Klebsiella pneumoniae is a severe opportunistic strain of enteric bacteria that is a major cause of urinary tract infection and pneumonia. This study was conducted in Baghdad City during September 2020-November 2020 on 50 clinical samples of urine, vaginal, sputum, wound swabs, ear swabs, and burn swabs. strains were identified using the VITEK-2 compact system and tested in K. pneumoniae terms of susceptibility to various antimicrobial drugs by Kirby-Bauer test. The isolates were more predominant in the females (56%) compared to males (44%). The antibiotic resistance rate of varied among different isolated clinical sample sources. K. pneumoniae K. pneumoniae isolated from different clinical specimens differed with respect

فقدان الزوج يعد حدثًا يغير حياة النساء، مما يضطرهن إلى السباحة في عالم جديد مليء بالحزن والوحدة والشكوك. مع الوقت، تطورت طريقة تصوير الأرامل بشكل كبير تعكس التغييرات في الآراء والقيم الثقافية. تُمثل الأرامل تقليديًا بأنهن ضعيفات ومعتمدات في الأدب، مستندة إلى افتراض أنهن يفتقدن الدعم المالي بعد وفاة شركائهن. ومع ذلك، فإن هذا التصوير لا يعترف بتأثير الأرملة على الرفاهية والهوية الشخصية. يسعى هذا النص إلى

The aim of this study is to propose reliable equations to estimate the in-situ concrete compressive strength from the non-destructive test. Three equations were proposed: the first equation considers the number of rebound hummer only, the second equation consider the ultrasonic pulse velocity only, and the third equation combines the number of rebound hummer and the ultrasonic pulse velocity. The proposed equations were derived from non-linear regression analysis and they were calibrated with the test results of 372 concrete specimens compiled from the literature. The performance of the proposed equations was tested by comparing their strength estimations with those of related existing equations from literature. Comparis

Today, the science of artificial intelligence has become one of the most important sciences in creating intelligent computer programs that simulate the human mind. The goal of artificial intelligence in the medical field is to assist doctors and health care workers in diagnosing diseases and clinical treatment, reducing the rate of medical error, and saving lives of citizens. The main and widely used technologies are expert systems, machine learning and big data. In the article, a brief overview of the three mentioned techniques will be provided to make it easier for readers to understand these techniques and their importance.

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

In recent years, the world witnessed a rapid growth in attacks on the internet which resulted in deficiencies in networks performances. The growth was in both quantity and versatility of the attacks. To cope with this, new detection techniques are required especially the ones that use Artificial Intelligence techniques such as machine learning based intrusion detection and prevention systems. Many machine learning models are used to deal with intrusion detection and each has its own pros and cons and this is where this paper falls in, performance analysis of different Machine Learning Models for Intrusion Detection Systems based on supervised machine learning algorithms. Using Python Scikit-Learn library KNN, Support Ve

Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has