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Twitter has become one of the most popular micro-blogging platforms recently. Millions of users can share their thoughts and opinions about different aspects and events on the micro-blogging platform. Therefore, Twitter is considered as a rich source of information for decision making and sentiment analysis. Obtaining the real time tweets on the particular topic is one of the challenging tasks. There are numbers of related publications, but which have limitations; also their methods are not much clear and mostly based on Linux based system and uses integrated tools, which is most complex job. Therefore, in this research paper we develop the indigenous Windows based user friendly application in Java to extract, process and classify the real time social network tweet. The tweets are processed for removal of hash, tags and URL and removed the stop words from sentence and tried to detect, analyze the abbreviations or slangs. The meaningful real time tweets are obtained and used for sentimental analysis.

Extraction Of Realtime Tweets, Processing Tweets

[1]. Farhan Hassan Khan et. al. , TOM: Twitter opinion mining framework using hybrid classification scheme, Decision Support Systems, 2013, http://dx.doi.org/
[2]. 10.1016/j.dss.2013.09.004
[3]. A. Bifet, E. Frank, Sentiment Knowledge Discovery in Twitter Streaming Data,, Springer-Verlag, Berlin, Heidelberg, 2010, pp. 1–15.
[4]. A. Bifet, G. Holmes, B. Pfahringer, MOA-Tweet Reader: real-time analysis in twitter, streaming data, in: T. Elomaa, J. Hollm'en, H. Mannila (Eds.), DS 2011, LNCS 6926, Springer-Verlag, Berlin Heidelberg, 2011, pp. 46–60.
[5]. Argamon S., Bloom K., Esuli A., Sebastiani F., Automatically determining attitude type and force for sentiment analysis, in: Vetulani Z., Uszkoreit H. (Eds.), LTC2007, LNAI 5603, Springer-Verlag, Berlin Heidelberg, 2009, pp. 218–231.
[6]. Fu X., Guo Y., Guo W., Wang Z., et al., Aspect and sentiment extraction based oninformation-theoretic co-clustering, in: Wang J., Yen G.G., Polycarpou M.M.(Eds.),ISNN 2012, Part II, LNCS 7368, Springer-Verlag, Berlin, Heidelberg, 2012, pp. 326–335.
[7]. Lambodar Jena, Narendra Kumar Kamila, Data Extraction and Web page Categorization using Text Mining, IJAIEM, ISSN 2319 – 4847, Volume 2, Issue 6, June 2013
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[13]. Georgios Petasis1,2, Dimitrios Petasis1. BlogBuster: A tool for extracting corpora from the blogosphere. Software and Knowledge Engineering Laboratory National Centre for Scientific Research (N.C.S.R.) “Demokritos”, Athens, Greece, 2010.
[14]. Baroni, M., Chantree, F., Kilgarri, A., Sharo, S. (2008). Cleaneval: a competition for cleaning web pages. In Proceedings of the 4th Web as Corpus Workshop (WAC4), - Can we beat Google?. N. Calzolari, K. Choukri, B.Maegaard, J. Mariani, J. Odjik, S. Piperidis, and D. Tapias, editors, Proceedings of the 6th International Language Resources and Evaluation (LREC 2008). Marrakech, Morocco, 2008
[15]. S. Evert. A lightweight and ecient tool for cleaning web pages. In N. Calzolari, K. Choukri, B. Maegaard, J. Mariani, J. Odjik, S. Piperidis, and D. Tapias, editors, Proceedings of the Sixth International Language Resources and Evaluation (LREC'08), Marrakech, Morocco, may 2008. European Language Resources Association (ELRA). http://www.lrec-conf.org/proceedings/lrec2008/N.B. Salem, and J-P Hubaux, “Securing Wireless Mesh Networks”, IEEE Wireless Communications, Vol.13, Issue-2, 2006, pp.50-55.
[16]. S. Han, E. Chang, L. Gao, T. Dillon, T., Taxonomy of Attacks on Wireless Sensor Networks, in the Proceedings of the 1st European Conference on Computer Network Defence (EC2ND), University of Glamorgan, UK, Springer Press, SpringerLink Date: December 2007.
[17]. C. Karlof and D. Wagner, “Secure routing in wireless sensor networks: attacks and countermeasures,” Ad Hoc Networks 1, 2003, pp. 293-315.
[18]. Y. Yang, Y. Gu, X. Tan and L. Ma, “A New Wireless Mesh Networks Authentication Scheme Based on Threshold Method,” 9th International Conference for Young Computer Scientists (ICYCS-2008), 2008, pp. 2260-2265

