Nada Alasbali’s research while affiliated with King Khalid University and other places

The integration of wearable medical devices into surgical practice has transformed the field, enabling enhanced precision, informed decision-making, and improved patient outcomes. These devices, which include biosensors and augmented reality (AR) headsets, continuously collect and provide real-time data to support surgeons during complex procedures. A key advancement in this domain is the incorporation of artificial intelligence (AI), which enables these devices to analyze large datasets, generate predictive insights, and adapt to changing clinical scenarios, thus assisting in surgical decision-making. AI-driven analytics, combined with wearable technology, optimize surgical performance by detecting potential complications early and allowing for dynamic adjustments to surgical plans. However, widespread adoption faces challenges, including data privacy concerns, device interoperability issues, and regulatory compliance requirements. This paper presents a comprehensive review of wearable medical devices in surgery, examining their applications, limitations, and transformative role of AI in advancing surgical care. Additionally, the paper discusses strategies to overcome barriers, supporting the integration of these technologies into future surgical practices.

The Internet of Things (IoT) and unmanned aerial vehicles (UAVs) continue to advance the low-carbon smart agriculture technologies for next-generation consumer electronics and unlock more informed agricultural practices. Reinforcement learning (RL), federated learning (FL), and federated reinforcement learning (FRL) have demonstrated notable achievements in resolving complex problems, including resource allocation, energy efficiency, anomaly detection, and bandwidth utilization for multimodal tasks. This research explores multimodal data analysis and resource optimization using FRL for agricultural consumer electronics. The proposed framework employs IoT devices to monitor temperature, humidity, soil temperature, and soil moisture in real time, while UAVs provide aerial imagery for soil moisture, crop growth, and pest identification across three fields. This framework supports distributed learning, which trains local RL models on each node and combines them into the global model. The proposed FRL model demonstrated significant enhancements, including a 17% reduction in energy consumption for IoT devices and a 15% reduction for UAVs compared to non-FRL methods. This research emphasizes the effectiveness of FRL in integrating IoT and UAV for efficient resource allocation, energy efficiency, and reduced carbon emissions for low-carbon agricultural consumer electronics.

The Distributed Network Protocol 3 (DNP3) protocols focus on securing critical infrastructure communication in sectors such as energy and supervisory control and data acquisition (SCADA) systems. The security of DNP3 is paramount, employing features such as encryption, authentication, and secure key management to safeguard against cyber threats. The robust security framework ensures the reliability and integrity of data exchange, fortifying the resilience of industrial control systems against potential cyber-attacks. This study investigates Smart Grid (SG) DNP3 communication security and provides a deep learning-driven approach to detect and prevent cyber-attacks in the SG. Securing communication in SG is a critical challenge, particularly for protocols such as DNP3, which is essential to SCADA systems. This study explores the potential for enhancing intrusion detection in DNP3 communications and the associated industrial control system traffic through the application of state-of-the-art deep learning (DL) algorithms. A Segmented Neural Network (SNN) architecture is employed to analyze the DNP3 dataset, which is captured using CICFlowMeter3 and a DNP3 Parser, integrating Deep Neural Network (DNN), Long Short Term Memory (LSTM), and Random Neural Network (RandNN) models. In CICFlowMeter3, the model achieved an accuracy of 99.86%, whereas, on the DNP3 Parser, it improved to 99.75%, demonstrating outstanding performance. These findings show that the proposed framework is efficient and resilient with complicated and varied data streams. The results show that the proposed SNN-based solution improved the security and resilience of SG operations to detect anomalies in industrial control systems (ICS) in real-time.

