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I There are many methods for enhancement of degraded document images. In the process of improving degraded document images segmentation is one of the difficult task due to background and foreground variation such as uneven illumination, document smear such as smudging of text, seeping of ink to the other side of paper, degradation of paper ink due to aging etc. A number of methodologies have been proposed by several researchers on image segmentation using binarization technique. In document analysis, binarization is easily affected by noise, surrounding illumination, gray-level distribution, local shading effects, weak contrast, and the presence of dense non-text components such as photographs. So binarization can become a challenging job under varying illumination and noise. This survey aims to evaluate the principles of image binarization techniques. The main objective of this paper is to evaluate the different image binarization techniques to find the gaps in existing techniques.

Degraded document image binarization, Global thresholding, Local thresholding, Dynamic thresholding, Adaptive binarization, Hybrid binarization

Otsu, N.:”A threshold selection method from gray-level histogram”.IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
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framework for adaptive binarization of degraded document images”. Pattern Recogn, 43(6), 2186–2198 (2010)
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[6] Ntirogiannis, K., Gatos, B., Pratikakis,” A combined
approach for the binarization of Hand written document images”. Pattern Recogn.Lett.35,3–15(2014).(ISSN 0167-8655, http://dx.doi.org/10.1016/j.patrec.2012.09.026)
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document image based on feature space partitioning and classification”. Int. J. Doc.Anal.Recogn. (IJDAR) 15(1), 57–69 (2012)
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adaptive local binarization method for document images based on a novel thresholding method and dynamic windows”. Pattern Recogn.Lett.32 (14), 1805–1813 (2011)
[9] B. Su, S. Lu, and C. L. Tan, “Binarization of historical handwritten document images using local maximum and minimum filter”, International Workshop on Document Analysis Systems, pp. 303–314, June 2010.
[10] Jagroop Kaur1, Dr.Rajiv Mahajan,” A Review of
Degraded Document Image Binarization Techniques”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 5, May 2014, ISSN (Print) : 2319-5940 ISSN (Online) : 2278-1021
[11] N. Chaki et al., “A Comprehensive Survey on Image
Binarization Techniques”, Exploring Image Binarization Techniques, Studies in Computational Intelligence 560, © Springer India 2014

Remya.A.R and M.Azath, "Survey on Image Binarization Techniques for Degraded Document Images," International Journal of Computer Sciences and Engineering, Vol.2, Issue.12, pp.90-93, 2014.

Cloud computing refers to both the application delivers services over the internet and the hardware and system software in the data centers that provide those services. Cloud is attracted by many users because of its security and storage features. The main attack faced by cloud is Distributed Denial of Services (DDOS), in which multiple hosts attack made simultaneously in all network. Security is an important issue in the cloud computing, but the problem is how effectively mitigating intruders and chooses correct counter measures. To counterattack insecure attacks from the virtual machines installed in the cloud proposing vulnerability detection, measurement, along with countermeasure mechanism known as NICE(Network Intrusion detection and Countermeasure Evaluation). In this survey aims to analyze intrusion detection and effective countermeasure mechanisms for achieving security on the virtual machines installed in cloud.

Cloud Computing, Cloud Security, DDOS Attacks, Intrusion Detection

[1]http://en.wikipedia.org/wiki/Cloud_computing
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[10]G. Gu, J. Zhang, and W. Lee, “BotSniffer: Detecting Botnet Command and Control Channels in Network Traffic,” Proc. 15th Ann. Network and Distributed System Security Symp. (NDSS’08), Feb.2008.
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[13] B. Morin, L. Me´, H. Debar, M. Ducasse´, M2D2: a formal data model for IDS alert correlation, in: Proceedings of the 5th International Symposium on Recent Advances in Intrusion Detection (RAID’02), 2002, pp. 115–137.
[14]L. Wang, A. Liu, and S. Jajodia, “Using Attack Graphs for Correlating, Hypothesizing, and Predicting Intrusion Alerts,” Computer Comm., vol. 29, no. 15, pp. 2917-2933, Sept. 2006.
[15]X. Ou, S. Govindavajhala, and A.W. Appel, “MulVAL: A Logic- Based Network Security Analyzer,” Proc. 14th USENIX Security Symp., pp. 113-128, 2005.
[16]Chun-Jen Jung, Pankaj Khatkar, Tianyi Xing, Jeongkeun Lee, Dijiang Huang,NICE-Network Intrusion Detection and Countermeasure Selection in Virtual Network System, IEEE Transactions on Dependable and Secure Computing, Issue Vol 10 No 4 2013.

