Machine learning's model-agnostic interpretability on the prediction of students' academic performance in video-conference-assisted online learning during the covid-19 pandemic by Eka Miranda, Mediana Aryuni, Mia Ika Rahmawati, Siti Elda Hiererra, Albert Verasius Dian Sano

“…Objective: This study aims to develop machine learning (ML) model-agnostic interpretability that could predict students' academic performance in VCAOL. …”
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Hippocampal Functional Radiomic Features for Identification of the Cognitively Impaired Patients from Low-Back-Related Pain: A Prospective Machine Learning Study by Yang Z, Liang X, Ji Y, Zeng W, Wang Y, Zhang Y, Zhou F

“…After calculating the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC) and degree centrality (DC) imaging, the radiomic features (n = 819) of bilateral hippocampi were extracted from these images, respectively. …”
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From reactive to proactive: Machine learning models for continuous positive airway pressure adjustments using heart rate variability and oximetry-related parameters by Chih-Fan Kuo, Yi-Chih Lin, Ze-Yu Chen, Jiunn-Horng Kang, Cheng-Chen Chang, Zhihe Chen, Arnab Majumdar, Yen-Ling Chen, Yi-Chun Kuan, Kang-Yun Lee, Po-Hao Feng, Kuan-Yuan Chen, Hsin-Chien Lee, Wun-Hao Cheng, Wen-Te Liu, Cheng-Yu Tsai

“…Methods CPAP titration data were first collected from a sleep center in northern Taiwan. …”
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Multi-Physics Simulation of 6/4 Switched Reluctance Motor by Finite Element Method by Renata R. C. Reis, Marcio L. M. Kimpara, João O. P. Pinto, Babak Fahimi

“…Afterwards, the natural frequencies and vibration modes were found through modal analysis. …”
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AN EMPIRICAL ANALYSIS OF TRADITIONAL RECOGNITION METHODS USING EXAMPLES OF IDENTIFYING WORDS SPOKEN BY NATIVE SPEAKERS by Elchin Ismailov

“…In the proposed approach, the sound signal is considered as a onedimensional representation of sound wave oscillations with a certain sampling frequency. To implement the task, classical DTW and DDTW methods, as well as methods based on the Fourier transform, discrete and continuous wavelet transforms are used. …”
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Early warning forecasting of river ecological flow in a typical Karst basin, southwest China: Leveraging baseflow segmentation and interpretable machine learning by Chongxun Mo, Haiyang Wang, Shufeng Lai, Yi Huang, Wei Gao, Lingling Tang, Siting Zhong

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Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis. by Shang-Ming Zhou, Fabiola Fernandez-Gutierrez, Jonathan Kennedy, Roxanne Cooksey, Mark Atkinson, Spiros Denaxas, Stefan Siebert, William G Dixon, Terence W O'Neill, Ernest Choy, Cathie Sudlow, UK Biobank Follow-up and Outcomes Group, Sinead Brophy

“…A machine learning based scheme was used to identify patients with rheumatoid arthritis from primary care EHRs via the following steps: i) selection of variables by comparing relative frequencies of Read codes in the primary care dataset associated with disease case compared to non-disease control (disease/non-disease based on the secondary care diagnosis); ii) reduction of predictors/associated variables using a Random Forest method, iii) induction of decision rules from decision tree model. …”
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Comparison of Eddy Current Loss Calculation Techniques for Axial Flux Motors with Printed Circuit Board Windings by Andreas Bauer, Daniel Dieterich, Sven Urschel

“…A recommendation is provided for the method that offers the best balance between accuracy and computation time for the early-stage design of slotless axial flux machines.…”
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Speech Databases, Speech Features, and Classifiers in Speech Emotion Recognition: A Review by G. H. Mohmad Dar, Radhakrishnan Delhibabu

“…It has been done in the past using low-level descriptors (LLDs) like Mel-Frequency Cepstral Coefficients (MFCCs), linear predictive coding (LPC), and pitch-based features in methods like Support Vector Machines (SVM), Random Forests (RF), and Gaussian Mixture Models (GMM). …”
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UHVDC Transmission Line Fault Identification Method Based on Generalized Regression Neural Network by XIE Jia, LIU Feng, KE Yanguo, YIN Zhen, RUAN Wei, YAO Jinming

