Aboveground biomass (AGB) is one of the strategic biophysical variables of interest in vegetation studies. The main objective of this study was to evaluate the Support Vector Machine (SVM) and Partial Least Squares Regression (PLSR) for estimating th...
This paper deals with the development of a Machine Learning (ML)-based regression for the construction of complex-valued surrogate models for the analysis of the frequency-domain responses of electromagnetic (EM) structures. The proposed approach rel...
The long-term demand forecast for annual national electricity and energy consumption plays a vital role in future strategic planning, power system installation programming, energy investment planning, and next-generation unit construction. Three mach...
We describe an Electronic Nose (ENose) system which is able to identify the type of analyte and to estimate its concentration. The system consists of seven sensors, five of them being gas sensors (supplied with different heater voltage values), the r...
The least squares support vector method is a popular data-driven modeling method which shows better performance and has been successfully applied in a wide range of applications. In this paper, we propose a novel coupled least squares support vector...
Visible and near infrared (Vis/NIR) spectroscopy was investigated for the fast analysis of superoxide dismutase (SOD) activity in barley (Hordeum vulgare L.) leaves. Seven different spectra preprocessing methods were compared. Four regression methods...
Notoginseng is a classical traditional Chinese medical herb, which is of high economic and medical value. Notoginseng powder (NP) could be easily adulterated with Sophora flavescens powder (SFP) or corn flour (CF), because of their similar tastes and...
Accurate rice yield prediction is essential for optimizing water management and supporting decision-making in agricultural systems, particularly in arid environments where irrigation efficiency is critical. This study assessed five machine learning a...
Recently, the quality of fresh water resources is threatened by numerous pollutants. Prediction of water quality is an important tool for controlling and reducing water pollution. By employing superior big data processing ability of deep learning it...
Due to solar radiation and other meteorological factors, photovoltaic (PV) output is intermittent and random. Accurate and reliable photovoltaic power prediction can improve the stability and safety of grid operation. Compared to solar power point pr...
Miniaturized near-infrared (NIR) spectrometers are revolutionizing the agri-food industry thanks to their compact size and ultra-fast analysis capabilities. This work compares the analytical performance of a handheld NIR spectrometer and a benchtop F...
High-throughput field phenotyping using close remote sensing platforms and sensors for non-destructive assessment of plant traits can support the objective evaluation of yield predictions of large breeding trials. The main objective of this study was...
In the current scenario of anthropogenic climate change, carbon credit security is becoming increasingly important worldwide. Topsoil is the terrestrial ecosystem component with the largest carbon sequestration capacity. Since soil organic matter (SO...
Monthly rainfall forecasts can be translated into monthly runoff predictions that could support water resources planning and management activities. Therefore, development of monthly rainfall forecasting models in reservoir watersheds is essential for...
Gentiana rigescens is a precious herbal medicine in China because of its liver-protective and choleretic effects. A method for the qualitative identification and quantitative evaluation of G. rigescens from Yunnan Province, China, has been developed...
Accurate modeling for nonlinear and nonstationary rainfall-runoff processes is essential for performing hydrologic practices effectively. This paper proposes two hybrid machine learning models (MLMs) coupled with variational mode decomposition (VMD)...
Ensuring the accuracy of wind power prediction is paramount for the reliable and stable operation of power systems. This study introduces a novel approach aimed at enhancing the precision of wind power prediction through the development of a multisca...
Recently, forest management faces new challenges resulting from increasing temperatures and drought occurrences. For sustainable, site-specific management strategies, the availability of up to date soil information is crucial. Proximal soil sensing t...
Soil contamination by heavy metals is of particular concern, due to the direct negative impact on crop yield, food quality and human health. Although the conventional approach to monitor heavy metals relies on field sampling and lab analysis, the pro...
An electronic nose (e-nose) was used to characterize sesame oils processed by three different methods (hot-pressed, cold-pressed, and refined), as well as blends of the sesame oils and soybean oil. Seven classification and prediction methods, namely...
Eddy current (EC) measurements, widely used for diagnostics of conductive materials, are highly dependent on physical properties and geometry of a sample as well as on a design of an EC-sensor. For a sensor of a given design, the conductivity and thi...
This paper aims to retrieve the temporal dynamics of soil moisture from 2015 to 2019 over an agricultural site in Southeast Australia using the Soil Moisture Active Passive (SMAP) brightness temperature. To meet this objective, two machine learning a...
Urban sprawl related increase of built-in areas requires reliable monitoring methods and remote sensing can be an efficient technique. Aerial surveys, with high spatial resolution, provide detailed data for building monitoring, but archive images usu...
Real estate price prediction is crucial for the establishment of real estate policies and can help real estate owners and agents make informative decisions. The aim of this study is to employ actual transaction data and machine learning models to pre...
Sinkholes are geohazards that commonly form in karstifiable terrain and are an ever-present danger to infrastructure and human life. This paper aims to answer the question: Can a cover–subsidence sinkhole’s volume be determined using fibr...
