Global air quality index prediction using integrated spatial observation data and geographics machine learning by Tania Septi Anggraini, Hitoshi Irie, Anjar Dimara Sakti, Ketut Wikantika

“…This study aims to detect and improve the accuracy of the Global Air Quality Index from Remote Sensing (AQI-RS) by integrating AQI from ground-based stations with driving factors such as meteorological, environmental, sources of air pollution, and air pollution magnitude from satellite observation parameters as independent variables using Geographics Machine Learning (GML). This study utilizes 425 air pollution stations and the driving factors data globally from 2013 to 2024. …”
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Deep learning for enhanced prediction of diabetic retinopathy: a comparative study on the diabetes complications data set by Weijun Gong, You Pu, Tiao Ning, Yan Zhu, Gui Mu, Jing Li

“…To enhance the interpretability of the deep learning model, SHAP analysis was employed to assess feature importance and provide insights into the key drivers of retinopathy prediction.ConclusionDeep learning models can accurately predict retinopathy in diabetic patients. …”
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Machine learning model for differentiating malignant from benign thyroid nodules based on the thyroid function data by Quan Zhou, Lihua Zhang, Nan Xiang, Lele Zhang, Fuqiang Ma, Fengchang Yu, Shenhui Lv, Zhilin Lu, He-Rong Mao

“…Objectives To develop and validate a machine learning (ML) model to differentiate malignant from benign thyroid nodules (TNs) based on the routine data and provide diagnostic assistance for medical professionals.Setting A qualified panel of 1649 patients with TNs from one hospital were stratified by gender, age, free triiodothyronine (FT3), free thyroxine (FT4) and thyroid peroxidase antibody (TPOAB).Participants Thyroid function (TF) data of 1649 patients with TNs were collected in a single centre from January 2018 to June 2022, with a total of 273 males and 1376 females, respectively.Measures Seven popular ML models (Random Forest, Decision Tree, Logistic Regression (LR), K-Neighbours, Gaussian Naive Bayes, Multilayer Perception and Gradient Boosting) were developed to predict malignant and benign TNs, whose performance indicators included area under the curve (AUC), accuracy, recall, precision and F1 score.Results A total of 1649 patients were enrolled in this study, with the median age of 45.15±13.41 years, and the male to female ratio was 1:5.055. …”
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Enhanced stroke risk prediction in hypertensive patients through deep learning integration of imaging and clinical data by Hui Li, Tianyu Zhang, Guochao Han, Zonghui Huang, Huiyu Xiao, Yunzhe Ni, Bo Liu, Wennan Lin, Yuan Lin

“…Objective This study aimed to develop a deep learning-based multimodal stroke risk prediction model by integrating carotid ultrasound imaging with multidimensional clinical data to enable precise identification of high-risk individuals among hypertensive patients. …”
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Genomics and integrative clinical data machine learning scoring model to ascertain likely Lynch syndrome patients by Ramadhani Chambuso, Takudzwa Nyasha Musarurwa, Alessandro Pietro Aldera, Armin Deffur, Hayli Geffen, Douglas Perkins, Raj Ramesar

“…We scored the clinicopathologic and somatic genomics data automatically using a machine learning model to discriminate between likely-LS and sporadic CRC cases. …”
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Improved Liquefaction Hazard Assessment via Deep Feature Extraction and Stacked Ensemble Learning on Microtremor Data by Oussama Arab, Soufiana Mekouar, Mohamed Mastere, Roberto Cabieces, David Rodríguez Collantes

“…We created a synthetic dataset of 1000 samples using realistic feature ranges that mimic the Rif data region to test model performance and conduct sensitivity analysis. …”
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Classifying Dry Eye Disease Patients from Healthy Controls Using Machine Learning and Metabolomics Data by Sajad Amouei Sheshkal, Morten Gundersen, Michael Alexander Riegler, Øygunn Aass Utheim, Kjell Gunnar Gundersen, Helge Rootwelt, Katja Benedikte Prestø Elgstøen, Hugo Lewi Hammer

