Musaed Alhussein’s research while affiliated with King Saud University and other places

The integration of Reconfigurable Intelligent Surfaces (RIS) with Non-Orthogonal Multiple Access (NOMA) and thermal energy harvesting presents a novel approach to enhancing wireless communication networks. RIS technology optimizes signal propagation and improves network efficiency through programmable surface elements, while NOMA increases spectral efficiency by allowing multiple users to share the same frequency resource. When combined with thermal energy harvesting, which captures ambient heat and converts it into electrical power, this integration offers a sustainable solution to power the RIS infrastructure. This paper explores the synergistic benefits of RIS using NOMA with thermal energy harvesting, examining its impact on network performance, energy efficiency, and sustainability. Through a review of recent advancements and research, we discuss how this combined approach can address key challenges in modern wireless communications and contribute to the development of greener, more efficient networks.

The correction of Light Detection and Ranging (LiDAR) intensity data is of great significance for enhancing its application value. However, traditional intensity correction methods based on Terrestrial Laser Scanning (TLS) technology rely on manual site setup to collect intensity training data at different distances and incidence angles, which is noisy and limited in sample quantity, restricting the improvement of model accuracy. To overcome this limitation, this study proposes a fine-grained intensity correction modeling method based on Mobile Laser Scanning (MLS) technology. The method utilizes the continuous scanning characteristics of MLS technology to obtain dense point cloud intensity data at various distances and incidence angles. Then, a fine-grained screening strategy is employed to accurately select distance-intensity and incidence angle-intensity modeling samples. Finally, based on these samples, a high-precision intensity correction model is established through polynomial fitting functions. To verify the effectiveness of the proposed method, comparative experiments were designed, and the MLS modeling method was validated against the traditional TLS modeling method on the same test set. The results show that on Test Set 1, where the distance values vary widely (i.e., 0.1–3 m), the intensity consistency after correction using the MLS modeling method reached 7.692 times the original intensity, while the traditional TLS modeling method only increased to 4.630 times the original intensity. On Test Set 2, where the incidence angle values vary widely (i.e., 0°–80°), the MLS modeling method, although with a relatively smaller advantage, still improved the intensity consistency to 3.937 times the original intensity, slightly better than the TLS modeling method’s 3.413 times. These results demonstrate the significant advantage of the modeling method proposed in this study in enhancing the accuracy of intensity correction models.

Cardiovascular disease (CVD) remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis, driven by risk factors such as hypertension, high cholesterol, and irregular pulse rates. Traditional diagnostic methods often struggle with the nuanced interplay of these risk factors, making early detection difficult. In this research, we propose a novel artificial intelligence-enabled (AI-enabled) framework for CVD risk prediction that integrates machine learning (ML) with eXplainable AI (XAI) to provide both high-accuracy predictions and transparent, interpretable insights. Compared to existing studies that typically focus on either optimizing ML performance or using XAI separately for local or global explanations, our approach uniquely combines both local and global interpretability using Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). This dual integration enhances the interpretability of the model and facilitates clinicians to comprehensively understand not just what the model predicts but also why those predictions are made by identifying the contribution of different risk factors, which is crucial for transparent and informed decision-making in healthcare. The framework uses ML techniques such as K-nearest neighbors (KNN), gradient boosting, random forest, and decision tree, trained on a cardiovascular dataset. Additionally, the integration of LIME and SHAP provides patient-specific insights alongside global trends, ensuring that clinicians receive comprehensive and actionable information. Our experimental results achieve 98% accuracy with the Random Forest model, with precision, recall, and F1-scores of 97%, 98%, and 98%, respectively. The innovative combination of SHAP and LIME sets a new benchmark in CVD prediction by integrating advanced ML accuracy with robust interpretability, fills a critical gap in existing approaches. This framework paves the way for more explainable and transparent decision-making in healthcare, ensuring that the model is not only accurate but also trustworthy and actionable for clinicians.

Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks. However, existing methods often fail in dynamic and high-demand environments, leading to resource bottlenecks and increased energy consumption. This study aims to address these limitations by proposing the Quantum Inspired Adaptive Resource Management (QIARM) model, which introduces novel algorithms inspired by quantum principles for enhanced resource allocation. QIARM employs a quantum superposition-inspired technique for multi-state resource representation and an adaptive learning component to adjust resources in real time dynamically. In addition, an energy-aware scheduling module minimizes power consumption by selecting optimal configurations based on energy metrics. The simulation was carried out in a 360-minute environment with eight distinct scenarios. This study introduces a novel quantum-inspired resource management framework that achieves up to 98% task offload success and reduces energy consumption by 20%, addressing critical challenges of scalability and efficiency in dynamic fog computing environments.

In the fast-evolving world of social media, user engagement is key to platform success. This study presents a novel approach to enhancing engagement through advanced classification algorithms for personalized content delivery, moving beyond generic strategies. The framework analyzes user behavior to provide tailored recommendations, adapting to changing interests and improving the overall experience. The classification algorithms effectively identify user preferences, resulting in more relevant content and higher interaction rates. The implementation and impact of these algorithms demonstrate that personalized engagement boosts content discoverability and strengthens user-platform relationships. Additionally, this article introduces a technology for classifying Facebook users using Particle Swarm Optimization (PSO). As social media evolves, this research aims to refine engagement strategies, highlighting the need for personalized content delivery to create a user-centric experience.

