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Cloud computing in an improve form for grid computing, cluster computing and distributed computing. Cloud computing provides sharing of computing resources such as platform, software, infrastructure and data over the network, on pay and use basis. Cloud computing provides service PaaS, IaaS and SaaS to various cloud users, by supporting various cloud models private, public, community and hybrid. Cloud computing reduces overall cost and efforts. Day by day numbers of cloud user are increasing rapidly. Higher number of cloud users are requires high computing resources on time, which creates a big challenge for cloud service providers so serve computing resources on time. Various cloud researchers are working on improvement on cloud performance by correct load distribution among cloud user request and computing resources. In this survey paper we are presenting a comparative study of various load distribution method for cloud computing.

Cloud Computing ,Load Balancing ,Grid Computing ,Cloud Services

[1]. Sidra Aslam, Munam Ali Shah, �Load Balancing Algorithms in Cloud Computing: A Survey of Modern Techniques�, National Software Engineering Conference (NSEC 2015), IEEE, PP 30-36
[2]. Geethu Gopinath P P(1), Shriram K Vasudevan, An in-depth analysis and study of Load balancing techniques in the cloud computing environment, 2nd International Symposium on Big Data and Cloud Computing (ISBCC�15), Science Direct, Procedia Computer Science 50 ( 2015 ) 427 � 432
[3]. Aarti Singha, Dimple Junejab, Manisha Malhotraa,� Autonomous Agent Based Load Balancing Algorithm in Cloud Computing,� International Conference on Advanced computing Technologies and Applications (ICACTA-2015), Science direct, Procedia Computer Science 45 ( 2015 ) 832 � 841
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[7]. J. Hu, J. Gu, G. Sun, T. Zhao. A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment in Proc. PAAP, 2010, pp. 89-96.
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[12]. S Jing and K She (2011 April). A Novel Model for Load Balancing in Cloud Data Centre. Journal of Convergence Information Technology. , pp. 29-38.
[13]. .S. Ray and A.D. Sarkar (2012, October). Execution Analysis of Load Balancing Algorithms in Cloud Computing Environment. International Journal on Cloud Computing: Services and Architecture (IJCCSA). 2(5), pp. 1-13.
[14]. S. S. Moharana, R. D. Ramesh & D.Powar,(2013, May). Analysis of Load Balancers in Cloud Computing. International Journal of Computer Science & Engineering (IJCSE), pp.: 101-108.
[15]. S.C. Wang,K.Q. Yan, W.P.Liao, S.S. Wang. Towards a Load Balancing in a three-Level Cloud Computing Network. In Proc. ICCSIT, 2010, pp.108-113.
[16]. T. Desai, J. Prajapati,(2013, Nov). A Survey of Various Load Balancing Techniques And Challenges In Cloud Computing. International Journal of Scientific & Technology Research, [Online]. 2(11), pp.158-161.
[17]. Y.Xu, L. Wu, L. Guo, Z.Chen, L.Yang, Z.Shi. An Intelligent Load Balancing Algorithms Towards Efficient Cloud Computing. In Proc. AAAI Workshop, 2011, pp. 27-32.

R. Upadhyay, U. Lilhore, "Review of Various Load Distribution Methods for Cloud Computing, to Improve Cloud Performance," International Journal of Computer Sciences and Engineering, Vol.4, Issue.12, pp.61-64, 2016.

Gray Scale Image Processing (GSI) technique has developed using MATLAB�s Digital Signal Processing toolbox to remove the unwanted frequencies present in the SODAR facsimiles and also to enhance the pixel quality (intensity) of the facsimiles for a better view. In general, the sources of these noises (unwanted frequencies) are airplanes, Choppers, Hi-frequency sound systems and man-made noises nearby SODAR antenna. Already some filtering techniques like Gaussian filter, median filter, and other DSP filter techniques are employed to remove the noises from facsimiles but these techniques are incapable of enhancing the pixel clarity. This GSI computing techniques could be applied directly to SODAR facsimiles, which is not possible with other filtering techniques. The beauty of the technique is that time was taken to complete the process within 10 to 20 seconds. Here in this paper, all mathematical computing techniques are designed and developed through MATLAB.

Computing, Facsimile, Filter, Gaussian, Grayscale, Histogram, Image, MATLAB, Noise, Pixel, SODAR

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M.H. Babu, M.B.N. Bhushanamu, D.S.S.N Raju, M. Purnachandra Rao, "Development of Gray Scale Image Processing computing Technique to Remove the Unwanted Frequency Patches on Sodar Facsimiles," International Journal of Computer Sciences and Engineering, Vol.4, Issue.12, pp.65-71, 2016.

Visual surveillance has been a very active research topic in the last few years due to its growing importance in security, law enforcement, and military applications. The project presents moving object detection based on background subtraction for video surveillance system. In all computer vision system, the important step is to separate moving object from background and thus detecting all the objects from video images. The main aim of this paper is to design a bounding box concept for the human detection and tracking system in the presence of crowd. The bounding box around each object can track the moving objects in each frame and it can be used to detect crowd and the estimation of crowd. This paper gives the implementation results of bounding box for detecting objects and its tracking. In order to remove some unwanted pixels, morphological erosion and dilation operation is performed for object edge smoothness. The simulated result shows that used methodologies for effective object detection has better accuracy and with less processing time consumption rather than existing methods.

