An Adaptive Weight Physics-Informed Neural Network for Vortex-Induced Vibration Problems by Ping Zhu, Zhonglin Liu, Ziqing Xu, Junxue Lv

“…This ensures balanced training speeds for each loss term and improves both the prediction precision and robustness of the network model. …”
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‘Machine Learning’ multiclassification for stage diagnosis of Alzheimer’s disease utilizing augmented blood gene expression and feature fusion by Manash Sarma, Subarna Chatterjee

“…DL classifier is used for developing models of both categories while GB (Gradient Boost), SVM (Support Vector Machine) classifier based models are built to identify AD stages from NCBI participants. …”
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Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets by Catarina Lopes, Andreia Brandão, Manuel R. Teixeira, Mário Dinis-Ribeiro, Carina Pereira

“…Leveraging transcriptomic data from the Gene Expression Omnibus (GEO), we constructed and validated predictive models through machine learning algorithms within the tidymodels framework. …”
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Reduced Complexity of Pulse Rate Is Associated With Faster Cognitive Decline in Older Adults by Chenlu Gao, Andrew S. P. Lim, Shahab Haghayegh, Ruixue Cai, Jingyun Yang, Lei Yu, Agustin Ibanez, Aron S. Buchman, David A. Bennett, Lei Gao, Kun Hu, Peng Li

“…Subclinical cardiovascular functional changes, such as cardiac autonomic dysfunction, precede cardiovascular diseases and improve risk stratification. Continuous monitoring of heart rate or pulse rate is a commonly used approach to evaluate cardiac cycle and autonomic regulation. …”
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Refining source-specific lung cancer risk assessment from PM2.5-bound PAHs: Integrating component-based potency factors and machine learning in Ningbo, China by Lord Famiyeh, Ke Chen, Fiseha Berhanu Tesema, Celeb Kelly, Dongsheng Ji, Hang Xiao, Lei Tong, Zongshuang Wang, Jun He

“…We identified a moderate PAH exposure risk level (>1.0 ×10⁻⁶) in Ningbo and used advanced machine learning (ML) algorithms, random forest (RF), extremely randomized trees (ERT), and extreme gradient boosting (XGBoost), to improve the accuracy of source-specific LECR assessments. …”
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Addition of Social Determinants of Health to Coronary Heart Disease Risk Prediction: The Multi‐Ethnic Study of Atherosclerosis by Brittany Saldivar Murphy, Yunbi Nam, Robyn L. McClelland, Isaac Acquah, Miguel Cainzos‐Achirica, Khurram Nasir, Wendy S. Post, Melinda C. Aldrich, Andrew P. DeFilippis

“…Incorporation of SDoH into the MESA Risk Score did not improve predictive ability of the model. Our findings do not support the incorporation of SDoH into current risk prediction algorithms.…”
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Optimizing Stroke Risk Prediction: A Primary Dataset‐Driven Ensemble Classifier With Explainable Artificial Intelligence by Md. Maruf Hossain, Md. Mahfuz Ahmed, Md. Rakibul Hasan Rakib, Mohammad Osama Zia, Rakib Hasan, Md. Rakibul Islam, Md. Shohidul Islam, Md Shahariar Alam, Md. Khairul Islam

“…Future work will focus on incorporating deep learning techniques and medical imaging to further improve classification accuracy and model performance.…”
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Federated Deep Learning for Scalable and Privacy-Preserving Distributed Denial-of-Service Attack Detection in Internet of Things Networks by Abdulrahman A. Alshdadi, Abdulwahab Ali Almazroi, Nasir Ayub, Miltiadis D. Lytras, Eesa Alsolami, Faisal S. Alsubaei, Riad Alharbey

“…ResNet improves feature extraction, VGGNet optimises feature refining, and Swin-Transformer captures contextual dependencies, making the model sensitive to complicated attack patterns across varied network circumstances. …”
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Comparative Analysis of Random Forest and Logistic Regression Methods in Predicting Leukemia Blood Cancer Using Microscopic Blood Cell Images by Jepri Banjarnahor, Galuh Wira Relungwangi

“…These findings suggest that ensemble methods like RF are particularly well-suited for detecting one of the most deadly blood cancers, leukemia, due to their ability to handle complex feature interactions in medical imaging data. While both algorithms have potential as clinical decision support, future research can test deep learning techniques and larger datasets to improve the accuracy and reliability of the model.…”
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Explainable Artificial Intelligence to Predict the Water Status of Cotton (<i>Gossypium hirsutum</i> L., 1763) from Sentinel-2 Images in the Mediterranean Area by Simone Pietro Garofalo, Anna Francesca Modugno, Gabriele De Carolis, Nicola Sanitate, Mesele Negash Tesemma, Giuseppe Scarascia-Mugnozza, Yitagesu Tekle Tegegne, Pasquale Campi

