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Now exponentially increased the use of information exchange and the multimedia applications. Security is the essential criterion in cryptography for the transmission of data or message in the secured form in all the applications. For preserving the privacy of data onto different applications encryption is a crucial technique. Advanced Encryption Standard algorithm is used for encrypting the image. The genetic algorithm is an important method of solving the optimization problem. This paper is based on the genetic algorithm used for making the encryption key stronger. Using parallel AES algorithm the experimental results show that it improves the fastness of the AES algorithm and meet the security demand.

AES, Genetic Algorithm, Cryptography, HD Image, Security

[1] Bhavin Patel, Neha Pandya, “Data Transfer Security Solution for Wireless Sensor Network”, International Journal of Computer Application Technology and Research Volume-2, Issue 01, 63-66, 213.
[2] Divyani UdayKumar Singh et al, “Separable Reversible Data Hiding in Image Using Advanced Encryption Standard with Fake Data Generation”, International Journal of Computer Science and Information Technologies, Volume 5(3), 214.
[3] Sourabh Singh, Anurag Jain, “An Enhanced Text to Image Encryption Technique using RGB Substitution and AES”, International Journal of Engineering Trends and Technology (IJETT) – Volume 04, 213.
[4] Salim M. Wadi, Nasharuddin Zainal, “Rapid Encryption Method Based on AES Algorithm for Grey Scale HD Image Encryption”, International Conference on Electrical Engineering and Informatics (ICEEI), 213.
[5] K. Brindha, G. Ramya, Rajpal Amit Jayantila, “Secured Data Transfer in Wireless Networks Using Hybrid Cryptography”, International Journal of Advanced Research in Computer Science and Software Engineering , Volume 03, 213.
[6] Adam Berent, “Advanced Encryption Standard by Example”.
[7] Kamali S. H., Shakerian R., Hedayati M., Rahmani M., “A New Modified Version of Advanced Encryption Standard Based Algorithm for Image Encryption”, International Conference on Electronics and Information Engineering, 210.
[8] NIST, Advanced Encryption Standard (AES), Federal Information Processing Standards Publication 197, 202.
[9] Ahmed Bashir Abugharsa, Abd Samad Bin Basari, Hasan Hamida Almangush, “A New Image Encryption Approach using The Integration of A Shifting Technique and The Aes Algorithm”, International Journal of Computer Applications (0975-8887), Volume 42- No.09, March 212.
[10] M. I .Youssef, A. E. Emam, S. M. Saafan, M. Abd Elghany, “Secured Image Encryption Scheme Using both Residue Number System and DNA Sequence”, International Journal of Emerging Science and Engineering (IJESE) ISSN: 2319-6378,Volume 01, Issue12, October 213.
[11] Aarti Soni, Suyash Agrawal, “Key Generation Using Genetic Algorithm for Image Encryption”, International Journal of Computer Science and Mobile Computing (IJCSMC,), Volume 02, Issue. 06, June 213.
[12] Dr. Poornima G. Naik, Girish R. Naik, “Asymmetric Key Encryption using Genetic Algorithm”, International Journal of Latest Trends in Engineering and Technology (IJLTET), ISSN: 2278-621X, Volume 03, Issue 03, January 214.
[13] Somalina Chowdhury, Sisir Kumar Das, Annapurna Das, “Application of Genetic Algorithm in Communication Network Security”, International Journal of Innovative Research in Computer and Communication Engineering, Volume 03, Issue 01, January 215.
[14] Vijaya Madhavi Lakshmi. Challa, Manasa. Bezawada, Anusha. Tenali, “An Image encryption Approach using Multilayer Crossover and Mutation Procedures”, International Journal of Advanced Research in Computer Science and Software Engineering, ISSN: 2277 128X, Volume 05, Issue 02, February 215.
[15] Sliman Arrag, Abdellatif Hamdoun, Abderrahim Tragha and Salah eddine Khamlich, “Replace AES Key Expansion Algorithm By Modified Genetic Algorithm”, Applied Mathematical Sciences, no. 144, 7161 – 7171, Volume 07, 213.
[16] Sonia Goyat, “Genetic Key Generation for Public Key Cryptography”, International Journal of Soft Computing and Engineering (IJSCE), ISSN: 2231-2307, Volume 02, Issue 03, July 212.
[17] Roza Afarin, Saeed Mozaffari, “Gray Level Image Encryption”, International Journal of Computer, Control, Quantum and Information Engineering, Volume 08, No:06, 214.
[18] Aarti Soni, Suyash Agrawal, “Using Genetic Algorithm for Symmetric key Generation in Image Encryption”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), ISSN: 2278 – 1323, Volume 01, Issue 10, December 212.
[19] Ankita Agarwal, “Secret Key Encryption Algorithm Using Genetic Algorithm”, International Journal of Advanced Research in Computer Science and Software Engineering, ISSN: 2277 128X, Volume 02, Issue 04, April 212.
[20] Suvarna Patil, Rahul Patil, “Faster Transfer of AES Encrypted Data over Network”, International Journal of Computer Science and Information Technologies (IJCSIT), 7674-7676, Volume 5(6), 2014.
[21] R. Kanimozhi, “A Novel Secure Multimedia message Hiding Algorithm behind the Image” SSRG International Journal of Computer Science and Engineering (SSRG-IJCSE), ISSN-2348-8387, Volume 01, Issue 08, October 214.

