Investigation of predictive factors for fatty liver in children and adolescents using artificial intelligence by Aliakbar Sayyari, Amin Magsudy, Yasamin Moeinipour, Amirhossein Hosseini, Hamidreza Amiri, Mohammadreza Arzaghi, Fereshteh Sohrabivafa, Seyedeh Fatemeh Hamzavi, Ashkan Azizi, Tahereh Hatamii, AmirAli Okhovat, Naghi Dara, Negar Imanzadeh, Farid Imanzadeh, Mahmoud Hajipour

“…Several machine learning models were trained and evaluated, including CatBoost, AdaBoost, Random Forest, and others. …”
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Explainable Artificial Intelligence to Predict the Water Status of Cotton (<i>Gossypium hirsutum</i> L., 1763) from Sentinel-2 Images in the Mediterranean Area by Simone Pietro Garofalo, Anna Francesca Modugno, Gabriele De Carolis, Nicola Sanitate, Mesele Negash Tesemma, Giuseppe Scarascia-Mugnozza, Yitagesu Tekle Tegegne, Pasquale Campi

“…Different machine learning algorithms, including random forest, support vector regression, and extreme gradient boosting, were evaluated using Sentinel-2 spectral bands as predictors. …”
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Identification and validation of the nicotine metabolism-related signature of bladder cancer by bioinformatics and machine learning by Yating Zhan, Min Weng, Yangyang Guo, Dingfeng Lv, Feng Zhao, Zejun Yan, Junhui Jiang, Yanyi Xiao, Lili Yao

“…Integrative machine learning combination based on 10 machine learning algorithms was used for the construction of robust signature. …”
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SHERA: SHAP-Enhanced Resource Allocation for VM Scheduling and Efficient Cloud Computing by Ashwin Singh Slathia, Abhiram Sharma, P. B. Krishna, Saksham Anand, Ayush Rathi, Linda Joseph, Xiao-Zhi Gao

“…Three machine learning models&#x2014;Random Forest, Na&#x00EF;ve Bayes, and Support Vector Machine (SVM) were trained and assessed based on Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and an Equivalent Accuracy metric. …”
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River total dissolved gas prediction using a hybrid greedy-stepwise feature selection and bidirectional long short-term memory model by Khabat Khosravi, Salim Heddam, Changhyun Jun, Sayed M. Bateni, Dongkyun Kim, Essam Heggy

“…Each model is trained and evaluated at one station and validated at the second station to assess transferability and generalization capability. …”
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Breast Tumor-Like-Masses Segmentation From Scattering Images Obtained With an Ultrahigh-Sensitivity Talbot-Lau Interferometer Using Convolutional Neural Networks by Ionut-Cristian Ciobanu, Nicoleta Safca, Elena Anghel, Dan Popescu

“…The experimental setup utilized an ultrahigh-sensitivity Talbot-Lau interferometer operated with a conventional X-ray tube to generate scattering images, which were processed using a Fourier Transform-based algorithm. Five CNN architectures - U-Net, ResNet50, DeepLabV3, PSPNet, and SegNet -were trained and tested on an augmented dataset of 320 images. …”
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Machine learning to improve HIV screening using routine data in Kenya by Jonathan D. Friedman, Jonathan M. Mwangi, Kennedy J. Muthoka, Benedette A. Otieno, Jacob O. Odhiambo, Frederick O. Miruka, Lilly M. Nyagah, Pascal M. Mwele, Edmon O. Obat, Gonza O. Omoro, Margaret M. Ndisha, Davies O. Kimanga

“…We trained four machine learning algorithms including logistic regression, Random Forest, AdaBoost and XGBoost. …”
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The use of heart rate variability, oxygen saturation, and anthropometric data with machine learning to predict the presence and severity of obstructive sleep apnea by Rafael Rodrigues dos Santos, Matheo Bellini Marumo, Alan Luiz Eckeli, Helio Cesar Salgado, Luiz Eduardo Virgílio Silva, Renato Tinós, Rubens Fazan

“…The Apnea-Hypopnea Index (AHI) was used to categorize into severity classes of OSA (normal, mild, moderate, severe) to train multiclass or binary (normal-to-mild and moderate-to-severe) classification models, using the Random Forest (RF) algorithm. …”
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Integrating deep learning in public health: a novel approach to PICC-RVT risk assessment by Yue Li, Yue Li, Shengxiao Nie, Lei Wang, Dongsheng Li, Shengmiao Ma, Ting Li, Hong Sun

“…Existing models often assess PICC-RVT risk as static and discrete outcomes, which may limit their practical application.ObjectivesThis study aims to evaluate the effectiveness of seven diverse machine learning algorithms, including three deep learning and four traditional machine learning models, that incorporate time-series data to assess PICC-RVT risk. …”
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Functional Monitoring of Patients With Knee Osteoarthritis Based on Multidimensional Wearable Plantar Pressure Features: Cross-Sectional Study by Junan Xie, Shilin Li, Zhen Song, Lin Shu, Qing Zeng, Guozhi Huang, Yihuan Lin

