An Improved Extreme Learning Machine (ELM) Algorithm for Intent Recognition of Transfemoral Amputees With Powered Knee Prosthesis by Yao Zhang, Xu Wang, Haohua Xiu, Wei Chen, Yongxin Ma, Guowu Wei, Lei Ren, Luquan Ren

“…To overcome the challenges posed by the complex structure and large parameter requirements of existing classification models, the authors propose an improved extreme learning machine (ELM) classifier for human locomotion intent recognition in this study, resulting in enhanced classification accuracy. …”
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A robust pressure drop prediction model in vertical multiphase flow: a machine learning approach by Fahd Saeed Alakbari, Mohammed Abdalla Ayoub, M. A. Awad, Tarek Ganat, Mysara Eissa Mohyaldinn, Syed Mohammad Mahmood

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Seismic Shear Strength Prediction of Reinforced Concrete Shear Walls by Stacking Multiple Machine Learning Models by Siming Tian, Xiangyong Ni, Yang Wang

“…Numerous factors influence RCSWs’ shear strength capacity, and the analytical models find it challenging to fully account for each factor’s impact on RCSWs’ shear-bearing capacity. Machine learning (ML) technology can deeply capture the mapping relationship between each input feature and the target value, and provide a more flexible and effective prediction method for RCSW shear-bearing capacity. …”
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Qualitative enhancement in machining efficiency of sncm8 alloy through hybrid ann-taguchi optimization approach by Sharad Chaudhari, Neeraj Sunheriya, Jayant Giri, Mohammad Kanan, Rajkumar Chadge, T. Sathish

“…Optimizing parameter settings increased machining efficiency, reduced tool wear by 25%, and improved surface quality, revealing sustainable production techniques.…”
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Predicting groundwater withdrawals using machine learning with limited metering data: Assessment of training data requirements by Dawit Asfaw, Ryan G. Smith, Sayantan Majumdar, Katherine Grote, Bin Fang, B.B. Wilson, V. Lakshmi, J.J. Butler, Jr.

“…Metering of pumping is key for implementing robust groundwater management, but metering is limited in most aquifers. Although machine learning methods have been used to estimate pumping over certain regions, these studies have not fully demonstrated the data quantity and input parameter requirements to accurately estimate regional groundwater pumping. …”
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COMPARISON OF POROSITY PREDICTION FROM SEISMIC DATA IN THE F3 BLOCK, NETHERLANDS USING MACHINE LEARNING by Urip Nurwijayanto Prabowo, Sudarmaji Sudarmaji, Jarot Setyowiyoto, Sismanto Sismanto

“…To address this issue, we employ machine learning, consisting of both an inversion generator and a forward generator, to estimate porosity from seismic data. …”
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Machine learning integration in thermodynamics: Predicting CO2 mixture saturation properties for sustainable refrigeration applications by Carlos G. Albà, Ismail I.I. Alkhatib, Lourdes F. Vega, Fèlix Llovell

“…Next, a constant, temperature-independent binary parameter is used to estimate the solubility profiles of CO2-derived mixtures in selected refrigerants. …”
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Non-destructive identification of commercial jerky types based on multi-band hyperspectral imaging with machine learning by Yuanxi Han, Liang Li, Siyuan Jiang, Pengpeng Sun, Wenliang Wu, Zhendong Liu

“…The findings demonstrate short-wave-near-infrared hyperspectral imaging combined with linear models (logistic regression and Support Vector Machine with linear kernel parameter settings) is better for identifying the types of jerky. …”
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Global air quality index prediction using integrated spatial observation data and geographics machine learning by Tania Septi Anggraini, Hitoshi Irie, Anjar Dimara Sakti, Ketut Wikantika

“…This study aims to detect and improve the accuracy of the Global Air Quality Index from Remote Sensing (AQI-RS) by integrating AQI from ground-based stations with driving factors such as meteorological, environmental, sources of air pollution, and air pollution magnitude from satellite observation parameters as independent variables using Geographics Machine Learning (GML). …”
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Machine Learning Analysis of Hydrological and Hydrochemical Data from the Abelar Pilot Basin in Abegondo (Coruña, Spain) by Javier Samper-Pilar, Javier Samper-Calvete, Alba Mon, Bruno Pisani, Antonio Paz-González

“…The Abelar pilot basin in Coruña (northwestern Spain) has been monitored for hydrological and hydrochemical data to assess the effects of eucalyptus plantation and manure applications on water resources, water quality, and nitrate contamination. Here, we report the machine learning analysis of hydrological and hydrochemical data from the Abelar basin. …”
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Classification of Epileptic Seizures by Simple Machine Learning Techniques: Application to Animals’ Electroencephalography Signals by Illia Pidvalnyi, Anna Kostenko, Oleksandr Sudakov, Dmytro Isaev, Oleksandr Maximyuk, Oleg Krishtal, Olena Iegorova, Ievgen Kabin, Zoya Dyka, Steffen Ortmann, Peter Langendorfer

