Optimizing Concrete Mix Design for Cost and Carbon Reduction Using Machine Learning by Angga T. Yudhistira, Arief S. B. Nugroho, Iman Satyarno, Tantri N. Handayani, Malindu Sandanayake, Rimba Erlangga, Jonathan Lianto, Alfa Rosyid Ernanto

“…This study aims to create an optimal concrete mixture of cost and minimal carbon emissions, but the compressive strength meets the requirements. XGBoost Machine Learning Algorithm is used to make predictions, and PSO is used to obtain the optimal mixture. …”
Get full text

Predicting the reduction in heatstroke and heart disease-related mortality under urban modification scenarios using machine learning by Yukitaka Ohashi, Ko Nakajima, Yuya Takane, Yukihiro Kikegawa, Tomohiko Ihara, Kazutaka Oka

“…This study proposes a novel approach combining machine learning (ML) techniques with meteorological model simulations to evaluate the heat-related mortality reduction potential of a climate change adaptation measure, namely, the installation of energy-saving or temperature-decreasing modifications in an urban area (e.g. greening, high-albedo paints, and photovoltaics). …”
Get full text

Revolutionizing the future of hydrological science: Impact of machine learning and deep learning amidst emerging explainable AI and transfer learning by Rajib Maity, Aman Srivastava, Subharthi Sarkar, Mohd Imran Khan

“…Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are revolutionizing hydrology, driving significant advancements in water resource management, modeling, and prediction. …”
Get full text

Design of a Machine Learning-Based Platform for Currency Market Prediction: A Fundamental Design Model by Kevin Gordillo-Orjuela, Paulo Alonso Gaona-García, Carlos Enrique Montenegro-Marín

Subjects: Get full text

Hydrodynamic Shape Optimization of a Naval Destroyer by Machine Learning Methods by Andrea Serani, Matteo Diez

“…This paper explores the integration of advanced machine learning (ML) techniques within simulation-based design optimization (SBDO) processes for naval applications, focusing on the hydrodynamic shape optimization of the DTMB 5415 destroyer model. …”
Get full text

An Explainable Deep Learning-Enhanced IoMT Model for Effective Monitoring and Reduction of Maternal Mortality Risks by Sherine Nagy Saleh, Mazen Nabil Elagamy, Yasmine N. M. Saleh, Radwa Ahmed Osman

Get full text

Research on Slope Stability Based on Bayesian Gaussian Mixture Model and Random Reduction Method by Jingrong He, Tao Deng, Shouxing Peng, Xing Pang, Daochun Wan, Shaojun Zhang, Xiaoqiang Zhang

Get full text

Deep Learning Model of Image Classification Using Machine Learning by Qing Lv, Suzhen Zhang, Yuechun Wang

“…Not only were traditional artificial neural networks and machine learning difficult to meet the processing needs of massive images in feature extraction and model training but also they had low efficiency and low classification accuracy when they were applied to image classification. …”
Get full text

Interpretable Machine Learning Models for PISA Results in Mathematics by Ismael Gomez-Talal, Luis Bote-Curiel, Jose Luis Rojo-Alvarez

“…To address this question, we proposed using advanced Machine Learning techniques through possibly non-linear predictive models that identify key drivers of Mathematics performance to inform data-driven educational policies and interventions that improve learning outcomes. …”
Get full text

An interpretable integrated machine learning framework for genomic selection by Jinbu Wang, Jia Zhang, Wenjie Hao, Wencheng Zong, Mang Liang, Fuping Zhao, Longchao Zhang, Lixian Wang, Huijiang Gao, Ligang Wang

“…Although machine learning (ML) methods have shown growing promise for genomic selection (GS), several key challenges hinder their widespread application. …”
Get full text

Simultaneous OPEX and carbon footprint reduction with hydrogen enhancement in autothermal reforming: a machine learning–based surrogate modeling and optimization framework by Sahar Shahriari, Davood Iranshahi

Get full text

Energy Optimization in Ultrasound Tomography Through Sensor Reduction Supported by Machine Learning Algorithms by Bartłomiej Baran, Tomasz Rymarczyk, Dariusz Majerek, Piotr Szyszka, Dariusz Wójcik, Tomasz Cieplak, Marcin Gąsior, Marcin Marczuk, Edmund Wąsik, Konrad Gauda

Get full text

Machine Learning-Driven Optimization for Solution Space Reduction in the Quadratic Multiple Knapsack Problem by Diego Yanez-Oyarce, Carlos Contreras-Bolton, Fredy Troncoso-Espinosa, Carlos Rey

Get full text

A feature selection and scoring scheme for dimensionality reduction in a machine learning task by PHILEMON UTEN EMMOH, christopher ifeanyi Eke, Timothy Moses

Get full text

Enhancing Security in Industrial IoT Networks: Machine Learning Solutions for Feature Selection and Reduction by Ahmad Houkan, Ashwin Kumar Sahoo, Sarada Prasad Gochhayat, Prabodh Kumar Sahoo, Haipeng Liu, Syed Ghufran Khalid, Prince Jain

“…Six machine learning algorithms—Decision Trees, k-nearest neighbors, Gaussian Support Vector Machine, Neural Network, Support Vector Machines kernel, and Logistic Regression Kernel—were evaluated for both binary and multi-class classification using feature sets of 4, 12, 23, 50, and 79 features. …”
Get full text

Quadratic descriptors and reduction methods in a two-layered model for compound inference by Jianshen Zhu, Naveed Ahmed Azam, Shengjuan Cao, Ryota Ido, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu

Get full text

An interpretable machine learning model to predict hospitalizations by Hagar Elbatanouny, Hissam Tawfik, Tarek Khater, Anatoliy Gorbenko

“…This paper explores the application of machine learning techniques in addressing the challenge of hospitalization during pandemics. …”
Get full text

Evaluating soiling effects to optimize solar photovoltaic performance using machine learning algorithms by Muhammad Faizan Tahir, Anthony Tzes, Tarek H.M. El-Fouly, Mohamed Shawky El Moursi, Nauman Ali Larik

Subjects: Get full text

Explicating the Role of Agricultural Socialized Services on Chemical Fertilizer Use Reduction: Evidence from China Using a Double Machine Learning Model by Lulu Wang, Jie Lyu, Junyan Zhang

“…This study utilized field survey data from 894 maize farmers across three northeastern provinces of China. A double machine learning modeling framework was established to empirically examine the impact and mechanism of agricultural socialized services on chemical fertilizer use of farm households. …”
Get full text

Insights into landslide susceptibility: a comparative evaluation of multi-criteria analysis and machine learning techniques by Zuleide Ferreira, Bruna Almeida, Ana Cristina Costa, Manoel do Couto Fernandes, Pedro Cabral

Get full text