Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Deci

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Tre

The availability of different processing levels for satellite images makes it important to measure their suitability for classification tasks. This study investigates the impact of the Landsat data processing level on the accuracy of land cover classification using a support vector machine (SVM) classifier. The classification accuracy values of Landsat 8 (LS8) and Landsat 9 (LS9) data at different processing levels vary notably. For LS9, Collection 2 Level 2 (C2L2) achieved the highest accuracy of (86.55%) with the polynomial kernel of the SVM classifier, surpassing the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) at (85.31%) and Collection 2 Level 1 (C2L1) at (84.93%). The LS8 data exhibits similar behavior. Conv

In this research, the effects of both current and argon gas pressure on the bending properties of welded joints were studied. Using the possible ranges of welding gas pressures and currents, Tungsten inert gas welding (TIG) of stainless steel (304) sheet was used to obtain their influence on the maximum bending force of the (TIG) welded joints. Design of experiment (DOE) ‘version 10' was used to determine the design matrix of experiments depending on the used levels of the input factors. Response surface methodology (RSM) technique was used to obtain an empirical mathematical model for the maximum bending force as a function of welding parameters (Current and Argon gas pressure). Also, the analysis of variance (ANOVA) was used to verif

This study examines the opportunity presented by the COVID-19 pandemic for city planners and leaders to learn from the crisis and build resilient cities with long-term societal, economic, and environmental resilience against future disasters. The research focuses on the relationship between urban planning and policies and the extent of their resilience, particularly in response to pandemic-related disasters. The study evaluates the ability of the city of Baghdad to respond to the pandemic and identifies gaps in its resilience. The study uses the scorecard measurement instrument to examine the disaster resilience of cities, with a focus on governance and financial capability, disaster planning and preparedness, and disaster response

Background: Saliva is one of the most important etiological host factors in relation to dental caries. It affects the carious process by its organic and inorganic constituents; in addition to its physiological functions as (flow rate, pH and buffer capacity). The aims of this study were to determine the concentrations of major elements (calcium and phosphorus) and trace elements (ferrous iron, nickel, chromium and aluminum) in saliva among a group of adolescent girls, and to explore the relation of these elements, flow rate and pH with dental caries. Material & Methods: The study group consisted of 25 girls with an age of 13-15 years old. Dental caries was diagnosed by both clinical and radiographical examinations following the criteria of

The aim of this work is to provide an efficient selection technique as a part of planning process to guide the decision makers to decide the preferences of one supplier over another for purchasing lab instruments in education domain. Fuzzy Analytical Hierarchy Process has used as a multi-criteria decision process, as an industrial engineering tool with certain emphasis on the qualitative aspects required to the decision makers. While the concept of degree of possibility for each criterion is used to reach its relative weights, a specific methodology created to reach the final objective decision of supplier selection. A questionnaire form was developed and distributed to five universities located in Baghdad province with a total