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

In this paper, an adaptive integral Sliding Mode Control (SMC) is employed to control the speed of Three-Phase Induction Motor. The strategy used is the field oriented control as ac drive system. The SMC is used to estimate the frequency that required to generates three phase voltage of Space Vector Pulse Width Modulation (SVPWM) invertor . When the SMC is used with current controller, the quadratic component of stator current is estimated by the controller. Instead of using current controller, this paper proposed estimating the frequency of stator voltage since that the slip speed is function of the quadratic current . The simulation results of using the SMC showed that a good dynamic response can be obtained under load

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

 Heritage is considered as the civilization and cultural wealth accumulated over the . centuries, whereas architectural heritage is the physical witness of that civilization. Despite the fact that architectural heritage is the most important effort for economic development of any communit,، it suffers from deterioration and neglection especially in the Arab communities. Recently awareness has increased about the importance of investing on architectural heritage generally and sustainable investment particularly. The goal of investment process in heritage areas is to revive economic activity in addition to attempt to revive the heritage and community values. Research aims to examine the relationship between sustainable investment and

The Wheat husk is one of the common wastes abundantly available in the Middle East countries especially in Iraq. The present study aimed to evaluate the Wheat husk as low cost material, eco-friendly adsorbents for the removal of the carcinogenic dye (Congo red dye) from wastewater by investigate the effect of, at different conditions such as, pH(3-10), amount of adsorbents (1-2.3gm/L),and particle size (125-1000) μm, initial Congo red dye concentration(10, 25 , 50 and 75mg/l)  by batch experiments. The results showed that the removal percentage of dye increased with increasing adsorbent dosage, and decreasing particle size. The maximum removal and uptake reached (91%) , 21.5mg/g, respectively for 25 initial concent

Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an