Toward Smart Condition Monitoring of Rotatory Machines: An Optimized Probabilistic Signal Reconstruction Methodology for Fault Prediction With Multisource Uncertainties by Xiaomo Jiang, Weijian Tang, Haixin Zhao, Xueyu Cheng

“…Three signal reconstruction methods, that is, Bayesian wavelet multiscale decomposition, probabilistic principal component analysis, and auto-associative kernel regression, were seamlessly integrated to address the noise, high dimensionality, and correlation in the sensed multivariate vibration data for accurate fault prediction. The bandwidth parameter in the auto-associative kernel regression approach was optimized to represent the health status of the rotatory machine. …”
Get full text

Machine learning for predicting metabolic-associated fatty liver disease including NHHR: a cross-sectional NHANES study. by Liyu Lin, Yirui Xie, Zhuangteng Lin, Cuiyan Lin, Yichun Yang

“…Finally, a metabolic - associated fatty liver disease (MAFLD) prediction model was developed using seven machine learning methods, including eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Multilayer Perceptron (MLP), Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and logistic regression. …”
Get full text

Variogram modelling optimisation using genetic algorithm and machine learning linear regression: application for Sequential Gaussian Simulations mapping by André William Boroh, Alpha Baster Kenfack Fokem, Martin Luther Mfenjou, Firmin Dimitry Hamat, Fritz Mbounja Besseme

“…This work presents a novel integration of GA and machine learning for variogram modelling, offering an automated, efficient approach to parameter estimation. …”
Get full text

Machine Learning-Driven Optimization of Transport Layers in MAPbI₃ Perovskite Solar Cells for Enhanced Performance by Velpuri Leela Devi, Piyush Kuchhal, Debasis de, Abhinav Sharma, Neeraj Kumar Shukla, Mona Aggarwal

“…This study aims to analyse the performance of MAPbI3-based perovskite solar cells (PSCs) by integrating machine learning (ML) models with the SCAPS-1D simulator. …”
Get full text

The Effect of Hydraulic Partitioning on Prediction the Rate of Bed Load Transport in Gravel-bed Rivers using Support Vector Machine by Kiyoumars Roushangar, Mohammad Hosseini, Saman Shahnazi

“…Considering the influential parameters to predict bed load transport rate in 20 gravel-bed rivers, in this study, the accuracy of support vector machine was investigated in different intervals of hydraulic and sediment parameters. …”
Get full text

Optimization of heat and mass transfer in chemically radiative nanofluids using Cattaneo-Christov fluxes and advanced machine learning techniques by Shazia Habib, Saleem Nasir, Zeeshan Khan, Abdallah S. Berrouk, Waseem Khan, Saeed Islam

Get full text

An artificial intelligence and machine learning-driven CFD simulation for optimizing thermal performance of blood-integrated ternary nano-fluid by Mohib Hussain, Du Lin, Hassan Waqas, Qasem M. Al-Mdallal

“…The heat transfer ability of ternary nano-fluid is enhanced with an increase in the couple stress parameter while, a rising Hartmann number results in more thermal diffusion. …”
Get full text

Performance Optimization of Multi-Roller Flat Burnishing Process in Terms of Surface Properties by Minh-Thai Le, An-Le Van, Trung Thanh Nguyen

Get full text

Space-Based Mapping of Pre- and Post-Hurricane Mangrove Canopy Heights Using Machine Learning with Multi-Sensor Observations by Boya Zhang, Daniel Gann, Shimon Wdowinski, Chaohao Lin, Erin Hestir, Lukas Lamb-Wotton, Khandker S. Ishtiaq, Kaleb Smith, Yuepeng Li

Get full text

Estimating Winter Canola Aboveground Biomass from Hyperspectral Images Using Narrowband Spectra-Texture Features and Machine Learning by Xia Liu, Ruiqi Du, Youzhen Xiang, Junying Chen, Fucang Zhang, Hongzhao Shi, Zijun Tang, Xin Wang

“…Correlation analysis and autocorrelation analysis were utilized to determine the final spectral feature scheme, texture feature scheme, and spectral-texture feature scheme. Subsequently, machine learning algorithms were applied to develop estimation models for winter canola biomass. …”
Get full text

Machine Learning Aided Tapered Four-Port MIMO Antenna for V2X Communications With Enhanced Gain and Isolation by Nagesh Kallollu Narayanaswamy, Yazeed Alzahrani, Krishna Kanth Varma Penmatsa, Ashish Pandey, Ajay Kumar Dwivedi, Vivek Singh, Manoj Tolani

