AgriSenAI: Automating UAV thermal and multispectral image processing for precision agriculture by Boaz B. Tulu, Fitsum Teshome, Yiannis Ampatzidis, Niguss Solomon Hailegnaw, Haimanote K Bayabil

“…Unmanned Aerial Vehicles (UAVs) equipped with thermal and multispectral imaging capabilities, which offer high spatial and temporal resolutions, are becoming increasingly valuable for timely crop monitoring and informed decision-making in precision agriculture. However, processing and extracting useful information from UAV images is often complex, time-consuming, and requires specialized software, which limits its broader adoption for practical field implementations. …”
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Machine-Learning-Based Biomechanical Feature Analysis for Orthopedic Patient Classification with Disc Hernia and Spondylolisthesis by Daniel Nasef, Demarcus Nasef, Viola Sawiris, Peter Girgis, Milan Toma

“…The performance of various ML models, including decision trees, support vector machines, and neural networks, is evaluated using metrics such as accuracy, AUC, recall, precision, F1, Kappa, and MCC. …”
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Machine learning algorithms to predict feeding practices during diarrheal disease and its determinants among under-five children in East Africa by Tirualem Zeleke Yehuala, Nebebe Demis Baykemagn, Bewuketu Terefe

“…We employed four ML algorithms, such as Random Forest (RF), Decision Tree (DT), XGB (Extreme Gradient Boosting), and Logistic Regression (LR). …”
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Longitudinal Analysis of Risk Factors for Pulmonary Function Decline in Chronic Lung Diseases Over Five Years by Li L, Meng J, Chen J

“…A random forest model was developed using Python, and the importance of the feature was visualized through the SHAP method. …”
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Surface-Enhanced Raman Scattering Combined with Machine Learning for Rapid and Sensitive Detection of Anti-SARS-CoV-2 IgG by Thais de Andrade Silva, Gabriel Fernandes Souza dos Santos, Adilson Ribeiro Prado, Daniel Cruz Cavalieri, Arnaldo Gomes Leal Junior, Flávio Garcia Pereira, Camilo A. R. Díaz, Marco Cesar Cunegundes Guimarães, Servio Túlio Alves Cassini, Jairo Pinto de Oliveira

“…The machine learning models used were k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Decision Trees (DTs), logistic regression (LR), and Light Gradient Boosting Machine (LightGBM). …”
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Operational hydrodynamic service as a tool for coastal flood assessment by X. Sánchez-Artús, V. Gracia, M. Espino, M. Grifoll, G. Simarro, J. Guillén, M. González, A. Sanchez-Arcilla

“…The outcome assists in decision-making relating to such events by utilising the operational system. …”
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Exploring machine learning algorithms for predicting fertility preferences among reproductive age women in Nigeria by Zinabu Bekele Tadese, Teshome Demis Nimani, Kusse Urmale Mare, Fetlework Gubena, Ismail Garba Wali, Jamilu Sani

“…Six machine learning algorithms, namely, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, and eXtreme Gradient Boosting, were employed on a total sample size of 37,581 in Python 3.9 version. …”
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Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches by Hamed Ghaderi, Nasibe Alipour, Hossein Safari

“…The two models include the support vector machine (SVM) and 1D convolutional neural network (1D-CNN), which use ZMs, compared with the other three classification models of 2D-CNN, ResNet50, and VGG16 that apply the features from original images. …”
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Closing the gap in the clinical adoption of computational pathology: a standardized, open-source framework to integrate deep-learning models into the laboratory information system by Miriam Angeloni, Davide Rizzi, Simon Schoen, Alessandro Caputo, Francesco Merolla, Arndt Hartmann, Fulvia Ferrazzi, Filippo Fraggetta

“…Methods Development and testing of the framework were carried out in a fully digitized Italian pathology department. A Python-based server-client architecture was implemented to interconnect through HL7 messaging the anatomic pathology laboratory information system (AP-LIS) with an external artificial intelligence-based decision support system (AI-DSS) containing 16 pre-trained DL models. …”
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Reinforcement learning based route optimization model to enhance energy efficiency in internet of vehicles by Quadeer Hussain, Ahmad Shukri Mohd Noor, Muhammad Mukhtar Qureshi, Jianqiang Li, Atta-ur Rahman, Aghiad Bakry, Tariq Mahmood, Amjad Rehman

