MB Mahmood, BN Dhannoon

In this study three reactive dyes (blue B, red R and yellow Y) in single , binary and ternary solution were adsorbed by activated carbon AC in equilibrium and kinetic experiments. Surface area, Bulk and real density, and porosity were carried out for the activated carbon.
Batch Experiments of pH (2.5-8.5) and initial concentration (5-100) mg/l were carried out for single solution for each dye. Experiments of adsorbent dosage effect (0.1-1)g per 100 ml were studied as a variable to evaluate uptake% and adsorption capacity for single dyes(5, 10) ppm, binary and ternary (10) ppm of mixture solutions solution of dyes. Langmuir, and Freundlich, models were used as Equilibrium isotherm models for single solution. Extended Langmuir and Freun

Matching between wind site characteristics and wind turbine characteristics for three selected sites in Iraq was carried out. Site-turbine matching for potential wind power application in Iraq has not yet been well reported on. Thus, in this study, five years’ wind speed data for sites located in Baghdad (33.34N, 44.40E), Nasiriyah (31.05N, 46.25E), and Basrah (30.50N, 47.78E) were collected. A full wind energy analysis based on the measured data, Weibull distribution function, and wind turbine characteristics was made. A code developed using MATLAB software was used to analyse the wind energy and wind turbines models. The primary objective was to achieve a standard wind turbine-site matching based on the capacity factor. Another matching

With the vast usage of network services, Security became an important issue for all network types. Various techniques emerged to grant network security; among them is Network Intrusion Detection System (NIDS). Many extant NIDSs actively work against various intrusions, but there are still a number of performance issues including high false alarm rates, and numerous undetected attacks. To keep up with these attacks, some of the academic researchers turned towards machine learning (ML) techniques to create software that automatically predict intrusive and abnormal traffic, another approach is to utilize ML algorithms in enhancing Traditional NIDSs which is a more feasible solution since they are widely spread. To upgrade t

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

Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has