This paper reviews advanced Neural Network (NN) techniques available to process
hyperspectral data, with a special emphasis on plant disease detection. Firstly, we provide a …

A recurrent neural network (RNN) has demonstrated its outstanding ability in sequence
tasks and has achieved state of the art in many applications, such as industrial and medical …

Transformer architectures have become increasingly popular in healthcare applications.
Through outstanding performance in natural language processing and superior capability to …

An algorithm called bidirectional long short-term memory networks (BLSTM) for processing
sequential data is introduced. This supervised learning method trains a special recurrent …

Both protease-and reactive oxygen species (ROS)-mediated proteolysis are thought to be
key effectors of tissue remodeling. We have previously shown that comparison of amino acid …

Optical Neural Networks (ONNs) can be promising alternatives for conventional electrical
neural networks as they offer ultra-fast data processing with low energy consumption …

Neural network systems are highly capable of deriving knowledge from complex data, and
they are used to extract patterns and trends which are otherwise hidden in many …

Depth-based reconstruction of three-dimensional (3D) shape of objects is one of core
problems in computer vision with a lot of commercial applications. However, the 3D …

Plant resistance (R) proteins play a significant role in the detection of pathogen invasion.
Accurately predicting plant R proteins is a key task in phytopathology. Most plant R protein …

This paper presents a composite multi-layer classifier system for predicting the subcellular
localization of proteins based on their amino acid sequence. The work is an extension of our …