Global burden of vertebral fractures from 1990 to 2021 and projections for the next three decades by Honghui Lei, Zebin Huang, Fangyong Wang, Tao Liu, Yang Yu, Sitong Su, Meiling Cheng, Haoyuan Chen

“…The annual percentage change (EAPC) was calculated to represent temporal trends from 1990 to 2021. Machine learning was used to predict the global burden of vertebral fractures over the next 30 years. …”
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SGA-Driven feature selection and random forest classification for enhanced breast cancer diagnosis: A comparative study by Abrar Yaqoob, Navneet Kumar Verma, Mushtaq Ahmad Mir, Ghanshyam G. Tejani, Nashwa Hassan Babiker Eisa, Hind Mamoun Hussien Osman, Mohd Asif Shah

“…The mean accuracies ranged from 85.35 to 94.33%, highlighting a balance between feature reduction and classification accuracy. Future work will explore the integration of other nature-inspired algorithms and deep learning models to further enhance performance and clinical applicability.…”
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Soil Organic Matter Content Prediction Using Multi-Input Convolutional Neural Network Based on Multi-Source Information Fusion by Li Guo, Qin Gao, Mengyi Zhang, Panting Cheng, Peng He, Lujun Li, Dong Ding, Changcheng Liu, Francis Collins Muga, Masroor Kamal, Jiangtao Qi

“…Incorporating multi-source data into traditional machine learning models (SVM, RF, and PLS) also improved prediction accuracy, with R² improvements ranging from 4% to 11%. …”
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A comprehensive IoT cloud-based wind station ready for real-time measurements and artificial intelligence integration by Décio Alves, Fábio Mendonça, Sheikh Shanawaz Mostafa, Fernando Morgado-Dias

“…The proposed cloud-computing Internet of Things-based Automated Weather Station framework demonstrates significant potential for accurate and efficient wind measurement and monitoring, paving the way for future advancements in high temporal resolution wind monitoring systems capable of producing big data prepared for subsequent machine learning model approaches.…”
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Revolutionizing Clear-Sky Humidity Profile Retrieval with Multi-Angle-Aware Networks for Ground-Based Microwave Radiometers by Yinshan Yang, Zhanqing Li, Jianping Guo, Yuying Wang, Hao Wu, Yi Shang, Ye Wang, Langfeng Zhu, Xing Yan

“…Based on the 7-year (2018–2024) in situ measurements from Beijing, Nanjing, and Shanghai, validation results reveal that AngleNet achieves substantial improvements, with an average R2 of 0.71 and a root mean square error (RMSE) of 10.39%, surpassing conventional models such as LGBM (light gradient boosting machine) and RF (random forest) by over 10% in both metrics, and demonstrating a remarkable 41% increase in R2 and a 10% reduction in RMSE compared to the previous BRNN method (batch normalization and robust neural network). …”
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TELEPROM Psoriasis: Enhancing patient-centered care and health-related quality of life (HRQoL) in moderate-to-severe plaque psoriasis by Gabriel Mercadal-Orfila, Gabriel Mercadal-Orfila, Piedad López Sánchez, Aranzazu Pou Alonso, Olatz Ibarra-Barrueta, Emilio Monte-Boquet, Joaquin Borrás Blasco, Nuria Padullés Zamora, Patricia Sanmartin-Fenollera, Cristina Capilla Montes, M. Ángeles Bernabéu Martínez, Salvador Herrera-Pérez

“…Machine learning models, particularly Random Forest (AUC = 0.98) and Support Vector Machine (AUC = 0.96), effectively predicted patient engagement. …”
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Risk assessment of water inrush from coal floor based on enhanced samples with class distribution by Shiwei Liu, Jiaxin Zhao, Hao Yu, Jiaqi Chen

“…This method was used to generate virtual samples and enhance the measured database. A prediction model of the water inrush risk for the coal seam floor was established using a coupled algorithm of extreme learning machines, self-adaptive differential evolution, and CDMTD (PCA-CDMTD-SaDE-ELM) and was used to evaluate the water inrush risk in the 19,105 working face of the Yunjialing Mine. …”
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Projections of single-level indirect lumbar interbody fusion volume and associated costs for Medicare patients to 2050 by Kyle A. Mani, BS, Samuel N. Goldman, BS, Noel Akioyamen, MD, Emily Kleinbart, BS, Yaroslav Gelfand, MD, Saikiran Murthy, DO, Jonathan Krystal, MD, Ananth Eleswarapu, MD, Reza Yassari, MD, Mitchell S. Fourman, MD, MPhil

“…Methods: Data was acquired from the Centers for Medicare and Medicaid Services (CMS) from January 1, 2000 to December 31, 2022, using CPT codes to identify ALIF/OLIF/LLIF procedures. The Prophet machine learning algorithm, using Bayesian Inference, was applied to data from 2000 to 2019 to generate point forecasts for 2020 to 2050 with 95% forecast intervals (FIs). …”
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Fusion of BIM and SAR for Innovative Monitoring of Urban Movement – Towards 4D Digital Twin by C.-H. Yang, C. Stemmler, N. Wolf, M. Koppe, T. Rudolph, A. Müterthies

