Conformal prediction quantifies wearable cuffless blood pressure with certainty by Zhan Shen, Tapabrata Chakraborti, Christopher R. S. Banerji, Xiaorong Ding

“…First, a quantile loss-based Gradient Boosting Regression Tree (GBRT) model was trained to obtain ambulatory BP estimates along with model uncertainty. …”
Get full text

Permeability Predictions for Tight Sandstone Reservoir Using Explainable Machine Learning and Particle Swarm Optimization by Jing-Jing Liu, Jian-Chao Liu

“…The particle swarm optimization algorithm is then used to optimize the hyperparameters of the XGBoost model. …”
Get full text

A Ship Underwater Radiated Noise Prediction Method Based on Semi-Supervised Ensemble Learning by Xin Huang, Rongwu Xu, Ruibiao Li

“…Second, a semi-supervised ensemble (ESS) framework integrating dynamic pseudo-label screening and uncertainty bias correction (UBC) is established, which can dynamically select pseudo-labels based on local prediction performance improvement and reduce the influence of pseudo-labels’ uncertainty on the model. …”
Get full text

Prognostic prediction of gastric cancer based on H&E findings and machine learning pathomics by Guoda Han, Xu Liu, Tian Gao, Lei Zhang, Xiaoling Zhang, Xiaonan Wei, Yecheng Lin, Bohong Yin

“…Features selected via minimum Redundancy - Maximum Relevance (mRMR)- recursive feature elimination (RFE) screening were used to train a model using the Gradient Boosting Machine (GBM) algorithm. …”
Get full text

Fecal occult blood affects intestinal microbial community structure in colorectal cancer by Wu Guodong, Wu Yinhang, Wu Xinyue, Shen Hong, Chu Jian, Qu Zhanbo, Han Shuwen

“…Characteristic gut bacteria were screened, and various machine learning algorithms were applied to construct CRC risk prediction models. …”
Get full text

RRBM-YOLO: Research on Efficient and Lightweight Convolutional Neural Networks for Underground Coal Gangue Identification by Yutong Wang, Ziming Kou, Cong Han, Yuchen Qin

“…Coal gangue identification is the primary step in coal flow initial screening, which mainly faces problems such as low identification efficiency, complex algorithms, and high hardware requirements. …”
Get full text

Explainable illicit drug abuse prediction using hematological differences by Aijun Chen, Yinchu Shen, Yu Xu, Jinhui Cai, Bo Ye, Jiaxue Sun, Jinze Du, Deshenyue Kong

“…Abstract This study aimed to develop a reliable and explainable predictive model for illicit drug use (IDU). The model uses a machine learning (ML) algorithm to predict IDU using hematological differences between illicit drug users (IDUr) and non-users (n-IDUr). …”
Get full text

FedeAMR-CFF: A Federated Automatic Modulation Recognition Method Based on Characteristic Feature Fine-Tuning by Meng Zhang, Jiankun Ma, Zhenxi Zhang, Feng Zhou

“…Specifically, the clients extract representative features through distance-based metric screening, and the server aggregates model parameters via the FedAvg algorithm and fine-tunes the model using the collected features. …”
Get full text

Edge Artificial Intelligence (AI) for real-time automatic quantification of filariasis in mobile microscopy. by Lin Lin, Elena Dacal, Nuria Díez, Claudia Carmona, Alexandra Martin Ramirez, Lourdes Barón Argos, David Bermejo-Peláez, Carla Caballero, Daniel Cuadrado, Oscar Darias-Plasencia, Jaime García-Villena, Alexander Bakardjiev, Maria Postigo, Ethan Recalde-Jaramillo, Maria Flores-Chavez, Andrés Santos, María Jesús Ledesma-Carbayo, José M Rubio, Miguel Luengo-Oroz

“…It achieved an overall precision of 94.14%, recall of 91.90% and F1 score of 93.01% for the screening algorithm and 95.46%, 97.81% and 96.62% for the species differentiation algorithm respectively. …”
Get full text

A Three-Dimensional Phenotype Extraction Method Based on Point Cloud Segmentation for All-Period Cotton Multiple Organs by Pengyu Chu, Bo Han, Qiang Guo, Yiping Wan, Jingjing Zhang

“…Experimental data show that, in the task of organ segmentation throughout the entire cotton growth cycle, the ResDGCNN model achieved a segmentation accuracy of 67.55%, with a 4.86% improvement in mIoU compared to the baseline model. …”
Get full text

Explainable machine learning for predicting lung metastasis of colorectal cancer by Zhentian Guo, Zongming Zhang, Limin Liu, Yue Zhao, Zhuo Liu, Chong Zhang, Hui Qi, Jinqiu Feng, Peijie Yao

