An advanced deep learning models-based plant disease detection: A review of recent research
Plants play a crucial role in supplying food globally. Various environmental factors lead to
plant diseases which results in significant production losses. However, manual detection of …
plant diseases which results in significant production losses. However, manual detection of …
Deep learning techniques to classify agricultural crops through UAV imagery: A review
During the last few years, Unmanned Aerial Vehicles (UAVs) technologies are widely used
to improve agriculture productivity while reducing drudgery, inspection time, and crop …
to improve agriculture productivity while reducing drudgery, inspection time, and crop …
Deep learning for safe autonomous driving: Current challenges and future directions
Advances in information and signal processing technologies have a significant impact on
autonomous driving (AD), improving driving safety while minimizing the efforts of human …
autonomous driving (AD), improving driving safety while minimizing the efforts of human …
Vision-based human activity recognition: a survey
Human activity recognition (HAR) systems attempt to automatically identify and analyze
human activities using acquired information from various types of sensors. Although several …
human activities using acquired information from various types of sensors. Although several …
Digital twin: Values, challenges and enablers from a modeling perspective
Digital twin can be defined as a virtual representation of a physical asset enabled through
data and simulators for real-time prediction, optimization, monitoring, controlling, and …
data and simulators for real-time prediction, optimization, monitoring, controlling, and …
A novel CNN-GRU-based hybrid approach for short-term residential load forecasting
Electric energy forecasting domain attracts researchers due to its key role in saving energy
resources, where mainstream existing models are based on Gradient Boosting Regression …
resources, where mainstream existing models are based on Gradient Boosting Regression …
A survey on video-based human action recognition: recent updates, datasets, challenges, and applications
Abstract Human Action Recognition (HAR) involves human activity monitoring task in
different areas of medical, education, entertainment, visual surveillance, video retrieval, as …
different areas of medical, education, entertainment, visual surveillance, video retrieval, as …
Human activity recognition using tools of convolutional neural networks: A state of the art review, data sets, challenges, and future prospects
Abstract Human Activity Recognition (HAR) plays a significant role in the everyday life of
people because of its ability to learn extensive high-level information about human activity …
people because of its ability to learn extensive high-level information about human activity …
Artificial Intelligence of Things-assisted two-stream neural network for anomaly detection in surveillance Big Video Data
In the last few years, visual sensors are deployed almost everywhere, generating a massive
amount of surveillance video data in smart cities that can be inspected intelligently to …
amount of surveillance video data in smart cities that can be inspected intelligently to …
Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: A systematic survey
Deep neural networks (DNN) have remarkably progressed in applications involving large
and complex datasets but have been criticized as a black-box. This downside has recently …
and complex datasets but have been criticized as a black-box. This downside has recently …