Chinese Paintings Emotion Recognition Based on End-to-end Weakly Supervised Learning Network Model by LU Ke-bin, YIN Shou-lin

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Short-Term Highway Traffic Flow Prediction via Wavelet–Liquid Neural Network Model by Yongjun Wu, Hongyun Kang, Weipin Wang, Shuli Zhao, Xuening He, Jingyao Chen

“…Notably, the wavelet-LNN model features the fewest parameters (<2% of typical deep learning models).…”
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The Heavy‐Tailed Inverse Power Lindley Type‐I Model: Reliability Inference and Actuarial Applications by Amal S. Hassan, Diaa S. Metwally, Mohammed Elgarhy, Rokaya Elmorsy Mohamed

“…The flexibility and goodness‐of‐fit performance of the proposed model are also demonstrated by re‐analyzing two actuarial real datasets and comparing its fit with those obtained by extended inverse Lindley, extended exponentiated inverse Lindley, inverse power Lindley, inverse Lindley, and alpha power‐transformed inverse Lindley distributions.…”
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YOLOv8n-SSDW: A Lightweight and Accurate Model for Barnyard Grass Detection in Fields by Yan Sun, Hanrui Guo, Xiaoan Chen, Mengqi Li, Bing Fang, Yingli Cao

“…A new WIoU loss function was developed to improve bounding box classification and regression accuracy. Comprehensive performance analysis demonstrated that YOLOv8n-SSDW outperformed state-of-the-art models. …”
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Construction and validation of a predictive model for lymph node metastasis in patients with papillary thyroid carcinoma by Yanhong Hao, Yanjing Zhang, Yuan Su, Liping Liu

“…ObjectiveTo study the occurrence of lymph node metastasis in patients with papillary thyroid carcinoma (PTC) and construct a predictive model to assess its predictive performance.MethodsWe retrospectively analyzed the data of 432 patients with PTC. …”
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Coal Mine Water Inflow Prediction Model Based on Multi-Factor Pearson Correlation Analysis by Liang Ma, Zaibing Liu, Weiming Chen, Junjie Hu, Hongjian Ye, Tao Fan, Lin An

“…To validate the performance of the present prediction model, a case study is conducted, where the predicted coal mine water inflow is close to the collected coal mine water inflow. …”
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Wavelet-Enhanced Hybrid LSTM-XGBoost Model for Predicting Time Series Containing Unpredictable Events by Ali Ajder, Hisham A. A. Hamza, Ramazan Ayaz

“…By incorporating these wavelet coefficients as features, the model is sensitized to anomalous events, resulting in more accurate forecasts over a more extended period without the need for frequent retraining. …”
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Model Predictive Control for Autonomous Ship Navigation with COLREG Compliance and Chart-Based Path Planning by Primož Potočnik

“…The method generates feasible reference routes using maritime charts and predefined waypoints, while the MPC controller ensures precise path following and dynamic re-planning in response to nearby vessels and coastal obstacles. Coastal features and shorelines are modeled using Global Self-consistent, Hierarchical, High-resolution Geography data, enabling MPC to treat landmasses as static obstacles. …”
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Kyber, Saber, and SK-MLWR Lattice-Based Key Encapsulation Mechanisms Model Checking with Maude by Duong Dinh Tran, Kazuhiro Ogata, Santiago Escobar, Sedat Akleylek, Ayoub Otmani

“…We use Maude, a specification language supporting equational and rewriting logic and a high-performance tool equipped with many advanced features, such as a reachability analyzer that can be used as a model checker for invariant properties, to model the three KEMs as state machines. …”
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Application of a Latent Diffusion Model to Plant Disease Detection by Generating Unseen Class Images by Noriyuki Mori, Hiroki Naito, Fumiki Hosoi

“…In this study, we used images of healthy and diseased grape leaves as training datasets and utilized the latent diffusion model, known for its superior performance in image generation, to generate images of diseased apple leaves that were not included in this dataset. …”
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Early Identification of Pediatric Inflammatory Bowel Disease Based on a Noninvasive Multivariable Predictive Model by Wu H, Sun Y, Tang Z, Qin X, Wang Y, Huang Y

