A Versatile, Machine-Learning-Enhanced RF Spectral Sensor for Developing a Trunk Hydration Monitoring System in Smart Agriculture by Oumaima Afif, Leonardo Franceschelli, Eleonora Iaccheri, Simone Trovarello, Alessandra Di Florio Di Renzo, Luigi Ragni, Alessandra Costanzo, Marco Tartagni

“…This innovative system enables non-invasive monitoring of wood hydration levels by analyzing scattering parameters (S-parameters). These S-parameters are then processed using ML techniques to automate the monitoring process, enabling real-time and predictive analysis of moisture levels.…”
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Integration of multi-temporal SAR data and robust machine learning models for improvement of flood susceptibility assessment in the southwest coast of India by Pankaj Prasad, Sourav Mandal, Sahil Sandeep Naik, Victor Joseph Loveson, Simanku Borah, Priyankar Chandra, Karthik Sudheer

“…The Boruta algorithm has been applied to determine the importance of each flood conditioning parameter. Six efficient machine learning models, namely support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (ANN), random forest (RF), partial least squares (PLS) and penalized discriminant analysis (PDA) have been applied to delineate the flood susceptible areas of the study region. …”
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Autonomous Detection of Mineral Phases in a Rock Sample Using a Space-prototype LIMS Instrument and Unsupervised Machine Learning by Salome Gruchola, Peter Keresztes Schmidt, Marek Tulej, Andreas Riedo, Klaus Mezger, Peter Wurz

“…In situ mineralogical and chemical analyses of rock samples using a space-prototype laser ablation ionization mass spectrometer along with unsupervised machine learning are powerful tools for the study of surface samples on planetary bodies. …”
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Exploration of the Ignition Delay Time of RP-3 Fuel Using the Artificial Bee Colony Algorithm in a Machine Learning Framework by Wenbo Liu, Zhirui Liu, Hongan Ma

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Machine learning-based predictive modeling of angina pectoris in an elderly community-dwelling population: Results from the PoCOsteo study. by Shahrokh Mousavi, Zahrasadat Jalalian, Sima Afrashteh, Akram Farhadi, Iraj Nabipour, Bagher Larijani

“…<h4>Background</h4>Angina pectoris, a comparatively common complaint among older adults, is a critical warning sign of underlying coronary heart disease. We aimed to develop machine learning-based models using multiple algorithms to predict and identify the predictors of angina pectoris in an elderly community-dwelling population.…”
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Validation of Electromechanical Transient Model for Large-Scale Renewable Power Plants Based on a Fast-Responding Generator Method by Dawei Zhao, Yujie Ning, Chuanzhi Zhang, Jin Ma, Minhui Qian, Yanzhang Liu

“…The complete scheme comprising model validation, error localization, parameter sensitivity analysis, and parameter correction is proposed. …”
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A machine learning based estimation method of beach slopes at a national scale: a case study of New Zealand by Hao Xu, Nan Xu, Chi Zhang, Shanhang Chi, Yuan Li, Wenyu Li, Yifu Ou, Jiaqi Yao, Han-Su Zhang, Fan Mo, Hui Lu

“…Beach slope is a critical parameter for understanding coastal geomorphological dynamics, yet the acquisition of comprehensive datasets at large scales remains a significant challenge. …”
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Petrophysical evaluation of clastic formations in boreholes with incomplete well log dataset by using joint inversion technique and machine learning algorithms by Felipe Santana-Román, Ambrosio Aquino López, Manuel Romero Salcedo (+), Raúl del Valle García, Oscar Campos Enriquez

“…To determine petrophysical parameters (i.e., volumes of laminar, structural and disperse shale) in clastic rocks from incomplete well log data we followed three approaches which are based on a hierarchical model, and on a joint inversion technique: 1) Available well log data (excluding the incomplete well log) are used to train machine learning algorithms to generate a predictive model; 2) the first step of the second approach machine learning algorithms are used to generate the missing data which are subsequently included a joint inversion; 3) in the third approach, machine learning process is used to estimate the missing data which are subsequently included in the prediction of the petrophysical properties. …”
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Prediction of Clavien Dindo Classification ≥ Grade III Complications After Epithelial Ovarian Cancer Surgery Using Machine Learning Methods by Aysun Alci, Fatih Ikiz, Necim Yalcin, Mustafa Gokkaya, Gulsum Ekin Sari, Isin Ureyen, Tayfun Toptas

“…BayesNet was found to be the most effective prediction model. No dominant parameter was observed in the Bayesian net importance matrix plot. …”
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Research into operation efficiency of single-bucket hydraulic excavators by R. F. Salikhov, V. N. Kuznetsova, I. S. Kuznetsov

