STCYOLO: Subway Tunnel Crack Detection Model with Complex Scenarios by Jia Zhang, Hui Li, Weidong Song, Jinhe Zhang, Miao Shi

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Frequency-Domain Guided Image Classification With Large Model Assistance by Xia Hua, Lei Han

“…Second, most sport image classification techniques directly transfer image classification methods from other domains, with few approaches specifically designed based on the unique features of sport images. To address this problem, we devise a Frequency-domain Guided sport image classification method with Large model Assistance, named FGLA. …”
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Survival Prediction of Esophageal Cancer Using 3D CT Imaging: A Context-Aware Approach With Non-Local Feature Aggregation and Graph-Based Spatial Interaction by Fuce Guo, Chen Huang, Shengmei Lin, Yongmei Dai, Qianshun Chen, Shu Zhang, Xunyu XU

“…In the current study, we aimed to develop an effective EC survival risk prediction using only 3D computed tomography (CT) images.The proposed model consists of two essential components: 1) non-local feature aggregation module(NFAM) that integrates visual features from tumor and lymph nodes at both local and global scales, 2) graph-based spatial interaction module(GSIM) that explores the latent contextual interactions between tumors and lymph nodes.The experimental results demonstrate that our model achieves superior performance compared to state-of-the-art survival prediction methods, emphasizing its robust predictive capability. …”
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Radiomics machine learning based on asymmetrically prominent cortical and deep medullary veins combined with clinical features to predict prognosis in acute ischemic stroke: a retr... by Hongyi Li, Cancan Chang, Bo Zhou, Yu Lan, Peizhuo Zang, Shannan Chen, Shouliang Qi, Ronghui Ju, Yang Duan

“…The DeLong test revealed that the predictive performance of the joint model was better than that of the individual models, with a test set AUC of 0.996, sensitivity of 0.905, and specificity of 1.000 (P < 0.05). …”
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Structure-Aware and Format-Enhanced Transformer for Accident Report Modeling by Wenhua Zeng, Wenhu Tang, Diping Yuan, Hui Zhang, Pinsheng Duan, Shikun Hu

“…Further analysis reveals an inverted U-shaped performance trend across varying report lengths and highlights the role of attention sparsity and label distribution in long-text modeling. …”
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The possibilities of debtor segmentation algorithmic modeling in the context of commercial lending by Chernichenko S., Kotov R.

“…The analytical and financial-engineering component of the scientific work which is of fundamental importance (as it reflects the implementation of the research focus), consists of five stages: 1) identification and thematic grouping of the creditor's risk indicators; 2) indicators unification in the form of the commercial debtor segmentation algorithm; 3) algorithmic model testing and performance evaluation; 4) explanation of cause-effect relationships strength between the interpenetrating exponents of the algorithmic model; 5) identification of the most valuable parameters (identifying factors) of credit analysis in the commercial lending sector. …”
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Design of experiments for the stochastic unit commitment with economic dispatch models by Nahal Sakhavand, Jay Rosenberger, Victoria C.P. Chen, Harsha Gangammanavar

“…To address this challenge, we develop a novel DACE approach that exploits the structure of the first-stage unit commitment decision space in a design of experiments, uses features based upon solar generation, and trains a multivariate adaptive regression splines model to approximate the second stage of the stochastic unit commitment-economic dispatch problem. …”
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A Lightweight Multi-Scale Model for Speech Emotion Recognition by Haoming Li, Daqi Zhao, Jingwen Wang, Deqiang Wang

“…Experiments have been carried out on IEMOCAP, EMO-DB and SAVEE datasets to verify the performance of the proposed model and compare with the state-of-the-art SER models. …”
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Multilayer Concept Drift Detection Method Based on Model Explainability by Haolan Zhang, Xinyi Chen, Min Hu, Vijayan Sugumaran

“…Lastly, a drift point backtracking mechanism is introduced to accurately pinpoint the initial drift point, facilitating the rapid creation of new models. Experiments show that HCDD-SHAP exhibits superior performance on synthetic and real-world datasets, surpassing conventional drift detectors in terms of precision, recall, and detection delay across diverse detection scenarios. …”
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Apprentice bot model design and implementation for psychological clients’ therapy by Kibrom Gidey, Hailay Beyene, Fiseha Haileslassie, Haben Berihu

