Artificial neural networks in predicting impaired bone metabolism in diabetes mellitus by S. S. Safarova

“…Growing incidence of diabetes mellitus (DM), given significant socioeconomic consequences that low-trauma fractures entail, determines a need to improve diagnostic standards and minimize the risk of medical errors, which will reduce costs and contribute to better treatment outcomes in this category of patients.Aim. …”
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Development of a manipulator kinematic model using Abb Robot Studio by Артем Бронніков, Марія Бендеберя

“…Conclusions: a model and software module were developed for the adaptive execution of the production process as part of the virtual model of the ABB IRB 1200 robot, which allows to increase the efficiency of the roduction process, reduce downtime, and improve product quality. The optimization of trajectories and collision management allowed to reduce the maintenance and operation costs of the robot, which led to an overall reduction in production costs, which will also be reflected in long-term use. …”
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An Interpretable Method for Asphalt Pavement Skid Resistance Performance Evaluation Under Sand-Accumulated Conditions Based on Multi-Scale Fractals by Yuhan Weng, Zhaoyun Sun, Huiying Liu, Yingbin Gu

“…The performance of mainstream machine learning models is compared, and the eXtreme Gradient Boosting (XGBoost) model is optimized using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. …”
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A Comprehensive Review of the Diagnostics for Pediatric Tuberculosis Based on Assay Time, Ease of Operation, and Performance by Soumya Basu, Subhra Chakraborty

“…Gaps in the existing diagnostic methods, algorithms, and operational costs were also reviewed. …”
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Deep Q-Networks for Minimizing Total Tardiness on a Single Machine by Kuan Wei Huang, Bertrand M. T. Lin

“…Due to its computational intractability, exact approaches such as dynamic programming algorithms and branch-and-bound algorithms struggle to produce optimal solutions for large-scale instances in a reasonable time. …”
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Remote Sensing Target Tracking Method Based on Super-Resolution Reconstruction and Hybrid Networks by Hongqing Wan, Sha Xu, Yali Yang, Yongfang Li

“…And obtaining high-resolution images by optimizing algorithms will save a lot of costs. Aiming at the problem of large tracking errors in remote sensing target tracking by current tracking algorithms, this paper proposes a target tracking method combined with a super-resolution hybrid network. …”
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Cardiac Computed Tomography Measurements in Pulmonary Embolism Associated with Clinical Deterioration by Anthony J. Weekes, Angela M. Pikus, Parker L. Hambright, Kelly L. Goonan, Nathaniel O’Connell

“…Introduction: Most pulmonary embolism response teams (PERT) use a radiologist-determined right ventricle to left ventricle ratio (RV:LV) cut-off of 1.0 to risk-stratify pulmonary embolism (PE) patients. …”
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Comparative Assessment of Several Effective Machine Learning Classification Methods for Maternal Health Risk by Md Nurul Raihen, Sultana Akter

“…Maternal risk analysis can improve prenatal care, improve mother and baby health, and optimize healthcare resources by identifying misclassified observations using machine learning algorithms such as LDA, QDA, KNN, Decision Tree, Random Forest, Bagging, and Support Vector Machine, all of which have a significant impact on maternity health risk assessment. …”
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A Rapid Design Method for Centrifugal Pump Impellers Based on Machine Learning by Y. Chen, W. Li, Y. Luo, L. Ji, S. Li, Y. Long

“…Variations in the cross-sectional area of the flow passage influence the internal flow state of the centrifugal pump, ultimately impacting its hydraulic efficiency. A genetic algorithm, guided by variations in the cross-sectional area of the flow passage, optimizes the blade profile, achieving an improved impeller flow path and completing the rapid design of the centrifuge. …”
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Machine learning enables legal risk assessment in internet healthcare using HIPAA data by Shixian Liu, Hailing Liu, Siyu Fan, Leming Song, Zeyu Wang