B. M. Bandgar and Binod Kumar, "Real Time Extraction and Processing of Social Tweets," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.1-6, 2015.

With the mounting significance of data security over the network, steganography and cryptography are two emerging fields of research. Steganography is the technique in which confidential data is concealed in a cover medium. Cryptography is an approach of translating the personal data to some unintelligible form to keep it safe from intruders. Steganography and Cryptography attempt to accomplish identical objective of data security via different means. In this paper, the authors state a novel system to mask secret data. The confidential text is encrypted using modified Playfair cipher in the first step. In the second step, the cipher text is subjected to image steganography. In this step, the cipher text is embedded into the image using LSB encoding. To further enhance the concealment of the message, the image is subjected to a two-level simple but secure symmetric key encryption which comprises of two image encryption algorithms. These encryption algorithms use two pseudo random number generators that is Linear Congruential Generator and Blum Blum Shum algorithm.

Modified Playfair cipher, Image Steganography, LSB encoding, Image Encryption, Linear Congruential Generator, Blum Blum Shub, Pseudo Random Number Generators

[1] Behrouz A. Forouzan, “Cryptography and Network Security” special Indian Edition 2007, Tata McGraw- Hill Publishing Company Limited, New Delhi
[2] Derek Bruff, Ph.D, The Playfair Cipher Revealed Wynne MLAS 280-07 Cryptography July 13, 2009
[3] William Stallings,” Cryptography and Network Security”, 5th Edition
[4] Ravindra Babu K, S. Uday Kumar, A. Vinay Babu, I.V.N.S Aditya, P. Komuraiah, “An Extension to Traditional Playfair Cryptographic Method”, International Journal of Computer Applications (0975 – 8887)Volume 17– No.5, March 2011
[5] S. Das, B. Bandyopadhyay and S. Sanyal, “Steganography and Steganalysis: different approaches”, Cornell University Library, 2011
[6] Ayushi, “A Symmetric Key Cryptographic Algorithm” International Journal of Computer Applications (09758887) Volume 1 – No. 15
[7] “A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications”, National Institute of Standards and Technology, Special Publication 800-22 Revision 1a.
[8] An article on Linear Congruential Generator is available “http://en.wikipedia.org/wiki/Linear_congruential_generator”.
[9] Nishith Sinha, Anirban Bhowmick, Kishore B, “Encrypted Information Hiding using Audio Steganography and Audio Cryptography”, International Journal of Computer Applications (0975 – 8887) Volume 112 – No. 5, February 2015
[10] M U Bokhari, Shadab Alam, Faheem Syeed Masoodi, “Cryptanalysis techniques for Stream Cipher: A Survey”, International Journal of Computer Applications (0975 – 8887) Volume 104 – No 15, October 2014
[11] Andrew S Tanenbaum, “Computer Networks”, 4th Edition
[12] Yambem Jina Chanu, Kh. Manglem Singh, Themrichon Tuithung, “Image Steganography and Steganalysis: A Survey”, International Journal of Computer Applications (0975 – 8887) Volume 52– No.2, August 2012
[13] Shreenandan Kumar, Suman Kumari, Sucheta Patro, Tushar Shandilya, Anuja Kumar Acharya, “Image Steganography using Index based Chaotic Mapping”, International Journal of Computer Applications (0975 – 8887) International Conference on Distributed Computing and Internet Technology (ICDCIT-2015)
[14] Saurabh V. Joshi Ajinkya A. Bokil Nikhil A. Jain Deepali Koshti, “Image Steganography Combination of Spatial and Frequency Domain”, International Journal of Computer Applications (0975 – 8887) Volume 53– No.5, September 2012
[15] Nitin Kaul, Nikesh Bajaj, “Audio in Image Steganography based on Wavelet Transform”, International Journal of Computer Applications (0975 – 8887) Volume 79 – No3, October 2013
[16] Ajit Singh, Swati Malik, “Securing Data by Using Cryptography with Steganography”, International Journal of Advanced Research in Computer Science and Software Engineering Volume 3, Issue 5, May 2013 ISSN: 2277 128X
[17] Firas A. Jassim, “A Novel Steganography Algorithm for Hiding Text in Image using Five Modulus Method”, International Journal of Computer Applications (0975 – 8887) Volume 72– No.17, June 2013
[18] Dipti Kapoor Sarmah, Neha Bajpai, “Proposed System for Data Hiding Using Cryptography and Steganography”, International Journal of Computer Applications (0975 – 8887)Volume 8– No.9, October 2010
[19] Unik Lokhande, A. K. Gulve, “Steganography using Cryptography and Pseudo Random Numbers”, International Journal of Computer Applications (0975 – 8887) Volume 96– No.19, June 2014
[20] V. Lokeswara Reddy, B. Sailendar, “Enhanced Chaos based Image Steganography using Edge Adaptive and Cat Mapping Techniques”, International Journal of Computer Applications (0975 – 8887) Volume 104 – No 11, October 2014

