Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the  most effective parameter, particularly when Age<49.5. Whereas  Ki67  appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimu

In this paper, new brain tumour detection method is discovered whereby the normal slices are disassembled from the abnormal ones. Three main phases are deployed including the extraction of the cerebral tissue, the detection of abnormal block and the mechanism of fine-tuning and finally the detection of abnormal slice according to the detected abnormal blocks. Through experimental tests, progress made by the suggested means is assessed and verified. As a result, in terms of qualitative assessment, it is found that the performance of proposed method is satisfactory and may contribute to the development of reliable MRI brain tumour diagnosis and treatments.

An intrusion detection system (IDS) is key to having a comprehensive cybersecurity solution against any attack, and artificial intelligence techniques have been combined with all the features of the IoT to improve security. In response to this, in this research, an IDS technique driven by a modified random forest algorithm has been formulated to improve the system for IoT. To this end, the target is made as one-hot encoding, bootstrapping with less redundancy, adding a hybrid features selection method into the random forest algorithm, and modifying the ranking stage in the random forest algorithm. Furthermore, three datasets have been used in this research, IoTID20, UNSW-NB15, and IoT-23. The results are compared with the three datasets men

Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural

The main idea of this research is that the researcher believes that media research remains useless unless its goals and results are achieved by using the correct scientific tools. The researcher chooses 100 research papers of about 35% of the published ones, 10 of them are excluded because they are outside media. We use a simple and randomized sample including the three departments of media: journalism, television and radio journalism and public relations. The researcher adopts statistical methods such as Fay coefficient, correlation coefficient, Pearson correlation coefficient and straight line equation.
The researcher uses an analytical form followed by analysis of content, them the scale. The results are found in 58 researches, w

Three types of extracts ( aquatic, alcoholic, and oily ) were prepared from the fruits of coconuts, and a series of chemical tests were conducted in addition to the use of the FTIR equipment to determine the active locations in the prepared extracts. The results indicated the presence of active compounds (tannins, saponins, flavonoids, turbines and steroids) in the extracts prepared from the fruits of coconuts, also the antimicrobial capability of these extracts were tested on pathogenic bacteria isolated from wounds and burns infections cases. The results proved that the concentration 80 mg/ml of the aquatic extract is the minimum inhibitory concentration for the microbes: Proteus vulgaris and Pseudomonas fluorescence, while the

Global date palm production is steadily increasing and adopting technologies such as unmanned aerial vehicles (UAVs) and deep learning can reduce costs, save time, and improve productivity. To address this issue, the authors have proposed an innovative approach that uses UAVs for high-resolution aerial imaging. These images, collected by the Department of Computer Engineering at Al-Salam University in Baghdad and the Institute of Machine Design, Faculty of Mechanical Engineering, Poznan University of Technology, support improved orchard management, palm counting, and yield estimation. Precise spraying and pollination are also facilitated and accelerated, reducing overall cultivation costs. The proposed methodology involves processing captur

The liability limit liability limit and reinsurance re factors insurance are important in influencing the achievement of company's competitive goals and achieve success and excellence in the market and because the limit of liability and reinsurance aimed to increase output with the stability of inputs leading to increased insurance business and increase profits. The study was based on two variables presidents interact with each other to form the intellectual and philosophical framework has two ( the limit of liability and reinsurance ) . The study was launched from a problem expressed by a number of targeted questions answered from the elucidation of theoretical philosophy and intellectual goals of these variables and highlight the impor