Web-based machine learning application for interpretable prediction of prolonged length of stay after lumbar spinal stenosis surgery: a retrospective cohort study with explainable... by Paierhati Yasheng, Paierhati Yasheng, Alimujiang Yusufu, Yasenjiang Yimiti, Haopeng Luan, Cong Peng, Xinghua Song

“…Postoperative complications and socioeconomic effects are evaluated using the clinical parameter of hospital length of stay (LOS). This study aimed to develop a machine learning-based tool that can calculate the risk of prolonged length of stay (PLOS) after surgery and interpret the results.MethodsPatients were registered from the spine surgery department in our hospital. …”
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Risk prediction of QTc prolongation occurrence in cancer patients treated with commonly used oral tyrosine kinase inhibitors: machine learning modeling or conventional statistical... by Hsiang-Wen Lin, Tien-Chao Lin, Chien-Ning Hsu, Tzu-Pei Yeh, Yu-Chieh Chen, Liang-Chih Liu, Chen-Yuan Lin

“…Both univariate logistic regression (LR) and supervised machine learning (ML) approaches were used for variable selection. …”
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Water quality index modelling and its application on artificial intelligence (AI) in conjunction with machine learning (ML) methodologies for mapping surface water potential zones... by Abhijeet Das

“…Subsequently, irrigation parameters showed that the model as a whole did a good job of forecasting the suitability of irrigation. …”
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Early prediction of grape disease attack using a hybrid classifier in association with IoT sensors by Apeksha Gawande, Swati Sherekar, Ranjit Gawande

“…Machine learning with IoT practices in the agriculture sector has the potential to address numerous challenges encountered by farmers, including disease prediction and estimation of soil profile. …”
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An Experimental Comparison of Self-Adaptive Differential Evolution Algorithms to Induce Oblique Decision Trees by Rafael Rivera-López, Efrén Mezura-Montes, Juana Canul-Reich, Marco-Antonio Cruz-Chávez

“…The findings highlight the potential of self-adaptive differential evolution algorithms to improve the effectiveness of oblique decision trees in machine learning applications.…”
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Software engineering meets legal texts: LLMs for auto detection of contract smells by Moriya Dechtiar, Daniel Martin Katz, Hongming Wang

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A Bayesian inversion supervised learning framework for the enzyme activity in graphene field-effect transistors by Ehsan Khodadadian, Samaneh Mirsian, Shahrzad Shashaani, Maryam Parvizi, Amirreza Khodadadian, Peter Knees, Wolfgang Hilber, Clemens Heitzinger

“…We present a pseudocode to explain the implementation of machine learning Bayesian inversion framework.…”
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A comprehensive review on the integration of artificial intelligence in friction stir welding for monitoring, modelling, and process optimization by Mostafa Akbari, Ezatollah Hassanzadeh, Yaghuob Dadgar Asl, Amirhossein Moghanian

“…The discussion is organized into three distinct sections, each focusing on the critical roles of AI and machine learning (ML) in FSW. The first section addresses process prediction, showcasing how AI techniques predict welding outcomes using historical data and process parameters, which enhances decision-making prior to actual implementation. …”
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A Study of Low-Frequency Vibration-Assisted Bandsawing of Metallic Parts by Tobias Tandler, Thomas Stehle, Hans-Christian Möhring

“…Subsequently, these parameter combinations were investigated on a real sawing machine with an excitation unit, analysing the extent to which the results from the analogue tests could be transferred to the real process.…”
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A spatiotemporal recurrent neural network for missing data imputation in tunnel monitoring by Junchen Ye, Yuhao Mao, Ke Cheng, Xuyan Tan, Bowen Du, Weizhong Chen

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Performance evaluation of rock fragmentation prediction based on RF‐BOA, AdaBoost‐BOA, GBoost‐BOA, and ERT‐BOA hybrid models by Junjie Zhao, Diyuan Li, Jian Zhou, Danial J. Armaghani, Aohui Zhou

“…However, accurate prediction of rock fragmentation after blasting is challenging due to the complicated blasting parameters and rock properties. For this reason, optimized by the Bayesian optimization algorithm (BOA), four hybrid machine learning models, including random forest, adaptive boosting, gradient boosting, and extremely randomized trees, were developed in this study. …”
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Correlation of the L-mode density limit with edge collisionality by A.D. Maris, C. Rea, A. Pau, W. Hu, B. Xiao, R. Granetz, E. Marmar, the EUROfusion Tokamak Exploitation team, the Alcator C-Mod team, the ASDEX Upgrade team, the DIII-D Team, the EAST Team, and the TCV team

