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. …”
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Wavelet Transform-Based 3D Landscape Design and Optimization for Digital Cities by Yang Chen, Xiaolin Wang, Chang Zhang

“…The algorithm extracts mixed feature information of local long path and local short path based on the information retention module, and decomposes the information by combining wavelet transform, inputs the different components obtained from the decomposition into the network for training, and removes the noise by subsequent feature screening of the network structure. …”
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Multi-Class Classification Using Improved Mahalanobis-Taguchi System Based on Binary Tree and Its Application by Niu Junlei

“…Aiming at the inadequacy of Mahalanobis-Taguchi System(MTS), an improved MTS optimization model(MTSO) is proposed. The core idea is that a number of optimization objectives are proposed based on the purpose and characteristics of the data classification problem and optimization model is used for screening important variables instead of orthogonal arrays and signal-noise-ratio. …”
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Breast cancer detection and classification with digital breast tomosynthesis: a two-stage deep learning approach by Yazeed Alashban

“…CLINICAL SIGNIFICANCE: The proposed two-tier DL algorithm, combining a modified VGG19 model for image classification and YOLOv5-CBAM for lesion detection, can improve the accuracy, efficiency, and reliability of breast cancer screening and diagnosis through innovative artificial intelligence-driven methodologies.…”
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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. …”
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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. …”
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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). …”
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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. …”
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Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer by Pu Zhou, Pu Zhou, Hongyan Qian, Pengfei Zhu, Jiangyuan Ben, Jiangyuan Ben, Guifang Chen, Qiuyi Chen, Lingli Chen, Jia Chen, Ying He, Ying He

“…Subsequently, construction of clinical predictive models and Rad score joint clinical predictive models using ML algorithms for optimal diagnostic performance. …”
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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. …”
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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. …”
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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. …”
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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. …”
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A prospective multicenter randomized controlled trial on artificial intelligence assisted colonoscopy for enhanced polyp detection by Dong Kyun Park, Eui Joo Kim, Jong Pil Im, Hyun Lim, Yun Jeong Lim, Jeong-Sik Byeon, Kyoung Oh Kim, Jun-Won Chung, Yoon Jae Kim

“…Abstract Colon polyp detection and removal via colonoscopy are essential for colorectal cancer screening and prevention. This study aimed to develop a colon polyp detection program based on the RetinaNet algorithm and verify its clinical utility. …”
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A phase separation-related gene signature for prognosis prediction and immunotherapy response evaluation in gastric cancer with targeted natural compound discovery by Yanjuan Jia, Yuanyuan Ma, Zhenhao Li, Wenze Zhang, Rukun Lu, Wanxia Wang, Chaojun Wei, Chunyan Wei, Yonghong Li, Xiaoling Gao, Tao Qu

“…Immune checkpoint inhibitor (ICI) response between PS-related high- and low-risk groups was evaluated using TIDE algorithm scores. Potential therapeutic agents targeting signature genes were screened via Connectivity Map and HERB database analyses, followed by molecular docking validation. …”
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Development of a method for differential diagnosis of iron deficiency anemia and anemia of chronic disease based on demographic data and routine laboratory tests using machine lear... by N. V. Varekha, N. I. Stuklov, K. V. Gordienko, R. R. Gimadiev, O. B. Shchegolev, S. N. Kislaya, E. V. Gubina, A. A. Gurkina

“…The study of machine learning methods, a branch of artificial intelligence science, is relevant for the development of optimal screening strategies, identification of risk groups, and application of less expensive and more accessible laboratory tests to assess the body iron status.   …”
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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. …”
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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.…”
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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. …”
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Research on predicting the risk level of coal mine roof accident based on machine learning by Zhao-Yang Guan, Jin-Ling Xie, Shen-Kuang Wu, Chao Liang

“…Finally, KNN, SVM and DT algorithms are used to evaluate the model performance. …”
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