Suvarna Patil, "A Parallel AES of Faster Image Transfer Using Genetic Algorithm Key Generation," International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.283-287, 2015.

Text detection and recognition in camera captured images have been considered as very important problems in computer vision community. Text detection and recognition is a hot topic for researchers in the field of image processing. Text detection and extraction is performed in a four-step approach that consists of the pre-processing which include binarization and noise removal of an image, image segmentation using connected component analysis, feature extraction using variance generation and finally classification by choosing a threshold value of variance property. The goal of the project is to develop an Android-platform based text detection application that will be able to recognize the text captured by a mobile phone camera. Optical character recognition (OCR) methods recognize the characters and can be really useful when you have got a paper document you want in digital, editable form. Character which can be used to assist a wide variety of applications, such as image understanding, image indexing and search, geolocation or navigation, and human computer interaction. Optical character recognition is very important technique that is used for recognition of characters and it is very useful when we want our paper document in digital form and with the help of this technique we can edit our form.

Pre-processing, Segmentation, Optical Character Recognition (OCR)

[1] Hyung Il Koo, Member, IEEE, and Duck Hoon Kim, Member, IEEE,” Scene Text Detection via Connected Component Clustering and Nontext Filtering”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 22, NO. 6, JUNE 2013

[2] Rodrigo Minetto ,Nicolas Thome , Matthieu Cord , Neucimar J. Leite and Jorge Stolfi ,” SnooperText: A text detection system for automatic indexing of urban Scenes” Computer Vision and Image Understanding 2013
[3] Vandana Gupta and Kanchan Singh,”A Novel Approach for Detection and Extraction of Textual Information from Scanned Document Images and Scene Images”, International Journal of Advanced Research in Computer Science and Software Engineering Volume 3, Issue 10, October 2013
[4] Yi-Feng Pan, Xinwen Hou, and Cheng-Lin Liu, Senior Member, IEEE,” A Hybrid Approach to Detect and Localize Texts in Natural Scene Images”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 20, NO. 3, MARCH 2011
[5] Jung-Jin Lee∗, Pyoung-Hean Lee∗, Seong-Whan Lee∗, Alan Yuille∗† and Christof Koch,” AdaBoost for Text Detection in Natural Scene” International Conference on Document Analysis and Recognition
[6] Chucai Yi, Student Member, IEEE and YingLi Tian, Senior Member, IEEE, Aries Arditi,” Portable Camera-based Assistive Text and Product Label Reading from Hand-held Objects for Blind Persons” IEEE/ASME Transactions on Mechatronics
[7] Cong Yao, Xiang Bai, Member, IEEE, and Wenyu Liu, Member, IEEE,” A Unified Framework for Multi-Oriented Text Detection and Recognition”, IEEE TRANSACTIONS ON IMAGE PROCESSING 2014
[8] Shangxuan Tian, Shijian Lu, Bolan Su and Chew Lim Tan,” Scene Text Segmentation with Multi-level Maximally Stable Extremal Regions”
[9] Khyati Vaghela and Narendra Patel,” AUTOMATIC TEXT DETECTION USING MORPHOLOGICAL OPERATIONS AND INPAINTING”, International Journal of Innovative Research in Science, Engineering and Technology Vol. 2, Issue 5, May 2013
[10] Shalin A. Chopra, Amit A. Ghadge, Onkar A. Padwal, Karan S. Punjabi and Prof. Gandhali S. Gurjar,” Optical Character Recognition”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 1, January 2014
[11] Ravina Mithe, Supriya Indalkar and Nilam Divekar,” Optical Character Recognition”, International Journal of Recent Technology and Engineering (IJRTE) Volume-2, Issue-1, March 2013
[12] Prof. Amit Choksi, Nihar Desai, Ajay Chauhan, Vishal Revdiwala and Prof. Kaushal Patel,” Text Extraction from Natural Scene Images using Prewitt Edge Detection Method”, International Journal of Advanced Research in Computer Science and Software Engineering Volume 3, Issue 12, December 2013
[13] Fathima A Muhammadali,” Survey on Localizing Text in Scene Images” INTERNATIONAL JOURNA L FOR RES EARCH IN AP PL I ED SC IENC E AND ENGINEERING TECHNOLO GY (I JRAS ET) Vol. 2 Issue V, May 2014
[14] Rodrigo Minetto ,Nicolas Thome , Matthieu Cord , Neucimar J. Leite and Jorge Stolfi ,” SnooperText: A text detection system for automatic indexing of urban Scenes” Computer Vision and Image Understanding 2013
[15] Yi-Feng Pan, Xinwen Hou, and Cheng-Lin Liu, Senior Member, IEEE,” A Hybrid Approach to Detect and Localize Texts in Natural Scene Images”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 20, NO. 3, MARCH 2011
[16] k. Sruthi nivetha, m. Surya, saany varghese, 4s.r.vidhya, 5p. Venkateswara rao,” detection of scene text based on machine learning classifiers” Proceedings of 5th IRF International Conference, Chennai, 23rd March. 2014, ISBN: 978-93-82702-67-2

Aarti Arjun Andhale and Rishikesh Yeolekar, "Optimized Solution for Efficient Detection of Text from Images," International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.288-293, 2015.