Due to the increasing prevalence of medical diseases, accurately diagnosing patients has become a significant challenge. Medical data is often raw and unstructured, requiring normalization to convert it into a suitable format for disease prediction. Even once data is appropriately formatted, additional challenges remain, such as handling imbalanced datasets, selecting effective features, and choosing suitable machine learning algorithms to achieve reliable predictive accuracy. This research focuses on predicting the seminal quality of men, addressing these challenges through a series of methodologies. The study utilizes the Fertility Dataset and employs preprocessing techniques to convert categorical values into normalized domain values based on WHO 2010 criteria. To handle class imbalance, the SMOTE algorithm is applied. Feature selection is optimized using CFS-Subset Evaluator and Best-First Search techniques to identify the most relevant features. Several machine learning models, including Naïve Bayes and Multi-layer Perceptron (non-ensemble), and ensemble methods like Bagging, Random Forest, and XG-Boost, are evaluated. Both percentage split and 10-fold cross-validation methods are employed for model validation. The highest accuracy achieved in this study is 96.2%.

Over the years, researchers have been using deep learning in different fields of science including disease diagnosis. Retinal vessel segmentation has seen significant advancements through deep learning techniques, resulting in high accuracy. Despite this progress, challenges remain in automating the segmentation process. One of the most pressing and often overlooked issues is computational complexity, which is critical for developing portable diagnostic systems. To address this, this study introduces a CBAM‐Attention Gate‐based U‐Netmodel aimed at reducing computational complexity without sacrificing performance on evaluation metrics. The performance of the model was analyzed using four publicly available fundus image datasets: CHASE_DB1, DRIVE, STARE, and HRF, and it achieved sensitivity, specificity, accuracy, AUC, and MCC performances (0.7909, 0.9975, 0.9723, 0.9867, and 0.8011), (0.8217, 0.9816, 0.9674, 0.9849, and 0.9778), (0.8346, 0.9790, 0.9680, 0.9855, and 0.7810), and (0.8082, 0.9769, 0.9638, 0.9723, and 0.7575), respectively. Moreover, this model comprises of only 0.8 million parameters, which makes it one of the lightest available models used for retinal vessel segmentation. This lightweight yet efficient model is most suitable for use in low‐end hardware devices. The attributes of significantly lower computational complexity along with improved evaluation metrics advocates for its deployment in portable embedded devices to be used for population‐level screening programs.

Federated learning (FL) is a promising technique for training machine learning models on distributed, privacy-aware datasets. Nevertheless, FL faces difficulties with agent/client participation, model performance, and the heterogeneous nature of networked data sources when it comes to distributed healthcare systems. When these agents work together in the system, it is imperative to tackle the complexities of distributed deep learning. We suggest a novel approach that uses a voting mechanism and dynamic SelectOut inside the FL framework to address these problems. Local medical imaging datasets frequently show diversity in distribution and data imbalances. In certain situations, traditional FL techniques like FedProx and federated averaging, which depend on data size to weight contributions, might not be the optimal choice. In order to improve parameter aggregation and client selection unpredictability and increase the model’s adaptability to imbalanced and heterogeneous datasets, our proposed FedVoteNet model introduces SelectOut techniques based on voting methodology. Based on how much their local performance has improved from the last communication cycle, we arbitrarily remove clients. Additionally eliminated are clients whose model weights when combined with the global model adversely affect its performance. Our method is further enhanced by the inclusion of a voting mechanism. At the conclusion of each communication cycle, clients that improve both their local performance and their contribution to the global model are awarded higher voting values. This encourages more significant and effective contributions from clients by providing incentives for them to actively increase the diversity of their training data. We assess our approach on a dataset of medical images, including magnetic resonance imaging scans, and find that the FL model performs noticeably better (F1 Score = 0.968, Sensitivity = 0.977, Specificity = 0.945, and AUC = 0.950). The voting system and the dynamic SelectOut algorithms improve the convergence of the FL model and successfully handle the difficulties presented by uneven and heterogeneous datasets. To sum up, our proposed approach uses voting and dynamic SelectOut techniques to improve FL performance on a variety of uneven, distributed, and varied datasets. This strategy has a lot of potential to improve FL across a range of applications, especially those that prioritize data privacy, diversity, and performance.

Chronic kidney disease (CKD) is a major global health concern caused mostly by high blood pressure and glucose levels. Detecting CKD early is critical for reducing its negative consequences since it can lead to increased mortality rates. With CKD's rising incidence expected to make it the fifth biggest cause of death by 2040, rapid advances in diagnostic approaches are required. This study presents the Reciprocal Domain Adaptation Network (RDAN) as a potential approach to the various issues of CKD diagnosis. RDAN is a neural network model that will help to traverse the complexity of CKD diagnosis by smoothly combining diverse data sets. RDAN consists of two critical units at its foundation: Mutual Model Adaptation (MMA) and Domain Model Learning. The MMA unit uses a powerful Global and Local Pyramid Pooling technique to extract rich features from a variety of data domains. Meanwhile, the DML unit uses semi‐supervised domain‐independent features combined with MMA features to improve representation learning. RDAN includes a reciprocal regularizer to promote cross‐domain knowledge transfer, maximising feature representation for accurate CKD identification. An analysis of RDAN's performance on a variety of real‐world datasets showed remarkable results in terms of accuracy (96.94%), precision (98.81%), recall (98.73%), F1‐Score (98.88%), and area under the curve (AUC—99.35%). These results highlight the unmatched expertise of RDAN in managing data bias, domain changes, and privacy issues related to CKD diagnosis. Beyond statistical measures, RDAN's implications promise revolutionary breakthroughs in early CKD identification and subsequent therapeutic therapies. RDAN stands out as a groundbreaking method for diagnosing CKD. It delivers exceptional accuracy and can be seamlessly applied in various clinical environments.