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Data is currency in today’s world. Security teams are now tasked with protecting the brand and intellectual property through the protection of the second-most important asset of a company: data (the first one being people). There are two approaches here. The first is to label – or classify – information so users know if they can place it in the cloud or not. The second is to look at how IT provision can be changed to make security less burdensome. As tablets and smartphones become the primary work computing device, offering easy access to the cloud, users will be less tolerant of VPN, multiple logins etc. Smaller organizations typically rely on services such as iCloud. For these businesses, it would make sense to implement additional security measures provided such as two-factor authentication. In this paper I have highlighted the options which can be useful in the two-factor authentication.

SLA. QoS, RBAC, IDM, OTP

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Surabhi Shukla and Neelam Joshi, "BYOD with Multi-Factor Authentication," International Journal of Computer Sciences and Engineering, Vol.3, Issue.6, pp.104-107, 2015.

The basic requirement of physical document analysis system is to digitalize the physical document. Recently number of researcher presented numerous techniques that can vary in sensitivity, quality and some more control parameters. Document binarization plays an important role in preserving the historical documents. The document image binarization focuses on extracting the text and background of the image. In doing this the edge detection approach also play the crucial role. This paper presents general review on the various approaches of document binarization. Various edge detection approaches are also been discussed. In addition various available data sets for image binarization developed in Document Image Binarization Contest (DIBCO) 2009 and Handwritten Document Image Binarization Competition (H-DIBCO) 2011 has also discussed.

Document Digitization, Edge Detection, Gaussian Filter

[1] Reza Farrahi Moghaddamn, Mohamed Cheriet “AdOtsu: An adaptive and parameterless generalization of Otsu’s method for document image binarization”, Elsevier transaction of Pattern Recognition,2012, pg no- 2419–2431.
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[3] Pratikakis, I., Gatos, B., Ntirogiannis, K.: ICDAR 2011 document image binarization contest (DIBCO 2011), International Conference on Document Analysis and Recognition,2011, pp. 1506–1510.
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[5] R. Farrahi Moghaddam, M. Cheriet, “A multi-scale framework for adaptive binarization of degraded document images”, Pattern Recognition 43 (6),2010, pp. 2186–2198.
[6] B. Gatos, I. Pratikakis, S.J. Perantonis, “Adaptive degraded document image Binarization”, Pattern Recognition 39 (3),2006, pp. 317–327.
[7] B. Gatos, K. Ntirogiannis, I. Pratikakis, DIBCO 2009: document image binarization contest, International Journal on Document Analysis and Recognition, 2010,pp. 1-10.
[8] J. Fabrizio, B. Marcotegui, M. Cord, “Text segmentation in natural scenes using toggle-mapping”, ICIP’09, 2009, pp. 2373–2376.
[9] B. Gatos, K. Ntirogiannis, I. Pratikakis, ICDAR 2009 document image binarization contest (DIBCO 2009), in: ICDAR’09,2009, pp. 1375–1382.
[10] R. Hedjam, R. Farrahi Moghaddam, M. Cheriet, “A spatially adaptive statistical method for the binarization of historical manuscripts and degraded document images”, Pattern Recognition 44 (9),2011, pp.2184–2196.
[11] B. Su, S. Lu, C.L. Tan, “A self-training learning document binarization frame work”, ICPR’10,2010, pp. 3187–3190.

Bharti Bansinge and R.K.Pateriya, "Data Document Image Binarization for Preserving Historical: A Review," International Journal of Computer Sciences and Engineering, Vol.3, Issue.6, pp.108-112, 2015.

Data cannot be managed by the traditional database management systems when it comes in a large amount. So there comes the Big Data. Hadoop and MapReduce are the solution to handle, manage and analyze Big Data. Hadoop is an open source implementation of MapReduce programming paradigm which is a parallel distributed programming model for handling large data intensive applications. In this paper, we present our experimental work done on Hadoop with pseudo-distributed mode on different machines and analyze the time taken by Hadoop to perform the same operations on different machines.

