iosr-JCE   National Conference on Advances in Engineering, Technology & Management (AETM'15)

Abstract: There are huge number of validation systems for system access focused around declaration, biometric, smart card with pin, password and so forth. One of the new and more prevalent validation procedures is OTP. The principle focal point of that is it is more secure as every time new secret key is utilized so recalling the password is no more needed. Each time new OTP is produced so no password is used over and over. If OTP generator is good then it makes this technique more secure. This paper describes EAP protocol and proposed EAP method for authentication.
Keywords: EAP, MD5, OTP, PEAP, SHA

[1] Mark Vandenwauver, Rene Govaerts and Joos Vandewalle, Overview of Authentication Protocols, Katholieke University Leuven, Belgium.
[2] D S. Stienne, Nathan Clarke and Paul Reynolds, Strong Authentication for Web Services using Smartcards, 7th Australian Information Security Management Conference, 2013.
[3] B. Lloyd and W. Simpson, PPP Authentication Protocol, Internet Engineering Task Force (IETF) RFC 4017 October 1992.
[4] Khidir M. Ali and Ali Al-Khalifah, A Comparative Study of Authentication Methods For Wi-Fi Networks, 3rd International Conference on Computational Intelligence Communication System and Network, July 2011, 190-194.
[5] C. Shyamala Kumari and M. Deepa Rani, Hacking Resistance Protocol For Securing Passwords Using Personal Device, 7th International Conference on ISCO, Jan 2013, 458-463.

Abstract:With the technological advancement in the development of small-scale unmanned aerial vehicles (UAVs) also known as drones and their decreasing costs, there is a growing emphasis on the utilization of these UAVs for various purposes such as disaster management, surveillance, payload delivery etc.
In this paper we try to develop a system for aerial imaging using multiple cameras mounted on the drones. Real time data will be transmitted by drones hovering over a large area. We then use aerial triangulation to make calibration in the distortion of the obtained images.
Keywords: Collaborative Drones, Autonomous Systems, Photogrammetry, Automatic Aerial Triangulation, Aerial Imaging, Camera Calibration.

[1] YASTIKLI N. and JACOBSEN K., 2002, Investigation of Direct Sensor Orientation for DEM Generation, Proceedings of ISPRS Commission I Symposium"Integrated Remote Sensing at the Global Regional andLocal Scale", Denver, USA.
[2] YUAN Xiuxiao, 2000, Principle, Software and Experiment of GPS-supported Aerotriangulation, Geo-Spatial Information Science, 3(1), pp.24-33.
[3] ACKERMANN F., 1994, Practical Experience with GPS-supported Aerial Triangulation, PhotogrammetricRecord, 16(84), pp.861-874.
[4] Wang Zhizhuo, 1979. Principle of photogremmetry. Publishing House of Surveying and Mapping, Beijing, China.
[5] Pinkney F, et al. UAV communications payload development. MILCOM 97 Proc. Volume: 1, 2-5 Nov. 1997. pp: 403-407.

Abstract: In medicine at present, neural networks are a 'hot' research area, particularly in cardiology, radiology, urology, oncology etc. In the area of computer science, this new technology has been accepted. The purpose of a neural network is to map an input into a desired output. Combining neurons into layers permits artificial neural networks to solve highly complex classification problems. The various types of neural networks are explained and established. In medicine, applications of neural networks like ANNs are described, and a detailed historical background is provided. This paper focuses on the role of neural network in medical imaging.
Keywords: Neural Network, Neurons, Oncology, Radiology

[1]. Muhm JR, Miller WE, Fontana RS, Lung cancer detected during a screening program using 4-month chest radiographs. Radiology, 148, 1983, 609–615.
[2]. Austin JHM, Romney BM, Goldsmith LS, Missed bronchogenic carcinoma: radiographic findings in 27 patients with a potentially resectable lesion evident in retrospect. Radiology, 182, 1992, 115–122.
[3]. Lin J S, Ligomenides P A, Lo S B, , Freedman M T and Mun S K, ―An application of convolution neural networks: reducing False- positives in lung nodule detection‖, Nuclear Science Symposium and Medical Imaging Conference, 1994,IEEE Conference Record (Volume:4 )1994, 1842-1846.
[4]. Zhenghao S., Lifeng H., ―Application of Neural Networks in Medical Image Processing‖, Proceedings of the Second International Symposium on Networking and Network Security (ISNNS '2010), 23-26.
[5]. Taher F, Sammouda R, ―Lung Cancer Detection By Using Artificial Neural Network and Fuzzy Clustering Methods‖, IEEE GCC Conference and exhibition(GCC), 2011, 295- 298.

