Prediction of all-cause mortality in Parkinson’s disease with explainable artificial intelligence using administrative healthcare data by You Hyun Park, Yong Wook Kim, Dae Ryong Kang, Sang Chul Lee, Seo Yeon Yoon

“…The most important contributing feature to PD mortality was age, followed by male sex and pneumonia. Using XAI models, the nonlinear association between contributing factors and PD mortality was assessed, and an optimal target value to reduce mortality was found. …”
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Study on Cutting Performance and Wear Resistance of Biomimetic Micro-Textured Composite Cutting Tools by Youzheng Cui, Dongyang Wang, Minli Zheng, Qingwei Li, Haijing Mu, Chengxin Liu, Yujia Xia, Hui Jiang, Fengjuan Wang, Qingming Hu

“…Based on the simulation results, a response surface model was constructed to optimize key texture parameters, and the optimal texture configuration was obtained. …”
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Intelligent resource allocation in internet of things using random forest and clustering techniques by Nahideh Derakhshanfard, Lida Hosseinzadeh, Fahimeh Rashid Jafari, Ali Ghaffari

“…A Random Forest model is then trained to accurately predict the resource needs of each cluster, enabling optimal allocation. …”
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An Improved YOLOv7-Tiny-Based Algorithm for Wafer Surface Defect Detection by Mengyun Li, Xueying Wang, Hongtao Zhang, Xiaofeng Hu

“…To address the shortcomings of manual inspection and the limitations of existing machine learning methods, this paper proposes a wafer defect detection algorithm based on an improved YOLOv7-tiny. …”
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Multiobjective Parallel Algorithms for Solving Biobjective Open Shop Scheduling Problem by Seyed Hassan Shams Lahroudi, Farzaneh Mahalleh, Seyedsaeid Mirkamali

“…This kind of problem includes m machines and n jobs, each job contains a certain number of operations, and each operation has a predetermined processing time on its corresponding machine. …”
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Use of Google Earth Engine in predicting future giant cane (Arundinaria gigantea (Walter) Muhl.) restoration sites by Sanjeev Sharma

“…However, identifying optimal sites for restoration in modified landscapes remains a challenge. …”
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Smart Congestion Control in 5G/6G Networks Using Hybrid Deep Learning Techniques by Saif E. A. Alnawayseh, Waleed T. Al-Sit, Taher M. Ghazal

“…This proposed model is applied to a training dataset to govern the congestion in a 5G/6G network. …”
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Research on Spatial Heterogeneity, Impact Mechanism, and Carbon Peak Prediction of Carbon Emissions in the Yangtze River Delta Urban Agglomeration by Pin Chen, Xiyue Wang, Zexia Yang, Changfeng Shi

“…Urban agglomerations with a high economic activity and population density are key areas for carbon emissions and pioneers in achieving carbon peaking and the Sustainable Development Goals (SDGs). This study combines machine learning with an extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model to uncover the mechanisms driving carbon peaking disparities within these regions. …”
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Whole‐Blood RNA Sequencing Profiling of Patients With Rheumatoid Arthritis Treated With Tofacitinib by Chiara Bellocchi, Ennio Giulio Favalli, Gabriella Maioli, Elena Agape, Marzia Rossato, Matteo Paini, Adriana Severino, Barbara Vigone, Martina Biggioggero, Elena Trombetta, Roberto Caporali, Lorenzo Beretta

“…The capability of DEGs to predict a successful treatment was tested via machine learning modeling after extensive internal validation. …”
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A Novel Transformer-Based Self-Supervised Learning Method to Enhance Photoplethysmogram Signal Artifact Detection by Thanh-Dung Le, Clara Macabiau, Kevin Albert, Philippe Jouvet, Rita Noumeir

“…Recent research has revealed that traditional machine learning methods, such as semi-supervised label propagation and K-nearest neighbors, outperform Transformer-based models in artifact detection from photoplethysmogram (PPG) signals, mainly when data is limited. …”
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Graph Neural Network Learning on the Pediatric Structural Connectome by Anand Srinivasan, Rajikha Raja, John O. Glass, Melissa M. Hudson, Noah D. Sabin, Kevin R. Krull, Wilburn E. Reddick

