[PDF][PDF] Using IoT and ML for forest fire detection, monitoring, and prediction: a literature review
Forests are large areas gathering trees and other plants. They are so important for protecting
the environment; they filter air and water, provide food and shelter for animals, and help …
the environment; they filter air and water, provide food and shelter for animals, and help …
Transfer-learning-based intrusion detection framework in IoT networks
Cyberattacks in the Internet of Things (IoT) are growing exponentially, especially zero-day
attacks mostly driven by security weaknesses on IoT networks. Traditional intrusion …
attacks mostly driven by security weaknesses on IoT networks. Traditional intrusion …
[PDF][PDF] Crime prediction using a hybrid sentiment analysis approach based on the bidirectional encoder representations from transformers
Sentiment analysis (SA) is widely used today in many areas such as crime detection
(security intelligence) to detect potential security threats in realtime using social media …
(security intelligence) to detect potential security threats in realtime using social media …
[PDF][PDF] A lightweight optimized deep learning-based host-intrusion detection system deployed on the edge for IoT
The Internet of Things (IoT) is now present in every domain from applications in smart
homes, Smart Cities, Industrial Internet of Things (IIoT), such as e-Health, and beyond. The …
homes, Smart Cities, Industrial Internet of Things (IIoT), such as e-Health, and beyond. The …
[PDF][PDF] Review on forest fires detection and prediction using deep learning and drones
Forests everywhere in the world are essential components for protecting the biosphere.
They strongly contribute to the global carbon cycle and sustain a wide variety of plant and …
They strongly contribute to the global carbon cycle and sustain a wide variety of plant and …
[PDF][PDF] An unsupervised generative adversarial network based-host intrusion detection system for internet of things devices
Machine learning (ML) and deep learning (DL) have achieved amazing progress in diverse
disciplines. One of the most efficient approaches is unsupervised learning (UL), a sort of …
disciplines. One of the most efficient approaches is unsupervised learning (UL), a sort of …
[PDF][PDF] A hybrid deep learning-based intrusion detection system for IoT networks
The Internet of Things (IoT) is a rapidly evolving technology with a wide range of potential
applications, but the security of IoT networks remains a major concern. The existing system …
applications, but the security of IoT networks remains a major concern. The existing system …
A hybrid approach for efficient feature selection in anomaly intrusion detection for IoT networks
The exponential growth of Internet of Things (IoT) devices underscores the need for robust
security measures against cyber-attacks. Extensive research in the IoT security community …
security measures against cyber-attacks. Extensive research in the IoT security community …
[PDF][PDF] Optimal text-to-image synthesis model for generating portrait images using generative adversarial network techniques
The advancements in artificial intelligence research, particularly in computer vision, have led
to the development of previously unimaginable applications, such as generating new …
to the development of previously unimaginable applications, such as generating new …
[PDF][PDF] Early wildfire detection using machine learning model deployed in the fog/edge layers of IoT
The impact of wildfires, even following the fire's extinguishment, continues to affect harmfully
public health and prosperity. Wildfires are becoming increasingly frequent and severe, and …
public health and prosperity. Wildfires are becoming increasingly frequent and severe, and …