Abstract: Malicious URL detection has become increasingly difficult due to the evolution of phishing campaigns and efforts to avoid attenuation black list. The current stateof cybercrimehas allowedpiratesto hostcampaignswith shorterlife cycles, which reduces the effectiveness of theblacklist.Asthe same time, normal supervised learning algorithms are known to generalize in specific patterns observed in the training data, which makes them a better alternative against piracy campaigns. However, the highly dynamic environment of these campaigns requires models updated regularly, which poses new challenges as most typical learning algorithms are too computationally expensive retraining.
Keywords: Computer Security, Adware Classification, Malicious web page analysis, Machine Learning

[1]. JitendraApteand Marina Lima Roesler. "Interactive Multimedia Advertising and Electronic Commerce on a Hypertext Network." U.S. Patent No. 7,225,142. 29 May 2007.
[2]. Ravula and Ravindar Reddy. "Classification of Malware using Reverse Engineering and Data Mining Techniques." M.S. Dissertation, University of Akron, CS Dept., 2011.
[3]. Anup K. Ghosh and Tara M. Swaminatha. "Software Security and Privacy Risks in Mobile E-Commerce." In Communications of the ACM,vol.44, issue 2, 2001.
[4]. Justin Ma, Lawrence K. Saul, Stefan Savage and Geoffrey M. Voelker. "Beyond Blacklists: Learning to Detect Malicious Web Sites From Suspicious URLs." in Proceedings of the 15th ACM international conference on Knowledge discovery and data mining, 2009.
[5]. "Gap between Google Play and AV vendors on adware classification", HispasecSistemas, S. L. "Virustotal malware intelligence service." 2011.

Abstract: In this paper, various tree comparison metrices have been discussed, where tree is showing Structure information of species or other related data. Some algorithms enable us to find the distance between the two trees efficiently. This paper focuses on tree pattern mining and tree validation methods. While comparing species trees, we can even gain information about their evolution and also the relationship that exists between several organisms. A comprehensive comparison of various metrics is also shown taking common dataset of species.
Keywords: Maximum Agreement Subtree, Nodal Distance, Phylogenetic tree, Robinson Foulds-Distance

[1] Mohammed J. Zaki, Member, Efficiently Mining Frequent Trees in a Forest: Algorithms and Applications, IEEE, August 2005, Volume 17
[2] John Bluis and Dong-Guk Shin, Nodal Distance Algorithm: Calculating a Phylogenetic Tree Comparison Metric, Computer Science and Engineering University of Connecticut Storrs, CT 06269-3155, USA, Bioinformatics and Bioengineering, IEEE 2003, pp. 87-94.
[3] D. F. ROBINSON, L. R. FOULDS, Comparison of Phylogenetic Trees, MATHEMATICAL BIOSCIENCES 53,1981, pp.131-141
[4] Yu Lin,Vaibhav Rajan,and Bernard M.E.Moret, A metric For Phylogenetic Trees Based On Matching, IEEE, July 2012, Volume 9, pp.1014-1022
[5] Hong Huang and Yongji Li, MASTtreedist: Visualization of Tree Space based on Maximum Agreement Subtree, Journal of Computational Biology,Issue:Jan 7,2013,pp.42-49

Abstract: CBIR Systems retrieve the relevant images by using various perceptions and mathematical calculations for example size, shape and other features of various objects in the image. These systems are being largely used in various fields. Here we use them for Retrieval of malarial positive images. In this paper retrieval could be done by using image or text as a query. To achieve best performance in order to help doctors we have proposed a methodology that does annotation of images by use of ontology. Further, more the number of objects in image larger is the need to do annotation. This methodology focuses on decrease in semantic gap by use of ontology and hence leading to increase in recall and precision. The results obtained are compared with previously defined algorithms and are found to be better than them.
Keywords: CBIR, Thresholding, Ontology, Similarity comparison.

[1]. J. Sanghavi, and D. Kayande, Content based Image Retrieval (CBIR) System for Diagnosis of Blood Related Diseases, National Conference on Innovative Paradigms in Engineering & Technology (NCIPET), 2013, 11-15.
[2]. H.Müller, N.Michoux, D.Bandon and A.Geissbuhler, A Review of Content- Based Image Retrieval Systems in Medical Applications – Clinical Benefits and Future Direction, International Journal of Medical Informatics, 73, 2004 , 1-23.
[3]. T. M. Lehmann, B. B.Wein, J.Dahmen, J.Bredno, F.Vogelsang and M.Kohnen, Content-based image retrieval in medical applications: A novel multistep approach, In Proc. of IS&T/SPIE Conference on Storage and Retrieval for Media Databases, 43( 4), 2004 , 312-320.
[4]. S. Ghosh, and A. Ghosh, Content Based Retrival Of Malaria Positive Images From A Clinical Database, Proceedings of the 2013 IEEE Second International Conference on Image Information Processing, 2013 , 313-318.
[5]. W.C.Seng, and S.H.Mirisaee, A Content-Based Retrieval System for Blood Cells Images, International Conference on Future Computer and Communications , Kuala Lumpur, 2009 , 412-415.

