Machine learning and uncertainty quantification to track and monitor natural frequencies in vibration-based SHM applied to offshore wind turbines by Maximillian Weil, Carlos Sastre Jurado, Wout Weijtjens, Christof Devriendt

“…This is typically a four-step process, comprising estimation, tracking, data normalization, and decision-making. …”
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Prediction of esophageal fistula in radiotherapy/chemoradiotherapy for patients with advanced esophageal cancer by a clinical-deep learning radiomics model by Yuxin Zhang, Xu Cheng, Xianli Luo, Ruixia Sun, Xiang Huang, Lingling Liu, Min Zhu, Xueling Li

“…Arterial phase enhanced CT images were used to extract handcrafted and deep learning radiomic features. Along with clinical information, a 3-step feature selection method (statistical tests, Least Absolute Shrinkage and Selection Operator, and Recursive Feature Elimination) was used to identify five feature sets in training cohort for constructing random forest EF prediction models. …”
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Revolutionizing clean energy labs: Robotic imitation learning for efficient fabrication AI-powered electrical units assembly platform by Xi Xu, Yijun Gu, Tianyi Zhang, Jiwen Yu, Stephen Skinner

“…AI-driven robotics was employed with an imitation learning method to effectively learn skills directly from human demonstrations, thereby alleviating researchers from labor-intensive tasks. …”
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Enhancing runoff simulation by combining superflex with deep learning methods in China's Qinghai Lake Basin, Northeast Tibetan Plateau by Kaixun Liu, Na Li, Sihai Liang

“…Our approach leverages Superflex as a pre-training step for the neural networks and incorporates key process variables from the physical model as inputs to the network, creating a physically-driven deep learning framework. …”
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Segmentation of the iliac crest from CT-data for virtual surgical planning of facial reconstruction surgery using deep learning by Stefan Raith, Tobias Pankert, Jônatas de Souza Nascimento, Srikrishna Jaganathan, Florian Peters, Mathias Wien, Frank Hölzle, Ali Modabber

“…The complete automation of this step allows for efficient and reliable preparation of anatomical data for reconstructive surgeries.…”
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A scoping review of COVID-19 economic response policies in the MENA countries: lessons learned for Iran for future pandemics by Alireza Hajizadeh, Mohammad Seyedmohammadi, Shirin Nosratnejad, Behzad Najafi, Homayoun Sadeghi-bazargani, Ali Imani

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Deploying machine learning for long-term road pavement moisture prediction: A case study from Queensland, Australia by Ayesh Dushmantha, Ruixuan Zhang, Yilin Gui, Jinjiang Zhong, Chaminda Gallage

“…Using data collected from an instrumented road in Brisbane, Australia, which includes pavement moisture and climatic factors, the study develops predictive models to forecast moisture content at future time steps. The approach incorporates current moisture content, rather than averaged values, along with seasonality (both daily and annual), and key climatic factors to predict next step moisture. …”
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Detection of Body Packs in Abdominal CT scans Through Artificial Intelligence; Developing a Machine Learning-based Model by Sayed Masoud Hosseini, Seyed Ali Mohtarami, Shahin Shadnia, Mitra Rahimi, Peyman Erfan Talab Evini, Babak Mostafazadeh, Azadeh Memarian, Elmira Heidarli

“…Methods: In this cross-sectional study, abdominal CT scan images were employed to create a machine learning-based model for detecting body packs. A single-step object detection called RetinaNet using a modified neck (Proposed Model) was performed to achieve the best results. …”
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Enhancing Heart Disease Diagnosis Using ECG Signal Reconstruction and Deep Transfer Learning Classification with Optional SVM Integration by Mostafa Ahmad, Ali Ahmed, Hasan Hashim, Mohammed Farsi, Nader Mahmoud

“…<b>Methods</b>: This study presents a comprehensive deep learning (DL) framework that integrates advanced ECG signal segmentation with transfer learning-based classification, aimed at improving diagnostic performance. …”
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Genetic diversity insights from population genomics and machine learning tools for Nordic Arctic charr (Salvelinus alpinus) populations by Christos Palaiokostas, Khrystyna Kurta, Fotis Pappas, Henrik Jeuthe, Ørjan Hagen, José Beirão, Matti Janhunen, Antti Kause

