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Although there are many chances to improve organisational performance and decision-making due to the quick adoption of Big Data Analytics (BDA), its practical implementation in the healthcare industry is still understudied. The five fundamental Big Data Analytics Capabilities (BDAC)-organizational, technological, analytical, cognitive, and social-are integrated into a predictive framework in this study, which assesses how well these BDACs predict the quality of decision-making in Jordanian healthcare facilities. 205 valid survey responses from healthcare experts were used to gather data, which was then preprocessed, feature-integrated, and classified. Four machine learning algorithms-Artificial Neural Networks (ANN), Naïve Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM)-were used to build prediction models employing a total of 65 features that represented the five BDAC dimensions as input variables. Confusion matrices and performance measures, such as accuracy, sensitivity, specificity, and precision, were used to evaluate the model. The findings showed that while ANN and DT produced comparable outcomes under various operational priorities, NB had the best prediction performance (87% accuracy). The trade-offs between sensitivity and specificity among classifiers were further brought to light by a comparison study, highlighting the significance of matching algorithm selection to the requirements of healthcare decision-making. By putting forth a capability-based prediction model that deepens knowledge of how BDAC improves the quality of healthcare decisions, the study adds to the body of literature. For hospital administrators and legislators looking to use data-driven tactics to improve institutional performance, it also has useful implications.

Recommendation system is one of the modern applications to solve information overload problem in a way to provide recommendations of interest to users. Websites such as Amazon, Netflix, Facebook, YouTube, and others apply recommendation system in recommending their products. This also includes recommending news to the readers. However, the systems suffer from some challenges such as high dimensional data, data sparsity, and cold start. To address these problems, deep learning techniques have recently been integrated with recommendation systems and achieve good performance. Autoencoder is one of the most widely used deep learning techniques in recommender systems, especially used for feature extraction, data dimensionality reduction, fast convergence, unsupervised learning, and data reconstruction. In this paper, a performance comparison between three different models of autoencoder is presented which applying in the recommendation systems to further improve the quality of recommendations provided to users. The models are Hybrid Collaborative Recommendation via Semi-AutoEncoder (HRSA), Recommendation via Dual-Autoencoder (ReDa), and Hybrid Collaborative Recommendation method via Dual-Autoencoder (HCRDa). These models work by retrieving the potential latent factors from the sparse rating matrix and predict the missing ratings. The performance is compared based on reconstruction loss that applies to the MovieLens 100K dataset with three different sets of training data: 70%, 80%, and 90%. As a result, it was found that the HCRDa is outperformed the other models in terms of reconstruction loss based on the RMSE evaluation metric and the use of side information in the model. Thus, it is the most effective technique in terms of enhancing the quality of user recommendations.

There is a huge content of Arabic text available over online that requires an organization of these texts. As result, here are many applications of natural languages processing (NLP) that concerns with text organization. One of the is text classification (TC). TC helps to make dealing with unorganized text. However, it is easier to classify them into suitable class or labels. This paper is a survey of Arabic text classification. Also, it presents comparison among different methods in the classification of Arabic texts, where Arabic text is represented a complex text due to its vocabularies. Arabic language is one of the richest languages in the world, where it has many linguistic bases. The researche in Arabic language processing is very few compared to English. As a result, these problems represent challenges in the classification, and organization of specific Arabic text. Text classification (TC) helps to access the most documents, or information that has already classified into specific classes, or categories to one or more classes or categories. In addition, classification of documents facilitate search engine to decrease the amount of document to, and then to become easier to search and matching with queries.

Software applications have become a fundamental part in the daily work of modern society as they meet different needs of users in different domains. Such needs are known as software requirements (SRs) which are separated into functional (software services) and non-functional (quality attributes). The first step of every software development project is SR elicitation. This step is a challenge task for developers as they need to understand and analyze SRs manually. For example, the collected functional SRs need to be categorized into different clusters to break-down the project into a set of sub-projects with related SRs and devote each sub-project to a separate development team. However, functional SRs clustering has never been considered in the literature. Therefore, in this paper, we propose an approach to automatically cluster functional requirements based on semantic measure. An empirical evaluation is conducted using four open-access software projects to evaluate our proposal. The experimental results demonstrate that the proposed approach identifies semantic clusters according to well-known used measures in the subject.