Anirban Bhowmick, Vishal Kapur and Surya Teja Paladi, "Concealing Cipher Data using an Amalgam of Image Steganography and two-level Image Cryptography," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.7-12, 2015.

Noises in image is a random variation of color and brightness information produced by the sensor and circuitary of a scanner, or digital camera, or transmission of images. Noise can degrade the visual quality of the image. Due to this, we lost so many information from the image. Image Denoising means to restore the noisy image in original form and extract the maximum information as much as possible. In this paper, we studied the various types of noises in the image, how its behavior, with some parameters, with the help of PDF functions using MATLAB.

Speckle Noise, Gaussian Noise, Uniform Noise, Exponential Noise, Gamma Noise, Rayleigh Noise, Impulsive Noise

[1] Rohitt Verma, and Jahid Ali, “A comparative study of various types of image noise and efficient noise removal Techniques”, International journal of advanced research in computer science and software engineering, volume 3, issue 10, October 2013, ISSN: 2277128X.
[2] Priyanka Kamboj and Versha Rani, “A brief study of various noise model and filtering techniques”, Journal of global research in computer science, Volume 4, Number 4, April 2013, ISSN: 2229371X.
[3] Divya Sharma, “A comparative analysis of thresholding techniques used in image Denoising through wavelets”, Thesis, Electrical and Instrumentation Engineering Department, Thapar University, Patiala, June 2008.
[4] http://en.wikipedia.org/wiki/Gaussian_noise
[5] Alan C. Bovik, “Handbook of image and video processing”, Academic press, ISBN 0-12-119792-1, 2005.
[6] Mr. Amit Agrawal and Ramesh Raskar, “Optimal single image capture for motion deblurring”, In Proc. IEEE Conference on Computer Vision and Pattern Recognition, pages 2560{2567, 2009.
[7] Mr. Pawan Patidar and et al. , “Image De-noising by Various Filters for Different Noise”, International Journal of Computer Applications (0975 – 8887) Volume 9– No.4, November 2010.
[8] MATLAB R2013a, Image Processing Toolbox, Import, Export and conversion, Synthetic images.
[9] Charles Boncelet, “Image Noise Models”. In Alan C. Bovik. Handbook of Image and Video Processing. Academic Press. ISBN 0-12-119792-1.
[10] http://www.mif.vu.It/atpazinimas/dip/FIP/fip-Quantiza.html
[11] Thomas S. Huang, “ Advances in Computer Vision and Image Processing”, . JAI Press. ISBN 0-89232-460-0, 1986.
[12] Joseph G. Pellegrino et al., “Infrared Camera Characterization”. In Joseph D. Bronzino. Biomedical Engineering Fundamentals. CRC Press. ISBN 0-8493-2122-0, 2006.
[13] http://en.wikipedia.org/wiki/Image_noise
[14] http://en.m.wikipedia.org/wiki/Laplace_distribution

