et al. Ontology-based information extraction and integration from heterogeneous data sources. International Journal of Human-Computer Studies 66.11 (2008): 759-788.

Abstract: Worldwide interoperability for Microwave Access (WIMAX) that based on 802.16d/e Orthogonal Frequency Division Multiplexing (OFDM) and based on adaptive Physical Layer (PHY), It provides wireless broadband to fixed and mobile terminals, The basic WIMAX feature is wireless transmission infrastructure which allows a fast use as well as low maintenance costs, Modulation techniques enable WIMAX network to be optimized. WIMAX supports BPSK, 16-QAM, DPSK, and 64-QAM as a modulation technique..............

Keyword: WIMAX, BER, SNR, MOS loss Rate, Modulation, OFDM.

[1] Jeffrey G. Andrews, Ph.D, Arunabha Ghosh, Ph.D, "Fundamentals of WiMAX Understanding Broadband Wireless Networking," First Edition, Prentice Hall, 2007
[2] Ramjee Prasad l Fernando J. Velez "WiMAX Networks Techno-Economic Vision and Challenges" Library of Congress Control Number: 2010928698
[3] A. Ghosh et al., Feb. 2005: "Broadband Wireless Access with WIMAX/802.16: Current Performance Benchmarks and Future Potential," IEEE Commune. Mag., vol. 43, pp. 129–36
[4] SYED AHSON MOHAMMAD ILYAS ,WIMAX Applications ISBN 9781420045474 , 2008 by Taylor & Francis Group, LLC
[5] ANDREW S. TANENBAUM Vrije Universiteit Amsterdam, The Netherlands, DAVID J. WETHERALL University of Washington Seattle, WA "computer networks fifth edition"

Abstract: In this paper Legal statute related to dowry acts has been processed to obtain a distinct set of legal keywords which don't have a common occurrence in day to day dowry case judgments. This effort coupled with the knowledge of legal expert would give a very much broadened scope for the BoW. These keywords are very rich in concept and well connected to the domain of dowry acts. The earlier work [22] constructed BoW for dowry case notes of judgments. Current work tries to improve the BoW by widening the scope of dowry related cases..............

Keyword:Bag-of-Words, Legal Statue, Dowry-Act, Enhancemen

[1]. Eman Isamil and Walaa Gad, "CBER: An Effective Classification Approach Based on Enrichment Representation for Short Text Documents", University Press, Egypt, 2016.
[2]. Martin Vita, "Computing Semantic Textual Similarity based on Partial Textual Entailment", NLP Centre, 2015.
[3]. Prof. Sarika N Zaware, Mr. Asmit Gautam, Ms. Sumedha Nashte, Ms. Puneet Khanuja, "AN EFFECTUAL APPROACH FOR CALCULATING COSINE SIMILARITY", IJAERD, Vol.2, Issue 4, 2015.
[4]. Quoc Le, Tomas Mikolov,"Distributed Representations of Sentence and Documents", ICML conference, China, 2014.
[5]. S. Mizzaro, M. Pavan, I. Scagnetto and M. Valenti, "Short text Categorization Exploiting Contextual Enrichment and External Knowledge", Conference SoMeRa-14, ACM 978-1-4503-3022-0/14/07, 2014.

Abstract: The main goal of this research is to develop a novel optimum neuro-fuzzy system for diagnosis the complex and dynamic systems. .It has used the Particle Swarm Optimization (PSO) technique for training the Adaptive Neuro Fuzzy Inference System (ANFIS) off-line. The proposed system has applied for diagnosis the faults of two complex Photovoltaic (PV) systems. They are used to feed the power for lighting and pumps in a synchrotron building inside a radiation centre and the power for a house in a rural village. Its achieved results are compared with three ANFIS' diagnostic systems. They are............

Keyword: Fault diagnosis, neuro-fuzzy system, Practice Swarm Optimization (PSO), Optimization, and PV systems.

[1] Y. Riffonneau, S. Bacha, F. Barruel, and S. Ploix, (July 2011), "Optimal Power Flow Management for Grid Connected PV Systems With Batteries ", IEEE Transactions on Sustainable Energy, Vol. 2, No. 3, pp.309-320.
[2] P. Romano, R. Candela, M. Cardinale, Vi. Li Vigni, D. Musso, and E. R. Sanseverino, "Optimization of Photovoltaic Energy Production through an Efficient Switching Matrix", (2013), Journal of Sustainable Development of Energy, Water and Environment Systems, Vol. (1), Issue (3), pp. 227-236.
[3] Wilson, P., Storey, J., and Bagnall, D., (2012), Improved Optimization Strategy for Irradiance Equalization in Dynamic Photovoltaic Arrays, IEEE Transactions on Power Electronics, vol. 28, no. 6, pp. 2946–2956.
[4] K. Ishaque and Z. Salam, (2011), "An improved modeling method to determine the model parameters of photovoltaic (PV) modules using differential evolution (DE),‟ Solar Energy, Vol. 85, pp. 2349–2359.
[5] S. T. BRASSAI, S. HAJDU and T. TĂMAS, (2015), " Embedded Adaptive Neuro Fuzzy Inference System with Hardware Implemented Real Time Parameter Update ", MACRo 2015- 5th International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics, pp.211-221.

