Mustafa Abdul Salam

Vehicle-to-everything (V2X) communication has become an end-to-end cyber–physical evidence problem rather than a radio-interface problem alone: sensed events, transmitted messages, edge computation, trust decisions, and control or service outcomes must be evaluated as a connected chain. The nearest recent surveys clarify V2X architectures, datasets, protocols, cybersecurity, cooperative perception, and energy services, but they do not provide a study-level synthesis that systematically separates architectural claims from evaluated cross-layer effects while preserving validation context and metric compatibility. This paper addresses that gap through a systematic evidence-to-inference map, reported in accordance with PRISMA 2020, built on an analytical corpus of 235 full-text V2X/ICV research publications whose first public versions appeared from 1 January 2025 to 4 August 2026. The reference list contains 249 entries: 235 coded corpus publications and 14 supporting methodological, standards, or contextual sources outside the analytical denominator. Each corpus publication is assigned to one of ten mutually exclusive primary domains and coded across six analytical layers: sensing/perception, representation/fusion, communication, computation, trust/security, and decision/control/outcome. Claimed and evaluated layer integration are coded separately, validation maturity is assessed on an E0–E4 scale, and communication-to-outcome evidence is synthesized only when metric families, denominators, and experimental contexts are compatible. The synthesis identifies recurring fragmentation between broad cross-layer architectural claims and narrower evaluated evidence, limited high-maturity validation, incomplete communication-to-downstream-outcome linkage, and reporting heterogeneity that limits direct quantitative comparison across studies. The resulting framework produces evidence-gap maps, layer co-evaluation patterns, metric-bundle interpretations, domain-level engineering implications, and testable priorities for benchmark refinement. It provides a practical route for moving V2X evaluation from isolated layer performance toward operationally credible and auditable cross-layer evidence.

Team Formation (TF) problems represent one of the most significant areas in computer science and optimization. The challenge lies in forming the best team of experts capable of completing a specific task at the lowest cost, which is a highly complex problem. Furthermore, the TF problem involves multiple attributes, each of which can be treated as a distinct objective that needs to be optimized. The problem solution varies according to the specified objectives. In this study, the TF problem is formulated as a bi-objective optimization problem, and a novel algorithm, Chaotic Jellyfish Search with Enhanced Swap Operator (CJSESOS), is proposed. This method is based on the Jellyfish Search Optimizer (JSO), a recent swarm intelligence algorithm known for its superior performance in various optimization tasks. CJSESOS introduces two major enhancements: (1) a chaotic sequence generated via a logistic map to improve solution diversity and exploration (CJSO), and (2) an enhanced swap sequence operator that increases the algorithm’s ability to escape local optima. The CJSESOS algorithm was adapted to enhance its exploration capabilities during the search for Pareto-optimal solutions. The effectiveness of the proposed bi-objective Chaotic Jellyfish search optimizer (Bi-CJSESOS) was evaluated using a different dataset with different skill numbers, in addition to twelve benchmark functions. Experimental results showed that, for the same fault discovery rate, the Bi-CJSESOS can find an optimal team and satisfy the two objectives more than the other comparative algorithms.

This paper proposed a model to detect fires in the industrial field using You Only Look Once Version 8 (YOLOv8) framework. The proposed model is based on three primary stages which are data pre-processing, feature selection, and evaluating the results using a variety of metrics. Images are resized, enhanced, noise is reduced, and videos and images are labeled with bounding boxes surrounding the fires during the data pre-processing step. The YOLOv8 model's speed and accuracy make it the preferred choice for feature selection. The performance of the proposed model is assessed using a variety of metrics, including accuracy, precision, and recall. Furthermore, the suggested model is trained in a real-time system that is capable of processing camera feeds in real time. When a fire is detected, the building's fire alarm should go on to alert people and tell them to evacuate. The experiment's findings show that the recommended model produced results with 98.1% accuracy, 98.9% precision, 95.3% recall, and 98.1% mAP. Finally, the proposed model is contrasted with existing methods on the same dataset. يتم تغيير حجم الصور وتحسينها ، ويتم تقليل الضوضاء ، ويتم تصنيف مقاطع الفيديو والصور مع صناديق محيطة تحيط بالحرائق أثناء خطوة المعالجة المسبقة للبيانات.

Arabic language processing poses significant challenges owing to the scarcity of resources and intricate nature of its linguistic structures. To address these issues, this paper introduces MSG-ATS, a groundbreaking approach to abstractive text summarization in Arabic. The primary problem is the difficulty in creating coherent and high-quality summaries due to the complex syntactic, semantic, and contextual features of the Arabic language. This challenge is compounded by the lack of extensive linguistic and effective models tailored for Arabic summarization. MSG-ATS aims to overcome these challenges by leveraging multi-level representations within semantic graphs. This novel approach enhances conventional text construction and embedding techniques, allowing for a more nuanced capture of syntactic, semantic, and contextual information. This comprehensive capture is crucial for generating summaries that are both coherent and contextually relevant. By utilizing a deep neural network, MSG-ATS effectively generates high-quality summaries. To validate MSG-ATS, rigorous evaluations are conducted, comparing its performance with word2vec, a foundational word embedding method. These evaluations are supported by a novel dataset specifically compiled for this study. The generated summaries undergo thorough assessment, including automated evaluation using the Rouge metric. The results are compelling, demonstrating MSG-ATS’s superior performance with a significant 42.4% improvement in precision, a notable 23.8% improvement in recall, and an impressive 38.3% overall advancement over the baseline model.

