Machine learning unveils multiple Pauli blockades in the transport spectroscopy of bilayer graphene double-quantum dots by Ankan Mukherjee, Anuranan Das, Adil Anwar Khan, Bhaskaran Muralidharan

“…Through numerical predictions and validations against test data, we identify where and how many Pauli blockades are likely to occur. …”
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A GRNN based frame work to test the influence of nano zinc additive biodiesel blends on CI engine performance and emissions by Chiranjeeva Rao Seela, B. Ravisankar, B.M.V.A. Raju

“…The neural network predictions are corroborated with the experimental results and are found in good agreement. …”
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Consistency regularization for few shot multivariate time series forecasting by Yumei She, Yi Hong, Shikai Shen, Bin Yang, Liyi Zhang, Jianxiao Wang

“…Abstract Multivariate time series forecasting aims to accurately predict future trends by capturing and analyzing various features of the time series. …”
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Substrate-aware computational design of two-dimensional materials by Arslan Mazitov, Ivan Kruglov, Alexey V. Yanilkin, Aleksey V. Arsenin, Valentyn S. Volkov, Dmitry G. Kvashnin, Artem R. Oganov, Kostya S. Novoselov

“…This study presents a novel method for predicting the atomic structure of 2D materials on substrates by combining an evolutionary algorithm, a lattice-matching technique, an automated machine-learning interatomic potentials training protocol, and the ab initio thermodynamics approach. …”
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CatSkill: Artificial Intelligence-Based Metrics for the Assessment of Surgical Skill Level from Intraoperative Cataract Surgery Video Recordings by Binh Duong Giap, PhD, Dena Ballouz, MD, Karthik Srinivasan, MD, MS, Jefferson Lustre, BS, Keely Likosky, BS, Ossama Mahmoud, MD, Shahzad I. Mian, MD, Bradford L. Tannen, MD, JD, Nambi Nallasamy, MD

“…Three CSAMs were computed to analyze 430 cataract surgeries (254 attendings and 176 residents). An ML algorithm was developed to predict surgeon training level using only CSAMs. …”
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Multimodal ultrasound deep learning to detect fibrosis in early chronic kidney disease by Xiachuan Qin, Xiaoling Liu, Linlin Xia, Qi Luo, Chaoxue Zhang

“…The AUC of the multimodal US DL model was significantly better than that of the single-mode DL and clinical models. The DL algorithm developed using multimodal US images can effectively predict early fibrosis in patients with CKD with significantly greater accuracy than single-mode DL or clinical models.…”
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Evaluating a Procedural Content Orchestrator Gameplay Data and Classifying User Profiles by Leonardo Tórtoro Pereira, T. Yuji Teoi, Claudio Fabiano Motta Toledo

“…Our extended results suggest that we can predict player’s profiles through gameplay metrics and data augmentation, even for small samples. …”
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Classifying detrital zircon U-Pb age distributions using automated machine learning by Jack W. Fekete, Glenn R. Sharman, Xiao Huang

“…Applied to the North American Cordillera dataset, AutoML achieves an ∼0.91 F1 score when predicting between foreland and forearc basin tectonic settings and an ∼0.71 F1 score when predicting subbasins within these settings, outperforming both RF and R2. …”
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Chlorophyll-a in the Chesapeake Bay Estimated by Extra-Trees Machine Learning Modeling by Nikolay P. Nezlin, SeungHyun Son, Salem I. Salem, Michael E. Ondrusek

“…Our approach leverages the Extra-Trees (ET) algorithm, a tree-based ensemble method that offers predictive accuracy comparable to that of other ensemble models, while significantly improving computational efficiency. …”
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Smart estimation of protective antioxidant enzymes’ activity in savory (Satureja rechingeri L.) under drought stress and soil amendments by Amin Taheri-Garavand, Mojgan Beiranvandi, Abdolreza Ahmadi, Nikolaos Nikoloudakis

“…On the other hand, POX had a lower predictive correlation (R = 0.8737), indicating a lower capacity of the ANN system in forecasting this parameter. …”
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Integrating Street View Images, Deep Learning, and sDNA for Evaluating University Campus Outdoor Public Spaces: A Focus on Restorative Benefits and Accessibility by Tingjin Wu, Deqing Lin, Yi Chen, Jinxiu Wu

“…On this basis, restorative benefit evaluation models were established, including the explanatory and predictive models. The explanatory model used Pearson’s correlation and multiple linear regression analysis to identify the key indicators affecting restorative benefits, and the predictive model used the XGBoost 1.7.3 algorithm to predict the restorative benefit scores on the campus scale. …”
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The Prognostic Value of Serum HBV-RNA during Hepatitis B Virus Infection is Related to Acute-on-Chronic Liver Failure by Keli Qian, Ying Xue, Hang Sun, Ting Lu, Yixuan Wang, Xiaofeng Shi

