Varied uses of international rivers in the past few decades dramatically, resulting in this multiplicity of uses and all associated with it for the occurrence of freshwater scarcity activities, and thus an increase in conflicts and disputes around on the rights of each of the riparian countries to benefit from the waters of the river at various purposes, particularly the establishment of dams on some of them as is the case (Renaissance Ethiopian) big impact on downstream countries Dam (Egypt and Sudan), due to the Oukuahma at the end of the Nile Valley made them vulnerable to environmental fluctuations, political crises facing the Nile basin countries, and any reduction in the proportion of water is not only the Nile River, but for all r

A substantial portion of today’s multimedia data exists in the form of unstructured text. However, the unstructured nature of text poses a significant task in meeting users’ information requirements. Text classification (TC) has been extensively employed in text mining to facilitate multimedia data processing. However, accurately categorizing texts becomes challenging due to the increasing presence of non-informative features within the corpus. Several reviews on TC, encompassing various feature selection (FS) approaches to eliminate non-informative features, have been previously published. However, these reviews do not adequately cover the recently explored approaches to TC problem-solving utilizing FS, such as optimization techniques.

Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

The objective of this study is to evaluate the efficacy and safety of rowatinex and tamsulosin in the treatment of patients with ureteric stone.

Forty patients with ureteric stone ranged (4- 12) mm, were included in this study. They were randomized into two groups where the first group includes twenty patients treated with Rowatinex three times daily (Group 1), and the second group includes twenty patients treated with tamsulosin 0.4mg/day (Group 2). All patients were randomly assigned to receive the designed standard medical therapy for a maximum of 3 weeks.

Each group was given an antibiotic as prophylaxis and an injectable non-steroidal anti-inflammatory drug used on demand. At the outpatient clinic all subjects were a

     Binary relations or interactions among bio-entities, such as proteins, set up the essential part of any living biological system. Protein-protein interactions are usually structured in a graph data structure called "protein-protein interaction networks" (PPINs). Analysis of PPINs into complexes tries to lay out the significant knowledge needed to answer many unresolved questions, including how cells are organized and how proteins work. However, complex detection problems fall under the category of non-deterministic polynomial-time hard (NP-Hard) problems due to their computational complexity. To accommodate such combinatorial explosions, evolutionary algorithms (EAs) are proven effective alternatives to heuristics in solvin

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