iosr-JCE   Volume-19 ~ Issue-6 ~ Nov-Dec 2017

Abstract: It has been observed that coverage is a basic issue in remote sensor organize (WSN).This issue can be elucidated by survey the less number of the handle vivacious at the specific time. The sensors from the dynamic groups defines all objectives and at danger to transmit the information to the base station and remaining center points are in rest mode. This paper discusses a couple of critical computations - Modified K-Means, PSO figuring and AOMDV utilized as a bit of remote sensor systems. The Modified K-Means is utilized to scope the range, AOMDV used for multipath coordinating and PSO is utilized to locate the perfect way.

[1] Aneesh Kumar V.N., AyanaAjith, "Hole and Border Detection Methods and Coverage Enhancement in WSN: A Survey", International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 6, June 2015 ISSN: 2277 128X,
[2] Feng Li, Jun Luo, "LAACAD: Load balancing k-Area Coverage through Autonomous Deployment in Wireless Sensor Networks", Distributed Computing Systems (ICDCS), 2012 IEEE 32nd International Conference, 21 June 2012, DOI: 10.1109/ICDCS.2012.34,
[3] A. Capone, M. Cesana, D. De Donno, I. Filippini, "Optimal Placement of Multiple Interconnected Gateways in Heterogeneous Wireless Sensor Networks", International Conference on Research in Networking 2009, DOI: 10.1007/978-3-642-01399-7_35,
[4] KoenLangendoen ,NielsReijers, "Distributed localization in wireless sensor networks: a quantitative comparison", Elsevier Journal Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Wireless sensor networks, Volume 43 Issue 4, 15 November 2003, doi>10.1016/S1389-1286(03)00356-6,

Abstract: In present scenario where network traffic is so high due to which speed and efficiency of the network also reduce. Network traffic also leads to delay in time as many nodes energies got reduced in span of time. So in this research paper we proposed a improved leach protocol which is improvement over the energy efficient routing protocol low energy adaptive clustering hierarchy (LEACH) for wireless sensor networks. We use TDMA concept here for communication between cluster head and nodes. In improved leach here the transmission takes place from hop to hop rather than direct transmission from cluster head to base station that takes place in leach protocol. So when we compare these protocol we find that multi hop leach protocol is better than energy efficient routing protocol low energy adaptive clustering hierarchy (LEACH) for wireless network

Keywords: Leach, Cluster Head, Base Station, Nodes

[1] Poonam Tyagi, Ravinder Prakash Gupta, Rakesh Kumar Gill,"Comparative Analysis of Cluster Based Routing Protocols used in Heterogeneous Wireless Sensor Network", International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, vol. 1, no. 5, pp. 2231-2307, Nov. 2014
[2] Xu, G. Yang, Z.-Yu. Chen, L. Chen, and Z. Yang, "Performance analysis of data aggregation algorithms in wireless sensor networks," in Proceedings of the International Conference on Electrical and Control Engineering (ICECE '11), September 2013.
[3] Akyildiz and M. C. Vuran, Wireless Sensor Networks,University Publishers, Tsinghua, China, 2011
[4] Nam and H. Min, "An Energy-Efficient Clustering Using a Round-Robin Method in a Wireless Sensor Network".5th ACIS International Conference on Software Engineering Research, Management & Applications, 2013, pp. 54-60.
[5] N.M.A. Latiff, C.C. Tsimenidis, and B.S. Sharif, "Performance Comparison of Optimization Algorithm for Clustering in Wireless Sensor Networks," IEEE International Conference on Mobile Adhoc and Sensor Systems, 2010, pp. 1-4.

Abstract: Clustering is a technique to analyze the data in efficient manner and generate required information. To cluster the dataset, the technique of k-medoid is applied which is a partitioning based method. It uses k as a parameter, divide n objects into k clusters. In K-medoid clustering data points are used as the centers of the clusters. Here, we will work on prediction analysis in which final clustered data will be analyzed and evaluated according to the requirement. In order to analyze the clusters the relationship among the attributes of the dataset must be known which is a difficult task in case of complex dataset is. In this work, a technique will be proposed using back propagation to derive relationship among the attributes of the complex dataset for better analysis of clustered data. This technique will lead to the improvement in accuracy, precision, F-measure and recall of the clusters.