Input video, Frame separation, Background subtraction, Morphological Filtering, Performance measurement

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A.C. Saindane, P.S. Patil, "An Efficient Human Recognition Using Background Subtraction and Bounding Box Technique for Surveillance Systems," International Journal of Computer Sciences and Engineering, Vol.4, Issue.12, pp.72-77, 2016.

A wireless sensor network comprises a number of small sensors that communicate with each other. Each sensor collects the data and communicates through the network to a single processing center that is a base station. The communication of node and process of message passing consumes energy. This energy consumption by the nodes to transmit data decreases the network lifetime significantly. Clustering is by far the best solution to save the energy consumption in the context of such network. Clustering divides the sensors into groups, so that sensors communicate information only to cluster heads and then the cluster heads communicate the aggregated information to the processing center so as to save energy. This paper studies and discusses various dimensions and approaches of some broadly discovered algorithms for clustering. It also presents a comparative study of various clustering algorithms and discussion about the potential research areas and the challenges of clustering in wireless sensor networks.

base station; clustering; cluster head;multi hop;nodes; sensors; single hop; WSN

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V. Sharma , "Clustering and Energy Efficiency in Wireless Sensor Networks: A Study," International Journal of Computer Sciences and Engineering, Vol.4, Issue.12, pp.78-82, 2016.

Impact amplification is aware of augment the good thing about infective agent promoting in informal organizations. The defect of impact growth is that it does not acknowledge specific shoppers from others, despite the likelihood that some things are often useful for the actual shoppers. For such things, it`s a superior system to consider boosting the impact on the actual shoppers. During this paper, we tend to detail an effect boost issue as question handling to acknowledge specific shoppers from others. We tend to demonstrate that the question handling issue is NP-hard and its target capability is sub secluded. We tend to propose a need model for the estimation of the target capability and a fast covetous primarily based shut estimation strategy utilizing the need model. For the need model, we tend to explore a relationship of the way between shoppers. For the covetous technique, we tend to estimate a productive progressive overhauling of the negligible addition to our goal capability. We tend to lead trials to assess the planned technique with real datasets, and distinction the outcomes and people of existing systems that area unit adjusted to the problem. From our trial results, the planned strategy is not any but asking of extent speedier than the prevailing routines by and enormous whereas accomplishing high truth. Also we are implementing Maximum Coverage algorithm in which will post or spread add(product list) as per category wise means we will divide the age category in different age group range by using Maximum Coverage algorithm and that particular adds will be displayed to particular age group users. This allows the marketers to plan and evaluate strategies online for advertised products.

Graph Algorithms, Influence Maximization, Independent Cascade Model, Social Networks

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[14] Analytics: An Intelligent Approach in Clinical Trail Management Ankit Lodha* Analytics Operations Lead, Amgen, Thousand Oaks, California, USA.
[15] Agile: Open Innovation to Revolutionize Pharmaceutical Strategy Ankit Lodha University of Redlands, 333 N Glenoaks Blvd #630, Burbank, CA 91502.
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S.S. Kamble, T.I. Bagban, "Competitive Influence Maximization in Social Networks," International Journal of Computer Sciences and Engineering, Vol.4, Issue.12, pp.83-86, 2016.

Visual data are transmitted as the high quality digital images in the major fields of communication in all of the modern applications. These images on receiving after transmission are most of the times corrupted with noise. This thesis focused on the work which works on the received image processing before it is used for particular applications. We applied image denoising which involves the manipulation of the DWT coefficients of noisy image data to produce a visually high standard denoised image. This works consist of extensive reviews of the various parametric and non parametric existing denoising algorithms based on statistical estimation approach related to wavelet transforms connected processing approach and contains analytical results of denoising under the effect of various noises at different intensities .These different noise models includes additive and multiplicative type�s distortions in images used. It includes Gaussian noise and speckle noise. The denoising algorithm is application independent and giving a very high speed performance with desired noise less image even in the presence of high level distortion. Hence, it is not required to have prior knowledge about the type of noise present in the image because of the adaptive nature of the proposed denoising algorithm.

Image - denoising, DWT, Gaussian noise , PCA ,K mean clustering

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D. Tripathi, V.K. Shukla, "DWT Based PCA and K-Means Clustering Block Level Approach for SAR Image De-Noising," International Journal of Computer Sciences and Engineering, Vol.4, Issue.12, pp.87-91, 2016.

Scanning Laser ophthalmoscopes (SLOs) are going to be used for early detection of retinal diseases. it`s a method of examination of the attention. The advantage of exploitation SLO is its wide field of scan, which can image associate outsized an area of the membrane for higher identification of the retinal diseases. On the opposite aspect, throughout the imaging methodology, artefacts like eyelashes and eyelids are also imaged in conjunction with the retinal space. This brings an enormous challenge on the thanks to exclude these artefacts. In planned novel approach to automatically extract out true retinal house from associate SLO image based mostly on image method and machine learning approaches. the straightforward Linear unvaried cluster (SLIC) is that the rule utilised in super-pixel calculation. To decrease the unpredictability of image preparing errands and supply associate advantageous primitive image vogue. to scale back the quality of image method tasks and provide a convenient primitive image pattern, conjointly to classified pixels into utterly totally different regions primarily based on the regional size and compactness, referred to as super-pixels. The framework then calculates image based mostly choices reflective textural information and classifies between retinal house and artefacts. The survey presents different methods that are used to detect the artefacts.

