Optimizing Space Heating in Buildings: A Deep Learning Approach for Energy Efficiency by Fernando Almeida, Mauro Castelli, Nadine Corte-Real, Luca Manzoni

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Classification of intracranial tumors based on optical-spectral analysis by I. D. Romanishkin, T. A. Savelieva, A. Ospanov, K. G. Linkov, S. V. Shugai, S. A. Goryajnov, G. V. Pavlova, I. N. Pronin, V. B. Loschenov

“…In case the number of parameters exceeds a couple of dozens, it is necessary to use machine learning algorithms  to build a intraoperative decision support system for the surgeon. …”
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Failure Detection of Laser Welding Seam for Electric Automotive Brake Joints Based on Image Feature Extraction by Diqing Fan, Chenjiang Yu, Ling Sha, Haifeng Zhang, Xintian Liu

“…In response to these challenges, this article proposes a defect detection and classification method for laser welding seams of automotive brake joints based on machine vision inspection technology. Laser-welded automotive brake joints are subjected to weld defect detection and classification, and image processing algorithms are optimized to improve the accuracy of detection and failure analysis by utilizing the high efficiency, low cost, flexibility, and automation advantages of machine vision technology. …”
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Forecasting Hospitalization for Adult Asthma Patients in Emergency Departments Based on Multiple Environmental and Clinical Factors by Xi H, Zhang Y, Li W, Zhang C, Sun Y, Ji H, He Z, Chang C

“…After integrating ambient air pollutant and meteorological features, the RF model consistently outperformed the other models, achieving an AUC of 0.8555. The most critical parameters for predicting hospitalization were found to be illness severity, oxygen saturation, age, and heart rate.Interpretation: Machine learning (ML) models based on clinical, meteorological, and air pollution data can rapidly and accurately predict hospitalization of adult asthma patients in EDs.Keywords: asthma exacerbation, machine learning, emergency department…”
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Energy storage efficiency modeling of high-entropy dielectric capacitors using extreme learning machine and swarm-based hybrid support vector regression computational methods by Yas Al-Hadeethi, Taoreed O. Owolabi, Mouftahou B. Latif, Bahaaudin M. Raffah, Ahmad H. Milyani, Saheed A. Tijani

“…This work employs single hidden layer extreme learning machine (ELM) algorithm and hybrid particle swarm optimization-based support vector regression (PS-SVR) for determining energy storage efficiency of high-entropy ceramics. …”
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Development of a Drought Monitoring System for Winter Wheat in the Huang-Huai-Hai Region, China, Utilizing a Machine Learning–Physical Process Hybrid Model by Qianchuan Mi, Zhiguo Huo, Meixuan Li, Lei Zhang, Rui Kong, Fengyin Zhang, Yi Wang, Yuxin Huo

“…The existing simulation methods like physical process models and machine learning (ML) algorithms have limitations: physical models struggle with parameter acquisition at regional scales, while ML algorithms face difficulties in agricultural settings due to the presence of crops. …”
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Crosslinking of the Bioplast plastic material as a means of increasing its biomechanical stability and the effectiveness of keratoplasty for corneal ulcers by S. V. Flora, E. V. Chentsova, E. N. Iomdina

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Discrete‐time full‐order sensorless control of high‐speed interior permanent magnet synchronous motor in electric vehicle traction system by Dongcui Wang, Yuan Zhu, Sibei Wu, Lu Liu

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Clinical study on basal blood perfusion in the major arteries of the limbs by Rongji Zhang, Rongji Zhang, Hao Wang, Ji Shi, Ji Shi, Minghan Gao, Jianhui Li, Jianzheng Zhang

“…However, the interaction effect of diabetes status on limb blood flow was not significant (p > 0.05).ConclusionQuantitative ultrasound-derived limb perfusion parameters and their BMI/BSA correlations enable hemodynamic customization for machine perfusion systems in limb replantation. …”
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Attitude-Predictive Control of Large-Diameter Shield Tunneling: PCA-SVR Machine Learning Algorithm Application in a Case Study of the Zhuhai Xingye Express Tunnel by Hui Li, Yijun Tan, Decheng Zeng, Dong Su, Shiye Qiao

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A Machine Learning Model Integrating Tongue Image Features and Myocardial Injury Markers Predicts Major Adverse Cardiovascular Events in Patients with Coronary Heart Disease by Zhou M, Li J, Lim J, Xiao X, Xia Y, Wang Q, Xu Z

