Factors influencing rideshare satisfaction in a university community by Deema Almaskati, Sharareh Kermanshachi, Jay Michael Rosenberger, Apurva Pamidimukkala, Chen Kan, Ann Foss

“…A variety of models were developed to assess the influence of several trip parameters on ride ratings, and the best performing model, the random forest model, was selected for further evaluation. …”
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Cervical cancer screening uptake and its associated factor in Sub-Sharan Africa: a machine learning approach by Fetlework Gubena Arage, Zinabu Bekele Tadese, Eliyas Addisu Taye, Tigist Kifle Tsegaw, Tsegasilassie Gebremariam Abate, Eyob Akalewold Alemu

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基于临床特征、SPECT/CT脑灌注显像参数和颈动脉超声斑块特征的多因素模型对无症状性颈动脉狭窄患者认知功能下降的风险评估(Risk Assessment of Cognitive Decline in Asymptomatic Carotid Artery Stenosis Patients: A Multifactor Model Based on Cl... by 王梦蝶，韩硕，刘志翔，谢海(WANG Mengdie, HAN Shuo, LIU Zhixiang, XIE Hai)

“…A prediction model was constructed, and its performance was evaluated using the ROC curve. …”
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RS-DETR: An Improved Remote Sensing Object Detection Model Based on RT-DETR by Hao Zhang, Zheng Ma, Xiang Li

“…To enhance remote sensing target detection performance, this study proposes a new model, the remote sensing detection transformer (RS-DETR). …”
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Optimization and predictive performance of fly ash-based sustainable concrete using integrated multitask deep learning framework with interpretable machine learning techniques by Bhupesh P. Nandurkar, Jayant M. Raut, Pawan K. Hinge, Boskey V. Bahoria, Tejas R. Patil, Sachin Upadhye, Vikrant S. Vairagade, Sagar D. Shelare

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Multi-channel based edge-learning graph convolutional network by Shuai YANG, Ruiqin WANG, Hui MA

“…Usually the edges of the graph contain important information of the graph.However, most of deep learning models for graph learning, such as graph convolutional network (GCN) and graph attention network (GAT), do not fully utilize the characteristics of multi-dimensional edge features.Another problem is that there may be noise in the graph that affects the performance of graph learning.Multilayer perceptron (MLP) was used to denoise and optimize the graph data, and a multi-channel learning edge feature method was introduced on the basis of GCN.The multi-dimensional edge attributes of the graph were encoded, and the attributes contained in the original graph were modeled as multi-channel.Each channel corresponds to an edge feature attribute to constrain the training of graph nodes, which allows the algorithm to learn multi-dimensional edge features in the graph more reasonably.Experiments based on Cora, Tox21, Freesolv and other datasets had proved the effectiveness of denoising methods and multi-channel methods.…”
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Multimodal machine learning-based model for differentiating nontuberculous mycobacteria from mycobacterium tuberculosis by Hong-ling Li, Ri-zeng Zhi, Hua-sheng Liu, Mei Wang, Si-jie Yu

“…The model performance was further verified using external new MTB and NTM patient data. …”
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Fast Modelling Algorithm for Realistic Three-Dimensional Human Face for Film and Television Animation by Limin Xu

“…The experimental results show that the algorithm in this paper has good reconstruction performance and real-time performance and can realize the rapid modelling of human face.…”
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Automated Early-Stage Glaucoma Detection Using a Robust Concatenated AI Model by Wheyming Song, Ing-Chou Lai

“…The model employs two classification approaches: a convolutional neural network approach for processing image features and an artificial neural network approach for analyzing quantitative retinal thickness parameters. …”
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Exploring Darcy dissipation modulation of nanofluid with titanium dioxide (TiO2) and copper (Cu) for enhanced thermal performance in a vertical sheet by P.K. Pattnaik, MD. Shamshuddin, S.R. Mishra, Subhajit Panda

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Submarine Topography Classification Using ConDenseNet with Label Smoothing Regularization by Jingyan Zhang, Kongwen Zhang, Jiangtao Liu

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Evaluation of thermal and mechanical properties of demonstration wall utilizing phase change cementitious materials by Jan Červenka, Michaela Herzfeldt, Antonio Caggiano, Eduardus Koenders

“…The numerical model is then used to demonstrate the performance of the wall layer design. …”
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Automatic detecting multiple bone metastases in breast cancer using deep learning based on low-resolution bone scan images by Jialin Shi, Ruolin Zhang, Zongyao Yang, Zhixian Chen, Zhixin Hao, Li Huo, Ji Wu, Qiang Sun, Yali Xu

“…Considering the difficulties of feature extraction caused by low resolution and multiple lesions, we innovatively propose the plug-and-play position auxiliary extraction module and feature fusion module to enhance the ability of global information extraction. …”
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Efficient spatio-temporal modeling for sign language recognition using CNN and RNN architectures by Kasian Myagila, Kasian Myagila, Devotha Godfrey Nyambo, Mussa Ally Dida

“…These results show that more effort is required to improve signer independence performance, including the challenges of hand dominance by optimizing spatial features.…”
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Explainable tuned machine learning models for assessing the impact of corrosion on bond strength in concrete by Maryam Bypour, Alireza Mahmoudian, Mohammad Yekrangnia, Mahdi Kioumarsi

“…Furthermore, tuned models resulted in more accurate performance than the default models. …”
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MC Classifier: A Classifier for 3D Mechanical Components Based on Geometric Prior Using Graph Neural Network and Attention by Zipeng Lin, Zhenguo Nie

“…We benchmark the performance of MC Classifier against state-of-the-art models and demonstrate its competitive potential in 3D mechanical component classification. …”
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Predictive modeling of punchouts in continuously reinforced concrete pavement: a machine learning approach by Ghazi Al-Khateeb, Ali Alnaqbi, Waleed Zeiada

“…Future research could focus on refining models, exploring additional features, and validating results through real-world implementation trials. …”
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Development and validation of MRI-based radiomics model for clinical symptom stratification of extrinsic adenomyosis by Man Sun, Jianzhang Wang, Ping Xu, Libo Zhu, Gen Zou, Shuyi Chen, Yuanmeng Liu, Xinmei Zhang

“…The multi-signature model (radiomic + clinical features) showed improved performance, with the nomogram demonstrating good stratification ability: AUCs of 0.78/0.78 (pain), 0.87/0.85 (AUB), 0.89/0.88 (infertility), and 0.84/0.81 (no symptom) in the training/test cohort.Conclusion We identified the correlation between key radiomic features and clinical symptom of extrinsic adenomyosis. …”
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A Cross-Project Defect Prediction Model Based on Deep Learning With Self-Attention by Wanzhi Wen, Ruinian Zhang, Chuyue Wang, Chenqiang Shen, Meng Yu, Suchuan Zhang, Xinxin Gao

“…However, traditional features lack sufficient semantic information of source code resulting in poor performance of the prediction models. …”
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Improving Wind Turbine Gearbox Reliability: A Hybrid Deep Learning Approach Using Bispectrum Image Analysis by Muhammad Zacky Asy'Ari, Jun Kondoh, Benyamin Kusumoputro

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