Sumit Kushwaha and Rabindra Kumar Singh, "Study of Various Image Noises and Their Behavior," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.13-17, 2015.

Free/Open Source Software (F/OSS) has emerged as a novel model of software development and distribution during the last decade. An F/OSS project generally evolves by receiving submissions from its volunteers in form of source code, bug identification, feature request, support request, translation request, documentation etc. The present paper uses F/OSS defect data extracted from a research collaboratory. Then it applies Goal/Question/Metric approach to determine the effectiveness of Defect Reporting and efficiency of Defect Resolution. The research findings of present work provide empirical evidences about Defect Management which F/OSS Projects may use to improve software quality.

Free Software; Open Source; Defect Management; GQM

[1] Eric S. Raymond, "The Cathedral and the Bazaar", First Monday, Volume -3, No. 3, 1998.
http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/578/499
[2] Joseph Feller, Brian Fitzgerald, Scott A. Hissam and Karim R. Lakhani, “Perspectives on Free and Open Source Software”, 2005, The MIT Press.
http://mitpress.mit.edu/books/chapters/0262562278.pdf
[3] “SourceForge”, http://sourceforge.net/
[4] Audris Mockus, Roy Fielding and James D. Herbsleb, “Two Case Studies of Open Source Software Development: Apache and Mozilla” ACM Transactions on Software Engineering and Methodology, Volume- 11, No.-3, Page No. - (309-324),2002.
[5] Dawid Weiss, “A Large Crawl and Quantitative Analysis Of Open Source Projects Hosted On Sourceforge”, Research Report ra-001/05(2005), Institute of Computing Science, Pozna University of Technology, Poland.
http://www.cs.put.poznan.pl/dweiss/xml/publications/index.xml
[6] Chiara Francalanci and Francesco Merlo, “Empirical Analysis of the Bug Fixing Process in Open Source Projects”, Open Source Development, Communities and Quality, Springer Boston, Volume- 275, Page No.- (187-196), 2008.
[7] Martin Michlmayr and Anthony Senyard, “A Statistical Analysis of Defects in Debian and Strategies for Improving Quality in Free Software Projects”, The Economics of Open Source Software Development, Elsevier B.V., Page No.- (131–148), 2006.
[8] Kevin Crowston and Barbara Scozzi, “Bug Fixing Practices within Free/Libre Open Source Software Development Teams”, Journal of Database Management, Volume- 19, No.- 2,Page No. -(1-30), 2008.
[9] David A. Wheeler, “Estimating Linux's Size Version 1.04”,
May 2001. http://www.dwheeler.com/sloc/
[10] Daniel German and Audris Mockus, “ Automating the Measurement of Open Source Projects”, Proceedings of the 3rd Workshop on Open Source Software Engineering, International Conference on Software Engineering, May 2003, Portland, Oregon, USA.
[11] Stefan Koch, “Effort Modeling and Programmer Participation in Open Source Software Projects '', Information Economics and Policy, Volume- 20, No. 4, Page No. – (345-355), 2008.
[12] Ionic Stamelos, Lefteris Angelis, Apostolos Oikonomou and Georgios L. Bleris, “Code Quality Analysis in Open Source Software Development”, Information Systems Journal, Volume - 12, No. 1,Page No. (43-60), 2002.
[13] Navica’s Open Source Maturity Model (OSMM)”,
http://www.navicasoft.com/pages/osmm.htm
[14] Frans-Willem Duijnhouwer and Chris Widdows, “Capgemini Expert Letter Open Source Maturity Model”, Capgemini, 2003.
http://pascal.case.unibz.it/retrieve/1097/GB_Expert_Letter_Open_Source_Maturity_Model_1.5.31.pdf
[15] “Qualification and Selection of Open Source Software (QSOS)”,
http://www.qsos.org/methode.php
[16] “Open Business Readiness Rating”,
http://www.openbrr.org/wiki/index.php/Home
[17] “Software Quality Observatory for Open Source Software (SQO-OSS)”,
http://www.sqo-oss.eu/
[18] G. Madey, The SourceForge Research Data Archive (SRDA), University of Notre Dame, http://zerlot.cse.nd.edu/
[19] A. Gupta, R.K. Singla,” Qualitative Evaluation of Defect Resolution in Free/Open Source Software Projects”, International Journal HIT Transactions on ECCN, ISSN: 0973-6875 Volume - 3, No. 9, Page No.- (27-36), 2009.
[20] V. Basili, G. Caldiera, and H. D. Rombach. The Goal Question Metric Approach, John Wiley & Sons Inc., 1994.
[21] Stephen H. Kan, “Metrics and Models in Software Quality Engineering”, Second Edition, Pearson Education, 2003.

