FG Mohammed, HM Al-Dabbas, Iraqi journal of science, 2018 - Cited by 6

In recent years, the world witnessed a rapid growth in attacks on the internet which resulted in deficiencies in networks performances. The growth was in both quantity and versatility of the attacks. To cope with this, new detection techniques are required especially the ones that use Artificial Intelligence techniques such as machine learning based intrusion detection and prevention systems. Many machine learning models are used to deal with intrusion detection and each has its own pros and cons and this is where this paper falls in, performance analysis of different Machine Learning Models for Intrusion Detection Systems based on supervised machine learning algorithms. Using Python Scikit-Learn library KNN, Support Ve

The road transportation system is considered as major component of the infrastructure in any country, it affects the developments in economy and social activities. The Asphalt Concrete which is considered as the major pavement material for the road transportation system in Baghdad is subjected to continuous deterioration with time due to traffic loading and environmental conditions, it was felt that implementing a comprehensive pavement maintenance management system (PMMS), which should be capable for preserving the functional and structural conditions of pavement layers, is essential. This work presents the development of PMMS with Visual inspection technique for evaluating the Asphalt Concrete pavement surface condition; common types o

The ground level ozone concentration at different locations in Baghdad city was identified. Five
different sites have been chosen to identify the ground level ozone concentration. Al- Dora and Al-
Za'afarania were chosen as areas contained point source ( power plant station ) in addition to high traffic
load , while Al –Uma park, Aden square and Al-Mawal square were chosen as area contained heavy
traffic only (line source). The measurement focuses on spring and fall because these periods display
favorable meteorology to ozone formation. During the research period the maximum values (peaks) for
ground level ozone concentration were observed at fall: at Al-Za'afarania area 101ppb as an average, at
Al-Dora 87 ppb as a

Trickle bed reactor was used to study the hydrogenation of nitrobenzene over Ni/SiO2 catalyst. The catalyst was prepared using the Highly Dispersed Catalyst (HDC) technique. Porous silica particles (capped cylinders, 6x5.5 mm) were used as catalyst support. The catalyst was characterized by TPR, BET surface area and pore volume, X-ray diffraction, and Raman Spectra. The trickle bed reactor was packed with catalyst and diluted with fine glass beads in order to decrease the external effects such as mass transfer, heat transfer and wall effect. The catalyst bed dilution was found to double the liquid holdup, which increased the catalyst wetting and hence, the gas-liquid mass transfer rate. The main product of the hydrogenation reaction of n

The different parameters  on  mechanical  and  microstructural  properties  of  aluminium  alloy  6061-T6 Friction  stir-welded  (FSW) joints  were investigated in the  present study. Different welded  specimens were produced by employing variable rotating  speeds and welding speeds. Tensile strength of the produced joints  was tested at room  temperature and the the effecincy was assessed, it was 75% of the base metal at rotational speed 1500 rpm and weld speed 50 mm/min. Hardness of various zones of FSW welds are presented and  analyzed by  means of  brinell hardness number . Besides to thess tests the bending properties  investigat

Twitter is becoming an increasingly popular platform used by financial analysts to monitor and forecast financial markets. In this paper we investigate the impact of the sentiments expressed in Twitter on the subsequent market movement, specifically the bitcoin exchange rate. This study is divided into two phases, the first phase is sentiment analysis, and the second phase is correlation and regression. We analyzed tweets associated with the Bitcoin in order to determine if the user’s sentiment contained within those tweets reflects the exchange rate of the currency. The sentiment of users over a 2-month period is classified as having a positive or negative sentiment of the digital currency using the proposed CNN-LSTM