In recent years, credit card transaction fraud has resulted in massive losses for both consumers and banks. Subsequently, both cardholders and banks need a strong fraud detection system to reduce cardholder losses. Credit card fraud detection (CCFD) is an important method of fraud prevention. However, there are many challenges in developing an ideal fraud detection system for banks. First off, due to data security and privacy concerns, various banks and other financial institutions are typically not permitted to exchange their transaction datasets. These issues make traditional systems find it difficult to learn and detect fraud depictions. Therefore, this paper proposes federated learning for CCFD over different frameworks (TensorFlow federated, PyTorch). Second, there is a significant imbalance in credit card transactions across all banks, with a small percentage of fraudulent transactions outweighing the majority of valid ones. In order to demonstrate the urgent need for a comprehensive investigation of class imbalance management techniques to develop a powerful model to identify fraudulent transactions, the dataset must be balanced. In order to address the issue of class imbalance, this study also seeks to give a comparative analysis of several individual and hybrid resampling techniques. In several experimental studies, the effectiveness of various resampling techniques in combination with classification approaches has been compared. In this study, it is found that the hybrid resampling methods perform well for machine learning classification models compared to deep learning classification models. The experimental results show that the best accuracy for the Random Forest (RF); Logistic Regression; K-Nearest Neighbors (KNN); Decision Tree (DT), and Gaussian Naive Bayes (NB) classifiers are 99,99%; 94,61%; 99.96%; 99,98%, and 91,47%, respectively. The comparative results show that the RF outperforms with high performance parameters (accuracy, recall, precision and f score) better than NB; RF; DT and KNN. RF achieve the minimum loss values with all resampling techniques, and the results, when utilizing the proposed models on the entire skewed dataset, achieved preferable outcomes to the unbalanced dataset. Furthermore, the PyTorch framework achieves higher prediction accuracy for the federated learning model than the TensorFlow federated framework but with more computational time.

The exponential growth of online content has made the task of locating specific information increasingly challenging, thus highlighting the necessity for automated text summarization. Deep learning techniques, particularly neural abstractive models such as Seq2Seq, have emerged as prominent solutions to this issue. The utilization of pre-trained models, including GPT, BERT, BART, and T5, has notably enhanced the quality of text summarization by addressing key aspects such as saliency, fluency, and semantic coherence. However, while these advancements have greatly benefited the English language, there remains a significant gap in support for low-resource languages. To bridge this gap, monolingual BERT and multilingual Seq2Seq models have been developed, enabling the application of state-of-the-art summarization techniques to languages like Arabic. Our research capitalizes on pre-trained Seq2Seq models to achieve superior results in Arabic text summarization tasks by leveraging datasets such as XLSum and Hindawi Books. Notably, the titles within these datasets serve as robust benchmarks for evaluating the effectiveness of our summarization techniques, underscoring the importance of high-quality input data. Additionally, one of our key contributions lies in the implementation of fine-tuning for the model using reinforcement learning. This innovative approach enhances the adaptability and performance of the model. Our findings indicate that monolingual BERT models outperform their multilingual counterparts, yielding a notable 2.4% increase in Rouge scores, further improving the quality of text summarization in Arabic. Our study encompasses cross-dataset evaluations, exploration of various text generation methodologies, and in-depth preprocessing analysis tailored specifically for Arabic text. By presenting a comprehensive approach to address the challenges in Arabic text summarization, our study contributes to the advancement of the field and underscores the significance of supporting low-resource languages in natural language processing tasks.

Harris Hawk Optimization (HHO) is a well-known nature-inspired metaheuristic model inspired by the distinctive foraging strategy and cooperative behavior of Harris Hawks. As with numerous other algorithms, HHO is susceptible to getting stuck in local optima and has a sluggish convergence rate. Several techniques have been proposed in the literature to improve the performance of metaheuristic algorithms (MAs) and to tackle their limitations. Chaos optimization strategies have been proposed for many years to enhance MAs. There are four distinct categories of Chaos strategies, including chaotic mapped initialization, randomness, iterations, and controlled parameters. This paper introduces SHHOIRC, a novel hybrid algorithm designed to enhance the efficiency of HHO. Self-adaptive Harris Hawk Optimization using three chaotic optimization methods (SHHOIRC) is the proposed algorithm. On 16 well-known benchmark functions, the proposed hybrid algorithm, authentic HHO, and five HHO variants are evaluated. The computational results and statistical analysis demonstrate that SHHOIRC exhibits notable similarities to other previously published algorithms. The proposed algorithm outperformed the other algorithms by 81.25%, compared to 18.75% for the prior algorithms, by obtaining the best average solutions for 13 benchmark functions. Furthermore, the proposed algorithm is tested on a real-life problem, which is the maximum coverage problem of Wireless Sensor Networks (WSNs), and compared with pure HHO, and two well-known algorithms, Grey Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). For the maximum coverage experiments, the proposed algorithm demonstrated superior performance, surpassing other algorithms by obtaining the best coverage rates of 95.4375% and 97.125% for experiments 1 and 2, respectively.

Three contributions are proposed. Firstly, a novel hybrid classifier (HHO-SVM) is introduced, which is a combination between the Harris hawks optimization (HHO) and a support vector machine (SVM) is introduced. Second, the performance of the HHO-SVM is enhanced using the conventional normalization method. The final contribution is to improve the efficiency of the HHO-SVM by adopting a parallel approach that employs the data distribution. The proposed models are evaluated using the Wisconsin Diagnosis Breast Cancer (WDBC) dataset. The results show that the HHO-SVM achieves a 98.24% accuracy rate with the normalization scaling technique, outperforming other related works. On the other hand, the HHO-SVM achieves a 99.47% accuracy rate with the equilibration scaling technique, which is better than other previous works. Finally, to compare the three effective scaling strategies on four CPU cores, the parallel version of the proposed model provides an acceleration of 3.97.