Ankitha.M.M and M. Azath, "Survey on Secure Intrusion Detection and Countermeasures in Cloud," International Journal of Computer Sciences and Engineering, Vol.2, Issue.12, pp.94-97, 2014.

Rain act as a noise that affect videos and images. Mostly, noises are observed due to weather conditions that will affect audio correspondence, object recognition, motion segmentation, and object tracking. While editing movie or any security surveillance if any problem is found due to rain constraints the object cannot be tracked well. Rain drops are spatially distributed which falls at very high velocities. Hence, it leads to produce sharp intensity variations in an image where each drop refracts and reflects the environment. Such group of falling rain drops generates a complex time varying signal in both images as well as in videos. Random rain pixel detection and noise filtrations lead to achieve the high performance in dynamic videos having various vision-based applications. So, by extracting the key frames from the large size video we can compress it to smaller one which helps to retrieve dynamic frame through indexing and labeling. After the key frame selection rain pixel recovery algorithm will provide the compressed video with the rain pixel recovery from highly dynamic scenes.

Rain Detection, Properties, Background Subtraction, Spatial-Temporal, Rain Removal, Static Weather Condition, Dynamic Weather Condition, Frame Indexing and Labeling

[1]Jie Chen and Lap-Pui Chau, ‘‘A Rain Pixel Recovery Algorithm for Videos with Highly Dynamic Scenes”, IEEE Image Processing, vol. 23, no. 3, March2014.
[2] Kshitiz Garg and Shree K. Nayar, “Vision and Rain”, International Journal of Computer Vision 75(1), 3–27, February 2007
[3] K. Garg and S.K. Nayar, "Detection and removal of rain from videos," in IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 528-535, 2004.
[4] X. Zhang, H. Li, Y. Qi, W.K. Leow and T.K. Ng, "Rain Removal in Video by Combining Temporal and Chromatic Properties," in IEEE Int. Conf. Multimedia and Expo, pp. 461-464, 2006.
[5] Peter C. Barnum ,Srinivasa Narasimhan ,Takeo Kanade “Analysis of Rain and Snow in Frequency Space”, Springer International Journal of Computer Vision 86,256-274, 2010.
[6] Ming Zhou, Zhichao Zhu, Rong, Deng, Shuai Fang,Rain, “Detection and Removal of Sequential Images”, IEEE Chinese Control and Decision Conference (CCDC), pp. 615-618, 2011.
[7] Abhishek Kumar Tripathi, Sudipta Mukhopadhyay, “A Probabilistic Approach for Detection and Removal of Rain from Videos”, IETE Journal of Research, vol. 57, pp. 82-91, 2011.
[8] Stuart, A., and Ord, J.K., Kendall’s Advanced Theory of Statistics, 5th edition, 1987, vol 1, section 10.15.
[9]http://mathworld.wolfram.com/PearsonsSkewnessCoefficients.html
[10] Jeremie Bossu, Nicolas Hautiere, Jean-Philippe Tarel, “Rain or Snow Detection in Image Sequences Through Use of a Histogram of Orientation of Streaks”, Springer International Journal of Computer Vision, January 2011.

Punam P. Kansare and Ashwini Meshram, "A Review on Frame Indexing and Labeling in Dynamic Rainy Video Scenes with Rain Pixel Recovery," International Journal of Computer Sciences and Engineering, Vol.2, Issue.12, pp.98-100, 2014.

In this paper we have implement offline notification system through Bulk sms for android application. Short message service is a message technology (only for mobile) that enables the sending and receiving of messages between any mobile phones. Bulk Messaging is the dissemination of large numbers of SMS messages for delivery to any mobile phone terminals. It is used by media companies, enterprises, banks, colleges and consumer brands for a variety of purposes including entertainment, enterprise and mobile marketing. for also purpose of notification system. Bulk messaging is commonly used for alerts, reminders, marketing, and notification but also for information and communication between both staff and customers. Bulk messaging lets you deliver SMS messages to mobile handsets almost anywhere in the world.

