Devdiscourse

From data to safety: Artificial intelligence transforms coal mine risk management

The researchers identify critical limitations that restrict the full realization of AI’s potential in mine safety. A major ...
Scientific Research Publishing

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ()

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ...
Mirage News

Water, AI Innovator Wins IEE Person of Year Award

Growing up in Chengdu, China, Chaopeng Shen remembers the rivers of his hometown transforming during his childhood."They ...

Naizhong Cui Enhances Urban Traffic Engineering Through Data-Driven Forecasting and Infrastructure Optimization Frameworks

A comprehensive framework integrates statistical modeling, machine learning, and simulation to optimize urban traffic forecasting, capacity ...

AI-powered model enhances atmospheric calibration precision for astronomical observation and geodetic measurement

Researchers from the Xinjiang Astronomical Observatory of the Chinese Academy of Sciences have developed a hybrid deep learning model that can accurately predict atmospheric delay, a key source of ...
GitHub

Time series forecasting using LSTM-Neural-Networks

The primary objective was to design, train, and evaluate a three-layer LSTM model for accurate price prediction, using a dataset of minute-by-minute Bitcoin prices from January 1, 2021, to March 1, ...
IEEE

Cross-LSTM: Integrating Cross Attention With Long Short-Term Memory Neural Networks in Estimating Joint Moments From Wearable Sensors

Abstract: Precise estimation of joint moments is essential for designing rehabilitation interventions and optimising the control of assistive devices. Current methods, including physics-based ...
IEEE

LSTM Neural Networks for Brain Signals and Neuromorphic Chip

Abstract: Long Short-Term Memory (LSTM) is a type of RNN architecture commonly employed in natural language processing, speech recognition, and various sequence modeling applications. A normal ...
Frontiers

A hybrid LSTM–transformer model for accurate remaining useful life prediction of lithium-ion batteries

With the widespread application of lithium-ion batteries in electric vehicles and energy storage systems, health monitoring and remaining useful life prediction have become critical components of ...
Frontiers

Research on short-term line loss rate prediction method of distribution network based on RF-CNN-LSTM

Under the background of the new distribution network, the power fluctuation on the line is increasing, which leads to more uncertainties in the predicted line loss rate, thus affecting the economic ...
GitHub

EEG-Depression-Classification-HybridSNN-LSTM

EEG samples are converted into spike trains using NeuCube. The spatio-temporal spiking patterns are encoded across a 3D SNN reservoir. The resulting spike outputs are stored in out_neucube_open.h5.