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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A novel lightweight Machine Learning framework for IoT malware classification based on matrix block mean Downsampling by Mahmoud E. Farfoura, Ibrahim Mashal, Ahmad Alkhatib, Radwan M. Batyha, Didi Rosiyadi

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Technical note: Towards atmospheric compound identification in chemical ionization mass spectrometry with pesticide standards and machine learning by F. Bortolussi, H. Sandström, F. Partovi, F. Partovi, J. Mikkilä, P. Rinke, P. Rinke, P. Rinke, P. Rinke, M. Rissanen, M. Rissanen

“…In this study, we apply machine learning to a reference dataset of pesticides in two standard solutions to build a model that can provide insights from CIMS analyses in atmospheric science. …”
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A multi-objective, multi-interpretable machine learning demonstration verified by domain knowledge for ductile thermoelectric materials by Xiangdong Wang, Yan Cao, Jialin Ji, Ye Sheng, Jiong Yang, Xuezhi Ke

Subjects: “…multi-interpretable machine learning…”
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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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Machine learning assisted classification RASAR modeling for the nephrotoxicity potential of a curated set of orally active drugs by Arkaprava Banerjee, Kunal Roy

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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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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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Advanced automated machine learning framework for photovoltaic power output prediction using environmental parameters and SHAP interpretability by Muhammad Paend Bakht, Mohd Norzali Haji Mohd, Babul Salam KSM Kader Ibrahim, Nuzhat Khan, Usman Ullah Sheikh, Ab Al-Hadi Ab Rahman

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Prediction of Systemic Risk Contagion Based on a Dynamic Complex Network Model Using Machine Learning Algorithm by Jiannan Yu, Jinlou Zhao

“…Cascading defaults are also generated in the simulation according to different crisis-triggering (targeted defaults) methods. We also use machine learning techniques to identify the synthetic features of the network. …”
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