Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination by Ying Liu, Lihua Huang

“…In our work, we propose an ensemble support vector machine model to solve the risk assessment of supply chain finance, combined with reducing noises method. …”
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Detection of power theft in sensitive stations based on generalized robust distance metric and multi-classification support vector machine by Wei Zhang, Qiong Cao, Shuai Yang, Hao Guo

“…Therefore, this paper studies the detection method of power stealing in sensitive areas based on generalized robust distance measurement and multi-classification support vector machine. Based on the non-technical linear loss characteristics of sensitive area, a mathematical model of electricity stealing behavior scene is constructed. …”
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Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images by K. Lakshmi, Sibi Amaran, G. Subbulakshmi, S. Padmini, Gyanenedra Prasad Joshi, Woong Cho

“…Finally, an improved radial movement optimization model is employed for the hyperparameter tuning of the BRANN technique. …”
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Identification of Maize Kernel Varieties Using LF-NMR Combined with Image Data: An Explainable Approach Based on Machine Learning by Chunguang Bi, Xinhua Bi, Jinjing Liu, He Chen, Mohan Wang, Helong Yu, Shaozhong Song

“…The precise identification of maize kernel varieties is essential for germplasm resource management, genetic diversity conservation, and the optimization of agricultural production. To address the need for rapid and non-destructive variety identification, this study developed a novel interpretable machine learning approach that integrates low-field nuclear magnetic resonance (LF-NMR) with morphological image features through an optimized support vector machine (SVM) framework. …”
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Stratified Metamodeling to Predict Concrete Compressive Strength Using an Optimized Dual-Layered Architectural Framework by Geraldo F. Neto, Bruno da S. Macêdo, Tales H. A. Boratto, Tiago Silveira Gontijo, Matteo Bodini, Camila Saporetti, Leonardo Goliatt

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Fractional-Order Control of a Nonlinear Time-Delay System: Case Study in Oxygen Regulation in the Heart-Lung Machine by S. J. Sadati, A. Ranjbar Noei, R. Ghaderi

“…Primarily a nonlinear single-input single-output (SISO) time-delay model which was obtained previously in the literature is introduced for the oxygen generation process in the heart-lung machine system and we will complete it by adding some new states to control it. …”
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A smarter approach to liquefaction risk: harnessing dynamic cone penetration test data and machine learning for safer infrastructure by Shubhendu Vikram Singh, Sufyan Ghani

“…This study establishes a threshold criterion based on the ratio of the penetration rate to the dynamic resistance (e/qd), where values exceeding four indicate high liquefaction susceptibility. ML models, including Support Vector Machine (SVM) optimized with Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Firefly Algorithm (FA), were employed to predict the e/qd ratio using key geotechnical parameters, such as fine content, peak ground acceleration, reduction factor, and penetration rate. …”
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Probability-Weighted Optimal Control for Nonlinear Stochastic Vibrating Systems with Random Time Delay by R. C. Hu, Q. F. Lü, X. F. Wang, Z. G. Ying, R. H. Huan

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Feature-Driven Density Prediction of Maraging Steel Additively Manufactured Samples Using Pyrometer Sensor and Supervised Machine Learning by Rajesh Kumar Balaraman, Shaista Hussain, John Kgee Ong, Qing Yang Tan, Nagarajan Raghavan

“…Finally, the feature-driven model incorporating optimal machine, pyrometer, and physical features was utilized for density prediction through ML. …”
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Computational fluid dynamics analysis and machine learning study of heat transfer in solar air heaters with distinct ribs configuration by Eid S. Alatawi

“…This research pioneers SAH optimization by uniquely integrating Computational Fluid Dynamics (CFD) with machine learning (ML) to analyze and predict the performance of 15 SAH designs featuring distinct curved rib configurations. …”
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In-Hospital Mortality Prediction among Intensive Care Unit Patients with Acute Ischemic Stroke: A Machine Learning Approach by Jack A. Cummins, Ben S. Gerber, Mayuko Ito Fukunaga, Nils Henninger, Catarina I. Kiefe, Feifan Liu

“…Identifying patients with stroke at high risk of mortality is crucial for timely intervention and optimal resource allocation. This study aims to develop and validate machine learning-based models to predict in-hospital mortality risk for intensive care unit (ICU) patients with acute ischemic stroke and identify important associated factors. …”
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Investigation of the Effect of Tool Rotation Rate in EDM Drilling of Ultrafine Grain Tungsten Carbide Using Predictive Machine Learning by Sai Dutta Gattu, Lucas Pardo Bernardi, Jiwang Yan

