Beyond raw data: AI-driven biosensor fusion for enhancing athletic performance by Peng Luo, Jun Song

“…SPARTA leverages AI algorithms to analyze real-time physiological data - including heart rate, oxygen saturation, skin conductance, and cortisol levels - enabling dynamic adjustments to training loads and recovery protocols. …”
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Efficiency in the classification of chest X-ray images through generative parallelization of the Neural Architecture Search by Felix Mejía Cajicá, John Anderson García Henao, Carlos Jaime Barrios Hernandéz, Michel Riveill

“…Training on 187,641 images, the sequential algorithm took 190.2 hours for an AUC-ROC of 0.869. …”
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Deep Learning-Based Tumor Segmentation of Murine Magnetic Resonance Images of Prostate Cancer Patient-Derived Xenografts by Satvik Nayak, Henry Salkever, Ernesto Diaz, Avantika Sinha, Nikhil Deveshwar, Madeline Hess, Matthew Gibbons, Sule Sahin, Abhejit Rajagopal, Peder E. Z. Larson, Renuka Sriram

“…Multiple combinations of the algorithm and training images for different sites were evaluated for inference quality. …”
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Pavement Diagnosis Accuracy With Controlled Application of Artificial Neural Network by Andrzej Pożarycki

“…By default, the artificial neural network training set has not included the reinforced pavement sections. …”
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Combination of dynamic TOPMODEL and machine learning techniques to improve runoff prediction by Pin‐Chun Huang

“…An innovative training method is suggested to elevate the model's performance by integrating the Long Short‐Term Memory (LSTM) algorithm and a topological classification, which relies on the evolving spatial distribution of runoff conditions during floods. …”
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Challenges of Vehicle Classification Using Acoustics by Raiden Evans, Stephanie Mullins, George Noel

“…The work presented also evaluates classification performance on combinations of acoustic feature sets and common machine learning algorithms. …”
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Effective human–object interaction recognition for edge devices in intelligent space by Haruhiro Ozaki, Dinh Tuan Tran, Joo-Ho Lee

“…In the experiment, a scenario in which a human is working on a desk is simulated and an algorithm is trained on object-specific interactions. …”
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Prediction model for the selection of patients with glioma to proton therapy by Jesper Folsted Kallehauge, Siri Grondahl, Camilla Skinnerup Byskov, Morten Høyer, Slavka Lukacova

“…Prediction models were built using logistic regression algorithms and support vector machines (SVMs) and evaluated using the area under the precision-recall curve (AUC-PR). …”
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Optimizing Metro Passenger Flow Prediction: Integrating Machine Learning and Time-Series Analysis with Multimodal Data Fusion by Li Wan, Wenzhi Cheng, Jie Yang

“…Subsequently, the decomposed data from STL and EEMD are partitioned into training and test sets and normalized. The training set is utilized to train the model for optimal performance in predicting subway short-time passenger flow. …”
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Integrating CEUS Imaging Features and LI-RADS Classification for Postoperative Early Recurrence Prediction in Solitary Hepatocellular Carcinoma: A Machine Learning-Based Prognostic... by Liang L, Pang J, Zhang B, Que Q, Gao R, Wu Y, Peng J, Zhang W, Bai X, Wen R, He Y, Yang H

“…Patients were randomly assigned to training (n = 196) and validation (n = 83) cohorts in a 7:3 ratio. …”
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Deep learning-assisted screening and diagnosis of scoliosis: segmentation of bare-back images via an attention-enhanced convolutional neural network by Xingyu Duan, Xiaojun Ma, Mengqi Zhu, Linan Wang, Dingqi You, Lili Deng, Ningkui Niu

“…The performance of the model was evaluated in the test set. After automatic segmentation of the back contour, a back asymmetry index was calculated via computer vision algorithms to classify scoliosis into different severities. …”
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Engineered feature embeddings meet deep learning: A novel strategy to improve bone marrow cell classification and model transparency by Jonathan Tarquino, Jhonathan Rodríguez, David Becerra, Lucia Roa-Peña, Eduardo Romero