Wireless Sensor Network contains tens to hundreds of nodes used to monitor the environment conditions. The data monitor by these nodes is sent to the main location. This data can be humidity, pressure, temperature, density, etc. Current applications of WSN are area monitoring, Health care monitoring, Forest fire detection, Water quality monitoring, battlefield monitoring, etc. Many of these applications can have sensitive data to transfer to the base station. This data can be forged by the adversary during its transfer. Hence security is one issue in Wireless Sensor Network. The recent research shows that the security can be improved using modified cryptographic algorithms in the sensor network. In this paper, we propose one simple cryptographic algorithm using ASCII values of original data. This algorithm can be used to encrypt the sensed data before its transfer.

WSN; cryptography; security; ASCII

[1] Hasan Tahir and Syed Asim Ali Shah, “Wireless Sensor Networks – A Security Perspective”, Multitopic conference, INMIC 2008, IEEE International, 2008, pp.189-193.
[2] Satyajeet R. Shinge, S. S. Sambare, “ Survey of different Clustering Algorithms used to Increase the Lifetime of Wireless Sensor Networks”, International Journal of Computer Applications, 2014, vol. 108, pp. 15-18.
[3] Hero Modares, Rosli Salleh and Amirhossein Moravejosharieh, “Overview of Security Issues in Wireless Sensor Networks”, Third International Conference on Computational Intelligence, Modelling & Simulation, IEEE 2011, pp.308-311.
[4] Shiva Murthy G, Robert John D’Souza and Golla Varaprasad “Digital Signature-Based Secure Node disjoint Multipath Routing Protocol for Wireless Sensor Networks”, IEEE Sensors Journal, VOL. 12, Issue-10, 2012, pp.2941-2949.
[5] G.Rohini, “Dynamic Router Selection and Encryption for Data Secure in Wireless Sensor Networks, Information Communication and Embedded Systems (ICICES), IEEE 2013 International Conference on , 2006, pp. 256 - 259.
[6] Sangeetha R. and Yuvaraju M. “Secure Energy-Aware Multipath Routing Protocol with Transmission Range Adjustment for Wireless Sensor Networks”, Computational Intelligence & Computing Research (ICCIC), 2012 IEEE International Conference on, 2012, pp. 1-4.
[7] Suraj Kumar Sharma and Sanjay Kumar Jena, “SCMRP: Secure Cluster Based Multipath Routing Protocol for Wireless Sensor Networks,” Sixth International Conference on Wireless Communication and Sensor Networks (WCSN), IEEE 2010, pp. 1-6.
[8] Guiyi Wei and Xueli Wang “Detecting Wormhole Attacks Using Probabilistic Routing and Redundancy Transmission,” International Conference on Multimedia Information Networking and Security, IEEE 2010, pp. 496-500.
[9] Aasma Abid, Mukhtar Hussain and Firdous Kausar, “Secure Routing andBroadcast Authentication in Heterogeneous Sensor Networks”, International Conference on Network-Based Information Systems, IEEE 2009, pp. 316-320.
[10] Al-Sakib Khan Pathan, Hyung-Woo Lee and Choong Seon Hong. “Security in Wireless Sensor Networks: Issues and Challenges”, 8th Conference on Advanced Communication Technology, IEEE 2006, pp. 1043-1048.
[11] Akanksha Mathur, “A Research paper: An ASCII value based data encryption algorithm and its comparison with other symmetric data encryption algorithms”, International Journal on Computer Science and Engineering (IJCSE), 2012, Vol. 4, pp. 1650-1657.

Satyajeet Shinge and S. S. Sambare, "New Cryptography Algorithm to Provide Security for Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.294-296, 2015.

Optical Character Recognition is the process of transforming printed or handwritten text in to a form in which computer can understand and manipulate. An important task of any Optical Character Recognition(OCR)system is segmentation. Characters, words and lines are separated from image text documents by segmentation. Depending on the segmentation algorithm which is being used can affect the accuracy of OCR system. Segmentation of handwritten Kannada script poses challenges due to writing styles, skewed lines, overlapping lines, inter and intra word gaps. In this paper we have proposed method for segmentation of handwritten Kannada documents based on bounding box and morphological operations, an average segmentation rate of 92% for lines is obtained.

