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Nowadays, compressing large data using different compression methods increase rapidly. This explains the recent importance and popularity of compressing data of multimedia applications as well as wavelet transforms in this field. Wavelet transforms tend to benefit of block-based transforms, including the Discrete Cosine Trans-form (DCT). DCT is responsible for displaying blocking artifacts while wavelets have compact support and can offer a DCT an adaptable substitute to DCT. The popularity of single-wavelets, formed through converting and expanding of single approximation functions as well as detail functions, offered high multi-resolution function-approximation bases. This paper discusses the idea of the image compression using two levels DWT with two-dimensional DCT on every 8x8 block. Hence, the low-frequency sub-band is reduced. The DC-Column stores the DC-coefficients. As a result of using Huffman coding the DC-Column will be coded. Meanwhile the other AC-Coefficient has to be quantized in order to gain additional zeros, allowing it to be converted easily to bits through the Huffman coding. HL2, HH2, as well as LH2 are other high-frequencies coefficients that are coded using the Minimize-Matrix-Size Algorithm. The mentioned proposed algorithm converts the three high-frequency coefficients into a single real number. Nevertheless, the use of the proposed algorithm; one-dimensional-array that has many real values will be reduced and will be converted it to many bits. The results of the compression algorithm are based on Mean Square Error ( MSE).

Minimize Matrix Size; Huffman Coding; DWT; DCT

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[7] Mohammed Mustafa Siddeq, � Using Two Levels DWT with Limited Sequential Search Algorithm for Image Compression�,Journal of Signal and Information Processing,3, 51-62 doi:10.4236/jsip.2012.
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[11] M.M. Siddeq and RODRIGUES, Marcos, � A novel 2D image compression algorithm based on two levels DWT and DCT transforms with enhanced minimize-matrix-size algorithm for high resolution structured light 3D surface reconstruction�. 3D Research-Springer, 6 (3), p. 26. DOI 10.1007/s13319-015-0055-6, 2015b

M.A. Anwer, D.A. Anwar and S.A. Anwer , "Image Compression: Combination of Discrete Transformation and Matrix Reduction," International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.1-6, 2017.

In everyday life recommendation system plays an important role and collaborative filtering (CF) is used widely in many e-commerce applicationsfor online product recommendation. A recommender system is mainly used for better predictions as better decision making by using preferences during searching, shopping etc. The preferences of other users, user`s past preferences and big data is the driving force behind Recommendation systems. In this paper, we present collaborative filter types and their main challenges. Using the open source library Apache Mahout, we implemented collaborative filter using single-criteria to recommend items to particular users. Also, we showed the flow of Apache Mahout command�s execution to process the huge data using LogLikelihood similarity algorithm in big data scenario.

Recommendation system; Collaborative filtering; Apache Mahout; Big data; Hive

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M.N. Manu, B. Ramesh , "Single-criteria Collaborative Filter Implementation using Apache Mahout in Big data," International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.7-13, 2017.

Internet Technology and its constant evolvement gives humans an opportunity to be an active explorer over social media and micro blogging sites. Due to which people have resorted to a route of Internet with images, a tempting platform which is hard avoid. This theme gained an overwhelming response as a communication medium for the obvious reason that people nowadays like to share or express their thoughts through images. Unlike text, images helps to annotate viewers` flow of emotions much conveniently with depth. But people relying more on power imagery to share information over unreliable channel, well-known as Internet invites exploitation and misapplication of confidential data which should be avoided. Considering the importance of the issue raised in the light of protecting images, encryption is a way to assure security. Image encryption techniques converts an original image to illegible form, so only authorized access is possible with a key. Over years, different image encryption schemes had been put forward with various issues being addressed according to the requirement of applications. In this paper, we proposed a novel image encryption algorithm based on permutation polynomials over integer rings which makes an attempt to overcome the limitations of existing methods. Here, the original image is scrambled by applying permutation polynomial to its rows and columns. Eventually, the experimental results and calculating the evaluation parameters using MATLAB shows that the proposed encryption scheme achieve satisfactory hiding aspect. Also, the comparison with respect to existing ones is made to analyze performance of the proposed technique.

Cryptography; Image Encryption; Permutation Polynomials; Internet; integer rings; MATLAB

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N.B. Wagh, M. Kolhekar, "A Novel Approach utilizing Permutation Polynomials over integer rings as a Cryptological Application for Effective Encryption of Digital Images," International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.14-21, 2017.

