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

 5-Fluorouracil is one of the commonly used chemotherapy drugs in anticancer therapy; unfortunately treatment with 5-FU by solely has many drawbacks  low lipophilicity, low permeability, low molecular weight, and its relatively poor plasma protein binding; also a brief half-life therefore frequent administration is required to maintain the optimal therapeutic plasma level which in addition to its poor selectivity, drug resistance and limited penetration to cancer cells; leads to increased incidence of side-effects to healthy cells/tissues and low response rates. In order to minimize these drawbacks; 5-FU was chemically  conjugated with pyrrolidine dithiocarbamate (PDTC) in a mutual prodrug moiety (S-(9H-purin-6-yl) 3-(

The use of real-time machine learning to optimize passport control procedures at airports can greatly improve both the efficiency and security of the processes. To automate and optimize these procedures, AI algorithms such as character recognition, facial recognition, predictive algorithms and automatic data processing can be implemented. The proposed method is to use the R-CNN object detection model to detect passport objects in real-time images collected by passport control cameras. This paper describes the step-by-step process of the proposed approach, which includes pre-processing, training and testing the R-CNN model, integrating it into the passport control system, and evaluating its accuracy and speed for efficient passenger flow