This paper proposed a new method for network self-fault management (NSFM) based on two technologies: intelligent agent to automate fault management tasks, and Windows Management Instrumentations (WMI) to identify the fault faster when resources are independent (different type of devices). The proposed network self-fault management reduced the load of network traffic by reducing the request and response between the server and client, which achieves less downtime for each node in state of fault occurring in the client. The performance of the proposed system is measured by three measures: efficiency, availability, and reliability. A high efficiency average is obtained depending on the faults occurred in the system which reaches to

Photodetector based on Rutile and Anatase TiO₂ nanostructures/n-Si Heterojunction

Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor

RA Ali, LK Abood, Int J Sci Res, 2017 - Cited by 2

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature

Several previous investigations and studies utilized silica fume (SF) or (micro silica) particles as supplementary cementitious material added as a substitute to cement-based mortars and their effect on the overall properties, especially on physical properties, strength properties, and mechanical properties. This study investigated the impact of the inclusion of silica fume (SF) particles on the residual compressive strengths and microstructure properties of cement-based mortars exposed to severe conditions of elevated temperatures. The prepared specimens were tested and subjected to 25, 250, 450, 600, and 900 °C. Their residual compressive strengths and microstructure were evaluated and compared with control samples (C

Protecting information sent through insecure internet channels is a significant challenge facing researchers. In this paper, we present a novel method for image data encryption that combines chaotic maps with linear feedback shift registers in two stages. In the first stage, the image is divided into two parts. Then, the locations of the pixels of each part are redistributed through the random numbers key, which is generated using linear feedback shift registers. The second stage includes segmenting the image into the three primary colors red, green, and blue (RGB); then, the data for each color is encrypted through one of three keys that are generated using three-dimensional chaotic maps. Many statistical tests (entropy, peak signa

Global concerns are rising due to complications associated with the use of chemical agents and antibiotic resistance. Consequently, research focus has shifted towards the quest for effective agents of biological origin. The aim of the present study was to assess the antioxidant and antimicrobial potentials of aqueous and organic extracts derived from various parts of Alcea kurdica. Different parts of A. kurdica were obtained and prepared into leaf, flower and root powders. The powders were extracted with aqueous and organic solvents. The antimicrobial activity of these extracts was assessed against bacterial pathogens using the agar well-diffusion assay. Additionally, the antioxidant effects of the extracts were evaluated using the

abstract

the research discussed a stage of strategic management. The strategic of the evaluation of the proposed strategy through feedback is to ensure that it is implemented with the least possible variation. The research aims at evaluation  a proposed strategy for the Ministry of Planning for the years 2018-2022 in line with the orientations of the state, taking into account the surrounding environmental conditions. It relies on scientific bases and steps to formulate the strategy The extent of the strategy suitability was tested through a set of statistical means and its objectivity was verified through a survey of a number of specialized experts who were selected in accordance with the principle