GSM,SMS,ETSI,GCM

[1] Veena K.Katankar, Dr.V.M.Thakare, “Short Message Service using SMS Gateway”, International Journal on Computer Science and Engineering, Vol. 02, No. 04, 2010, 1487-1491.
[2] Rahul D. Sadafule, “Mobile App Development for the Indian Market “ ,IEEE software ,MAY/JUNE 2014
[3] Zerfos, P. ; Samanta, V. ; Wong, S.H.Y.,” Analysis of the Reliability of a Nationwide Short Message Service”,IEEE
For Book
[4] Zachary Cleaver,Software Architecture, Push Notification Services: Google and Apple.Chapter 4: page no 1-3.
[5] William Enck, Patrick Traynor, Patrick McDaniel, and Thomas La Porta,” Exploiting Open Functionality in SMS-Capable Cellular Networks”,
[6] Telecom Intelligence,”Assuring the feature of SMS”, White paper, Feb-2013,Page 1-8
For web link
[7] http://www.developershome.com/sms/smsIntro.asp#1.1.What%20is%20SMS%20%28Short%20Message%20Service%29_|outline

Harshad Kale, Ganesh Rane, Sagar Shende and Swapnil Shinde, "Short Message Service: Offline Notification System through Bulk SMS for Android Application," International Journal of Computer Sciences and Engineering, Vol.2, Issue.12, pp.101-103, 2014.

Construction high Excellence Cloud requests become an immediately compulsory investigation problematic in Cloud calculating technology. Non-functional presentation of cloud facilities is typically defined by Quality-of-Facility (Qos). To acquire Qos values, practical practice of facilities candidates are typically required. At this time, there is no outline that cans little operators to approximation cloud facilities and vigorous they founded on their Qos values. This paper intends to outline and a maneuver that measures the excellence and positions cloud facilities for the users. Cloud vigorous outline by taking the benefit of past facility practice experiences of extra users. So it can evade the time overwhelming and luxurious real life facility invocation. This practice determines the Qos location straight using the two modified Qos location forecast way namely, CloudRank1 and CloudRank2. These events make unquestionable that the lively facilities are properly ranked. The core willpower is location forecast of client lateral Qos properties, which likely have unlike values for dislike operators of the same Cloud service. It approximations all the applicant facilities at the user-lateral and vigorous the facilities founded on the experiential Qos values.