This paper presents a methodology to calculate day-ahead wind speed predictions based on historical measurements done by weather stations. The methodology was tested for three locations: Colombia, Ecuador, and Spain. The data is input into the proces...
This study developed a rapid, non-destructive method for the quantitative detection of protein in cottonseed by integrating near-infrared (NIR) fiber spectroscopy with chemometric machine learning. The establishment of this method holds significant i...
Fruit juices are one of the most adulterated beverages, usually because of the addition of water, sugars, or less expensive fruit juices. This study presents a method based on Fourier transform infrared spectroscopy (FT-IR), in combination with machi...
The rapid and accurate estimation of the nitrogen content of fruit trees helps to achieve a precise management of orchards. Hyperspectral data were collected from leaves of apple tree canopies at different fertility stages through field experiments t...
The advent of hyperspectral remote sensing represented a breakthrough in the accurate, fast, and non-invasive estimation of important soil fertility parameters. The present study utilizes non-imaging hyperspectral data in the spectral range of 350&nd...
Aiming at the problems of large active control errors and long control times in the active control method for high-speed motor rotor eccentric vibration, an active control method for high-speed motor rotor eccentric vibration based on a least squares...
Photothermal techniques are infrared remote sensing techniques that have been used for biomedical applications, as well as industrial non-destructive testing (NDT). Machine learning is a branch of artificial intelligence, which includes a set of algo...
Potentiometric multisensor systems were shown to be very promising tools for the quantification of numerous analytes in complex radioactive samples deriving from spent nuclear fuel reprocessing. Traditional multivariate calibration for these multisen...
To overcome the performance degradation in the presence of steering vector mismatches, strict restrictions on the number of available snapshots, and numerous interferences, a novel beamforming approach based on nonlinear least-square support vector r...
For potato external defect detection, ordinary spectral technology has limitations in detail detection and processing accuracy, while the machine vision method has the limitation of a long feedback time. To realize accurate and rapid external defect...
The problems that the key biomass variables in Pichia pastoris fermentation process are difficult measure in real time; this paper mainly proposes a multi-model soft sensor modeling method based on the piecewise affine (PWA) modeling method, which is...
Several attempts have been made for estimating the vital swelling index parameter conducted by the expensive and time-consuming Oedometer test. However, they have only focused on the neuron network neglecting other advanced methods that could have in...
To accurately, safely, and efficiently evaluate the bitterness of Traditional Chinese Medicines (TCMs), a robust predictor was developed using robust partial least squares (RPLS) regression method based on data obtained from an electronic tongue (e-t...
Parametric cycle analysis, an on-design engine study, specifies the required design characteristics that optimize engine performance. This study aimed to conduct a parametric analysis of a low-bypass turbofan engine with an afterburner, F100-PW229, a...
Traditional compensation methods based on temperature-related parameters are not effective for complex total reflection prism laser gyro (TRPLG) bias variation. Because the high frequency oscillator voltage (UHFO) fundamentally affects the TRPLG bias...
A solution for forecasting the dissolved gases in oil-immersed transformers has been proposed based on the wavelet technique and least squares support vector machine. In order to optimize the hyper-parameters of the constructed wavelet LS-SVM regress...
Linear partial least square and non-linear support vector machine regression analysis with various preprocessing techniques and their combinations were used to determine the soluble solids content of hardy kiwi fruits by a handheld, portable near-inf...
Mangosteens can develop a postharvest physiological disorder, called “hardening”, which affects their marketability and is not detectable using visual inspection. The hardening disorder of mangosteens was determined by firmness value usin...
In this paper, we explore a novel model for pricing Chinese convertible bonds that seamlessly integrates machine learning techniques with traditional models. The least squares Monte Carlo (LSM) method is effective in handling multiple state variables...
Wind speed (WS) is an important factor in wind power generation. Because of this, drastic changes in the WS make it challenging to analyze accurately. Therefore, this study proposed a novel framework based on the stacking ensemble machine learning (S...
Natural gas is often described as the cleanest fossil fuel. The consumption of natural gas is increasing rapidly. Accurate prediction of natural gas spot prices would significantly benefit energy management, economic development, and environmental co...
Rapid, non-destructive, and accurate quantitative determination of the effective components in traditional Chinese medicine (TCM) is required by industries, planters, and regulators. In this study, near-infrared hyperspectral imaging was applied for...
To address the limitations of a traditional Fourier-transform infrared (FTIR) spectrometer, including its bulky size, high cost, and unsuitability for on-site industrial detection, this study developed a Fourier-transform near-infrared (FT-NIR) absor...
Fourier transform infrared spectroscopy (FT-IR) and multispectral imaging (MSI) were evaluated for the prediction of the microbiological quality of poultry meat via regression and classification models. Chicken thigh fillets (n = 402) were subjected...
The intensity of the odor in food-grade paraffin waxes is a pivotal quality characteristic, with odor panel ratings currently serving as the primary criterion for its assessment. This study presents an innovative method for assessing odor intensity i...
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