Comparing machine learning models for osteoporosis prediction in Tibetan middle aged and elderly women by Peng Wang, Qiang Yin, Kangzhi Ding, Huaichang Zhong, Qundi Jia, Zhasang Xiao, Hai Xiong

“…We built a predictive model of osteoporosis in three steps. First, we performed feature selection to identify factors associated with osteoporosis. …”
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ACSAformer: A crime forecasting model based on sparse attention and adaptive graph convolution by Zhenkai Qin, Baozhong Wei, Baozhong Wei, Caifeng Gao, Caifeng Gao, Feng Zhu, Feng Zhu, Weiqi Qin, Weiqi Qin, Qian Zhang

“…The incorporation of adaptive graph convolution significantly enhances the model’s ability to represent multivariate spatiotemporal sequences, enabling it to capture complex inter-feature relationships and dynamic correlations, thereby improving generalization and predictive accuracy. …”
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Marine Mammal Call Classification Using a Multi-Scale Two-Channel Fusion Network (MT-Resformer) by Xiang Li, Chao Dong, Guixin Dong, Xuerong Cui, Yankun Chen, Peng Zhang, Zhanwei Li

“…The model employs a multi-level neural perceptron (MLP) to dynamically regulate the weighting of the two channels, enabling flexible feature fusion. …”
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Sequential Model Predictive Torque Control for Six-Phase Machines Without Weighting Factors by Jorge Rodas, Osvaldo Gonzalez, Paola Maidana, Christian Medina, Jesus Doval-Gandoy, Margarita Norambuena, Magno Ayala, Jose Rodriguez

“…Model predictive control has become a powerful and versatile control strategy, particularly effective in controlling multiphase machines. …”
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Construction and interpretation of tobacco leaf position discrimination model based on interpretable machine learning by Ranran Kou, Cong Wang, Jinxia Liu, Ran Wan, Zhe Jin, Le Zhao, Youjie Liu, Junwei Guo, Feng Li, Hongbo Wang, Song Yang, Cong Nie

“…The experimental results showed that among all models, the SVM- hybrid kernel demonstrated the most robust and accurate performance, achieving discrimination accuracies of 98.17% and 96.33% on the training and test sets, respectively. …”
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Spatiotemporal Soil Moisture Prediction Using a Causal-Guided Deep Learning Model by Tingtao Wu, Lei Xu, Ziwei Pan, Ruinan Cai, Jin Dai, Shuang Yang, Xihao Zhang, Xi Zhang, Nengcheng Chen

“…Ablation experiments further confirm the critical role of the causal attention module in improving model performance. These findings demonstrate that integrating causal knowledge with deep learning models effectively enhances the modeling capabilities of complex spatiotemporal systems, providing a novel solution for broader spatiotemporal prediction tasks.…”
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A machine learning-based efficient anomaly detection system for enhanced security in compromised and maligned IoT Networks by Anita Punia, Manish Tiwari, Sourabh Singh Verma

“…The model exhibits high performance in the case of binary classification with 99.9 % accuracy. …”
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Development technologies and models of different types of gas reservoirs in Ordos Basin, NW China by Ailin JIA, Dewei MENG, Guoting WANG, Guang JI, Zhi GUO, Naichao FENG, Ruohan LIU, Suqi HUANG, Shuai ZHENG, Tong XU

“…The current challenges and future development directions are also discussed. Mature development models have been formed for the three representative types of gas reservoirs in the Ordos Basin: (1) Low-permeability carbonate reservoir development model featuring groove fine-scale characterization and three-dimensional vertical succession between Upper and Lower Paleozoic formations. (2) Low-permeability sandstone reservoir development model emphasizing horizontal well pressure-depletion production and vertical well pressure-controlled production. (3) Tight sandstone gas reservoir development model focusing on single-well productivity enhancement and well placement optimization. …”
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Domain-Specific Languages for Algorithmic Graph Processing: A Systematic Literature Review by Houda Boukham, Kawtar Younsi Dahbi, Dalila Chiadmi

“…Our findings reveal the prevalence of property graphs (with 60% of surveyed DSLs explicitly adopting this model), as well as notable similarities in syntax and features. …”
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Loss values of style transfer from UBM to AS-OCT images for plateau iris classification by Natsuda Kaothanthong, Boonsong Wanichwecharungruang, Pantid Chantangphol, Warisara Pattanapongpaiboon, Thanaruk Theeramunkong

“…In this study, we propose a novel semi-supervised approach that eliminates the need for paired images. A generative model learns to distinguish plateau and non-plateau features from UBM images. …”
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An Interpretable XGBoost Framework for Predicting Oxide Glass Density by Pawel Stoch