Segmentation; Handwriting; Text lines; OCR; Bounding Box

[1] Priyadharshini N and Vijaya MS , "Genetic Programming for Document Segmentation and Region Classification using Discipulus Perceptron", (IJARAI) International Journal of Advanced Research in Artificial Intelligence ,Vol.2 ,No.2, 2013
[2] Rafael C. Gonzalez, Richard E. Woods and Steven L. Eddins ,"Digital Image Processing using MATLAB , Indian Edition,2009,pp. 348-361.
[3] Pulagam Soujanya, Vijaya Kumar Koppula , Kishore Gaddam and P. Sruthi , "Comparative Study of Text Line Segmentation Algorithms on Low Quality Documents", Special Issue of International Journal of Computer Science & Informatics (IJCSI) ,ISSN (PRINT): 22315292 , Vol .II , Issue1 , 2.
[4] Mamatha HR and Srikantamurthy K , "Morphological Operations and Projection Profiles based Segmentation of Handwritten Kannada Document", International Journal of Applied Information Systems (IJAIS)– ISSN:2249-0868 Foundation of Computer Science FCS,2012.
[5] Laurence Likforman- Sulem , Abderrazak Zahour and Bruno Taconet,"Text line segmentation of historical documents: a survey", IJDAR9:123–138 DOI 10.1007/s10032-006-0023-z . M , 2007.
[6] Munish Kumar, R.K. Sharma and M.K. Jindal , "Segmentation of Lines and Words in Handwritten Gurumukhi Script Documents", Indian Institute of Information Technology Allahabad, India.
[7] Vijaya Kumar Koppula and Atul Negi , "Using Fringe Maps for Text Line Segmentation in Printed or Handwritten Document Images", 2010 ,pp.8388.
[8] Mamatha H R and Srikantamurthy K ,"Skew Detection, Correction and Segmentation of Handwritten Kannada Document", International Journal of Advanced Science and Technology ,Vol. 48, November,2012.
[9] Nagabhushan P, Alireza Alaei and Umapada pal, "A Benchmark Kannada Handwritten Document Dataset and its Segmentation", International Conference on Document Analysis and Recognition,2011.
[10] Laurence Likforman- Sulem and Ana hid Hanimyan , "A Hough Based Algorithm for Extracting Text Lines in Handwritten Documents", Claudie Faure Ecole Nationale SupCrieure des T&communications, CNRS-URA 82046 rue Barrault,1995.
[11] M. Arivazhagan, H. Srinivasan and S. N. Srihari , "A Statistical Approach to Handwritten Line Segmentation", In Proceedings of SPIE Document Recognition and Retrieval XIV, SanJose , CA,February2007.
[12] A.V. Aho, J.E. Hopcroft and J.D. Ullman , "Data Structures and Algorithms", Addison- Wesley, 1983.
[13] A. Alaei, U. Pal and P. Nagabhushan, "A new scheme for unconstrained handwritten text-line segmentation" , Pattern Recognition,44(4), pp.917–928, 2011.
[14] V. N. Manjunath Aradhya and C Naveena ,"Text Line Segmentation of Unconstrained Handwritten Kannada Script", In the proceedings of ICCCS’11,pp.231-23, 2011.
[15] M.K Jindal, R. K. Sharma & G.S. Lehal , "Segmentation of Horizontally Overlapping Lines in Printed Indian Scripts", International Journal of Computational Intelligence Research, ISSN 0973-1873 Vol.3, No.4, pp. 277–286,2007.
[16] G. Louloudis, B. Gatos, I. Pratikakis & K.Halatsis, "A Block-Based Hough Transform Mapping for Text Line Detection in Handwritten Documents", Proceedings of the Tenth International Workshop on Frontiers in Handwriting Recognition, La Baule, Oct. 2006.
[17] B.M.Sagar, Dr.Shobha G and Dr. Ramakanth kumar P, "OCR for printed kannada text to Machine editable format using Database approach", 9th WSEAS International Conference on AUTOMATION and INFORMATION (ICAI'08) , Bucharest , Romania , June24-26 , 2008.

Chethana H T and Mamatha H R, "Text Line Extraction of Handwritten Kannada Documents Based on Bounding Box Technique," International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.297-303, 2015.

The total number of consumers that are using smartphones has increased in the past year and continue to grow. Mobile users are concerned about their sensitive data, but often do not understand the security risks involved while performing financial transactions through a mobile application. This research work mainly focused on application permissions and whether they are categorized as dangerous or normal as per the Android developer guidelines. These two types of permissions help to compute the possible danger of each mobile application. Results showed that risky permissions apps are harmful for user’s data preservation and classification of normal and dangerous apps clearly. Our work proposes a system which would help the users in analyzing and removing harmful apps and thereby protecting their security and privacy. This is achieved by analyzing the various permissions used by an application that it has requested during installation. The overall process of analyzing apps is done using weka data mining tool and classification techniques. The major objective of the proposed system is to detect and remove the potentially risky apps that are present in the user’s Android device.

Android;Classification; Dangerous Permission; Harmful Apps; Normal Permission.