“…Compared to traditional convolutional neural networks, generalized regression neural networks, support vector machines, and other methods, the fault recognition accuracy of the proposed method in this paper has been improved by 6. 6% , 0. 65% , and 7. 69% , respectively, meeting the requirements of protection speed and reliability.…”
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Optimasi Algoritma Support Vector Machine Berbasis Kernel Radial Basis Function (RBF) Menggunakan Metode Particle Swarm Optimization Untuk Analisis Sentimen by Cucun Very Angkoso, Khozainul Asror, Ari Kusumaningsih, Andi Kurniawan Nugroho

“…The study investigates the effectiveness of the Particle Swarm Optimization (PSO) method for balanced and unbalanced datasets and how well it improves sentiment analysis accuracy when applied to the Support Vector Machine (SVM) algorithm when using Radial Basis Function (RBF) kernel. …”
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Machine Learning Modeling for Building Energy Performance Prediction Based on Simulation Data: A Systematic Review of the Processes, Performances, and Correlation of Process-Relate... by Damla Kömürcü, Ecem Edis

“…Regarding these steps, the frequency of the methods used, strategies followed against the limitations, common sources of concerns, and intertwined workflows are analyzed with their effects on prediction performance in terms of accuracy. …”
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A Fault Identification Method of Mechanical Element Action Unit Based on CWT-2DCNN by Hongyu Ge, Yujiao Guo, Tianyu Luo, Manzhi Yang, Chuanwei Zhang

“…Firstly, the vibration signals of each fault state of the element action unit are CWT transformed into the corresponding two-dimensional time-frequency diagram; then, the 2DCNN fault identification model is established, and the time-frequency diagrams of various faults are input to the network as characteristic diagrams for training and testing. …”
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Automated Design Method Based on Boosting Algorithms for Improving the Radiation Performance of Microstrip Antenna Arrays by Sina Hasibi Taheri, Ali Lalbakhsh, Amirhassan Zareanborji, Slawomir Koziel

“…This paper presents an automated design methodology to improve the radiation performance of microstrip antenna arrays using boosting-based machine learning (ML) algorithms in the X-band frequency range. …”
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SICNN: Soft Interference Cancellation Inspired Neural Network Equalizers by Stefan Baumgartner, Oliver Lang, Mario Huemer

“…We compare the bit error ratio performance of the proposed NN-based equalizers with state-of-the-art model-based and NN-based approaches, highlighting the superiority of SICNNv1 over all other methods for SC-FDE. Exemplarily, to emphasize its universality, SICNNv2 is additionally applied to a unique word orthogonal frequency division multiplexing (UW-OFDM) system, where it achieves state-of-the-art performance. …”
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RESEARCH ON FAULT DIAGNOSIS METHOD OF ROLLING BEAR BASED ON SYMBOLIC ANALYSIS OF INTRINSIC MODE FUNCTION by HOU HePing, XU ZhuoFei, LIU Kai

“…And then realize the fault diagnosis with the help of some kind of pattern recognize method. The results show that there is a good recognition effect for typical bearing fault and printing machine fault. …”
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Enhanced Blade Fault Diagnosis Using Hybrid Deep Learning: A Comparative Analysis of Traditional Machine Learning and 1D Convolutional Transformer Architecture by Syed Asad Imam, Meng Hee Lim, Ahmed Mohammed Abdelrhman, Iftikhar Ahmad, Mohd Salman Leong

“…This research investigates blade FD, comparing traditional machine learning approaches with a novel hybrid deep learning fused model based on a one‐dimensional (1D) convolutional transformer. …”
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Determination of seed sowing quality by dotted-nesting method using video file processing technology by Strygin Sergei, Konkina Viktoriya, Korobova Irina, Rybachok Maksim, Pustovarov Nikita

“…The research work presents a method for assessing the qualitative performance of a sowing machine using the dotted-nest sowing method. …”
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Research on Characteristic Analysis and Identification Methods for DC-Side Grounding Faults in Grid-Connected Photovoltaic Inverters by Wanli Feng, Lei Su, Cao Kan, Mingjiang Wei, Changlong Li

“…In the simulation case study, the proposed fault identification method, which combines mechanism characteristics and statistical characteristics, achieved an accuracy rate of 99%, which was significantly superior to traditional methods based solely on statistical characteristics and other machine learning algorithms. …”
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Remaining useful life prediction method of centrifugal pump rolling bearings based on digital twins by ShengWen Zhou, Li Zhang, Xiaoming Yang, Ruiping Luo, BaiGang Du, Wenhui Zeng

“…To minimize the deviation between simulated and measured data, we introduce a finite element model correction method using a stacked autoencoder–long short-term memory (SAE–LSTM) network. …”
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