Keywords: Clustering, K-medoid clustering, back Propagation, Diabetes, Prediction Analysis

[1]. E. Turcato, O. Bosello, V. Di Francesco, T. B. Harris, E. Zoico, L. Bissoli, E. Fracassi, and M. Zamboni, "Waist circumference and abdominal sagittal diameter as surrogates of body fat distribution in the elderly: their relation with cardiovascular risk factors," Int. J. Obes. Relat. Metab. Disord., vol. 24, no. 8, pp. 1005–1010, Aug. 2000.
[2]. Aastha Joshi, Rajneet kaur, "A Review: Comparative Study of Various Clustering Techniques in Data Mining", International Journal of Advanced Research in Computer Science and Software Engineering Volume 3, Issue 3, March 2013.
[3]. S.Wild,G.Roglic, A. Green,R. Sicree, and H.King, "Global prevalence of diabetes: estimates for the year 2000 and projections for 2030," Diabetes Care, vol. 27, no. 5, pp. 1047–1053, May 2004.
[4]. M. B. Snijder, R. M. Van Dam, M. Visser, and J. C. Seidell, "What aspects of body fat are particularly hazardous and how do we measure them?" Int. J. Epidemiol., vol. 35, no. 1, pp. 83–92, Feb. 2006.

Abstract: Automatic text classification is becoming increasingly vital nowadays. The two most common approaches for text classification using Naive Bayes Approach consider either Multivariate Bernoulli model or Multinomial model. The Multinomial model is usually observed to give better results for large number of features. We suggest some improvements in the Multivariate Bernoulli Naive Bayes Approach. We were able to achieve an average accuracy of about 99% as opposed to about 85% using basic Multivariate Naive Bayes Approach. We also reduced the number of features required making the classifier space and time efficient.

Keywords: Text classification, naïve bayes, multivariate, bernaulli.

[1] Andrew McCallum And Kamal Nigam, A Compariison of Event Models for Naïve Bayes Text Classification, Pittsburgh,PA 15213.
[2] Lingling Yuan, An Improved Naive Bayes Text Classification Algorithm In Chinese Information Processing,Henan Polytechnic University, Jiaozuo, China, 2010.
[3] Hetal Doshi and maruti Zalte, Performance of Naïve Bayes Classifier – Multinomial Model on Different Categories of Documents, Vidyavihar, Mumbai.
[4] Karl-Michael Schneider,On Word Frequency Information and Negative Evidence in Naive Bayes Text Classifications, University of Passau, Germany.

Abstract: Information system security is important in an institution whose routine operations expose its information system to threat. The objectives of the study were to establish the effect of systems security threats on the utilization of information resources and to find out the challenges of information system threats on organizational information resources in Kenya School of Government. The study employed the descriptive survey research design. The study targeted 100 staff drawn from the ICT Department, secretarial and records departments. Census sampling was employed to select he sample size. Data was collected using self administered questionnaires and analyzed using descriptive statistics with the aid of Microsoft Excel. The findings were presented using tables and graphs.............

Keywords: Computer security policy, Computer Security, E-government, Information security strategy implementation, Information System Security Threat, Information system, Policy, Public Organization, Security breach, System integrity, Threat

[1] Ana-Maria, S., Mihai, B. & Florin, G. (2010). Audit for Information Systems Security, Information Economica vol. 14, no. 1.
[2] Baker, B. (2015). The Future of Cyber-Security-Threats and Opportunities, Global Corporate Venturing,
[3] Barbara, G. & Edward, A. (1995). An Introduction to Computer Security: The NIST Handbook, NIST Special Publication 800=12, US Department of Commerce, USA.
[4] Bobert, S. & Kenneth, K. (2009). Local government IT Implementation issues: a challenge for public administration, Hawaii International Conference on System Sciences, Hawaii, USA.
[5] Caralli, R. A. (2004). Managing for Enterprise Security, Software Engineering Institute, Carnegie Mellon University, Pittsburgh, PA.

Abstract: With the rapid growth of cloud computing, many users shared sensitive data and multiple applications should be effectively managed via different cloud models and tools and it is needed for the safe management of identities and to avoid negotiating data privacy. The models and tools that speak federated cloud identity management, and it is essential that they use privacy appliances to assist in compliance with current law. So, this article aims to present a study of privacy in federated cloud identity management, presenting and comparing main features and challenges described in the literature. At the end, the use of privacy and future research directions are discussed.

