A review on linear regression comprehensive in machine learning

D Maulud, AM Abdulazeez - Journal of applied science and technology …, 2020‏ - jastt.org
Perhaps one of the most common and comprehensive statistical and machine learning
algorithms are linear regression. Linear regression is used to find a linear relationship …

Prediction of higher heating value of coal based on gradient boosting regression tree model

N Xu, Z Wang, Y Dai, Q Li, W Zhu, R Wang… - International Journal of …, 2023‏ - Elsevier
Higher heating value, also known as the coal calorific value, is an important indicator of coal
quality. Nevertheless, traditional experimental determination of higher heating value is …

Feasibility study of the grid-connected hybrid energy system for supplying electricity to support the health and education sector in the metropolitan area

MR Ahmed, MR Hasan, S Al Hasan, M Aziz, ME Hoque - Energies, 2023‏ - mdpi.com
One of the biggest issues impeding a country's progress is the lack of power. To overcome
this issue, hybrid renewable energy systems (HRES) play an important role. Due to rising …

Estimation of lower limb joint moments based on the inverse dynamics approach: a comparison of machine learning algorithms for rapid estimation

M Mansour, K Serbest, M Kutlu, M Cilli - Medical & Biological Engineering …, 2023‏ - Springer
The aim of this study is to estimate the joint moments of the ankle, knee, and hip joints during
walking. A sit-to-stand (STS) movement analysis was first performed on 20 participants with …

Accurate Identification of Harmonic Distortion for Micro-Grids using Artificial Intelligence-Based Predictive Models

AM Abed, RA El-Sehiemy, BB Touati… - IEEE Access, 2024‏ - ieeexplore.ieee.org
This paper proposes an accurate harmonic identification strategy for microgrids and
distributed power systems. The harmonic identification strategy is one of the complex tasks …

[PDF][PDF] Machine learning classification of rainfall forecasts using Austin weather data

TT Tin, EHC Sheng, LS **an… - … Journal of Innovative …, 2024‏ - pdfs.semanticscholar.org
The paper examines the machine learning classification of rainfall forecasts using Austin
weather data. Rain is a natural phenomenon that is essential for the Earth's water cycle …

Neural network-based regression for heat transfer and fluid flow over in-line cylinder arrays with random pitch distances at low Reynolds number

G Choi, SJ Kim, S Shin - Engineering Applications of …, 2023‏ - Taylor & Francis
Finding an arrangement, leading to a higher heat transfer and lower pressure drop, is crucial
in the design of heat exchangers. Previous studies have primarily focused on regular …

Prediction of water content in subgrade soil in road construction using hyperspectral information obtained through UAV

K Lee, J Park, G Hong - Applied Sciences, 2024‏ - mdpi.com
In road construction, the compaction of the subgrade layer, which is one of the earthwork
fields, is an essential procedure to support the pavement layer and traffic load. For the …

Evaluating the Performance of Machine Learning Models for Energy Load Prediction in Residential HVAC Systems

PB Asamoah, E Shittu - Energy and Buildings, 2025‏ - Elsevier
Given that heating and cooling systems account for roughly 40% of overall building energy
usage, accurately predicting and analyzing the energy needed for HVAC operations in …

Iterative training of neural networks for intra prediction

T Dumas, F Galpin, P Bordes - IEEE Transactions on Image …, 2020‏ - ieeexplore.ieee.org
This paper presents an iterative training of neural networks for intra prediction in a block-
based image and video codec. First, the neural networks are trained on blocks arising from …