Research and development on task offloading over the Internet of Drones (IoD) has expanded rapidly in the last few years. Task offloading in a fog IoD environment is very challenging due to the high dynamics of the IoD topology, which cause intermittent connections, as well as the stringent requirements of task offloading, such as reduced delay. To overcome these challenges, in this paper, we propose a task-offloading optimization strategy using a heuristic genetic algorithm (GA) with hybrid fog computing technology for the Internet of Drones, named GA Hybrid-Fog. The proposed solution employs a GA for task offloading from edge Unmanned Aerial Vehicles (UAVs) to both fog base stations (FBSs) and fog UAVs (FUAVs) in order to optimize offloading delays (transmission and fog computing delays) and guarantee higher storage and processing capacity. Experimental results show that GA Hybrid-Fog achieves greater improvements in task-offloading delays compared to other IoD technologies (GA BS-Fog, GA UAV-Fog, and GA UAV-Edge).

Cardiovascular diseases (CVDs) continue to be a primary cause of global mortality, requiring prompt detection and care to avert serious consequences. Electrocardiograms (ECGs) are vital diagnostic instruments for cardiovascular disorders, yet traditional diagnostic methods encounter considerable obstacles, such as class imbalance and substantial heterogeneity in the available data. This study introduces a novel ECG classification technique utilizing GAN and LSTM. Generative Adversarial Networks (GANs) are employed to produce additional synthetic ECG samples, addressing class imbalance in the training dataset. The suggested design integrates LSTM units into the GAN generator to collect and maintain temporal signal patterns, while anomaly detection is performed using a CNN-LSTM model. Experimental findings indicate a significant enhancement in classification performance, achieving an accuracy of up to 94.74%. The findings indicate that the proposed GAN-LSTM model significantly improves ECG anomaly detection and classification, offering substantial potential for the rapid and precise identification of arrhythmias.

Covid_19 pandemic is considered a global emergency that has caused great human as well as economic loss. To attack this pandemic, we developed a convolutional neural network model (CNN) the detection of COVID-19 using Chest X-Ray images (CXR). The model is then deployed on the Android app RT2022 which is tested on real CXR images and the first results obtained, after experiments, testify to the performance of the RT2022 model. In COVID-19 detection, we achieved an accuracy of 90.60%

Premature birth remains a major challenge in neonatal care, especially in resource-constrained settings, where continuous monitoring and timely intervention are limited. Most existing neonatal incubators offer limited real-time monitoring, unreliable alerting, and lack communication redundancy, potentially delaying critical responses. This paper presents a comprehensive internet of thing (IoT) enabled connected incubator with redundant communication (Wi-Fi and GSM) for real-time monitoring of physiological and environmental parameters. The system integrates sensing, processing, cloud connectivity, a mobile application, and multi-channel alerts (App notifications, SMS, voice calls, and local alarms). It was experimentally evaluated under controlled laboratory conditions. Quantitative evaluation shows a cloud transmission success rate of 99.1%, end-to-end communication latency below 1 second via Wi-Fi and 2.2 seconds via GSM, with 98% of alerts successfully delivered within 6 seconds. The proposed system provides a low-cost, reliable platform that enhances neonatal safety, supports timely clinical decisions, and is scalable for resource-constrained healthcare environments.