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Cloud computing is one of the most promising technology. When using hybrid cloud we all don’t know in which order the processes will be submitted to the private and public cloud. As some processes need to be more secure than other processes. Private Cloud is meant for security and privacy than public cloud. They need some mechanism that how these processes will be executed on private cloud or public cloud. So better is to prioritize the processes. A novel way is presented where an Artificial Neural Network model is designed to reprioritize the cloud computing processes with extended parameters. ANN being an Artificial Intelligence Technique is meant for accuracy. The results shows that the proposed technique helps in improving accuracy

Cloud Computing, Hybrid Cloud, Resource Provisioning, Artificial Neural Network

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Pooja Chopra, R.P.S. Bedi, "A Novel way to Reprioritize Cloud Computing Process Requests with Extended Parameters using ANN," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.365-370, 2018.

Snag discovery and evasion can be considered as the focal issue in planning portable robots. In this paper, a snag maintaining a strategic distance from the robot is outlined which can distinguish snags in its way and move around them without making any crash. It is a robot vehicle that chips away at Arduino Microcontroller and utilizes an ultrasonic sensor to distinguish impediments. The Arduino board was chosen as the microcontroller and Arduino software to complete the programming. The ultrasonic sensor gives higher exactness in identifying encompassing hindrances. Being a completely independent robot, it effectively moved in obscure situations with no crash. The equipment utilized in this undertaking is broadly accessible and reasonable which makes the robot effortlessly replicable.

Snag, Arduino Microcontroller, Ultrasonic sensor, Arduino software

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P.P. Bhatt, J.A. Trivedi, "Development of Snag Discovery Robot utilizing Arduino and Ultrasonic Sensor," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.371-373, 2018.

Wireless ad-hoc network (or) Mobile Ad Hoc Network is a set of mobile nodes in the combination of some computing contrivances which could be placed randomly everywhere in the network to do a specific task inclusive of dispensing and transferring of messages for sundry applications. The nodes can broadcast information in meticulous range and the host is capable of changing their position very repeatedly. If the information is not transferred within a scrupulous moment, retransmission of data packets is done by the protocols, which enhance the communication overhead in MANET. Broadcasting is playing a vital role for path discovery. Most probably, four types of broadcasting methods are used. They are simple flooding, location-predicated methods, probability predicated strategies and Neighbour cognizance predicated. This article exposes a widespread learning and investigation of various routing overhead reducing techniques proposed by many researchers. This paper additionally reveals the routing overhead percentage achieved using the prominent techniques proposed by contemporary researchers.

MANET, Routing Overhead, Flooding, Broadcasting, Probability based, Area based, Neighbor Knowledge

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[4] Kamalaesh chandravanshi and D.K.Mishra, “Minimization of Routing Overhead on the basis Multipath and Destination Distance Estimation Mechanism under MANET”, In the proceedings of 2016 IEEE International Conference on ICT in Business Industry and Government,pp.no:1-6, 2017 .
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En Bo Wang ,IEEE Transaction on Mobile Computing,vol:12, Number 3,2013
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A Survey”,International Journal of Pure and Applied Mathematics” volume 119 No.12 2018.

P.Tamilselvi, T.N.Ravi, "A Survey on Various Techniques to Minimize Routing Overhead in MANET," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.374-379, 2018.

The extent of communicated information through internet has augmented speedily over the past few years. Image compression is the preeminent way to lessen the size of the image. JPEG is the one the best technique related to lossy image compression. In this paper a novel JPEG compression algorithm with Fuzzy-Morphology techniques was proposed. The efficacy of the proposed algorithm compared to JPEG is presented with metrics like PSNR, MSE, No of bits transmitted. The proposed approaches lessen the number of encoded bits as a result tumbling the quantity of memory needed. The Planned approaches are best appropriate for the images corrupted with Gaussian, Speckle, Poisson, Salt & Pepper noises.

Compression, Morphology, PSNR, MSE, RMS

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[11] Dillip Ranjan Nayak, Ashutosh Bhoi” Image Enhancement Using Fuzzy Morphology”, Journal of Engineering, Computers & Applied Sciences”, Vol.3,NO.3, pp no:22-24, March 2014.
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Vanitha Kakollu, P Chandrasekhar reddy, "Fuzzy Morphology Based JPEG compression for Image Quality Enhancement of Noisy Images," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.380-384, 2018.

