The modern and contemporary history of Libya has attracted the attention of researchers during the nineties of the last century and beyond.
     The subject of ((Jewish activity in Libya 1911 AD - 1951 AD)), and the nature of follow - up economic and social activities and political participation has remained poor in terms of study and follow - up, because of the scarcity of sources, especially documentary ones. The research will reveal within its axes the most important of these activities.

Brain Fingerprinting (BF) is one of the modern technologies that rely on artificial intelligence in the field of criminal evidence law. Brain information can be obtained accurately and reliably in criminal procedures without resorting to complex and multiple procedures or questions. It is not embarrassing for a person or even violates his human dignity, as well as gives immediate and accurate results. BF is considered one of the advanced techniques related to neuroscientific evidence that relies heavily on artificial intelligence, through which it is possible to recognize whether the suspect or criminal has information about the crime or not. This is done through Magnetic Resonance Imaging (EEG) of the brain and examining

The Security Council has an active role in addressing international crises and dealing with their causes. The Libyan crisis is one of the most important real tests of the Security Council and its role in maintaining international peace and security, as the Council has proven so far an ineffective role in resolving the crisis and dealing withtheir causes, which has prolonged its duration and increased its complexities and dangerous repercussions, perhaps the most prominent of which is the threat of the recently achieved cease-fire and the formation of a new transitional government led by Abdel Hamid al-Dabaiba, the growing significant obstacles facing the political process, foremost of which is the continued presence of foreign forces , m

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a

In recent years, literary studies have witnessed a remarkable shift towards employing digital technologies, particularly artificial intelligence tools, in analyzing literary texts and exploring their linguistic and semantic structures. This trend has provided researchers with new possibilities for understanding texts in quantitative and qualitative ways that transcend traditional methods based solely on critical reading. The current research aims to introduce professors and students of Arabic to artificial intelligence tools that contribute to the analysis of literary texts, focusing on exploring their mechanisms for studying style, meaning, structure, and emotion. It also seeks to highlight the most prominent challenges facing researchers

This study proposes a pioneering Ethical Artificial Intelligence (EAI) framework for advancing sustainable development in Iraq by integrating eight multidimensional sustainability indicators—administrative, technological, economic, environmental, social, legal, security, and governance. Utilizing data from 60 completed development projects, the framework combines SPSS statistical analysis, the SMART-AI model, and Artificial Neural Networks (ANN) to identify key determinants of project success and failure. Results reveal a 37% project failure rate, with administrative and technological deficiencies emerging as the most influential predictors. The SMART-AI model achieved an accuracy of 91.3% using stratified k-fold cross-validation. A bilin