Robust object counting through distribution uncertainty matching and optimal transport by Sabri Boughorbel, Fethi Jarray, Rachida Zegour, Nauman Ullah Gilal, Khaled Al Thelaya, Marco Agus, Jens Schneider

“…Although such labeling is cost-effective, trained models can be sensitive to annotation noise. …”
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Developing advanced datadriven framework to predict the bearing capacity of piles on rock by Kennedy C. Onyelowe, Shadi Hanandeh, Viroon Kamchoom, Ahmed M. Ebid, Fabián Danilo Reyes Silva, José Luis Allauca Palta, José Luis Llamuca Llamuca, Siva Avudaiappan

“…This research presents an advanced data-driven framework that integrates multiple machine learning algorithms to predict the bearing capacity of piles based on geotechnical and in-situ test parameters. …”
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Enhancing Breast Cancer Diagnosis With Multi-Resolution Vision Transformers and Robust Decision-Making by Margo Sabry, Hossam Magdy Balaha, Khadiga M. Ali, Tayseer Hassan A. Soliman, Dibson Gondim, Mohammed Ghazal, Norah Saleh Alghamdi, Ayman El-Baz

“…A stacking ensemble method combines predictions from ViT models trained at these levels, improving classification robustness. …”
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Uniform Physics Informed Neural Network Framework for Microgrid and Its Application in Voltage Stability Analysis by Renhai Feng, Khan Wajid, Muhammad Faheem, Jiang Wang, Fazal E. Subhan, Muhammad Shoaib Bhutta

“…Accurately extracting the parameters of these models is essential for effectively controlling and optimizing the overall stability of Chongqing power system (CPS). Despite numerous algorithms proposed to tackle this issue, accurately and reliably extracting the parameters of these remains a significant challenge. …”
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Deep Residual Learning Image Recognition Model for Skin Cancer Disease Detection and Classification by Jamal Mustafa Al-Tuwaijari, Naeem Th. Yousir, Nafea Ali Majeed Alhammad, Salama Mostafa

“…However, due to the variety of skin cancer tumour shapes and colours, deep learning algorithms misclassify whether a tumour is cancerous or benign. …”
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A deep learning approach to real-time Markov modeling of ion channel gating by Efthymios Oikonomou, Yannick Juli, Rajkumar Reddy Kolan, Linda Kern, Thomas Gruber, Christian Alzheimer, Patrick Krauss, Andreas Maier, Tobias Huth

“…Finally, we tested the algorithm with data recorded on a patch-clamp setup. …”
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Early prediction of cognitive impairment in adults aged 20 years and older using machine learning and biomarkers of heavy metal exposure by Ali Nabavi, Farimah Safari, Mohammad Kashkooli, Sara Sadat Nabavizadeh, Hossein Molavi Vardanjani

“…Variables included demographics, medical history, lifestyle factors, and blood and urine levels of lead, cadmium, manganese, and other metals. Machine learning algorithms were trained on 90 % of data and evaluated on 10 %. …”
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Real-time congestion control using cascaded LSTM deep neural networks for deregulated power markets by G. Madhu Mohan, T. Anil Kumar, A. Srujana, Yasser Fouad, Alexey Mikhaylov, Nora Baranyai, Kitmo, Ch. Rami Reddy

“…The suggested approach was evaluated on an IEEE 30 bus system and demonstrated to be highly effective.…”
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An enhanced machine vision system for smart poultry farms using deep learning by P. Natho, S. Boonying, P. Bonguleaum, N. Tantidontanet, L. Chamuthai

“…The model YOLOv11 was trained using the NVIDIA Jetson Orin Nano Developer Kit and evaluated in terms of precision, recall, and mean average precision (mAP). …”
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Machine Learning for Health Insurance Prediction in Nigeria by Victor Enemona Ochigbo, Oluwasogo Adekunle Okunade, Emmanuel Gbenga Dada, Oluyemi Mikail Olaniyi, Oluwatoyosi Victoria Oyewande

