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

The need for participants’ performance assessments in academia and industry has been a growing concern. It has attendance, among other metrics, is a key factor in engendering a holistic approach to decision-making. For institutions or organizations where managing people is an important yet challenging task, attendance tracking and management could be employed to improve this seemingly time-consuming process while keeping an accurate attendance record. The manual/quasi-analog approach of taking attendance in some institutions could be unreliable and inefficient, leading to inaccurate computation of attendance rates and data loss. This work, therefore, proposes a system that employs embedded technology and a biometric/ w

In this paper a mathematical model that analytically as well as numerically
the flow of infection disease in a population is proposed and studied. It is
assumed that the disease divided the population into five classes: immature
susceptible individuals (S1) , mature individuals (S2 ) , infectious individual
(I ), removal individuals (R) and vaccine population (V) . The existence,
uniqueness and boundedness of the solution of the model are discussed. The
local and global stability of the model is studied. Finally the global dynamics of
the proposed model is studied numerically.

Statistical learning theory serves as the foundational bedrock of Machine learning (ML), which in turn represents the backbone of artificial intelligence, ushering in innovative solutions for real-world challenges. Its origins can be linked to the point where statistics and the field of computing meet, evolving into a distinct scientific discipline. Machine learning can be distinguished by its fundamental branches, encompassing supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Within this tapestry, supervised learning takes center stage, divided in two fundamental forms: classification and regression. Regression is tailored for continuous outcomes, while classification specializes in c

kinetic studies were carried out the uterine homogenate time course of the association of with LH in benign and malignant uterine

Hair is an excellent indicator for abnormal concentration of toxic elements , In this study a random samples from girls hair of 12 cm long were irradiated by a flux of neutrons (4x10^ n/ cm^.s) obtained from an Am-Be neutron source of 5-Ci activitity . The y-ray activity measurements were carried out by using a " 5x5 " well- type Nal (Tl) detector. The study indicates clearly that the maximum concentration of elements was at about 7 cm hair length.