Keywords: Cloud Identity, Cloud Computing, Federated Cloud, Federated Cloud Identity Management, Safe Management of Identity, Data Privacy.

[1] R. Buyya, C.S. Yeo, S. Venugopal, "Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities, in: High Performance Computing and Communications", 2008. HPCC '08. 10th IEEE International Conference on, 2008, pp. 5–13, DOI: 10.1109/HPCC.2008.172.
[2] J. Werner, G.A. Geronimo, C.B. Westphall, F.L. Koch, R. Freitas, C.M. Westphall, "Environment, Services and Network Management for Green Clouds", CLEI Electron. J., 15 (2), (2012). http://www.clei.org/cleiej/paper.php?id=238.
[3] https://auth0.com/learn/cloud-identity-access-management/
[4] M. Hansen, A. Schwartz, A. Cooper, "Privacy and Identity Management", IEEE Secur. Privacy 6 (2), (2008) 38–45, DOI: 10.1109/MSP.2008.41.
[5] O. Foundation, "Openid Connect", 2016, Retrieved: June, 2016, http://openid.net

Abstract: Database replication is performed to back up the data in second server in order to provide efficient data to the required users. However there are numerous issues in the database replication process. In this paper, some of the major database replication issues are discussed in general followed by the presentation of a case study for merge replication. Specifically, different replication types, replication topologies and replication agents are discussed and the details about merge replication are provided. In particular, the necessary conditions for merge replication and the methods for data transfer among multiple servers are examined. For illustrating the case study in simple terms.............

Keywords: Database,Replication, Merge Replication, Dynamic Data Transfer, DBMS

[1] S. Meine, Fundamentals of SQL Server 2012 Replication, TairwaysHandbook, ISBN – 978-1-906434-98-4, 2013.
[2] M. C. Mazilu,Database Replication, Database Systems Journal, 1(2),2010, 33-38.
[3] Merge Replication, https://www.techopedia.com/definition/24730/merge-replication, Access Date: April 25, 2017.
[4] R.N. Sangwan, SQLServer2012Replication, https://www.codeproject.com/Articles, Access Date: April 25, 2017.
[5] Chigrik, A., and A. Chigrik. Setting up merge replication: A step-by-step guide. Online] Tersedia: http://www. databasejournal.
com/features/mssql (2001).
[6] H. Lamehamedi, B. Szymanski, Z. Shentu, E. Deelman, Data Replication Strategies in Grid Environments, 5th International
Conference on Algorithms and Architecture for Parallel Processing, IEEE Computer Society Press, Los Alamitos, CA, pp. 378-
383, 2002.

Abstract: Decision support system has emerged as an important information technology tool to many sectors especially to managerial department or missions ; in order to save time, get maximum benefits from technology, get precise decisions based on the user roles. So DSS came to resolve some issues that managerial department faced when they resolve some semi-structured or unstructured problems that need important decisions, however, DSS can effectively provide precise decisions to managers at any position. There are many DSS implementations nowadays at many sectors.............

Keywords: Decision Support System, DSS, Scientific Promotion, SP.

[1] Z. Jingning, "Promotion criteria, faculty experiences and perceptions: A qualitative study at a key university in China," Int. J. Educ. Dev., vol. 33, no. 2, pp. 185–195, Mar. 2013.

[2] I. A. Archibong, O. E. David, D. Omoike, and A. O. Edet, "Academic Staff Disposition To Promotion Criteria In Nigerian Universities," J. Coll. Teach. Learn. TLC, vol. 7, no. 10, Nov. 2010.

[3] M. A. Orf, "Criteria for Initial Appointment in Rank and Subsequent Promotion for Faculty in Two-Year Public Colleges," University of Arkansas, 2013.

[4] M. M. Reeve, "Development and implementation of a faculty evaluation system for clinical nursing," Ed.D., Nova University, United States -- Florida, 1989.

[5] F. F.-H. Tien, "Promotion, motivation, and faculty research productivity: Theory testing and model construction for a Taiwanese setting," Ph.D., University of Michigan, United States -- Michigan, 1994.