“…Model performance was assessed via bootstrapped internal validation (1000 resamples), and temporally validated in a prospective cohort of 66 children (IBD, 19; non-IBD, 47).Results: In the importance assessment, the ultrasound feature of Limberg level &gt; 1 was identified as the most valuable feature, followed by the erythrocyte sedimentation rate, fecal calprotectin, C-reactive protein and hypoalbuminemia. …”
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An AI based cross‐language aspect‐level sentiment analysis model using English corpus by Jing Chen, Li Pan

“…First, a multi‐channel XLNet (Multi XLNet) network model is used to perform autoregressive encoding operations on different languages, fully extracting contextual information from the text and better characterizing the ambiguity of the text. …”
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BertADP: a fine-tuned protein language model for anti-diabetic peptide prediction by Xueqin Xie, Changchun Wu, Yixuan Qi, Shanghua Liu, Jian Huang, Hao Lyu, Fuying Dao, Hao Lin

“…Among them, BertADP (a fine-tuned ProtBert model) demonstrated superior performance in the independent test set, outperforming existing ADPs prediction tools with an overall accuracy of 0.955, sensitivity of 1.000, and specificity of 0.910. …”
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Accurate classification of benign and malignant breast tumors in ultrasound imaging with an enhanced deep learning model by Baoqin Liu, Shouyao Liu, Zijian Cao, Junning Zhang, Xiaoqi Pu, Junjie Yu

“…On the external test set, RcdNet achieved an accuracy of 0.9351, a precision of 0.9168, a recall of 0.9495, and an F1-score of 0.9290, demonstrating superior classification performance and strong generalization ability. Furthermore, heat map visualizations confirm that RcdNet accurately attends to clinically relevant features such as tumor edges and irregular structures, aligning well with radiologists’ diagnostic focus and enhancing the interpretability and credibility of the model in clinical applications.ConclusionThe RcdNet model proposed in this study performs well in the classification of benign and malignant breast ultrasound images, with high classification accuracy, strong generalization ability and good interpretability. …”
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A text-speech multimodal Chinese named entity recognition model for crop diseases and pests by Ruilin Liu, Xuchao Guo, HongMei Zhu, Lu Wang

“…In this paper, we propose a multimodal named entity recognition model (CDP-MCNER) based on cross-modal attention to solve the issue of the performance degradation of the NER model caused by potential word segmentation errors. …”
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Automatic Identification Model for Landslide Disaster Using Remote Sensing Images Based on Improved Multiresunet by Zhenyu Zhao, Shucheng Tan, Qinghua Zhang, Hui Chen

“…Additionally, the Canny operator is employed to design a lightweight boundary-focused attention (BFA) mechanism, enhancing the model&#x2019;s ability to emphasize landslide edge features. …”
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Blueberry Remaining Shelf-Life Prediction Based on the PSO-CNN-BiLSTM-MHA Model by Mengya Liu, Xu Cheng, Yu Cao, Qian Zhou

“…In this study, seven key features were selected from fifteen quality indicators of blueberries using the embedded method, and the PSO algorithm was used to determine the optimal hyperparameter combination of the model, which effectively improved its prediction performance. …”
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Visual Detection on Aircraft Wing Icing Process Using a Lightweight Deep Learning Model by Yang Yan, Chao Tang, Jirong Huang, Zhixiong Cen, Zonghong Xie

“…The model is optimized using the transfer learning method, where pre-trained weights are utilized to accelerate convergence and enhance performance. …”
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Prediction of coal mine water conduction fracture zone height based on integrated learning model by Meng Wang, Xufeng Zhang, Xin Li, Zhongzheng Fang, Yafei Wei, Hongyan Qin, Shuai Wang, Tianyu Liu, Yaqiang Zheng

“…The model also achieves an R² of 0.98 and RMSE of 2.08, indicating excellent predictive performance. …”
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Clinical assessment and interpretation of dysarthria in ALS using attention based deep learning AI models by Michele Merler, Carla Agurto, Julian Peller, Esteban Roitberg, Alan Taitz, Marcos A. Trevisan, Indu Navar, James D. Berry, Ernest Fraenkel, Lyle W. Ostrow, Guillermo A. Cecchi, Raquel Norel

“…Abstract Speech dysarthria is a key symptom of neurological conditions like ALS, yet existing AI models designed to analyze it from audio signal rely on handcrafted features with limited inference performance. …”
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