Big Data; Hadoop; MapReduce; Pseudo-distributed Mode; Distributed Programming

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[2] M. Maurya, S. Mahajan,”Performance Analysis of MapReduce Programs on Hadoop Cluster”, in proc. of 2012 World Congress on Information and Communication Technologies, ISBN:978-1-4673-4806-5,Page No (505-510), Oct 30-Nov 2,2012.
[3] M. Ishii, Jungkyu Han, H. Mankino,”Design and Performance Evaluation for Hadoop Clusters on Virtualized Environment”, in proc. of 2103 International Conference on Information Networking, E-ISBN:978-1-4673-5741-8, Page No (244-249), Jan 28-30,2013.
[4] Han Jungkyu, M. Ishii, H. Makino,”A Hadoop Performance Model For Multi-Rack Clusters”,in proc. of 2013 5th International Conference on Computer Science and Information Technology, Page No (265-274), Mar 27-27,2013.
[5] Zhuoyao Zhang, Ludmila Cherksova, Boon Thau Loo,”Performance Modeling od MapReduce Jobs in Heterogeneous Cloud Environments”, in proc. of the 2013 IEEE Sixth International Conference on Cloud Computing, ISBN: 978-0-7695-5028-2, Page No (839-846), June 28- July 3,2013.
[6] J. Nandimath, E. Banerjee, A.Patil, P. Kakade, “Big Data Analysis using Apache Hadoop”, in proc. of 2013 IEEE 14th International Conference on Information Reuse and Intergration, Page No (700-703), Aug 14-16,2013.
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[8] Invanilton Polato, Reginaldo Re, Alfredo Goldman, Fabio Kon, “A Comprehensive view of Hadoop Research- A Systematic Literature Review”, Elsevier- Journal of Network and Computer Applications,Volume-46,Page No (1-25), Aug 2014.
[9] Chia-Wei Lee, Kuang-Yu Hsieh Sun-Yuan Hsieh , Hung-Chang Hsiao,”A Dynamic Data Placement Strategy for Hadoop in Heterogeneous Environments ”, Elsevier-Big Data Research, Volume-1, Page No (14-22), Aug 2014.
[10] D. Dev, R. Patgiri, “Performance Evaluation of HDFS in Big Data Management”, in proc. of 2014 International Conference on High Performance Computing and Applications,ISBN: 978-1-4799-5957-0, Page No (1-7), Dec 22-24,2014.
[11] M.F. Hyder, M.A. Ismail, H. Ahmed, “Performance Comparison of Hadoop Clusters Configured on Virtual Machines and as a Cloud Service”, in proc. of 2014 International Conference on International Technologies,ISBN: 978-1-4799-6088-0, Page No (60-64), Dec 8-9,2014.
[12] Hadoop Tutorial [online]. Available: https:// hadoop.apache.org

Ruchi Mittal and Ruhi Bagga, "Performance Analysis of Hadoop with Pseudo-Distributed Mode on Different Machines," International Journal of Computer Sciences and Engineering, Vol.3, Issue.6, pp.113-117, 2015.

Recently, the usages of Cloud Computing are increasing rapidly and gained tremendous success over the internet. Therefore, security is the major challenge in Cloud computing and one of the major issues is to protect the Cloud resources and the services against network intrusions. So Network Intrusion Detection System (NIDS) are installed in the Cloud networks to detect the intrusions in the system. In this paper we proposed an NIDS based on Naïve Bayes Classifier to be implemented in Cloud. The main aim of the NIDS is to improve the performance by preparing the training dataset which can detect the malicious connections that exploit the Cloud HTTP services. In the training phase, the Naïve Bayes Classifiers select the important Network traffic that can be used to detect the attacks. In the testing and execution phases the proposed IDS using the Naïve Bayes Classifier classifies the services based on the selected features into normal or attacks. The proposed IDS carried out on NSL-KDD’99 dataset and results in high detection with low false alarm as compared with other similar IDS.

Cloud Computing; Cloud security;Network based Intrusion Detection System; Naïve Bayes Classifier

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[13] Mohamed M. Abd-Eldayem, “A proposed HTTP service based IDS”, Egyptian Informatics Journal (2014) 15, 13–24.
[14] The NSL-KDD data set http://nsl.cs.unb.ca/NSL-KDD/.

Sudhansu Ranjan Lenka and Bikram Keshari Rath , "HTTP service based Network Intrusion Detection System in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.3, Issue.6, pp.118-123, 2015.