Abstract: Medical Image processing has become an accelerating subject of interest these days. Technology is growing day by day to capture the accurate internal body images of human beings to diagnose the abnormalities efficiently. To aggravate the efficiency Artificial Neural Networks (ANNs) can be used as a very good tool. This paper is an approach to classify the processed MRI images of human brain for the presence or absence of a tumor. The Artificial Neural Network (ANN) technology developed here further locates the tumor
in case of its presence. The technology is based on the concept of segmenting the MRI image of the human brain and then finding out the gradient of pixels in different segments. On the basis of the pixel densities at different segments the ANN cluster the image into an image with a tumor or without a tumor. Further the position of the tumor is calculated.

Keywords: ANN, Brain Tumor, MRI, Pixel density gradient, SOM.

[1] E. Abdel-Maksoud et al, Brain Tumor Segmentation Based on a Hybrid Clustering Technique, Egyptian Informatics Journal, 16,
2016, 71-81.
[2] R. Yogamangalam and B. Karthikeyan, Segmentation Techniques Comparision in Image Processing, International Journal of
Engineering and Technology, 5(1), 2013, 307-313.
[3] Y. Esquenazi et al, Critical Care Management of Cerebral Edema in Brain Tumors, Journal of Intensive Care Medicine, 32(1),
2017, 15-24.
[4] A. Drevelegas, N. Papanikolaou, Imaging Modalities in Brain Tumors, in A. Drevelegas (Ed.), Imaging of Brain Tumors with
Histological Correlations. (Berlin: Springer Heidelberg, 2011), 13-33.
[5] I. Njeh et al, 3D Multidimensional MRI Brain Glioma Tumor and Edema Segmentation: A Graph Cut Distribution Matching
Approach, Computerized Medical Imaging and Graphics, 40, 2014, 108-119.

Abstract: In wireless sensor networks, mobility and pause time have a major impact that directly influences the energy consumption and the lifetime of the network. This paper analyzes the performances of three hierarchicals protocols LEACH, LEACH-C and PEGASIS with respect to different mobility models under various pauses time for wireless sensor networks. In fact, the evaluation is based on metric performance such as packet delivery ratio (PDR), average throughput, consumption and lifetime network in a simulation environment NS2. Simulation results show that PEGASIS for mobility Random Waypoint model (RWP), Manhattan model.........

Keywords: LEACH, PEGASIS, LEACH-C, Random Waypoint, Manhattan Grid, Gauss Markov, Performance Metrics, Wireless Sensor Networks.

[1]. X. Lai, Q. Liu, X. Wei, W. Wang, G. Zhou, and G. Han, "A Survey of Body Sensor Networks," Sensors, vol. 13, pp. 5406-5447, 2013.
[2]. S. B. Awwad, C. Ng, N. Noordin, M. F. Rasid, and A. R. H. Alhawari, "Mobility and Traffic Adapted Cluster Based Routing for Mobile Nodes (CBR-Mobile) Protocol in Wireless Sensor Networks," in Ad Hoc Networks. vol. 49, pp. 281-296, 2010.
[3]. Kim, D.S.; Chung, Y.J. Self-Organization routing protocol supporting mobile nodes for wireless sensor network, First International Multi-Symposiums on Computer and Computational Sciences (IMSCCS 06), Hanzhou, Zhejiang, 2006, vol. 2, pp. 622 - 626.
[4]. Priti Kumari, M. P Singh, Prabhat Kumar "Cluster head selection in mobile wireless sensor networks: a survey" ICACC, 2013.
[5]. SamerA. B. Awwad ·CheeKyun Ng ·NorK.Noordin · Mohd. Fadlee A. Rasid, "Cluster Based Routing protocol for Mobile Nodes in Wireless Sensor Network", Wireless Pers Commun (2011) 61:251–281.

Abstract: The huge amount of circulated images around the world need a big size of capacity in addition high speed of transmission to overcome these problems. This leading to continues need of improving the issue of image compression. This paper concentrated on the design an efficient approach for image compression using discrete wavelet transform. This approach leads to minimize the redundancies and the unwanted information in addition the original image can be presented in a good form. Wavelet transform is a multispectral analysis that can decompose image into sub-band of images. The implemented approach of image compression used the benefits of correlation operation. This based on the prediction of edges from the low-low band from the lowest resolution level to predicate edges on the other bands.

Keywords: Image Compression, Image Decomposition, 2D DWT, Statistical Parameters.