“…The models’ performance was assessed using R² and root mean square error (RMSE). …”
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NLP-Based Fusion Approach to Robust Image Captioning by Riccardo Ricci, Farid Melgani, Jose Marcato Junior, Wesley Nunes Goncalves

“…Despite indisputable improvements in performance, we argue that relying on the output of a single model can be critical, especially when data scarcity limits the generalization capability of the trained algorithms. …”
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Experimental and numerical investigations on the bidirectional thermal contact performance by Chen Wang, Mingjun Qiu, Huijing Liu, Jun Hong, Feiyu Gu, Lifei Chen, Tao Wang, Hao Guan, Qiyin Lin

“…Additionally, a prediction model for TCR was developed using the Levenberg-Marquardt (L-M) algorithm. …”
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An Ensemble Learning-Based Predictive Parameterization Approach for Permanent Magnet Synchronous Machines by Sema Nur Ipek, Nur Bekiroglu, Murat Taskiran

“…An averaging voting ensemble model is developed by integrating the two highest-performing algorithms, LRNN and TRF, leveraging the strengths of both algorithms. …”
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Swarm intelligence for energy-efficient heating, ventilation, and air conditioning (HVAC) systems: A case study in smart buildings by Vinoth Kanna I, Raja Subramani, Maher Ali Rusho, Shubham Sharma, Ramachandran T, Abinash Mahapatro, Deepak Gupta, Jasmina Lozanovic

“…The results illustrate the outstanding performance of hybrid swarm intelligence algorithms to improve HVAC efficiency and occupant comfort, a scalable and inexpensive solution for smart building management. …”
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A Novel Data-Driven Method of Real-Time Transient Stability Assessment for AC/DC Hybrid Power Systems by Haifeng Li, Zhiwei Wang, Tao Jin, Xian Xu, Lin Shi

“…With such a design, the attention mechanism improves the model&#x2019;s attention to instability samples, and the high-precision assessment of the systems&#x2019; transient stability is achieved. …”
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Exploring Gender Bias in AI for Personalized Medicine: Focus Group Study With Trans Community Members by Nataly Buslón, Davide Cirillo, Oriol Rios, Simón Perera del Rosario

“…Participants expressed apprehensions about potential misdiagnoses or inappropriate treatments due to cisnormative data models. However, they also identified opportunities for AI to enhance health care outcomes, advocating for community-led data collection initiatives and improved algorithmic transparency. …”
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Exploring the capabilities of hyperspectral remote sensing for soil texture evaluation by Mohammad Hosseinpour-Zarnaq, Mahmoud Omid, Fereydoon Sarmadian, Hassan Ghasemi-Mobtaker, Reza Alimardani, Pouya Bohlol

“…Additionally, we compared the performance of random forest (RF) algorithms with partial least squares regression (PLSR), multiple linear regression (MLR), support vector machine regression (SVR), decision trees (DTs), and multilayer perceptron (MLP) neural networks, addressing the effects of feature selection and irregular soil data on the modeling procedure. …”
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Machine learning predicts spinal cord stimulation surgery outcomes and reveals novel neural markers for chronic pain by Jay Gopal, Jonathan Bao, Tessa Harland, Julie G. Pilitsis, Steven Paniccioli, Rachael Grey, Michael Briotte, Kevin McCarthy, Ilknur Telkes

“…Machine and deep learning hold enormous potential to predict patient responses to SCS therapy resulting in refined patient selection and improved patient outcomes.…”
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Addressing data imbalance in collision risk prediction with active generative oversampling by Li Li, Xiaoliang Zhang

“…Our method selectively enriches minority class samples through QBC and diversity metrics to enhance the diversity of sample generation, thereby improving the performance of fault classification algorithms. …”
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DMSO-free cryopreservation of hiPSC-derived cardiomyocytes: low temperature characterization and protocol development by Akshat S. Mallya, Tessa Burrows, Jeanne Hsieh, Troy Louwagie, James R. Dutton, Brenda M. Ogle, Allison Hubel

“…Abstract Background Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have attracted significant interest for use in disease modeling, drug discovery and potential therapeutic applications. …”
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