Design and Optimization of a Novel Compliant Z-Positioner for the Nanoindentation Testing Device by Minh Phung Dang, Thanh Dat Le, Hieu Giang Le, Chi Thien Tran

Get full text

High accuracy prediction of Thai rice glycemic index using machine learning by Yusuf Durmus

“…This study investigated the effectiveness of machine learning (ML) models in estimating the glycemic index (GI) of Thai rice starches from their physicochemical characteristics. …”
Get full text

Image Reconstruction Algorithm Based on Extreme Learning Machine for Electrical Capacitance Tomography by SU Ziheng, CHEN Deyun, WANG Lili

“…Aiming at the problem that the traditional ECT is not accurate in complex situations, this paper proposes a depth learning based inversion method Through the improvement and optimization of the traditional extreme learning machine, the image feature information obtained by the reconstructed image method is used as the training data, and the result obtained by inputting the data into the predictive model is used as the prior information The cost function is used to encapsulate the prior knowledge and domain expertise, and spatial regularizers and time regularizers are introduced to enhance sparsity The separated Bregman (SB) algorithm and the iterative shrinkage threshold (FIST) method are used to solve the specified cost function The final imaging result is obtained The simulation results show that the image reconstructed by this method has less than 10% error compared with the original flow pattern, and reduces artifacts and distortion, which improves the reconstructed image quality…”
Get full text

A review of machine learning approaches for predicting lettuce yield in hydroponic systems by Sabrina Sharmin, Md. Tazel Hossan, Mohammad Shorif Uddin

“…A comparative analysis of existing ML models also highlights their strengths and limitations. …”
Get full text

Machine Learning Prediction of Airfoil Aerodynamic Performance Using Neural Network Ensembles by Diana-Andreea Sterpu, Daniel Măriuța, Grigore Cican, Ciprian-Marius Larco, Lucian-Teodor Grigorie

“…Reliable aerodynamic performance estimation is essential for both preliminary design and optimization in various aeronautical applications. In this study, a hybrid deep learning model is proposed, combining convolutional neural networks (CNNs) and operating directly on raw airfoil geometry, with parallel branches of fully connected deep neural networks (DNNs) that process operational parameters and engineered features. …”
Get full text

Research on the Evaluation of the Node Cities of China Railway Express Based on Machine Learning by Chenglin Ma, Mengwei Zhou, Wenchao Kang, Haolong Wang, Jiajia Feng

“…By comparing the evaluation performance of six machine learning models, an optimal decision-making model is identified, and the evaluation indicators are rigorously screened to provide robust decision-support for the establishment of CR Express assembly centers. …”
Get full text

Machine learning-based approach for bandwidth and frequency prediction of circular SIW antenna by Md Mahabub Alam, Nurhafizah Abu Talip Yusof, Ahmad Afif Mohd Faudzi, Md Raihanul Islam Tomal, Md Ershadul Haque, Md. Suaibur Rahman

“…Abstract Machine Learning (ML) has significantly transformed antenna design by enabling efficient optimization of geometrical parameters, modeling complex electromagnetic behavior, and accelerating performance prediction with reduced computational cost. …”
Get full text

Toward Automatic Detection of Pi2 Magnetic Pulsation Using Machine Learning by Mohamed S. Abdalzaher, Essam Ghamry, Khairul Adib Yusof, Mostafa Shaaban

“…A comprehensive analysis of various linear, ensemble, and non-linear ML models was conducted, employing hyperparameter optimization to identify the optimal model with high classification performance and minimal computational overhead during testing. …”
Get full text

Method of formation of numerical projection-grid algorithm on basis of «three-dimensional regular element» for calculation of 3D-models of magnetic field in cylindrical coordinate... by A. A. Tatevosyan, E. G. Andreeva

“…The paper proposes a method of forming a numerical projectiongrid algorithm on a regular triangulation network for the calculation of three-dimensional models of the magnetic field of synchronous magnetoelectric machines with excitation from permanent magnets (SMEM PM) using recurrent expressions obtained on the basis of a «three-dimensional regular element» for a cylindrical coordinate system. …”
Get full text

Optimal Knowledge Distillation through Non-Heuristic Control of Dark Knowledge by Darian Onchis, Codruta Istin, Ioan Samuila