Anu Gupta , "Applying GQM Approach towards Evaluation of Defect Management in Free/Open Source Software Projects," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.18-23, 2015.

In this fast-paced and dynamic world of data communication, systems and software development, and uncontrolled network traffic, security is becoming more and more of an issue. Surprisingly, many organizations, as well as individual users regard security as more of an afterthought (proper security implementation is often enacted after an unauthorized intrusion has already occurred) , a process that is overlooked in favor of increased productivity, convenience, ease of use, and budgetary concerns. Security on Linux systems never stays static. Once secured, the system does not perpetually stay secure. Indeed, the longer one uses the system, the less secure it becomes. This document provides general practices, procedures, planning and tools for creating a secured computing environment for the data center, workplace, or at home. It is aimed for engineers, IT managers, security and system administrators, who are assumed to possess basic system administration skills for Unix-like systems. It addresses basic security vulnerabilities, local and remote intrusion, exploitation and malicious activity techniques valid for all Linux systems.

Linux, Unix, Hardening, Security, Server

[1] J. Turnbull, “Hardening Linux”, Apress, 2005
[2] K. Fenzi, Dave Wreski, “Linux Security HOWTO ”, v2.3, 22 January 2004,
http://www.tldp.org/HOWTO/Security-HOWTO/
[3] National Security Agency, “Guide to the Secure Configuration of Red Hat Enterprise Linux 5 ”,
Revision 4.1, February 28, 2011, www.nsa.gov/ia/_files/os/redhat/rhel5-guide-i731.pdf
[4] M. Prpič, T. Čapek, S. Wadeley, Y. Ruseva, M. Svoboda, R. Krátký, “Red Hat Enterprise Linux 6 Security Guide”,
Red Hat, Inc., 2013, access.redhat.com/documentation/ en-US/Red_Hat_Enterprise_Linux/6/html/Security_Guide/

Yogender Bhardwaj, "Server Hardening: Securing Unix-like workstations," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.24-32, 2015.

In this paper we have reviewed and analyzed different car-license-plate detection and recognition technique. We have reviewed different image processing Edge Detection techniques and recognition technique like Sobel operator, Canny operator, ANN, BPNN, Template matching. We have also represented analysis of these techniques in the form of table considering different factors of Edge Detection and recognition like visual quality, security, robustness and computational complexity. It is concluded that in edge detection techniques vertical edge detection technique are best with some factors like processing time, accuracy, while in recognition technique BPNN are best for some factors like execution speed or other image processing operations and security. The visual quality and computational complexity are common factors for both the technique.

