Mohammed Hasan Ali Aljbory

Today, COVID-19-patient health monitoring and management are major public health challenges for technologies. This research monitored COVID-19 patients by using the Internet of Things. IoT-based collected real-time GPS helps alert the patient automatically to reduce risk factors. Wearable IoT devices are attached to the human body, interconnected with edge nodes, to investigate data for making health-condition decisions. This system uses the wearable IoT sensor, cloud, and web layers to explore the patient’s health condition remotely. Every layer has specific functionality in the COVID-19 symptoms’ monitoring process. The first layer collects the patient health information, which is transferred to the second layer that stores that data in the cloud. The network examines health data and alerts the patients, thus helping users take immediate actions. Finally, the web layer notifies family members to take appropriate steps. This optimized deep-learning model allows for the management and monitoring for further analysis.

Supervised intrusion detection system is a system that has the capability of learning from examples about the previous attacks to detect new attacks. Using artificial neural network (ANN)-based intrusion detection is promising for reducing the number of false negative or false positives, because ANN has the capability of learning from actual examples. In this paper, a developed learning model for fast learning network (FLN) based on particle swarm optimization (PSO) has been proposed and named as PSO-FLN. The model has been applied to the problem of intrusion detection and validated based on the famous dataset KDD99. Our developed model has been compared against a wide range of meta-heuristic algorithms for training extreme learning machine and FLN classifier. PSO-FLN has outperformed other learning approaches in the testing accuracy of the learning