The objective of the research is to uncover the effect of the strategy of Quranic verses in the collection of science and systemic intelligence for second-grade students. The research sample consisted of (48) students of second grade students in the middle of Al Rasheed Boys School of the second Karkh Directorate, Distribution in the two divisions, Division of (b) and experimental group that studied strategy of Quranic verses, and the Division (a) control group which studied the regular way, and results indicated a statistically significant differences for the experimental group students studied using the strategy Verses in systemic intelligence collection.

Mammary tumors (CMT) in dogs in Iraq may be induced by carcinogenic war ordnance. In our study, 10 virgin un- spayed military/pet bitches aged 5-15 years presented with abnormal masses in the abdomen with painful oedema, swelling, anorexia, weight loss, weakness and mild fever. Examination of regional lymph nodes and thoracic radiography confirmed metastasis. Tumors were excised and determined to be mostly adenocarcinomas involving multiple glands, solid in texture, 5-15 cm in size, mostly in the inguinal mammary glands at stage T3: >5 cm. Microscopy confirmed presence of adenocarcinoma in 8 dogs and solid carcinoma in 2 with half of tumors being grade III. Tumors had pleomorphic hyperchromatic cell nuclei in stroma, epithelial cells of duc

Pseudomonas aeruginosa is a Gram-negative opportunistic pathogen and a model bacterium for studying virulence and bacterial social traits. While it can be isolated in low numbers from a wide variety of environments including soil and water, it can readily be found in almost any human/animal-impacted environment. It is a major cause of illness and death in humans with immunosuppressive and chronic conditions, and infections in these patients are difficult to treat due to a number of antibiotic resistance mechanisms and the organism’s propensity to form multicellular biofilms. One hundred twenty clinical samples and forty hospital environmental samples (various sources) were collected from hospitals in Baghdad city during the period from Oc

t: - Sports training in itself is a means, not an end. It is the core process that involves using exercises to provide an opportunity for athletes to perform their duties with the highest level of efficiency by developing and enhancing their capabilities and potential, using them to gain the greatest benefit for themselves and, consequently, for their team. This requires the selection of various methods and different means of training, focusing on specific physical and skill-related aspects. Therefore, training plans should take into account individual differences between players. Many researchers and experts in the field of sports in general, and combat sports in particular, emphasize the importance of having a holistic approach in the tra

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Tre

Imitation learning is an effective method for training an autonomous agent to accomplish a task by imitating expert behaviors in their demonstrations. However, traditional imitation learning methods require a large number of expert demonstrations in order to learn a complex behavior. Such a disadvantage has limited the potential of imitation learning in complex tasks where the expert demonstrations are not sufficient. In order to address the problem, we propose a Generative Adversarial Network-based model which is designed to learn optimal policies using only a single demonstration. The proposed model is evaluated on two simulated tasks in comparison with other methods. The results show that our proposed model is capable of completing co

Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet

Classifying butterfly species is crucial in biodiversity studies and environmental monitoring. However, manual classification is often a laborious process that requires specialized expertise and is prone to error, especially when species have similar visual characteristics. To address these drawbacks, this paper presents a hybrid approach that combines machine learning with deep learning for feature extraction. To enhance the visibility of important features, preprocessing techniques such as background removal and binarization are applied to butterfly images. Feature extraction was performed using the SqueezeNet convolutional neural network, pretrained on the ImageNet dataset. By discarding the final classification layer, the networ

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Deci

Patients infected with the COVID-19 virus develop severe pneumonia, which typically results in death. Radiological data show that the disease involves interstitial lung involvement, lung opacities, bilateral ground-glass opacities, and patchy opacities. This study aimed to improve COVID-19 diagnosis via radiological chest X-ray (CXR) image analysis, making a substantial contribution to the development of a mobile application that efficiently identifies COVID-19, saving medical professionals time and resources. It also allows for timely preventative interventions by using more than 18000 CXR lung images and the MobileNetV2 convolutional neural network (CNN) architecture. The MobileNetV2 deep-learning model performances were evaluated