Abstract: Comfort characteristics along with the aesthetic appeal of textile structures for varied applications like next to skin layer, sportswear and active wear are of paramount importance for today's consumer demanding comfort as well as latest trends. Comfort although a subjective term can be objectively evaluated to determine the suitability and end use requirements of apparel fabrics. The effects of different fibre, yarn and fabric parameters on physical, thermal, mechanical and moisture management properties of textile structures can not only be objectively and subjectively determined but the properties can be predicted using soft computing techniques like artificial neural networks with high degree of accuracy. Nonlinear relationship of different fabric parameters with the comfort properties of textiles and close relationship of parameters with each other pauses problems in statistical modeling. Neural networks provide an effective tool for prediction of thermal, moisture management and hand values of textile structures. Another advantage of ANN is its stability and suitability for a variety of fabric types and an opportunity to generate database of fabric properties and eventually in development and engineering of new fabrics or updating the existing fabrics to keep pace with fashion. The present paper discusses the objective evaluation and prediction of thermal properties of textiles using the artificial neural network with emphasis on the structure and training of neural network using multi-layer perceptron (MLP).
Keywords: Comfort, Neural network, Soft computing, Textile, Thermal.

[1]. S. W. Wong, Y. Li and P. K. W. Yeung, Predicting clothing sensory comfort with artificial intelligence hybrid models, Textile Research Journal, 74 (1), 2004,13-19.
[2]. Bivainyteand D. Mikucioniene,Investigation on the air and water vapour permeability of double-layered weft knitted fabrics,Fibres and Textiles in Eastern Europe, 19, 2011, 69-73.
[3]. E.Onofrei, A. Rocha and A. Catarino, The influence of knitted fabrics structure on the thermal and moisture management properties, Journal of Engineered Fiber and Fabrics, 6, 2011,11-22.
[4]. Majumdar, S. Mukhopadhyayand R. Yadav, Thermal properties of knitted fabrics made from cotton and regenerated bamboo cellulosic fibres, International Journal of Thermal Science, 30,2010, 1-7
[5]. V. K. Kothari, Thermal transmission properties of fabrics : Paper presentedin QIP, Functional clothing, IIT Delhi, 2009, 1-7.

Abstract: In challenging wireless network, where there is no end to end connection between source and destination, delay tolerance network is proposed. In delay tolerance network, dynamic topology is used where nodes are completely mobile and based on the principle of store carry and forward. Delay tolerant network uses various routing schemes and movement models. Mechanism through which message is transmitted from one node to other depends on various routing schemes like first contact, spray and wait and max prophetetc. and node mobility is decided using various movement models like Random way point, Shortest path map based, Map based movement model etc.In this paper, an attempt has been made to compare various existing movement models using different metrics like delivery ratio, average message delay, and overheadratio.
Keywords: DTN (Delay Tolerant Network), ONE (Opportunistic Network Environment), Routing protocols, Movement Models.

[1]. Fall K., A delay tolerantnetwork architecture forchallenged internets.SIGCOMM‟03:Proceedingsofthe2003conference onApplications,technologies,architectures,and protocols for computercommunications, ACM,New York,NY, USA, pp.27–34.
[2]. NelsonI.Dopico,Álvaro GutiérrezandSantiagoZazo. Performanceanalysisofadelay tolerantapplicationforherd localization.2011Elsevier B.V.Allrights reserved.
[3]. SeunghunCha,ElmurodTalipovandHojung Cha.Data delivery schemeforintermittently connectedmobilesensor networks.0140-3664/$,2012Elsevier.
[4]. ShyamKapadia,BhaskarKrishnamachariandLinZhang. DataDelivery inDelayTolerantNetworks:ASurvey. www.intechopen.com.
[5]. Haigang Gongand Lingfei Yu .Study on Routing Protocols for Delay To lerant Mobile Networks. Volume2013,ArticleID145727,HindawiPublishingCorporation.

