Tuning structural dynamics through architected inner material designs: Numerical, experimental, and machine learning analysis by Irfan Ul Hassan, Agyapal Singh, Dimitrios C. Rodopoulos, Alexander Morawietz, Andrea Bergamini, Nikolaos Karathanasopoulos

“…Evidence is provided that the appropriate microstructure selection allows for a controlled appearance of torsional and bending modes below 2000 Hz. Furthermore, machine learning models are developed to predict and explain the recorded performance, classifying the importance of underlying influential parameters. …”
Get full text

A Machine Learning Approach to Quantitative Analysis of Enamel Microstructure from Scanning Electron Microscopy Images by Carli Marsico, Cameron Renteria, Jack R. Grimm, Juliana Fernandez‐Arteaga, Donna Guillen, Dwayne Arola

“…A validation of this segmentation method is presented, in addition to its application to calculate relevant microstructural parameters for images of tooth enamel from selected mammalian species. …”
Get full text

Hierarchical Information-Extreme Machine Learning of Hand Prosthesis Control System Based on Decursive Data Structure by Anatolii Dovbysh, Vladyslav Piatachenko, Mykyta Myronenko, Mykyta Suprunenko, Julius Simonovskiy

“…The modified Kullback–Leibler information measure was the optimization criterion for machine learning parameters. The proposed hierarchical information-extreme machine learning method was implemented using electromyographic biosignals of cognitive commands for six finger and hand movements as an example.…”
Get full text

Improving the Efficiency of Screen Type Potato Sorting Machines by a Modification of the Sieve Drive Movement Algorithm by A. G. Ivanov, M. N. Erokhin, S. P. Kazantsev, P. V. Dorodov, I. I. Khuzyakhmetov, I. T. Khakimov

“…The paper emphasizes the relevance of developing effective methods for designing and calculating the parameters of this type sorting devices. (Research purpose) To upgrade the sizing machine drive by modifying the sieve movement patterns for improving its operation efficiency. …”
Get full text

Probabilistic analysis of active earth pressures in spatially variable soils using machine learning and confidence intervals by Tran Vu-Hoang, Tan Nguyen, Jim Shiau, Duy Ly-Khuong, Hung-Thinh Pham-Tran

“…To improve computational efficiency and prediction accuracy, machine learning models, such as Multivariate Adaptive Regression Splines (MARS), are utilized to predict failure probabilities based on key spatial variability parameters. …”
Get full text

Investigating the Efficiency of Machine Learning Methods for Simulating the Effects of the Paper-Pulp Industry on Aqua Ecosystem Pollution by Mahmoud Ahmadi, Mehran Davallo, Mohsen Jahanshahi, Vahid Kiarostami, Majid Peyravi

“…To overcome these difficulties, this paper aims to use machine learning tools for analyzing the pollutant space. …”
Get full text

Recent Advances in Early Earthquake Magnitude Estimation by Using Machine Learning Algorithms: A Systematic Review by Andrés Navarro-Rodríguez, Oscar Alberto Castro-Artola, Enrique Efrén García-Guerrero, Oscar Adrian Aguirre-Castro, Ulises Jesús Tamayo-Pérez, César Alberto López-Mercado, Everardo Inzunza-Gonzalez

“…Rapid detection and characterization of seismic parameters, including location and magnitude, are crucial for real-time seismological applications, including Earthquake Early Warning (EEW) systems. …”
Get full text

Investigating the Machining Behavior of the Additively Manufactured Polymer-Based Composite Using Adaptive Neuro-Fuzzy Learning by Anastasios Tzotzis, Dumitru Nedelcu, Simona-Nicoleta Mazurchevici, Panagiotis Kyratsis

“…A series of machining trials were conducted under dry conditions, with cutting speed (<i>Vc</i>), feed (<i>f</i>), and depth-of-cut (<i>ap</i>) as the primary input parameters. …”
Get full text

Machine Learning-Based Seismic Response Prediction for Nuclear Power Plant Structures Considering Aging Deterioration by Hyunsu Kim, Soyeon Lee, Junsu Jang, Sihyeon An

“…Thirteen intensity measures of artificial earthquakes and four material properties were employed as input parameters for the training database. The floor response spectrum of the example structure was chosen as the output for the database. …”
Get full text

Classifying the Mortality of People with Underlying Health Conditions Affected by COVID-19 Using Machine Learning Techniques by Rami Mustafa A. Mohammad, Malak Aljabri, Menna Aboulnour, Samiha Mirza, Ahmad Alshobaiki

