Short-term load forecasting of long-short term memory neural network based on genetic algorithm
, 2020 Accurate load forecasting is conducive to the reasonable arrangement of power grid dispatching plans. Traditional load forecasting methods cannot handle the time series and nonlinear characteristics of load well.
Li WT(李婉婷) +3 more
core
A Hybrid Transfer Learning Framework for Brain Tumor Diagnosis
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.A novel hybrid transfer learning approach for brain tumor classification achieves 99.47% accuracy using magnetic resonance imaging (MRI) images. By combining image preprocessing, ensemble deep learning, and explainable artificial intelligence (XAI) techniques like gradient‐weighted class activation mapping and SHapley Additive exPlanations (SHAP), the ...
Sadia Islam Tonni +11 more
wiley +1 more source
Empirical Evaluation of A New Approach to Simplifying Long Short-term Memory (LSTM)
CoRR, 2016 5 pages, 5 ...
openaire +3 more sources
Predicting daily reference evapotranspiration (ETo) plays a significant role in numerous environmental and agricultural applications. It aids in optimizing agricultural practices, enhancing drought resilience, supporting environmental conservation ...
Farzan Mohajeri +13 more
core +1 more source
Advanced Intelligent Systems, EarlyView.Inspired by human touch, a tendon‐driven soft robotic finger combines multimodal tactile sensing and deep learning to simultaneously perceive texture and softness on deformable surfaces. A CNN‐LSTM model fuses pressure, accelerometer, and gyroscope signals to accurately classify material properties, achieving up to 95.4% texture and 97.0% softness ...
Gorkem Anil Al +3 more
wiley +1 more source
Benchmarking Data‐Driven Control of Octopus‐Inspired Soft Arms in Underwater Environment
Advanced Intelligent Systems, EarlyView.Underwater soft robots present safe, compliant interaction, yet reproducible control remains scarce. This work presents an open benchmark for octopus‐inspired arms: a smooth, velocity‐diverse data‐collection scheme produces a compact dataset to train a vanilla policy.
Muhammad Sunny Nazeer +6 more
wiley +1 more source
Prediksi pergerakan harga indeks saham berdasarkan metode Long Short-Term Memory – Convolutional Neural Network (LSTM-CNN): Studi kasus Indeks Harga Saham Gabungan Indonesia [PDF]
Seiring dengan meningkatnya kebutuhan untuk memprediksi pergerakan harga saham secara lebih akurat, berbagai teknik machine learning telah dikembangkan.
Mutmainah, A'syifa
core +1 more source
Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy
Advanced Intelligent Systems, EarlyView.This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne +5 more
wiley +1 more source
QKAN-LSTM: Quantum-inspired Kolmogorov-Arnold Long Short-term Memory
CoRRLong short-term memory (LSTM) models are a particular type of recurrent neural networks (RNNs) that are central to sequential modeling tasks in domains such as urban telecommunication forecasting, where temporal correlations and nonlinear dependencies dominate.
Yu-Chao Hsu +7 more
openaire +3 more sources
Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term Memory
, 2020 This study attempts to predict stock index prices using multivariate time series analysis. The study’s motivation is based on the notion that datasets of stock index prices involve weak periodic patterns, long-term and short-term information, for which ...
Xiaolu Wei +3 more
core +1 more source