import numpy as np import tensorflow as tf print(f"TensorFlow: {tf.__version__}") # Load model (handle legacy LSTM parameter) class CustomLSTM(tf.keras.layers.LSTM ...

Qubic has given AIGarth, the AI model it has been training while it attacked privacy blockchain Monero, a social media account. AIGarth has faced public ridicule after it failed to solve basic math ...

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 ...

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 ...

The goal of this project is to predict future stock prices by training on historical stock data. Please describe the specific objective here, such as: [e.g., to predict the closing price for a ...

ABSTRACT: 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 ...

ABSTRACT: The application of artificial intelligence in stock price forecasting is an important area of research at the intersection of finance and computer science, with machine learning techniques ...

Abstract: Water quality prediction is an important way to protect water sample area. Most Marine environmental pollution comes from the pollution of shore-based water quality, so the monitoring and ...

Abstract: While Long Short-Term Memory (LSTM) networks excel in handling time series data, Bayesian optimisation techniques offer significant advantages in tuning model parameters to adapt to variable ...