Enhancing structural health monitoring with machine learning for accurate prediction of retrofitting effects by A. Presno Vélez, M. Z. Fernández Muñiz, J. L. Fernández Martínez

“…Structural health monitoring (SHM) systems used sensors to detect damage indicators such as vibrations and cracks, which were crucial for predicting service life and planning maintenance. Machine learning (ML) enhanced SHM by analyzing sensor data to identify damage patterns often missed by human analysts. …”
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Optimization Design of Lazy-Wave Dynamic Cable Configuration Based on Machine Learning by Xudong Zhao, Qingfen Ma, Jingru Li, Zhongye Wu, Hui Lu, Yang Xiong

“…To address this challenge, this study proposes a closed-loop optimization framework that couples machine learning with intelligent optimization algorithms for a dynamic cable configuration design. …”
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Optimised Extension of an Ultra-Low-Power RISC-V Processor to Support Lightweight Neural Network Models by Qiankun Liu, Sam Amiri

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Machine learning and multicriteria analysis for prediction of compressive strength and sustainability of cementitious materials by Khuram Rashid, Fatima Rafique, Zunaira Naseem, Fahad K. Alqahtani, Idrees Zafar, Minkwan Ju

“…In the initial phase, three machine learning models—Decision Tree, Random Forest, and Multi-layer Perceptron—were developed and trained on a dataset of 1030 records to predict sustainable concrete's compressive strength accurately. …”
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Machine learning analysis of CO2 and methane adsorption in tight reservoir rocks by Mehdi Maleki, Mohammad Rasool Dehghani, Moein Kafi, Ali Akbari, Yousef Kazemzadeh, Ali Ranjbar

“…In this study, the adsorption behavior of CO2 and CH4 in tight reservoirs is examined using experimental data and advanced machine learning (ML) techniques. The dataset incorporates key variables such as temperature, pressure, rock type, total organic carbon (TOC), moisture content, and the CO2 fraction in the injected gas. …”
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Equitable hospital length of stay prediction for patients with learning disabilities and multiple long-term conditions using machine learning by Emeka Abakasanga, Rania Kousovista, Georgina Cosma, Ashley Akbari, Francesco Zaccardi, Navjot Kaur, Navjot Kaur, Danielle Fitt, Gyuchan Thomas Jun, Reza Kiani, Satheesh Gangadharan

“…However, there is limited research on the application of machine learning (ML) models to this population. Furthermore, approaches designed for the general population often lack generalisability and fairness, particularly when applied across sensitive groups within their cohort.MethodThis study analyses hospitalisations of 9,618 patients with LD in Wales using electronic health records (EHR) from the SAIL Databank. …”
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Machine Learning Innovations for Improving Mineral Recovery and Processing: A Comprehensive Review* by Korie, Josephmartin Izuchukwu*, Chudi-Ajabor, Ogochukwu Gabriela, Ezeonyema, Chukwudalu Chukwuekezie, Oshim, Francisca Ogechukwu

“…To overcome the limitations of traditional mineral processing and recovery methods, cutting-edge technologies, including Machine learning (ML), emerge as a paradigm shift in this sector, offering predictive insights, data analysis, and real-time monitoring capabilities. …”
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Revealing the effects of environmental and spatio-temporal variables on changes in Japanese sardine (Sardinops melanostictus) high abundance fishing grounds based on interpretable... by Yongchuang Shi, Lei Yan, Shengmao Zhang, Fenghua Tang, Shenglong Yang, Wei Fan, Haibin Han, Yang Dai

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Ecological risks of PFAS in China’s surface water: A machine learning approach by Xinmiao Huang, Huijuan Wang, Xiaoyong Song, Zilin Han, Yilan Shu, Jiaheng Wu, Xiaohui Luo, Xiaowei Zheng, Zhengqiu Fan

“…This study investigated the ecological risks of PFAS in surface water in China under different Shared Socioeconomic Pathways (SSPs) using machine learning modeling, based on concentration data collected from 167 published papers. …”
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Efficient Hotspot Detection in Solar Panels via Computer Vision and Machine Learning by Nayomi Fernando, Lasantha Seneviratne, Nisal Weerasinghe, Namal Rathnayake, Yukinobu Hoshino