“…This two-parameter boundary succeeds at predicting L-mode density limits by robustly identifying the radiative state preceding the terminal MHD instability. …”
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Investigating performance and variability of NIF ICF experiments with deep learning by M. Pokornik, S.F. Khan, J.A. Gaffney, B. MacGowan, A. Maris, K. Humbird, S. Haan

“…The use of machine learning to overcome these challenges has gained popularity and has had several successful applications by the scientific community. …”
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Comparing IOL refraction prediction accuracy and A-constant optimization for cataract surgery patients across South Indian and Midwestern United States populations by Omer Siddiqui, Elisa Warner, Miles Greenwald, Tingyang Li, Karthik Srinivasan, Aravind Haripriya, Nambi Nallasamy

“…Conclusions Substantial clinical and biometric differences exist between South Indian and Midwestern US cataract populations. Machine learning-based IOL refraction prediction formulas performed the best on the South Indian dataset both before and after population-specific parameter optimization. …”
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A Hybrid GRA-TOPSIS-RFR Optimization Approach for Minimizing Burrs in Micro-Milling of Ti-6Al-4V Alloys by Rongkai Tan, Abhilash Puthanveettil Madathil, Qi Liu, Jian Cheng, Fengtao Lin

“…This approach innovatively combines GRA and TOPSIS with a random forest regression (RFR) model, facilitating the exploration of nonlinear and complex relationships between input parameters and machining outcomes. Specifically, the effects of spindle speed, depth of cut, and feed rate per tooth on surface roughness and burr width generated during both down-milling and up-milling processes were systematically investigated using the proposed methodology. …”
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Investigation of a low-temperature-dominant HFCVD composite process for high-toughness diamond coated milling cutters in 5G-PCB applications by Yu Qiao, Zheng He, Shifei Chen, Enzhi Liu, Xinchang Wang

“…The specific design of the composite process parameters is guided by a coupling model that establishes the relationship between the performance characteristics of coated tools (diamond quality, tool toughness, and film-substrate adhesion strength), the inherent cobalt distribution within coated tools, and critical preparation parameters (substrate temperature Ts, cobalt removal depth D, grain size of diamond coatings G, and coating thickness h). …”
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Contribution of hydrogeological, well logs and machine learning in predicting the aquifer hydraulic properties in arid regions: a case study of Nubian Sandstone aquifer, Farafra Oa... by Ahmed Nosair, Muhammad Nabih, Ahmed Bakry

“…Traditionally, these parameters are obtained through pumping tests and well log data. …”
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Exploring the fresh and rheology properties of 3D printed concrete with fiber reinforced composites (3DP-FRC): a novel approach using machine learning techniques by Risul Islam Rasel, Md Minaz Hossain, Md Hasib Zubayer, Chaoqun Zhang

“…These parameters include OPC, W/B, W/S, FA, LP, SF, SP, VMA, W, h _f , R _i , AR, t _sf , F _t , and S _time /R _time . …”
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Development of a mathematical model of an autonomous power supply source with a free piston motor on the basis of a synchronous electric returning machine with a permanent magnets by A. R. Safin, I. V. Ivshin, E. I. Gracheva, T. I. Petrov

“…The created procedure for calculating the parameters of the electromagnetic component of the force of a synchronous machine with permanent magnets allows you to calculate and optimize the design parameters of the inductor and stator element of the electric motor under consideration. …”
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Interpretable machine learning model integrating contrast-enhanced CT environmental radiomics and clinicopathological features for predicting postoperative recurrence in lung adeno... by Song Lin, Song Lin, Yanli Niu, Yanli Niu, Lina Song, Yingjian Ye, Jinfang Yang, Junjie Liu, Xin Zhou, Xin Zhou, Peng An, Peng An

“…PurposeThis study aims to develop an interpretable predictive model combining contrast-enhanced CT (CECT) radiomics features with clinicopathological parameters to assess 3-year recurrence risk after surgery for lung adenocarcinoma (LA).MethodsA retrospective cohort of 350 LA patients (126 recurrence, 224 non-recurrence) from Xiangyang NO.1 People’s Hospital (2016–2023) was included. …”
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