Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne

Resumen

El presente trabajo nace de una inquietud por la enseñanza del español en Irak a nivel universitario especialmente ante las dificultades que los alumnos árabes en general, e iraquíes en particular, encuentran en su proceso de aprendizaje. Nuestra primera inclinación fue, pues, prestar una atención directa  y cercana al alumno como sujeto del aprendizaje, así como a lo que el alumno produce como resultado del mismo. En el presente trabajo pretendemos dotar al estudiante de los conocimientos lingüísticos necesarios para poder interaccionar en una variedad de situaciones y enfrentarse a problemas cotidianos, de manera que desarrolle las destrezas comunicativas que le permitan establecer una co

The investigation of machine learning techniques for addressing missing well-log data has garnered considerable interest recently, especially as the oil and gas sector pursues novel approaches to improve data interpretation and reservoir characterization. Conversely, for wells that have been in operation for several years, conventional measurement techniques frequently encounter challenges related to availability, including the lack of well-log data, cost considerations, and precision issues. This study's objective is to enhance reservoir characterization by automating well-log creation using machine-learning techniques. Among the methods are multi-resolution graph-based clustering and the similarity threshold method. By using cutti

Manual fruit picking is labor-intensive and can damage fruit. Fully mechanized picking is efficient, but it also risks fruit damage. Therefore, semi-automated tools are needed to improve bitter orange picking. This paper presents a smart manual picker designed to facilitate picking while predicting fruit maturity based on picking force as well as various chemical and physical parameters using machine learning (ML). The study methodology consists of five stages: (1) manufacturing the smart picker, (2) picking 50 bitter orange samples, (3) measuring the characteristics of the bitter oranges in the laboratory, (4) training different ML models, and (5) identifying the most accurate model for predicting fruit maturity. The results indicate that

Abstract

The aim of the current research is to prepare an integrated learning program based on mathematics standards for the next generation of the NYS and to investigate its impact on the development of the teaching performance of middle school mathematics teachers and the future thinking skills of their students. To achieve the objectives of the research, the researcher prepared a list of mathematics standards for the next generation, which were derived from a list of standards. He also prepared a list of the teaching competencies required for middle school mathematics teachers in light of the list of standards, as well as clarified the foundations of the training program and its objectives and the mathematical

In this work, the preparation of new multidentate Schiff-base lig and and its metal complexes are described. The formation of the lig and{ 2,2`((5-methyl-1,3-phenylene)-bis-(oxy))-bis-N`(E`)-2- hydroxybenzylideneacetohydrazide}[H2L] was prepared from the reaction {2,2-((5-methyl-1,3-phenylene)-bis-(oxy))- di-(acetohydrazide)}[M]precursor and salicylaldehyde in a 1:2 mole ratio, respectively. The reaction of the lig and [H2L] with (Cr+3 , Mn+2 and Fe+2 )metal ions in a 1:2 (L:M) mole ratio. Ligand and complexes were characterised via spectroscopic analyses; [FT-IR, UV-Vis spectroscopy,(C.H.N) microanalysis, chloride content, thermal analysis(TG), electrospray mass, magnetic susceptibility and conductivity measurements. The characterisation d

В статье рассматриваются понятие наглядности и ее классификация, эффективное применение принципа наглядности, возможность и преимущество использования наглядности и современные инструменты ее реализации в ираке.

Вследствие глобальных изменений во всем мире изменилась и роль иностранного языка в системе образования. Современный человек, шагающий в ногу со временем, не мыслит себя без зна

Metal corrosion is a destructive process for many industrial operations, including oil well acidizing and acid pickling. Therefore, numerous efforts made by many researchers to control the steel corrosion. In the present work, A (E)-4-(((4-(5-mercapto-1,3,4-oxadiazol-2-yl) phenyl) amino) methyl)-2-methoxyphenol (MOPM) has been synthesized and characterized as a new corrosion inhibitor for mild steel in 0.1 M hydrochloric acid. FTIR and 1 HNMR were used in the diagnosis of MOPM, while electrochemical polarization technique was employed to test the performance of inhibitor at various temperatures and inhibitor concentrations. Electrochemical studies showed that MOPM acts as a mixed-type inhibitor with a maximum inhibition efficiency of

The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized s