Optimizing Virtual Power Plants with Parallel Simulated Annealing on High-Performance Computing by Ali Abbasi, Filipe Alves, Rui A. Ribeiro, João L. Sobral, Ricardo Rodrigues

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Artificial Intelligence in Orthopedic Surgery: Current Applications, Challenges, and Future Directions by Fei Han, Xiao Huang, Xin Wang, Yong‐feng Chen, Chuang Lu, Shasha Li, Lu Lu, Da‐Wei Zhang

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Creating prognostic systems for cancer patients: A demonstration using breast cancer by Mathew T. Hueman, Huan Wang, Charles Q. Yang, Li Sheng, Donald E. Henson, Arnold M. Schwartz, Dechang Chen

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Contextual Regularization-Based Energy Optimization for Segmenting Breast Tumor in DCE-MRI by Priyadharshini Babu, Mythili Asaithambi, Sudhakar Mogappair Suriyakumar

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Loan classification using logistic regression by U. I. Behunkou, M. Y. Kovalyov

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Establishment and validation of a combined diagnostic model for aldosterone-producing adenoma of the adrenal gland based on CT radiomics and clinical features by ZHANG Mingquan, LIU Jingjing, LIN Xin, FU Min, FENG Ying, CHEN Jingjing

“…The Pearson correlation coefficient and the least absolute shrinkage and selection ope-rator (LASSO) algorithm were used to identify the radiomic features on the plain CT and contrast-enhanced CT images of the adrenal gland, and a CT radiomic model was established. …”
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Improving early epidemiological assessment of emerging Aedes-transmitted epidemics using historical data. by Julien Riou, Chiara Poletto, Pierre-Yves Boëlle

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Development and validation of a radiomics-based nomogram for predicting pathological grade of upper urinary tract urothelial carcinoma by Yanghuang Zheng, Hongjin Shi, Shi Fu, Haifeng Wang, Xin Li, Zhi Li, Bing Hai, Jinsong Zhang

“…The maximum relevance minimum redundancy algorithm, least absolute shrinkage and selection operator, and various machine learning (ML) algorithms—including random forest, support vector machine, and eXtreme gradient boosting—were employed to select radiomics features and calculate radiomics scores. …”
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UAV Swarm Confrontation Using Hierarchical Multiagent Reinforcement Learning by Baolai Wang, Shengang Li, Xianzhong Gao, Tao Xie

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A data-driven approach to solve the RT scheduling problem by Mruga Gurjar, Jesper Lindberg, Thomas Björk-Eriksson, Caroline Olsson

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The relationship between epigenetic biomarkers and the risk of diabetes and cancer: a machine learning modeling approach by Shiqi Zhang, Shiqi Zhang, Jianan Jin, Benfeng Xu, Qi Zheng, Haibo Mou

“…Nine machine learning algorithms were used to build models: AdaBoost, GBM, KNN, lightGBM, MLP, RF, SVM, XGBoost, and logistics. …”
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Investigating the Use of Electrooculography Sensors to Detect Stress During Working Activities by Alessandra Papetti, Marianna Ciccarelli, Andrea Manni, Andrea Caroppo, Gabriele Rescio

“…Employing supervised machine learning (ML) algorithms—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbors (KNN)—the analysis revealed accuracy rates exceeding 80%, with RF leading at 85.8% and 82.4% for two classes and three classes, respectively. …”
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Predicting postoperative trauma-induced coagulopathy in patients with severe injuries by machine learning by Xiaohui Du, Wei Wang, Bo Xu, Jiang Zheng, Victor W. Xia, Yi Guo, Shuai Feng, Qingxiang Mao, Hong Fu

“…The study employed various machine learning algorithms, including random forests, logistic regression, gradient boosting decision trees, support vector machines, backpropagation artificial neural networks, extreme gradient boosting, and naïve Bayes. …”
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An MRI-based fusion model for preoperative prediction of perineural invasion status in patients with intrahepatic cholangiocarcinoma by Zuochao Qi, Hao Yuan, Qingshan Li, Pengyu Chen, Dongxiao Li, Kunlun Chen, Bo Meng, Peigang Ning, Haibo Yu, Deyu Li

“…Methods A retrospective collection of 192 ICC patients from three medical centers (training set: n = 147; external test set: n = 45) was performed. …”
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A non-invasive nomogram for predicting heart failure with preserved ejection fraction in taiwanese outpatients with unexplained dyspnea and fatigue by Yi-Wei Chung, Jen-Fang Cheng, Yen-Liang Lin, Hung-Jui Chuang, Chia-Chuan Chuang, Cheng-Wei Chen, Wei-Ming Huang, Cho-Kai Wu, Lian-Yu Lin

“…The nomogram's performance was assessed and validated using the concordance index (C-index), area under the curve (AUC), calibration curves, and decision curve analysis. Results: Multivariate logistic regression analyses identified five independent noninvasive variables for developing an HFpEF nomogram, including dyslipidemia (OR = 5.264, p = 0.010), diabetes (OR = 3.929, p = 0.050), left atrial area (OR = 1.130, p = 0.046), hemoglobin <13 g/dL (OR = 5.372, p = 0.010), and NT-proBNP ≥245 pg/mL (OR = 5.108, p = 0.027). …”
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Electrophysiological-based automatic subgroups diagnosis of patients with chronic dysimmune polyneuropathies by Sara Ballanti, Piergiuseppe Liuzzi, Paolo Luca Mattiolo, Maenia Scarpino, Sabrina Matà, Bahia Hakiki, Francesca Cecchi, Calogero Maria Oddo, Andrea Mannini, Antonello Grippo

“…Five different classification algorithms based on electrophysiological data (conduction velocity, latency, and amplitude of sensory and motor responses from different nerves) were implemented to classify three types of neuropathies and identify discriminative neurographic parameters. …”
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Multimodal data-driven prognostic model for predicting long-term outcomes in older adult patients with sarcopenia: a retrospective cohort study by Mengdie Liu, Wen Guo, Jin Peng, Jinhui Wu

“…Feature selection was performed using Lasso Regression, XGBoost, and Random Forest machine learning algorithms, and a nomogram model was developed using univariate and multivariate Cox regression analyses, with validation of its accuracy, concordance, and clinical applicability.ResultsA total of 12 feature variables were identified through the combined use of three machine learning methods. …”
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Machine-Learning-Based Biomechanical Feature Analysis for Orthopedic Patient Classification with Disc Hernia and Spondylolisthesis by Daniel Nasef, Demarcus Nasef, Viola Sawiris, Peter Girgis, Milan Toma

“…The second task further classifies patients into three groups: Normal, Disc Hernia, and Spondylolisthesis (3C). …”
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MANAGEMENT AND ORGANIZATIONAL AND ECONOMIC CONDITIONS OF STRENGTHENING THE MARKETING ACTIVITY OF THE ENTERPRISE AND MAINTAINING EFFICIENT AGRO BUSINESS by L. Lomovskykh, M. Ponomarova, L. Chip, E. Krivosheya, O. Lisova

“…JEL Classification M11, M31, Q13 Formulas: 1; fig.: 3; tabl.: 0; bibl.: 22. …”
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Appropriateness of NHS 111 Wales outcomes—using the Call Prioritisation Streaming System: a RAND/UCLA modified Delphi method by Craig Brown, Conrad Fivaz, Rebecca Malin, Mike Brady, Rhiannon Elizabeth Roynon, Peter Noblett

“…CPSS is a sophisticated Computer Decision Support Software designed to enhance decision-making processes. …”
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