Exploring the integration of self-regulated learning into digital platforms to improve students’ achievement and performance by Ahmed Elmabaredy, Nurgun Gencel

“…Abstract This study aimed to explore the integration of self-regulated learning into digital platforms to improve students' achievement and performance. Moodle platform was used with additional modifications to integrate the features of self-regulated learning. …”
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Application of Open-Source, Low-Code Machine-Learning Library in Python to Diagnose Parkinson's Disease Using Voice Signal Features by Daniel Hilário da Silva, Caio Tonus Ribeiro, Leandro Rodrigues da Silva Souza, Adriano Alves Pereira

“…Among these algorithms, Extra Trees Classifier (ETC), Gradient Boosting Classifier (GBC), and K Neighbors Classifier (KNN) exhibited the best performance for the given dataset. Furthermore, to enhance the models' performance, we employed various techniques, including Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance, feature selection based on correlation, and hyperparameter tuning. …”
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Causal inference-based graph neural network method for predicting asphalt pavement performance by CHEN Kai;WANG Xiaohe;SHI Xinli;CAO Jinde

“…The model comprises four modules: global feature extraction, local feature extraction,causal inference, and dual-channel graph convolution. …”
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MLGFENet: Multiscale Local–Global Feature Enhancement Network for High-Resolution Remote Sensing Image Change Detection by Huanhuan Lv, Xianqi Yan, Hui Zhang, Cuiping Shi, Ruiqin Wang

“…Next, a cascading feature decoder is employed to perform upsampling on the extracted features. …”
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Evaluation of Time-Domain Acoustic Signature in TIG Welding of 5083 Aluminum Alloy: A Methodological Comparison of Feature Reduction Approaches by V M Gautham, A Sumesh, E V Jithin, K Rameshkumar, Dinu Thomas Thekkuden

“…The sound signatures of weld conditions are captured using a microphone with a sample rate of 10 kHz. The feature selection and feature reduction are performed on the sound signature data using ANOVA, MRMR, Chi-Square, reliefF, and Kruskal-Wallis methods. …”
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Driver Injury Prediction and Factor Analysis in Passenger Vehicle-to-Passenger Vehicle Collision Accidents Using Explainable Machine Learning by Peng Liu, Weiwei Zhang, Xuncheng Wu, Wenfeng Guo, Wangpengfei Yu

“…Moreover, by integrating the SHAP model interpretation method, we conducted detailed feature analysis at global, local, and individual case levels, thereby filling the gap in PV-PV accident severity prediction and feature analysis.…”
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Identification of PET/CT radiomic signature for classification of locally recurrent rectal cancer: A network-based feature selection approach by Sara Dalmonte, Maria Adriana Cocozza, Dajana Cuicchi, Daniel Remondini, Lorenzo Faggioni, Paolo Castellucci, Andrea Farolfi, Emilia Fortunati, Alberta Cappelli, Riccardo Biondi, Arrigo Cattabriga, Gilberto Poggioli, Stefano Fanti, Gastone Castellani, Francesca Coppola, Nico Curti

“…This work aimed to develop a machine learning model for predicting LRRC using radiomic features extracted from 18F-FDG Positron Emission Tomography/Computed Tomography (PET/CT). …”
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BUILDING HIGH PERFORMANCE STRATEGY OF MILITARY EXPENDITURES: THE UTILITY FUNCTION IN THE MIDDLE OF DEFENCE BUDGETING by MARITANA SEDYSHEVA

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MS3D: A Multi-Scale Feature Fusion 3D Object Detection Method for Autonomous Driving Applications by Ying Li, Wupeng Zhuang, Guangsong Yang

“…The Adam optimizer is employed for efficient adaptive parameter tuning, significantly improving detection performance. On the KITTI dataset, MS3D achieves average precisions of 93.58%, 90.91%, and 88.46% in easy, moderate, and hard scenarios, respectively, surpassing state-of-the-art models like VoxelNet, SECOND, and PointPillars.…”
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Research on Arc Fault Detection Based on Conditional Batch Normalization Convolutional Neural Network with Cost-Sensitive Multi-Feature Extraction by Xin Ning, Tianli Ding, Hongwei Zhu

“…The experimental results show that the proposed method outperforms traditional models in terms of its accuracy and misjudgment rate while maintaining a lower computational cost, demonstrating its superior detection performance. …”
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Comparing raw score difference, multilevel modeling, and structural equation modeling methods for estimating discrepancy in dyads by Amber McEnturff, Qi Chen, Robin K. Henson, Ryan Glaman, Wen Luo

