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[HTML][HTML] Evaluating electrical power yield of photovoltaic solar cells with k-Nearest neighbors: A machine learning statistical analysis approach
The increasing demand for sustainable and renewable energy solutions reflects the critical
importance of advancing photovoltaic (PV) technology and its operational efficiency. In …
importance of advancing photovoltaic (PV) technology and its operational efficiency. In …
Enhancing building sustainability through aerodynamic shading devices: an integrated design methodology using finite element analysis and optimized neural …
In the quest for sustainable building solutions, attention has increasingly turned to innovative
structural designs and technologies that minimize environmental impact. Within this context …
structural designs and technologies that minimize environmental impact. Within this context …
Response to letter to the Editor from Y. Takefuji on “Beyond principal component analysis: Enhancing feature reduction in electronic noses through robust statistical …
Background Principal Component Analysis (PCA) is extensively utilized in Electronic Nose
(E-nose) research for dimensionality reduction, allowing simplification of high-dimensional …
(E-nose) research for dimensionality reduction, allowing simplification of high-dimensional …
[HTML][HTML] Forecasting sustainable water production in convex tubular solar stills using gradient boosting analysis
Water scarcity is an important global issue that necessitates the development of sufficient
and sustainable desalination technologies. This study forecasts the productivity of two solar …
and sustainable desalination technologies. This study forecasts the productivity of two solar …
UAV propeller fault diagnosis using deep learning of non-traditional χ2-selected Taguchi method-tested Lempel–Ziv complexity and Teager–Kaiser energy features
Fault detection and isolation in unmanned aerial vehicle (UAV) propellers are critical for
operational safety and efficiency. Most existing fault diagnosis techniques rely basically on …
operational safety and efficiency. Most existing fault diagnosis techniques rely basically on …
Environmental engineering solutions for efficient soil classification in southern Syria: a clustering-correlation extreme learning approach
The classification of soil types for agricultural management systems and environmental
engineering is extremely important for land use planning and environmental conservation …
engineering is extremely important for land use planning and environmental conservation …
[HTML][HTML] Leak detection and localization in water distribution systems using advanced feature analysis and an Artificial Neural Network
This study capitalizes on a dataset, originally including 280 sensory measurements from a
laboratory-scale water distribution system, to advance the concept of leakage diagnosis and …
laboratory-scale water distribution system, to advance the concept of leakage diagnosis and …
Naïve Bayes algorithm for timely fault diagnosis in helical gear transmissions using vibration signal analysis
Vibration signal analysis assumes a critical role in the diagnosis of faults within helical gear
transmissions, facilitating early detection and mitigation of potential failures. This paper is …
transmissions, facilitating early detection and mitigation of potential failures. This paper is …
[HTML][HTML] Innovative Machine Learning Approaches for Complexity in Economic Forecasting and SME Growth: A Comprehensive Review
Economic forecasting and small and medium-sized enterprises (SMEs) growth prediction
have become essential tools for guiding policy, business strategy, and economic …
have become essential tools for guiding policy, business strategy, and economic …
A data fusion analysis and random forest learning for enhanced control and failure diagnosis in rotating machinery
In a heavily-subjected-to-failure field of rotating machinery, the need for accurate and
reliable detection methods is paramount. This paper aims to advance fault detection …
reliable detection methods is paramount. This paper aims to advance fault detection …