Factors influencing the effectiveness of SM-VCE method in solving 3D surface deformation by Xupeng Liu, Guangyu Xu, Mingkai Chen, Tengxu Zhang

“…Currently, most research focuses on improving the above types of methods. However, some key factors in the coseismic 3D surface deformation inversion are rarely mentioned, such as the influence of window size on the inversion results in the strain model and variance component estimation method (SM-VCE), and whether the outliers in the observational data are considered. …”
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TECHNOLOGICAL ADVANCES IN ELECTROPLATING: ARTIFICIAL INTELLIGENCE TO PREDICT ZINC COATING THICKNESS ON SAE 1008 LOW CARBON STEELS by Luciano M. L. de Oliveira, Fabiana L. da Silva, Paulo R. Janissek, Juliano C. Toniolo

“…Statistical analysis and supervised machine learning algorithms, including multivariate regression, random forest, and extreme gradient boosting (XGBoost), were employed to develop prediction models. …”
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Experimental and numerical investigations on the bidirectional thermal contact performance by Chen Wang, Mingjun Qiu, Huijing Liu, Jun Hong, Feiyu Gu, Lifei Chen, Tao Wang, Hao Guan, Qiyin Lin

“…Additionally, a prediction model for TCR was developed using the Levenberg-Marquardt (L-M) algorithm. …”
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Interpretable prediction of hospital mortality in bleeding critically ill patients based on machine learning and SHAP by Bingkui Ren, Yuping Zhang, Siying Chen, Jinglong Dai, Junci Chong, Yifei Zhong, Mengkai Deng, Shaobo Jiang, Zhigang Chang

“…External validation using the REFRAIN cohort confirmed the robustness of model(AUC = 0.776). Conclusions The interpretable predictive model improves mortality risk stratification in ICU patients with hemorrhage, supporting clinicians in optimizing treatment plans and resource allocation. …”
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High-Dimensional Projected Clustering for Learner Competency Analysis in Medical Training Programs by Sandhya Harikumar, C. S. Jayamohan Pillai, V. Vani Chithra, Raghu Raman, Mr Kaimal, Kai-Yu Tang, Prema Nedungadi

“…The system helps learners refine their skills by identifying areas that need improvement and providing timely assistance to struggling learners. …”
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Ensemble Transformer–Based Detection of Fake and AI–Generated News by Md. Ishraquzzaman, Mohammed Ashraful Islam Chowdhury, Shahreen Rahman, Riasat Khan

“…The proposed ensemble model is optimized by applying model pruning (reducing parameters from 265M to 210M, improving training time by 25%) and dynamic quantization (reducing model size by 50%, maintaining 95.68% accuracy), enhancing scalability and efficiency while minimizing computational overhead. …”
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Research Progress and Prospect of Green Infrastructure with Public Health Promotion Function by Tongyu LI, Junyi LIXU, Binxia XUE, Yan (USA) SONG

“…To manage the multidimensionality of GI research, cluster analysis is performed using a Word2Vec model combined with a K-means algorithm to integrate different GI forms into a coherent classification system.ResultsThe results show that GI can be clearly divided into different categories, such as urban green spaces and parks, high-interaction spaces, trees in built-up areas, water management and biofiltration systems, community and residential greening, green roofs and facades, linear green networks, and broader macro-GI strategies. …”
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A Matheuristic Approach Based on Variable Neighborhood Search for the Static Repositioning Problem in Station-Based Bike-Sharing Systems by Julio Mario Daza-Escorcia, David Álvarez-Martínez

“…To solve this problem, we propose a <i>matheuristic</i> based on a <i>variable neighborhood search</i> combined with several improving algorithms, including an <i>integer linear programming model</i> to optimize loading instructions. …”
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A comprehensive review of data analytics and storage methods in geothermal energy operations by Ali Basem, Ahmed Kateb Jumaah Al-Nussairi, Dana Mohammad Khidhir, Narinderjit Singh Sawaran Singh, Mohammadreza Baghoolizadeh, Mohammad Ali Fazilati, Soheil Salahshour, S. Mohammad Sajadi, Ali Mohammadi Hasanabad

“…The study also delves into the potential of machine learning to optimize geothermal design, monitor performance, improve performance, find errors, and more. …”
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InvMOE: MOEs Based Invariant Representation Learning for Fault Detection in Converter Stations by Hao Sun, Shaosen Li, Hao Li, Jianxiang Huang, Zhuqiao Qiao, Jialei Wang, Xincui Tian

“…To overcome these issues, we propose InvMOE, a novel fault detection algorithm with two core components: (1) invariant representation learning, which captures task-relevant features and mitigates background noise interference, and (2) multi-task training using a mixture of experts (MOE) framework to adaptively optimize feature learning across tasks and address label sparsity. …”
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Enhancing Consumer Decision-Making in Skincare: Implementation of the VIKOR Method for Product Recommendation Systems by Diah Arifah Prastiningtyas, Greta Septy Purwiantono, Febry Eka Purwiantono, Addin Aditya