Cloud Services, Cloud Rank, Quality-Of-Service, And Location Prediction

[1] Yeonjoon Chung: Electron. & Telecommun. Res. Inst., Daejeon; Min Ho Park; Eui Hyun Paik “A QoS negotiable service framework for multimedia services connected through subscriber networks “Published in: Consumer Electronics, 2006. ISCE '06. 2006 IEEE Tenth International Symposium on Date of Conference: 0-0 0 Page(s): 1 – 4.
[2] Ito ,Y.; Dept. of Computer. Sci. & Eng., Nagoya Inst. of Technol., Nagoya; Tasaka, S. “QRPp1-1: User-Level QoS Assessment of a Multipoint-to-Multipoint TV Conferencing Application over IP Networks” Published in: Global Telecommunications Conference, 2006. GLOBECOM '06. IEEE Date of Conference: Nov. 27 2006-Dec. 1 2006 Page(s): 1 – 6.
[3] Bejerano, Y.; Lucent Technol. Bell Labs., Murray Hill, NJ, USA ; Breitbart, Y. ; Orda, A. ; Rajeev Rastogi more authors “Algorithms for computing QoS paths with restoration “Published in: INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications. IEEE Societies (Volume: 2) Date of Conference: March 30 2003-April 3 2003 Page(s): 1435 - 1445 vol.2.
[4] Matsui,Y.; Nippon Telegraph & Telephone Corp., Japan ; Kihara, S. ; Mitsuzawa, A. ; Moriai, S. more authors “An extensible object model for QoS specification in adaptive QoS systems “Published in: Object-Oriented Real-Time Distributed Computing, 1999. (ISORC '99) Proceedings. 2nd IEEE International Symposium on Date of Conference: 1999 Page(s): 129 – 132.
[5] Xiao Liu ; Fac. of Inf. & Commun. Technol., Swinburne Univ. of Technol. Hawthorn, Melbourne, VIC, Australia; Yun Yang ; Dong Yuan ; Gaofeng Zhang more authors “A Generic QoS Framework for Cloud Workflow Systems “ Published in: Dependable, Autonomic and Secure Computing (DASC), 2011 IEEE Ninth International Conference on Date of Conference: 12-14 Dec. 2011 Page(s): 713 – 720.
[6] Benharref, A. ; Eng. & Comput. Sci., Abu Dhabi Univ., Abu Dhabi, United Arab Emirates ; Serhani, M.A. ; Bouktif, S. ; Bentahar, J. “A managerial community of Web Services for management of communities of Web Services “Published in: New Technologies of Distributed Systems (NOTERE), 2010 10th Annual International Conference on Date of Conference: May 31 2010-June 2 2010 Page(s): 97 – 104.
[7] Mokarizadeh, S. ; R. Inst. of Technol., Stockholm, Sweden ; Kungas, P. ; Matskin, M. “Utilizing Web Services Networks for Web Service Innovation” Published in: Web Services (ICWS), 2014 IEEE International Conference on Date of Conference: June 27 2014-July 2 2014 Page(s): 646 – 653.
[9] Meng Li ; Sch. of Electron. Eng. & Comput. Sci., Peking Univ., Beijing, China ; Junfeng Zhao ; Lijie Wang ; Sibo Cai more authors “CoWS: An Internet-Enriched and Quality-Aware Web Services Search Engine “Published in: Web Services (ICWS), 2011 IEEE International Conference on Date of Conference: 4-9 July 2011 Page(s): 419 – 427.
[10] Noh-Sam Park ; Network Technol. Lab., Electron. & Telecommun. Res. Inst., Daejeon, South Korea ; Gil-haeng Lee “Agent-based Web services middleware” Published in: Global Telecommunications Conference, 2003. GLOBECOM '03. IEEE (Volume:6 ) Date of Conference: 1-5 Dec. 2003 Page(s): 3186 - 3190 vol.6.
[11] Elgazzar, K. ; Sch. of Comput., Queen''s Univ., Kingston, ON, Canada ; Hassan, A.E. ; Martin, P. “Clustering WSDL Documents to Bootstrap the Discovery of Web Services “Published in: Web Services (ICWS), 2010 IEEE International Conference on Date of Conference: 5-10 July 2010 Page(s): 147 – 154.
[12] Siegel, J. ; Carnegie Mellon Univ. Silicon Valley, Mountain View, CA, USA ; Perdue, J. “Cloud Services Measures for Global Use: The Service Measurement Index (SMI) “Published in: SRII Global Conference (SRII), 2012 Annual Date of Conference: 24-27 July 2012 Page(s): 411 – 415.
[13] Breiter, G. ; IBM Software Group, Boeblingen, Germany ; Naik, V.K.” A Framework for Controlling and Managing Hybrid Cloud Service Integration “Published in: Cloud Engineering (IC2E), 2013 IEEE International Conference on Date of Conference: 25-27 March 2013 Page(s): 217 – 224.
[14] Nagireddi, V.S.K. ; Inst. for Dev. & Res. in Banking Technol., Hyderabad, India ; Mishra, S. “An ontology based cloud service generic search engine” Published in: Computer Science & Education (ICCSE), 2013 8th International Conference on Date of Conference: 26-28 April 2013 Page(s): 335 – 340.
[15] Ruozhou Yu ; Sch. of Comput. Sci., Beijing Univ. of Posts & Telecom, Beijing, China ; Xudong Yang ; Jun Huang ; Qiang Duan “QoS-aware service selection in virtualization-based Cloud computing “Published in: Network Operations and Management Symposium (APNOMS), 2012 14th Asia-Pacific Date of Conference: 25-27 Sept. 2012 Page(s): 1 – 8.
[16] Zheng Li ; Sch. of CS, NICTA & ANU, Canberra, ACT, Australia ; OBrien, L. ; Cai, R. ; He Zhang “Building an Expert System for Evaluation of Commercial Cloud Services “Published in: Cloud and Service Computing (CSC), 2012 International Conference on Date of Conference: 22-24 Nov. 2012 Page(s): 168 – 175.

