Deep learning modelling techniques: current progress, applications, advantages, and challenges
Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …
Machine learning in agriculture: A comprehensive updated review
The digital transformation of agriculture has evolved various aspects of management into
artificial intelligent systems for the sake of making value from the ever-increasing data …
artificial intelligent systems for the sake of making value from the ever-increasing data …
Machine learning in geo-and environmental sciences: From small to large scale
In recent years significant breakthroughs in exploring big data, recognition of complex
patterns, and predicting intricate variables have been made. One efficient way of analyzing …
patterns, and predicting intricate variables have been made. One efficient way of analyzing …
Microgrid energy management and monitoring systems: A comprehensive review
Microgrid (MG) technologies offer users attractive characteristics such as enhanced power
quality, stability, sustainability, and environmentally friendly energy through a control and …
quality, stability, sustainability, and environmentally friendly energy through a control and …
A smart meter infrastructure for smart grid IoT applications
Electric infrastructures have been pushed forward to handle tasks they were not originally
designed to perform. To improve reliability and efficiency, state-of-the-art power grids …
designed to perform. To improve reliability and efficiency, state-of-the-art power grids …
Machine learning‐reinforced noninvasive biosensors for healthcare
The emergence and development of noninvasive biosensors largely facilitate the collection
of physiological signals and the processing of health‐related data. The utilization of …
of physiological signals and the processing of health‐related data. The utilization of …
[HTML][HTML] Energy data generation with wasserstein deep convolutional generative adversarial networks
Residential energy consumption data and related sociodemographic information are critical
for energy demand management, including providing personalized services, ensuring …
for energy demand management, including providing personalized services, ensuring …
Artificial intelligence as a sustainable tool in wastewater treatment using membrane bioreactors
Efforts are currently in progress to commercialize membrane bioreactor (MBR) technologies
already developed at laboratory and pilot scale. To attain this goal, the efficiency of MBRs …
already developed at laboratory and pilot scale. To attain this goal, the efficiency of MBRs …
A comparative study of convolutional neural networks and conventional machine learning models for lithological map** using remote sensing data
Lithological map** is a critical aspect of geological map** that can be useful in studying
the mineralization potential of a region and has implications for mineral prospectivity …
the mineralization potential of a region and has implications for mineral prospectivity …
HSIC bottleneck based distributed deep learning model for load forecasting in smart grid with a comprehensive survey
Load forecasting is a vital part of smart grids for predicting the required electrical power
using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the …
using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the …