Activation functions in deep learning: A comprehensive survey and benchmark

SR Dubey, SK Singh, BB Chaudhuri - Neurocomputing, 2022‏ - Elsevier
Neural networks have shown tremendous growth in recent years to solve numerous
problems. Various types of neural networks have been introduced to deal with different types …

[HTML][HTML] Recurrent neural networks: A comprehensive review of architectures, variants, and applications

ID Mienye, TG Swart, G Obaido - Information, 2024‏ - mdpi.com
Recurrent neural networks (RNNs) have significantly advanced the field of machine learning
(ML) by enabling the effective processing of sequential data. This paper provides a …

Activation functions: Comparison of trends in practice and research for deep learning

C Nwankpa, W Ijomah, A Gachagan… - arxiv preprint arxiv …, 2018‏ - arxiv.org
Deep neural networks have been successfully used in diverse emerging domains to solve
real world complex problems with may more deep learning (DL) architectures, being …

Deep reinforcement learning assisted federated learning algorithm for data management of IIoT

P Zhang, C Wang, C Jiang… - IEEE Transactions on …, 2021‏ - ieeexplore.ieee.org
The continuous expanded scale of the industrial Internet of Things (IIoT) leads to IIoT
equipments generating massive amounts of user data every moment. According to the …

Induction motor fault diagnosis using support vector machine, neural networks, and boosting methods

MC Kim, JH Lee, DH Wang, IS Lee - Sensors, 2023‏ - mdpi.com
Induction motors are robust and cost effective; thus, they are commonly used as power
sources in various industrial applications. However, due to the characteristics of induction …

A novel malware classification and augmentation model based on convolutional neural network

A Tekerek, MM Yapici - Computers & Security, 2022‏ - Elsevier
The rapid development and widespread use of the Internet have led to an increase in the
number and variety of malware proliferating via the Internet. Malware is the general …

Three decades of activations: A comprehensive survey of 400 activation functions for neural networks

V Kunc, J Kléma - arxiv preprint arxiv:2402.09092, 2024‏ - arxiv.org
Neural networks have proven to be a highly effective tool for solving complex problems in
many areas of life. Recently, their importance and practical usability have further been …

PKRT-Net: Prior knowledge-based relation transformer network for optic cup and disc segmentation

S Lu, H Zhao, H Liu, H Li, N Wang - Neurocomputing, 2023‏ - Elsevier
Glaucoma causes irreversible vision loss, and early detection of glaucoma is essential to
protect the vision of patients. The optic cup (OC) and optic disc (OD) are two critical …

[HTML][HTML] Formwork pressure prediction in cast-in-place self-compacting concrete using deep learning

Y Gamil, J Nilimaa, T Najeh, A Cwirzen - Automation in Construction, 2023‏ - Elsevier
The prediction of formwork pressure exerted by self-compacting concrete (SCC) remains a
challenge not only to researchers but also to engineers and contractors on the construction …

A hybrid supervised machine learning classifier system for breast cancer prognosis using feature selection and data imbalance handling approaches

YS Solanki, P Chakrabarti, M Jasinski, Z Leonowicz… - Electronics, 2021‏ - mdpi.com
Nowadays, breast cancer is the most frequent cancer among women. Early detection is a
critical issue that can be effectively achieved by machine learning (ML) techniques. Thus in …