Deep neural networks are at the heart of artificial intelligence, ranging from pattern recognition to large language and ...

In order to produce effective targeted therapies for cancer, scientists need to isolate the genetic and phenotypic ...

Scientists at ETH Zurich have broken new ground by generating over 400 types of nerve cells from stem cells in the lab, far ...

Interpreting a deep Convolutional Neural Network (CNN) involves identifying the features in a hierarchy of layers that contribute to recognition. Although the current approaches serve as methods to ...

The framework consists of two main modules: the multi-modal molecular representation learning module and the structure-awareness module. The multi-modal molecular representation learning module ...

Perceptual image hashing has emerged as a crucial forensic tool within the Internet of Things (IoT) ecosystem. Traditional perceptual hashing algorithms predominantly rely on global image features to ...

With the widespread application of deep learning and Convolutional Neural Networks (CNN) in image classification, how to effectively improve model performance and reduce its complexity has become a ...

With the continuous improvement of deep learning technology, it is crucial to improve the accuracy of model prediction. Existing classification prediction models often struggle with low classification ...

A multitask 2D CNN model is designed for integrated monitoring stress and damage in concrete specimens utilizing the raw impedance signatures of capsule-like smart aggregated (CSA). The fundamental ...

According to the World Report by WHO on Hearing published in March, 2021, nearly 2.5 billion people will be deaf or hard of hearing by the year 2050. In 2020, the uncorrected hearing loss in the world ...

Musical instrument recognition is the act of counting on machine learning or the application of signal processing to isolate and classify various musical instruments in an audio track. Such capability ...

Increased network traffic demands have led to ex-tremely dense network deployments. This translates to significant growth in energy consumption at the radio access networks, resulting in high network ...