Get full text

Application of artificial intelligence and machine learning in lung transplantation: a comprehensive review by Xiting Liu, Wenqian Chen, Wenwen Du, Pengmei Li, Pengmei Li, Xiaoxing Wang

“…Artificial intelligence (AI) and machine learning (ML) excel in handling such complex data and contribute to preoperative assessment and postoperative management of LTx, including the optimization of organ allocation, assessment of donor suitability, prediction of patient and graft survival, evaluation of quality of life, and early identification of complications, thereby enhancing the personalization of clinical decision-making. …”
Get full text

Research Progress on Machine Learning Prediction of Compressive Strength of Nano-Modified Concrete by Ruyan Fan, Ankang Tian, Yikun Li, Yue Gu, Zhenhua Wei

“…It reduces trial-and-error efforts and supports mix design optimization. Currently, machine learning is more adept at handling complicated datasets than experimental and traditional statistical models. …”
Get full text

Identification of Rotary Machines Excitation Forces Using Wavelet Transform and Neural Networks by Francisco Paulo Lepore, Marcelo Braga Santos, Rafael Gonçalves Barreto

“…Unbalance and asynchronous forces acting on a flexible rotor are characterized by their positions, amplitudes, frequencies and phases, using its measured vibration responses. The rotary machine dynamic model is a neural network trained with measured vibration signals previously decomposed by wavelets. …”
Get full text

Solar Energy Forecasting Using Machine Learning Techniques for Enhanced Grid Stability by Attuluri R. Vijay Babu, N. Bharath Kumar, Rajanand Patnaik Narasipuram, Soundhar Periyannan, Alireza Hosseinpour, Aymen Flah

“…Accurate short-term forecasting is essential to ensure grid stability and optimize energy resource allocation. This study proposes a comprehensive data-driven framework for solar energy forecasting using multiple machine learning (ML) techniques, including Multiple Linear Regression, Ridge, Lasso, Decision Tree Regression, Support Vector Regression, and ensemble-based models such as Random Forest, AdaBoost, Bagging, and Gradient Boosting Regressors. …”
Get full text

Variance Reduction Optimization Algorithm Based on Random Sampling by GUO Zhenhua, YAN Ruidong, QIU Zhiyong, ZHAO Yaqian, LI Rengang

“…The stochastic gradient descent (SGD) algorithms have been applied to machine learning and deep learning due to their superior performance. …”
Get full text

Automated Graphic Divergent Thinking Assessment: A Multimodal Machine Learning Approach by Hezhi Zhang, Hang Dong, Ying Wang, Xinyu Zhang, Fan Yu, Bailin Ren, Jianping Xu

Get full text

Exploration of ductility for refractory high entropy alloys via interpretive machine learning by Shaolong Zheng, Lingwei Yang, Liyang Fang, Chenran Xu, Guanglong Xu, Yifang Ouyang, Xiaoma Tao

“…This study constructs an ML model for accurate ductility prediction from sparse compositional data, accelerating the design of ductile RHEAs within infinite compositional space. …”
Get full text

Impact wear behavior of austenitic steel bucket teeth based on machine learning by Zhihui Cai, Jianfeng Yan, Yandong Qiao, Rongjie Li, Yanchun Shi, Junping Zhang, Zhixiong Zhang

“…Using a random forest approach, the critical parameters influencing wear depth were identified. A particle swarm optimization support vector machine was employed to accurately predict wear depth. …”
Get full text

Anomaly detection using unsupervised machine learning algorithms: A simulation study by Edmund Fosu Agyemang

“…Future work should explore parameter optimization, the impact of dataset characteristics on model performance, and the application of these models to real-world datasets to validate their efficacy in practical anomaly detection scenarios.…”
Get full text

Integer linear programming for unsupervised training set selection in molecular machine learning by Matthieu Haeberle, Puck van Gerwen, Ruben Laplaza, Ksenia R Briling, Jan Weinreich, Friedrich Eisenbrand, Clémence Corminboeuf

“…We argue that the reason for the improved performance is due to the selection that is based on the notion of local similarity (i.e. per-atom) and a unique ILP approach that finds optimal solutions efficiently. Altogether, this work provides a practical algorithm to improve the performance of physics-inspired ML models and offers insights into the conceptual differences with existing training set selection approaches.…”
Get full text