“…Extensive simulations were conducted using NS2 and Python, demonstrating that OptiE2ERL significantly outperforms existing models like LEACH, PEGASIS, and EER-RL across various performance metrics. …”
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Machine learning-based prediction model for arteriovenous fistula thrombosis risk: a retrospective cohort study from 2017 to 2024 by Peng Shu, Ling Huang, Xia Wang, Zhuping Wen, Yiqi Luo, Fang Xu

“…A comprehensive analysis of 55 features was conducted utilizing Python. The dataset was partitioned into a training set and a test set, comprising 70% and 30% of the samples, respectively. …”
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Machine learning in assessing the association between the size and structure of the ascending aortic wall in patients with aortic dilatation of varying severity by V. E. Uspenskiy, V. L. Saprankov, V. I. Mazin, D. G. Zavarzina, A. B. Malashicheva, O. B. Irtyuga, O. M. Moiseeva, M. L. Gordeev

“…During the random forest analysis, the first stage was to construct decision trees to predict three following outcomes: the presence of CMN, atherosclerosis, or normal aortic structure. …”
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Influencing factors for hyporesponsiveness to erythropoiesis-stimulating agents in patients on maintenance hemodialysis and construction of a predictive model by YIN Jiaming, LUO Congjuan, SHEN Bing, YUAN Nan, SUN Lijie, CHAI Yalin

“…The calibration curve showed a good degree of fitting between the actual value and the predictive value, and the decision curve analysis showed a high net benefit in predicting hyporesponsiveness to ESAs in MHD patients within a range of 0.4-0.6 for threshold probability. …”
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Predicting suicidal behavior outcomes: an analysis of key factors and machine learning models by Mohammad Bazrafshan, Kourosh Sayehmiri

“…Five binary classification algorithms were utilized, including Random Forest, Logistic Regression, and Decision Trees, with hyperparameters optimized using the area under the receiver operating characteristic curve (AUC) and F1 score metrics. …”
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The most frequent clinical symptoms in newly diagnosed HIV-1 patients with high and low HIV-1 viral load by Alicja Zofia Maj, Izabela Olejnik, Agnieszka Lembas, Tomasz Mikuła, Karolina Domosud, Karol Knysak, Alicja Wiercińska-Drapało, Tomasz Mruzek

“…Patient data were collected by physicians from various sources, including patients’ registers, lab test records, and electronic medical files. …”
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Alfalfa stem count estimation using remote sensing imagery and machine learning on Google Earth Engine by Hazhir Bahrami, Karem Chokmani, Saeid Homayouni, Viacheslav I. Adamchuk, Md Saifuzzaman, Rami Albasha, Maxime Leduc

“…Our proposed framework model can offer valuable information to growers and decision-makers, enabling them to make timely and informed decisions.…”
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Predicting breast self-examination awareness in Sub-Saharan Africa using machine learning by Nebebe Demis Baykemagn, Meron Asmamaw Alemayehu, Tirualem Zeleke Yehuala, Agmasie Damtew Walle, Andualem Enyew Gedefaw, Abraham Keffale Mengistu

“…The model performance was evaluated by ROC-AUC, AUC, accuracy, F1 score, recall, and precision. The Decision Tree model was the best-performing one, with an accuracy of 82% and an AUC of 0.87. …”
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Vegetation Monitoring of Palm Trees in an Oasis Environment (Boudenib, Morocco) Using Automatic Processing of Medium-Resolution Remotely Sensed Data by Kaoutar Badioui, Ann Van Griensven, Boud Verbeiren

“…These results have important implications for water management in the area and can help decision-makers to make better decisions about prevention of water scarcity in the region.…”
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Optimizing automated external defibrillator deployment within the walking golden window for out-of-hospital cardiac arrest cases: a case study from a Chinese city by Zhaohui Qin, Jia Li, Shuyao Zheng, Da Xu, Wei Zhang, Lu Lu, Xianliang Yan, Tie Xu, Ningjun Zhao, Yan Xu

“…BackgroundIrreversible brain injury may begin 4–6 min after the onset of out-of-hospital cardiac arrest (OHCA) if no cardiopulmonary resuscitation (CPR) is provided. …”
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Real-Time Detection and Localization of Force on a Capacitive Elastomeric Sensor Array Using Image Processing and Machine Learning by Peter Werner Egger, Gidugu Lakshmi Srinivas, Mathias Brandstötter

“…Machine learning models such as linear regression, Support Vector Machine, decision tree, and Gaussian Process Regression were evaluated to correlate force with capacitance values. …”
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