Car-License-Plate Detection, Vertical Edge Detection Algorithm, Edge Detection, License Plate, Adaptive Thresholding

[1] Sharifi, M.; Fathy, M.; Mahmoudi, M.T.; " A classified and comparative study of edge detection algorithms", International Conference on Information Technology: Coding and Computing, Proceedings, Page (s): 117 – 120, 8-10 April 2002.
[2] WenshuoGao, “An improved Sobel edge detection”,
Computer Science and Information Technology (ICCSIT),
2010 3rd IEEE International Conference, China, Volume:
5,pp. 67 – 71, 9-11 July 2010.
[3] S. Gendy, C.L Smith, S. Lachowicz, "Automatic car registration plate recognition using fast Hough transform," Security Technology Proceedings. The Institute of Electrical and Electronics Engineers 31st Annual 1997 International Carnahan Conference on , vol., no., pp.209-218, 15-17, 1997.
[4] M. Fukumi, Y. Takeuchi, H. Fukumoto, Y. Mitsura, and M. Khalid, “Neural network based threshold determination for Malaysia license plate character recognition,” in Proc. 9th Int. Conf. Mechatron. Technol., 2005, pp. 1–5.
[5] Muhammad Sarfraz, Mohammed Jameel Ahmed, Syed A. Ghazi, "Saudi Arabian License Plate Recognition System," gmag, pp.36, 2003 International Conference on Geometric Modeling andGraphics (GMAG'03), 2003.
[6] SherrZheng Wang and HsMian Lee, “Detection and Recognition of License Plate Characters with Different
Appearences”, in proc. Conf. Intelligent Transportation Systems, vol. 2, 2003, pp. 979 - 984.
[7] Ahmed, M.J.; Sarfraz, M.; Zidouri, A.; Al-Khatib, W.G., "License Plate recognition system," Electronics, Circuits and Systems, 2003. ICECS 2003. Proceedings of the 2003 10th IEEE International Conference on , vol.2, no., pp. 898-901 Vol.2, 2003.
[8] Dr. P.K.Suri, Dr. EktaWalia, Er. AmitVerma, "Vehicle License Plate Detection using Sobel Edge Detection Technique," IJCST Vol. 1, Issue 2, December 2010”
[9] Harish Kumar Reddy Medipally, "license plate localization and recognition Algorithm using edge detection technique." 2,July 2010”
[10] Ming-Kan Wu, Jing-Siang Wei, Hao-Chung Shih, and Chian C. Ho#, "2-Level-Wavelet-Based License Plate Edge Detection.," 2009 Fifth International Conference on Information Assurance and Security”.
[11] Mr. HoWingTeng, Mr. Yap Wooi Hen, Dr. Tay Yong Haur,Computer Vision and Intelligent Systems (CVIS) Group University Tunku Abdul Rahman, Malaysia.
[12] Shapiro, Vladimir, Gluhchev, Georgi, Dimov, Dimo, “Towards a Multinational Car License Plate Recognition System,” MVA(17), No. 3, pp. 173-183, 2006.
[13] Russ, “The image processing handbook”, CRC Press, 2002
[14] Albovik,“Handbook of Image and Video Processing”, Academic Press, 2000.
[15] Rothwell, Ch., J. Mundy, B. Hoffman and V. Nguyen, 1994.Driving Vision by Topology. TR-2444 – Programme 4, INRIA, pp. 1-29.
[16] Vladimir Shapiro, Stefan Bonchev, VeselinVelichkov,
[17] GeorgiGluhchev “Adaptive Multi-National License Plate Extraction,” CYBERNETICS AND INFORMATION TECHNOLOGIES • Volume 4, No 1
[18] Mahmood Ashoori-Lalimi, Sedigheh Ghofrani, "
[19] An Efficient Method for Vehicle License Plate Detection in Complex Scenes," Circuits and Systems, 2011, 2, 320-325 doi:10.4236/cs.2011.24044 Published Online October 2011 (http://www.scirp.org/journal/cs)
[20] Feng Tang and Hai Tao, “ Fast Multi-scale Template Matching Using Binary Features” Department of Computer Engineering, University of California, Santa Cruz, USA.
[21] A MORPHOLOGICAL-BASED LICENSE PLATE LOCATION,"Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran”
[22] K. Debi, H.-U. Chae, and C. K.-H. Jo, “Parallelogram and histogram based vehicle license plate detection,” in Proc. IEEE Int. Conf. Smart Manuf. Appl., Gyeonggi-do, Korea, 2008, pp. 349–353.

