Machine Learning Modeling Reveals Divergent Air Pollutant Responses to Stringent Emission Controls in the Yangtze River Delta Region by Qiufang Yao, Linhao Wang, Wenjing Qiu, Yutong Shi, Qi Xu, Yanping Xiao, Jiacheng Zhou, Shilong Li, Haobin Zhong, Jinsong Liu

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Exploring the potential of machine learning and magnetic resonance imaging in early stroke diagnosis: a bibliometric analysis (2004–2023) by Jian-cheng Lou, Xiao-fen Yu, Jian-jun Ying, Da-qiao Song, Wen-hua Xiong

“…The development in this field is marked by a coexisting pattern of interdisciplinary integration and refinement within major disciplinary branches.ConclusionThe application of machine learning in the early prediction and personalized medical plans for stroke patients using neuroimaging characteristics offers significant value. …”
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Spatial Prediction of High-Risk Areas for Asthma in Metropolitan Areas: A Machine Learning Approach Applied to Tehran, Iran by Alireza Mohammadi, Elahe Pishgar, Juan Aguilera

“…Additionally, GBM achieved the highest R-squared values (0.95 for training and 0.76 for testing) and lower MAE values (0.43 for training and 0.88 for testing). Effective pattern recognition was confirmed by EV values of 0.95 for training and 0.75 for testing, along with a Moran’s I value of 0.17, indicating minimal spatial autocorrelation.…”
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Using Fuzzy C-Means clustering and PCA in public health: A machine learning approach to combat CVD and obesity by Gamal Saad Mohamed Khamis, Nasser S. Alqahtani, Sultan Munadi Alanazi, Mohammed Muharrab Alruwaili, Mariam Shabram Alenazi, Maneaf A. Alrawaili

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Machine Learning-Assisted 3D Flexible Organic Transistor for High-Accuracy Metabolites Analysis and Other Clinical Applications by Caizhi Liao, Huaxing Wu, Luigi G. Occhipinti

“…Machine learning algorithms further enhance the analytical capabilities of FOT sensors by effectively processing complex data, identifying patterns, and predicting diagnostic outcomes with 100% high accuracy. …”
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A Novel Strategy for Automatic Selection of Cross‐Basin Data to Improve Local Machine Learning‐Based Runoff Models by Congyi Nai, Xingcai Liu, Qiuhong Tang, Liu Liu, Siao Sun, Paul P. J. Gaffney

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Machine learning-random forest model was used to construct gene signature associated with cuproptosis to predict the prognosis of gastric cancer by Xiaolong Liu, Pengxian Tao, He Su, Yulan Li

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Green cover change detection using a modified adaptive ensemble of extreme learning machines for North-Western India by Madhu Khurana, Vikas Saxena

“…It has already started affecting the weather patterns leading to disruption of normal life. Detecting change helps to monitor and plan the Earth’s resources in an efficient manner. …”
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Prediction and management of physical injuries caused by gym equipment and facilities using a Support Vector Machine (SVM) algorithm by Javad Shahlaei Bagheri

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BanglaNewsClassifier: A machine learning approach for news classification in Bangla Newspapers using hybrid stacking classifiers. by Tanzir Hossain, Ar-Rafi Islam, Md Humaion Kabir Mehedi, Annajiat Alim Rasel, M Abdullah-Al-Wadud, Jia Uddin

“…Previous studies mostly focused on traditional models, overlooking the potential of hybrid techniques to handle the ever-growing complex dataset and its linguistic patterns in Bangla to achieve higher accuracy. Addressing the challenge, this study presents a comprehensive approach to classify Bangla news articles into eight distinct categories using various machine learning and deep learning techniques. …”
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Optimum Combination of Spectral Variables for Crop Mapping in Heterogeneous Landscapes based on Sentinel-2 Time Series and Machine Learning by J. G. de Oliveira Júnior, J. C. D. M. Esquerdo, J. C. D. M. Esquerdo, R. A. C. Lamparelli, R. A. C. Lamparelli

