Abstract This article reports on designing and implementing a multiclass sentiment classification approach to handle the imbalanced class distribution of Arabic documents. The proposed approach, sentiment classification of Arabic documents (SCArD), combines the advantages of a clustering-based undersampling (CBUS) method and an ensemble learning model to aid machine learning (ML) classifiers in building accurate models against highly imbalanced datasets. The CBUS method applies two standard clustering algorithms: K-means and expectation–maximization, to balance the ratio between the major and the minor classes by decreasing the number of the major class instances and maintaining the number of the minor class instances at the cluster level. The merits of the proposed approach are that it does not remove the majority class instances from the dataset nor injects the dataset with artificial minority class instances. The resulting balanced datasets are used to train two ML classifiers, random forest and updateable Naïve Bayes, to develop prediction data models. The best prediction data models are selected based on F1-score rates. We applied two techniques to test SCArD and generate new predictions from the imbalanced test dataset. The first technique uses the best prediction data models. The second technique uses the majority voting ensemble learning model, which combines the best prediction data models to generate the final predictions. The experimental results showed that SCArD is promising and outperformed the other comparative classification models based on the F1-score rates.

This research presents a new hybridized evolutionary artificial intelligence (AI) model for modeling depth scouring under submerged weir ( d s ). The proposed model is based on the hybridization of the Extreme Gradient Boosting (XGBoost) model and genetic algorithm (GA) optimizer. The GA is hybridized to solve the hyper-parameter problem of the XGBoost model and to recognize the influential input predictors of d s . The proposed XGBoost-GA model is developed based on the incorporation of fifteen physical parameters of submerged weir. The feasibility of the XGBoost-GA model is validated against several well-established AI models introduced in the literature in addition to a hybrid XGBoost-Grid model. Several statistical performance metrics is computed for the modeling evaluation in parallel with a graphical assessment. Based on the attained prediction results, the proposed model revealed an optimistic and superior predictability performance with a maximum coefficient of determination ( R 2 = 0.933) and a minimum root mean square error ( RMSE = 0.014 m). In addition, the XGBoost-GA model demonstrated reliable feature selection for the essential physical parameters. The fifteen parameters are re-scaled to seven parameters based on their essential impacts on the d s determination.

Knowledge management systems can be defined as information and communication technology based systems developed to support the management of knowledge assets in the organization. This support is performed by facilitating collecting, organizing, storing, sharing, searching and retrieving knowledge and sharing it among knowledge workers. In this research we describe a framework of a semantic based knowledge management system for aerospace collaborative working environments. We also identify the design principles, system architecture and technological implantation of the system which is developed and implemented as part of integrated and virtually produced tools and functionalities for a collaborative and distributed working environment. The introduced system depends mainly on Latent semantic Indexing techniques and a set of evolving ontologies as underlying resource for searching and retrieving shared knowledge documents according to the semantic correlations with the knowledge worker's queries. The main goal of this research is to help knowledge workers effectively share, search and retrieve required knowledge from shared and vast knowledge bases in their collaborative working environment.