“…The multidimensional gait features extracted from the data and physical characteristics were used to establish the KOA functional feature database for the plantar pressure measurement system. 40mFPWT and TUGT regression prediction models were trained using a series of mature machine learning algorithms. …”
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Modelling water table depth at rewetted peatlands with Sentinel-1 and Sentinel-2 by Eoin Reddin, Jennifer Hanafin, Mingming Tong, Laurence Gill, Mark G. Healy

“…The aims of this paper are to (1) systematically test the relationship between radar backscatter and water table depth (2) compare decision tree regression algorithms to evaluate the potential of multi-sensor remote sensing in peatland management, and (3) make novel estimations of site-wide water table depth using a multi-sensor approach. …”
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CAFiKS: Communication-Aware Federated IDS With Knowledge Sharing for Secure IoT Connectivity by Ogobuchi Daniel Okey, Demostenes Zegarra Rodriguez, Frederico Gadelha Guimaraes, Joao Henrique Kleinschmidt

“…Specifically, CAFiKS leverages knowledge distillation (KD), where a high-capacity teacher model trains a smaller, more efficient student model by transferring essential knowledge. …”
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Enhancing Typhoid Fever Diagnosis Based on Clinical Data Using a Lightweight Machine Learning Metamodel by Fariha Ahmed Nishat, M. F. Mridha, Istiak Mahmud, Meshal Alfarhood, Mejdl Safran, Dunren Che

“…A machine learning metamodel, integrating Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), and Decision Tree classifiers with a Light Gradient Boosting Machine (LGBM), was trained and evaluated using k-fold cross-validation. …”
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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

“…(1) <b>Background</b>: The exploration of various machine learning (ML) algorithms for classifying the state of Lumbar Intervertebral Discs (IVD) in orthopedic patients is the focus of this study. …”
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Acceleration Data Reveal Behavioural Responses to Hunting Risk in Scandinavian Brown Bears by Jeanne Clermont, Andreas Zedrosser, Ludovick Brown, Frank Rosell, Gunn Elisabeth Sydtveit Rekvik, Jonas Kindberg, Fanie Pelletier

“…We used a random forest algorithm trained with observations of captive brown bears to classify the accelerometry data into four behaviours, running, walking, feeding and resting, with an overall precision of 95%. …”
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RadiomiX for Radiomics Analysis: Automated Approaches to Overcome Challenges in Replicability by Harel Kotler, Luca Bergamin, Fabio Aiolli, Elena Scagliori, Angela Grassi, Giulia Pasello, Alessandra Ferro, Francesca Caumo, Gisella Gennaro

“…<b>Background/Objectives:</b> To simplify the decision-making process in radiomics by employing RadiomiX, an algorithm designed to automatically identify the best model combination and validate them across multiple environments was developed, thus enhancing the reliability of results. …”
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LSTM-Enhanced Deep Reinforcement Learning for Robust Trajectory Tracking Control of Skid-Steer Mobile Robots Under Terra-Mechanical Constraints by Jose Manuel Alcayaga, Oswaldo Anibal Menéndez, Miguel Attilio Torres-Torriti, Juan Pablo Vásconez, Tito Arévalo-Ramirez, Alvaro Javier Prado Romo

“…Four state-of-the-art DRL algorithms, i.e., Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), and Soft Actor–Critic (SAC), are selected to evaluate their ability to generate stable and adaptive control policies under varying environmental conditions. …”
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VGGBM-Net: A Novel Pixel-Based Transfer Features Engineering for Automated Coffee Bean Diseases Classification by Muhammad Shadab Alam Hashmi, Azam Mehmood Qadri, Ali Raza, Saleem Ullah, Aseel Smerat, Changgyun Kim, Muhammad Syafrudin, Norma Latif Fitriyani

“…In this research, the proposed approach is trained using a dataset USK-Coffee of coffee beans to classify healthy and unhealthy beans. …”
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Improving T2D machine learning-based prediction accuracy with SNPs and younger age by Cynthia AL Hageh, Andreas Henschel, Hao Zhou, Jorge Zubelli, Moni Nader, Stephanie Chacar, Nantia Iakovidou, Haralampos Hatzikirou, Antoine Abchee, Siobhán O’Sullivan, Pierre A. Zalloua

“…Background: This study aimed to evaluate whether integrating clinical and genomic data improves the performance of machine learning (ML) models for predicting Type 2 Diabetes (T2D) risk. …”
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An Optimal Internet of Things-Driven Intelligent Decision-Making System for Real-Time Fishpond Water Quality Monitoring and Species Survival by Saima Kanwal, Muhammad Abdullah, Sahil Kumar, Saqib Arshad, Muhammad Shahroz, Dawei Zhang, Dileep Kumar

“…Advanced machine learning techniques, with feature transformation and balancing, were applied to preprocess the dataset, which includes water quality metrics and species-specific parameters. Multiple algorithms were trained and evaluated, with the Decision Tree classifier emerging as the optimal model, achieving remarkable performance metrics: 99.8% accuracy, precision, recall, and F1-score, a 99.6% Matthews Correlation Coefficient (MCC), and the highest Area Under the Curve (AUC) score for multi-class classification. …”
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