P.Thamizharasan and M.Saravanan, "Attain Grade Exactitude Using Web Ranking Framework for Web Services," International Journal of Computer Sciences and Engineering, Vol.2, Issue.12, pp.104-108, 2014.

Digital images have produced a large amount of image data in various areas, such as entertainment, fashion design, education, graphics, medicine, industry, etc. However in order to effectively retrieve the desired images from a large image database, a content-based image retrieval (CBIR) system has become an important research issue to be developed. As no. of the proposed approaches emphasize on finding the best representation for different image features. Because of that a user-oriented mechanism for CBIR method based on an interactive genetic algorithm (IGA) is developed. Color, texture and edge features of an image are extracted quickly using content-based image retrieval (CBIR) system with IGA. The mean value, the standard deviation, and the image bitmap of a color image are used as the features for retrieval. In addition to that entropy based on the gray level co-occurrence matrix and the edge histogram of an image is also considered. The experiments show that the method developed is very fast and retrieval performance achieved 100%.

Interactive Genetic Algorithm (IGA), Content Based Image Retrieval (CBIR), Gray Level Concurrence Matrix (GLCM)

[1] M. Antonelli, S. G. Dellepiane, and M. Goccia, “Design and implementation of Web-based systems for image segmentation and CBIR,” IEEE Trans. Instrum. Meas., vol. 55, no. 6, Dec. 2006, pp. 1869–1877.
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[12] P.S.Suhasini, Dr. K.Sri Rama Krishna, Dr. I. V. Murali Krishna “CBIR using color histogram processing” Journal of Theoretical and Applied Information Technology Vol6. No1. June 2009, pp.116 - 122.
[13] Ricardo da S. Torresa, Alexandre X. Falcãoa, Marcos A. Gonçalvesb, João P. Papaa, Baoping Zhangc,Weiguo Fanc, Edward A. Foxc “A genetic programming framework for content-based image retrieval” Pattern Recognition 42 (2009) .June 2009,pp.283 – 292.
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[21] Shrikant Chavate, Prof. Parul Dihulia Prof. Vikas Gupta” An approach used for user Oriented Content Based Image Retrieval using Interactive Genetic Algorithm” International Journal of Advanced Research in Computer Science and Software Engineering Volume 3, Issue 11, November 2013.
[22] Micheline Najjar, Jean Pierre Cocquerez, and Christophe Ambroise” A Semi-supervised Learning Approach to Image Retrieval”.
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Mamta R. Nagpure, Suchita S. Mesakar, Sonali R. Raut and Vanita P.Lonkar, "Image Retrieval System with Interactive Genetic Algorithm Using Distance," International Journal of Computer Sciences and Engineering, Vol.2, Issue.12, pp.109-113, 2014.

Mobile ad hoc networks (MANET) are widely used in that places where there is no available infrastructure. It is also called infrastructure less network. It is a collection of mobile nodes that dynamically form a temporary network without infrastructure. Each mobile node can move freely in any direction and changes their links to other devices frequently. In MANET different types of routing protocols have been recommended. Ad hoc On demand Distance Vector (AODV) is one of the most suitable routing protocol for the MANETs and it is more vulnerable to black hole attack by the malicious nodes. In this attack, the malicious node advertises itself as having the shortest path to the destination and falsely replies to the route requests, and drops all receiving packets. In this paper, we have surveyed and compare the existing solutions to multiple black hole attacks on AODV protocol and their drawbacks.

