Identifying potential three key targets gene for septic shock in children using bioinformatics and machine learning methods by Wei Guo, Hao Chen, Feng Wang, Yingjiao Chi, Wei Zhang, Shan Wang, Kezhu Chen, Hong Chen

“…Three machine learning algorithms LASSO, random forest (RF), and support vector machine recursive feature elimination (SVM-RFE) were used to finally screen out three core genes: CD163, MCEMP1 and RETN. …”
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Modeling Worldwide Tree Biodiversity Using Canopy Structure Metrics from Global Ecosystem Dynamics Investigation Data by Jin Xu, Kjirsten Coleman, Volker C. Radeloff, Melissa Songer, Qiongyu Huang

“…Using Forest Global Earth Observatory (ForestGEO) data, we developed three models using the random forest algorithm to predict global tree species richness across climate zones, including a dynamic habitat index (DHI)-only model, a GEDI-only model, and a combined GEDI-DHI model. …”
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Artificial intelligence for severity triage based on conversations in an emergency department in Korea by Jae Won Seo, Sung-Joon Park, Young Jae Kim, Jung-Youn Kim, Kwang Gi Kim, Young-Hoon Yoon

“…To achieve this, artificial intelligence algorithms that consider the frequency and order of words used in the conversation were employed alongside neural network models. …”
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Vehicle-to-everything decision optimization and cloud control based on deep reinforcement learning by Zhenhai Gao, Dayu Liu, Chengyuan Zheng

“…In the decision-making module, deep reinforcement learning algorithms are applied to optimize decision processes by maximizing expected rewards. …”
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Methodical approaches to the economic assessment of costs in the operation of cars with axial load of 27 tons at the section Kachkanar—Smychka by G. A. Granovskaya, A. I. Safonova, O. A. Suslov, N. S. Okhotnikov

“…The article describes method of calculating the coefficient reflecting the change in the impact of cars with an axial load of 27 tons on the roadbed during transportation in estimated cars compared to transportation in equivalent cars. Algorithms for calculating changes in the cost of fuel and energy costs for train traction and maintenance of the track infrastructure on the site during the operation of trains formed from cars with an axial load of 27 tons are given, as well as methods for determining the initial data for the calculation.Authors provide values of the coefficient reflecting the change in the impact of vertical and horizontal forces on the railway line when passing freight cars with an axial load of 27 tons compared to analogue cars, and the coefficient of change of the main specific resistance to motion separately for loaded and empty cars.Developed calculation algorithms and methods for obtaining baseline data allow an economic assessment of changes in infrastructure maintenance costs and fuel and energy resources for the operation of trains formed from cars with an axial load of 27 tons compared to those formed from cars with a load of 23.5 tons at the experimental section Kachkanar—Smychka.The cost change assessment carried out in 2017 shows a generally definite economic effect, while there is a reduction in costs associated with the consumption of electricity for train traction as a result of the operation of the estimated cars in the experimental section and an increase in the cost of maintaining the track superstructure and the roadbed, which is quite expected for the conditions of the organization of traffic with increased axial loads.…”
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Monitoring the dynamics of coastal wetlands ecosystems in Brittany (France) using LANDSAT time series and machine learning by Adrien Le Guillou, Simona Niculescu

“…The study exploits the potential of satellite image time series (SITS), machine learning (ML), and Random Forest (RF) algorithms.These algorithms enable the software to learn autonomously from multiple datasets, including Landsat 4/5 and 8 SITS archive images. …”
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Enabling scalable single-cell transcriptomic analysis through distributed computing with Apache spark by Asif Adil, Namrata Bhattacharya, Aadam, Naveed Jeelani Khan, Mohammed Asger

“…We demonstrate the utility of our framework and algorithms through a series of experiments on real-world scRNA-seq data. …”
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Comparative Study of Adaptive <i>l</i>₁-Regularization for the Application of Structural Damage Diagnosis Under Seismic Excitation by Weilin Wu, Junfang Wang, Jianfu Lin, Xuanyu Liu

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Enhancing Laser-Induced Breakdown Spectroscopy Quantification Through Minimum Redundancy and Maximum Relevance-Based Feature Selection by Manping Wang, Yang Lu, Man Liu, Fuhui Cui, Rongke Gao, Feifei Wang, Xiaozhe Chen, Liandong Yu

“…This study validates the effectiveness of the mRMR algorithm for LIBS feature extraction and highlights the potential of feature selection techniques to enhance predictive accuracy. …”
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ScRNA-seq reveals novel immune-suppressive T cells and investigates CMV-TCR-T cells cytotoxicity against GBM by Wei Gao, Qing Liu, Xiang Lin, Yiming Zhao, Minghua Wu, Shuai Chen, Peng Du, Meng Huang, Cheng Huang, Xiangyu Wang, Xinmiao Long, Zuping Zhang, Yuzhe Li, Kun Deng, Xiaoling She, Minfu Zhang, Shiyi Wang, Yinfei Du

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Comprehensive multi-omics integration uncovers mitochondrial gene signatures for prognosis and personalized therapy in lung adenocarcinoma by Wenjia Zhang, Lei Zhao, Tiansheng Zheng, Lihong Fan, Kai Wang, Guoshu Li