“…We selected the best algorithm and visualized it using SHAP. We conducted a validation of the model utilizing data from a Chinese hospital to assess its practicality. …”
Get full text

A Fuzzy Supplier Selection Application Using Large Survey Datasets of Delivery Performance by Jonathan Davis, Margaret F. Shipley, Gary Stading

“…A model is developed using fuzzy probability to screen survey data across relevant criteria for selecting suppliers based on fuzzy expected values. …”
Get full text

Perceived age estimation from facial image and demographic data in young and middle-aged South Korean adults by Ilkoo Ahn, Younghwa Baek, Bok-Nam Seo, Su Eun Lim, Kyoungsik Jung, Ho Seok Kim, Jeongkyun Kim, Sukyung Lee, Siwoo Lee

“…The averaging models of Lasso, XGBoost, and CatBoost showed a mean absolute error of 2.2944, indicating that this algorithm can be used as a screening method for general health status in the population.…”
Get full text

3D Morphology Distribution Characteristics and Discrete Element Simulation of Sand-Gravel Mixtures by Wu Qi, Sun Suyu, Gao Guangliang, Fang Yi, Chen Guoxing

“…Retrospective simulation of the laboratory tests using the proposed model showed good agreement, and the reliability of the model is effectively verified. …”
Get full text

The Marine Safety Simulation based Electronic Chart Display and Information System by Xin Yu Zhang, Yong Yin, Jin YiCheng, XiaoFeng Sun, Ren HongXiang

“…The man-machine conversation method is taken to amend planned route to obtain autodeciding of feasibility according to ECDIS information, and the route monitoring algorithm is improved by enhancing its precision caused by screen coordinate conversion. …”
Get full text

Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response by Sha-Zhou Li, Hai-Ying Sun, Yuan Tian, Liu-Qing Zhou, Tao Zhou

“…Therefore, the identification of reliable biomarker is crucial to enhance the accuracy of screening and treatment strategies for HNSCC.MethodTo develop and identify a machine learning-derived prognostic model (MLDPM) for HNSCC, ten machine learning algorithms, namely CoxBoost, elastic network (Enet), generalized boosted regression modeling (GBM), Lasso, Ridge, partial least squares regression for Cox (plsRcox), random survival forest (RSF), stepwise Cox, supervised principal components (SuperPC), and survival support vector machine (survival-SVM), along with 81 algorithm combinations were utilized. …”
Get full text

A clinical scoring system to prioritise investigation for tuberculosis among adults attending HIV clinics in South Africa. by Yasmeen Hanifa, Katherine L Fielding, Violet N Chihota, Lungiswa Adonis, Salome Charalambous, Nicola Foster, Alan Karstaedt, Kerrigan McCarthy, Mark P Nicol, Nontobeko T Ndlovu, Edina Sinanovic, Faieza Sahid, Wendy Stevens, Anna Vassall, Gavin J Churchyard, Alison D Grant

“…<h4>Participants</h4>Representative sample of adult HIV clinic attendees; data from participants reporting ≥1 symptom on the WHO screening tool were split 50:50 to derive, then internally validate, a prediction model.…”
Get full text

Road Perception for Autonomous Driving: Pothole Detection in Complex Environments Based on Improved YOLOv8 by Siyuan Kong, Qiao Meng, Xin Li, Zhijie Wang, Xin Liu, Bingyu Li

“…To address this, this paper proposes an innovative improved algorithm, which is based on the YOLOv8 model, and introduces the MSF-HFEB module in the innovative design. …”
Get full text

Cheetah optimized CNN: A bio-inspired neural network for automated diabetic retinopathy detection by V. K. U. Ahamed Gani, N. Shanmugasundaram

“…The proposed CO-CNN approach shows superior performance compared to that of state-of-the-art methods, offering potential applications in telemedicine, treatment planning, early detection, screening, and patient education. Integrating fuzzy logic enhances the model’s interpretability and robustness, paving the way for improved healthcare outcomes in diabetic retinopathy management.…”
Get full text

Cost-effectiveness analysis of best management practices for non-point source pollution in watersheds: A review by CHANG Jian, YU Jie, WANG Fei’er, ZHENG Siyuan

“…According to the accounting results, two optimization criteria, namely cost minimization and benefit maximization, were employed to screen for the most cost effective measures. Application of cost-effectiveness analysis method included three categories, coupling NPS model with empirical calculation methods, coupling NPS model with economic model and cost-effectiveness analysis based on optimization algorithm. …”
Get full text