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

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.

Diabetic retinopathy (DR) is a severe complication of diabetes that affects the retinal structures and can lead to significant visual impairment or even blindness. Early diagnosis is crucial for reducing and preventing the progression of this condition. However, detecting DR’s early stages remains challenging due to subtle symptoms that are difficult to recognize independently. Our proposed model leverages to analyze both sequential and non-sequential fundus images for effective DR detection. By incorporating a multi-layered transfer learning approach, 86 layers are used for processing color fundus images, while the remaining 86 layers focus on grayscale images. The model undergoes thorough pre-processing and testing phases, utilizing eight layers of convolutions at each stage to handle various data matrices and integrate global and specialized features. The chi-square testing mechanism refines the evaluation of test cases, contributing to the model’s overall performance. Using multi-decision hybrid techniques, the model achieves a detection accuracy of 98.1%, outperforming other existing models.

The investigation of the flow patterns of a trihybrid nanofluid flow situated in the conical gap that is created among a revolving disc and a stationary cone computationally examined in this study. Three different types of nanoparticles, Al2O3, TiO2 and Ag are examined with blood as the base fluid according to flow properties and energy phenomenon. While discussing the heat transfer mechanism in trihybrid nanofluid flow crossing through the disc and cone, four different types of cases are explored likewise the disc and cone may be rotating at the same rate or at different rates, or one may be stationary about the other. The numerical scheme is planted to observe the fluid flow and heat transfer patterns. In the current article, we investigated rheological parameters, including rotating speed, cone angle, and concentration of nanoparticles, and the effect of heat transfer performance over velocity and temperature patterns. The results shed light on the complex interactions between the geometric and nanofluid characteristics, providing useful information for fluid dynamics and thermal management applications. This fluid model is also useful for the study of blood pressure, arthritis, brain therapy, and malignant tumors. The graphs are plotted using the MATLAB program BVP4C to ensure convergence. Several variables, such as a magnetic parameter, Prandtl's number, and Reynold's number, have an impact on temperature and velocity profiles. It is evident that the amalgamation of the fraction of three nanoparticles reduces the velocity and enhances the temperature of the nanofluid. The momentum boundary layer expands when the cone and disc rotate in the same direction.

The investigation of the flow patterns of a trihybrid nanofluid flow situated in the conical gap that is created among a revolving disc and a stationary cone computationally examined in this study. Three different types of nanoparticles, Al2O3, TiO2 and Ag are examined with blood as the base fluid according to flow properties and energy phenomenon. While discussing the heat transfer mechanism in trihybrid nanofluid flow crossing through the disc and cone, four different types of cases are explored likewise the disc and cone may be rotating at the same rate or at different rates, or one may be stationary about the other. The numerical scheme is planted to observe the fluid flow and heat transfer patterns. In the current article, we investigated rheological parameters, including rotating speed, cone angle, and concentration of nanoparticles, and the effect of heat transfer performance over velocity and temperature patterns. The results shed light on the complex interactions between the geometric and nanofluid characteristics, providing useful information for fluid dynamics and thermal management applications. This fluid model is also useful for the study of blood pressure, arthritis, brain therapy, and malignant tumors. The graphs are plotted using the MATLAB program BVP4C to ensure convergence. Several variables, such as a magnetic parameter, Prandtl's number, and Reynold's number, have an impact on temperature and velocity profiles. It is evident that the amalgamation of the fraction of three nanoparticles reduces the velocity and enhances the temperature of the nanofluid. The momentum boundary layer expands when the cone and disc rotate in the same direction.

Cloud computing has emerged as a vital platform for processing resource-intensive workloads in smart manufacturing environments, enabling scalable and flexible access to remote data centers over the internet. In these environments, Virtual Machines (VMs) are employed to manage workloads, with their optimal placement on Physical Machines (PMs) being crucial for maximizing resource utilization. However, achieving high resource utilization in cloud data centers remains a challenge due to multiple conflicting objectives, particularly in scenarios involving inter-VM communication dependencies, which are common in smart manufacturing applications. This manuscript presents an AI-driven approach utilizing a modified Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, enhanced with improved mutation and crossover operators, to efficiently place VMs. This approach aims to minimize the impact on networking devices during inter-VM communication while enhancing resource utilization. The proposed algorithm is benchmarked against other multi-objective algorithms, such as Multi-Objective Evolutionary Algorithm with Decomposition (MOEA/D), demonstrating its superiority in optimizing resource allocation in cloud-based environments for smart manufacturing.

The pupil, iris, vitreous, and retina are parts of the eye, where any defect due to physical damage or chronic diseases to these parts of the eye can lead to partial vision loss or complete blindness. Changes in retinal structure due to diabetes or high blood pressure lead to diabetic retinopathy (DR). The early diagnosis of DR using computer-aided automated tools is possible due to tremendous advancements in machine and deep learning models in the last decade. Devising and implementing innovative deep learning models for retinal structural analysis is crucial to the early diagnosis of DR and other eye diseases. In this work, we have developed a new approach, which involves the development of a lightweight convolutional neural network (CNN)-based model for segmentation of retinal vessels and a mobile application for DR grading. This paper covers the development process of an Android application that leverages the power of CNN-based deep learning model to detect DR regardless of its stage. To achieve this, two models have been created and compared, the best one having an accuracy of 96.72 %. An Android application has then been developed, that makes calls to this model and then displays the results on screen with a simple-to-understand interface developed using the Kivy framework.

In this article, we evaluate the throughput of NOMA with energy harvesting from vibrations. NOMA is the best candidate for 6 G communications as it offers larger data rates than 5 G. However, NOMA’s performance and throughput have not been yet derived when the source harvests energy from vibrations. Indeed, energy harvesting from vibrations is a technology that consists in converting mechanical vibrations into electrical energy. The vibrations sources can come from industrial machinery, vehicles, or human motion and they can be captured by using piezoelectric materials. The harvested energy from vibrations is proportional to the the product of the squared value of the mechanical deformation d and the frequency of the mechanical vibration f. Both f and d follow a Gaussian distribution. The harvested power depends also on the capacity of the piezoelectric element C and the force factor g. The harvesting process was also optimized in order to maximize data rates. We have derived the statistics of the harvested energy from vibrations as well as that of the Signal to Interference plus Noise Ratio (SINR) to compute and maximize the throughput.

Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS) represent a cutting-edge technology in wireless communications, allowing surfaces to both transmit and reflect signals simultaneously. This dual functionality provides greater flexibility and efficiency in signal propagation and spectrum management. Integrating energy harvesting from vibrations into STAR-RIS creates an innovative and sustainable solution for powering these systems, enabling autonomous operation in environments where conventional power sources are unavailable. By converting ambient mechanical energy into electrical power, vibration-based energy harvesting supports the continuous operation of STAR-RIS without reliance on external energy sources. This advancement has significant implications for the deployment of future wireless networks, including smart cities, the Internet of Things, and remote sensing applications, offering a pathway to greener and more efficient communication infrastructure.