An Interpretable Deep Learning-Based Feature Reduction in Video-Based Human Activity Recognition by Micheal Dutt, Morten Goodwin, Christian W. Omlin

Get full text

Physics-informed transformation toward improving the machine-learned NLTE models of ICF simulations by Min Sang Cho, Paul E. Grabowski, Kowshik Thopalli, Thathachar S. Jayram, Michael J. Barrow, Jayaraman J. Thiagarajan, Rushil Anirudh, Hai P. Le, Howard A. Scott, Joshua B. Kallman, Branson C. Stephens, Mark E. Foord, Jim A. Gaffney, Peer-Timo Bremer

“…By replacing the costly nonlocal thermodynamic equilibrium (NLTE) model with machine-learning models, significant reductions in calculation time have been achieved. …”
Get full text

Enhancing Early Breast Cancer Detection with Infrared Thermography: A Comparative Evaluation of Deep Learning and Machine Learning Models by Reem Jalloul, Chethan Hasigala Krishnappa, Victor Ikechukwu Agughasi, Ramez Alkhatib

“…This study investigates and compares the performance of various deep learning and machine learning models in analyzing thermographic data to classify breast tissue as healthy, benign, or malignant. …”
Get full text

ScTCN-LightGBM: a hybrid learning method via transposed dimensionality-reduction convolution for loading measurement of industrial material by Zihua Chen, Runmei Zhang, Zhong Chen, Yu Zheng, Shunxiang Zhang

Subjects: Get full text

A Triple-Optimized Extreme Learning Machine Model for Power Load Forecasting by Haoxiang Gao, Weixin Kang, Miao Fan

Get full text

Machine learning in modeling, analysis and control of electrochemical reactors: A tutorial review by Wenlong Wang, Zhe Wu, Dominic Peters, Berkay Citmaci, Carlos G. Morales-Guio, Panagiotis D. Christofides

“…The complexity of these systems – arising from coupled electrochemical reactions with mass, heat and charge transport phenomena – poses significant challenges in modeling, analysis, and control. Machine learning (ML) has emerged as a promising tool for addressing these challenges by providing data-driven solutions to complex process modeling, optimization, and advanced control. …”
Get full text

Influence of high Andean grasslands on landslide reduction in Peru by Albert Franco Cerna-Cueva, Katherin Lourdes Uriarte-Barraza, Grecia Isabel Lobatón-Tarazona, Wensty Saenz-Corrales, Casiano Aguirre-Escalante, Peter Coaguila-Rodriguez, Manuel Reategui-Inga

Get full text

Optimized machine learning model for Alzheimer and epilepsy detection from EEG signals by P. Jasphin Jeni Sharmila, T. S. Shiny Angel

“…However, machine learning-based models lag in performance due to high dimensional EEG features. …”
Get full text

Empowering Diagnostics: An Ensemble Machine Learning Model for Early Liver Disease Detection by Abdulrahman Ahmed Jasim, Hajer Alwindawi, Layth Rafea Hazim

Get full text

HARNESSING MACHINE LEARNING IN HPV DIAGNOSTICS: MODEL PERFORMANCE, EXPLAINABILITY, AND CLINICAL INTEGRATION by Bahar Senel

“…Future research should prioritize explainable AI (XAI), federated learning, and robust validation studies to enhance model generalizability and real-world applicability. …”
Get full text

Modeling Political Discourse in Indonesia’s 2024 Election Using Unsupervised Machine Learning by Malikhatul Ibriza, Maya Rini Handayani, Wenty Dwi Yuniarti, Khothibul Umam

Get full text

A novel machine learning-based approach to determine the reduction factor for punching shear strength capacity of voided concrete slabs by Alireza Mahmoudian, Mussa Mahmoudi, Mohammad Yekrangnia, Nima Tajik, Mostafa Mohammadzadeh Taleshi

“…In this study, an innovative application of machine learning is proposed to address this challenge. …”
Get full text

Analysis of different IDS-based machine learning models for secure data transmission in IoT networks by Gladić Dejana, Petrovački Jelena, Sladojević Srdan, Arsenović Marko, Ristić Sonja

“…The aim of this study is to develop a machine learning (ML) model for network traffic classification and attack detection in IoT environments. …”
Get full text

Urban Flood Susceptibility Mapping for Toronto, Canada, Using Supervised Regression and Machine Learning Models by Baljeet Kaur, Andrew Binns, Edward McBean, Dan Sandink, Karen Castro, Bahram Gharabaghi

Get full text

Optimizing Superior Seed Bunches: A New Approach to Artificial Pollination with Machine Learning Models by Yabani Yabani, Retna Astuti Kuswardani

“…Keywords: Seed bunch optimization, pollination strategy, pollen viability, optimization model, machine learning, k-means clustering, multi-objective optimization.…”
Get full text

Correcting errors in seasonal Arctic sea ice prediction of Earth system models with machine learning by Z. He, Z. He, Z. He, Y. Wang, J. Brajard, X. Wang, X. Wang, Z. Shen, Z. Shen

“…In this study, we integrate a multilayer perceptron (MLP) machine learning (ML) model into the Norwegian Climate Prediction Model (NorCPM) to improve seasonal sea ice predictions. …”
Get full text

Machine learning models for predicting in-hospital mortality from acute pancreatitis in intensive care unit by Shuxing Wei, Hongmeng Dong, Weidong Yao, Ying Chen, Xiya Wang, Wenqing ji, Yongsheng Zhang, Shubin Guo

“…This study focuses on the development of advanced machine learning (ML) models to accurately predict in-hospital mortality among AP patients admitted to intensive care unit (ICU). …”
Get full text

Predicting Olympic Medal Performance for 2028: Machine Learning Models and the Impact of Host and Coaching Effects by Zhenkai Zhang, Tengfei Ma, Yunpeng Yao, Ningjia Xu, Yujie Gao, Wanwan Xia

“…This study develops two machine learning models to predict the medal performance of countries at the 2028 Olympic Games while systematically analyzing and quantifying the impacts of the host effect and exceptional coaching on medal gains. …”
Get full text

Machine learning techniques for estimating high–temperature mechanical behavior of high strength steels by C. Yazici, F.J. Domínguez-Gutiérrez

“…Machine learning (ML) has emerged as a powerful tool for predicting the mechanical behavior of materials by analyzing stress-strain data from tensile tests. …”
Get full text

A novel double machine learning approach for detecting early breast cancer using advanced feature selection and dimensionality reduction techniques by Suganya Athisayamani, Tamilazhagan S, A. Robert Singh, Jae-Yong Hwang, Gyanendra Prasad Joshi

“…The DML models learn the primary features using machine learning and deep learning models. …”
Get full text