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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DGL-STFA: Predicting lithium-ion battery health with dynamic graph learning and spatial–temporal fusion attention by Zheng Chen, Quan Qian

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Coordinated Volt/VAR Control in Distribution Networks Considering Demand Response via Safe Deep Reinforcement Learning by Dong Hua, Fei Peng, Suisheng Liu, Qinglin Lin, Jiahui Fan, Qian Li

Subjects: “…safe deep reinforcement learning…”
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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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A Novel Ensemble Meta-Model for Enhanced Retinal Blood Vessel Segmentation Using Deep Learning Architectures by Mohamed Chetoui, Moulay A. Akhloufi

Subjects: “…deep learning…”
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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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Enhancing zero-shot relation extraction with a dual contrastive learning framework and a cross-attention module by Diyou Li, Lijuan Zhang, Jie Huang, Neal Xiong, Lei Zhang, Jian Wan

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Learning experience on sentinel cases of COVID-19 at a public healthcare institution: sharing on operating room processes by Frederick H Koh, Keen-Chong Chau, Siok-Peng Ng, Li-Ming Teo, Sharon GK Ong, Wai-Keong Wong, Biauw-Chi Ong, Min-Hoe Chew

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Multisignal VGG19 Network with Transposed Convolution for Rotating Machinery Fault Diagnosis Based on Deep Transfer Learning by Jianye Zhou, Xinyu Yang, Lin Zhang, Siyu Shao, Gangying Bian

“…To realize high-precision and high-efficiency machine fault diagnosis, a novel deep learning framework that combines transfer learning and transposed convolution is proposed. …”
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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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Underwater DVL Optimization Network (UDON): A Learning-Based DVL Velocity Optimizing Method for Underwater Navigation by Feihu Zhang, Shaoping Zhao, Lu Li, Chun Cao

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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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Hybrid dung beetle optimization based dimensionality reduction with deep learning based cybersecurity solution on IoT environment by Amal K. Alkhalifa, Nuha Alruwais, Wahida Mansouri, Munya A. Arasi, Mohammed Alliheedi, Fouad Shoie Alallah, Alaa O. Khadidos, Abdulrhman Alshareef

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English goes digital: Framing pre-service teachers’ perceptions of a learning management system in their EFL studies by Kapranov Oleksandr

Subjects: “…learning management platform…”
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Exploring the Role of Artificial Intelligence on Educational Dynamics: Evaluating its Impact on Pedagogical Practices and Student Learning Outcomes by Sarah Abou Karroum, Nour-Eldin Elshaiekh

“…The review of literature suggests role of AI in terms of profiling & prediction, tutoring, accessing student performance, grading & evaluation and personalization of learning experience which influences learning outcomes amongst students using mediating role of pedagogical beliefs of teachers.  …”
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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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A Comparative Study of Anomaly Detection Techniques for IoT Security Using Adaptive Machine Learning for IoT Threats by Dheyaaldin Alsalman

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The association of lifestyle with cardiovascular and all-cause mortality based on machine learning: a prospective study from the NHANES by Xinghong Guo, Mingze Ma, Lipei Zhao, Jian Wu, Yan Lin, Fengyi Fei, Clifford Silver Tarimo, Saiyi Wang, Jingyi Zhang, Xinya Cheng, Beizhu Ye

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