Enas Rawashdeh

Feature selection plays an important role in medical classification, particularly when datasets contain high-dimensional and redundant information. At the same time, the performance of a Random Weight Network (RWN) depends on its configuration, making the joint optimization of feature subsets and network parameters a relevant problem. This study proposes a metaheuristic-based framework that integrates feature selection with RWN configuration optimization. The framework simultaneously determines the selected features, active hidden neurons, regularization parameter, and activation function. Eight binary transfer functions, including four S-shaped and four V-shaped functions, are investigated to convert continuous search variables into binary decisions for feature and hidden-neuron selection. The framework is evaluated using four metaheuristic algorithms: Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE). Experiments are conducted on 20 benchmark medical datasets under consistent experimental conditions. The results show that the S2 transfer function provides the most favorable overall performance among the investigated transfer functions. Using S2, GWO achieves the best overall Friedman ranking among the four optimizers, although the overall differences are not statistically significant at the 5% level for some evaluation measures. The resulting GWO-S2-RWN configuration also demonstrates competitive performance when compared with established feature-selection and classification methods reported in the literature. Overall, the findings show that jointly optimizing feature selection and RWN configuration provides a flexible approach for balancing classification performance and model complexity in medical classification tasks.

Online social networks have become a crucial medium for global communication, with many users accessing their accounts multiple times a day. This trend was particularly pronounced during the Covid-19 crisis, as quarantines and extended periods at home drove a significant increase in online activity. However, this surge in usage also brought about negative societal behaviors, such as heightened instances of offensive language, hate speech, and online bullying. Therefore, detecting these abuses electronically has become necessary, despite the slowness that accompanies manual detection, particularly with large data. This paper proposes a model for the automatic identification of offensive language in Arabic tweets using fine-tuned word embedding models. The proposed GWO-RWN model combines a Random Weight Network (RWN) as the classifier with a Grey Wolf Optimizer (GWO) to optimize the RWN configuration. Experiments were conducted on the ArCybC dataset of 4505 Arabic tweets, using an 60% training, 20% validation and 20% testing split. Word2Vec embeddings with CBOW and Skip-G architectures and GloVe embeddings were evaluated, and the proposed GWO-RWN was compared with GA-, PSO-, and ABC-optimized RWN models over 30 independent runs. The proposed approach achieved an accuracy of 90.5%, with precision, recall, and F1-score of 0.891, 0.888, and 0.889, respectively, using Skip-G embeddings. This represents a 4.8-percentage-point improvement over the non-optimized RWN, which achieved an accuracy of 85.7%.This exceeds previously reported results on the same dataset, including 88.2% accuracy with a GA-optimized SVM and 86.3% accuracy with an SVM using word embeddings.

Efficiency is a crucial factor when handling the retrieval and storage of data from vast amounts of records in a Big Data repository. These systems require a subset of data that can be accommodated within the combined physical memory of a cluster of servers. It becomes impractical to analyze all of the data if its size exceeds the available memory capacity. Retrieving data from virtual storage, primarily hard disks, is significantly slower compared to accessing data from main memory, resulting in increased access time and diminished performance. To address this, a proposed model aims to enhance performance by identifying the most suitable data locality structure within a big data set and reorganizing the data schema accordingly; by locality, it has been referred to as a particular access pattern. This allows transactions to be executed on data residing in the fastest memory layer, such as cache, main memory, or disk cache