MANET, AODV, DRS, OLSR, DSDV

[1] Pooja Jaiswal, Dr. Rakesh Kumar “Prevention of Black Hole Attack in MANET”, IRACST – International Journal of Computer Networks and Wireless Communications (IJCNWC), ISSN: 2250-3501 Vol.2, No5, October 2012.Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
[2] Y. Khamayseh, A. , Bader, W. Mardini and M. BaniYasein, “A New Protocol for Detecting Black Hole Nodes in Ad Hoc Networks “International Journal of Communication Networks and Information Security (IJCNIS) Vol. 3, No. 1, April 2011.
[3] Bounpadith Kannhavong, Hidehisa Nakayama, Yoshiaki Nemoto, and Nei Kato; “A SURVEY OF ROUTING ATTACKS IN MOBILE AD HOC NETWORKS”, IEEE Wireless Communications • October 2007. PP: 85-90.
[4] Pooja Vinod Kumar Department of Computer Science and Applications”A Review on Detection of Blackhole Attack Techniques in MANET” Volume 4, Issue 4, April 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering.
[5] “ Modified AODV Protocol against Black hole Attacks in MANET” by K. Lakshmi1, S.Manju Priya, A.Jeevarathinam, K.Rama, K.Thilagam
[6] Ming-Yang Su; Kun-Lin Chiang; Wei-Cheng Liao, "Mitigation of Black-Hole Nodes in Mobile Ad Hoc Networks," Parallel and Distributed Processing with Applications (ISPA), 2010 International Symposium on, vol., no., pp.162-167, 6-9 Sept. 2010
[7] Jaspal Kumar, M. Kulkarni, Panipat Institute of Engineering & Technology, India National Institute of Technology, Karnataka, India “Effect of Black Hole Attack on MANET Routing Protocols” I. J. Computer Network and Information Security, 2013, 5, 64-72 Published Online April 2013 in MECS
[8] C. Kim, E. Talipov and B. Ahn, "A Reverse AODV Routing Protocol in Ad Hoc Mobile Networks," Pro- ceeding from EUC'06: The 2006 International Confer- ence on Emerging Directions in Embedded and Ubiqui- tous Computing, Seoul, 1-4 August 2006, pp. 522-531.
[9] Ravi KantvM.tech ScholarvABES EC, Ghaziabad” A Literature Survey on Black Hole Attacks on AODV Protocol in MANET” International Journal of Computer Applications (0975 – 8887) Volume 80 – No 16, October 2013
[10] S. Dokurer, Y. M. Erten and E. A. Can, "Performance Analysis of Ad-Hoc Networks under Black Hole Attacks," Proceeding from SECON'07: IEEE Southeast Conference, Richmond, 22-25 March 2007, pp. 148-153.
[11] K. Makki, N. Pissinou, and H. Huang, “Solutions to the black hole problem in mobile ad-hoc network,” 5th World Wireless Congress, pp.508–512, 2004.Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
[12] S. Lu, L. Li, K.Y. Lam, L. Jia, “SAODV: A MANET Routing Protocol that can Withstand Black Hole Attack.,” International Conference on Computational Intelligence and Security, 2009.
[13] Bhoomika Patel Department of Information Technology, Parul Institute of Engineering & Technology, Limda,Vadodara, India.” A Review - Prevention and Detection of Black Hole Attack in AODV based on MANET” (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014,
[14] Nirali Modi, Vinit Kumar Gupta Department of computer engineering Hasmukh Goswami College of Engineering, Ahmedabad, India” Prevention Of Black hole Attack using AODV Routing Protocol in MANET” (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014,
[15] Neha Kaushik, M.Tech Student Ajay Dureja, Assistant Prof. PDM College of Engineering for Women, B’Garh” Performance Evaluation Of Modified Aodv Against Black Hole Attack In Manet” European Scientific Journal June 2013 edition vol.9, No.18

Bhagyashree Thakur, Sharda Patel, Ashok Verma and Shivendu Dubey, "Simulation Of Blackhole Nodes And Prevention Using IDS For MANET Reactive Routing Protocol AODV," International Journal of Computer Sciences and Engineering, Vol.2, Issue.12, pp.114-120, 2014.

Cloud computing is an accepted trend in current computing which provide cheap and easy access to computational resources. In cloud computing, multiple cloud users can simultaneously request cloud services. Accessing of applications and associated data from anywhere is possible using clouds. Current cloud providers do not allocate the resources efficiently. Services are delivered to large number of users as demand grows up. This survey reviews various resource allocation methods.

