This research aims to investigate the direct and formal effect of visual aesthetics on customers’ willingness to participate in an online marketing campaign, given that visual aesthetics is considered a salient design characteristic that customers tend to refer to when expressing their experience with the online retailer environment. This research study incorporates the Theory of Reasoned Action (TRA) and the Technology Acceptance Model (TAM) to propose and estimate this direct effect through structural aesthetics TAM model. In order to validate the proposed model, an online survey with interactive experience was constructed utilizing an open-source platform and was primarily sent to scholars specializing in HCI, informatics, user experience, and digital marketing research in the United Kingdom. The data were obtained from 360 participants, and Partial Least Squares (PLS) path modeling using SmartPLS 4 was employed to evaluate the proposed model. The empirical evaluation demonstrated and confirmed the authenticity and novelty of significant direct and formal effects of customers’ perceived visual aesthetics on their willingness to participate in an online marketing campaign (β = 0.462, t = 10.847, p < 0.001), and their usage attitude (β = 0.789, t = 39.622, p < 0.001). These findings highlight the importance of visual aesthetics in online marketing campaign design, suggesting that concise, well-organized, and visually appealing user interfaces is more likely to engage customers than a complex, cluttered one.

Feature selection problem is one of the main important problems in the text and data mining domain. This paper presents a comparative study of feature selection methods for Arabic text classification. Five of the feature selection methods were selected: ICHI square, CHI square, Information Gain, Mutual Information and Wrapper. It was tested with five classification algorithms: Bayes Net, Naive Bayes, Random Forest, Decision Tree and Artificial Neural Networks. In addition, Data Collection was used in Arabic consisting of 9055 documents, which were compared by four criteria: Precision, Recall, F-measure and Time to build model. The results showed that the improved ICHI feature selection got almost all the best results in comparison with other methods.

Semantic segmentation of aerial and drone imagery remains a demanding task due to class imbalance, scale diversity, and the visual similarity between land-cover categories. Although encoder–decoder architectures such as U-Net and U-Net++ have demonstrated strong performance, reliance on a single model often limits generalization across heterogeneous datasets. This study introduces a Dynamic Confidence-Aware Mask Fusion (DCAMF) framework that reformulates model fusion as a pixel-wise, uncertainty-guided decision process. Instead of applying static averaging or conventional voting schemes, the proposed approach integrates heterogeneous U-Net and U-Net++ models equipped with multiple backbone encoders and assigns adaptive weights based on entropy-derived confidence measures. The framework was evaluated on two multi-class aerial datasets with differing levels of semantic complexity. Experimental findings indicate consistent improvements in mean Intersection over Union and overall pixel accuracy compared with individual architectures and traditional ensemble strategies. The gains were particularly evident in boundary regions and visually ambiguous areas, where model disagreement is common. By incorporating uncertainty modeling directly into mask-level aggregation, DCAMF enhances robustness without introducing architectural modifications or excessive computational overhead. The results demonstrate that confidence-aware decision fusion offers a practical and scalable pathway for improving segmentation reliability in real-world aerial imaging applications. Received: 15 November 2025 | Revised: 27 May 2026 | Accepted: 19 June 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Ahmad Qasim AlHamad: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Ahmad Mousa Odat: Validation. Abdullah Ahmad Al Sokkar: Supervision. Mohammed Abdallah Otair: Conceptualization, Methodology, Resources, Writing – review & editing. Suhier Nadi Odah: Formal analysis. Omar Hussain Tarawneh: Project administration.

Today use computer networks all the time for everything—our phones, computers, internet of thing (IoT), and cloud services. Because of this, networks often get attacked by things like denial of service (DoS), user to remote attack (U2R) We try to stop these attacks with Intrusion Detection Systems (IDSs). However, today's IDSs struggle to find brand-new types of attacks. To make them work better, we first need to pick out only the most useful features of information before the system runs. This paper introduces a Deterministic version of the Arithmetic Optimization Algorithm (DAOA) to solve the feature selection problem in classification. The classifier employs K-Nearest Neighbors (KNN) using a wrapper-based approach. to find the optimal solutions. In contrast, all previous studies have introduced a probabilistic version of the Arithmetic Optimization Algorithm (BAOA). This study uses NF-UNSW-NB15-V2 dataset as benchmark datasets from the collection by the university of Queensland The results demonstrate that DAOA outperformed the Binary Arithmetic Optimization Algorithm(BAOA), Binary Grey Wolf Optimizer (GWO), Binary Particle Swarm Optimization (BPSO), and Binary Harmony Search optimization (HS),Binary Ant Colony Optimization for Real-valued domains (ACOR), when various performance metrics were used, including classification accuracy, selected features, The tested algorithms were ranked using the Friedman Test, and pairwise comparisons were performed using the Wilcoxon Signed-Rank Test. After running the algorithms for 30 iterations and 20 epochs, the results showed that the DAOA achieved the highest classification accuracy while selecting the smallest feature set compared to all other tested algorithms.