Hybrid-Driven Dynamic Position Prediction of Robot End-Effector Integrating Parametric Dynamic Model and Machine Learning by Hepeng Ni, Cong Xu, Yingxin Ye, Bo Chen, Shuangsheng Luo, Shuai Ji

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Layer-by-layer phase transformation in Ti3O5 revealed by machine-learning molecular dynamics simulations by Mingfeng Liu, Jiantao Wang, Junwei Hu, Peitao Liu, Haiyang Niu, Xuexi Yan, Jiangxu Li, Haile Yan, Bo Yang, Yan Sun, Chunlin Chen, Georg Kresse, Liang Zuo, Xing-Qiu Chen

“…This is enabled by developing an efficient machine learning potential with near first-principles accuracy through an on-the-fly active learning method and an advanced sampling technique. …”
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A novel agricultural drought index based on multi-source remote sensing data and interpretable machine learning by Hao Chen, Ni Yang, Xuanhua Song, Chunhua Lu, Menglan Lu, Tan Chen, Shulin Deng

Subjects: “…Interpretable machine learning drought index (IMLDI)…”
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A Comparative Study between Time Series and Machine Learning Technique to Predict Dengue Fever in Dhaka City by Tanzina Akter, Md. Tanvirul Islam, Md. Farhad Hossain, Mohammad Safi Ullah

“…Therefore, from this research, the author suggests machine learning gives better prediction performance than time series analysis for any other prediction performance.…”
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Artificial Intelligence in Educational Data Mining and Human-in-the-Loop Machine Learning and Machine Teaching: Analysis of Scientific Knowledge by Eloy López-Meneses, Luis López-Catalán, Noelia Pelícano-Piris, Pedro C. Mellado-Moreno

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A Novel Approach for Detection of Cyber Attacks in MQTT-Based IIoT Systems Using Machine Learning Techniques by Serkan Gönen

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Uncovering glycolysis-driven molecular subtypes in diabetic nephropathy: a WGCNA and machine learning approach for diagnostic precision by Chenglong Fan, Guanglin Yang, Cheng Li, Jiwen Cheng, Shaohua Chen, Hua Mi

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Improving tropical cyclone rapid intensification forecasts with satellite measurements of sea surface salinity and calibrated machine learning by Ryan Eusebi, Hui Su, Longtao Wu, Pingping Rong, Karthik Balaguru, Ruby Leung, Yong-Sang Choi, Pak Wai Chan, Jianping Gan, Mark DeMaria, Galina Chirokova

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An Empirical Study of Macroeconomic Factors and Stock Returns in the Context of Economic Uncertainty News Sentiment Using Machine Learning by Ayesha Jabeen, Muhammad Yasir, Yasmeen Ansari, Sadaf Yasmin, Jihoon Moon, Seungmin Rho

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Fortifying IoT Infrastructure Using Machine Learning for DDoS Attack within Distributed Computing-based Routing in Networks by Sharaf Aldeen Abdulkadhum Abbas, Abdullahi Abdu Ibrahim

“…This research aims to compare the key machine learning approaches, Namely Support Vector Machines (SVM), Random Forest (RF) and Decision Trees (DT), in their ability to classify Intrusion Detection Systems (IDS) via routing networks over distributed computing systems. …”
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Development and Validation of a Machine Learning Model for the Prediction of Bloodstream Infections in Patients with Hematological Malignancies and Febrile Neutropenia by Antonio Gallardo-Pizarro, Christian Teijón-Lumbreras, Patricia Monzo-Gallo, Tommaso Francesco Aiello, Mariana Chumbita, Olivier Peyrony, Emmanuelle Gras, Cristina Pitart, Josep Mensa, Jordi Esteve, Alex Soriano, Carolina Garcia-Vidal

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Diagnosis of Malignant Endometrial Lesions from Ultrasound Radiomics Features and Clinical Variables Using Machine Learning Methods by Shanshan Li, Jiali Wang, Li Zhou, Hui Wang, Xiangyu Wang, Jian Hu, Qingxiu Ai

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Estimation of the Visibility in Seoul, South Korea, Based on Particulate Matter and Weather Data, Using Machine-learning Algorithm by Bu-Yo Kim, Joo Wan Cha, Ki-Ho Chang, Chulkyu Lee

