Deep learning for medical image-based cancer diagnosis
Simple Summary Deep learning has succeeded greatly in medical image-based cancer
diagnosis. To help readers better understand the current research status and ideas, this …
diagnosis. To help readers better understand the current research status and ideas, this …
A systematic literature review on hardware reliability assessment methods for deep neural networks
Artificial Intelligence (AI) and, in particular, Machine Learning (ML), have emerged to be
utilized in various applications due to their capability to learn how to solve complex …
utilized in various applications due to their capability to learn how to solve complex …
Via: A novel vision-transformer accelerator based on fpga
Since Google proposed Transformer in 2017, it has made significant natural language
processing (NLP) development. However, the increasing cost is a large amount of …
processing (NLP) development. However, the increasing cost is a large amount of …
Multibit, Lead‐Free Cs2SnI6 Resistive Random Access Memory with Self‐Compliance for Improved Accuracy in Binary Neural Network Application
In the realm of neuromorphic computing, integrating Binary Neural Networks (BNN) with non‐
volatile memory based on emerging materials can be a promising avenue for introducing …
volatile memory based on emerging materials can be a promising avenue for introducing …
Diagnostic strategies for breast cancer detection: from image generation to classification strategies using artificial intelligence algorithms
JA Basurto-Hurtado, IA Cruz-Albarran… - Cancers, 2022 - mdpi.com
Simple Summary With the recent advances in the field of artificial intelligence, it has been
possible to develop robust and accurate methodologies that can deliver noticeable results in …
possible to develop robust and accurate methodologies that can deliver noticeable results in …
Fully parallel stochastic computing hardware implementation of convolutional neural networks for edge computing applications
CF Frasser, P Linares-Serrano… - … on Neural Networks …, 2022 - ieeexplore.ieee.org
Edge artificial intelligence (AI) is receiving a tremendous amount of interest from the
machine learning community due to the ever-increasing popularization of the Internet of …
machine learning community due to the ever-increasing popularization of the Internet of …
[HTML][HTML] Explaining deep learning models for ozone pollution prediction via embedded feature selection
MJ Jiménez-Navarro, M Martínez-Ballesteros… - Applied Soft …, 2024 - Elsevier
Ambient air pollution is a pervasive global issue that poses significant health risks. Among
pollutants, ozone (O 3) is responsible for an estimated 1 to 1.2 million premature deaths …
pollutants, ozone (O 3) is responsible for an estimated 1 to 1.2 million premature deaths …
Deep-GHBP: improving prediction of Growth Hormone-binding proteins using deep learning model
Growth hormone-binding proteins (GHBPs) are carrier proteins that interact with other
growth hormone proteins in a selective and non-covalent fashion. GHBPs perform significant …
growth hormone proteins in a selective and non-covalent fashion. GHBPs perform significant …
Dependable dnn accelerator for safety-critical systems: A review on the aging perspective
In the modern era, artificial intelligence (AI) and deep learning (DL) seamlessly integrate into
various spheres of our daily lives. These cutting-edge disciplines have given rise to …
various spheres of our daily lives. These cutting-edge disciplines have given rise to …
[HTML][HTML] A Deep Reinforcement Learning Approach to DC-DC Power Electronic Converter Control with Practical Considerations
In recent years, there has been a growing interest in using model-free deep reinforcement
learning (DRL)-based controllers as an alternative approach to improve the dynamic …
learning (DRL)-based controllers as an alternative approach to improve the dynamic …