“…The article presents the results of research of technical parameters reflecting the use of single-bucket hydraulic excavators by time. …”
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Predictive Model for Erosion Rate of Concrete Under Wind Gravel Flow Based on K-Fold Cross-Validation Combined with Support Vector Machine by Yanhua Zhao, Kai Zhang, Aojun Guo, Fukang Hao, Jie Ma

“…To address this, the study utilized a machine learning (ML) model for a more precise prediction and evaluation of CER. …”
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Predicting soybean seed germination using the tetrazolium test and computer intelligence by Marcio Alves Fernandes, Izabela Cristina de Oliveira, Marcio Dias Pereira, Breno Zaratin Alves, Alan Mario Zuffo, Charline Zaratin Alves

“…The data analysis used the correlation coefficient and mean absolute error as accuracy parameters of the algorithms. The results highlighted the support vector machine as the most effective algorithm for predicting germination, with the viability and vigor + viability inputs showing the best results. …”
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Machine learning models for predicting metabolic dysfunction-associated steatotic liver disease prevalence using basic demographic and clinical characteristics by Gangfeng Zhu, Yipeng Song, Zenghong Lu, Qiang Yi, Rui Xu, Yi Xie, Shi Geng, Na Yang, Liangjian Zheng, Xiaofei Feng, Rui Zhu, Xiangcai Wang, Li Huang, Yi Xiang

“…Using eight demographic and clinical characteristics (age, educational level, height, weight, waist and hip circumference, and history of hypertension and diabetes), we built predictive models for MASLD (classified as none or mild: controlled attenuation parameter (CAP) ≤ 269 dB/m; moderate: 269–296 dB/m; severe: CAP > 296 dB/m) employing 10 machine learning algorithms: logistic regression (LR), multilayer perceptron (MLP), extreme gradient boosting (XGBoost), bootstrap aggregating, decision tree, K-nearest neighbours, light gradient boosting machine, naive Bayes, random forest, and support vector machine. …”
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Gait Recognition With Wearable Sensors Using Modified Residual Block-Based Lightweight CNN by Md. Al Mehedi Hasan, Fuad Al Abir, Md. Al Siam, Jungpil Shin

“…Our proposed model outperforms all the state-of-the-art methods regarding recognition accuracy and is more than 85 percent efficient on average in terms of model parameters and memory consumption.…”
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A New Method for Constructing the Static-Voltage-Stability Boundary with a Conversion Strategy by Peng Shi, Yongcan Wang, Xi Wang, Chengwei Fan, Jiayu Bai, Baorui Chen, Jing Li, Deqiang Gan

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Spatial and Temporal Variability of Chlorophyll-a and the Modeling of High-Productivity Zones Based on Environmental Parameters: a Case Study for the European Arctic Corridor by Kuzmina Sofia, Lobanova Polina, Chepikova Svetlana Sergeevna

“…Then, using a Random Forest Machine Learning algorithm in the Classifier modification, we created models for each sea to predict the position of high-productivity zones (Chl-a > 1 mg m−3) using environmental parameters. …”
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Analysis of the Influence of Drill Tip Geometry on the Dry Drilling Process in CFRP Thermoset Laminate by Francisco de A. Toti, Amilton J. C. de Freitas, José J. de Oliveira, Rita de Cássia M. Sales-Contini

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Retrieval of non-optical active water quality parameters in complex Lake environments using a novel zoning-based ensemble modeling strategy by Cheng Cai, Linlin Liu, Ziming Wang, Wei Pang, Congshuo Bai, Huanxue Zhang

“…However, accurate retrieval of non-optically active water quality parameters (NAWQPs) remains challenging due to their weak spectral responses and interference from diverse aquatic vegetation. …”
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Characterising the spatio-temporal patterns of water quality parameters in the cradle of humankind world heritage site using Sentinel-2 and random forest regressor by Sinesipho Ngamile, Sinesipho Ngamile, Mahlatse Kganyago, Sabelo Madonsela, Sabelo Madonsela, Vuyelwa Mvandaba

“…For optically active parameters, suspended solids showed the highest prediction accuracy under high-flow conditions using Model 2 (R2p = 0.55; RMSE = 118.19). …”
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Prediction of sugar beet yield and quality parameters using Stacked-LSTM model with pre-harvest UAV time series data and meteorological factors by Qing Wang, Ke Shao, Zhibo Cai, Yingpu Che, Haochong Chen, Shunfu Xiao, Ruili Wang, Yaling Liu, Baoguo Li, Yuntao Ma

“…End-of-season yield and quality parameters were forecasted using UAV-derived time series data and meteorological factors collected at three critical growth stages, providing a timely and practical tool for farm management. …”
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