“…MLM is not recommended as it featured comparatively poor reliability.…”
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Performance Enhancement of EEG Signatures for Person Authentication Using CNN BiLSTM Method by Ashish Ranjan Mishra, Rakesh Kumar, Rajkumar Saini

“…We propose a multiscale convolutional neural network (CNN) and a Bidirectional LSTM (BiLSTM) model called CNN-BiLSTM to extract features and classify raw EEG data. …”
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Improving resectable gastric cancer prognosis prediction: A machine learning analysis combining clinical features and body composition radiomics by Gianni S.S. Liveraro, Maria E.S. Takahashi, Fabiana Lascala, Luiz R. Lopes, Nelson A. Andreollo, Maria C.S. Mendes, Jun Takahashi, José B.C. Carvalheira

“…To identify the relevant features for the prognosis, recursive feature inclusion (RFI) was performed using SHAP Importance ranking. …”
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Exploring genomic feature selection: A comparative analysis of GWAS and machine learning algorithms in a large‐scale soybean dataset by Hawlader A. Al‐Mamun, Monica F. Danilevicz, Jacob I. Marsh, Cedric Gondro, David Edwards

“…Emphasizing the “small n large p” dilemma prevalent in contemporary genomic studies, we compared the efficacy of traditional genome‐wide association studies (GWAS) with two prominent machine learning tools, random forest and extreme gradient boosting, in pinpointing predictive features. Utilizing the expansive soybean dataset, we assessed the performance of these methodologies in selecting features that optimize predictive modeling for various phenotypes. …”
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A Systematic Review of Surface Electromyography in Sarcopenia: Muscles Involved, Signal Processing Techniques, Significant Features, and Artificial Intelligence Approaches by Alessandro Leone, Anna Maria Carluccio, Andrea Caroppo, Andrea Manni, Gabriele Rescio

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Vibration-based gearbox fault diagnosis using a multi-scale convolutional neural network with depth-wise feature concatenation. by Van-Trang Nguyen, Quoc Bao Diep

“…Specifically, MixNet utilizes multi-scale convolutional layers combined with depth-wise feature concatenation to extract discriminative features from spectrogram representations of vibration signals, generated via the Short-time Fourier transform (STFT). …”
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Cascade drive: a unified deep learning framework for multi-featured detection and control in autonomous electric vehicles on unstructured roadways by Kushal Kumar Raju, B. Prahal Bhagavath, M. K. Nallakaruppan, Rajesh Kumar Dhanaraj, Soufiane Ben Othman, Obaid Ali

“…The core innovation lies in the unified framework that simultaneously processes lane boundaries and critical objects at 6 frames per second on resource-constrained hardware, with intelligent prioritization of safety features. Performance metrics are exceptional with measures of 97.26% accuracy for lane detection using DeepLabv3+, 0.92 mAP for object detection with YOLOv5, and 0.83 mAP for pothole detection using YOLOv7. …”
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Modeling of torsional vibration in harmonic drives by Ryszard LENIOWSKI

“…Simulation results show that the developed model has satisfactory features and accuracy and can be used in ongoing research to develop variant of MRAC-type controllers for vibration cancellation.…”
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Hybrid Deep Learning for Survival Prediction in Brain Metastases Using Multimodal MRI and Clinical Data by Cristian Constantin Volovăț, Călin Gheorghe Buzea, Diana-Ioana Boboc, Mădălina-Raluca Ostafe, Maricel Agop, Lăcrămioara Ochiuz, Ștefan Lucian Burlea, Dragoș Ioan Rusu, Laurențiu Bujor, Dragoș Teodor Iancu, Simona Ruxandra Volovăț

“…<b>Results:</b> The hybrid model based on EfficientNet-B0 achieved state-of-the-art performance, attaining an R² score of 0.970 and a mean absolute error of 3.05 days on the test set. …”
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Detailed PV Monitor: A Highly Generalized Photovoltaic Panels Segmentation Network Integrating Context-Aware and Deep Feature Reconstruction by Xiaopu Zhang, Huayi Wu, Kunlun Qi, Yuehui Qian, Yongxian Zhang, Ligang Wang, Jianxun Wang

“…Experimental results demonstrate that DPVM exhibits outstanding robustness and broad adaptability, ensuring stable performance across diverse scenarios. Specifically, DPVM excels in complex backgrounds, significantly reducing PV panel missed detections, improving edge delineation, and outperforming classical and state-of-the-art segmentation models in key metrics.…”
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