Abstract: Deployment of recommendation engines in social websites and e-commerce Websites has greatly facilitated the growth of number of users and the customer satisfaction. Recommenders are automotive tools that make a significant contribution to a better understanding of customers' behavior and their interest, so as to provide them useful suggestion of items from a plethora of available information. Item is a generic term which may refer to video, song, book, friend list, location etc. There exist various recommender algorithms that have been proposed in recent years for generating different kind of user recommendations. This paper surveys the existing recommender algorithms that effectively and extensively produce suggestions to users. A comparison is made between content based and collaborative filtering recommendation engines that helps in alleviating the severe issues related to them and their effectiveness in making recommendations to users. In nutshell, an attempt has been made to provide an overview of recommenders, their evolution, and the exposures in areas of future implementation.
Keywords: Automotive Tools, Collaborative, Content-based, Hybrid, Items, Recommendations

[1] P. Resnick and H. R. Varian, Recommender Systems, Communications of the ACM, 40(3), 1997.
[2] C-N Ziegler, S. M. McNee, J. A. Konstan and G. Lousen, Improving Recommendation Lists Through Topic Diversification, GroupLens Research in Minneapolis, International World Wide Web Conference Committee (IW3C2), 2005.
[3] R. M. Bell and Y. Koren, Improved Neighbourhood- based Collaborative Filtering, AT &T Labs Research, 2007.
[4] D. Lemire and A. Maclachlan, Slope One Predictors for Online Rating-Based Collaborative Filtering, In SDM, Vol. 5, 2008, 1-5.
[5] Z. Zheng, H. Ma, M. R. Lyu and I. King, WSRec: A Collaborative Filtering Based Web Service Recommender System, IEEE International Conference on Web Services, 2009.

Abstract: Computers are ubiquitous in education,offices, business,communication,and shopping and data storage.They have revolutionized our lives across all dimensions saving our time and effort to do work but these tremendous benefits is not proceeding without issue.It is daunting to see how the widespread use of computers is profoundly affecting our health,depleting our natural resources and polluting our environment.The concept of green computing hence becomes extremely relevant and significant in the present computer age. Green Computing refers to the environmentally sustainable use of computers and related resources right from the stage ofmanufacture,through delivery, use, maintenance, recycling and disposal.The goal of green computing is to design such systems which features reduced use of hazardous material,are energy efficient during the products lifetime and whose defunct products and factory waste can be recycledthus reducing pollution footprint.Such systems will haveno or minimum effect on environment. This paper offers an insight into what green computing is,its origin, its need and implementation, IT companies initiatives towards green computing, examines the awareness of IT usersof four diverse organizations about green technology through a case study/comparative analysis,the findings of this study and finallytheconclusion.
Keywords: Eco-friendly,Energy-efficient,Energy Star,Green IT, Recycling

[1]. Richard L.Brandt,Four Companies that went Green,www.enterprisetech.com/2013/01/04/
[2]. Scott Hammers,Apple taking over Jefferson County hydro project,www.bendbulletin.com/localstate/1979627-151
[3]. Shalabh Agarwal , Arnab Datta, Asoke Nath ,IMPACT OF GREEN COMPUTING IN IT INDUSTRY TO MAKE ECO FRIENDLY ENVIRONMENT, Journal of Global Research in Computer Science, 5(4),2014,5-10.
[4]. Sharmila Shinde , Simantini Nalawade, Ajay Nalawade, Green Computing: Go Green and Save Energy,International Journal of Advanced Research in Computer Science and Software Engineering,3(7),2013,1033-103.
[5]. Gaurav Jindal , Manisha Gupta , Green Computing "Future of Computers" , International Journal of Emerging Research in Management & Technology,2012,14-18.

Abstract: Data mining turned into astimulating, operative, and widely acceptable methodology for analyzing central repository from different perspective even on the same data andattaining knowledge while elaborating various insights through the applicability of obtainable mining tools. The advantage of using these refined and efficient tools are that besides the required information, they aid in highlighting the same data sets in different modes to provide additionalinvestigation. The present paper underlining the implication of one of the finest tools of data mining for examining the data of 164 Judokas and to demonstrate various relationships between different entities, their grouping and pictographic representation of the classification through the k-means clustering, k-nearest neighbor method and decision tree classification.
Keywords: Clustering, Mining, Decision tree.

[1] https://rapidminer.com/wp-content/uploads/2013/10/RapidMiner_RapidMinerInAcademicUse_en.pdf.
[2] https://rapidminer.com/
[3] http://sourceforge.net/projects/rapidminer/
[4] https://rapidminer.com/wp-content/uploads/2014/10/RapidMiner-v6-user-manual.pdf
[5] http://www.slideshare.net/dataminingtools/rapidminer-introduction-to-datamining