Software defect prediction is one of the most active research areas in software engineering. Machine learning approaches are good in solving these. A predictive model is constructed by using machine learning approaches and classified them into defective and non-defective modules. Clustering is an unsupervised classification method aims at creating groups of objects, or clusters, in such a way that objects in the same cluster are very similar and objects in different clusters are quite distinct. In this paper we proposed a new hybrid approach of K-means clustering algorithm combined with Genetic Algorithm to get the optimum no of clusters. From the present studies it is shown that the performance of the proposed optimized hybrid algorithm is better than the conventional k-means algorithm without optimization.

Unsupervised classifier, Clustering, K-means, Genetic Algorithm, Software Defect Prediction

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Manjula C, Lilly Florence, "Optimized Machine Learning Approach for Software Defect Prediction using K-means with Genetic Algorithms," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.385-390, 2018.

Brain tumor detection and extraction within the time frame to offer better healthcare is vital and very important, but a time-consuming task performed by clinical supervisors or radiologists. Its accuracy for the brain tumor detection from modern imaging modalities also depends on their experience only. So the use of computer-aided methodology is very important to overcome these limitations. Generally, Cerebrum a tumor begins in the glial cells called Gliomas. Gliomas can be moderate developing (slow rate) or quickly developing (high rate). Doctors utilize the review of a mind tumor in light of gliomas to choose which treatment a patient needs. The state of the tumor is of indispensable significance for the treatment. In this paper, we propose a mechanized framework to separate between typical mind and strange cerebrum with tumor in the MRI pictures and furthermore additionally arrange the anomalous cerebrum tumors into High Rate or Low Rate tumors. The proposed framework utilizes KMFCM as the division strategy for grouping while Discrete Wavelet Transform (DWT) Principal Component Analysis (PCA) and Support Vector Machine (SVM)are the primary algorithms used. The calculated values of Cho/Cr and Cho/NAA of 15 different patients of different ages of both genders data is extracted from Brats-2017dataset are used classify into tumor grades.

Tumors, Cho/Cr,Cho/NAA, DWT, PCA, High rate, Low rate, Gliomas

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B Nandan, Kunjam Nageswara Rao, "Automatic Segmentation and Categorization of the Brain Tumors," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.391-397, 2018.

This paper assesses comparatively the performance of image fusion in different color channels using an image matting based multi focus image fusion technique, the JR method. This is a solely vicinity-based image matting algorithm that relies on the close pixel clusters in the input images. Color spaces provide powerful information for image processing by means of color variants, color histogram, color texture etc.. In our assessment, firstly we transform RGB color model of multi focus source images in to 6 different color spaces that are HSV, L*a*b, YUV, YIQ, YCbCr and XYZ. Next, each color channel of input images (RGB-R, RGB-G, RGB-B, LAB-L, LAB-A, LAB-B, HSV-H, HSV-S, HSV-V, YUV-Y,YUV-U, YUV-V, XYZ-X, XYZ-Y,XYZ-Z, YCbCr-Y, YCbCr-Cb, YCbCr –Cr, YIQ-Y, YIQ-I, YIQ-Q) are used in fusion process using the image matting based multi focus image fusion with optimal cluster size (the JR method). Finally the fused images are assessed with standard image quality metrics. The results certainly show better results in LAB-L and YIQ-Q color channals.

Color spaces, Multi focus image fusion, image color models, color image fusion

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J.R.Dharmaraj, D.C.Durairaj, J.J.Melodina, "Comparative Assessment of Color Models for Multi-Focus Image Fusion With Optimal Cluster Size," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.398-403, 2018.

The brain tumor detection is the approach which can detect the tumor portion from the MRI image. To detect tumor from the image various techniques has been proposed in the previous times. The major challenge of robust brain tumor nuclei/cell detection is to handle significant variations in cell appearance and to split touching cells. The technique which is proposed in this research paper is based on morphological scanning and naïve bayes classification. The morphological scanning will scan the input image and naïve bayes classifier mark the tumor portion from the MRI image. The proposed algorithm is implemented in MATLAB and results are analyzed in terms of qualitatively and quantitatively in various parameters like false positive rate, false negative rate, execution time, PSNR, MSE, Accuracy and Fault Detection and also calculate overlapping area with dice coef. The proposed method has been tested on data set with more than 25 slide scanned images. This proposed method achieved accuracy with 86% best cell detection.

