Kamal Ghoumid

Forest ecosystems play a crucial role in providing a wide range of ecological, social, and economic benefits. However, the increasing frequency and severity of forest fires pose a significant threat to the sustainability of forests and their functions, highlighting the need for early detection and swift action to mitigate damage. The combination of drones and artificial intelligence, particularly deep learning, proves to be a cost-effective solution for accurately and efficiently detecting forest fires in real-time. Deep learning-based image segmentation models can not only be employed for forest fire detection but also play a vital role in damage assessment and support reforestation efforts. Furthermore, the integration of thermal cameras on drones can significantly enhance the sensitivity in forest fire detection. This study undertakes an in-depth analysis of recent advancements in deep learning-based semantic segmentation, with a particular focus on model’s mask region convolutional neural network (Mask R-CNN) and you only look once (YOLO) v5, v7, and v8 variants. Emphasis is placed on their suitability for forest fire monitoring using drones equipped with RGB and/or thermal cameras. The conducted experiments have yielded encouraging outcomes across various metrics, underscoring its significance as an invaluable asset for both fire detection and continuous monitoring endeavors.

Improving the bit error rate (BER) in dense wireless medical sensor networks (WMSNs) using small areas is challenging. Ensuring the relay of big data in healthcare, such as human virus vaccine signs, is always a huge challenge. This paper proposes a new potent hierarchical algorithm (PHA) for controlling several vaccine types in real-time using a uniquely challenging laboratory before being recommended and authorized. This healthcare research paper offers a new medical application in the virus testing communication of several separate and independent patient groups (subnets), exploiting a smart laboratory. The health data aggregator (HDA) nodes of the patients are wirelessly connected to a relay network (RN) via the central nodes (CN). We develop an efficient communication scheme, improving BER values versus signal-to-noise ratio (SNR), proposing a PHA that requires three main creation stages. The first stage is responsible for collecting big medical data. In the second and third stages, we use coding approaches based on network coding (NC) and low-density parity-check code (LDPC). The proposed PHA reduces BER in deep fading environment receivers for different creating parameters. This paper introduces an innovative approach to tracking the evolution of virus vaccines by integrating IoMT, Big Data, and Artificial Intelligence (AI). This integration enables identifying and selecting the optimal and most effective vaccine.