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Abstract: To improve the low accuracy of the SGP4 model in short-term orbit prediction for medium Earth orbit satellites and the instability in LSTM model training, this paper proposes and develops an ...

Background: Accurate forecasting of lung cancer incidence is crucial for early prevention, effective medical resource allocation, and evidence-based policymaking. Objective: This study proposes a ...

This study proposes a hybrid modeling approach that integrates a Physics Informed Neural Network (PINN) and a long short-term memory (LSTM) network to predict river water temperature in a defined ...

Abstract: A novel forecasting model of the bidirectional LSTM with self-attention (Bi-LSTM-SA) is introduced to address the need to upgrade the accurate projection of electrical load forecasting. The ...

Mesoscale eddies are the most important mesoscale phenomena in the oceans, and determining how to predict their spatial and temporal characteristics is a very challenging task. Most previous studies ...

The National Oceanic and Atmospheric Administration reports a 95% decline in the oldest Arctic ice over the last 33 years [1], while the National Aeronautics and Space Administration states that ...

Task 1: Preprocess and Explore the Data 1.1 Load Historical Data (TSLA, BND, SPY) Using yfinance python CopyEdit import yfinance as yf import pandas as pd import numpy as np import matplotlib.pyplot ...