MRI, Naïve Bayes, Morphological Scanning, Brain Tumor, Clustering

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[8] Mohammed Y. Kamil, “Brain Tumor Area Calculation in CT-scan image using Morphological Operations”, IOSR Journal of Computer Engineering (IOSR-JCE) ISSN: 2278-8727, Volume 17, Issue 2, Ver.-V, PP 125-128, Mar – Apr. 2015.
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[10] Kim Mey Chew, Ching Yee Yong , Rubita Sudirman , Syvester Tan Chiang Wei, “Bio-Signal Processing and 2D Representation for Brain Tumor Detection Using Microwave Signal Analysis”, 2018, IEEE
[11] Navpreet Kaur (Student), Manvinder Sharma (Assistant Professor), “Brain Tumor Detection using Self-Adaptive K-Means Clustering”, 2018, IEEE
[12] Animesh Hazra , Ankit Dey , Sujit Kumar Gupta , Md. Abid Ansari, “Brain Tumor Detection Based on Segmentation using MATLAB”, 2017, IEEE
[13] Saumya Chauhan, Aayushi More, Ritumbhra Uikey, Pooja Malviya, Asmita Moghe, “Brain Tumor Detection and Classification in MRI Images using Image and Data Mining”, 2017, IEEE
[14] Reema Mathew, A Dr. Ba bu Anto P, “Tumor detection and classification of MRI Brain image using Wavelet Transform and SVM”, 2017,IEEE
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Gurkarandesh Kaur, Ashish Oberoi, "Development of an Efficient Clustering Technique for Brain Tumor Detection for MR Images," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.404-409, 2018.

Face is an important biometric trait that has been used for various application of today’s technology. Face recognition comprises recognition of an individual identity to others on the basis of facial features. Facial features provide low accuracy due to variation in illumination and resolution of the images. In this paper an approach has been proposed that overcomes the issue of low recognition accuracy under low resolution captured images. In this paper texture features based approach that has been developed from the local ternary pattern by using two different groups of neighbour pixel values. On the basis of these two different groups texture features have been computed by comparing these values with centre pixel value of particular regions. By using local codes developed from the particular region whole image features have been computed using different codes from different regions. In this paper an approach that is combination of filtering and feature extraction has been proposed so that better face recognition can be achieved. On the basis of parameters analysis proposed work outperforms as compare to exiting approaches.

Face recognition, DCT, DWT, LDA, LTP, LBP and PCA

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[10] Prabhjot Singh and Anjana Sharma, "Face Recognition Using Principal Component Analysis in MATLAB", International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.1, pp.1-5, 2015
[11] Ratnesh Kumar Shukla, Ajay Agarwal, Anil Kumar Malviya, "An Introduction of Face Recognition and Face Detection for Blurred and Noisy Images", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.39-43, 2018

Alka Rani, Narinder Kumar, "Face Recognition under Variation in Resolution Using Enhanced Local Ternary Pattern," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.410-415, 2018.

In this paper, online handwritten numeral recognition for Gujarati is proposed. Online handwritten character recognition is in trend for research due to a rapid growth of handheld devices. The authors have compared Support Vector Machine (SVM) with linear, polynomial, and radial basis function kernels. The authors have used hybrid feature set. The authors have used zoning and chain code directional features which are extracted from each stroke. The dataset of the system is of 2000 samples and was collected by 200 writers and tested by 50 writers. The authors have achieved an accuracy of 92.60%, 95%, and 93.80% for linear, polynomial, RBF kernel and an average processing time of 0.13 seconds, 0.15seconds, and 0.18 seconds per stroke for linear, polynomial, RBF kernel.

Online Handwritten Character Recognition (OHCR), Handwritten Character Recognition (HCR), Optical Character Recognition (OCR), Support Vector Machine (SVM), Gujarati Numeral, Gujarati Digits

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V. A. Naik, A. A. Desai, "Online Handwritten Gujarati Numeral Recognition Using Support Vector Machine," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.416-421, 2018.