Predicting the permeability and compressive strength of pervious concrete using a stacking ensemble machine learning approach by Fan Yu, Wei Chu, Rui Zhang, Zhang Gao, Yunan Yang

“…This enhancement can be attributed to the model’s ability to effectively address the limitations posed by the linear assumptions of traditional empirical formulations through base-learner feature reweighting and meta-learner dynamic fusion mechanisms. …”
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SAVE: Self-Attention on Visual Embedding for Zero-Shot Generic Object Counting by Ahmed Zgaren, Wassim Bouachir, Nizar Bouguila

“…This paper proposes a fully automated zero-shot method outperforming both zero-shot and few-shot methods. By exploiting feature maps from a pre-trained detection-based backbone, we introduce a new Visual Embedding Module designed to generate semantic embeddings within object contextual information. …”
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Thyroid disease classification using generative adversarial networks and Kolmogorov-Arnold network for three-class classification by Aysel Topşir, Ferdi Güler, Ecesu Çetin, Mehmet Furkan Burak, Melih Ağraz

“…Various machine learning models including logistic regression, random forest, support vector machines, multilayer perceptrons, and KANs were trained and evaluated. The results indicate that the application of GAN-based data augmentation has significantly improved classification accuracy, particularly for minority classes. …”
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Diet quality of Chilean schoolchildren: How is it linked to adherence to dietary guidelines? by Anna Christina Pinheiro Fernandes, Jacqueline Araneda Flores, Daiana Quintiliano Scarpelli Dourado, Tito Pizarro Quevedo, Maria Rita Marques de Oliveira

“…Dietary intake was assessed using a validated semi-quantitative frequency survey featuring images of food groups aligned with DGBF, as well as HPF. …”
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Spatiotemporal associations between air pollution and emergency room visits for cardiovascular and cerebrovascular diseases in Korea using a multivariate graph autoencoder modeling... by Sohee Wang, Seungpil Jeong, Eunhee Ha

“…The model was trained separately for each month and region to estimate the strength of pollutant–disease associations. …”
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YOLOv8 framework for COVID-19 and pneumonia detection using synthetic image augmentation by Uddin A Hasib, Raihan Md Abu, Jing Yang, Uzair Aslam Bhatti, Chin Soon Ku, Lip Yee Por

“…Synthetic images are generated using Feature Interpolation through Linear Mapping and principal component analysis to enrich dataset diversity and balance class distribution. …”
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Towards Identifying Objectivity in Short Informal Text by Chaowei Zhang, Cheng Zhao, Zewei Zhang, Yuchao Huang

“…Upon that, we further propose a two-stage objectivity identification approach: (1) a UVO quantification module is implemented via a proposed OpenIE and large language model (LLM)-based triple feature quantification procedure; (2) an objectivity identification module employs pre-trained base models like BERT or RoBERTa that are constrained with the quantified UVO. …”
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Machine Learning Approach to Model Soil Resistivity Using Field Instrumentation Data by Md Jobair Bin Alam, Ashish Gunda, Asif Ahmed

“…This research leverages various machine learning algorithms to develop predictive models trained on a comprehensive dataset of sensor-based soil moisture, matric suction, and soil temperature obtained from prototype ET covers, with known resistivity values. …”
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Exploring vision transformers and XGBoost as deep learning ensembles for transforming carcinoma recognition by Akella Subrahmanya Narasimha Raju, K. Venkatesh, B. Padmaja, CH. N. Santhosh Kumar, Pattabhi Rama Mohan Patnala, Ayodele Lasisi, Saiful Islam, Abdul Razak, Wahaj Ahmad Khan

“…This improvement enabled more precise feature extraction and improved classification results. …”
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Multimodal GNSS-R self-supervised learning as a generalist Earth surface monitor by Daixin Zhao, Konrad Heidler, Milad Asgarimehr, Conrad M. Albrecht, Jens Wickert, Xiao Xiang Zhu, Lichao Mou

“…Yet, these models are typically trained using supervised learning, which requires extensive feature engineering and application-specific annotations. …”
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A Comparative Study of a Deep Reinforcement Learning Solution and Alternative Deep Learning Models for Wildfire Prediction by Cristian Vidal-Silva, Roberto Pizarro, Miguel Castillo-Soto, Ben Ingram, Claudia de la Fuente, Vannessa Duarte, Claudia Sangüesa, Alfredo Ibañez

