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A content delivery network or content distribution network (CDN) using cloud resources such as storage and compute have started to emerge. Unlike traditional CDNs hosted on private data centers, cloud-based CDNs take advantage of the geographical availability and the pay-as-you-go model of cloud platforms. The Cloud-based CDNs (CCDNs) promote content-delivery-as-a-service cloud model. Though CDNs and CCDNs share similar functionalities, introduction of cloud impose additional challenges that have to be addressed for a successful CCDN deployment. Several papers have tried to address the issues and challenges around CDN with varying degree of success. However, to the best of our knowledge, there is no clear articulation of issues and challenge problems within the context of cloud-based CDNs. Hence, this paper aims to identify the open challenges in cloud-based CDNs. In this regard, we present an overview of cloud-based CDN followed by a detailed discussion on open challenges and research dimensions.

Content delivery networks, Cloud, Cloud-based CDN, pay-as-you-go model, content-delivery-as-a-service

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V. Suresh, B. Venkatesh, A. Anjaneyulu, "Emerging Cloud based Content Delivery Networks," International Journal of Computer Sciences and Engineering, Vol.4, Issue.9, pp.64-71, 2016.

To obtain retrieval accuracy of content based images retrieval systems, the prime notice is on reduction of �semantic gaps� between the visual features and human linguistics than designing low-level feature extraction algorithm. This paper elucidates a comprehensive study on recent technical updates in high-level semantic-based image retrieval. Major recent publications are enclosed during this survey covering different aspects of the research during this space, as well as low-level image feature extraction, similarity mensuration, and deriving high-level linguistics options. We have a tendency to establish 5 major classes of the progressive techniques in narrowing down the� linguistics gap�: (1) victimisation object metaphysics to outline high-level concepts; (2) victimisation machine learning ways to associate low-level options with question concepts; (3) victimisation relevance feedback to find out user�s intention; (4) generating linguistics template to support high-level image retrieval; (5) fusing the evidences from markup language text and also the visual content of pictures for computer network image retrieval. Other connected problems reminiscent of image workand retrieval performance evaluation are mentioned.

CBIR,Feedback,Machine Learning,semantic,Linguistic template

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[9] Aasish Sipani, Phani Krishna and Sarath Chandra, "Content Based Image Retrieval Using Extended Local Tetra Patterns", International Journal of Computer Sciences and Engineering, Volume-02, Issue-11, Page No (11-17), Nov -2014
[10] Shraddha S.Katariya and Ulhas B.Shinde, "Review of Content Based Image Retrieval Using Low Level Features", International Journal of Computer Sciences and Engineering, Volume-04, Issue-03, Page No (91-97), Mar -2016, E-ISSN: 2347-2693

G. Vidya, S. Omprakash, "Survey on Recent Researches on High Level Image Retrieval," International Journal of Computer Sciences and Engineering, Vol.4, Issue.9, pp.72-77, 2016.

The proposed algorithm expands the meaning of a user�s goal using ontology then derives a group of keywords to discover services and web composition are used to select the web services based on QoS to find the optimality solution of their user goal. The efficiency of the web service matching and composition becomes more important than ever because of the vast number of the web services. For this purpose we propose a web service composition algorithm based on the annotated ontology using semantic matching to achieve exact service for user�s constraint. We design a resource graph to represent the semantic relationship among Web resources. By analyzing the relations among Web resources and using ontologies, A semantic web services would require careful usage combined technologies this semantic web service is realized to show that they ensure interoperability. Four aspects of web services are presented 1) Standard of XML web services 2) Semantic annotation 3) Web service composition 4) Performance Evolution. Our framework can generate ad-hoc processes for composing Web resources. We have built a prototype to demonstrate that the repetitive tasks in the Web resources can be automatically and tracked and the user can change simple Web resources into reusable services by annotating the data with them.

Ontology, Service-oriented architecture, Service composition, Service discovery and Web services

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G. Narayanan, P. Periasamy, "Web Resources Development Methdology Based on Web Composition Using Ontology for User�s Optimal Goal," International Journal of Computer Sciences and Engineering, Vol.4, Issue.9, pp.78-86, 2016.

