Advanced Machine Learning Techniques for Predictive Modeling of Property Prices
Real estate price prediction is crucial for informed decision making in the dynamic real
estate sector. In recent years, machine learning (ML) techniques have emerged as powerful …
estate sector. In recent years, machine learning (ML) techniques have emerged as powerful …
Explainable artificial intelligence (XAI) for predicting the need for intubation in methanol-poisoned patients: a study comparing deep and machine learning models
The need for intubation in methanol-poisoned patients, if not predicted in time, can lead to
irreparable complications and even death. Artificial intelligence (AI) techniques like machine …
irreparable complications and even death. Artificial intelligence (AI) techniques like machine …
[HTML][HTML] Advanced techniques for wind energy production forecasting: Leveraging multi-layer Perceptron+ Bayesian optimization, ensemble learning, and CNN-LSTM …
Due to the shortage of fossil fuels in many countries, power plants that rely on fossil fuels will
be phased out in favor of wind turbines as the primary source of energy generation. These …
be phased out in favor of wind turbines as the primary source of energy generation. These …
Hybrid framework combining grey system model with Gaussian process and STL for CO2 emissions forecasting in developed countries
Accurate forecasting of carbon dioxide (CO 2) emissions is crucial for achieving carbon
neutrality early, as CO 2 is the primary component of greenhouse gases. The time series of …
neutrality early, as CO 2 is the primary component of greenhouse gases. The time series of …
Carbon emissions trading price forecasting based on temporal-spatial multidimensional collaborative attention network and segment imbalance regression
H Yin, Y Yin, H Li, J Zhu, Z ** low-
carbon economies and constructing carbon markets. Making accurate predictions is …
carbon economies and constructing carbon markets. Making accurate predictions is …
Assessment of the neutrosophic Fuzzy-AHP and predictive power of some machine learning approaches for maize silage soil quality
Agricultural soil quality has become a noteworthy subject for study because of the rising
awareness surrounding sustainable farming, the decrease in farmland due to urban growth …
awareness surrounding sustainable farming, the decrease in farmland due to urban growth …
Detection of small foreign objects in Pu-erh sun-dried green tea: An enhanced YOLOv8 neural network model based on deep learning
Z Wang, S Zhang, Y Chen, Y **a, H Wang, R **… - Food Control, 2025 - Elsevier
To efficiently and accurately detect minuscule foreign objects in the processing of Pu-erh
sun-dried green tea, ensuring food quality and consumer safety, this study innovatively …
sun-dried green tea, ensuring food quality and consumer safety, this study innovatively …
[HTML][HTML] Machine learning and transfer learning techniques for accurate brain tumor classification
Brain tumors, resulting from uncontrolled and rapid cell growth, pose significant health risks
if not treated early. Despite numerous advancements, accurate segmentation and …
if not treated early. Despite numerous advancements, accurate segmentation and …
[HTML][HTML] Exploring machine learning to study and predict the chloride threshold level for carbon steel reinforcement
N Maamary, IG Ogunsanya - Cement and Concrete Composites, 2024 - Elsevier
Chloride-induced corrosion of steel reinforcing bar (rebar) is the primary cause of
deterioration in reinforced concrete structures, posing a significant infrastructure challenge …
deterioration in reinforced concrete structures, posing a significant infrastructure challenge …
Leveraging LSTM-SMI and ARIMA architecture for robust wind power plant forecasting
Wind power prediction is a critical goal for power engineers, aimed at forecasting the power
output for applicable power plants. However, the complex, nonlinear, non-stationary, and …
output for applicable power plants. However, the complex, nonlinear, non-stationary, and …