“…A principal component analysis was applied for feature selection before using a support vector machine for the detection of seizures. Hjorth’s parameters and Daubechies discrete wavelet transform coefficients were found to be the most informative features of EEG data. …”
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Machine learning model for reproducing subjective sensations and alleviating sound-induced stress in individuals with developmental disorders by Itsuki Ichikawa, Yukie Nagai, Yukie Nagai, Yasuo Kuniyoshi, Yasuo Kuniyoshi, Yasuo Kuniyoshi, Makoto Wada

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Photoplethysmography-based HRV analysis and machine learning for real-time stress quantification in mental health applications by Ying-Ying Tsai, Yu-Jie Chen, Yen-Feng Lin, Fan-Chi Hsiao, Ching-Han Hsu, Lun-De Liao

“…Machine learning can capture complex nonlinear relationships among HRV parameters during stress-inducing tasks, adapts to individual stress response variations, and provides real-time stress level predictions. …”
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Pemodelan Sistem Monitoring Kualitas Udara Pintar Berbasis Internet of Things dengan Pendekatan Machine Learning by Eddy Prasetyo Nugroho, Ani Anisyah, Deva Shofa Al Fathin, Muhammad Nur Yasin Amadudin, Muhammad Satria Ramadhani, Yosafat

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Research on deterioration mechanism of graded gravel in high-speed railway subgrade layer based on machine vision by Tai-feng Li, Xian-pu Xiao, Rong-hui Yan, Kang Xie, Jia-shen Li, Ruo-han Dai

“…In the DEM simulation, with the increase of Rc, the mechanical strength of graded gravel decreased and the micro indicators (e.g., sliding rate, strong force chains, and anisotropy parameter an) inside the fillers gradually decreased, while the particle rotation continuously increased. …”
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Real‐Time Self‐Optimization of Quantum Dot Laser Emissions During Machine Learning‐Assisted Epitaxy by Chao Shen, Wenkang Zhan, Shujie Pan, Hongyue Hao, Ning Zhuo, Kaiyao Xin, Hui Cong, Chi Xu, Bo Xu, Tien Khee Ng, Siming Chen, Chunlai Xue, Zhanguo Wang, Chao Zhao

“…This approach guides the dynamic optimization of growth parameters, allowing real‐time feedback control to adjust the QDs emission for lasers. …”
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Modeling the determinants of attrition in a two-stage epilepsy prevalence survey in Nairobi using machine learning by Daniel M. Mwanga, Isaac C. Kipchirchir, George O. Muhua, Charles R. Newton, Damazo T. Kadengye, Abankwah Junior, Albert Akpalu, Arjune Sen, Bruno Mmbando, Charles R. Newton, Cynthia Sottie, Dan Bhwana, Daniel Mtai Mwanga, Damazo T. Kadengye, Daniel Nana Yaw, David McDaid, Dorcas Muli, Emmanuel Darkwa, Frederick Murunga Wekesah, Gershim Asiki, Gergana Manolova, Guillaume Pages, Helen Cross, Henrika Kimambo, Isolide S. Massawe, Josemir W. Sander, Mary Bitta, Mercy Atieno, Neerja Chowdhary, Patrick Adjei, Peter O. Otieno, Ryan Wagner, Richard Walker, Sabina Asiamah, Samuel Iddi, Simone Grassi, Sloan Mahone, Sonia Vallentin, Stella Waruingi, Symon Kariuki, Tarun Dua, Thomas Kwasa, Timothy Denison, Tony Godi, Vivian Mushi, William Matuja

“…Background: Attrition is a challenge in parameter estimation in both longitudinal and multi-stage cross-sectional studies. …”
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High‐Resolution National‐Scale Water Modeling Is Enhanced by Multiscale Differentiable Physics‐Informed Machine Learning by Yalan Song, Tadd Bindas, Chaopeng Shen, Haoyu Ji, Wouter J. M. Knoben, Leo Lonzarich, Martyn P. Clark, Jiangtao Liu, Katie vanWerkhoven, Sam Lamont, Matthew Denno, Ming Pan, Yuan Yang, Jeremy Rapp, Mukesh Kumar, Farshid Rahmani, Cyril Thébault, Richard Adkins, James Halgren, Trupesh Patel, Arpita Patel, Kamlesh Arun Sawadekar, Kathryn Lawson

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Stability Domain Construction and Tuning for External Loop Controller Parameters Based on Permanent Magnet Wind Power Generation Systems by Hao Liang, Fei He, Guangtao Zhang, Chuan Qin

“…Based on the closed-loop frequency domain model of permanent magnet synchronous generator–based wind power generation system (PMSG-WPGS), the stability domain of the control parameters is constructed through the D-partition method in this paper to acquire the range of machine-side converter (MSC) and grid-side converter (GSC) parameters. …”
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Prediction of unobserved bifurcation by unsupervised extraction of slowly time-varying system parameter dynamics from time series using reservoir computing by Keita Tokuda, Yuichi Katori

“…However, predicting the behavior of systems with temporal parameter variations without knowledge of true parameter values remains a significant challenge.MethodsThis study uses reservoir computing framework to address this problem by unsupervised extraction of slowly varying system parameters from time series data. …”
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