3D Modeling and Printing in Physics Education: The Importance of STEM Technology for Interpreting Physics Concepts by Indira Usembayeva, Bakitzhan Kurbanbekov, Sherzod Ramankulov, Aknur Batyrbekova, Kazhymukan Kelesbayev, Asem Akhanova

“…In addition, it is necessary to determine the features of the use of 3D modeling and printing in the interpretation of physics concepts. …”
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Electronic Health Data Records for Diabetes Patients Based on Deep Learning Models: A Review by Dalal Hamid, Mohammed Younis

“…Hybrid and ensemble techniques also show promise in enhancing performance. Despite these advancements, challenges such as data availability, model interpretability, and generalizability remain. …”
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Detection and Counting Model of Soybean at the Flowering and Podding Stage in the Field Based on Improved YOLOv5 by Yaohua Yue, Wei Zhang

“…A phenotype survey on soybean flower and pod drop conducted by agricultural experts revealed issues such as poor real-time performance and strong subjectivity. Based on the YOLOv5 detection model, a microscale detection layer is added and the size of the initial anchor box is improved to enhance feature expression ability. …”
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LiteMP-VTON: A Knowledge-Distilled Diffusion Model for Realistic and Efficient Virtual Try-On by Shufang Zhang, Lei Wang, Wenxin Ding

“…To reduce model size while maintaining performance, we adopt an attention-guided distillation strategy that transfers semantic and structural knowledge from MP-VTON to a lightweight model, LiteMP-VTON. …”
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A novel hybrid TCN-TE-ANN model for high-precision solar irradiance prediction by Murat Isik

“…By leveraging TCN’s temporal feature extraction, TE’s attention mechanisms, and ANN’s dense layer refinements, the model demonstrates significant advancements over existing methods.…”
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Sentiment Analysis of Emoji and Latinized Arabic in Indonesian Youtube Comments: A LABERT-LSTM Model by M. Noer Fadli Hidayat, Didik Dwi Prasetya, Triyanna Widiyaningtyas

“…The proposed LABERT-LSTM model integrates BERT for deep feature extraction and Bi-LSTM to capture word sequence context effectively. …”
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Extracting product competitiveness through user-generated content: A hybrid probabilistic inference model by Ming-Fang Li, Guo-Xiang Zhang, Lu-Tao Zhao, Tao Song

“…Compared with the traditional model, BMB model has better performance in product feature mining in three aspects of feature diversity, feature long tail and attribute difference. …”
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A New Model Design for Combating COVID -19 Pandemic Based on SVM and CNN Approaches by Sura Monther Alnedawe, Hadeel K. Aljobouri

“…In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial lung CT-scans into two groups (COVID-19 and NonCOVID-19) had been proposed. …”
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Weamba: Weather-Degraded Remote Sensing Image Restoration with Multi-Router State Space Model by Shuang Wu, Xin He, Xiang Chen

“…However, these methods either suffer from limited receptive fields or incur quadratic computational overhead, leading to an imbalance between performance and model efficiency. In this paper, we propose an effective vision state space model (called Weamba) for remote sensing image restoration by modeling long-range pixel dependencies with linear complexity. …”
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MWG-UNet++: Hybrid Transformer U-Net Model for Brain Tumor Segmentation in MRI Scans by Yu Lyu, Xiaolin Tian

“…Incorporating WGAN for data augmentation addresses the challenge of limited medical imaging datasets to generate high-quality synthetic images that enhance model training and generalization. Our comprehensive evaluation demonstrates that this hybrid model significantly improves segmentation performance. …”
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A Propagation Model for Subsurface and Through-Wall Imaging Applications under the Frequency Dispersion Perspective by Ana Vazquez Alejos, Muhammad Dawood

“…The internal multireflection model has been considered as the most suitable model to describe the transmission process underlying both subsurface and through-wall imaging technologies. …”
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Bearing fault diagnosis based on improved DenseNet for chemical equipment by Wu Huiyong, Jiang Kuan, Wang Yanyu

“…The alternating stacking strategy of channel and spatial attention further improves the feature extraction ability at different scales. This optimized structure increases the diversity and discriminative power of feature representations, enhancing the model’s performance in fault diagnosis tasks. …”
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A Combined Windowing and Deep Learning Model for the Classification of Brain Disorders Based on Electroencephalogram Signals by Dina Abooelzahab, Nawal Zaher, Abdel Hamid Soliman, Claude Chibelushi

“…Methods: The model consists of three key components: data selection, feature extraction, and classification. …”
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Prediction Model of Phosphorus Content at the End Point of Electroslag Remelting Based on MI and XGBoost Algorithms by Liu　Yuxiao, Dong　Yanwu, Jiang　Zhouhua, Chen　Xi

“…The dataset after feature selection serves as the input variables for the model.The MI-XGBoost model is trained and validated using production data. …”
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Risk factors and transitional probability of clinical events in Korean CKD patients using the multistate model by Ji Hye Kim, Jinheum Kim, Jayoun Kim, Ji Yong Jung, Jong Cheol Jeong, Seung Hyeok Han, Kook-Hwan Oh

“…Multivariable multi-state model analysis was performed to investigate the study outcomes associated with the five transitions. …”
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MAET-SAM: Magneto-Acousto-Electrical Tomography segmentation network based on the segment anything model by Shuaiyu Bu, Yuanyuan Li, Guoqiang Liu, Yifan Li

“…Based on this dataset, we proposed a MAET tomography segmentation network based on the Segment Anything Model (SAM), termed as MAET-SAM. Specifically, we froze the encoder weights of SAM to extract rich feature information of image and design, an adaptive decoder with no prompts. …”
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Advanced predictive disease modeling in biomedical IoT using the temporal adaptive neural evolutionary algorithm by Chandragandhi S, Arvind C, Srihari K

“…TANEA leverages temporal data patterns, adapts to dynamic changes in sensor readings, and optimizes feature selection through an evolutionary mechanism, resulting in a more precise and reliable predictive model. …”
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Building Lightweight 3D Indoor Models from Point Clouds with Enhanced Scene Understanding by Minglei Li, Mingfan Li, Min Li, Leheng Xu

“…Indoor scenes often contain complex layouts and interactions between objects, making 3D modeling of point clouds inherently difficult. In this paper, we design a divide-and-conquer modeling method considering the structural differences between indoor walls and internal objects. …”
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A Planning Model for Optimal Sizing of Integrated Power and Gas Systems Capturing Frequency Security by Yi Wang, Goran Strbac

“…Additionally, a deep learning-based clustering method featured by concurrent and integrated learning is introduced in the planning model to effectively generate representative days. …”
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A fuzzy-optimized multi-level random forest (FOMRF) model for the classification of the impact of technostress by Gabriel James, Ifeoma, David, John, Samuel, Enefiok, Imeh Umoren, Ubong Etuk, Aloysius, Anietie, Saviour, Chikodili

“…Benchmarking showed that FOMRF outperformed existing methods in predictive performance, flexibility, and accuracy. These findings emphasize the potential of fuzzy-enhanced machine learning models to effectively detect and mitigate technostress, thereby improving the quality of digital work environments. …”
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