Wireless Sensor Networks are being deployed in industrial application which requires routing path that can delivery sensed data in reliable, energy efficient and low end-to-end delay. A velocity based protocol proposed in [13] co-relates velocity offered by two hop neighbor to required delay of application and determines successor node in path to sensed data toward sink. However, velocity based protocol have more control packets overhead. We propose a Real-Time Reliable Data delivery based on Virtual Coordinates Routing (RRDVCR) protocol, which uses the hop count between source and destination instead of geographic distance. Further, process of finding forwarding node is on advancement of packet offered by node�s two-hop neighbor, success probability of link and residual energy at the two-hop neighbor nodes, the data packets are serviced depending on their real-time requirements. To provide differentiated services to each packet, packet progress, link quality and residual energy parameters are co-related by co-relation factor. To reduce control overhead selective acknowledgment scheme is used in proposed protocol. Simulation results of proposed protocol demonstrate that decrease in energy utilization is about 22% and 9.5% in comparison to SPEED [8] and THVR [13] protocol, packet success delivery is about 16% and 38% increase in comparison to THVR [13] and SPEED [8] protocol, and control overhead in proposed protocol is 50% less than THVR[13].

Link Reliability, Maximum Transmission Count [MTX], Virtual Coordinating Routing, Dynamic Co-relation Factor: f(rt).

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Venkatesh, C.S. Sengar, K.R. Venugopal , S.S. Iyengar, L.M. Patnaik, "RRDVCR: Real-Time Reliable Data Delivery Based on Virtual Coordinating Routing for Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.4, Issue.9, pp.87-95, 2016.

Colorization is a practice of adding colors to gray scale images and videos. This research aims at replacing each pixel value of grayscale image by the required color pixel value. Adding colors to grayscale images makes them more attractive and observable. It is easy for human visual system to recognize color information more proficiently as compared to gray information. Automation of this process is very important as manual colorization takes a lot of time and effort. In the proposed method, Kekre�s Proportionate Error (KPE) codebook is used and five different vector quantization (VQ) codebook sized alias 32, 64, 128, 256 and 512 are considered. By using eight different color models: RGB, Kekre�s LUV, YCbCr, YUV, YIQ, and Kekre�s Biorthogonal color models and five VQ codebooks, total 40 versions of proposed colorization method are found. Testing is done on 30 images of different classes and results are compared with the existing method of LBG based image coloring. It is observed that the use of higher codebook sizes and YCbCr color model enhances the colorization.

Colorization; Codebook; VQ; LBG; KPE; Color Model

[1] Ambika Kalia, Balwinder Singh, �Colorization of Grayscale Images: An Overview�, Journal of Global Research in Computer Science, vol. 2, no. 8, pp. 34-37, August 2011.
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[4] Sudeep D. Thepade , Padale Supriya, Atul Pawar, �Performance Comparison of Color Spaces in Thepade`s Transform Error Vector Rotation Algorithms of Vector Quantization for Grayscale Image Colorization with Slant and Hartley transforms�, IEEE Conference on Industrial Instrumentation and Control (ICIC), pp. 400-405, May 2015.
[5] H. B. Kekre, Sudeep D. Thepade, �Color Traits Transfer to Grayscale Images�, First International Conference on Emerging Trends in Engineering and Technology, IEEE Conference, pp. 82-85, July 2008.
[6] H. B. Kekre, Dhirendra Mishra, Rakhee S. Saboo, �Comparison of image fusion techniques in RGB & Kekre`s LUV color space�, Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), IEEE Conference, pp. 114-120, Feb 2015.
[7] Sudeep D. Thepade, Vandana Mhaske, Vedant Kurhade, �New Clustering Algorithm for Vector Quantization using Slant Transform�, Emerging Trends and Applications in Computer Science (ICETACS), IEEE Conference, pp. 161-166, Sept 2013.
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[10] Dr. H. B. Kekre, Dr. Sudeep D. Thepade, Dr. Tanuja K. Sarode, Ms. Nikita Bhandari, �Colorization of Grayscale Images using LBG VQ Codebook for different Color Spaces�, International Journal of Engineering Research and Applications (IJERA), vol. 1, issue 4, pp.1274-1283, Feb 2011.