The quality of life of the citizens is highly relies on the efficiency and effectiveness of its transportation system. The main goal of transportation is to get easy access to each and every location in the city. Effective transportation system helps to reduce time consumption as well as pollution at some extent. This paper presents an analysis of the Aurangabad city transportation system, role of Geographic Information System in transportation, pitfalls in the existing system and discuss different techniques for network analysis. It will show us clear picture where we need to focus to make Aurangabad as a smart city.

Geographical Information System (GIS), Transportation, Smart City

[1] Khaja Fareeduddin, “Urban Transportation Planning, Challenges And Policy Initiatives Ways For Hyderabad City – A GIS Approach”, International Journal of Environmental Research and Development, Vol. 04, No. 04, page no. 309-316, 2014.
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[3] Pallavi U. Pandagale, “Geospatial Technology for Tourism Management in Aurangabad City”, International Journal of Computer Applications, Vol. 102, No.16, 2014.
[4] Ajay D. Nagne, Amol D. Vibhute, Bharti W.Gawali, Suresh C. Mehrotra, “Spatial Analysis of Transportation Network for Town Planning of Aurangabad City by using Geographic Information System”, International Journal of Scientific & Engineering Research, Vol. 04, Issue 07, 2013.
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[18] Jean-François Cordeau, Gilbert Laporte, Martin W.P. Savelsbergh , Daniele Vigo , “Vehicle Routing”, Handbook in OR & MS , Elsevier, Vol. 14, 2007.
[19] Sang Gu Lee, Mark Hickman, and Daoqin Tong, “Development of a temporal and spatial linkage between transit demand and landuse patterns”, The Journal of Transport and Land Use, vol.06 no.02, page no 33–46, 2013.
[20] Musara Chipumuro, Romeo Mawonike and Tendai Makoni “Optimizing Routing Of Residential Solid Waste Collection: Case Study Of Chikova Residential Area In Zimbabwe”, International Research Journal Of Mathematics, Engineering & IT, Vol. -01, Issue-03, 2014.
[21] EunSu Lee, Alan D. Dybing and Sumadhur Shakya, “Facility Location Problem of Beverage Distribution Considering Time Window and Land Use Plan Using GIS, vol 01, page no. 55-69, 2015.
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[23] Danhuai Guo, Ziqi Zhao, Wei Xu, Jinsong Lan, Tao Zhang, Shuguang Liu, Jianhui Li and Yuanchun Zhou, “How to Find a Comfortable Bus Route - Towards Personalized Information Recommendation Services”, Data Science Journal, 14: 14, page no. 1-11, 2015.
[24] Ajay D. Nagne, Dr. Bharti W.Gawali, “Transportation Network Analysis By Using Remote Sensing & GIS A Review”, Vol. 03, Issue 03, page no. 070-076, 2013.
[25] Mukti Advani, B.Srirama, S.K.Pathan, “Improvement In Transit Service Using GIS – Case Study Of Bhavnagar State Transport Depot”, Proceedings ESRI National Conference held at Noida, India, 2005.

Dhananjay B. Nalawade, Sumedh D. Kashid , Rajesh K. Dhumal, Ajay D. Nagne, Karbhari V. Kale, "Analysis of Present Transport System of Aurangabad City Using Geographic Information System," International Journal of Computer Sciences and Engineering, Vol.3, Issue.6, pp.124-128, 2015.

With the advance of technology the use of human computer interaction (HCI) has improved day by day. Computer vision plays an important role to provide information to design more simple and efficient approaches for HCI. The proposed approach uses skin color model to identify the hand from the image, and further preprocessing is done in order to remove unwanted noise and areas. Blob analysis is done in-order to extract the hand gesture from the image considering that the largest blob is the hand. Then the blob is resized into a standard size in order to eliminate size variant constraint. Sampling of the boundary line of the hand gesture is done by overlapping grid lines and extracting the point of intersection of the grid line and the boundary. Freeman chain code is used to represent the boundary of the hand gesture. In order to minimize the length of chain code run-length encoding is done. Finding the first difference of the chain code its shape number is obtained. Shape number can be used to identify each of the gesture uniquely.

Human Computer Interaction, Computer Vision, Static Gesture, Nepali Sign Language, Blob, Freeman Chain Code, Shape Number

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Jhuma Sunuwar and Ratika Pradhan, "Hand Gesture Recognition for Nepali Sign Language Using Shape Information," International Journal of Computer Sciences and Engineering, Vol.3, Issue.6, pp.129-135, 2015.