“…The stacking ensemble method, combining these models, was used to improve the accuracy of the antenna performance prediction. By leveraging machine learning, the final design was achieved more efficiently, significantly reducing the simulation time and enabling more precise parameter tuning for optimal performance. …”
Get full text

Machine learning prediction of anxiety symptoms in social anxiety disorder: utilizing multimodal data from virtual reality sessions by Jin-Hyun Park, Yu-Bin Shin, Dooyoung Jung, Ji-Won Hur, Seung Pil Pack, Heon-Jeong Lee, Hwamin Lee, Chul-Hyun Cho, Chul-Hyun Cho

“…IntroductionMachine learning (ML) is an effective tool for predicting mental states and is a key technology in digital psychiatry. …”
Get full text

Predictive analysis of clinical features for HPV status in oropharynx squamous cell carcinoma: A machine learning approach with explainability by Emily Diaz Badilla, Ignasi Cos, Claudio Sampieri, Berta Alegre, Isabel Vilaseca, Simone Balocco, Petia Radeva

“…Materials and Methods:: We employed the RADCURE dataset clinical information to train six Machine Learning algorithms, evaluating them via cross-validation for grid search hyper-parameter tuning and feature selection as well as a final performance measurement on a 20% sample test set. …”
Get full text

Pemetaan Daerah Rawan Longsor di Kabupaten Bandung Barat menggunakan Metode Machine Learning dengan Teknik SVM by Afif Faadhilah, Hary Nugroho

“…Kata kunci: Longsor, Machine Learning, Pemetaan, dan Support Vector Machine.    …”
Get full text

Robustness of Machine Learning Predictions for Determining Whether Deep Inspiration Breath-Hold Is Required in Breast Cancer Radiation Therapy by Wlla E. Al-Hammad, Masahiro Kuroda, Ghaida Al Jamal, Mamiko Fujikura, Ryo Kamizaki, Kazuhiro Kuroda, Suzuka Yoshida, Yoshihide Nakamura, Masataka Oita, Yoshinori Tanabe, Kohei Sugimoto, Irfan Sugianto, Majd Barham, Nouha Tekiki, Miki Hisatomi, Junichi Asaumi

“…Although previous studies have explored the potential of machine learning (ML) to predict which patients might benefit from DIBH, none have rigorously assessed ML model performance across various MHD thresholds and parameter settings. …”
Get full text

The effects of snakebite on haematological and clotting parameters of snakebite victims attending the Snakebite Research, Training and Treatment Centre Kaltungo, Gombe State Nigeri... by Tokdung, M. , Sagir, A. , Akuyam, S.A. , Mohammed, Nuhu

“…The aim of this study is to evaluate the effect of haemotoxic snakebite on some clotting and haematological parameters in Kaltungo, Gombe State. It is a cross-sectional study involving 200 snakebite victims and 100 control subjects. …”
Get full text

How milling parameters influence surface texture and osteoblasts response when manufacturing Ti6Al4V medical parts by Michela Sanguedolce, Jessica Dal Col, Stefania Ferrisi, Francesco G. Modica, Vito Basile, Giuseppina Ambrogio, Luigino Filice

“…By varying the technological parameters such as the cutting speed and depth and, consequently, the surface condition, the number of cells after a 72-h culture was measured to correlate cell proliferation with the process parameters. …”
Get full text

Development and Validation of Predictive Models for Differentiating Resectable Stage III Peripheral SCLC from NSCLC Using Radiomic Features and Clinical Parameters by Junjie Zhang MD, Ligang Hao MD, Qiuxu Zhang MD, Lina Zheng MD, Qian Xu PhD, Fengxiao Gao MD

“…Radiomic feature selection was performed using the LASSO algorithm, and nine machine learning models were evaluated. The optimal model was employed to compute the radiomics score (Rad-score) and construct a clinical model. …”
Get full text

Environmentally sustainable color fading approaches of denim fabric using alternative garments dry process: An insight into chromatic parameters and physical properties by Md. Tareque Rahaman, Abdullah Al Rakib Shikder, Md. Abdullah Al Mamun

“…Process variables such as different sodium hydroxide concentrations (5 g/L, 10 g/L, 15 g/L, and 20 g/L) and heat treatment by stenter machine operating temperatures (140°C, 160°C, 180°C, and 200 °C) are used to conducting the research. …”
Get full text

Dynamics of quality of life parameters and echocardiography depending on adherence to treatment in patients with chronic rheumatic heart disease at 5-year follow-up by V. S. Petrov

“…To assess the dynamics of quality of life indicators and echocardiography parameters depending on adherence to treatment in patients with chronic rheumatic heart disease at 5-year follow-up.Material and methods. …”
Get full text