“…By demonstrating the efficacy of the VIKOR method, this research paves the way for its future application in various industries, offering a replicable and adaptable model for improving decision processes in consumer goods selection across diverse markets. …”
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Predicting the Remaining Useful Life of an Aircraft Engine Using a Stacked Sparse Autoencoder with Multilayer Self-Learning by Jian Ma, Hua Su, Wan-lin Zhao, Bin Liu

“…The grid search method is introduced in this paper to optimize the hyperparameters of the proposed aircraft engine RUL prediction model. …”
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Advanced Interpretation of Bullet-Affected Chest X-Rays Using Deep Transfer Learning by Shaheer Khan, Nirban Bhowmick, Azib Farooq, Muhammad Zahid, Sultan Shoaib, Saqlain Razzaq, Abdul Razzaq, Yasar Amin

“…Special deep learning algorithms went through a process of optimization before researchers improved their ability to detect and place objects. …”
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Predicting child mortality determinants in Uttar Pradesh using Machine Learning: Insights from the National Family and Health Survey (2019–21) by Pinky Pandey, Sacheendra Shukla, Niraj Kumar Singh, Mukesh Kumar

“…Four machine learning algorithms—Random Forests, Logistic Regression, K-Nearest Neighbors (KNN), and Naive Bayes—were applied alongside a traditional logistic regression model. …”
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Large-scale S-box design and analysis of SPS structure by Lan ZHANG, Liangsheng HE, Bin YU

“…A class of optimal linear transformation P over a finite field<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow> <msup> <mrow> <mrow><mo>(</mo> <mrow> <msubsup> <mi>F</mi> <mn>2</mn> <mi>m</mi> </msubsup> </mrow> <mo>)</mo></mrow></mrow> <mn>4</mn> </msup> </mrow></math></inline-formula> was constructed based on cyclic shift and XOR operation.Using the idea of inverse proof of input-output relation of linear transformation for reference, a proof method was put forward that transformed the objective problem of optimal linear transformation into several theorems of progressive relation, which not only solved the proof of that kind of optimal linear transformation, but also was suitable for the proof of any linear transformation.By means of small-scale S-box and optimal cyclic shift-XOR linear transformation P, a large-scale S-box model with 2-round SPS structure was established, and a series of lightweight large-scale S-boxes with good cryptographic properties were designed.Only three kind of basic operations such as look-up table, cyclic shift and XOR were used in the proposed design scheme, which improved the linearity and difference uniformity of large-scale S-boxes.Theoretical proof and case analysis show that, compared with the existing large-scale S-box construction methods, the proposed large-scale S-box design scheme has lower computational cost and better cryptographic properties such as difference and linearity, which is suitable for the design of nonlinear permutation coding of lightweight cryptographic algorithms.…”
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Policy Similarity Measure for Two-Player Zero-Sum Games by Hongsong Tang, Liuyu Xiang, Zhaofeng He

“…Policy space response oracles (PSRO) is an important algorithmic framework for approximating Nash equilibria in two-player zero-sum games. …”
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Efficient structure learning of gene regulatory networks with Bayesian active learning by Dániel Sándor, Péter Antal

“…Results We introduce novel acquisition functions for experiment design in gene expression data, leveraging active learning in both Essential Graph and Graphical Model spaces. We evaluate scalable structure learning algorithms within an active learning framework to optimize intervention selection. …”
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Deep learning radiomics nomogram predicts lymph node metastasis in laryngeal squamous cell carcinoma by Yun Liang, Yun Liang, Min He, Wenqing Chen, Wenqing Chen, Lizhen Li, Lizhen Li, Yumeng Dong, Yumeng Dong, Gang Liang, Hui Huangfu, Zengyu Jiang, Zengyu Jiang, Sheng He, Sheng He, Sheng He

“…Radiomics features were extracted from CT images, and 7 machine learning algorithms were used to develop 7 radiomics models, which were combined with deep learning features extracted from the ResNet50 deep learning network to form deep learning radiomics (DLR) models. …”
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Artificial Intelligence and Machine Learning Approaches for Target-Based Drug Discovery: A Focus on GPCR-Ligand Interactions by M. O. Otun

“…This review explores the integration of AI and ML techniques in GPCR-targeted drug discovery, highlighting their potential to accelerate lead identification, optimize ligand binding predictions, and improve structure-activity relationship modeling. …”
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Convergence of evolving artificial intelligence and machine learning techniques in precision oncology by Elena Fountzilas, Tillman Pearce, Mehmet A. Baysal, Abhijit Chakraborty, Apostolia M. Tsimberidou

“…Currently, many operational and technical challenges exist related to data technology, engineering, and storage; algorithm development and structures; quality and quantity of the data and the analytical pipeline; data sharing and generalizability; and the incorporation of these technologies into the current clinical workflow and reimbursement models.…”
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