“…This study compared three deep learning models for wildfire prediction: Deep Reinforcement Learning (DRL) with Actor–Critic architecture, Convolutional Neural Network (CNN), and Transformer-based models. The models were trained and evaluated using historical data from Chile (2000–2023), including wildfire occurrences, meteorological variables, topography, and vegetation indices. …”
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Multisensor Data Fusion for Coastal Boundary Detection by Res-U-Net Implementation Using High-Resolution UAV Imagery by Qin Wang, Nyasha J. Kavhiza, Fakhrul Islam, Ilyas Ahmad Huqqani, Mohsin Abbas, Sanjoy Barman

“…This work utilizes high-resolution UAV data to develop a deep learning framework based on a Residual U-Net architecture for shoreline semantic segmentation. …”
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Non-Invasive Painting Pigment Classification Through Supervised Machine Learning by Michal Piotr Markowski, Solongo Gansukh, Mateusz Madry, Robert Borowiec, Jaroslaw Rogoz, Boguslaw Szczupak

“…A total of 600 initial raw images were acquired, from which 4000 image patches were extracted for feature engineering. Feature vectors were obtained from visible reflectography, ultraviolet false-color imaging (UVFC), and infrared false-color imaging (IRFC) using statistical descriptors derived from RGB channels. …”
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Evaluation of classification algorithms in the Google Earth Engine platform for the identification and change detection of rural and periurban buildings from very high-resolution i... by Alejandro Coca-Castro, Maycol A. Zaraza-Aguilera, Yilsey T. Benavides-Miranda, Yeimy M. Montilla-Montilla, Heidy B. Posada-Fandiño, Angie L. Avendaño-Gomez, Hernando A. Hernández-Hamon, Sonia C. Garzón-Martinez, Carlos A. Franco-Prieto

“…In total, eight traditional classification algorithms, three unsupervised (K-means, X-Means y Cascade K-Means) and five supervised (Random Forest, Support Vector Machine, Naive Bayes, GMO maximum Entropy and Minimum distance) available at GEE were trained. Additionally, a deep neural network named Feature Pyramid Networks (FPN) was added and trained using a pre-trained model, EfficientNetB3 model. …”
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ML-Driven Transistor Self-Heating Analysis From TCAD to Large IP Circuits by Simon Thomann, Nico Mayr, Albi Mema, Hussam Amrouch

“…We explore our method to study self-heating in complex large circuits featuring over 3700 transistors. This includes foundries IPs like a complete high-performance SRAM-based register file. …”
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Prediction of carbon dioxide phase at bottomhole by adaptive factorization network considering well geometry by Sungil Kim, Tea-Woo Kim, Yongjun Hong, Hoonyoung Jeong

“…This study addresses these challenges by introducing a deep learning framework based on the adaptive factorization network (AFN), which enhances CO2 phase prediction accuracy by leveraging feature interactions. …”
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Net2Brain: a toolbox to compare artificial vision models with human brain responses by Domenic Bersch, Domenic Bersch, Martina G. Vilas, Martina G. Vilas, Sari Saba-Sadiya, Timothy Schaumlöffel, Timothy Schaumlöffel, Kshitij Dwivedi, Christina Sartzetaki, Radoslaw M. Cichy, Radoslaw M. Cichy, Radoslaw M. Cichy, Gemma Roig, Gemma Roig

“…To address these challenges, we present Net2Brain, a Python-based toolbox that provides an end-to-end pipeline for incorporating DNNs into neuroscience research, encompassing dataset download, a large selection of models, feature extraction, evaluation, and visualization. …”
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A high-throughput ResNet CNN approach for automated grapevine leaf hair quantification by Nagarjun Malagol, Tanuj Rao, Anna Werner, Reinhard Töpfer, Ludger Hausmann

“…Abstract The hairiness of the leaves is an essential morphological feature within the genus Vitis that can serve as a physical barrier. …”
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Arctic Sea Ice and Open Water Classification From Dual-Polarization Synthetic Aperture Radar Imagery and Deep Learning Models by Yiru Lu, Biao Zhang, William Perrie, Jinyu Sheng

“…The modified Atrous spatial pyramid pooling module is integrated into a customized dilated U-Net to create a multiscale feature extraction model (MS-DUNet). MS-DUNet is then trained and validated using 11 485 labeled patches extracted from 3565 RADARSAT-2 SAR images. …”
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Experimental investigation of shaft misalignment effects on bearing reliability through vibration signal analysis using machine learning and deep learning by Fransiskus Tatas Dwi Atmaji, Jamasri, Hari Agung Yuniarto, I Made Miasa

“…Statistical time-domain feature extraction notably improved the performance of classical models, with KNN achieving a maximum accuracy of 92.9%. …”
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