Abstract: The paper implements secure MIMO-OFDM systems to achieve the data confidentiality using the following three different cryptographic techniques, namely, AES block cipher, RC4 stream cipher and the proposed Partial Selective Encryption (PSE). These implemented systems have been investigated using PC with processor corei7, 6G RAM and running on Windows 10 as operating system. The investigation involves the measure of the impact of security and quality on the transmission of encrypted images between parties in the MIMO-OFDM wireless domain. The simulation results show the system performs well, the security of the system has been enhanced, and the good point is the enhancement of the Bit Error Rate (BER) by (1.5-1.9) dB..............

Keyword: MIMO-OFDM, AES, RC4, Image Transmission, PSNR, BER, Partial and Selective Image

[1] S. K. Mohammed, A. Zaki, A. Chockalingam, and B. S. Rajan, High-Rate Space–Time Coded Large-MIMO Systems: Low-Complexity Detection and Channel Estimation, IEEE J. Sel. Topics Signal Process,3(6), 2009, 958-974.
[2] T. Marzetta, Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas, IEEE Trans. Wireless Commun., 9(11), 3590-3600, 2010.
[3] R. Kudo, S. M. D. Armour, J. P. McGeehan, and M. Mizoguchi, A Channel State Information Feedback Method for Massive MIMO-OFDM, IEEE Journal of Communications and Networks, 15(4), 352-361, 2013.
[4] F. Rusek, D. Persson, B. K. Lau, E. G. Larsson, O. Edfors, F. Tufvesson, and T. L. Marzetta, Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays, IEEE Signal Process. Mag., 30, 40-60, 2013.
[5] M. Matthaiou, N. Chatzidiamantis, and G. Karagiannidis, A New Lower Bound on the Ergodic Capacity of Distributed MIMO Systems, IEEE Signal Process. Letter, 18(4), 227-230, 2011.

Abstract: Security is the biggest concern nowadays faced by various companies as security threats are more prevalent. This "openness‟ is the reason because of which protecting networks and business data is even more difficult. The solution in response to this threat is to provide a single aggregated view of all the threats observed in the network, to prioritize them so the worst are dealt first. It can happen only through "Security Suite". It offers insight of Front-end-Perimeter protection (FEP), Advanced Breach Detection (ABD), Distributed Denial-of –Service(DDOS) protection. But, what when they are not accessed from the same simulator. This paper refers to the working of Security Suite in real time, to provide a more user friendly working and reduce the tedious task of switching between simulators. It states how the products of the organization and the Security Suite can go hand in hand.

Keyword: Alerts, Security Suite, Simulators

[1]. Sajjad Arshad , Maghsoud Abbaspour , Mehdi Kharrazi , Hooman Sanatkar, "An Anomaly-based Botnet Detection Approach for Identifying Stealthy Botnets", 2011 IEEE International Conference on Computer Applications and Industrial Electronics (ICCAIE) , Penang, Malaysia, 4-7 December 2011, ISBN: 978-1-4577-2058-1, IEEE Catalog Number: CFP1189L-PRT.
[2]. Hayawardh Vijayakumar, Joshua Schiffman, and Trent Jaeger, "A Rose by Any Other Name or an Insane Root? Adventures in Name Resolution ", 2011 Seventh European Conference on Computer Network Defence (ECCND), Gothenburg, Sweden, 6 – 7 September 2011, ISBN: 978-1-4673-2116-7, IEEE Catalog Number: CFP1136F-PRT.
[3]. Junfeg Tian, Jianlei Feng, "Trust Model of Software Behaviour Based on Check point Risk Evaluation", Third International Symposium on Information Science and Engineering, , Pages 54-57, IEEE Computer Science Washington , DC, USA, 2010, ISBN:978-0-7695-4360-4,DOI:10.1109/ISISE.2010.79
[4]. S. Massoud Amin, Anthony M. Giacomoni, "Smart Grid Safe Secure Healing", IEEE Power and Energy, January 2012, 1540-7977/12, DOI: 10.1109/MPE.2011.943112

Abstract: Hacking and hacker are terms that generally tend to have negative effect on people. These days, largely due to popular media most people wrongly think of hackers as computer criminal and the person who doing something mischievous things in other computer, delete data, damage os & steal password and cause harm to system release viruses etc. "Hacker" is not computer criminal. The tech community now distinguishes between hackers, who identify security flaws in order to improve computer systems and crackers, who attempt to exploit those flaws to their own advantage. Now different sides of hacking is discussed.

Keyword: Trojan horse, Worm, Firewall.

[1]. Pragmatic Overview of Hacking & Its Counter Measures by Yogita Negi, 2011
[2]. Unofficial Guide to Ethical Hacking by Ankit Fadia
[3]. What is the difference between Hackers and Intruders by Asmaa Shaker, Prof. Sharad Gore, Int. Journal of Scientific & Engineering Research Vol. 2 Issue 7 July 2011 ISSN 2229-5518
[4]. Hacking: The Basics by Zachary Wilson, April 4, 2001.
[5]. www.wikipedia.org/wiki/Hacking