With the continuous increase in cyberattacks over the past few decades, the quest to
develop a comprehensive, robust, and effective intrusion detection system (IDS) in the …

With the continuous expansion and evolution of IoT applications, attacks on those IoT
applications continue to grow rapidly. In this systematic literature review (SLR) paper, our …

The significance of an intrusion detection system (IDS) in networks security cannot be
overstated in detecting and responding to malicious attacks. Failure to detect large-scale …

Nowadays, machine learning and deep learning algorithms are used in recent studies as
active security techniques instead of traditional ones to secure the cloud environment based …

The majority of the intrusion detection solutions proposed using machine learning and deep
learning approaches are based on known attack classes only. Comprehensive threat …

Convolutional neural networks (CNNs) have been originally used for computer vision tasks,
such as image classification. While several digital soil map** studies have been …

Soil Vis-NIR spectral response had been widely proposed as an alternative to costly and
time-consuming laboratory determination of soil physical and chemical properties. However …

As a proximal soil sensing technique, laboratory visible and near-infrared (Vis-NIR)
spectroscopy is a promising tool for the quantitative estimation of soil properties. However …

Soil nutrients play vital roles in vegetation growth and are a key indicator of land
degradation. Accurate, rapid, and non-destructive measurement of the soil nutrient content is …

Soil organic matter (SOM) is critical for soil fertility, crop growth, and plays an important role
in the global carbon cycle and climate change. Therefore, spatial prediction of SOM is …