A Preliminary Investigation into the Design of Driver Evaluator Using a Physics-Assisted Machine Learning Technique by Mingke Hou, Francis Assadian

“…Physics-assisted machine learning is a powerful framework that enhances data efficiency by integrating the strengths of conventional machine learning with physical knowledge. …”
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Turbidity Estimation by Machine Learning Modelling and Remote Sensing Techniques Applied to a Water Treatment Plant by Víctor Gauto, Enid Utges, Elsa Hervot, Maria Daniela Tenev, Alejandro Farías

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Forecasting tuberculosis in Ethiopia using deep learning: progress toward sustainable development goal evidence from global burden of disease 1990–2021 by Zinabu Bekele Tadese, Fetlework Gubena Arage, Tigist Kifle Tsegaw, Eyob Akalewold Alemu, Tsegasilassie Gebremariam Abate, Eliyas Addisu Taye

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Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning. by Jiayi Wu, Yong-Bei Ma, Charles Congdon, Bevin Brett, Shuobing Chen, Yaofang Xu, Qi Ouyang, Youdong Mao

“…Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. …”
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A low-cost IoT-based deep learning method of water gauge measurement for flood monitoring by Leila Hashemi-Beni, Megha Puthenparampil, Ali Jamali

“…Real-time and accurate measurement of the water level is a critical step in flood monitoring and management of water resources. …”
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Enhanced crayfish optimization algorithm: Orthogonal refracted opposition-based learning for robotic arm trajectory planning. by Yuefeng Leng, Chunlai Cui, Zhichao Jiang

“…Furthermore, an orthogonal refracted opposition-based learning strategy enhances solution quality and search efficiency by leveraging the dominant dimensional information. …”
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Teaching Graduate Student Practitioner-Scholars with Affordable Learning Materials (ALMs): Closing the Research-to-Practice Gap by Elizabeth Pope, Phillip Grant

“…And how can instructors create learning environments that support these activities? …”
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A guide to bayesian networks software for structure and parameter learning, with a focus on causal discovery tools by Francesco Canonaco, Francesco Canonaco, Joverlyn Gaudillo, Nicole Astrologo, Fabio Stella, Enzo Acerbi

“…These tasks, referred to as structural learning and parameter learning, are actively investigated by the research community, with several algorithms proposed and no single method having established itself as standard. …”
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Machine learning-enabled multiscale modeling platform for damage sensing digital twin in piezoelectric composite structures by Somnath Ghosh, Saikat Dan, Preetam Tarafder

“…The DT framework consists of a two-step computational process integrating multiscale-multiphysics modeling with machine learning (ML) tools to detect damage progression in the piezoelectric composite structure using electrical signal measurements at a few surface points. …”
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Multi-Task Learning-Based Traffic Flow Prediction Through Highway Toll Stations During Holidays by Xiaowei Liu, Yunfan Zhang, Zhongyi Han, Hao Qiu, Shuxin Zhang, Jinlei Zhang

“…Under the multi-task learning framework, the model shares spatial–temporal features between inbound flow and outbound flow, enhancing their representations and improving multi-step prediction accuracy. …”
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Automated assessment of bridge guardrails for regional prioritization based on open-source data and deep learning algorithms by Giuseppe Santarsiero

“…This latter step is integrated with Google Street View API, for the extraction of images of each bridge’s safety barriers to be analysed by YOLO. …”
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Integrated Machine Learning and Region Growing Algorithms for Enhanced Concrete Crack Detection: A Novel Approach by Wenxuan Yao, Hui Li, Yanlin Li

“…During structural repair, crack detection is the most critical step. Automatic detection significantly reduces the engineering cost and human factor error compared with manual detection. …”
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Predicting visual field global and local parameters from OCT measurements using explainable machine learning by Md Mahmudul Hasan, Jack Phu, Henrietta Wang, Arcot Sowmya, Erik Meijering, Michael Kalloniatis

“…To evaluate the models, a total of 268 glaucomatous eyes (86 early, 72 moderate, 110 advanced) and 226 normal eyes were included. The machine learning models outperformed recent OCT-based VF prediction deep learning studies, with correlation coefficients of 0.76, 0.80 and 0.76 for mean deviation, visual field index and pattern standard deviation, respectively. …”
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Domain knowledge-infused pre-trained deep learning models for efficient white blood cell classification by P. Jeneessha, Vinoth Kumar Balasubramanian

“…Abstract White blood cell (WBC) classification is a crucial step in assessing a patient’s health and validating medical treatment in the medical domain. …”
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AI Machine Learning–Based Diabetes Prediction in Older Adults in South Korea: Cross-Sectional Analysis by Hocheol Lee, Myung-Bae Park, Young-Joo Won

“… Abstract BackgroundDiabetes is prevalent in older adults, and machine learning algorithms could help predict diabetes in this population. …”
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Wind speed prediction based on variational mode decomposition and advanced machine learning models in zaafarana, Egypt by Ali Taha, Nathalie Nazih, Peter Makeen

“…This study proposes a multi-step methodology that integrates Variational Mode Decomposition (VMD) with advanced machine learning like Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Light Gradient Boosting Machine (LightGBM), K-Nearest Neighbor (KNN), and transformer-based model (Informer) to improve long-term wind speed forecasting. …”
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A Novel Dynamic Lane-Changing Trajectory Planning Model for Automated Vehicles Based on Reinforcement Learning by Cenxin Yu, Anning Ni, Jing Luo, Jinghui Wang, Chunqin Zhang, Qinqin Chen, Yifeng Tu

“…Most conventional lane-changing models need to establish and solve constrained optimization models during the whole process, while reinforcement learning can just take the current state as input and directly output actions to vehicles. …”
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Intrusion Detection Using Machine Learning for Risk Mitigation in IoT-Enabled Smart Irrigation in Smart Farming by Abhishek Raghuvanshi, Umesh Kumar Singh, Guna Sekhar Sajja, Harikumar Pallathadka, Evans Asenso, Mustafa Kamal, Abha Singh, Khongdet Phasinam

“…Crop irrigation is an important step in crop yield prediction. Field harvesting is very reliant on human supervision and experience. …”
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Stable Simulation of the Community Atmosphere Model Using Machine‐Learning Physical Parameterization Trained With Experience Replay by Jianda Chen, Minghua Zhang, Tao Zhang, Wuyin Lin, Wei Xue

“…The method uses experience replay with a multistep training scheme of the ML model in which the model's own output at the previous time step is used in the training. Predicted physics tendencies in the replay buffer with the most recent errors in the training iterations are reused, making the ML model learn from its own errors. …”
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Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins. by Moritz Ertelt, Vikram Khipple Mulligan, Jack B Maguire, Sergey Lyskov, Rocco Moretti, Torben Schiffner, Jens Meiler, Clara T Schoeder

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