“…The model's accuracy was 79.62%, with a 79.62% likelihood of delivering relevant responses. …”
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On the Non-Dimensional Modelling of Friction Hysteresis of Conformal Rough Contacts by Kristof Driesen, Sylvie Castagne, Bert Lauwers, Dieter Fauconnier

“…The non-dimensional groups are derived from a mixed friction model composed of the transient Reynolds equation, a statistical mixed friction contact model, and the load balance. …”
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Deep Sequential Model for Anchor Recommendation on Live Streaming Platforms by Shuai Zhang, Hongyan Liu, Jun He, Sanpu Han, Xiaoyong Du

“…To evaluate the performance of our proposed model, we conduct experiments on real datasets, and the results show that our proposed model outperforms state-of-the-art recommendation models.…”
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Application of Machine Learning for Academic Outcome Prediction: A Methodological Comparative Study by Md. Wira Putra Dananjaya, Putu Gita Pujayanti

“…Academic performance prediction is a crucial area in education; however, the complexity of influencing factors often cannot be adequately captured by simple linear models. …”
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Race course characteristics are the most important predictors in 48 h ultramarathon running by Beat Knechtle, David Valero, Elias Villiger, Katja Weiss, Pantelis T. Nikolaidis, Lorin Braschler, Rodrigo Luiz Vancini, Marilia Santos Andrade, Ivan Cuk, Thomas Rosemann, Mabliny Thuany

“…The country of origin of the athlete and the country where the event was hold were the most important features by the SHAP analysis, yielding the broader range of model outputs. …”
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Computer vision based automatic evaluation method of Y2O3 steel coating performance with SEM image by Jianhong Zhao, Huamin Yang, Yi Sui

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Spatial–Texture Hybrid MRI Model for Orbital Lymphoma Typing by Lunhao Li, Lai Wei, Jiahao Shi, Guangtao Zhai, Menghan Hu, Yixiong Zhou

“…Hence, for the first time, the relative spatial positional features are extracted between different orbital structures and the tumor, complemented by the texture characteristics of the tumor area, to perform hybrid modeling. …”
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Corn-associated weed detection model based on WAAP-YOLO by Zhiyong MENG, Yawei JIA, Xiuqing ZHANG, Yongjing NI, Ming ZHANG, Qi WU, Chenxi WU

“…Second, an aggregated attention mechanism was introduced to construct the C2f-AA module, improving the model′s ability to extract weed features in complex backgrounds. …”
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Memory Prefetching Evaluation of Scientific Applications on a Modern HPC Arm-Based Processor by Nam Ho, Carlos Falquez, Antoni Portero, Estela Suarez, Dirk Pleiter

“…We evaluated traditional and innovative prefetchers using a gem5-based model of Arm Neoverse V1 cores. The model features a 16-core architecture, using Amazon&#x2019;s Graviton 3 processor as a hardware reference, but substituting DDR5 by high bandwidth memory (HBM2). …”
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Chronobiologically-informed features from CGM data provide unique information for XGBoost prediction of longer-term glycemic dysregulation in 8,000 individuals with type-2 diabetes... by Jamison H Burks, Leslie Joe, Karina Kanjaria, Carlos Monsivais, Kate O'laughlin, Benjamin L Smarr

“…In this work, we leverage longitudinal Dexcom continuous glucose monitoring data to test the hypothesis that additional information about glycemic stability comes from chronobiologically-informed features. We develop a computationally efficient multi-timescale complexity index, and find that inclusion of time-of-day complexity features increases the performance of an out-of-the-box XGBoost model in predicting the change in glucose across days. …”
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GhostConv+CA-YOLOv8n: a lightweight network for rice pest detection based on the aggregation of low-level features in real-world complex backgrounds by Fei Li, Yang Lu, Qiang Ma, Shuxin Yin, Rui Zhao

“…Deep learning models for rice pest detection often face performance degradation in real-world field environments due to complex backgrounds and limited computational resources. …”
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