“…The extension of the adult models to the pediatric dataset and training on the smaller pediatric dataset were sub-optimal in their performance. …”
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Human-Intelligent Information System Collaboration in Digital Twin Environment: Value Proposition, Key Technologies, and Practical Approaches by Fangrui BAI, Shaobo LIANG, Dan WU, Yuheng REN, Fan YANG

“…[Purpose/Significance] Previous studies or reviews of digital twins have focused either on conceptual analysis and theoretical models, or on the current state of the art and implementation, with only a few studies analyzing human-digital twin interaction and collaboration. …”
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High temperature stress-strain data for SAE 5120 steel under various strain ratesDataverse by Philippe Moreau, Jean-Dominique Guérin, José Grégorio La Barbara Sosa, Eli Puchi Cabrera, André Dubois, Laurent Dubar

“…Finite Element simulations are often used to model and optimize these processes, requiring accurate material flow stress and strain hardening data under varying deformation conditions and microstructures. …”
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Suicide Risk Screening in Jails: Protocol for a Pilot Study Leveraging the Mental Health Research Network Algorithm and Health Care Data by Erin B Comartin, Grant Victor, Athena Kheibari, Brian K Ahmedani, Bethany Hedden-Clayton, Richard N Jones, Ted R Miller, Jennifer E Johnson, Lauren M Weinstock, Sheryl Kubiak

“…Several studies have indicated that machine learning (ML) models considerably improve accuracy and have positive predictive value in detecting suicide risk compared with practice as usual (PAU). …”
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Circuit Breaker Energy Storage State Identification Based on Quick Extraction of Vibration Signal Interval Features by Xiaofei XIA, Yufeng LU, Yi SU, Jian YANG

“…Then the signal envelope was extracted and used as the feature vector, and the ReliefF-SFS method was used to reduce the dimensionality of features to obtain the optimal feature subset. Finally, the fuzzy C-means clustering (KFCM) was used to pre-classify the features to obtain the optimal hyperplane with the least risk, and a training model was established with support vector machine (SVM) for state identification. …”
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Robust fault detection in electrochemical energy storage systems under label noise: applications to lithium-ion batteries and transformer windings by Tao He, Wei Liu, Xin Wu, Yu Wei

“…However, the performance of machine learning-based fault diagnosis models is often degraded in practice due to label noise in training data, caused by sensor inaccuracies, ambiguous fault transitions, and imperfect labeling processes. …”
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Online variational Gaussian process for time series data by Weidong Wang, Mian Muhammad Yasir Khalil, Leta Yobsan Bayisa

“…Unlike traditional methods that rely on a fixed number of inducing points, OLVGP adaptively adjusts the number of inducing points as new data arrives and optimizes them from the model, ensuring that the model remains computationally efficient while maintaining high predictive accuracy. …”
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An automated adaptive trading system for enhanced performance of emerging market portfolios by Cristiana Tudor, Robert Sova

“…The system incorporates an Autoregressive Moving Average-Generalized AutoRegressive Conditional Heteroskedasticity model that offers an interpretability advantage over machine-learning methods. …”
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An enhanced method of CNNs by incorporating the clustering-guided block for concrete crack recognition by Hui Li, Chenyu Liu, Ning Zhang, Wei Shi

“…Notably, the CG-DeepLabV3 + model significantly reduced the recognition error for locating crack edges to a mere 2.31 pixels. …”
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A hybrid MCDM approach for unveiling ChatGPT's effect on students' learning by Jannatul Ferdous Mim, Md. Mohibul Islam, Aizizul Haque Raza

“…After rigorous evaluation, the COBRA technique emerged as the most appropriate among the models considered, exhibiting comprehensive calculations and demonstrating the highest Spearman's correlation with other models.…”
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