A systematic review on overfitting control in shallow and deep neural networks

MM Bejani, M Ghatee - Artificial Intelligence Review, 2021 - Springer
Shallow neural networks process the features directly, while deep networks extract features
automatically along with the training. Both models suffer from overfitting or poor …

[HTML][HTML] A review on dropout regularization approaches for deep neural networks within the scholarly domain

I Salehin, DK Kang - Electronics, 2023 - mdpi.com
Dropout is one of the most popular regularization methods in the scholarly domain for
preventing a neural network model from overfitting in the training phase. Develo** an …

Pruning for compression of visual pattern recognition networks: a survey from deep neural networks perspective

SA Bhalgaonkar, MV Munot, AD Anuse - Pattern recognition and data …, 2022 - Springer
Abstract Visual Pattern Recognition Networks (VPRN) delivers high performance using deep
neural networks. With the advancements in deep neural networks VPR network has gained …

Optimized tongue driven system using artificial intelligence

MH Assaf, R Kumar, K Sharma… - Computer Methods in …, 2023 - Taylor & Francis
This paper presents a cost-effective design of a wearable wireless tongue drive system
(TDS) for disabled individuals, particularly with spinal cord injuries. We propose a basic TDS …

[การอ้างอิง][C] Optimization of AI Methods on Distributed-Memory Computing Architectures

D Coquelin - 2024 - Blekinge Institute of Technology