N. Bhandari, G. Kaur, "Colorization of Grayscale Images Using KPE and LBG Vector Quantization Techniques," International Journal of Computer Sciences and Engineering, Vol.4, Issue.9, pp.96-102, 2016.

: Scheduling and broadcasting of data through network tunnels is always a big challenge in closed network topologies. Each and individual tunnel or part of network will be having its own capacity to transmit and receive the packets. Adoptive and open networks are easy to transmit the data but the challenges will occur in synchronization of data transmission among them. So clustering, tracking, log maintenance of the data transmission among the channels or tunnels and retransmission with respect energy levels and synchronization can be achieved by incremental tracking retransmission (ITR[1]) approach. Energy levels will be monitored by network monitor and assigns scheduling depends on the network capacity of the available methodologies. Here the three methodologies are 1.Memory less channels[2] , 2. Modulated channel[3], 3.Joint and uniform scheduling[4] for data transmission with respect to scheduling. Considerable throughput criteria is framed with incremental flow with our work to end up fair and best accuracy levels. This ITR method is totally unique in open networks. Here open networks means which can adopt with legacy and other adoptive open networks in tunnelling or bridge level transmission. The packet buffering and delivery is always depends on previous cluster or next cluster and chance of losing the packets. So to overcome our work is practically implemented in chunks mechanism. Totally 3 or more chunks will be framed as clusters which acts as incremental growth in transmission with respect to losing of the packets. The central frame work which works as auto deployment methodology to track the tunnels. The loss of frequency is traceable using this frame work and adopts the lost and non lost packets addresses and flushes to next level to fulfil ITR method. The practical implementation depends on asynchronous services to roll back to any level/cluster. The feasible transmission is achieved in incremental level of clusters which will get the log or track information about the data from central frame work.

open networks ,topology, clusters, channel , tunnel. ITR

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[14] Umesh Kumar Singh, Shivlal Mewada, Lokesh Laddhani and Kamal Bunkar, �An Overview & Study of Security Issues in Mobile Ado Networks�, International Journal of Computer Science and Information Security (IJCSIS) USA, Vol-9, No.4, pp (106-111), April 2011.
[15] Zhou L. and Haas Z.J, �Securing Ad Hoc Networks,� IEEE Network Magazine, vol. 13, no. 6, 1999
[16] Shivlal Mewada, Umesh Kumar Singh and Pradeep Kumar Sharma, �Simulation Based Performance Evaluation of Routing Protocols for Mobile Ad-hoc Networks (MANET)", International Journal of Computer Science, Information Technology and Security, Vol. 2, No. 4, Aug 2012.
[17] M.Bhuvaneswari and L.VijayaKalyani, "Authentication Procedures For Ad-hoc Networks: Taxonomy and Investigation Subjects", International Journal of Computer Sciences and Engineering, Volume-02, Issue-09, Page No (90-99), Sep -2014
[18] Mayank Kumar and Tanya Singh, "A Survey on Security Issue in Mobile AD-HOC Network and Solutions", International Journal of Computer Sciences and Engineering, Volume-02, Issue-03, Page No (71-75), Mar -2014,
[19] Srihari Babu.Kolla and B.B.K. Prasad, "A Survey of Source Routing Protocols, Vulnerabilities and Security In Wireless Ad-hoc Networks", International Journal of Computer Sciences and Engineering, Volume-02, Issue-04, Page No (20-25), Apr -2014
[20] Rama Koteswara Rao.G , Satya Prasad.R , Patanjali Sastri.A , Prasad.PESN �Enhancing the impregnability of linux Servers, International Journal of network security & its applications(IJNSA) Vol6 ,No2, March2014

M.J. Bollarapu and G.R. Rao , "An Effective Re Deployment of Cooperative Network(S) to Transmit in Incremental Clusters Approach," International Journal of Computer Sciences and Engineering, Vol.4, Issue.9, pp.103-110, 2016.