Cloud Computing, Computational resources, Data Centers, Resource allocation, VM Provisioning and allocation

[1]http://en.wikipedia.org/wiki/Cloud_computing
[2]Lizhewang,JieTao,KunzeM.,Castellanos,A.C,Kramer,D.,Karl,w, ”High Performance Computing and Communications”,IEEE International Conference HPCC,2008,pp.825-830.
[3] V.Vinothina, Sr.Lecturer, Dr.R.Sridaran, Dean, Dr.PadmavathiGanapathi”A survey on resource allocation strategies in cloud computing” International Journal of Advanced Computer Science andApplications, Vol. 3, No.6, 2012
[4] Gunho Lee, Niraj Tolia, Parthasarathy Ranganathan, and Randy H. Katz, Topology aware resorce allocation for data-intensive workloads, ACM SIGCOMM Computer Communication Review, 41(1):120--124, 2011
[5] Abirami S.P. and Shalini Ramanathan, Linear scheduling strategy for resource allocation in cloud environment, International Journal on Cloud Computing: Services and Architecture(IJCCSA), 2(1):9--17, 2012
[6] HadiGoudarzi, MassoudPedram, Multi-dimensional SLAbased Resource Allocation for Multi-tier Cloud Computing Systems, in Proceedings of IEEE International Conference on Cloud Computing (CLOUD),Washington DC USA, 2011. [7] RerngvitYanggratoke, FetahiWuhib and Rolf Stadler: Gossip-based resource allocation for green computing in Large Clouds: 7th International conference on network and service management, Paris, France, 24-28 October, 2011.
[8] PradeepPadala, Kai-Yuan Hou Kang G. Shin, Xiaoyun Zhu, Mustafa Uysal, Zhikui Wang, SharadSinghal, Arif Merchant “Automated Control of Multiple Virtualized Resources”, The University of Michigan, Hewlett Packard Laboratories.
[9] Xiaomin zhua,chuan Hea,Kenli Li,Xiao Qin “Adaptive energy-efficient scheduling for real time tasks on DVS-enabled heterogeneous clusters”, J.Parallel Distrib. Comput, SciVerse ScienceDirect, 2012 Elsevier Inc.
[10] D. Gmach, J.RoliaandL.cherkasova, Satisfying service level objectives in a self-managing resource pool. In Proc. Third IEEE international conference on self-adaptive and self organizing system.(SASO’09) pages 243-253.IEEE Press 2009
[11] X.Zhu et al. Integrated capacity and workload management for the next generation data center. In proc.5th international conference on Automatic computing(ICAC’08),pages 172-181,IEEE Press 2008.
[12]. Amit Nathani, Sanjay Chaudharya, GauravSomani, “Policy based resource allocation inIaaS cloud”, Future Generation Computer Systems28 (2012)94–103 doi:10.1016/j.future.2011.05.016

Swathy Surendran, Sreetha E.S2 and M. Azath3, "Study on Resource Allocation in Cloud," International Journal of Computer Sciences and Engineering, Vol.2, Issue.12, pp.121-124, 2014.

One of the prominent challenges encountered with regard to web search engines is the large number of documents retrieved by the user in response to their queries. In this regard Various solutions have been proposed in the literature .One approach is to use clustering of web documents. In this paper we propose a genetic algorithm approach for clustering of web documents and study the effectiveness of using various similarity measures in this context. This paper proposes various similarities have been employed and the cosine similarity yields better results when compared to other similarity measures.

Web Page Clustering, vector space model, Genetic Algorithm

[1]A.Huang “Similarity measures for text document clustering” NZCSRS(2008)
[2]A.Strehl,J.Ghoesh “Impact of similarity measures”
[3]N.Oikonomakon,M.vazirginnn “A review of web document Approaches”
[4]R.kala,A.Shukla and R.Tiwang “ A novel Approach to clustering using genetic algorithm”International journal of engineering research 2010.
[5] U.Maulik,S.Bandyopadhyay “Genetic algorithm based clustering”

J.Usharani and K.Iyakutti, "An Analysis of the Effectiveness of Various Similarity Measures for Web Page Clustering," International Journal of Computer Sciences and Engineering, Vol.2, Issue.12, pp.125-127, 2014.

Automatic fire detection by real time vision based method got great importance in recent years. By the development in science and technologies it has drawn potential significance in last decades. There are different video based detection for smoke, fire and object’s movement .The importance of fire being detected as early as possible is to take prevention and precaution before it causes any material damage, human casualties. Survey aims to study the different methods and techniques used for flame detection and some related works.