“…A preceding dimensionality reduction step by PCA resulted in all three models, suggesting that the most probable number of clusters was three. …”
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Evaluation of LSTM vs. conceptual models for hourly rainfall runoff simulations with varied training period lengths by Mohamed M. Fathi, Md Abdullah Al Mehedi, Virginia Smith, Anjali M. Fernandes, Michael T. Hren, Dennis O. Terry

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Moving from crisis response to a learning health system: Experiences from an Australian regional primary care network by Bianca Forrester, Georgia Fisher, Louise A. Ellis, Andrew Giddy, Carolynn L. Smith, Yvonne Zurynski, Lena Sanci, Katherine Graham, Naomi White, Jeffrey Braithwaite

“…This Experience Report describes our progress toward a durable organizational learning health system (LHS) model through the COVID‐19 pandemic crisis and beyond. …”
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Barriers and facilitators to high-volume evidence-based innovation and implementation in a large, community-based learning health system by Cimone Durojaiye, Stephanie Prausnitz, Jennifer L. Schneider, Tracy A. Lieu, Julie A. Schmittdiel, Smita Rouillard, Yi-Fen Chen, Kristine Lee, Douglas A. Corley

“…The most commonly cited barriers were inadequate resource commitment for next-step implementation (n = 15), insufficient learning/implementation culture (n = 5), and insufficient individual-level willingness/ability for change (n = 5). …”
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Web-SpikeSegNet: Deep Learning Framework for Recognition and Counting of Spikes From Visual Images of Wheat Plants by Tanuj Misra, Alka Arora, Sudeep Marwaha, Ranjeet Ranjan Jha, Mrinmoy Ray, Rajni Jain, A. R. Rao, Eldho Varghese, Shailendra Kumar, Sudhir Kumar, Aditya Nigam, Rabi Narayan Sahoo, Viswanathan Chinnusamy

“…Computer vision with deep learning is emerging as a significant approach for non-invasive and non-destructive plant phenotyping. …”
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A Multimodal Pain Sentiment Analysis System Using Ensembled Deep Learning Approaches for IoT-Enabled Healthcare Framework by Anay Ghosh, Saiyed Umer, Bibhas Chandra Dhara, G. G. Md. Nawaz Ali

“…The first phase focuses on detecting the facial region from a video sequence, a crucial step for extracting facial patterns indicative of pain. …”
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Deep learning-based underwater metal object detection using input image data and corrosion protection of mild steel used in underwater study: A case study: Part A: Deep learning-ba... by Thankappan Sasilatha, Doss Hebciba Mary Suvakeen Amala, Rajendran Susai Santhammal, Časlav Lačnjevac, Gurmeet Singh

“…In this paper, introduced Deep learning-based Underwater Metal object detection using input Image data by using several step to improve the model performance. …”
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Automatic Segmentation of Asphalt Cracks on Highways After Large-Scale and Severe Earthquakes Using Deep Learning-Based Approaches by Mehmet Yilmaz, Erkut Yalcin, Fatih Demir, Ahmet Munir Ozdemir, Muhammed Atar, Aysegul Gunes, Beyza Furtana Yalcin, Ertugrul Cambay

“…This study develops a deep learning-based automated system for detecting and segmenting earthquake-induced asphalt cracks, offering a rapid and reliable solution for post-disaster road condition assessments. …”
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Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web by Shiwei Zhu, Guanwen Fang, Chichun Zhou, Jie Song, Zesen Lin, Yao Dai, Xu Kong

“…In this study, we propose a self-supervised method called contrastive learning to upgrade the unsupervised machine learning (UML) part of the USmorph framework, aiming to improve the efficiency of feature extraction in this step. …”
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Overheard at a conference: an in-class activity to foster classroom discussion on ethics and experimental systems in microbiology research by Yannick D. N. Tremblay, Jenny-Lee Thomassin

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Automation of Predictive Maintenance: An Experimental Framework for Aircraft Landing Gear by Izaak Stanton, Kamran Munir, Ahsan Ikram, Murad El‐Bakry

“…This research presents an experimental analysis comparing industry‐selected machine learning models and a hand‐picked selection of automated machine‐learning tools. …”
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