Performance of deep-learning models incorporating knee alignment information for predicting ground reaction force during walking by Tommy Sugiarto, Yi-Jia Lin, Hsiao-Liang Tsai, Chi-Tien Sun, Wei-Chun Hsu

“…A two-dimensional (2D)-CNN-LSTM hybrid model achieved the best results. Established models, such as ResNet50 and Inception, performed better when trained with pretrained ImageNet weights and subject-specific features, underscoring the value of pretrained knowledge and subject-specific information for improving accuracy. …”
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MTCDNet: Multimodal Feature Fusion-Based Tree Crown Detection Network Using UAV-Acquired Optical Imagery and LiDAR Data by Heng Zhang, Can Yang, Xijian Fan

“…Specifically, a transformer-based multimodal feature fusion strategy is proposed to adaptively learn correlations among multilevel features from diverse modalities, which enhances the model’s ability to represent tree crown structures by leveraging complementary information. …”
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Enhancement of Underwater Images through Parallel Fusion of Transformer and CNN by Xiangyong Liu, Zhixin Chen, Zhiqiang Xu, Ziwei Zheng, Fengshuang Ma, Yunjie Wang

“…Firstly, a novel transformer model is introduced to capture local features, employing peak-signal-to-noise ratio (PSNR) attention and linear operations. …”
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Incorporation of clinical features into a multivariate logistic regression model for the differential diagnosis of benign and malignant TI-RADS 4 thyroid nodules by Jun Hu, Xian Du, Yongbin Jiang, Yunle Wang, Lijuan Yang

“…A qualitative assessment of clinical and US features was performed, followed by univariable and multivariable logistic regression analyses using a training cohort, which contributed to the construction of the clinical TIRADS model. …”
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Using Features Extracted From Upper Limb Reaching Tasks to Detect Parkinson’s Disease by Means of Machine Learning Models by Giuseppe Cesarelli, Leandro Donisi, Francesco Amato, Maria Romano, Mario Cesarelli, Giovanni D'Addio, Alfonso M. Ponsiglione, Carlo Ricciardi

“…The ML analysis was performed twice: first, a leave-one out-cross validation was applied; then, a wrapper feature selection method was implemented to identify the best subset of features that could maximize the accuracy. …”
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RelVid: Relational Learning with Vision-Language Models for Weakly Video Anomaly Detection by Jingxin Wang, Guohan Li, Jiaqi Liu, Zhengyi Xu, Xinrong Chen, Jianming Wei

“…The key innovation of RelVid lies in the integration of auxiliary tasks, which guide the model to learn more discriminative features, significantly boosting the model’s performance. …”
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Small Scale Multi-Object Segmentation in Mid-Infrared Image Using the Image Timing Features–Gaussian Mixture Model and Convolutional-UNet by Meng Lv, Haoting Liu, Mengmeng Wang, Dongyang Wang, Haiguang Li, Xiaofei Lu, Zhenhui Guo, Qing Li

“…The approach integrates the Image Timing Features–Gaussian Mixture Model (ITF-GMM) and Convolutional-UNet (Con-UNet) to improve the accuracy of target detection. …”
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IoT-based prediction model for aquaponic fish pond water quality using multiscale feature fusion with convolutional autoencoder and GRU networks by Suma Christal Mary Sundararajan, Yamini Bhavani Shankar, Sinthia Panneer Selvam, Nalini Manogaran, Koteeswaran Seerangan, Deepa Natesan, Shitharth Selvarajan

“…The data needed to perform the developed water quality prediction model will be acquired from “a simple dataset of aquaponic fish pond IoT” database. …”
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Histologic and genetic features of remodeling of tissue-engineered small-diameter vascular grafts: outcomes of six-month implantation in a sheep model by E. A. Senokosova, E. O. Krivkina, E. A. Velikanova, A. V. Sinitskaya, A. V. Mironov, A. R. Shabaev, M. Yu. Khanova, E. A. Torgunakova, L. V. Antonova

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Key Drivers of Live Streaming Adoption: An Empirical Analysis Using the UTAUT Model by Retno Fuji Oktaviani

“…Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the assistance of SmartPLS software to test the model’s validity and the relationships between variables. …”
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Psychomedical named entity recognition method based on multi-level feature extraction and multi-granularity embedding fusion by Zixuan Liu, Guofang Zhang, Yanguang Shen

“…The features of radical embedding and pinyin embedding are extracted separately by the CNN model, and then feature fusion is performed. …”
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Integrating Machine Learning Algorithms: A Hybrid Model for Lung Cancer Outcome Improvement by Pradnyawant M. Gote, Praveen Kumar, Hemant Kumar, Prateek Verma, Moses Makuei Jiet

“…A comparative evaluation with existing models demonstrated substantial improvements in predictive accuracy, ranging from 0.44% to 9.69%, with Gradient Boosting and Random Forest models achieving the highest classification performance. …”
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Optimizing Traffic Speed Prediction Using a Multi-Objective Genetic Algorithm-Enhanced RNN for Intelligent Transportation Systems by C. Swetha Priya, F. Sagayaraj Francis

“…We evaluated our model using the Performance Measurement System (PeMS)-10 dataset and compared its performance against baseline and advanced models from existing literature. …”
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Applying complex network metrics to individual-tree diameter growth modeling by Leite e Lopes I, Sousa da Mata A, Oliveira Castro RV, Viana da Páscoa KJ, Souza Jarochinski e Silva C, Rezende Gomide L

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Artificial intelligence models for predicting the performance of proton exchange membrane water electrolyzers under steady and dynamic power by Thomas Waite, Alireza Sadeghi, Mohammad Yazdani-Asrami

“…This paper evaluated the ability of a range of AI techniques to model PEMWE performance degradation under steady-state and dynamic operation. …”
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Optimizing machine learning for network inference through comparative analysis of model performance in synthetic and real-world networks by Ruby Khan, Sumbal Khan, Bakht Pari, Krzysztof Puszynski

“…Real-world networks sometimes include extra complexities, like modularity, clustering, and scale-free features, which pose issues for these models. This study focuses on assessing the effectiveness of machine learning models in examining the structural features of networks across different scales and the related computational expenses. …”
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STDNet: Improved lip reading via short-term temporal dependency modeling by Xiaoer Wu, Zhenhua Tan, Ziwei Cheng, Yuran Ru

“…Methods: This article presents a spatiotemporal feature fusion network (STDNet) that compensates for the deficiencies of current lip-reading approaches in short-term temporal dependency modeling. …”
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Subcortical Brain Segmentation Based on a Novel Discriminative Dictionary Learning Method and Sparse Coding by Xiang Li, Ying Wei, Yunlong Zhou, Bin Hong

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Predictions of Multilevel Linguistic Features to Readability of Hong Kong Primary School Textbooks: A Machine Learning Based Exploration by Zhengye Xu, Yixun Li, Duo Liu

“…For both publisher-assigned and teacher-rated readability, the all-level-feature-RF and character-level-feature-RF models performed the best. …”
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PERFORMANCE ANALYSIS OF GRADIENT BOOSTING MODELS VARIANTS IN PREDICTING THE DIRECTION OF STOCK CLOSING PRICES ON THE INDONESIA STOCK EXCHANGE by Delvian Christoper Kho, Hindriyanto Dwi Purnomo, Hendry Hendry

“…Using four datasets from the Indonesia Stock Exchange, the research integrates technical, fundamental, and sentiment data, encompassing 37 features. Modeling and testing are conducted using Orange tools and Python, with performance evaluated through metrics such as Mean Absolute Percentage Error (MAPE), R-squared (R²), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). …”
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