“…This fusion step is implemented by machine learning, employing a novel distance metric adapted through dimensionality reduction. …”
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Borohydride Synthesis of Silver Nanoparticles for SERS Platforms: Indirect Glucose Detection and Analysis Using Gradient Boosting by Viktoriia Bakal, Olga Gusliakova, Anastasia Kartashova, Mariia Saveleva, Polina Demina, Ilya Kozhevnikov, Evgenii Ryabov, Daniil Bratashov, Ekaterina Prikhozhdenko

“…To enable glucose sensing, the substrates were further functionalized with glucose oxidase (GOx), allowing detection in the 1–10 mM range. Machine learning classification and regression models based on gradient boosting were employed to analyze SERS spectra, enhancing the accuracy of quantitative predictions (R² = 0.971, accuracy = 0.938, limit of detection = 0.66 mM). …”
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Agricultural Non-Point Source Pollution: Comprehensive Analysis of Sources and Assessment Methods by Fida Hussain, Shakeel Ahmed, Syed Muhammad Zaigham Abbas Naqvi, Muhammad Awais, Yanyan Zhang, Hao Zhang, Vijaya Raghavan, Yiheng Zang, Guoqing Zhao, Jiandong Hu

“…It assesses current evaluation models, encompassing field- and watershed-scale methodologies, and investigates novel technologies such as Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) that possess the potential to enhance pollution monitoring and predictive precision. …”
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Assessing Earthquake-Triggered Ecosystem Carbon Loss Using Field Sampling and UAV Observation by Wen Zeng, Baofeng Di, Yu Zhan, Wen He, Junhui Li, Ziquan Zuo, Siwen Yu, Tan Mi

“…This study quantifies ecosystem carbon loss from the Luding Earthquake by integrating field sampling, UAV-based LiDAR, and machine learning models to assess vegetation and soil carbon stocks. …”
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Performance analysis of dual-fuel engines using acetylene and microalgae biodiesel: The role of fuel injection timing by M. Sonachalam, R. Jayaprakash, V. Manieniyan, P.S. Raghavendra Rao, G. Vinodhini, Manish Sharma, Teku Kalyani, Mahammadsalman Warimani, Hasan Sh Majdi, T.M. Yunus Khan, Abdul Saddique Shaik, Keerthi Shetty

“…To predict engine performance and emission characteristics, advanced machine learning models were employed and evaluated using four statistical criteria, including R-squared, mean absolute error (MAE), and mean squared error (MSE). …”
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A service-oriented microservice framework for differential privacy-based protection in industrial IoT smart applications by Dileep Kumar Murala, K. Vara Prasada Rao, Veera Ankalu Vuyyuru, Beakal Gizachew Assefa

“…The architecture integrates Differential Privacy (DP) mechanisms into the machine learning pipeline to safeguard sensitive information. …”
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FiSC: A Novel Approach for Fitzpatrick Scale-Based Skin Analyzer’s Image Classification by Guillermo Crocker Garcia, Muhammad Numan Khan, Aftab Alam, Josue Obregon, Tamer Abuhmed, Eui-Nam Huh

“…Our method involves modeling image features as a nine-dimensional feature vector, followed by a dimensionality reduction process to identify the most influential features and dominant areas within the feature space, enabling deployment on low-power devices. …”
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Review on atomistic and quantum mechanical simulation approaches in chemical mechanical planarization by Seokgyu Ryu, Murali Ramu, Patrick Joohyun Kim, Junghyun Choi, Kangchun Lee, Jihoon Seo

“…Future research directions include development of machine learning-accelerated simulations, integration of multiphysics models connecting molecular-scale phenomena to wafer-scale uniformity, and predictive frameworks for novel slurry chemistries. …”
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Reconsidering the use of race, sex, and age in clinical algorithms to address bias in practice: A discussion paper by Reanna Panagides, Jessica Keim-Malpass

“…By applying a framework for understanding sources of harm throughout the machine learning life cycle and presenting case studies, this paper aims to examine sources of potential harms (i.e. representational and allocative harm) associated with including sex and age in clinical decision-making algorithms, particularly risk calculators. …”
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The role of artificial intelligence in promoting health and developing preventive strategies for diabetes by Ameneh Marzban

“…For instance, machine learning models can evaluate patient records, lifestyle factors, and genetic information to deliver precise risk assessments and personalized recommendations. …”
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Honeybee colony soundscapes: Decoding distance-based cues and environmental stressors by Nayan Di, Chunjing Zhu, Zongwen Hu, Muhammad Zahid Sharif, Baizhong Yu, Fanglin Liu

“…Using OpenL3 embeddings and machine learning models, the study achieved accurate classification of food source distances based on acoustic features, with the K-Nearest Neighbors (KNN) model demonstrating superior performance. …”
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Dataset on droplet spreading and rebound behavior of water and viscous water-glycerol mixtures on superhydrophobic surfaces with laser-made channelsMendeley Data by Matic Može, Samo Jereb, Robert Lovšin, Jure Berce, Matevž Zupančič, Iztok Golobič

“…It can help validate theoretical and numerical models of droplet spreading, retracting, and rebounding from poorly wettable surfaces, optimize superhydrophobic surfaces for applications such as self-cleaning and drag reduction, and contribute to machine learning models predicting droplet behavior. …”
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