Electrocardiography Denoising via Sparse Dictionary Learning from Small Datasets by Steinbrinker Tabea, Spicher Nicolai

“…Our results regarding baseline wander demonstrate that the algorithm outperforms the American Heart Association-recommended bandpass filter w.r.t. signal-to-noise ratio. …”
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DAG-based swarm learning: A secure asynchronous learning framework for Internet of Vehicles by Xiaoge Huang, Hongbo Yin, Qianbin Chen, Yu Zeng, Jianfeng Yao

“…In this paper, we propose a Directed Acyclic Graph (DAG) based Swarm Learning (DSL), which integrates edge computing, FL, and blockchain technologies to provide secure data sharing and model training in IoVs. To deal with the high mobility of vehicles, the dynamic vehicle association algorithm is introduced, which could optimize the connections between vehicles and road side units to improve the training efficiency. …”
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A novel method for power transformer fault diagnosis considering imbalanced data samples by Jun Chen, Yong Wang, Lingming Kong, Yilong Chen, Mianzhi Chen, Qian Cai, Gehao Sheng

“…We derive a sample parameter correlation quantization matrix from oil chromatography fault data using association rules, which serves as the initial value for the NCA algorithm’s training metric matrix. …”
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Adaptive Multiobjective Evolutionary Generative Adversarial Network for Metaverse Network Intrusion Detection by Dikai Xu, Bin Cao

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Seismic Failure Probability of a Curved Bridge Based on Analytical and Neural Network Approaches by K. Karimi-Moridani, P. Zarfam, M. Ghafory-Ashtiany

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Unsupervised Discretization of Continuous Variables in a Chicken Egg Quality Traits Dataset by Zeynel Cebeci, Figen Yıldız

“…Discretization is a data pre-processing task transforming continuous variables into discrete ones in order to apply some data mining algorithms such as association rules extraction and classification trees. …”
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Automated classification of online reviews of otolaryngologists by Jake G. Stenzel, Nicholas R. Schultz, Michael J. Marino

“…Using scikit‐learn, an NLP algorithm was trained and validated on the subsets, with F1 scores evaluating text classification accuracy against manual categorization. …”
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SCHOOL MODEL PATIENT IN PALLIATIVE CARE GERIATRICS: A NEW LOOK AT THE PROBLEM by I. P. Рonomareva, K. I. Рrashchayeu, A. N. Ilnitsky

“…Describes the algorithm for generating the patient as a medical technology school and social work in the provision of out-patient assistance to those elderly and senile patients with severe chronic disease and high demand for palliative care. …”
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SEMIOTICS, DIAGNOSTICS AND THERAPY TACTICS OF DEPRESSIVE DISORDERS IN CLINICAL MEDICINE by N. A. Kornetov

“…The forth module included training physicians in mental health skills. 39 theme improvements for 858 primary care setting physicians within the framework of faculty training program of physicians’ development were the part of other programs of physicians’ occupations.Results. …”
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Anoikis-related gene PDK4 and pathogenesis of type 2 diabetes mellitus： A bioinformatics-based study by ZHANG Ke, ZHANG Weiyi, SUN Haitian, CAO Mingfeng, ZHANG Xinhuan

“…Subsequently, key genes were identified using the random forest (RF) and least absolute shrinkage and selection operator (LASSO) algorithms. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated to evaluate the association strength between the expression levels of the identified key genes in pancreas islet tissues and T2DM, followed by validation in the GSE76895 dataset. …”
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Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning. by Zhuoran Wang, Anoop D Shah, A Rosemary Tate, Spiros Denaxas, John Shawe-Taylor, Harry Hemingway

“…For training the algorithm, we used texts classified as positive and negative according to their associated Read diagnostic codes, rather than by manual annotation. …”
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Target Tracking Using a Time-Varying Autoregressive Dynamic Model by Ralph J. Mcdougall, Simon J. Godsill

“…Target tracking algorithms commonly use structured dynamic models which require prior training of fixed model parameters. …”
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Assessing the generalization capabilities of TCR binding predictors via peptide distance analysis. by Leonardo V Castorina, Filippo Grazioli, Pierre Machart, Anja Mösch, Federico Errica

“…The DS algorithm controls the distance between training and testing peptides based on both sequence and structure, allowing for a more nuanced evaluation of model performance. …”
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Multi-Agent Reinforcement Learning With Action Masking for UAV-Enabled Mobile Communications by Danish Rizvi, David Boyle

“…Firstly, a weighted K-means-based clustering algorithm establishes UAV-user associations at regular intervals. …”
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Profit-Efficient Elastic Allocation of Cloud Resources Using Two-Stage Adaptive Workload Prediction by Lei Li, Xue Gao

“…First, we propose a new workload prediction method—named the adaptive two-stage multi-neural network based on long short-term memory (LSTM)—which can adaptively route prediction tasks to the corresponding LSTM sub-model according to the workload change trend (i.e., uphill and downhill categories), in order to improve the predictive accuracy. To avoid the cost associated with manual labeling of the training data, the first-order gradient feature is used with the <i>k</i>-means algorithm to cluster and label the original training data set automatically into uphill and downhill training data sets. …”
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A METHOD FOR INVESTIGATING MACHINE LEARNING ATTACKS ON ARBITER-TYPE PHYSICALLY UNCLONABLE FUNCTIONS by Yuri A. Korotaev

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Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep Learning by Jiaze Wu, Hao Liang, Changsong Ding, Xindi Huang, Jianhua Huang, Qinghua Peng

“…We aimed to investigate the determinants that can improve the accuracy of BP classification based on PPG and deep learning and establish a better algorithm for the prediction. Methods. The dataset from PhysioNet was accessed to extract raw PPG signals for testing and its corresponding BPs as category labels. …”
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Imaging-based machine learning to evaluate the severity of ischemic stroke in the middle cerebral artery territory by Gang Xie, Jin Gao, Jian Liu, Xuwei Zhou, Zhengkai Zhao, Wuli Tang, Yue Zhang, Lingfeng Zhang, Kang Li

“…Results After conducting correlation analysis, CT imaging features such as infarction side, basal ganglia area involvement, dense MCA sign, and infarction volume were found to be independently associated with NIHSS score (P < 0.05). The Logistic Regression algorithm was determined to be the optimal method for constructing the prediction model for stroke severity. …”
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Collaborative Federated Learning of Unmanned Aerial Vehicles in Space–Air–Ground Integrated Network by Huibo Li, Peng Gong, Siqi Li, Weidong Wang, Yu Liu, Xiang Gao, Dapeng Oliver Wu, Duk Kyung Kim, Guangwei Zhang, Jihao Zhang

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Auxiliary identification of depression patients using interpretable machine learning models based on heart rate variability: a retrospective study by Min Yang, Huiqin Zhang, Minglan Yu, Yunxuan Xu, Bo Xiang, Xiaopeng Yao

“…The study population was then randomly divided into training and test sets in a 7:3 ratio. Logistic regression (LR), support vector machine (SVM), random forest (RF) and eXtreme gradient boosting (XGBoost) algorithm models were used to construct risk prediction models in the training set, and the model performance was verified in the test set. …”
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