Static analysis examines program code and reasons over all possible behaviors that might arise at run time. Tools based on static analysis can be used to find defects in programs. Recent technology advances has brought forward tools that do deeper analyses that discover more defects and produce a limited amount of false warnings. The aim of this work is to succinctly describe static code analysis, its features and potential, giving an overview of the concepts and technologies behind this type of approach to software development as well as the tools that enable the usage of code reviewing tools to aid programmers in the development of applications, thus being able to improve the code and correct errors before an actual execution of the code.

static analysis, code review, code inspection, source code, bugs, dynamic analysis, software testing, manual review

[1] Ernst. M,� Static and dynamic analysis: synergy and duality�, MIT Lab for Computer Science, Cambridge, Workshop on Dynamic Analysis, ICSE�03 International Conference on Software Engineering Portland, Oregon (2003), Volume-05, Issue-07, Page No (9-16), Mar -2003.
[2] McGraw, G, Chess, B.�Static Analysis for Security�, IEEE Computer Society (2004), Volume-03, Issue-05, Page No (21-28), Oct -2004.
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[4] Humphrey, W,� The Personal Software ProcessSM (PSPSM)�, Carnegie Mellon University, Massachusetts (2000), IJCSCL, Volume-05, Issue-07, Page No (58-66), Mar -2000.
[5] Faria, J. P,� Software Reviews and Inspections�, FEUP, Porto(2008), Volume-09, Issue-02, Page No (36-42), June -2008.
[6] Basili, V,�Experimentation in Software Engineering. Experimental Software Engineering Group�, IJRIT, Volume-08, Issue-05, Page No (96-103), Mar -2002.
[7] Emanuelsson, P.Nilsson, �A Comparative Study of Industrial Static Analysis Tools�, University Electronic Press (2008),IJAR, Volume-03, Issue-08, Page No (221-228), July -2008.

N. Sudheer, S.H. Raju, "Different approach Analysis for Static Code in Software Development," International Journal of Computer Sciences and Engineering, Vol.4, Issue.9, pp.111-118, 2016.

There are various mechanisms that provide security to the users and resources in Wireless Networking. Password authentication is one of the important procedure that enhance the security measures of the system. Drawbacks of traditional password authentication system like stolen, forgotten etc. are overcome by the technologies used for authentication mechanism like Neural Network approaches. In this paper, the two algorithms of Neural Network have been taken for conducting the experiment. Neural Network is an emerging field of Artificial Intelligence that works like a human brain. One is the Backpropagation algorithm which follows feed forward procedure and the second one is Hopfield Neural Network which works on auto associative properties of the network.

Backpropagation algorithm; Hopfield Neural Network; Password authentication; Wireless Networking

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[2] L. Sachin, �Security in MANET: Vulnerabilities, Attacks and Solutions�, International Journal of Multidisciplinary and current research, Volume.02, Page No (62-68), Jan-Feb 2014.
[3] Shivlal Mewada, Aarti Shrivastava, Pradeep Sharma, N Purohit and S.S. Gautam" Performance Analysis of Encryption Algorithm in Cloud Computing", International Journal of Computer Sciences and Engineering, Volume-03, Issue-03, pp (83-89), Jun -2014
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[6] Neha Shukla, Meena Arora, "Prediction of Diabetes Using Neural Network & Random Forest Tree", International Journal of Computer Sciences and Engineering, Volume-04, Issue-07, Page No (101-104), Jul -2016
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[8] Dahiya. M, �Back Propagation Neural Network for Wireless Networking�, International Journal of Computer Science and Engineering, Volume.04, Issue-04, Page No (123-125), May 2016.
[9] ASN Chakravarthy, P S Avadhani, PESN Krishna Prasad, N. Rajeev, D. Rajasekhar Reddy, �A Novel Approach for Authenticating Textual or Graphical Passwords Using Hopfield Neural Network�, Advanced Computing: An International Journal (ACIJ), Volume.02, Issue-04, Page No (33-46), July 2011.
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M. Dahiya, "Password Authentication in Wireless Networking using Neural Network Techniques," International Journal of Computer Sciences and Engineering, Vol.4, Issue.9, pp.119-122, 2016.