“…<b>Methods:</b> To address this challenge, we conducted a comparative analysis of eight machine learning models on two metabolomics data sets from cataract patients with and without dry eye disease. …”
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Predicting Cardiovascular Aging Risk Based on Clinical Data Through the Integration of Mathematical Modeling and Machine Learning by Kuat Abzaliyev, Madina Suleimenova, Siming Chen, Madina Mansurova, Symbat Abzaliyeva, Ainur Manapova, Almagul Kurmanova, Akbota Bugibayeva, Diana Sundetova, Raushan Bitemirova, Nazipa Baizhigitova, Merey Abdykassymova, Ulzhas Sagalbayeva

“…Objective: This study aimed to develop and externally validate a mathematical model for predicting cardiovascular aging in individuals aged 65 and older, based on general clinical and behavioral data. Methods: The model was built using data from 800 individuals aged 65+ from Almaty, Kazakhstan. …”
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A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang, Jiexin Chen

“…To address the algorithmic limitations of deep learning-based echo extrapolation models, this study introduces three major improvements: (1) A Deep Convolutional Generative Adversarial Network (DCGAN) is integrated into the ConvLSTM-based extrapolation model to construct a DCGAN-enhanced architecture, significantly improving the quality of radar echo extrapolation; (2) Considering that the evolution of radar echoes is closely related to the surrounding meteorological environment, the study incorporates specific physical variable products from the initial zero-hour field of RMAPS-NOW (the Rapid-update Multiscale Analysis and Prediction System—NOWcasting subsystem), developed by the Institute of Urban Meteorology, China. …”
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Snow Depth Retrieval Using Sentinel-1 Radar Data: A Comparative Analysis of Random Forest and Support Vector Machine Models with Simulated Annealing Optimization by Yurong Cui, Sixuan Chen, Guiquan Mo, Dabin Ji, Lansong Lv, Juan Fu

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MACHINE LEARNING AS A CORPORATION'S TOOL FOR SELECTION OF SUPPLIERS by V. V. Baklushinskii, E. V. Pustynnikova

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Semi-supervised prediction of protein fitness for data-driven protein engineering by Alicia Olivares-Gil, José A. Barbero-Aparicio, Juan J. Rodríguez, José F. Díez-Pastor, César García-Osorio, Mehdi D. Davari

“…Data-driven strategies utilizing machine learning methods have emerged as a promising solution, yet their dependence on labelled training datasets poses a significant obstacle. …”
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A real-time predicting online tool for detection of people’s emotions from Arabic tweets based on big data platforms by Naglaa Abdelhady, Ibrahim E. Elsemman, Taysir Hassan A. Soliman

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Machine Learning Model for Predicting Net Environmental Effects by Sellappan Palaniappan, Rajasvaran Logeswaran, Shapla Khanam, Zhang Yujiao

“…This study presents a proof-of-concept machine learning model for predicting net environmental effects using synthetic data. …”
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Privacy preserving federated learning with convolutional variational bottlenecks by Daniel Scheliga, Patrick Mäder, Marco Seeland

“…Abstract Gradient Inversion (GI) attacks are a ubiquitous threat in Federated Learning as they exploit gradient leakage to reconstruct supposedly private training data. …”
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An intelligent identification for pest and disease detection in wheat leaf based on environmental data using multimodal data fusion by SHENG-HE XU, Sai Wang

“…Third, the data fusion process integrates image data for further analysis. …”
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Recent Progress in Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes by Sheng Du, Zixin Huang, Li Jin, Xiongbo Wan

“…With the advent of Industry 4.0, the amalgamation of sophisticated data analytics, machine learning, and artificial intelligence has become pivotal, unlocking new horizons in production efficiency, sustainability, and quality assurance. …”
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Survey of personalized federated learning for edge computing scenarios by HE Fan, WANG Yong, YANG Jing, YU Xu

“…Firstly, the background and scientific significance of personalized federated learning were elaborated, followed by rigorous analysis of data heterogeneity’s impacts. …”
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Container Truck High-Risk Events Prediction and Its Influencing Factors Analyses Based on Trajectory Data by Zhihao Zhu, Yuan Meng, Rongjun Cheng

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A review of machine learning applications in heart health by Ava Perrone, Taghi M. Khoshgoftaar

“…This review contributes an analysis of current machine learning methods in stroke and heart attack research, highlighting key gaps such as limited use of multimodal data, external validation, and class imbalance mitigation. …”
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