The recent advancements in security approaches have significantly increased the ability to identify and mitigate any type of threat or attack in any network infrastructure, such as a software-defined network (SDN), and protect the internet security architecture against a variety of threats or attacks. Machine learning (ML) and deep learning (DL) are among the most popular techniques for preventing distributed denial-of-service (DDoS) attacks on any kind of network. The objective of this systematic review is to identify, evaluate, and discuss new efforts on ML/DL-based DDoS attack detection strategies in SDN networks. To reach our objective, we conducted a systematic review in which we looked for publications that used ML/DL approach

Excess heat significantly reduces the efficiency and lifespan of electrical and optoelectronic devices. While passive radiative cooling is becoming more common, achieving active thermal control without physical reconfiguration remains challenging. Unlike conventional static absorbers, we propose a broadband plasmonic solar absorber designed to regulate energy absorption in the near-infrared (NIR) region without modifying the geometrical parameters. The design utilizes a coaxial cylindrical metal-insulator-metal (MIM) configuration, combining refractory copper (Cu), silicon dioxide (SiO2), and a trilayer graphene (Gr) that allows electrical tuning in a broadband solar absorber. Simulation results show a maximum broadband absorption efficienc

A new and hybrid deep learning-based approach for diagnosing faults in electric vehicle (EV) drive motors is proposed in this article. This article presents a new and hybrid deep learning-based method of diagnosing faults in the drive motors of electric vehicles (EV). In contrast to standard CNNLSTM approaches that depend on SoftMax classification, the introduced framework combines a Random Forest (RF) classifier to enhance the generalization, interpretability, and robustness of fault prediction. Furthermore meant for use on edge computing equipment with IoT integration, the design allows for real-time monitoring in resource-limited settings. The introduced algorithm utilizes a Random Forest (RF) classifier for accurate fault classification

Unter Exilliteratur versteht man „die literarische Produktion von Autoren, die wegen politischer, religiöser oder rassistischer Verfolgung gezwungen sind, sich an einem anderen als dem von ihnen gewünschten Lebens- und Arbeitsort aufzuhalten.“[1] Deswegen unterscheidet sich „Migration“ von „Exil“ dadurch, dass sie freiwillige Auswanderung ist. Bereits in der Antike und auch im Mittelalter gab es Schriftsteller, die vor Zensur oder politischer Verfolgung ins Exil geflohen sind, wie zum Beispiel der griechische Satiriker des 

Background Due to the intermittent, nonlinear, and uncertain behavior of renewable energy sources (res) such as solar and wind, grid stability and reliability require very high forecasting and optimization skills as widely reported in the literature. Traditional optimization methods work very well in small or static systems but are suffer difficulty on large-scale, dynamic and stochastic renewable environment due to their NP-hard nature. Methods The framework introduces the concept of a Machine Learning-Assisted Hybrid Cuckoo Search (ML-HCS) that combines CS with a hybrid metaheuristic and integrates Long Short-Term Memory (LSTM) networks for forecasting based on both regression models of LSTMs and hybrid optimization algorithm

Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on may greatly improve the chances of recovery by accurately predicting outcomes and developing suitable treatment plans. Grading breast cancer properly, especially evaluating nuclear atypia, is difficult owing to faults and inconsistencies in slide preparation and the intricate nature of tissue patterns. This work explores the capability of deep learning to extract characteristics from histopathology photos of breast cancer. The research introduces a new method called SMOTE-based Convolut

An analysis of three selected wells in the Ain Zala oil field (AZ-16, AZ-17, and AZ-24) in the northwestern part of Mosul Governorate. In terms of biostratigraphy, it consists of planktonic and suspended foraminifera, which comprise 37 species belonging to 17 genera. Based on the recorded planktonic foraminifera, three main biozones were identified: Globotruncana aegyptiaca Partial Zone, which refers to the age of the Late Campanian, Gansserina gansseri Partial Range Zone, which indicates the age of the Early Maastrichtian, and Abathomphalus mayaroensis Interval Zone, which indicates the age of the Late Maastrichtian. The Abathomphalus mayaroensis Interval Zone is divided into four subzones: Racemiguembelina fructicosa Partial Range

The geological modeling of an oil-bearing reservoir, which specifies the lithology and analyzes the petrophysical parameters of complex reservoir, its being created using Petrel software to construct reservoir models that describe and estimate porosity and water saturation distributions. 3D modeling of carbonate reservoirs has proven to be a highly effective technique for evaluate the economic benefit of the reservoir by estimating the formation petrophysical properties and calculating the oil reserves. The study aims constructed on evaluated Yamama formation in Four drilling wells (Nasiriyah-1, Nasiriyah-3, Nasiriyah-4, and Nasiriyah-5) entering the Yamama Formation of the Al-Nasiriyah field. In Al-Nasiriyah Oil field, and within t