“…By leveraging an ensemble of machine learning algorithms, we developed an Artificial Intelligence-Derived Prognostic Signature (AIDPS) model based on mitochondrial-related genes and validated its prognostic accuracy across multiple independent datasets. …”
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Weighting Optimization for Fuel Cell Hybrid Vehicles: Lifetime-Conscious Component Sizing and Energy Management by Xuanyu Xiao, Chen Shu, Huaiwei Dong, Yujun Tang, Jinfeng Feng, Hao Yuan, Shuzhan Bai, Sipeng Zhu, Guoxiang Li

“…Utilizing the dynamic programming (DP) algorithm, the total cost is optimized to derive the optimal weighting factors and component sizing, effectively addressing the multi-objective optimization problem and balancing efficiency and durability. …”
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Application of the Integral Energy Criterion and Neural Network Model for Helicopter Turboshaft Engines’ Vibration Characteristics Analysis by Serhii Vladov, Maryna Bulakh, Denys Baranovskyi, Eduard Kisiliuk, Victoria Vysotska, Maksym Romanov, Jan Czyżewski

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Characterizing and mapping the spatial variability of HIV risk among adolescent girls and young women: A cross-county analysis of population-based surveys in Eswatini, Haiti, and M... by Kristen N Brugh, Quinn Lewis, Cameron Haddad, Jon Kumaresan, Timothy Essam, Michelle S Li

“…Using geolocated survey data at enumeration clusters and high-resolution satellite imagery, we applied algorithms to predict the number and proportion of at-risk AGYW at hyperlocal levels.…”
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Impurity rates detection for pepper harvesting based on YOLOv8n-Seg-ASB and random forest by Lijian Lu, Jin Lei, Chenming Cheng, Shiguo Wang, Chengfu Wang, Xinyan Qin

“…To address the inaccuracies and inefficiencies of pepper impurity rates detection caused by complex material compositions and variable harvesting environments, this paper proposes a detection technique based on deep and machine learning algorithms. First, a machine vision-based image acquisition device for pepper material is designed to reliably capture high-quality real-time images. …”
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MODERN APPROACHES TO CLINICAL AND LABORATORY DIAGNOSTICS OF RHEUMATOID ARTHRITIS EARLY ONSET by D. G. Rekalov, S. Y. Dotsenko, A. V. Kylinich

“…The data on the sensitivity and specificity of the diagnostic classification and clinical criteria of eRA and an algorithm for the identification of the disease were presented. …”
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A Non-Invasive and Highly Accurate Multi-Wavelength Light Near-Infrared Glucose Sensor Using A Multilevel Metric Learning–Back Propagation Network by Yuwei Chen, Chenxi Li, Bo Gao, Huangrong Xu, Weixing Yu

“…Finally, the optimized data were utilized as the BP network input to predict blood glucose concentrations. The predicted results showed that the factor analysis algorithm had the best performance in our HMML-BP network and that all the predicted glucose values fell into region A, with a mean absolute relative difference of 9.98%, meeting the requirements of daily glucose monitoring. …”
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A new method for determining factors Influencing productivity of deep coalbed methane vertical cluster wells by HUANG Li, XIONG Xianyue, WANG Feng, SUN Xiongwei, ZHANG Yixin, ZHAO Longmei, SHI Shi, ZHANG Wen, ZHAO Haoyang, JI Liang, DENG Lin

“…This method leverages the advantages of multiple machine-learning algorithms, demonstrating strong operability and improving the accuracy of CBM dynamic predictions. …”
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Cerebral gray matter volume identifies healthy older drivers with a critical decline in driving safety performance using actual vehicles on a closed-circuit course by Handityo Aulia Putra, Kaechang Park, Kaechang Park, Fumio Yamashita

“…Feature selection and classification were performed using the Random Forest machine learning algorithm, optimized to identify the most predictive GM regions.ResultsOut of 114 GM regions, eleven were selected as optimal predictors: left angular gyrus, frontal operculum, occipital fusiform gyrus, parietal operculum, postcentral gyrus, planum polare, superior temporal gyrus, and right hippocampus, orbital part of the inferior frontal gyrus, posterior cingulate gyrus, and posterior orbital gyrus. …”
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Validity of the International Classification of Diseases 10th revision code for hospitalisation with hyponatraemia in elderly patients by Amit X Garg, Salimah Z Shariff, Sonja Gandhi, Jamie L Fleet, Matthew A Weir, Arsh K Jain

“…Objective To evaluate the validity of the International Classification of Diseases, 10th Revision (ICD-10) diagnosis code for hyponatraemia (E87.1) in two settings: at presentation to the emergency department and at hospital admission.Design Population-based retrospective validation study.Setting Twelve hospitals in Southwestern Ontario, Canada, from 2003 to 2010.Participants Patients aged 66 years and older with serum sodium laboratory measurements at presentation to the emergency department (n=64 581) and at hospital admission (n=64 499).Main outcome measures Sensitivity, specificity, positive predictive value and negative predictive value comparing various ICD-10 diagnostic coding algorithms for hyponatraemia to serum sodium laboratory measurements (reference standard). …”
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