The use of remotely sensed image has been a wide area in research. Different images of the same scene captured by the satellites at different times can provide information about the significant changes that held on earth with time. Many techniques for change detection has been developed while new techniques are also emerging. This paper gives a review of pixel based and transformation based techniques used for change detection. The paper begins with the discussion on pixel based techniques like image differencing, Image Ratioing and Image regression. Then, transformation based techniques like principal component analysis and change vector analysis has been discussed in detail. The paper has been concluded with the comparison of the discussed techniques based on advantages, limitations and applications.

Change Detection; Principal Component Analysis; Image Ratioing; Image Differencing

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A. Sharma, T. Gulati, "Review of Change Detection Techniques for Remotely Sensed Images," International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.22-25, 2017.

Extraction of features from the biomedical image using the texture and color space based image processing analysis algorithm is developed using hybrid of DWT, entropy filtering and watershed transform is discussed in this article. To extract the textures we have used entropy features using function on the MATLAB algorithm where it corresponds to the input image parameter with the use of spatial based parameters. The texture analysis based skin texture extraction algorithm consists of steps related to decomposing the input image into a set of binary images from which the color space dimensions of the resulting regions can be computed in order to describe segmented texture patterns.

Image segmentation, Image texture analysis, Image watershed transform, Image dwt2

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P. Ranjan, P.R. Khan, "Wavelet approximated texture data watershed transform (WATDWT) segmentation of Bio-Medical Images," International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.26-31, 2017.

Clustering is the process of organizing objects into several groups whose members are similar in some way and is very important technique in data mining as it has applications spread extensively example marketing, biology, pattern recognition etc. Various algorithms have been proposed, published, implemented for clustering like the one published by Rodriguez and Liao but this algorithm is dependent and sensitive to specified parameters and also faces difficulties in identification of ideal problems. Another one published by G. Wang and Q. Song this algorithm do not list all possible numbers of the nearest neighbors and the accuracy is not better in terms of Olivetti face data set then impact of this on performance. This paper overcome the problem faces by above algorithm so, here proposes a new clustering method that will identify cluster centers automatically via statistical testing. Here first define a new metric to evaluate the local density of an object which is named K-density and second metric is define to evaluate the distance of an object to its neighbors with higher density. Then, product of these two metrics is used to evaluate the centrality of each object. After analyzing the distribution of these metrics further transformed the clustering center identification into a problem of extreme-value detection from a long-tailed distribution Finally, apply outward statistical testing method to detect the clustering centers automatically and then completed the clustering process by assigning each of the rest objects to the cluster that contains its nearest neighbor with higher K-density.

Clustering, Clustering Center Identification, Long-tailed Distribution, Outward Statistical Testing

[1] G. Wang and Q. Song, "Automatic Clustering via Outward Statistical Testing on Density Metrics," in IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 8, pp. 1971-1985, Aug. 1 2016.
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A.A. Jadhav, V.S. Gaikwad, "Automatic Clustering Based On Outward Statistical Testing Using Advanced Density Metrics," International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.32-35, 2017.

The process of Learning distance function over different objects is called as Metric learning. In various of data mining processes like clustering, nearest neighbours etc. is very important problem that relies on distance function. For many types of data, linear model is not very useful but most of metric learning methods assumes linear model of distance. In the recent nonlinear data demonstrated potentialpower of non-Mahalanobis distance function, particularly tree-based functions. This leads to a more robust learning algorithm. We compare our method to a number of state-of-the-art benchmarks on k-nearest neighbour classification, large-scale image retrieval and semi supervised clustering problems. Then we find that our algorithm yields results comparable to the state-of-the-art. A novel tree-based non-linear metric learning method can have information from both constrained and unconstrained points. And hierarchical nature of training can minimize the constraint satisfaction problem as it won�t have to go through the constraint satisfaction process per object but per hierarchy. Combining the output of many of the resulting semi-random weak hierarchy metrics and by introducing randomness during hierarchy training, we can obtain a powerful and robust nonlinear metric model.

Similarity Measures, Clustering, , Image Retrieval, Classification, Data Mining,Constrained And Unconstrained Point

[1] D. M. Johnson, C. Xiong and J. J. Corso, "Semi-Supervised Nonlinear Distance Metric Learning via Forests of Max-Margin Cluster Hierarchies," vol. 28, no. 4, pp. 1035-1046, April 1 2016.
[2] A. Bellet, A. Habrard, and M. Sebban, �A survey on metric learning for feature vectors and structured data,� arXiv preprint arXiv:1306.6709, 2013.
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[6] Y. Ying and P. Li, �Distance metric learning with eigen value optimization,� J. Mach. Learn. Res., vol. 13, pp. 1�26, 2012.
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A. Mhetre, V.S. Gaikwad, "Relaxed Constraints Formulation for Non-linear Distance Metric Learning in Hierarchical Clustering," International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.36-39, 2017.

