Cybersecurity refers to the actions that are used by people and companies to protect themselves and their information from cyber threats. Different security methods have been proposed for detecting network abnormal behavior, but some effective attacks are still a major concern in the computer community. Many security gaps, like Denial of Service, spam, phishing, and other types of attacks, are reported daily, and the attack numbers are growing. Intrusion detection is a security protection method that is used to detect and report any abnormal traffic automatically that may affect network security, such as internal attacks, external attacks, and maloperations. This paper proposed an anomaly intrusion detection system method based on a

Cybersecurity refers to the actions that are used by people and companies to protect themselves and their information from cyber threats. Different security methods have been proposed for detecting network abnormal behavior, but some effective attacks are still a major concern in the computer community. Many security gaps, like Denial of Service, spam, phishing, and other types of attacks, are reported daily, and the attack numbers are growing. Intrusion detection is a security protection method that is used to detect and report any abnormal traffic automatically that may affect network security, such as internal attacks, external attacks, and maloperations. This paper proposed an anomaly intrusion detection system method based on a

Modern civilization increasingly relies on sustainable and eco-friendly data centers as the core hubs of intelligent computing. However, these data centers, while vital, also face heightened vulnerability to hacking due to their role as the convergence points of numerous network connection nodes. Recognizing and addressing this vulnerability, particularly within the confines of green data centers, is a pressing concern. This paper proposes a novel approach to mitigate this threat by leveraging swarm intelligence techniques to detect prospective and hidden compromised devices within the data center environment. The core objective is to ensure sustainable intelligent computing through a colony strategy. The research primarily focusses on the

Globally, chronic kidney disease (CKD) has emerged as a significant public health concern, characterized by high rates of morbidity and mortality. To assess the risk of kidney damage, researchers have identified tissue inhibitor of matrix metalloproteinase-2 (TIMP-2) and fatty acid-binding protein-1 (FABP-1) as valuable biomarkers. This study aims to analyse the effectiveness of specific biomarkers in assessing CKD and its associated mechanisms in Iraqi patients. The study was conducted from December 2023 to May 2024. Ninety subjects, aged 48–65 years; including 60 patients with CKD (38 male and 22 female) attended the Baghdad Teaching Hospital/ Medical City/ Dialysis Unit- Baghdad, Iraq. In addition, 30 healthy people (15 male an

Background: Adenoiditis is a common cause of otitis media with effusion (OME) in children & perhaps OME is one of the most common diseases leads to hearing impairment in children with subsequent impairment of speech development & learning difficulties , however, treatment remains controversial. Objectives: To evaluate if there is a significant advantage of tympanostomy tube insertion in association with adenoidectomy over adenoidectomy in association with myringotomy alone in treatment of children with OMEType of the study: This is a prospective study. Patients & methods: The study consisted from 63 children diagnosed as cases of bilateral OME & variable degrees of adenoid hypertrophy. The patients were divided randomly i

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

Echocardiography is a widely used imaging technique to examine various cardiac functions, especially to detect the left ventricular wall motion abnormality. Unfortunately the quality of echocardiograph images and complexities of underlying motion captured, makes it difficult for an in-experienced physicians/ radiologist to describe the motion abnormalities in a crisp way, leading to possible errors in diagnosis. In this study, we present a method to analyze left ventricular wall motion, by using optical flow to estimate velocities of the left ventricular wall segments and find relation between these segments motion. The proposed method will be able to present real clinical help to verify the left ventricular wall motion diagnosis.