Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas

Most recognition system of human facial emotions are assessed solely on accuracy, even if other performance criteria are also thought to be important in the evaluation process such as sensitivity, precision, F-measure, and G-mean. Moreover, the most common problem that must be resolved in face emotion recognition systems is the feature extraction methods, which is comparable to traditional manual feature extraction methods. This traditional method is not able to extract features efficiently. In other words, there are redundant amount of features which are considered not significant, which affect the classification performance. In this work, a new system to recognize human facial emotions from images is proposed. The HOG (Histograms of Or

LK Abood, RA Ali, M Maliki, International Journal of Science and Research, 2015 - Cited by 2

This research deals with the use of a number of statistical methods, such as the kernel method, watershed, histogram and cubic spline, to improve the contrast of digital images. The results obtained according to the RSME and NCC standards have proven that the spline method is the most accurate in the results compared to other statistical methods

In this paper, image compression technique is presented based on the Zonal transform method. The DCT, Walsh, and Hadamard transform techniques are also implements. These different transforms are applied on SAR images using Different block size. The effects of implementing these different transforms are investigated. The main shortcoming associated with this radar imagery system is the presence of the speckle noise, which affected the compression results.

This research deals with the use of a number of statistical methods, such as the kernel method, watershed, histogram, and cubic spline, to improve the contrast of digital images. The results obtained according to the RSME and NCC standards have proven that the spline method is the most accurate in the results compared to other statistical methods.

Graphite nanoparticles were successfully synthesized using mixture of H2O2/NH4OH with three steps of oxidation. The process of oxidations were analysis by XRD and optics microscopic images which shows clear change in particle size of graphite after every steps of oxidation. The method depend on treatments the graphite with H2O2 in two steps than complete the last steps by reacting with H2O2/NH4OH with equal quantities. The process did not reduces the several sheets for graphite but dispersion the aggregates of multi-sheets carbon when removed the Van Der Waals forces through the oxidation process.

In some cases, surgeons need to navigate through the computer system for reconfirmation patients’ details and unfortunately surgeons unable to manage both computer system and operation at the same time. In this paper we propose a solution for this problem especially designed for heart surgeon, by introducing voice activation system with 3D visualization of Angiographic images, 2D visualization of Echocardiography processed video and selected patient’s details. In this study, the processing, approximation of the 3D angiography and the visualization of the 2D echocardiography video with voice recognition control are the most challenging work. The work involve with predicting 3D coronary three from 2D angiography image and also image enhan

A content-based image retrieval (CBIR) is a technique used to retrieve images from an image database. However, the CBIR process suffers from less accuracy to retrieve images from an extensive image database and ensure the privacy of images. This paper aims to address the issues of accuracy utilizing deep learning techniques as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon, Kim, Kim, and Song (CKKS). To achieve these aims, a system has been proposed, namely RCNN_CKKS, that includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a