Reputation and trust are two important key factors in e-commerce applications where as sellers or product ratings. In ecommerce applications, reputation is used to select best sellers among different available sellers by users. The reputation system models are used to project the different sellers based on their offering services and quality they provide to users. The trustworthiness of sellers are computed based on different models and method they opt in each model. Feedback comments of trust computation will be good impression as the users are free to direct themselves in free text feedback reviews. In proposed work we have calculating good reputation scores from users feedbacks based on a multidimensional trust model. In order to work this model, we have used algorithm for weights and ratings computation by mining feedback comments in which NLP, Topic Modelling techniques are used.

Electronic commerce, CommTrust, text mining, Repudiation based models, Sentiment Analysis

[1] Xiuzhen Zhang, Lishan Cui, and Yan Wang, "Computing Multi-Dimensional Trust by Mining E-Commerce Feedback Comments " IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING VOL:26 NO:7 YEAR 2014 .
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[4] P. Resnick and R. Zeckhauser, �Trust among strangers in internet transactions: Empirical analysis of eBay�s reputation system,� Econ. Internet E-Commerce, vol.11, no. 11, pp. 127�157, Nov. 2002.
[5]J.O�Donovan,B.Smyth,V.Evrim,andD.McLeod,�Extracting and visualizing trust relationships from online auction feedback comments,� in Proc. IJCAI, San Francisco, CA, USA, 2007, pp. 2826�2831.
[6] M. De Marneffe, B. MacCartney, and C. Manning, �Generating typed dependency parses from phrase structure parses,� in Proc. LREC, vol. 6, 2006, pp. 449�454.
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P. Balaji, O. Nagaraju, D. Haritha, "Reputation Based Trust Evaluation in E-Commerce Applications by Using Feedback Comments," International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.40-42, 2017.

The Ad-hoc network is a collection of wireless mobile nodes which dynamically form a temporary network without the use of any existing network infrastructure or centralized administration. It may connect hundreds to thousands of mobile nodes. The mobile nodes communicate directly with each other without the aid of access points. They form an arbitrary topology, where the routers are free to move randomly and arrange themselves as required. In this paper, an attempt has been made to investigate the impact of mobility models on the performance of three MANET on-demand reactive routing protocols: AODV, DSR and DYMO. The mobility models that are used in this work are: Random Waypoint mobility model and Group mobility model. The performance differentials are analyzed using varying network size, varying pause time, and varying velocity. We used Qual-Net [18] from scalable networks for the simulation purpose. The performance analysis is based on different network metrics such as packet delivery ratio, throughput, and average end.

Mobile Ad-hoc Network (MANET) Routing protocol; AODV; DSR; DYMO; Random Waypoint mobility; Group mobility model

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[4] 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. ISSN: 2249-9555
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[10] Performance of AODV, DSR and DSDV Protocols under varying node Movement by Veena Anand and Suresh Chandra Gupta IEEE INFOCOM, 2015.
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[18] Er. Hanisha Goyal, Er. Parveen Kakkar, � Performance Investigation of DYMO, DSR, AODV and LAR Routing Protocols using Different Mobility in MANETs�, International Journal of Engineering Research and Technology, Vol. 2, Issue 12, December 2013.
[19] Rashmi Rohankar, et. al�s, �Performance Analysis of Various Routing Protocols (Proactive and Reactive) for Random Mobility Models of Adhoc Networks�, IEEE conf., 2012.
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P. Pal, R. Pal , "Observing of Mobility Model against Reactive Routing Protocols in MANETs of throughput, Average end-to-end delay, packet delivery ratio," International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.43-52, 2017.

In cognitive radio networks (CRN) an intelligent spectrum allocation is an immense subjects because of its deficiency of spectrum demand. Resources in CRN would be allocated based on dynamic access methods with respected to sensed radio environment. A principal research trial is that how would be allocated or assigned presented unused spectrum to unlicensed users. In this paper we provide a comprehensive survey of dynamic resource allocation and priority scheduling in MIMO-CRN. The appropriate portion of unmoving recurrence range existing together intellectual radios while amplifying all out transmission capacity utilization what`s more, reducing impedance is required for the dynamic range use in MIMO-CRN. The system for established extent segment came to achievement to less range utilization over the entire range. In this paper we presented the different approaches used for dynamic resource allocation and scheduling in heterogeneous MIMO-CRN.

Cognitive Radio; Dynamic Scheduling; Priority Queue; Heterogenetive Services, MIMO

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