Study elastoplastic bending of a sheet blank of different thickness while rolling by D. I. Chernyavsky, D. D. Chernyavsky

“…Conclusions are drawn and practical recommendations are given, which can be used in the process of adjustment of process parameters of rolling machines.…”
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End-of-Life Prediction for Milling Cutters Based on an Online Vibro-Acoustic System by Michele Perrelli, Romina Conte, Gabriele Zangara, Francesco Gagliardi

“…Sound signals were recorded at fixed main machining parameters, i.e., cutting speed, feed rate and depth of cut. …”
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Research on Dynamic Evolution of Residual Stress Based on Simulation of Piston Manufacturing Process by Dong Yang, Lizheng Li, Chuanlong Zhou, Qiang He

“…Subsequently, through the machining and heat treatment simulation of the piston, the variation law of residual stress before and after machining is analyzed. …”
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Justifying the structure of the improved mechanism for manual control of motor vehicles’ pedals by Vitaliy Korendiy, Nazarii Fedunyshyn, Vasyl Kozub

“…The research methodology involves the use of classical methods from the theory of mechanisms and machines to conduct the structural synthesis of an improved multi-link hinge-lever mechanism and its kinematic analysis, aimed at determining the main parameters of pedals’ movements in a vehicle under various control inputs from the driver’s hand. …”
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A Stride Toward Wine Yield Estimation from Images: Metrological Validation of Grape Berry Number, Radius, and Volume Estimation by Bernardo Lanza, Davide Botturi, Alessandro Gnutti, Matteo Lancini, Cristina Nuzzi, Simone Pasinetti

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An optimized approach for predicting water quality features and a performance evaluation for mapping surface water potential zones based on Discriminant Analysis (DA), Geographical... by Abhijeet Das

“…On this basis, physicochemical parameters related to water quality, including 19 parameters, were investigated at the chosen sites. …”
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Efficient UAV-Based Automatic Classification of Cassava Fields Using K-Means and Spectral Trend Analysis by Apinya Boonrang, Pantip Piyatadsananon, Tanakorn Sritarapipat

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Solid Oxide Fuel Cell Voltage Prediction by a Data-Driven Approach by Hristo Ivanov Beloev, Stanislav Radikovich Saitov, Antonina Andreevna Filimonova, Natalia Dmitrievna Chichirova, Egor Sergeevich Mayorov, Oleg Evgenievich Babikov, Iliya Krastev Iliev

“…White box and gray box models are unable to achieve real-time optimization of control parameters. A potential solution involves using data-driven machine learning (ML) black-box models. …”
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The accuracy of C-ARM in evaluating pelvic incidence and lumbar lordosis during surgery compared with EOS radiography after lumbar fixation by Hasan Toghraei Semiromi, Mohammadreza Chehrassan, Mansour Bahardoust, Hasan Ghandhari, Abbas Esmaeli, Farshad Nikouei

“…This study aimed to evaluate the accuracy of the mobile C-arm X-ray machine (C-ARM) for measuring lumbar lordosis (LL) and pelvic incidence (PI) parameters during lumbar fixation. …”
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Correction of crop water deficit indicators based on time-lag effects for improved farmland water status assessment by Yujin Wang, Zhitao Zhang, Yinwen Chen, Shaoshuai Fan, Haiying Chen, Xuqian Bai, Ning Yang, Zijun Tang, Long Qian, Zhengxuan Mao, Siying Zhang, Junying Chen, Youzhen Xiang

“…The indicator with the strongest correlation to SWC was selected and then predicted using four machine learning models. Results demonstrated that time-lag correction significantly enhanced the correlation between SWC and theoretical CWSI, empirical CWSI, gs, and ET, with increases of 0.15, 0.33, 0.11, and 0.21, respectively; Time-lag mutual information exhibited the highest effectiveness in correcting time-lag effects; The sudden decline in gs and the peak advancement in severe water stress treatments led to abrupt changes in time-lag parameters; The Convolutional Neural Network-Bidirectional Long Short-Term Memory-Adaptive Boosting model achieved the highest accuracy in predicting gs corrected by time-lag mutual information from 8:00–15:00 (R2=0.96). …”
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Less Is More: The Influence of Pruning on the Explainability of CNNs by Florian Merkle, David Weber, Pascal Schottle, Stephan Schlogl, Martin Nocker

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Rolling Bearing Fault Diagnosis Method Based on Fusion of CNN and CSSVM by LI Yunfeng, LAN Xiaosheng, SHEN Hongchang, XU Tongle

“…Finally, the extracted feature vectors were input into the support vector machine classification layer with optimized parameters by cuckoo search algorithm to realize the fault classification of rolling bearings. …”
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Study of Heart Rate Variability to Comprehend the Significance of Singing Bowl Meditation on the Functioning of the Autonomic Nervous System by Ritika Upadhyay, Biswajeet Champaty, Suraj Kumar Nayak

“…In the statistics-based t-test method, the HRV parameters were subjected to choose appropriate input for the ML model. …”
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Quantum chimp-enanced SqueezeNet for precise diabetic retinopathy classification by Anas Bilal, Muhammad Shafiq, Waeal J. Obidallah, Yousef A. Alduraywish, Alishba Tahir, Haixia Long

“…The classification process, QCOA optimizes the Support Vector Machine (SVM) parameters and performs feature selection. …”
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A highly generalized federated learning algorithm for brain tumor segmentation by Jun Wen, Xiusheng Li, Xin Ye, Xiaoli Li, Hang Mao

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Deep learning-based approach for extracting inflorescence morphology features in cut chrysanthemum by Shanpeng Xu, Jingshan Lu, Yin Wu, Huahao Liu, Fadi Chen, Fei Zhang, Sumei Chen, Weimin Fang, Zhiyong Guan

“…To address these limitations, we developed a lightweight deep learning and machine learning pipeline for automated trait extraction in over 30 chrysanthemum cultivars. …”
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Optimal control strategy based on artificial intelligence applied to a continuous dark fermentation reactor for energy recovery from organic wastes by Kelly Joel Gurubel Tun, Elizabeth León-Becerril, Octavio García-Depraect

“…First, experimental data from continuous dark fermentation are modeled using support vector machine (SVM) algorithm for response prediction and then, optimization algorithms are employed to identify the key parameters that enhance H2 production. …”
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Sleep Stage Classification Through HRV, Complexity Measures, and Heart Rate Asymmetry Using Generalized Estimating Equations Models by Bartosz Biczuk, Sebastian Żurek, Szymon Jurga, Elżbieta Turska, Przemysław Guzik, Jarosław Piskorski

“…This study investigates whether heart rate asymmetry (HRA) parameters offer insights into sleep stages beyond those provided by conventional heart rate variability (HRV) and complexity measures. …”
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Prediction of Final Phosphorus Content of Steel in a Scrap-Based Electric Arc Furnace Using Artificial Neural Networks by Riadh Azzaz, Mohammad Jahazi, Samira Ebrahimi Kahou, Elmira Moosavi-Khoonsari

“…This study aims to develop a machine learning model to estimate steel phosphorus content at the end of the process based on input parameters. …”
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Direction-of-Arrival (DOA) Estimation Based on Real Field Measurements and Modified Linear Regression by Luis Antonio Flores, Ismael Lomas, Lenin Guachalá, Pablo Lupera-Morillo, Robin Álvarez, Ricardo Llugsi

“…This study applied modified linear regression in machine learning (ML) to predict the direction of arrival (DoA) in cellular networks using field measurements and radiofrequency parameters. …”
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