Text mining is the process of acquiring high-quality information from text that is typically borrowed through the devising of patterns and trends such as statistical pattern learning .It usually involves the process of structuring the input text, deriving patterns within the structured data and finally evaluation and interpretation of the output. It can help an organization to acquire potentially valuable business insights from text-based content such as word documents, email and postings on social media streams like Facebook, Twitter and LinkedIn. Mining unstructured data with natural language processing (NLP), statistical modeling and machine learning techniques can be challenging because natural language text is often inconsistent. So, R is used to mine unstructured data which is the most exhaustive statistical analysis package and it incorporates all of the standard statistical tests, models and analyses for managing and manipulating data.

R ,S,Text mining,Statistical Modeling

[1] Mr. Rahul Patel, Mr. Gaurav Sharma,�A survey on text mining techniques�, Int. Journal of Engineering and Computer Science, Volume-03, Issue 5, Page No. (5621-5625), May 2014.
[2] Minakshi R. Shinde1, Parmeet C. Gill, �Pattern Discovery Techniques for the Text Mining and its Applications�, Int. Journal of Science and Research (IJSR), Volume-03 Issue 5, May 2014.
[3] S.S. Patil and V.M. Gaikwad , "Developing New Software Metric Pattern Discovery for Text Mining", International Journal of Computer Sciences and Engineering, Volume-02, Issue-04, Page No (119-125), Apr -2014
[4] Abhilasha Singh Rathor, Dr. Pankaj Garg,� Analysis on Text Mining Techniques�, Int. Journal of Advanced Research in Computer Science and Software Engineering, Volume -06, Issue 2, Page No (132-137), February 2016.
[5] Vishakha D. Bhope and Sachin N. Deshmukh, �Comparative Study on Information Retrieval Approaches for Text Mining�, Int. Journal of Computer Sciences and Engineering, Volume-03, Issue-3, Page No (102-106), Mar 2015.
[6] Vishakha D. Bhope and Sachin N. Deshmukh, "Comparative Study on Information Retrieval Approaches for Text Mining", International Journal of Computer Sciences and Engineering, Volume-03, Issue-03, Page No (102-106), Mar -2015
[7] StatisticalModeling,https://en.wikipedia.org/wiki/Statistical_model, June 2016.

M.S. Lakshmi, M.D.A. Sultana, "Text Mining of Unstructured Data Using R," International Journal of Computer Sciences and Engineering, Vol.4, Issue.9, pp.123-130, 2016.

This paper describes a method for attendance monitoring through a wireless network of smart devices (IoT). Today, IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. IoT is expected to bridge diverse technologies to enable new applications by connecting physical objects together in support of intelligent decision making. The basic premise here is to have smart sensors collaborate directly without human involvement to deliver a new class of application. Compared with the existing technology where local sensors are deployed to monitor attendance OR where often the technology is barely used requiring human intervention, our intention here is to provide a hassle free system which is mobile and requires no human intervention (except for the unavoidable initial set-up). Not only our solution eradicates the burden on human resource but also, provides a convenient way to interact by hosting a web-page for viewing the attendance report of every student.

Biometric attendance, Mobile attendance monitoring, Internet of Things

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[8] Umesh Kumar Singh, Shivlal Mewada, Lokesh Laddhani and Kamal Bunkar, �An Overview & Study of Security Issues in Mobile Ado Networks�, International Journal of Computer Science and Information Security (IJCSIS) USA, Vol-9, No.4, pp (106-111), April 2011.ISSN: 1947-5500.
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S.C. Kohalli, R. Kulkarni, M. Salimath, M. Hegde, R. Hongal, "Smart Wireless Attendance System," International Journal of Computer Sciences and Engineering, Vol.4, Issue.9, pp.131-137, 2016.