Introduction Major Depressive Disorder (MDD) remains a critical mental health concern, necessitating accurate detection. Traditional approaches to diagnosing MDD often rely on manual Electroencephalography (EEG) analysis to identify potential disorders. However, the inherent complexity of EEG signals along with the human error in interpreting these readings requires the need for more reliable, automated methods of detection. Methods This study utilizes EEG signals to classify MDD and healthy individuals through a combination of machine learning, deep learning, and split learning approaches. State of the art machine learning models i.e., Random Forest, Support Vector Machine, and Gradient Boosting are utilized, while deep learning models such as Transformers and Autoencoders are selected for their robust feature-extraction capabilities. Traditional methods for training machine learning and deep learning models raises data privacy concerns and require significant computational resources. To address these issues, the study applies a split learning framework. In this framework, an ensemble learning technique has been utilized that combines the best performing machine and deep learning models. Results Results demonstrate a commendable classification performance with certain ensemble methods, and a Transformer-Random Forest combination achieved 99% accuracy. In addition, to address data-sharing constraints, a split learning framework is implemented across three clients, yielding high accuracy (over 95%) while preserving privacy. The best client recorded 96.23% accuracy, underscoring the robustness of combining Transformers with Random Forest under resource-constrained conditions. Discussion These findings demonstrate that distributed deep learning pipelines can deliver precise MDD detection from EEG data without compromising data security. Proposed framework keeps data on local nodes and only exchanges intermediate representations. This approach meets institutional privacy requirements while providing robust classification outcomes.

Introduction: The accurate and timely diagnosis of skin diseases is a critical concern, as many skin diseases exhibit similar symptoms in the early stages. Most existing automated detection/classification approaches that utilize machine learning or deep learning poses privacy issues, as they involve centralized computing and require local storage for data training. Methods: Keeping the privacy of sensitive patient data as a primary objective, in addition to ensuring accuracy and efficiency, this paper presents an algorithm that integrates Federated learning techniques into an IoT-based edge-computing environment. The purpose of the proposed technique is to protect the sensitive data by training the model locally on the edge device and transferring only the weights to the central server where the aggregation takes place. This process ensures data security at the edge level and eliminates the need for centralized storage. Furthermore, the proposed framework enhances the network's real-time processing capabilities using IoT-integrated sensors, which in turn facilitates swift diagnoses. In addition, this paper also focuses on the design and execution of the federated framework, which includes the processing power, memory, and the number of nodes present in the network. Results: The accuracy and effectiveness of the proposed algorithm are demonstrated using precise parameters, such as accuracy, precision, f1-score, and recall, along with all the intricacies of the secure federated approach. The accuracy achieved by the proposed algorithm is 98.6%. As the model was trained locally, the bandwidth utilization was almost negligible. Discussion: The proposed model can assist skin specialists in diagnosing conditions. Additionally, with federated learning, the model continuously improves as new input data accumulates, enhancing the accuracy of subsequent training rounds. KEYWORDS federated learning, healthcare technology, internet of things (IoT), edge computing, decentralized network architecture, distributed computing OPEN ACCESS EDITED BY MS (2025) Privacy-enhanced skin disease classification: integrating federated learning in an IoT-enabled edge computing.

The ever-increasing security vulnerabilities in the Internet-of-Things (IoT) systems require improved threat detection approaches. This paper presents a compact and efficient approach to detect botnet attacks by employing an integrated approach that consists of traffic pattern analysis, temporal support learning, and focused feature extraction. The proposed attention-based model benefits from a hybrid CNN-BiLSTM architecture and achieves 99% classification accuracy in detecting botnet attacks utilizing the N-BaIoT dataset, while maintaining high precision and recall across various scenarios. The proposed model's performance is further validated by key parameters, such as Mathews Correlation Coefficient and Cohen's kappa Correlation Coefficient. The close-to-ideal results for these parameters demonstrate the proposed model's ability to detect botnet attacks accurately and efficiently in practical settings and on unseen data. The proposed model proved to be a powerful defense mechanism for IoT networks to face emerging security challenges.

The ever-increasing security vulnerabilities in the Internet-of-Things (IoT) systems require improved threat detection approaches. This paper presents a compact and efficient approach to detect botnet attacks by employing an integrated approach that consists of traffic pattern analysis, temporal support learning, and focused feature extraction. The proposed attention-based model benefits from a hybrid CNN-BiLSTM architecture and achieves 99% classification accuracy in detecting botnet attacks utilizing the N-BaIoT dataset, while maintaining high precision and recall across various scenarios. The proposed model's performance is further validated by key parameters, such as Mathews Correlation Coefficient and Cohen's kappa Correlation Coefficient. The close-to-ideal results for these parameters demonstrate the proposed model's ability to detect botnet attacks accurately and efficiently in practical settings and on unseen data. The proposed model proved to be a powerful defense mechanism for IoT networks to face emerging security challenges.