Supriya Muravade, Amruta shete, Priyanka Takale, Gyankamal Chhajed , "Review On Car-License-Plate Detection Systems," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.33-37, 2015.

Many carpool and ride-sharing solutions have been proposed and even developed in the previous decades, but they were rarely able to attain a global user base, at least not up until recently. That was mostly because many of them were not initially designed as scalable, leaving their users without user friendly user experiences as their user base grew, and often their cell-phone or desktop client reach was not unique enough, making them available only to a small portion of mobile client devices and/or desktop users. This paper explains and describes the design concepts, distribution and cloud computing strategies the authors feel any future global carpool and ride-sharing solution could follow, making it very scalable and unique enough to successfully fulfill user requirements with user friendly user interface.

Keywords: ABF, RSS, Carpooling system

[1] N.V.Pukhovskiv R.E.Lepshokov ,"Real time carpooling system",IJEIT,Volume-02,Issue-06,Dec 2012
[2] Miguel A. Vargas, Jose l. Walteros, Andres L.Medaglia, "Car Pooling Optimization: A case Study in Strasbourg(France)", Proceedings of the 2008 IEEE Systems and Information Engineering Design Symposium, University of Virginia, Charlottesville, VA, USA, April 25,2008.

Ketki Deshmukh, Mansi Mali, Surendra Patil and Kiran Jadhav, "Android Based Fuel and Resource Saving System," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.38-41, 2015.

Image quality assessment has involved the comparison of a corrupted image with an original or perfect version of that given image. Many real time cases, this perfect image is not easily available. This research introduces a new metric, that measures visual quality of a single given image and also quality of video images is considered. Operating in this no-reference framework, one new method is suited for real-world applications, such as television monitoring and digital camera quality sensing. Most of the theoretical basis of this work centers on the notion of level-of-detail. Knowing whether an image is very smooth or highly detailed is important in both the detection and assessment of errors. At this time, there are three types of errors that commonly arise in practice are considered, that are namely blur, noise, and compression. Every given image is assigned a score reflecting its perceived quality. Human test cases may validate the new techniques. In this paper, we discuss several open challenges in an image and video quality research. These challenges coming from lack of complete perceptual models for: supra threshold distortions, natural images, interactions between images and distortions, images containing nontraditional and multiple distortions, and images containing enhancements. Here we also discuss the challenges related to computational efficiency.

Image Quality Assessment (IQA), NR Method, objective QA

[1] Z. Wang, A. C. Bovik and L. Lu, “Why is image quality assessment so difficult?” IEEE International Conference on Acoustics, Speech, & Signal Processing, (2002).
[2] Damon M. Chandler, “Seven Challenges for Image Quality Research”, SPIE-IS&T/Vol. 9014 901402-2
[3] Fan Zhang, Songnan Li, “Limitation and challenges of Image Quality Measurement”.
[4] Deepa maria Thomas,”A survey on efficient No-reference Blur estimation methods”, IJRET, dec-2013 vol: 2,Issue:12.
[5] Anna Geomi George,”A Survey On Different Approaches Used In Image Quality Assessment”, IJETAE,feb-2013,vol-3,issue-2.
[6] Rafal K. Mantiuk, “Comparison of four subjective method for image quality assessment”, computer graphics forum Volume 0 (1981), Number 0 pp. 1–13.
[7] Zhou Wang, “Reduced-Reference Image Quality Assessment Using A Wavelet-Domain Natural Image Statistic Model”, Human Vision and Electronic Imaging X, Proc. SPIE, vol. 5666,SPIE.
[8] Marius Pedersen and Jon Yngve Hardeberg, “Survey of full-reference image quality metrics”, ISSN: 1890‐520X ISBN: 978-82-91313-20-7.
[9] Song Nan Li, Lawrence Chun-Man Mak, and King Ngai Nag,” Visual Quality Evaluation for Images and Videos”, W. Lin et al. (Eds.): Multimedia Analysis, Processing & Communications, SCI 346, pp. 497–544.
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[11] http://en.wikipedia.org/wiki/Recognition_memory.