“…A structured analytical workflow, combining Taguchi–Grey optimization, regression analysis, and classification models, was designed to capture discharge dynamics across macro- and micro-timescales. …”
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Unveiling farmers’ perceptions: a citizen science and machine learning approach to exploring drivers in the adequacy and fairness of water systems by Vasilios Plakandaras, Fahad Khan Khadim, Vassiliki Kazana, Emmanouil Anagnostou, Amvrossios C Bagtzoglou

“…Harnessing the analytical power of machine learning models in extracting patterns from data, the informational content of social surveys coupled with hydrological data for the survey region from a calibrated MODFLOW-NWT groundwater (GW) model, we draw inferences on the importance of socioeconomic rather than hydrological variables as drivers in agricultural decisions about crop selection and planting period, underscoring those factors as potential criteria in drawing successful agricultural policies for crop yield optimization in the Great South area. …”
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Machine Learning-Driven Prediction of Wear Rate and Phase Formation in High Entropy Alloy Coatings for Enhanced Durability and Performance by S. Sivaraman, N. Radhika, Muhammad Abubaker Khan

“…For phase prediction, four ML models including RF, GNB, ANN and Logistic regression were evaluated. …”
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BioInnovate AI: A Machine Learning Platform for Rapid PCR Assay Design in Emerging Infectious Disease Diagnostics by Hung-Hsin Lin, Hsing-Yi Chung, Tai-Han Lin, Chih-Kai Chang, Cherng-Lih Perng, Kuo-Sheng Hung, Katsunori Yanagihara, Hung-Sheng Shang, Ming-Jr Jian

“…Performance metrics, including the area under the curve (AUC), sensitivity, specificity, accuracy, and F1 score, were assessed to identify the optimal model for platform integration. <b>Results</b>: All machine learning models performed well, with the LGBM model achieving the highest metrics (AUC: 0.97, sensitivity: 0.93, specificity: 0.91). …”
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Impact of lightweight clay aggregate with slag and biomedical waste ash on self-compacting concrete using machine learning approach by Kennedy C. Onyelowe, Viroon Kamchoom, Shadi Hanandeh, Ahmed M. Ebid, Janneth Alejandra Viñan Villagran, Raúl Gregorio Martínez Pérez, Fausto Ulpiano Caicedo Benavides, Paul Awoyera, Siva Avudaiappan

“…This research enables the optimization of self-compacting concrete mix designs using machine learning, reducing experimental trials, enhancing material efficiency, lowering environmental impact, and promoting sustainable construction through the effective reuse of industrial by-products.…”
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High-Precision Pose Measurement of Containers on the Transfer Platform of the Dual-Trolley Quayside Container Crane Based on Machine Vision by Jiaqi Wang, Mengjie He, Yujie Zhang, Zhiwei Zhang, Octavian Postolache, Chao Mi

“…An enhanced EPnP optimization algorithm incorporating lockhole coplanar constraints is proposed, establishing a 2D–3D coordinate transformation model that reduces pose-estimation errors to millimeter level (planar MAE-P = 0.024 m) and sub-angular level (MAE-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>θ</mi></semantics></math></inline-formula> = 0.11°). …”
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Classification Based on the Support Vector Machine for Determining Operational Targets for Controlling Electricity Usage With Conventional Meters: A Case Study of Industrial and Bu... by Galih Arisona, Alief Pascal Taruna, Dwi Irwanto, Arif Bijak Bestari, Wildan Juniawan

“…This research aims to improve the detection of electricity theft through a machine learning-based model utilizing the Support Vector Machine (SVM) classification technique. …”
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Efficiency enhancement of energy supply chains using a machine learning-driven network evaluation framework for blockchain adoption by Ardavan Babaei, Erfan Babaee Tirkolaee, Shahryar Sorooshian, Sadia Samar Ali, Gongming Wang

“…A novel approach based on four network-based data-driven optimization models is utilized to investigate a case study in Norway's O&G sector over a five-year period (April to October 2020). …”
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Identifying key factors influencing maize stalk lodging resistance through wind tunnel simulations with machine learning algorithms by Guanmin Huang, Ying Zhang, Shenghao Gu, Weiliang Wen, Xianju Lu, Xinyu Guo

“…This study introduces an combining wind tunnel testing with machine learning algorithms to quantitatively evaluate stalk lodging resistance traits. …”
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