“…Using Unmanned Aerial Vehicle (UAV)-acquired thermal images from five datasets, the study compares five Machine Learning (ML) models and five Deep Learning (DL) models. …”
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Projecting Future Wetland Dynamics Under Climate Change and Land Use Pressure: A Machine Learning Approach Using Remote Sensing and Markov Chain Modeling by Penghao Ji, Rong Su, Guodong Wu, Lei Xue, Zhijie Zhang, Haitao Fang, Runhong Gao, Wanchang Zhang, Donghui Zhang

“…This study employs high-resolution projections from NASA’s Global Daily Downscaled Projections (GDDP) and the Intergovernmental Panel on Climate Change Sixth Assessment Report (IPCC AR6), combined with a machine learning and Cellular Automata–Markov (CA–Markov) framework to forecast the land cover transitions to 2040. …”
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A Rapid Design Method for Centrifugal Pump Impellers Based on Machine Learning by Y. Chen, W. Li, Y. Luo, L. Ji, S. Li, Y. Long

“…To reduce development time and costs, this paper proposes a rapid impeller design method focused on hydraulic performance, integrating traditional similarity design theory with machine learning. The proposed model uses neural networks to predict empirical coefficients, determine key dimensions such as the impeller’s inlet diameter, outlet diameter, outlet width, and axial distance. …”
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Advances in Composite Power System Reliability Assessment: Trends and Machine Learning Role by Chiranjeevi Yarramsetty, Tukaram Moger, Debashisha Jena, Veeranki Srinivasa Rao

“…A comparative examination of conventional and Machine Learning (ML)-based methods demonstrates that deep learning models, such as Convolutional Neural Networks, offer substantial reductions in computational time while maintaining reliability assessment precision. …”
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Hybrid high-performance computing enhanced machine learning framework for nano-thermal conductivity in MWNT-oil-based solar cooking systems by Nischal P. Mungle, Sumit Kumar, Dnyaneshwar M. Mate, Sham H. Mankar, Tejas R. Patil, Hirkani Padwad, Niteen T. Kakade, Nilesh Shelke, Haytham F. Isleem, Vikrant S. Vairagade

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An artificial intelligence optimization of NOx conversion efficiency under dual catalytic mechanism reaction based on multi-objective gray wolf algorithm by Zhiqing Zhang, Zicheng He, Yuguo Wang, Feng Jiang, Weihuang Zhong, Bin Zhang, Yanshuai Ye, Zibin Yin, Dongli Tan

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Data-driven insights of flow over heated elliptic cylinders: Machine learning and CFD perspectives on non-Newtonian forced convection by Anika Tahsin Meem, Md. Zhangir Hossain, Hasina Akter, Md. Mamun Molla

“…To alleviate the computational cost of high-fidelity CFD simulations, surrogate machine learning (ML) models — Random Forest, XGBoost, Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP) – are trained to predict CD, CL, and q′′¯. …”
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A Multispectral Automated Transfer Technique (MATT) for Machine-Driven Image Labeling Utilizing the Segment Anything Model (SAM) by James E. Gallagher, Aryav Gogia, Edward J. Oughton

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The Detection and Classification of Grape Leaf Diseases with an Improved Hybrid Model Based on Feature Engineering and AI by Fatih Atesoglu, Harun Bingol

“…The most well-known convolutional neural network (CNN) architectures, texture-based Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) methods, Neighborhood Component Analysis (NCA), feature reduction methods, and machine learning (ML) techniques are the methods used in this article. …”
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Enhancing Network Security: A Study on Classification Models for Intrusion Detection Systems by Abeer Abd Alhameed Mahmood, Azhar A. Hadi, Wasan Hashim Al-Masoody

“…This study leverages AI methods to develop nine classification models using supervised machine learning classifiers. …”
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COMPARATIVE ANALYSIS OF CLASSIFICATION MODELS FOR DETERMINING THE QUALITY OF WINE BY ITS CHEMICAL COMPOSITION by Vladimir S. Repkin, Artemy V. Li, Grigory Yu. Semenov, Nikita I. Sermavkin, Alexander S. Kovalenko, Nikolai S. Egoshin

“…Methods: machine learning methods for the formation of classification models; statistical methods for assessing the quality of classification and comparing classifiers. …”
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