With the proliferation of both Internet access and data traffic, recent breaches have brought into sharp focus the need for Network Intrusion Detection Systems (NIDS) to protect networks from more complex cyberattacks. To differentiate between normal network processes and possible attacks, Intrusion Detection Systems (IDS) often employ pattern recognition and data mining techniques. Network and host system intrusions, assaults, and policy violations can be automatically detected and classified by an Intrusion Detection System (IDS). Using Python Scikit-Learn the results of this study show that Machine Learning (ML) techniques like Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) can enhance the effectiveness of an Intrusi

Objectives: to assess chronic diseases patients’ knowledge toward stroke risk factors and warning signs, besides
determining the relationship between chronic diseases patients’ knowledge and their sociodemographical
characteristics.
Methodology: A descriptive study was carried out at public medical clinics which has started from December
2
nd, 2008 to August 8th, 2009. A purposive "non-probability" sample of (300) chronic diseases individuals who
were clients of Public Medical Clinics who have one or more of the following chronic diseases (hypertension,
diabetes mellitus, heart diseases, and previous stroke), in Baghdad city. The data were collected through the use
of a constructed questionnaire which consists

Introduction: The current study investigated the use of acid-treated rice husks to remove heavy metals and organic pollutants from water containing heavy metals (R2C and Cd2) and organic pollutants (phenol and atrazine). Methods: The adsorption effect of acid-treated rice husks was compared with other adsorbents such as activated carbon, chitosan, and bentonite clay. Result: both acid-treated rice husks and activated carbon were highly efficient materials, and thus, rice husks were established as a cost-effective alternative. It was revealed that acid treatment of rice husks enhanced adsorption capacity by half, and lead removal was nearly doubled. The most effective pH value for optimizing organic pollutants and heavy metals while

Osteoarthritis (OA) is the most common degenerative joint disorder that ultimately results in the progressive destruction of articular cartilage. The occurrence of knee osteoarthritis (OA) increases with obesity and is more common in women compared with men. Thirty patients (20 females, 10 males) clinically diagnosed with knee OA admitted to Baghdad teaching hospital between January 2015-June 2015. Patients between 35-70 year of age and 30 healthy subject (20 females, 10 males) ages matched. The results showed that increased body mass index (BMI) is a well-recognized risk factor for knee osteoarthritis, and the effect of obesity is a stronger predictor of developing knee OA symptoms in women than men. There was also a significant diffe

European Chemical Bulletin (ISSN 2063-5346) is a peer-reviewed journal that publishes original research papers, short communications, and review articles in all areas of chemistry. European Chemical Bulletin has eight sections, namely

Hydatidosis is a zoonotic, helminthes parasitic disease that cause veterinary and human problems due to economic burden. Amis of the current study was to estimate the prevalence rate of hydatidosis infecting different organs of sheep slaughtered in street. In addition, investigate the relationship between age, gender and the infected rates in some Baghdad areas during a period from may to end of July 2018.A total of 188 sheep was examined for detection of Hydatid cyst in different organs using macroscopic and microscopic examination The overall prevalence rate of 47.9% was recorded. The results indicate non significant differences according to age and gender, the highest rate 57% was observed in sheep more than one year old compare

Big data analysis is essential for modern applications in areas such as healthcare, assistive technology, intelligent transportation, environment and climate monitoring. Traditional algorithms in data mining and machine learning do not scale well with data size. Mining and learning from big data need time and memory efficient techniques, albeit the cost of possible loss in accuracy. We have developed a data aggregation structure to summarize data with large number of instances and data generated from multiple data sources. Data are aggregated at multiple resolutions and resolution provides a trade-off between efficiency and accuracy. The structure is built once, updated incrementally, and serves as a common data input for multiple mining an