[1] Veelasha Moonsamy, Jia Rong, Shaowu Liu, “Mining permission patterns for contrasting clean and malicious android applications”, www.elsevier.com/locate/fgcs,2014.
[2] Brett Ferris, Jay Stahle, and Ibrahim Baggili, “Quantifying the Danger of Mobile Banking Applications on the Android Platform”, 9th ANNUAL SYMPOSIUM ON INFORMATION ASSURANCE (ASIA’14), JUNE 3-4, 2014.
[3] Dimitris Geneiatakis ,Igor Nai Fovino , Ioannis Kounelis,Paquale Stirparo , “A Permission verification approach for android mobile applications”, www.sciencedirect.com,2015.
[4] Christoph Stach and Bernhard Mitschang ,”Design and Implementation of the Privacy Management Platform” 2014 IEEE 15th International Conference on Mobile Data Management
[5] Mohd Fauzi bin Othman,Thomas Moh Shan Yau,”Comparison of Different Classification Techniques Using WEKA for Breast Cancer”.Control and Instrumentation Department, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, Malaysia.
[6] Felt, A. P., Greenwood, K., & Wagner, D. (2011, June). “The effectiveness of application permissions”. In Proceedings of the 2nd USENIX conference on Web application development (pp. 7-7).
[7] Supriya S. Shinde, Prof. Rahul Patil,” Improving spam mail filtering using classification algorithms with discretization Filter” International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS), September-November, 2014, pp. 82-87.
[8] http://www.makeuseof.com/tag/the-seven-deadly-android-permissions-how-to-avoid-the-sin-of-slothful-preparedness/
[9] http://developer.android.com
[10] Ryan Farmer:” A Brief Guide to Android Security”(2012)

Supriya S. Shinde and Santosh S. Sambare, "Analysis of Android app Permissions for User’s Privacy Preservation," International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.304-308, 2015.

Round Robin is the most oldest and widely used scheduling algorithm but it has certain limitation because of static time quantum. Time quantum must be large so that context switching becomes reduces and it also effect the response time .So, in this paper we proposed a new algorithm called Optimized Performance Round Robin(OPRR) in which we focused on dynamic time quantum which give result as a very less context switching as well as average waiting time and average turnaround time. It also reduces the overhead of the CPU by adjusting the time quantum according to the highest burst time of the processes in the ready queue.

Operating System, Round Robin, Best Performance Round Robin, Turnaround time, Waiting time, Context Switch

[1] “Silberschatz, A., P.B. Galvin and G. Gagne, 2008”Operating Systems Concepts. 7th Edn., John Wiley and Sons, USA, ISBN: 13: 978-0471694663 , pp: 944.
[2] Pallab banerjee, probal banerjee, shweta sonali dhal, “ Comparative Performance Analysis of Average Max Round Robin Scheduling Algorithm (AMRR) using Dynamic Time Quantum with Round Robin Scheduling Algorithm using static Time Quantum ”,IJITEE, ISSN: 2278-3075, Volume-1, Issue-3, August 2012.
[3] “Tanebaun, A.S., 2008” Modern Operating Systems. 3rd Edn., Prentice Hall, ISBN: 13:9780136006633, pp: 1104.
[4] Pallab banerjee, probal banerjee, shweta sonali dhal, “Performance Evaluation of a New Proposed Average Mid Max Round Robin (AMMRR) Scheduling Algorithm with Round Robin Scheduling Algorithm”,IJARCSSE,ISSN:2277-128X, Volume-2, Issue-8, August 2012.
[5] Pallab banerjee, probal banerjee, shweta sonali dhal,“Comparative Performance Analysis of Even Odd Round Robin Scheduling Algorithm (EORR) using Dynamic Time Quantum with Round Robin Scheduling Algorithm using static Time Quantum” IJARCSSE,ISSN: 2277-128X,Volume-2, Issue-9, August 2012.
[6] Pallab banerjee, probal banerjee, shweta sonali dhal,“Improved High Performance Round Robin Scheduling Algorithm(HPRR)using Dynamic Time Quantum” International Journal of Computer Information System,ISSN: 2277-128X,Volume-5, No-3, 2012.
[7] Sarojhiranwal and D.r. K.C.Roy“Adaptive Round Robin Scheduling using Shortest Burst Approach Based on Smart Time Slice”.volume 2,No. 2,July-Dec 2011,pp. 319-32.
[8] Pallab banerjee, probal banerjee, shweta sonali dhal, “Comparative Performance Analysis of Mid Average Round Robin Scheduling Algorithm (MARR) using Dynamic TimeQuantum with Round Robin Scheduling Algorithm having static Time Quantum”,IJECSE,ISSN: 2277- 1956, Volume-1,Issue-4, August 2012.
[9] H. S. Behera, R. Mohanty, and D. Nayak, “A New Proposed Dynamic Quantum with Re-Adjusted Round Robin Scheduling Algorithm and Its Performance Analysis,” vol. 5,no. 5, pp. 10-15, August 2010.
[10] Sanjay Kumar Panda and Saurav Kumar Bhoi, “An Effective Round Robin Algorithm using Min-Max Dispersion Measure” ISSN : 0975-3397 ,Vol. 4 No. 01, January 2012.
[11] Tarek Helmy, Abdelkader Dekdouk “ Burst Round Robin: As a Proportional-Share Scheduling Algorithm”, IEEE Proceedings of the fourth IEEE-GCC Conference on towards Techno-Industrial Innovations, pp. 424-428, 11-14 November,2007.
[12] Yaashuwanth .C & R. Ramesh “Inteligent time slice for round robin in real time operating system”, IJRRAS 2 (2), February 2010.
[13] R. J. Matarneh, “ Seif-Adjustment Time Quantum in Round Robin Algorithm Depending on Burst Time of the Now Running Proceses ”, American Journal of Applied Sciences 6 (10),pp. 1831-1837, 2009.
[14] H. S. Behera, Rakesh Mohanty, Sabyasachi Sahu and Sourav Kumar Bhoi. “Comparative performance analysis of multi-dynamic time quantum round robin (mdtqrr) algorithm with arrival time”, ISSN : 0976-5166, Vol. 2, No. 2,Apr-May2011,pp.262-271.
[15]Pallab banerjee ,Prof Dr L.N.Padhy. “Comparative analysis of Maximum performance round robin(MPRR) by Dynamic Time Quantum with static time quantum”,ISSN:2277-128X,pg.372-377Vol-4,Issue-11,Nov-2014
[16] Pallab banerjee,Talat Zabin,Shweta Kumari,Pushpa Kumari “Comparative performance analysis of best performance of round robin scheduling algorithm(BPRR) using Dynamic Time quantum with priority based round robin(PBRR) CPU Scheduling algorithm in Real Time System”.ISSN:2277-1956,Vol-4,Number-2, pg. 151-159,May 2015.
[17] R.Nallakumar ,Dr.N.Sengottaiyan ,S.Nithya “A Servey of Task Scheduling Methods in Cloud Computing”IJCSE,ISSN:2347-2693,Volume-2,Issue-10 ,2014
[18] Abbas Noon,Ali Kalakech,Seifedine Kadry“A New Round Robin Scheduling Algorithm for Operating Systms: Dynamic Quantum Using Mean Average”IJCSI,ISSN:1694-0814,Vol-8,Issue-3,No-1,May-2011