[1] John W. Woods, "Multidimensional Signal, Image, and Video Processing and Coding", Second Edition, Academic Press, 2012, PP. 329–392.
[2] Zhiyong Zuo, XiaLan, Lihua Deng, Shoukui Yao, Xiaoping Wang, "An improved medical image compression technique with lossless region of interest", Optik - International Journal for Light and Electron Optics, Vol. 126, Issue 21, November 2015, PP. 2825-2831.
[3] Fatema Rashid, Ali Miri, Isaac Woungang, "Secure image deduplication through image compression", Journal of Information Security and Applications, Vol. 27–28, April–May 2016, Pages 54-64.

Abstract: The integration of knowledge management (Klick) and e-encyclopaedism (Altitude) become inevitable day by day. KM coating focuses in providing institutions with prick to enrich knowledge, while the EL focuses on managing the delivery of faculty member knowledge. But integration of both the areas is far more behind in research. This research proposed a KM theoretical account to provide 24x7 hour synchronous collaborative erudition. Requirement of KM in EL is the focus of this report by creating a new approach of KM and accenting on various outgrowth accompanying. Several Artificial Intelligence (AI) tools and technologies such as big data, linked data, semantic web, 3D visualization, etc. are incorporated hypothetically to achieve the collaborative learnedness environment.

Keywords: VLE, EL, KM, CMS, Collaborative learning, Semantic web, ES.

[1]. Mihalca, R.; Uta, A.; Andreescu, A.; Intorsureanu, I. (2008): Knowledge Management in E-Learning Systems, Revista Informatica, Economica, nr. 2(46), pp.60-65.
[2]. Sherwood, C. (2001): Knowledge management for e- Learning, International Conference on Engineering Education, Technical tour, Oslo and Bergen, Norway, pp.8D4-16 – 8D4-21.
[3]. Judrups, J. (2014): Analysis of Knowledge Management and E-Learning Integration Models, ICTE in Regional Development, Procedia Computer Science 43(2015), pp.154-162, Elsevier.
[4]. Khdour, T.; Salem, S. (2014): The effects of integrating knowledge management with E-Learning, Proceedings of 2014 zone 1 conference of the American Society for Engineering Education (ASEE Zone 1).
[5]. Putzhuber, W. (2003): From eLearning to Knowledge Management-Bridging the Gap. (Unpublished Master's Thesis), Graz University of Technology, Austria

Abstract: Internet of things (IoT) gadgets need aid quickly turning into universal same time IoT benefits would getting to be pervasive. Their achievement need not Run unnoticed and the number from claiming dangers What's more strike against IoT gadgets Also benefits would on the expansion too. Cyber-attacks are not new to IoT, However as IoT will be profoundly interlaced clinched alongside our exists and societies, it is getting to be important to venture up Furthermore detract digital guard genuinely. Hence, there is An true require on secure IoT, which need Subsequently brought about a necessity with comprehensively see all the the dangers and strike looking into IoT base. This paper may be an endeavor should arrange danger types, furthermore examine What's more describe intruders Also strike confronting IoT gadgets Furthermore administrations.

Keywords:Internet of Things, Cyber-attack, Security threats.

[1] L. Atzori, A. Iera, and G. Morabito, "The internet of things: A survey," Computer networks, vol. 54, no. 15, pp. 2787–2805, 2010.
[2] S. Andreev and Y. Koucheryavy, "Internet of things, smart spaces, and next generation networking," Springer, LNCS, vol. 7469, p. 464, 2012.
[3] J. S. Kumar and D. R. Patel, "Asurvey on internet of things: Security and privacy issues," International Journal of Computer Applications, vol. 90, no. 11, pp. 20–26, March 2014, published by Foundation of Computer Science, New York, USA.
[4] A. Stango, N. R. Prasad, and D. M. Kyriazanos, "A threat analysis methodology for security evaluation and enhancement planning," in Emerging Security Information, Systems and Technologies, 2009. SECURWARE'09. Third International Conference on. IEEE, 2009, pp. 262–267.
[5] D. Jiang and C. ShiWei, "A study of information security for m2m of iot," in Advanced Computer Theory and Engineering (ICACTE), 2010 3rd International Conference on, vol. 3. IEEE, 2010, pp. V3–576.