Probing nuclear quantum effects in electrocatalysis via a machine-learning enhanced grand canonical constant potential approach by Menglin Sun, Bin Jin, Xiaolong Yang, Shenzhen Xu

“…Abstract Proton-coupled electron transfer (PCET) is the key step for energy conversion in electrocatalysis. Atomic-scale simulation acts as an indispensable tool to provide a microscopic understanding of PCET. …”
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Machine Learning Classification of Fossilized <i>Pectinodon bakkeri</i> Teeth Images: Insights into Troodontid Theropod Dinosaur Morphology by Jacob Bahn, Germán H. Alférez, Keith Snyder

“…Although the manual classification of microfossils is possible, it can become burdensome. Machine learning offers an alternative that allows for automatic classification. …”
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Adoption deep learning approach using realistic synthetic data for enhancing network intrusion detection in intelligent vehicle systems by Said A. Salloum, Tarek Gaber, Mohammed Amin Almaiah, Rami Shehab, Romel Al-Ali, Theyazan H.H Aldahyani

“…The results of this research are significant, marking a step forward towards more flexible and preemptive security measures for intelligent vehicles, and effectively narrowing the gap between simulation-based testing and real-world network environments.…”
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The smart archive management practicing pipeline: a virtual online learning platform for AI literacy development in archival science by Lanxi Dong, Shuning Tang, Yuan Cheng, Ping Wang

“…The implementation of the SAMPP follows a three-step strategy: 1) an introductive and immersive 3D virtual environment to fix students’ attentions on AI learning; 2) AI-powered archive management tasks for skill development, and 3) an automated online exhibition project to enhance creativity and competency. …”
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Leveraging machine learning techniques for image classification and revealing social media insights into human engagement with urban wild spaces by Haider Khalid, Marcus J. Collier

“…This research explores how advanced machine learning techniques, leveraging social media data for image classification, can be used to gain deeper insights into public engagement with urban wild spaces. …”
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Parallel Multi-Model Energy Demand Forecasting with Cloud Redundancy: Leveraging Trend Correction, Feature Selection, and Machine Learning by Kamran Hassanpouri Baesmat, Zeinab Farrokhi, Grzegorz Chmaj, Emma E. Regentova

“…We also introduced a processing step to ensure accurate predictions for holidays. …”
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Building Damage Detection Using Deep Learning Architecture with Satellite Images: The Case of the 6 February 2023 Kahramanmaraş Earthquake by Zeynep Aygün, Merve Kocaman, Salih Aydemir, Berkant Konakoğlu

“…This study aims to support post-disaster rapid response and rescue operations by using deep learning techniques to detect and classify damaged and intact buildings from satellite images. …”
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Advancing Interstitial Cystitis/Bladder Pain Syndrome (IC/BPS) Diagnosis: A Comparative Analysis of Machine Learning Methodologies by Joseph J. Janicki, Bernadette M. M. Zwaans, Sarah N. Bartolone, Elijah P. Ward, Michael B. Chancellor

“…</b> This study aimed to improve machine learning models for diagnosing interstitial cystitis/bladder pain syndrome (IC/BPS) by comparing classical machine learning methods with newer AutoML approaches, utilizing biomarker data and patient-reported outcomes as features. …”
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Fusion-Based Deep Learning Approach for Renal Cell Carcinoma Subtype Detection Using Multi-Phasic MRI Data by Gulhan Kilicarslan, Dilber Cetintas, Taner Tuncer, Muhammed Yildirim

“…<b>Results</b>: The model performs RCC subtype classification at the end of a five-step process. These are regions of interest (ROI), preprocessing, augmentation, feature extraction, and classification. …”
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Machine Learning Developed a Programmed Cell Death Signature for Predicting Prognosis, Ecosystem, and Drug Sensitivity in Ovarian Cancer by Le Wang, Xi Chen, Lei Song, Hua Zou

“…The prognostic cell death signature (CDS) was constructed with an integrative machine learning procedure, including 10 methods, using TCGA, GSE14764, GSE26193, GSE26712, GSE63885, and GSE140082 datasets. …”
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Design of an Improved Model for Gear Fault Diagnosis Using Acoustic Data and EfficientNet-Based Deep Learning Process by Bundele Shubham, Kane P.V.