Pallab Banerjee and Anita Kumari and Puja Jha, "Comparative Performance Analysis of Optimized Performance Round Robin Scheduling Alg.(OPRR) with AN Based Round Robin Scheduling Alg. using Dynamic Time Quantum in Real Time System with Arrival Time," International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.309-316, 2015.

A Wireless Sensor Network (WSN) consists of a group of sensors which are geographically distributed and are capable of computing, communicating and sensing. One of the major challenges of WSN is to preserve energy. MAC protocols which operate at the data link layer have full control over the wireless radio; their design can contribute significantly to the overall energy requirements of a sensor node. The goal of the sensor MAC (S-MAC) protocol (Ye et al. 2002) is to reduce unnecessary energy consumption, while providing good scalability and collision avoidance. S-MAC adopts a duty-cycle approach, that is, nodes periodically transition between a listen state and a sleep state. The sleep deprivation torture attack also known as denial-of-sleep attack is a powerful attack in which an attacker prevents a sensor from going into sleep mode. It makes a device inoperable by draining the battery more quickly than it would be under normal usage. This paper provides a comparative study of 802.11 and SMAC protocol under this attack. A simulation has been carried out in NS2 and analysis shows that SMAC performs similar to 802.11 under such attack.

Wireless Sensor Networks ( WSN’S) , Media Access Control(MAC) , Sensor MAC (SMAC)

