AI-based method accurately segments and quantifies overlapping cell membranes

Researchers at University of Tsukuba have developed DeMemSeg, an AI-powered analysis pipeline that addresses a long-standing ...
The New York Times

In an Era of Deep Polarization, Unity Is Not Trump’s Mission

President Trump does not subscribe to the traditional notion of being president for all Americans. By Peter Baker Peter Baker has covered the past six presidencies. He reported from Washington. The ...
IEEE

Online Public Opinion Situation Assessment by Deep Sparse Autoencoder and Time-Series Modeling Convolutional Neural Network

Abstract: We have developed an innovative framework for analyzing online public sentiment, referred to as the Online Public Opinion Situation Assessment (OPOSA) model. This model employs Deep Sparse ...
Scientific Research Publishing

Traffic Speed Prediction Based on Autoencoder and Deep Learning ()

Traffic prediction is the core of intelligent transportation system, and accurate traffic speed prediction is the key to optimize traffic management. Currently, the traffic speed prediction model ...
Frontiers

BANSMDA: a computational model for predicting potential microbe-disease associations based on bilinear attention networks and sparse autoencoders

Introduction: Predicting the relationship between diseases and microbes can significantly enhance disease diagnosis and treatment, while providing crucial scientific support for public health, ...
GitHub

Enhancing EV Cost Prediction Using AutoEncoder-Based Feature Learning

Electric Vehicle (EV) cost prediction involves analyzing complex, high-dimensional data that often contains noise, multicollinearity, and irrelevant features. Traditional regression models struggle to ...
GitHub

Slow Deepsparse Benchmarking Result from Trained Model Using SparseML.

And here are related dependencies and training environment Libraries: torch==2.5.1 sparseml==1.8.0 deepsparse==1.8.0 ultralytics==8.0.124 onnx==1.14.1 onnxruntime==1.17.0 Training Environment: NVIDIA ...
IEEE

EEG Classification Using Reinforcement Learning and Swarm-Based Deep Sparse Autoencoder

Abstract: Analysis of electroencephalographic (EEG) data has shown to be a useful tool in the fields of Brain-computer interfaces (BCIs), and even business. Recently, deep learning architectures have ...