Scanning Laser Ophthalmoscope, retinal image analysis,feature selection, retinal artefacts extraction

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G. Gopi, M.R. Kavitha, K.K. Faisal , "A Survey on Retinal Area Detector Using SLO Images," International Journal of Computer Sciences and Engineering, Vol.4, Issue.12, pp.92-97, 2016.

All input is evil until proven otherwise!‖�so security technology come into play.With the rapid growth of interest in the Internet, network security has become a major concern to companies throughout the world. The fact that the information and tools needed to penetrate the security of corporate networks are widely available has increased that concern. Because of this increased focus on network security, network administrators often spend more effort protecting their networks than on actual network setup and administration. Tools that probe for system vulnerabilities, such as the Security Administrator Tool for Analyzing Networks (SATAN), and some of the newly available scanning and intrusion detection packages and appliances, assist in these efforts, but these tools only point out areas of weakness and may not provide a means to protect networks from all possible attacks. Thus, as a network administrator, you must constantly try to keep abreast of the large number of security issues confronting you in today`s world. This paper describes many of the security issues that arise when connecting a private network. Understand the types of attacks that may be used by hackers to undermine network security. For decades, technology has transformed almost every aspect of business, from the shop floor to the shop door. While technology was a fundamental enabler, it was often driven from an operational or cost advantage and seen as separate from business itself. The new reality is that technology doesn�t support the business�technology powers the business. IT risks are now business risks and IT opportunities are now business opportunities.

Security, Fires, IP networks, Internet, Filtering

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[5]. 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.
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[8]. M. Nachammai and N. Radha, "Survey on Black Hole and Gray Hole Attacks in MANET", International Journal of Computer Sciences and Engineering, Volume-04, Issue-05, Page No (66-70), May -2016, E-ISSN: 2347-2693
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A.J. Zaru, "Security Technology," International Journal of Computer Sciences and Engineering, Vol.4, Issue.12, pp.98-103, 2016.

A new swarm intelligent algorithm, called as Animal Migration Optimization (AMO). This paper discusses brief introduction of few optimization techniques. Optimization techniques used for finding optimal solutions. The efficiency of AMO is not appropriate due to its execution time. The efficiency of animal migration optimization algorithm (AMO )is increase by using few benchmark functions and which show the animal migration algorithm performance and it�s working in order to confirm the presentation of AMO including four benchmark functions � Sum, Ackley, Baele and Rosenbrock are employed. The benchmark functions which are considered as standard functions increase the efficiency and minimize the time.

Animal Migration Optimization,Cuckoo Search,Firefly Algorithm,Ant Bee Colony, Particle Swarm Optimization,Bat Algorithm

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R. Rai, V.S. Kushwah, "Animal Migration Optimization: A Survey," International Journal of Computer Sciences and Engineering, Vol.4, Issue.12, pp.104-107, 2016.

Face is an important biometric feature for personal identification. Human beings easily detect and identify faces in a scene but it is very challenging for an automated system to achieve such objectives. Hence there is need to have reliable identification method for user interactions. A computer application which automatically identifies or verifies a person from a digital image or a video frame from a video source, is presented and it is done by comparing selected facial features from the image and a facial database. One of the retrieving method is Content based image retrieval (CBIR), which retrieves images on the basis of automatically derived features. This paper draws points from it but, focuses on a low-dimensional feature based indexing technique for achieving efficient and effective retrieval performance. A static appearance based retrieving system for face recognition referred to as hierarchical model is presented based on singular value decomposition (SVD) is proposed in this paper and is different from principal component analysis (PCA), which effectively considers only Euclidean structure of face space for analysis and leads to poor classification performance in case of great facial variations such as expression, lighting, occlusion and so on, due to the fact the image gray value matrices on which they manipulate are very sensitive to these facial variations. It is a known fact that every image matrix can always have the well known singular value decomposition (SVD) and can be regarded as a composition of a set of base images generated by SVD and further it is pointed out that base images are sensitive to the composition of the face image. Finally the experimental results show that SVD has the advantage of providing a better representation and achieves lower error rates in face recognition but it has the disadvantage that it drags the performance evaluation. So, in order to overcome that, a controlling parameter �α �, which ranges from 0 to 1 is introduced a better result is achieved for α=0.4 when compared to the other value of �α� and it is also seen that it reduces classification redundancy.

Face recognition, Feature based methods, singular value decomposition Euclidean distance Original gray value matrix

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D. Narsaiah, R. Kulkarni, "Static Face Recognition Using Hierarchical Model," International Journal of Computer Sciences and Engineering, Vol.4, Issue.12, pp.108-112, 2016.