“…It is then used to train deep learning models, EfficientNetB0 and EfficientNetB3 that are superior on feature-extraction and computational efficiency. …”
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Supporting intraoperative margin assessment using deep learning for automatic tumour segmentation in breast lumpectomy micro-PET-CT by Luna Maris, Menekse Göker, Kathia De Man, Bliede Van den Broeck, Sofie Van Hoecke, Koen Van de Vijver, Christian Vanhove, Vincent Keereman

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High sensitivity in spontaneous intracranial hemorrhage detection from emergency head CT scans using ensemble-learning approach by Juuso Takala, Heikki Peura, Riku Pirinen, Katri Väätäinen, Sergei Terjajev, Ziyuan Lin, Rahul Raj, Miikka Korja

“…We developed a deep learning (DL) solution for spontaneous intracranial hemorrhage detection from head CT scans. …”
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Transfer Learning based Image Classification of Diseased Tomato Leaves with Optimal Fine-Tuning combined with Heat Map Visualization by Sivakumar Palanıswamy, Vijayakumar Vaıthyam Rengarajan, Sandhya Devi Ramıah Subburaj

“…It is presumed that the implementation of deep learning algorithms demands a large amount of data to learn complex features automatically and this can pose a challenge for applications with lesser data to achieve generalization. …”
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Efficient microstructure segmentation in three-dimensional imaging: Combining few-shot learning with the segment anything modelEarth/Chem by Po-Yen Tung, Richard J. Harrison

“…Pixel-level segmentation of each 2D image is the first step in any analysis pipeline, but creates a considerable human bottleneck in the workflow that can now be overcome using machine learning. …”
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Accelerated composition-process-properties design of precipitation-strengthened copper alloys using machine learning based on Bayesian optimization by Longjian Li, Jinchuan Jie, Xiaoyu Guo, Gaojie Liu, Huijun Kang, Zongning Chen, Enyu Guo, Tongmin Wang

“…We propose an alloy design strategy based on machine learning algorithms for navigating the enormous search space. …”
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Intelligent Diagnosis of Rolling Element Bearings Under Various Operating Conditions Using an Enhanced Envelope Technique and Transfer Learning by Ali Davoodabadi, Mehdi Behzad, Hesam Addin Arghand, Somaye Mohammadi, Len Gelman

“…The main innovation is applying Transfer Learning (TL) with fine-tuning to improve model accuracy in identifying REB conditions by leveraging features learned from diverse datasets. …”
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Introducing Spatial Heterogeneity via Regionalization Methods in Machine Learning Models for Geographical Prediction: A Spatially Conscious Paradigm by Lukas Boegl, Ourania Kounadi

“… This study addresses the challenge of incorporating spatial heterogeneity in predictive modeling by introducing regionalization methods in the preprocessing step of the modeling workflow. Spatial heterogeneity, where the mean of attribute values varies across spatial units, poses difficulties for traditional models. …”
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ANALYSIS OF REAL RELATIVE ASYMMETRY IN URBAN TRANSPORTATION NETWORK PROBLEMS USING SPACE SYNTAX, REDS, AND MACHINE LEARNING CONCEPTS by Robiatul Adawiyah, Fitriyatul Mardiyah, Dafik Dafik, Ika Hesti Agustin, Excelsa Suli Wildhatul Jannah, Marsidi Marsidi

“…The REDS analysis yields a resolving efficient domination number that determines the optimal quantity and placement of mobile ETLE units. GNN-based multi-step time series forecasting successfully predicts traffic violation trends across 29 road segments with a Mean Squared Error (MSE) equal to 0.0173. …”
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A machine learning-based recommendation framework for material extrusion fabricated triply periodic minimal surface lattice structures by Sajjad Hussain, Carman Ka Man Lee, Yung Po Tsang, Saad Waqar

“…To overcome these challenges, this study presents a machine learning (ML) and Deep Learning (DL) based framework recommending TPMS LS according to specific requirements. …”
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