Mohamed Ali Saip

Ecently, the widespread use of social media has increased, leading to increased concerns about cyberbullying. It has become imperative to intensify efforts and methods to detect and manage cyberbullying through social media. Arabic has recently received increasing attention to improve the classification of Arabic texts. Given the multitude of Arabic dialects used on social media platforms by Arabic speakers to express their opinions and communicate with each other, applying this approach to Arabic becomes extremely challenging due to its structural and morphological complexity. Analyzing Arabic dialects using Natural Language Processing (NLP) tools can be more challenging than Standard Arabic. In this paper, the impact of using stopword removal and derivation techniques on detecting cyberbullying in the Libyan dialect was presented. The efficiency of text classification was compared when using a Libyan dialect word list alongside pre-generated Modern Standard Arabic (MSA) lists. The texts were classified using Convolutional Neural Network (CNN) classifiers, and the experiments showed that when using Libyan dialect words, the accuracy results were 92% and 83%, and when using only Standard Arabic stop words, the accuracy results were dropped to 91% and 77%. Based on these results, the higher accuracy was obtained when using the presented stop words list which it is specific to the Libyan dialect, and they had a positive impact on the results, better than Standard Arabic stop words.

Small or tiny object detection has gained significant attention from researchers in recent years due to its wide range of applications across various domains. This highlights its status as a promising research area. The study employs bibliometric analysis to understand the current research status and future trends in small or tiny object detection. By analyzing documents published between 2015 and 2024 from the Scopus database, we map the quantitative structure of this research area, identify the most influential publications and key contributions, and uncover potential research directions. The contribution of this paper is to provide a comprehensive bibliometric analysis of a decade of research on small or tiny object detection, a topic that has not been previously published. A total of 1627 publications were analyzed. Our findings reveal a significant increase in the number of publications over the decade, indicating growing research interest. China leads the field, contributing the majority of publications, followed by the USA and South Korea. Foundational contributions, such as "Inside-Outside Net" and GAN-based approaches, are highlighted for their impact on detection methodologies. Key topics include deep learning, convolutional neural networks, feature extraction, and object recognition. The analysis also identifies major challenges such as data scarcity, computational efficiency, and environmental complexities like occlusion and background clutter. Emerging trends include lightweight architectures, multiscale feature fusion, and edge computing, reflecting a shift toward resource-efficient implementations for practical applications. This study provides foundational insights into the current state of the field and highlights directions for future research, aiming to guide scholars and practitioners in advancing the technology of small or tiny object detection.

Social network analysis is a process of studying social structures and relationships using graph theory and data analysis techniques. It involves mapping and measuring connections and entities in a network. However, on online selling platforms, identifying influential entities such as individuals and high-value products remains a challenge due to the complexity of customer and seller interactions. This study aims to assess seller performance and product lifetime value using AI-driven network analysis involving a measure of centrality. AI-driven network analysis utilises artificial intelligence (AI) to identify influential individuals and predict emerging trends in consumer engagement. It uses weighted degree and betweenness centrality to assess their effectiveness in identifying influential entities, including sellers, products, or organisations in a commercial network. Weighted degree centrality measures the strength and frequency of direct connections, while betweenness centrality identifies entities that act as intermediaries across different network segments. The analysis reveals that weighted degree centrality, with a value of 3190 for annual seller performance, is more closely aligned with actual sales performance and stakeholder assessments, making it a more suitable metric for supporting business decisions in this context. The findings demonstrate that AI-driven analytics enable businesses to consistently identify high-performing sellers and products based on their structural positions within the network. It contributes to the development of more targeted marketing strategies, systematic recognition of top performers, and enhanced customer engagement through data-informed decision-making. Future research may explore the integration of dynamic network modelling with multi-layered e-commerce networks, thereby increasing the depth of analysis across various platforms and industries.