“…All the patients were classified into two different groups according to follow-up results showed whether there was MACE, and the tongue image of each patient was performed using SMX System 2.0 to normalised acquisition was performed using SMX System 2.0, and tongue body (TC_) and tongue coating (CC_) data were converted to RGB and HSV model parameters. Five supervised machine learning classifiers, including XGBoost, logistic regression, KNN, LightGBM, AdaBoost, were used in building the MACE prediction model.Results: 1293 patients were finally included in this study, with MACE occurred in 279 (21.6%) participants. …”
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A Comparative Study of Machine Learning Techniques for Predicting Mechanical Properties of Fused Deposition Modelling (FDM)-Based 3D-Printed Wood/PLA Biocomposite by Prashant Anerao, Atul Kulkarni, Yashwant Munde, Namrate Kharate

“…The dataset has been carefully prepared to facilitate machine learning for both training and testing, and it contains the experimental results and associated process parameters. …”
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ACCOUNT OF PECULIARITIES PERTAINING TO FORMATION OF SURFACE RUN-OFF QUALITY FROM TERRITORIES OF MACHINE-BUILDING ENTERPRISES WHILE CONSTRUCTING AND OPERATING WASTE WATER TREATMENT... by A. N. Kolobaev, O. K. Novikova

“…The paper reveals an influence of storm water quality from territories of machine-building enterprises on parameters of waste water treatment systems. …”
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Dynamic, Interpretable, Machine Learning–Based Outcome Prediction as a New Emerging Opportunity in Acute Ischemic Stroke Patient Care: A Proof-of-Concept Study by Ivan Petrović, Sava Njegovan, Olivera Tomašević, Dmitar Vlahović, Sonja Rajić, Željko Živanović, Isidora Milosavljević, Ana Balenović, Nikola Jorgovanović

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Forest age estimation using UAV-LiDAR and Sentinel-2 data with machine learning algorithms- a case study of Masson pine (Pinus massoniana) by Jinjin Chen, Xuejian Li, Zihao Huang, Jie Xuan, Chao Chen, Mengchen Hu, Cheng Tan, Yongxia Zhou, Yinyin Zhao, Jiacong Yu, Lei Huang, Meixuan Song, Huaqiang Du

“…Thus, when the combined Sentinel-2 and LiDAR data were used to establish these parameters, the highest accuracy in the estimation of Masson pine was obtained. …”
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Analyzing vehicle emissions using a hybrid machine learning approach using weighted average based k-means clustering for sustainable transportation decision-making by Mohd Mobasshir, Praveen Pachauri, Pratibha Kumari, Faisal Khan, Azhar Equbal, Osama Khan, Mohd Parvez, Taufique Ahamad, Shadab Ahmad

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Machine Learning-Based Prediction of Unconfined Compressive Strength of Sands Treated by Microbially-Induced Calcite Precipitation (MICP): A Gradient Boosting Approach and Correlat... by Saeed Talamkhani

“…The dataset includes eight input parameters: median sand particle size, uniformity coefficient of sand, initial void ratio, calcium chloride concentration, urea concentration, urease activity, optical density of bacteria, and calcite content. …”
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Development of a novel constitutive model incorporating phase transformation and dynamic recrystallization effects for laser-assisted machining of Ti6Al4V alloy by Binbin Xu, Xin Liu, Hongguang Liu, Shijia Shi, Yuyang Tang, Jun Zhang

“…These results not only enhance our theoretical understanding of microstructural evolution under extreme conditions but also provide practical guidelines for optimizing machining parameters in high-performance manufacturing systems.…”
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AI-driven wear monitoring of PVD TiAlN coated carbide insert in sustainable machining of Hastelloy C276: An industry 4.0 perspective by Binayak Sen, Subhankar Saha, Raman Kumar, Ramdevsinh Jhala, Nagaraj Patil, Abinash Mahapatro, Abhijit Bhowmik, A․Johnson Santhosh

“…Machine learning techniques, including deep neural networks (DNN), extreme gradient boosting (XGBoost), and support vector regression (SVR), were utilized to predict tool wear based on machining parameters. …”
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Inverse System Decoupling Control of Composite Cage Rotor Bearingless Induction Motor Based on Support Vector Machine Optimized by Improved Simulated Annealing-Genetic Algorithm by Chengling Lu, Junhui Cheng, Qifeng Ding, Gang Zhang, Jie Fang, Lei Zhang, Chengtao Du, Yanxue Zhang

“…Subsequently, an SVM regression equation is established, and the SVM kernel function parameters are optimized using the ISA-GA to train a high-precision inverse system decoupling control model. …”
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