One of the major challenges of academic institutions is the student dropout rate. It negatively impacts students, universities, and society by precluding student success, wasting time and financial resources, and reducing graduation rates. Previous studies have used machine learning (ML), deep learning (DL), and explainable artificial intelligence (XAI) techniques to predict dropout. However, most of these studies focused primarily on academic factors and were often limited to a single educational institution. Although national reports indicate that 40% to 50% of university students in Saudi Arabia do not complete their programs, few studies have examined dropout at various universities in the country or considered a broader set of factors. Therefore, this research aims to develop predictive models for academic dropout in Saudi universities using ML and DL techniques, taking into account various academic, personal, social, and cultural factors. Data were collected from 4,560 students and then processed and analyzed using twelve feature selection-based learning models. Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were used as XAI models to identify the most influential features, including GPA, academic year, employment status, family/friends support, and prior major knowledge. The results indicate that the K-Nearest Neighbors (KNN)-based Recursive Feature Elimination (RFE) model achieves the best performance, with an accuracy of 97.6%. Additionally, the Local Explanation Evaluation Framework (LEAF) showed that LIME outperformed SHAP on all four metrics: fidelity, local concordance, prescriptivity, and stability. These findings provide insights into the key factors influencing student dropout at Saudi universities and support the development of early intervention strategies.
Cancer, a life-threatening disorder caused by genetic abnormalities and metabolic irregularities, is a substantial health danger, with lung and colon cancer being major contributors to death. Histopathological identification is critical in directing effective treatment regimens for these cancers. The earlier these disorders are identified, the lesser the risk of death. The use of machine learning and deep learning approaches has the potential to speed up cancer diagnosis processes by allowing researchers to analyse large patient databases quickly and affordably. This study introduces the Inception-ResNetV2 model with strategically incorporated local binary patterns (LBP) features to improve diagnostic accuracy for lung and colon cancer identification. The model is trained on histopathological images, and the integration of deep learning and texture-based features has demonstrated its exceptional performance with 99.98% accuracy. Importantly, the study employs explainable artificial intelligence (AI) through SHapley Additive exPlanations (SHAP) to unravel the complex inner workings of deep learning models, providing transparency in decision-making processes. This study highlights the potential to revolutionize cancer diagnosis in an era of more accurate and reliable medical assessments.
Background Sharing knowledge such as resources, research results, and scholarly documents, is of key importance to improving collaboration between researchers worldwide. Research results from the field of artificial intelligence (AI) are vital to share because of the extensive applicability of AI to several other fields of research. This has led to a significant increase in the number of AI publications over the past decade. The metadata of AI publications, including bibliometrics and altmetrics indicators, can be accessed by searching familiar bibliographical databases such as Web of Science (WoS), which enables the impact of research to be evaluated and identify rising researchers and trending topics in the field of AI. Problem description In general, bibliographical databases have two limitations in terms of the type and form of metadata we aim to improve. First, most bibliographical databases, such as WoS, are more concerned with bibliometric indicators and do not offer a wide range of altmetric indicators to complement traditional bibliometric indicators. Second, the traditional format in which data is downloaded from bibliographical databases limits users to keyword-based searches without considering the semantics of the data. Proposed solution To overcome these limitations, we developed a repository, named AI-SPedia. The repository contains semantic knowledge of scientific publications concerned with AI and considers both the bibliometric and altmetric indicators. Moreover, it uses semantic web technology to produce and store data to enable semantic-based searches. Furthermore, we devised related competency questions to be answered by posing smart queries against the AI-SPedia datasets. Results The results revealed that AI-SPedia can evaluate the impact of AI research by exploiting knowledge that is not explicitly mentioned but extracted using the power of semantics. Moreover, a simple analysis was performed based on the answered questions to help make research policy decisions in the AI domain. The end product, AI-SPedia, is considered the first attempt to evaluate the impacts of AI scientific publications using both bibliometric and altmetric indicators and the power of semantic web technology.
La transición de la guerra a la paz puede conllevar un cambio en el centro de gravedad de la violencia hacia micro-espacios deprimidos de las ciudades que constituyen lo que se puede denominar, adaptando el concepto de Guillermo O’Donnell, zonas marrones urbanas. Las situaciones de postconflicto altamente violento y las de alta violencia societal que corresponden al tipo de casos que se pueden caracterizar como casos de paz violenta, requieren un enfoque de seguridad ciudadana urbana que vaya en sintonía con el giro local que se ha dado en las aproximaciones críticas de la construcción de paz.