Identification of biomarkers in Alzheimer's disease and COVID-19 by bioinformatics combining single-cell data analysis and machine learning algorithms. by Juntu Li, Linfeng Tao, Yanyou Zhou, Yue Zhu, Chao Li, Yiyuan Pan, Ping Yao, Xuefeng Qian, Jun Liu

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Advanced neural network modeling with Levenberg–Marquardt algorithm for optimizing tri-hybrid nanofluid dynamics in solar HVAC systems by A. Aziz, S.A.H. Shah, H.M.S. Bahaidarah, T. Zamir, T. Aziz

“…The nonlinear differential equations derived from the physical model are solved using the three-step Lobatto IIIa method, ensuring precision and reliability. …”
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Screening potential diagnostic biomarkers for PLA2R‑associated idiopathic membranous nephropathy by WGCNA analysis and LASSO algorithm by Jinxu Huang, Yaqing Huang, Xiaoling Zeng, Yuhong Zhang, Junneng Zhang, Qingchu Hong, Yongtiao Peng

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Evolution, reconfiguration and low-carbon performance of green space pattern under diverse urban development scenarios: A machine learning-based simulation approach by Yujie Ren, Mengdie Zhou, Antian Zhu, Shucheng Shi, Hao Zhu, Yuzhu Chen, Shanshan Li, Tianhui Fan

“…In this study, we applied machine learning algorithms to model the non-linear relationships and threshold effects between green space evolution and carbon emissions/sequestration at different stages of ecological restoration in the Yangtze River Basin, China. …”
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Optimization for Nonlinear Uncertain Switched Stochastic Systems with Initial State Difference in Batch Culture Process by Jinlong Yuan, Jun Xie, Honglei Xu, Enmin Feng, Zhilong Xiu

“…Considering both the difficulty of finding analytical solutions and the complex nature of these subproblems, we develop a parallelized differential evolution (DE) algorithm to solve these approximation subproblems. …”
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Mesenteric panniculitis in rheumatologist practice by O. N. Egorova, A. V. Datsina

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A comparative analysis of emotion recognition from EEG signals using temporal features and hyperparameter-tuned machine learning techniques by Rabita Hasan, Sheikh Md. Rabiul Islam

“…Furthermore, cross-subject evaluation on the SEED dataset reinforced the approach’s robustness, where XGBoost achieved 86 % accuracy using HFD and 84 % using DE. …”
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Risk prediction and effect evaluation of complicated appendicitis based on XGBoost modeling by Sunmeng Chen, Jianfu Xia, Beibei Xu, Yi Huang, Miaomiao Teng, Juyi Pan

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Advanced music classification using a combination of capsule neural network by upgraded ideal gas molecular movement algorithm by Peiyan Chen, Jichi Zhang, Arsam Mashhadi

“…To address this issue, this research proposes a novel methodology that combines CapsNet with an upgraded version of the Ideal Gas Molecular Movement (UIGMM) optimization algorithm. By utilizing the UIGMM algorithm, the parameters of the CapsNet model can be fine-tuned, thereby enhancing its ability to accurately recognize and classify different music genres. …”
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Supervised machine learning algorithms for the classification of obesity levels using anthropometric indices derived from bioelectrical impedance analysis by Rodrigo Yáñez-Sepúlveda, Aldo Vásquez-Bonilla, Rodrigo Olivares, Pablo Olivares, Juan Pablo Zavala-Crichton, Claudio Hinojosa-Torres, Catalina Muñoz-Strale, Frano Giakoni-Ramírez, Josivaldo de Souza-Lima, Jacqueline Páez-Herrera, Jorge Olivares-Arancibia, Tomás Reyes-Amigo, Guillermo Cortés-Roco, Juan Hurtado-Almonacid, Eduardo Guzmán-Muñoz, Nicole Aguilera-Martínez, José Francisco López-Gil, Boryi A. Becerra-Patiño, Juan David Paucar-Uribe, Exal Garcia-Carrillo, Vicente Javier Clemente-Suárez

“…Traditional anthropometric measures such as body mass index (BMI) have limitations in differentiating between fat and lean mass. This study aimed to evaluate and compare the performance of various supervised machine learning algorithms in classifying obesity levels using anthropometric indices derived from bioelectrical impedance analysis (BIA). …”
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Identification and verification of mitochondria-related genes biomarkers associated with immune infiltration for COPD using WGCNA and machine learning algorithms by Meijuan Peng, Chen Jiang, Ziyu Dai, Bin Xie, Qiong Chen, Jianing Lin