“…To achieve our goal, we applied machine learning (ML) models on a new publicly available dataset, not investigated by any existing literature. …”
Get full text

Optimizing precision farming: enhancing machine learning efficiency with robust regression techniques in high-dimensional data by Nour Hamad Abu Afouna, Majid Khan Majahar Ali

“…In a recent study involving 435 drying parameters and 1,914 observations, two machine learning algorithms - Ridge and Lasso - were employed to analyze and compare the impact of two variable selection techniques, specifically the regularization methods Ridge and Lasso, before and after addressing heterogeneity in highly ranked variables (50, 100, 150, 200, 250, 300). …”
Get full text

Intrusion Detection Using Machine Learning for Risk Mitigation in IoT-Enabled Smart Irrigation in Smart Farming by Abhishek Raghuvanshi, Umesh Kumar Singh, Guna Sekhar Sajja, Harikumar Pallathadka, Evans Asenso, Mustafa Kamal, Abha Singh, Khongdet Phasinam

“…Performance comparisons of machine learning algorithms are evaluated on the basis of accuracy, precision, and recall parameters.…”
Get full text

Influence of Transformation Coefficient of Refrigerating Machine on Exergetic Efficiency of Air Conditioning System of Operating Clean Rooms by Volodymyr Labay, Kyrylo Andreiko

“…The aim of the model is to make computer estimation of exergetic efficiency of existing air conditioning system depending on different factors which have influence on its work, in particular the coefficient of transformation (or energy efficiency rate, EER) of its refrigeration machine. The dependence of the exergetic output-input ratio of implemented air conditioning system for operating clean rooms on coefficient of transformation of its refrigeration machine by different parameters of outdoor and indoor air and the temperature difference between the indoor and supply air were presented. …”
Get full text

Optimized Machine Learning Models for Enhanced Stock Market Predictions: A Case Study on the SSE Index by Vahid Babazadeh, Ahmad Faramarzi, Ali Rahnamaei

“…This study aims to introduce a machine learning-based model for Shanghai Stock Exchange Index (SSE) index prediction. …”
Get full text

Machine learning supported ground beef freshness monitoring based on near‐infrared and paper chromogenic array by Yihang Feng, Yi Wang, Burcu Beykal, Zhenlei Xiao, Yangchao Luo

“…Recent advancements in spectroscopic and chromogenic sensor array technologies have opened up new avenues for monitoring meat quality parameters, offering the potential for real‐time, accurate, and cost‐effective solutions. …”
Get full text

Electric load forecasting based on kernel extreme learning machine optimized by improved sparrow search algorithm by Diming Zhang, Yuchen Xu, Yuanjiang Li

“…These subsequences are then integrated with the Kernel Extreme Learning Machine (KELM) to develop a forecasting model named WHFSSA-KELM. …”
Get full text

Anomaly Detection Using Machine Learning in Hydrochemical Data From Hot Springs: Implications for Earthquake Prediction by Ruijie Zhu, Fengtian Yang, Xiaocheng Zhou, Jiao Tian, Yongxian Zhang, Miao He, Jingchao Li, Jinyuan Dong, Ying Li

“…Abstract This study explores the potential of machine learning algorithms for earthquake prediction, utilizing fluid chemical anomaly data from hot springs. …”
Get full text

Cultivating clean skies: unveiling the tapestry of air quality in Gujarat through innovative machine learning analysis by Gaddam Advitha, Allada Nagasai Varaprasad, Koti Vennela Khushi, Pullabhotla Vijay, Sukanta Nayak

“…This research delves into the utilization of Machine Learning (ML) algorithms, specifically Random Forest (RF) and XGBoost, to predict AQIs in Gujarat. …”
Get full text

Advanced water quality assessment using machine learning: Source identification and probabilistic health risk analysis by Amin Mohammadpour, Ehsan Gharehchahi, Mohammad Golaki, Majid Amiri Gharaghani, Fahime Ahmadian, Soroush Abolfathi, Mohammad Reza Samaei, Md Galal Uddin, Agnieszka I. Olbert, Amin Mousavi Khaneghah

“…These findings highlight the efficacy of integrating RMS-WQI with machine learning tools for a robust assessment of groundwater quality and associated health risks in arid environments.…”
Get full text

Prediction of groundwater level and potential zone identification in Keonjhar, Odisha based on machine learning and GIS techniques by B. Ritushree, Shubhshree Panda, Abinash Sahoo, Sandeep Samantaray, Deba P Satapathy

“…The models were trained and evaluated using historical GWL data and meteorological parameters such as rainfall, humidity, temperature, and soil moisture. …”
Get full text