[1] Chen C., Hui L., Pei Q., Ning L., Qingquan P, “An Effective Scheme for Defending Denial-of-Sleep Attack in Wireless Sensor Networks”, Proceedings of the 2009 Fifth International Conference on Information Assurance and Security, Vol. 02, IEEE CS, May 2009.
[2] David R. Raymond, Randy C. Marchany, Michael I. Brownfield, and Scott F. Midkiff, “Effect of Denial of sleep attacks on wireless sensor network MAC protocols” published by IEEE , June 2008.
[3] Dharam Vir, Dr.S.K.Agarwal, Dr.S.A.Imam:”WSN Performance Evauation of power consumption”,International Journal of Scientific and Research Publications, Volume 3, Issue 12, December 2013.
[4] Genita Gautam, Biswaraj Sen, “Survey on different types of Security threats in wireless Sen sor Networks”, International Journal of Computer Science and Information Technologies, Vol. 6 (1) , 2015, pp-770-774.
[5] Genita Gautam, Biswaraj Sen, “Design and simulation of Wireless Sensor networks in NS2”, International Journal of Computer Applications, Vol. 113 (16) , 2015, pp-14-16.
[6] Jianliang Zheng and Myung J. Lee,”A Comprehensive Performance Study of IEEE 802.15.4”, IEEE, Aug. 1999.
[7] Michael Brownfield, Yatharth Gupta, Mem and Nathaniel Davis IV :” Wireless Sensor Network Denial of sleep attack” , IEEE 2005.
[8] M. Riduan Ahmad, Eryk Dutkiewicz and Xiaojing Huang,” A Survey of Low Duty Cycle MAC Protocols in Wireless Sensor Networks”, www.intechopen.com, 07, February, 2011.
[9] P. Lin, C. Qiao, and X. Wang, “Medium access control with a dynamic duty cycle for sensor networks”, IEEE Wireless Communications and Networking Conference, Volume: 3,March 2004.
[10] Rajesh Yadav, Shirshu Varma, N. Malaviya,” A SURVEY OF MAC PROTOCOLS FOR WIRELESS SENSOR NETWORKS”, UbiCC Journal, Volume 4, Number 3, August 2009..
[11] R. Rugin, G. Mazzini, “A simple and efficient MAC-routing integrated algorithm for sensor network”, IEEE International Conference on Communications, Volume: 6, June 2006.
[12] S. Cui, R. Madan, A. J. Goldsmith, and S. Lall, “Joint Routing, MAC, and Link Layer optimization in Sensor Networks with Energy Constraints”, to appear at ICC'05, Korea, May, 2005.
[13] Security Model Using NS2 “International Journal of Latest Trends in Engineering and Technology (IJLTET), Vol. 4 Issue 1 May 2014.
[14] Shweta Agarwal, Varsha Jain, Kuldeep Goswami,” ENERGY EFFICIENT MAC PROTOCOLS FOR WIRELESS SENSOR NETWORK”, www.intechopen.com, 07, February, 2011.
[15] Shamneesh Sharma, Dinesh Kumar and Keshav Kishore,”Wireless Sensor Networks- A Review on Topologies and Node Architecture”, International Journal of Computer Sciences and Engineering”, Vol 1 Issue 2, Oct 2013.
[16] Tapalina Bhattasali, Rituparna Chaki, Sugata sanyal: “Sleep deprivation Attack Detection in Wireless Sensor network”, International Journal of Computer Applications, February 2012.
[17] Teerawat Issariyakul, Ekram Hossain,“Introduction to Network Simulator 2”, Springer US,2008, pp 1-18.
[18] T.V. Dam and K. Langendoen, “An Adaptive Energy-Efficient MAC Protocol for Wireless Sensor Networks”, The First ACM Conference on Embedded Networked Sensor Systems (Sensys‘03), Los Angeles, CA, USA, November, 2003.
[19] Waltenegus Dargie and Christian Poellabauer,” FUNDAMENTALS OF WIRELESS SENSOR NETWORKS”, 2nd Edition ,John Wiley & Sons, Ltd, pp.25-30.
[20] Vidya M, Reshmi S, “Denial-of-service Attacks in Wireless Sensor Ndetworks”, International Journal of Advanced Computer Theory and Engineering, Volume -3, Issue -2, 2014.

Genita Gautam and Biswaraj Sen, "Performance Analysis of 802.11 and SMAC Protocol under Sleep Deprivation Torture Attack in Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.317-322, 2015.

Big data analysis includes many theories and methods for prediction system. Statistical methods such as Person’s correlation, Regression analysis and Rough Set Theory etc are being used for predicting facts. Also theory like collaboration filtering uses word’s filtering to predict and provide recommendations. We have studied all these methods and selected most appropriate method for student’s psychology prediction. In our proposed work we have used Rough sets to extract the rules for prediction of student’s psychology. Rough Set is a comparatively recent method that has been effective in various fields such as medical, geological and other fields where intelligent decision making is required. Our experiments with rough sets in predicting student’s psychology produced attractive results.

Psychology; Prediction; RST

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( 2012 ) 980 – 984.
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[17] Umang Gupta, Niladri Chatterjee, “Personality Traits Identification using Rough sets based Machine Learning”, IEEE 2013 International Symposium on Computational and Business Intelligence.
[18] Niharika Upadhyay, Pragati Jain, “Applying Rough Set Theory In Feedback Analysis”, International Journal of Integrative Sciences, Innovation and Technology.
[19] Hameed Al-Qaheri, Aboul Ella Hassanien, Ajith Abraham, “A Generic Scheme for Generating Prediction Rules Using Rough Set”.
[20] Zdzisław Pawlak, “Rough Sets”, Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, ul. Bałtycka 5, 44 100 Gliwice, Poland.

Bhakti Ratnaparkhi, Lokesh Katore, J. S. Umale and Niharika Upadhyay, "Student Psychology Prediction and Recommendation System Using Rough Set Theory," International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.323-327, 2015.

With the advancement on the web, internet interactive media is rising as an administration. To provide rich media services, process, multimedia computing has emerged as a noteworthy technology to produce, edit and examine media contents, such as images, graphics, video, audio and so on. For media application and administrations over the internet and portable remote system, there are solid request for distributed computed computing on account of noteworthy measure of calculation needed for serving a huge number of internet or versatile client in the meantime. This paper audits the brief study on sight and sound distributed computing perspectives and described some security issues in cloud computing, including data integrity, data confidentiality, access control, data manipulation in the encrypted data domain etc. along with security algorithms.