“…Deep learning algorithms are a promising approach to automate this bone marrow cell evaluation. …”
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Development of a postoperative recurrence prediction model for stage Ⅰ non-small cell lung cancer patients using multimodal data based on machine learning by ZHANG Di, WU Yi, XU Yu

“…Among 6 machine learning algorithms, the adaptive boosting (Adaboost) model demonstrated the best overall predictive performance, with an area under the curve (AUC) of 0.866 (95% CI: 0.808~0.923; accuracy: 0.832, specificity: 0.884) in the training set and of 0.806 (95% CI: 0.630~0.983; accuracy: 0.795, specificity: 0.971) in the validation set. …”
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AI-aided short-term decision making of rockburst damage scale in underground engineering by Chukwuemeka Daniel, Shouye Cheng, Xin Yin, Zakaria Mohamed Barrie, Yucong Pan, Quansheng Liu, Feng Gao, Minsheng Li, Xing Huang

“…BO-RF model also ranked top in a multi-criteria evaluation framework. This devised ranking system underscores the importance of evaluating model performance on both training and unseen testing data to ensure robust generalization. …”
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Rapid prediction of poly(butylene adipate-co-terephthalate)/poly(glycolic acid) (PBAT/PGA) agricultural films based on UV-accelerated aging tests with applicability to the environm... by Zihan Jia, Minglong Li, Bo Wang, Dongsheng Li, Peng Guo, Mingfu Lyu, Zhiyong Wei, Lin Sang

“…Due to the UV-accelerated aging experimental conditions were well matched with natural environmental factors, the data derived from UAD and NED were highly correlated, indicating the feasibility of predicting film properties based on the UAD test. Random forest algorithm displayed superior stability and high accuracy in constructing degradation prediction model, achieving R2 of 0.984 and 0.979 for training and test sets, respectively. …”
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Predicting distant metastasis of bladder cancer using multiple machine learning models: a study based on the SEER database with external validation by Xin Chang Zou, Xue Peng Rao, Jian Biao Huang, Jie Zhou, Hai Chao Chao, Tao Zeng

“…Features were filtered using the least absolute shrinkage and selection operator (LASSO) regression algorithm. Based on the significant features identified, three ML algorithms were utilized to develop prediction models: logistic regression, support vector machine (SVM), and linear discriminant analysis (LDA). …”
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Risk factors and an interpretability tool of in-hospital mortality in critically ill patients with acute myocardial infarction by Rui Yang, Tao Huang, Renqi Yao, Di Wang, Yang Hu, Longbing Ren, Shaojie Li, Yali Zhao, Zhijun Dai

“…Lasso regression was used to build a compact model. Seven evaluation methods, PR and ROC curves were used to assess the model. …”
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Predicting the Displacement of Single Battered Pile in Sandy Soil under Pullout Loading Using Artificial Neural Network by Ahmed Mohammed Najemalden

“…The performance of the Artificial Neural Network (ANN) algorithm was evaluated using the Mean Squared Error (MSE) and the Coefficient of Determination (R^2). …”
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Incorporating Wave-ViT for Breast Cancer Diagnosis Using MRI Imaging by Sahil Mahey, Hamid Usefi

“…These included randomized training and testing splits using the Fisher-Yates shuffle, exploration of different Wave-ViT variants, and testing across multiple training set configurations. …”
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Development of Machine Learning–Based Risk Prediction Models to Predict Rapid Weight Gain in Infants: Analysis of Seven Cohorts by Miaobing Zheng, Yuxin Zhang, Rachel A Laws, Peter Vuillermin, Jodie Dodd, Li Ming Wen, Louise A Baur, Rachael Taylor, Rebecca Byrne, Anne-Louise Ponsonby, Kylie D Hesketh

“…ResultsThe average prevalence of infant RWG was 27%. In the training dataset, all ML algorithms showed acceptable to excellent discrimination with AUCs ranging from 0.75 to 0.86. …”
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