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, ...
Abstract: This article presents a deep neural network model that integrates long short-term memory (LSTM), fuzzy logic, and self-attention mechanism to enhance short-term power load forecasting, ...
Abstract: Precise estimation of joint moments is essential for designing rehabilitation interventions and optimising the control of assistive devices. Current methods, including physics-based ...
Deep learning-based image steganalysis has progressed in recent times, with efforts more concerted toward prioritizing detection accuracy over lightweight frameworks. In the context of AI-driven ...
Over the past decade, deep learning (DL) techniques such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks have played a pivotal role in advancing the field of ...
LSTM Recurrent Neural Network is a special version of the RNN model. It stands for Long Short-Term Memory. The simple RNN has a problem that it cannot remember the context in a long sentence because ...
This project explores and compares the effectiveness of different deep learning models for cryptocurrency price prediction. By analyzing historical Bitcoin data including OHLCV (Open, High, Low, Close ...
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jctc.5c00215. Derivation of cNMSSE; neural network architecture and ...
Comparative efficacy and safety of donafenib versus bevacizumab in combination with immune checkpoint inhibitors and interventional therapy for advanced hepatocellular carcinoma: A retrospective ...
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