“…Given the results found, the C2 classification scenario (with bands B3, B4, B5, B6, B7, B8, and B8A and the NDRE1, RESI, and MSR indexes) demonstrated the best LULC classification accuracy at the crop pattern level, compared to the other scenarios, with average values of 0.91, 0.88, 0.91, 0.89, and 0.89 (Global Accuracy, Producer Accuracy, User Accuracy, Kappa index, and F1-Score, respectively, for the TempCNN model), the which emphasized the importance of both qualitative and quantitative variability of sampling data and variables based on the Red Edge region for improving LULC classification processes in large-scale heterogeneous landscapes.…”
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Predictive Archaeological Risk Assessment at Reservoirs with Multitemporal LiDAR and Machine Learning (XGBoost): The Case of Valdecañas Reservoir (Spain) by Enrique Cerrillo-Cuenca, Primitiva Bueno-Ramírez

“…The model trained with 2018–2023 data exhibited the highest predictive performance (R² = 0.685), suggesting that sedimentary and erosional patterns are partially predictable. Finally, a multicriteria approach was applied using a DEM generated from 1957 aerial photographs to estimate past variations based on historical terrain conditions. …”
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Machine learning for automated electrical penetration graph analysis of aphid feeding behavior: Accelerating research on insect-plant interactions. by Quang Dung Dinh, Daniel Kunk, Truong Son Hy, Vamsi Nalam, Phuong D Dao

“…By integrating the plant and insect into an electrical circuit, EPG allows researchers to identify specific feeding behaviors based on their distinctive waveform patterns. However, the traditional manual analysis of EPG waveform data is time-consuming and labor-intensive, limiting research throughput. …”
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Navigating the data frontier in science assessment: Advancing data augmentation strategies for machine learning applications with generative artificial intelligence by Paul P. Martin, Nicole Graulich

“…Machine learning (ML) techniques are commonly seen as an inductive learning procedure, typically involving the identification of patterns in a specific training dataset to make predictions in novel contexts. …”
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Modeling of Systematic Errors and Precision Optimization Methods for Workpiece Clamping and Alignment System in Aeroengine Gearbox Automated Line Machining by DU Xueming, XIANG Yang, LIU Shun, JIN Sun

“…It then designs a Kolmogorov-Smirnov (K-S) test to verify the accuracy of the model, based on which, the accuracy loss patterns during multi-sub-master disc to master disc interchange processes is analyzed. …”
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Development of several machine learning based models for determination of small molecule pharmaceutical solubility in binary solvents at different temperatures by Mohammed Alqarni, Ali Alqarni

“…Given the complex, non-linear patterns in solubility behavior, three advanced regression approaches were utilized: Polynomial Curve Fitting, a Bayesian-based Neural Network (BNN), and the Neural Oblivious Decision Ensemble (NODE) method. …”
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Detection of Cumulative Bruising in Prunes Using Vis–NIR Spectroscopy and Machine Learning: A Nonlinear Spectral Response Approach by Lisi Lai, Hui Zhang, Jiahui Gu, Long Wen

“…Notably, the results revealed nonlinear reflectance variations in the near-infrared region associated with repeated low-energy impacts, highlighting the capacity of spectral response patterns to capture progressive physiological changes. …”
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Multi-Dimensional AE Signal Features in Eccentrically Loaded Concrete Structures: A Machine Learning Classification for Damage Progression by Shilong Ding, Alipujiang Jierula, Abudusaimaiti Kali, Tong Han, Tae-Min Oh

“…The integrated approach of clustering and random forest modeling demonstrates robust feasibility in identifying AE signal patterns associated with specific damage stages, achieving an 85% recognition rate for damage stage classification. …”
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Hybrid feature selection for real-time road surface classification on low-end hardware: A machine learning approach by Cong Ngo Van, Duc-Nghia Tran, Ton That Long, Nguyen Gia Minh Thao, Duc-Tan Tran

“…Vibration-based methods have proven effective in this field, utilizing vehicle vibration patterns to determine road surface conditions. One of the challenges in this field is using optimal datasets and classification models that meet real-time applications on low-end hardware devices. …”
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Machine learning approach for identifying and forecasting streamflow droughts in data limited basins of South Korea using threshold levels by Young-Ho Seo, Jang Hyun Sung, Byung-Sik Kim, Junehyeong Park

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