Split computing and early exiting for deep learning applications: Survey and research challenges
Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep
neural networks (DNNs) to execute complex inference tasks such as image classification …
neural networks (DNNs) to execute complex inference tasks such as image classification …
[HTML][HTML] Machine learning for landslides prevention: a survey
Landslides are one of the most critical categories of natural disasters worldwide and induce
severely destructive outcomes to human life and the overall economic system. To reduce its …
severely destructive outcomes to human life and the overall economic system. To reduce its …
On interpretability of artificial neural networks: A survey
Deep learning as performed by artificial deep neural networks (DNNs) has achieved great
successes recently in many important areas that deal with text, images, videos, graphs, and …
successes recently in many important areas that deal with text, images, videos, graphs, and …
Neuroevolution in deep neural networks: Current trends and future challenges
A variety of methods have been applied to the architectural configuration and learning or
training of artificial deep neural networks (DNN). These methods play a crucial role in the …
training of artificial deep neural networks (DNN). These methods play a crucial role in the …
Uncertainty-aware convolutional neural network for COVID-19 X-ray images classification
Deep learning (DL) has shown great success in the field of medical image analysis. In the
wake of the current pandemic situation of SARS-CoV-2, a few pioneering works based on …
wake of the current pandemic situation of SARS-CoV-2, a few pioneering works based on …
DeepThink IoT: the strength of deep learning in internet of things
Abstract The integration of Deep Learning (DL) and the Internet of Things (IoT) has
revolutionized technology in the twenty-first century, enabling humans and machines to …
revolutionized technology in the twenty-first century, enabling humans and machines to …
A variational autoencoder solution for road traffic forecasting systems: Missing data imputation, dimension reduction, model selection and anomaly detection
Efforts devoted to mitigate the effects of road traffic congestion have been conducted since
1970s. Nowadays, there is a need for prominent solutions capable of mining information …
1970s. Nowadays, there is a need for prominent solutions capable of mining information …
A review on data‐driven learning approaches for fault detection and diagnosis in chemical processes
Fault detection and diagnosis for process plants has been an active area of research for
many years. This review presents a concise overview on supervised and unsupervised data …
many years. This review presents a concise overview on supervised and unsupervised data …
[HTML][HTML] An overview of variational autoencoders for source separation, finance, and bio-signal applications
A Singh, T Ogunfunmi - Entropy, 2021 - mdpi.com
Autoencoders are a self-supervised learning system where, during training, the output is an
approximation of the input. Typically, autoencoders have three parts: Encoder (which …
approximation of the input. Typically, autoencoders have three parts: Encoder (which …
Recent advances in baggage threat detection: A comprehensive and systematic survey
X-ray imagery systems have enabled security personnel to identify potential threats
contained within the baggage and cargo since the early 1970s. However, the manual …
contained within the baggage and cargo since the early 1970s. However, the manual …