“…A clear hierarchical improvement in predictive accuracy was observed with increasing feature sophistication across all models. The XGBoost model combined with the Magpie feature set provided the best performance, achieving a coefficient of determination (R²) of 0.97. …”
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Deep convolutional fuzzy neural networks with stork optimization on chronic cardiovascular disease monitoring for pervasive healthcare services by Nuzaiha Mohamed, Reem Lafi Almutairi, Sayda Abdelrahim, Randa Alharbi, Fahad M. Alhomayani, Amer Alsulami, Salem Alkhalaf

“…For the feature selection process, the presented DCFNN-SOCVDC technique utilizes an arithmetic optimization algorithm model. …”
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A patient-derived HCC spheroid system to model the tumor microenvironment and treatment response by Emilie Crouchet, Nuno Almeida, Sarah C. Durand, Marie Parnot, Marine A. Oudot, Fabio Giannone, Cloé Gadenne, Natascha Roehlen, Antonio Saviano, Emanuele Felli, Patrick Pessaux, Hong Tuan Duong, Hideki Ohdan, Hiroshi Aikata, Kazuaki Chayama, Thomas F. Baumert, Catherine Schuster

“…This model will likely contribute to improve the therapeutic outcomes for patients with HCC. …”
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Improving the Extraction Ability of Thermospheric Mass Density Variations From Observational Data by Deep Learning by Wenbo Li, Libo Liu, Yiding Chen, Zhuowei Xiao, Huijun Le, Ruilong Zhang

“…It is vital to improving our understanding of the TMD features. Deep learning (DL) can learn complex representations directly from raw data, which makes it a compelling feature extraction and modeling tool for providing a novel perspective for TMD modeling. …”
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Enhanced Semi-Supervised Medical Image Classification Based on Dynamic Sample Reweighting and Pseudo-Label Guided Contrastive Learning (DSRPGC) by Kun Liu, Ji Liu, Sidong Liu

“…In semi-supervised learning (SSL) for medical image classification, model performance is often hindered by the scarcity of labeled data and the complexity of unlabeled data. …”
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A Deep Learning Approach for Identifying Intentional AIS Signal Tampering in Maritime Trajectories by Xiangdong Lv, Ruhao Jiang, Chao Chang, Nina Shu, Tao Wu

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Daily Runoff Prediction Model Based on Multivariate Variational Mode Decomposition and Correlation Reconstruction by DING Jie, TU Peng-fei, FENG Yu, ZENG Huai-en

“…Finally, the integrated prediction combining fluctuation and random terms under condition 5 yielded R2 of 0.87 and 0.93 for the overall prediction at Ankang and Baihe stations, respectively, demonstrating excellent model performance. [Conclusions](1) The MVMD decomposition method can control the number of decomposition layers, ensuring complete signal feature extraction without overfitting while improving processing speed.(2) Pearson correlation coefficient method enhances prediction accuracy through decomposed data classification.(3) The MEA-BP can improve signal-to-noise ratio, adapt to complex environments, enhance learning efficiency and generalization ability, and reduce computational complexity.(4) The GWO-ELM algorithm integrates grey wolf optimizer with extreme learning machine, providing a fast and adaptive solution for time-series prediction with reduced overfitting and improved efficiency.(5) The overall combined model can efficiently and stably process large amount of data while ensuring high accuracy.…”
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HGCS-Det: A Deep Learning-Based Solution for Localizing and Recognizing Household Garbage in Complex Scenarios by Houkui Zhou, Chang Chen, Zhongyi Xia, Qifeng Ding, Qinqin Liao, Qun Wang, Huimin Yu, Haoji Hu, Guangqun Zhang, Junguo Hu, Tao He

“…The HGCS-Det model is designed as follows: Firstly, the normalization attention module is introduced to calibrate the model’s attention to targets and to suppress the environmental noise interference information. …”
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VRU-YOLO: A Small Object Detection Algorithm for Vulnerable Road Users in Complex Scenes by Yunxiang Liu, Yuqing Shi

“…Moreover, in a generalization test conducted on the remote sensing small target dataset VisDrone2019, the VRU-YOLO model achieved an mAP50 of 31%. This study demonstrates that the improved model offers more efficient performance in small object detection scenarios, making it well-suited for VRU detection in complex road environments.…”
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Decom-UNet3+: A Retinal Vessel Segmentation Method Optimized With Decomposed Convolutions by Qun Li, Juntao Zhang, Licheng Hua, Songyin Fu, Chenjie Gu

“…To address this issue, we propose Decom-UNet3+, a model that optimizes the encoders by employing decomposed convolutions. …”
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