Forests are vital ecosystems composed of various plant and animal species that have evolved over years to coexist. Such ecosystems are often threatened by wildfires that can start either naturally, as a result of lightning strikes, or unintentionally caused by humans. In general, human-caused fires are more severe and expensive to fight because they are frequently located in inaccessible areas. Wildfires can spread quickly and become extremely dangerous, causing damage to homes and facilities, as well as killing people and animals. Early discovery of wildfires is vital to protect lives, property, and resources. Reinforced imaging technologies can play a key role to detect wildfires earlier. By applying deep learning (DL) over a dataset of images (collected using drones, planes, and satellites), we target to automate the forest fire detection. In this paper, we focus on building a DL model specifically to detect wildfires using transfer learning techniques from the best pretrained DL computer vision architectures available nowadays, such as VGG16, VGG19, Inceptionv3, ResNet50, ResNet50V2, InceptionResNetV2, Xception, Dense-Net, MobileNet, MobileNetV2, and NASNetMobile. Our proposed approach attained a detection rate of more than 99.9% over multiple metrics, proving that it could be used in real-world forest fire detection applications.

In this paper, we present a comparative evaluation of state-of-the-art deep learning models for object detection in underwater environments, with a focus on marine debris detection. We investigate the performance of four prominent object detection models: Faster R-CNN, SSD, YOLOv8, and YOLOv9,...

Forest fires are a global environmental problem that can cause significant damage to natural resources and human lives. The increasing frequency and severity of forest fires have resulted in substantial losses of natural resources. To mitigate this, an effective fire detection and monitoring system is crucial. This work aims to explore and review the current advancement in the field of forest fire detection and monitoring using both drones or unmanned aerial vehicles (UAVs), and deep learning techniques. The utilization of drones fully equipped with specific sensors and cameras provides a cost-effective and efficient solution for real-time monitoring and early fire detection. In this paper, we conduct a comprehensive analysis of the latest developments in deep learning object detection, such as YOLO (You Only Look Once), R-CNN (Region-based Convolutional Neural Network), and their variants, with a focus on their potential application in the field of forest fire monitoring. The performed experiments show promising results in multiple metrics, making it a valuable tool for fire detection and monitoring.

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.

Data preprocessing plays a critical role in the success of machine learning and deep learning models in the medical and healthcare field. As the availability of healthcare data continues to grow, ensuring its quality, reliability, and suitability for machine learning tasks becomes essential. In this paper, we will try to provide an in-depth exploration of data preprocessing techniques specifically tailored to the medical and healthcare domain. We will cover various steps involved in data preprocessing, including data types, data cleaning, data transforming, and data normalization. Additionally, challenges and considerations unique to medical data preprocessing are discussed.