A State of Art Survey for OS Performance Improvement

Through the huge growth of heavy computing applications which require a high level of performance, it is observed that the interest of monitoring operating system performance has also demanded to be grown widely. In the past several years since OS performance has become a critical issue, many research studies have been produced to investigate and evaluate the stability status of OSs performance. This paper presents a survey of the most important and state of the art approaches and models to be used for performance measurement and evaluation. Furthermore, the research marks the capabilities of the performance-improvement of different operating systems using multiple metrics. The selection of metrics which will be used for monitoring the performance depends on monitoring goals and performance requirements. Many previous works related to this subject have been addressed, explained in details, and compared to highlight the top important features that will very beneficial to be depended for the best approach selection

Image Steganalysis in Frequency Domain Using Co-Occurrence Matrix and Bpnn

In the last two decades, steganalysis has become a fertile research area to minimize the security risks left behind by Misuse of data concealment in digital computer files. As the propagation of hidden writing increased, the need for the steganalysis emerged and grew to a large extent necessary to deter illicit secret communications. This paper introduces a steganalysis system to detect hidden information in images through using co-occurrence matrix, frequency domain transform, the first three moments, and back propagation neural network (BPNN). Four varieties of the system implemented. Firstly, the co-occurrence matrix calculated for the input image, which suspected to be a carrier of hidden secret information. Second, three levels of discrete wavelet transform (DWT) are applied resulting in 12 subbands. Then, those subbands along with the original image are transformed by discrete Fourier transform (DFT) or discrete cosine transform (DCT) to produce 13 subbands. After that, the first three moments are calculated resulting feature vector with 39 features. Finally, BPNN is used as a classifier to determine whether the image is containing hidden information or not. The system is tested with and without co-occurrence matrix, each of them once using DFT and another time using DCT. The results have shown that using co-occurrence matrix with DFT has the highest performance, which was 81.82% on the Hiding Ratio of 0.5 bit per pixel. This work demonstrates a good effect comparing to previous works

Rider weed deep residual network-based incremental model for text classification using multidimensional features and MapReduce

Increasing demands for information and the rapid growth of big data have dramatically increased the amount of textual data. In order to obtain useful text information, the classification of texts is considered an imperative task. Accordingly, this article will describe the development of a hybrid optimization algorithm for classifying text. Here, pre-processing was done using the stemming process and stop word removal. Additionally, we performed the extraction of imperative features and the selection of optimal features using the Tanimoto similarity, which estimates the similarity between features and selects the relevant features with higher feature selection accuracy. Following that, a deep residual network trained by the Adam algorithm was utilized for dynamic text classification. Dynamic learning was performed using the proposed Rider invasive weed optimization (RIWO)-based deep residual network along with fuzzy theory. The proposed RIWO algorithm combines invasive weed optimization (IWO) and the Rider optimization algorithm (ROA). These processes are carried out under the MapReduce framework. Our analysis revealed that the proposed RIWO-based deep residual network outperformed other techniques with the highest true positive rate (TPR) of 85%, true negative rate (TNR) of 94%, and accuracy of 88.7%