“…A nomogram was developed to formulate an algorithm incorporating serum HBV-RNA for predicting the survival of HBV-ACLF patients. …”
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Comparative performance of deep learning architectures for diabetic peripheral neuropathy detection using corneal confocal microscopy: a retrospective single-centre study by Yuyang Deng, Wenqu Chen, Weihuang Xu, Jianzhang Hu

“…For single-image predictions in the three-class classification task of CCM images, the InceptionV3 model achieved a precision of 0.8385, a recall of 0.9083, an F1 score of 0.8720 and an AUC of 0.8769 for predicting DPN+.Conclusions The InceptionV3-based DLA model achieved superior performance compared with traditional convolutional neural network architectures like ResNet and DenseNet, and the Swin transformer model, highlighting its potential for effective DPN screening.…”
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Machine learning modeling of cancer treatment-related cardiac events in breast cancer: utilizing dosiomics and radiomics by Sefika Dincer, Muge Akmansu, Oya Akyol

“…Machine learning models were optimized using the Tree-based Pipeline Optimization Tool (TPOT), identifying the gradient-boosted classification as the best-performing algorithm. Feature selection was conducted using gradient-boosted recursive feature elimination. …”
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Prognostic Value of a Classification and Regression Tree Model in Patients with Open-Globe Injuries by Danica T. Esteban, MD, Karlo Marco D. Claudio, MD, Cheryl A. Arcinue, MD

“…Purposive sampling of hospital medical records was done to collect data from both in- and out-patient cases. The CART algorithm was utilized to determine the predicted visual outcome for each case, and the accuracy of prognostication was measured by computing for sensitivity, specificity, positive predictive value, and negative predictive value. …”
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Comparative Analysis of Automated Machine Learning for Hyperparameter Optimization and Explainable Artificial Intelligence Models by Muhammad Salman Khan, Tianbo Peng, Hanzlah Akhlaq, Muhammad Adeel Khan

“…The study focuses on predicting the ultimate moment capacity of Ultra-High-Performance Concrete (UHPC) beams and U-shaped girders. …”
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The BRCA1 variant p.Ser36Tyr abrogates BRCA1 protein function and potentially confers a moderate risk of breast cancer. by Charita M Christou, Andreas Hadjisavvas, Maria Kyratzi, Christina Flouri, Ioanna Neophytou, Violetta Anastasiadou, Maria A Loizidou, Kyriacos Kyriacou

“…PolyPhen algorithm predicted that the BRCA1 p.Ser36Tyr VUS identified in the Cypriot population was damaging, whereas Align-GVGD predicted that it was possibly of no significance. …”
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Development of an Intelligent Tablet Press Machine for the In-Line Detection of Defective Tablets Using Machine Learning and Deep Learning Models by Sun Ho Kim, Su Hyeon Han

“…The TPM was verified by sorting defective tablets in-line using a pretrained defect-detection algorithm. <b>Results:</b> The RF model demonstrated the highest predictive accuracy at 93.7% with an Area Under the Curve (AUC) of 0.895, while the ANN model achieved an accuracy of 92.6% with an AUC of 0.878. …”
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Systemic longitudinal immune profiling identifies proliferating Treg cells as predictors of immunotherapy benefit: biomarker analysis from the phase 3 CONTINUUM and DIPPER trials by Sai-Wei Huang, Wei Jiang, Sha Xu, Yuan Zhang, Juan Du, Ya-Qin Wang, Kun-Yu Yang, Ning Zhang, Fang Liu, Guo-Rong Zou, Feng Jin, Hai-Jun Wu, Yang-Ying Zhou, Xiao-Dong Zhu, Nian-Yong Chen, Cheng Xu, Han Qiao, Na Liu, Ying Sun, Jun Ma, Ye-Lin Liang, Xu Liu

“…Further validation through flow cytometry (n = 120) confirmed the predictive value of this Treg subset. Multiplex immunohistochemistry (n = 249) demonstrated that Ki67+ Tregs in tumors could predict immunotherapy benefit, with aPD1 improving EFS only in patients with low baseline levels of Ki67+ Tregs. …”
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Comparative effects of metformin and varying intensities of exercise on miR-133a expression in diabetic rats: Insights from machine learning analysis by Elahe Alivaisi, Sabrieh Amini, Karimeh Haghani, Hori Ghaneialvar, Fatemeh Keshavarzi

“…We used the CatBoost algorithm to develop a predictive model for miR-133a expression based on metabolic parameters. …”
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