Cloud Computing, Multimedia, Cryptography, Internet

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[3] Deyan Chen and Hong Zhao “Data Security and Privacy Protection Issues in Cloud Computing,” 2012 IEEE International Conference on Computer Science and Electronics Engineering.
[4] Dr.A.Padmapriya, P .Subhasri, “Cloud Computing: Security Challenges & Encryption Practices,” Volume 3, Issue 3, March 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering.
[5] Gartner: Seven cloud-computing security risks InfoWorld 2008-07-02.
[6] Gurpreet Kaur and Manish Mahajan, “Analyzing data Security for cloud computing using cryptographic algorithms,” ijera, vol.3, no.5, Sep-Oct 2013.
[7]http://www.mytestbox.com/miscellaneous/cloud-computing-grid-computing-utility- computing-list-top-providers/
[8] K.S.Suresh “ Security Issues and Security Algorithms in Cloud Computing,” International Journal of Advanced Research in Computer Science and Software Engineering.
[9]Leena Khanna “ Cloud Computing: Security Issues And Description Of Encryption Based Algorithms To Overcome Them,” Volume 3, Issue 3, March 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering.
[10] Maha TEBAA, Saïd EL HAJJI and Abdellatif EL GHAZI, “Homomorphic Encryption Applied to the Cloud Computing Security,” Proceedings of the World Congress on Engineering, Vol.1, WCE 2012, July 4 (2012), London, U.K .
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[13] Randeep Kaur and Supriya Kinger, “Analysis of security algorithms in cloud computing,” ijaiem, vol.3, no.3, March 2014.
[14] Uma Somani, Kanika Lakhani, and Manish Mundra “Implementing Digital Signature with RSA Encryption Algorithm to Enhance the Data Security of Cloud in Cloud Computing,” 2010 IEEE 1st International Conference on Parallel, Distributed and Grid Computing (PDGC - 2010).
[15] Vahid Ashktorab2, Seyed Reza Taghizadeh1 “Security Threats and Countermeasures in Cloud Computing,” International Journal of Application or Innovation in Engineering & Management (IJAIEM).
[16] Volker Fusenig and Ayush Sharma “Security Architecture for Cloud Networking,” 2012 IEEE International Conference on Computing, Networking and Communications, Cloud Computing and Networking Symposium.

Er. Ramandeep Kaur, Er.Gurjot Kaur, Er.Reena Sharma, Er.Varinder kaur, "Comprehensive Review on Encryption Algorithms for Multimedia Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.328-331, 2015.

There is a need to improve the service reliability, security, availability, privacy and regulation complaint requirements in public cloud along with private cloud. By using hybrid cloud environment we can improve those concerns. If the workload is managed properly in the cloud environment, availability will be automatically increased. A better Load Balancing algorithm should be a fault tolerant one. Good Load Balance technique will improve the performance of the entire Cloud. However, there is no common method that can adapt to all possible different situations. However, all the existing Load Balancing algorithms are applied to the entire Cloud Environment. This creates an overhead in maintaining all the status of the nodes. In the hybrid cloud, the Intelligent workload factoring (IWF) is designed for proactive workload management. The intelligent workload factoring has a three components workload profiling, based load threshold and fast factoring. Based on the internet video workload management streaming, user can divide the workload management as two zones. Base workload as one zone, Flash crowd workload as another zone. The proactive workload management factoring is a fast frequent data item detection algorithm as factorized the data volume and also the data content. This application architecture is increased the Quality of Services (QoS). The workload factoring is mainly concentrate with the smooth workload at all time in data center and the data volume along with the data content.From the real trace driven simulation analysis and evaluation on hybrid cloud of local computing platform the user have a reliable workload prediction and achieve resource efficiency.

Time Optimization, Cloud Computing, Cloud System

[1] “Amazon web services,” http://aws.amazon.com/.
[2] “Google app engine,” http://code.google.com/appengine/
[3] Hui Zhang, Guofei Jiang, Kenji Yoshihira, and Haifeng Chen(2014), “Proactive Workload Management in Hybrid Cloud Computing”, IEEE Transactions on Network and Service Management, VOL. 11, NO. 1, MARCH 2014
[4] Gaochao Xu, Junjie Pang & Xiaodong Fu(2013), “A Load balancing Model Based on Cloud Partitioning for Public Cloud ”, IEEE Transactions on Cloud Computing, Vol:18, No:1, pp:34-39.
[5] “Youtube,” http://www.youtube.com.
[6] “Gigaspaces,” http://www.gigaspaces.com.
[7] “Yahoo! video,” http://video.yahoo.com.

K.Karthika, K.Kanakambal ,R.Balasubramaniam, "Time Optimization Workload Management in Hybrid Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.332-334, 2015.