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Modelling the seasonal dynamics of Aedes albopictus populations using a spatio-temporal stacked machine learning model by Daniele Da Re, Giovanni Marini, Carmelo Bonannella, Fabrizio Laurini, Mattia Manica, Nikoleta Anicic, Alessandro Albieri, Paola Angelini, Daniele Arnoldi, Federica Bertola, Beniamino Caputo, Claudio De Liberato, Alessandra della Torre, Eleonora Flacio, Alessandra Franceschini, Francesco Gradoni, Përparim Kadriaj, Valeria Lencioni, Irene Del Lesto, Francesco La Russa, Riccardo Paolo Lia, Fabrizio Montarsi, Domenico Otranto, Gregory L’Ambert, Annapaola Rizzoli, Pasquale Rombolà, Federico Romiti, Gionata Stancher, Alessandra Torina, Enkelejda Velo, Chiara Virgillito, Fabiana Zandonai, Roberto Rosà

“…In our study, we utilized a recently published dataset documenting egg abundance observations of Aedes albopictus collected using ovitraps. and a set of environmental predictors to forecast the weekly median number of mosquito eggs using a stacked machine learning model. This approach enabled us to (i) unearth the seasonal egg-laying dynamics of Ae. albopictus for 12 years; (ii) generate spatio-temporal explicit forecasts of mosquito egg abundance in regions not covered by conventional monitoring initiatives. …”
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Machine learning meta-analysis identifies individual characteristics moderating cognitive intervention efficacy for anxiety and depression symptoms by Thalia Richter, Reut Shani, Shachaf Tal, Nazanin Derakshan, Noga Cohen, Philip M. Enock, Richard J. McNally, Nilly Mor, Shimrit Daches, Alishia D. Williams, Jenny Yiend, Per Carlbring, Jennie M. Kuckertz, Wenhui Yang, Andrea Reinecke, Christopher G. Beevers, Brian E. Bunnell, Ernst H. W. Koster, Sigal Zilcha-Mano, Hadas Okon-Singer

“…This research is a pre-registered individual-level meta-analysis to identify factors contributing to cognitive training efficacy for anxiety and depression symptoms. Machine learning methods, alongside traditional statistical approaches, were employed to analyze 22 datasets with 1544 participants who underwent working memory training, attention bias modification, interpretation bias modification, or inhibitory control training. …”
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Predicting egg production rate and egg weight of broiler breeders based on machine learning and Shapley additive explanations by Hengyi Ji, Yidan Xu, Ganghui Teng

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Machine Learning for Precision Health Economics and Outcomes Research (P-HEOR): Conceptual Review of Applications and Next Steps by Yixi Chen, Viktor V Chirikov, Xiaocong L Marston, Jingang Yang, Haibo Qiu, Jianfeng Xie, Ning Sun, Chengming Gu, Peng Dong, Xin Gao

“…Through a conceptualized example, the objective of this review is to highlight the capabilities and limitations of machine learning (ML) applications to P-HEOR and to contextualize the potential opportunities and challenges for the wide adoption of ML for health economics. …”
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Geospatial digital mapping of soil organic carbon using machine learning and geostatistical methods in different land uses by Yahya Parvizi, Shahrokh Fatehi

“…The SOC changes were simulated using multivariate analysis and machine learning methods including generalized linear model (GLM), linear additive model (LAM), cubist, random forest (RF), and support vector machine (SVM) models. …”
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Development of an interpretable machine learning model based on CT radiomics for the prediction of post acute pancreatitis diabetes mellitus by Xiyao Wan, Yuan Wang, Ziyi Liu, Ziyan Liu, Shuting Zhong, Xiaohua Huang

“…Abstract This study sought to establish and validate an interpretable CT radiomics-based machine learning model capable of predicting post-acute pancreatitis diabetes mellitus (PPDM-A), providing clinicians with an effective predictive tool to aid patient management in a timely fashion. …”
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Identification of Potential lncRNAs and miRNAs as Diagnostic Biomarkers for Papillary Thyroid Carcinoma Based on Machine Learning by Fei Yang, Jie Zhang, Baokun Li, Zhijun Zhao, Yan Liu, Zhen Zhao, Shanghua Jing, Guiying Wang

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