Renuka.H and M.Azath, "A Survey on Image/Video Quality Assessment- some Challenges and limitations," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.42-45, 2015.

Removal of noise is an essential and challengeable operation in image processing. Before performing any process in the image, it must be first restored. Images may be corrupted by noise during image acquisition and transmission. Nature of the noise removal depends upon the type of the noise corrupting the image. In an image there will be different type of noises like impulse noise, adaptive white Gaussian noise, short noise, quantization noise, film grain, these one or more are coupled together to form a mixed noise. To remove this type of noise there is a novel method comprises two stages: the first stage is to detect the noise in the image. In this stage, based on the intensity, the pixels are roughly divided into “noise-free pixel” and “noisy pixel”. Then, in the second stage it is to eliminate the noise from the image. In this, only the “noise-pixels” are processed. The “noise free pixels” are copied directly to the output image. The survey aims to study the different types of noises and noise removal techniques of an image.

Noise, Removal techniques, Pixels, Impulse Noise. Gaussian Noise, quantization Noise, Short Noise

[1] http://en.wikipedia.org/wiki/Image_noise
[2] “A Comparative Study of Various Types of Image Noise and Efficient Noise Removal Technique”s by Mr. Rohit Verma and Dr. Jahid International Journal of Advanced Research in Computer Science and Software Engineering
[3] “Survey of Image Denoising Techniques”By Mukesh C. Motwani ,Mukesh C. Gadiya, Rakhi C. Motwani and Frederick C. Harris, Jr.
[4] “Comparative Analysis of Various Image Denoising Techniques: A Review Paper” by Manoj Gabhel, Aashish Hiradhar International Journal of Science and Research (IJSR)
[5]” Image Denoising Techniques-An Overview” by Rajni and Anutam International Journal of Computer Applications (0975 – 8887)
[6] “A Survey on Image Noises and Denoise Techniques” by Toran Lal Sahu International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 1, Issue 9, November 2012
[7] “Mixed Noise Removal by Weighted Encoding with Sparse Nonlocal Regularization” by Jielin Jiang, Lei Zhang, and Jian Yang, Image Processing, IEEE Transactions on
[8] http://in.mathworks.com/help/images/noise-removal.
[9] “A Survey of Linear and Non-Linear Filters for Noise Reduction” by Pragati Agrawal, Jayendra Singh Verma International Journal of Advance Research in Computer Science and Management Studies.
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Mary Sruthy Pious and M.Azath, "Study on Noise and Its Removal Techniques," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.46-50, 2015.

Wireless network technology is most frequently used network technology. A number of variants are available on the basis of traditional wireless networking such as WSN, WMN, MANET and others. In all the variants of wireless networks routing plays the essential role. Additionally the attackers are mainly targeting the routing strategies for performing the malicious activities. In order to secure the wireless ad hoc network a new kind of security system is proposed in this presented paper. The proposed security architecture provides the security against the malicious attackers namely black-hole, wormhole, grey-hole and DDOS attacks. Additionally able to improve the performance of network in normal conditions as well as under attack conditions. The given paper includes the proposed system design and the concepts that are help to support the proposed security infrastructure

ad hoc network, clustered routing, implementation, simulation, performance evaluation

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Basant Kuamr Verma and Binod Kumar2, "An Improved Weighted Clustering for Ad-hoc Network Security New," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.51-55, 2015.