“…Using 12 distinct machine learning algorithms (comprising 143 predictive models), we identified the optimal diagnostic model, which includes five pivotal MitoDEGs: ERN1, FASTK, HIGD1B, NDUFA7 and NDUFB7. …”
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Evolution and taxonomic classification of alphapapillomavirus 7 complete genomes: HPV18, HPV39, HPV45, HPV59, HPV68 and HPV70. by Zigui Chen, Mark Schiffman, Rolando Herrero, Rob DeSalle, Kathryn Anastos, Michel Segondy, Vikrant V Sahasrabuddhe, Patti E Gravitt, Ann W Hsing, Robert D Burk

“…To study viral heterogeneity, the complete 8 kb genome of 128 isolates, including 109 sequenced for this analysis, were annotated and analyzed. Viral evolution was characterized by constructing phylogenic trees using maximum-likelihood and Bayesian algorithms. …”
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H-NMR metabolomics identifies three distinct metabolic profiles differentially associated with cardiometabolic risk in patients with obesity in the Di@bet.es cohort by Enrique Ozcariz, Montse Guardiola, Núria Amigó, Sergio Valdés, Wasima Oualla-Bachiri, Pere Rehues, Gemma Rojo-Martinez, Josep Ribalta

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Protection Algorithm Based on Two-Dimensional Spatial Current Trajectory Image and Deep Learning for Transmission Lines Connecting Photovoltaic Stations by Panrun Jin, Jianling Liao, Wenqin Song, Xushan Zhao, Yankui Zhang

“…A data-enhanced convolutional neural network (A-CNN) based on cross-overlapping data sets is used to identify trajectory features and locate faults. After performance evaluation, the protection algorithm has the advantages of adapting to new energy access, resisting transition resistance, and robustness to current transformer (CT) saturation, and outliers.…”
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High-Performance Deployment Operational Data Analytics of Pre-Trained Multi-Label Classification Architectures with Differential-Evolution-Based Hyperparameter Optimization (AutoDE... by Teo Prica, Aleš Zamuda

“…In this differential evolution (DE) optimization use case, we analyze how energy efficiently the DE algorithm performs with different DE strategies and ML models. …”
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Enhanced NSGA-II algorithm based on novel hybrid crossover operator to optimise water supply and ecology of Fenhe reservoir operation by Qinglai Xiong, Ling Dong, Hu Chen, Xueping Zhu, Xuehua Zhao, Xuerui Gao

“…This study addresses bi-objective optimisation problems by proposing a novel crossover evolution operator, known as the hybrid simulated binary and improved arithmetic crossover (SBAX) operator, based on the simulated binary cross (SBX) and arithmetic crossover operators, and applies it to the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) algorithm to improve the algorithm. …”
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Research progress on shale gas productivity evaluation: concepts, methods and future directions by ZHU Suyang, PENG Zhen, DI Yunting, PENG Xiaolong, LIU Dongchen, GUAN Wenjie

“…The results revealed that shale gas productivity was highly stage-dependent, with substantial variations in dominant controlling factors, flow mechanisms, and flow regimes across different production stages. Early and late production stages exhibit distinct controlling factors, leading to differentiated perspectives across the various evaluation methods. …”
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Toward a learnable Artificial Intelligence Model for Aerosol Chemistry and Interactions (AIMACI) based on the Multi-Head Self-Attention algorithm by Z. Xia, C. Zhao, C. Zhao, C. Zhao, Z. Yang, Q. Du, J. Feng, C. Jin, J. Shi, H. An, H. An

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Criteria for Psychological Evaluation of Legally Relevant Emotional States of Defendants: Methodological Recommendations by F. S. Safuanov, O. F. Savina, M. V. Morozova, I. V. Isaeva

“…An algorithm of forensic psychological evaluation of the defendant's emotional states is proposed, together with examples of sample wording of expert conclusions, and their legal meaning.…”
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Identification of novel metabolism-related biomarkers of Kawasaki disease by integrating single-cell RNA sequencing analysis and machine learning algorithms by Chenhui Feng, Zhimiao Wei, Xiaohui Li, Xiaohui Li

“…We analyzed the cellular heterogeneity of BAM and FAM at the single-cell level using R packages. Through differential expressed genes (DEG) analysis, high-dimensional Weighted Correlation Network Analysis (hdWGCNA) and machine learning algorithms, we identified signature genes associated with both BAM and FAM. …”
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