“…The dataset utilized for analysis comprises demographic information, socioeconomic factors, and health-related variables collected from a diverse sample across Nigeria. Four models are trained and evaluated: Logistic Regression widely accepted for its simplicity and interpretability. …”
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YOLOv9: A High-Performance Deep Learning Approach for Asphalt Pavement Distresses Detection in Roadway Images by Fahrizal, Siti Nurjanah, Yoan Purbolingga, Dila Marta Putri, Asde Rahmawati, Bastul Wajhi Akramunnas, Muhidin Arifin

“…This study proposes employing the deep learning algorithm YOLOv9, the latest You Only Look Once (YOLO) version, for identifying asphalt pavement distresses. …”
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Predicting 30-Day Venous Thromboembolism Following Total Joint Arthroplasty: Adjusting for Trends in Annual Length of Stay by Johnathan R. Lex, MBChB, MASc, Robert Koucheki, MD, MEng, Aazad Abbas, MD, Jesse I. Wolfstadt, MD, MSc, FRCSC, FAAOS, Alexander S. McLawhorn, MD, MBA, Bheeshma Ravi, MD, PhD, FRCSC

“…Predictive models (logistic regression, random forest, and XGBoost) were trained and tested based on year of surgery with different oversampling algorithms used to address data imbalance. …”
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An RTM-Driven Machine Learning Approach for Estimating High-Resolution FAPAR From LANDSAT 5/7/8/9 Surface Reflectance by Guodong Zhang, Gaofei Yin, Yi Zhang, Jiangchuan Hu, Zongyan Li, Changjing Wang, Dujuan Ma, Jiangliu Xie

“…The simulated dataset encompassing diverse canopy structural and atmospheric states is utilized to train a random forest model relating Landsat surface reflectance bands to FAPAR. …”
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Discussion of a Simple Method to Generate Descriptive Images Using Predictive ResNet Model Weights and Feature Maps for Recurrent Cervix Cancer by Destie Provenzano, Jeffrey Wang, Sharad Goyal, Yuan James Rao

“…Simulated images were generated as follows: [A] a ResNet model was trained to identify recurrent cervix cancer; [B] a model was evaluated on T2W MRI data for subjects to obtain corresponding feature maps; [C] most important feature maps were determined for each image; [D] feature maps were combined across all images to generate a simulated image; [E] the final image was reviewed by a radiation oncologist and an initial algorithm to identify the likelihood of recurrence. …”
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A sustainable neuromorphic framework for disease diagnosis using digital medical imaging by Rutwik Gulakala, Marcus Stoffel

“…The models are trained using enhanced and pre-processed X-ray images and are evaluated based on classification metrics. …”
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Advanced Deep Learning Based Predictive Maintenance of DC Microgrids: Correlative Analysis by M. Y. Arafat, M. J. Hossain, Li Li

“…The proposed models are trained and evaluated with a dataset of diverse real-world operational scenarios and environmental conditions. …”
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Development and validation of machine learning models for osteoporosis prediction in chronic kidney disease patients: Data from National Health and Nutrition Examination survey by Hui Li, Ya Zhang, Chong Zhang

“…Separate models for male and female CKD patients were developed using 59 potential predictors, with key variables selected through the Least Absolute Shrinkage and Selection Operator and Boruta algorithms. Seven single-base models, including logistic regression, support vector machine, extreme gradient boosting, K-nearest neighbors, gradient boosting decision tree, random forest (RF), and neural network, were trained. …”
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Respirable particles from cutting and grinding ceramic tiles: A Scanning Electron Microscopy investigation by Silvia Contessi, Francesca Borghi, Francesca Graziosi, Francesco S. Violante

“…An automated microscopy workflow based on image analysis with a trained deep learning algorithm was applied. The findings revealed that a consistent proportion of the respirable particles were smaller than 1 μm. …”
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Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning Technique by Moritz Schneider, Kevin Seeser-Reich, Armin Fiedler, Udo Frese

“…Convolutional neural networks (CNNs), residual networks (ResNets), convolutional long short-term memory networks (convLSTMs), and InceptionTime models were trained and evaluated over a range of temporal window lengths using a neural architecture search. …”
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The soft computing approaches in optimising multi-objective mechanical design of a weeding robot by Afsaneh Soleimani

“…Artificial neural network (ANN) models were then trained based on the dataset from these 60 specimens. …”
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