“…DNN demonstrates strong capabilities in handling complex nonlinear relationships, and XGBoost further improves classification accuracy by optimizing decision tree models through gradient boosting. …”
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Research on Measurement of Coal–Water Slurry Solid–Liquid Two-Phase Flow Based on a Coriolis Flow Meter and a Neural Network by Jie Liu, Lingfei Kong, Jiahao Ma, Xuemei Zhang, Chengjie Wang, Dongze Wu

“…The first correction results showed that the corrected error of the predictive model was 3.98%, a significant improvement compared to the 5.11% error measured by the X company’s meter. (2) Building on this, a second correction model was established through algorithm optimization, successfully reducing the corrected error of the predictive model to 1.01%. …”
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Deep Reinforcement Learning Based Transferable EMS for Hybrid Electric Trains by Yogesh Wankhede, Sheetal Rana, Faruk Kazi

“…To enhance the performance of the EMS, proposes to use of a deep reinforcement learning (DRL) algorithm specifically the deep deterministic policy gradient (DDPG) combined with transfer learning (TL) which can improve the system's efficiency when driving cycles are changed. …”
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Initialization Methods for FPGA-Based EMT Simulations by Xin Ma, Xiao-Ping Zhang

“…The performance of these four methods are also compared, and Method 4 can initialize instantly with the simplest code. To improve hardware adaptability, optimized strategies are developed for address sequence, interface, update modes and dataflow. …”
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Integration of Hash Encoding Technique with Machine Learning for Employee Turnover Prediction by Ahya Radiatul Kamila, Johanes Fernandes Andry, Francka Sakti Lee, Felliks F. Tampinongkol

“…It is part of the preprocessing stage, aiming to reduce memory usage, speed up data preprocessing, and improve model performance. After preprocessing is completed, the prediction model is trained using the Random Forest algorithm to predict employee turnover. …”
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Tribological Performance Enhancement in FDM and SLA Additive Manufacturing: Materials, Mechanisms, Surface Engineering, and Hybrid Strategies—A Holistic Review by Raja Subramani, Ronit Rosario Leon, Rajeswari Nageswaren, Maher Ali Rusho, Karthik Venkitaraman Shankar

“…Through material-level innovations, process optimization, and surface treatment techniques integration, the article provides actionable guidelines for researchers and engineers aiming at performance improvement of FDM and SLA-manufactured parts. …”
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On Flood Detection Using Dual-Polarimetric SAR Observation by Su-Young Kim, Yeji Lee, Sang-Eun Park

“…This study aims to elucidate the optimal exploitation of polarimetric scattering information in dual-pol SAR data. …”
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Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations by Yuchao Miao, Jiwei Li, Ruigang Ge, Chuanbin Xie, Yaoying Liu, Gaolong Zhang, Mingchang Miao, Shouping Xu

“…Abstract Background Accurate calculation of lung cancer dose using the Monte Carlo (MC) algorithm in CyberKnife (CK) is essential for precise planning. …”
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Enhancing phase change thermal energy storage material properties prediction with digital technologies by Minghao Yu, Jing Liu, Cheng Chen, Mingyue Li

“…Hierarchical feature fusion modules combine low-level atomistic descriptors with high-level continuum features.ResultsBenchmark evaluations show improved performance in predicting elastic modulus, thermal conductivity, and phase transition temperature across five material classes. …”
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Real-Time Common Rust Maize Leaf Disease Severity Identification and Pesticide Dose Recommendation Using Deep Neural Network by Zemzem Mohammed Megersa, Abebe Belay Adege, Faizur Rashid

“…We applied image preprocessing techniques such as image equalization, noise removal, and enhancement to improve model performance. Additionally, during training, we utilized batch normalization, dropout, and early stopping to reduce overfitting, improve accuracy, and improve execution time. …”
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Approach to knowledge management and the development of a multi-agent knowledge representation and processing system by E. I. Zaytsev, E. V. Nurmatova

“…Methods for distribution of intelligent software agents on the MKRPS nodes are proposed along with algorithms for optimizing the logical structure of the distributed knowledge base (DKB) to improve the performance of the MKRPS in terms of volume, cost and time criteria.Conclusions. …”
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