Fire Detection, Optical Flow, Optimal Mass Transport, Video Analytics, Vision-Based Method

[1] Glossary of Wildland Fire Terminology, National Wildfire Coordinating Group, November 2009, Retrieved 2008-12-18.
[2] Majid Bahrepour, Nirvana Meratnia, Paul Havinga, “A survey from wireless sensor network perspective”, in Proc. IEEE Int. Conf. Image Process, 2010.
[3] http://en.wikipedia.org/wiki/Flame_ionization_detector
[4] B.Ugur Toreyin, Yigithan Dedeoglu, Ugur Gudukbay, A.Enis Cetin, “Computer vision based method for real-time fire and flame detection”, in Proc. IEEE Int. Conf. Image Process, 2008.
[5] T. Celik, H. Demirel, “Fire detection in video sequences using a generic color model”, Fire Safety Journal (2008), doi:10.1016/j.firesaf.2008.05.005
[6] T. Celik,H. Demirel,H. Ozkaramanli and M.Uyguroglu, “Fire Detection using statical color model in video sequences”, in J. Vis. Commun. Image R., no 18, pp. 176-185, 2007
[7] S.Shanthi et al, International Journal of Computer Science and Mobile Computing, Vol.3 Issue.2, February- 2014, pg. 910-916
[8] M.Muller, P.Karasev, and I.Kolesov, “Optical flow estimation for flame detection in videos”, in IEEE Trans. On Image Process, vol.2, no.7,pp. 2786-2797, 2013.
[9] http://www.ijirset.com/upload/2014/icets/60_CS286.pdf
[10] S.Fazekas and D.Chetverikov, “Analysis and performance evaluation of optical flow features for dynamic texture recognition,” Signal Process, Image Commun, vol. 22, nos. 7–8, pp. 680–691, 2007.
[11] http://www.ijraset.com/fileserve.php?FID=414
[12] Sasirekha.SP, Chinnu Thomas, P. Dhivya, P.Sathyashrisharmila, “Detection of Flames Using Videos by Expectation Maximization and Flow Estimation Algorithm”, International Journal of Innovative Research in Science, Engineering and Technology, Vol 3, Special Issue 1, February 2014, Department of Computer Science and Engineering.
[13] Suchet Rinsurongkawong, Mongkol Ekpanyapong, and Matthew N. Dailey, “Fire Detection for Early Fire Alarm Based on Optical Flow Video Processing”, Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 9th International Conference on May 2012.
[14] http://en.wikipedia.org/wiki/Monocular
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[17] J.George Christober, A.Jeeva, S. Shanthi, “Estimation and Detection of Fire Flow by SVM Classifier”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 11, November 2013.
[18] George et al., International Journal of Advanced Research in Computer Science and Software Engineering 3(11), November - 2013, pp. 1443-1447
[19] Yair Weiss “Motion Segmentation using EM- A Short Tutorial”, MIT E10-120, Cambridge, USA.
[20] http://en.wikipedia.org/wiki/Support_vector_machine
[21] Ms.S.P.Sasirekha, Ms.S.Ramya, Mr. R.Mohan Prasanth, Mr.M.Nagarasan, Department of Computer Science and Engineering, “A Survey about Automatic Flame/Fire Detection in Videos” published in International Journal of Research in Advent Technology, Vol.2, No.2, February 2014.
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[24] Turgay, C, Huseyin, O. & Hasan, D. (2007). “Fire and smoke detection without sensors: Image processing based approach”.
[25] Xu, Z. & Xu, J. (2007). “Automatic fire smoke detection based on image visual features”. Proceedings of International Conference on Computational Intelligence and Security Workshops, December 2007, pp. 316 –319.
[26] Micky James. “Survey on Video Based Fire Detection Systems”, Department of Computer Science, MG University, Kerala, M.Tech Student, Viswajyothi College of Engineering & Technology, Vazhakulam, Kerala.
[27] http://en.wikipedia.org/wiki/Fire
[28] http://en.wikipedia.org/wiki/Conflagration
[29] John Adedapo Ojo, Jamiu Alabi Oladosu, “Video-based Smoke Detection Algorithms: A Chronological Survey”, Department of Electronic & Electrical Engineering, Ladoke Akintola University of Technology, P.M.B 4000, Ogbomoso, Nigeria.

Anju Thomas and M. Azath, "Survey on Flame Detection by Optical Flow Estimation," International Journal of Computer Sciences and Engineering, Vol.2, Issue.12, pp.128-131, 2014.