Research on AIGC Technology Driven Innovation Models and Risk Management in the Financial Sector by Gong Lipeng

“…Finally, this article proposes strategies such as improving the regulatory system, optimizing algorithm transparency, and strengthening data governance to promote the healthy development of AIGC technology in the financial sector.…”
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Enhanced Superpixel-Guided ResNet Framework with Optimized Deep-Weighted Averaging-Based Feature Fusion for Lung Cancer Detection in Histopathological Images by Karthikeyan Shanmugam, Harikumar Rajaguru

“…Segmentation and feature selection enhance performance, improving diagnostic accuracy. Future work may explore further optimizations and hybrid models.…”
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Deep learning-based classification of coronary arteries and left ventricle using multimodal data for autonomous protocol selection or adjustment in angiography by Arpitha Ravi, Philipp Bernhardt, Mathis Hoffmann, Florian Kordon, Siming Bayer, Stephan Achenbach, Andreas Maier

“…This model has the potential to streamline clinical workflows, improve diagnostic accuracy, and enhance safety for both patients and operators.…”
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Machine Learning Approach to Model Soil Resistivity Using Field Instrumentation Data by Md Jobair Bin Alam, Ashish Gunda, Asif Ahmed

“…Cross-validation and feature selection methods were used to optimize model performance and identify key variables that most significantly impact soil resistivity. …”
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Research review of digital modeling technologies with 3D spatial visualization for pipelines by Xin LIU, Shengwen TU, Yanfang HOU, Dong LIN, Jie SHU, Yu TANG

“…MethodsFrom the perspective of software and algorithm characteristics, this paper classifies visual modeling technologies for pipelines into three categories: 3D modeling software, secondary development based on existing components, and underlying development. …”
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FUR-DETR: A Lightweight Detection Model for Fixed-Wing UAV Recovery by Yu Yao, Jun Wu, Yisheng Hao, Zhen Huang, Zixuan Yin, Jiajing Xu, Honglin Chen, Jiahua Pi

“…Therefore, this paper proposes a lightweight visual detection model based on transformer architecture to further optimize computational efficiency. …”
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Research on Urban Rainfall Runoff Pollution Prediction Model Based on Feature Fusion by Junping Yao, Tianle Sun

“…Meanwhile, the characteristics of rainfall runoff pollution are analyzed from the perspectives of the main sources of rainfall runoff pollution, the types of rainfall runoff pollution, and the initial erosion. The neural network algorithm is optimized and trained according to the sample data to obtain the sample features; the sample data are predicted according to the extracted sample features, and the prediction model is generated by using the feature fusion technology for two groups of prediction results to generate the prediction model and realize the water drop prediction. …”
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Sustainable Self-Training Pig Detection System with Augmented Single Labeled Target Data for Solving Domain Shift Problem by Junhee Lee, Heechan Chae, Seungwook Son, Jongwoong Seo, Yooil Suh, Jonguk Lee, Yongwha Chung, Daihee Park

“…Then, the trained base model is improved through self-training, where a super-low threshold is applied to filter pseudo-labels. …”
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Machine learning-based e-commerce platform repurchase customer prediction model. by Cheng-Ju Liu, Tien-Shou Huang, Ping-Tsan Ho, Ping-Tsan Ho, Jui-Chan Huang, Ching-Tang Hsieh

“…Finally, through two sets of contrast experiments, it is proved that the algorithm selected in this paper can effectively filter the features, which simplifies the complexity of the model to a certain extent and improves the classification accuracy of machine learning. …”
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Observer-based finite-time event-triggered secure control for fuzzy implicit systems against deception attacks: A single-link flexible joint robotic application by M. Kchaou, L. Ladhar, M. Omri, R. Abbassi, H. Jerbi

“…Furthermore, the Gray Wolf Optimization (GWO) algorithm is employed alongside linear matrix inequality techniques to optimize the gains of both the controller and the observer. …”
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A Federated Learning Model for Detecting Cyberattacks in Internet of Medical Things Networks by Abdallah Ghourabi, Adel Alkhalil

“…The XGBoost models are further optimized using a Bayesian method and integrated with an aggregation algorithm to construct an adaptive global model. …”
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可穿戴设备在卒中风险预测及卒中后管理中的应用进展 Research Progress on the Application of Wearable Devices in Stroke Risk Prediction and Post-Stroke Management... by 吴雅婷1，魏宸铭1，桑振华1，陈乐1，梁怡凡1，武剑1，2，3 (WU Yating1, WEI Chenming1, SANG Zhenhua1, CHEN Le1, LIANG Yifan1, WU Jian1,2,3)

“…Wearable devices, with their real-time capabilities and portability features, present new solutions for stroke risk prediction and post-stroke management. By integrating with health management platforms and artificial intelligence algorithms, wearable devices can significantly enhance the accuracy of risk assessment, optimize rehabilitation treatment plans, and thus improve the patients’ outcomes. …”
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Advanced Deep Learning Approaches in Detection Technologies for Comprehensive Breast Cancer Assessment Based on WSIs: A Systematic Literature Review by Qiaoyi Xu, Afzan Adam, Azizi Abdullah, Nurkhairul Bariyah

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Evaluation of Shelf Life Prediction for Broccoli Based on Multispectral Imaging and Multi-Feature Data Fusion by Xiaoshuo Cui, Xiaoxue Sun, Shuxin Xuan, Jinyu Liu, Dongfang Zhang, Jun Zhang, Xiaofei Fan, Xuesong Suo

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Forecasting loan, deferred rate and customer segmentation in banking industry: A computational intelligence approach by Mahtab Vasheghani, Ebrahim Nazari Farokhi, Behrooz Dolatshah

“…This study proposes a novel hybrid model integrating Multi-Layer Perceptron (MLP) neural networks with Self-Adaptive Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) frameworks. …”
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Development of a machine learning-based surrogate model for friction prediction in textured journal bearings by Yujun Wang, Georg Jacobs, Shuo Zhang, Benjamin Klinghart, Florian König

“…This enhancement is achieved through an architecture design based on cross-validation and further optimization utilizing the genetic algorithm. Eventually, the average prediction accuracy is improved to 98.81% from 95.89%, with the maximum error reduced to 3.25% from 13.17%. …”
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Yield Diagnosis and Tuning for Emerging Semiconductors During Research Stage by Chunshan Wang, Zizhao Ma, Yuxuan Zhu, Chensheng Jin, Dongyu Chen, Chuxin Zhang, Yining Chen, Wenzhong Bao, Yufeng Xie

“…The process of taking a new semiconductor device from the lab to the factory involves a lot of time, funds and manpower, a large portion of which is spent on device yield improvement. In recent years new methods have been tried to rapidly improve yields and using machine learning (ML) algorithms is one option. …”
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Crack Parameters Identification Based on a Kriging Surrogate Model for Operating Rotors by Danyang Wang, Chunrong Hua, Dawei Dong, Biao He, Zhiwen Lu

“…This work proposes a new method to identify crack parameters in a rotor-bearing system based on a Kriging surrogate model and an improved nondominated sorting genetic algorithm-III (NSGA-III). …”
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Taking the lag out of jet lag through model-based schedule design. by Dennis A Dean, Daniel B Forger, Elizabeth B Klerman

“…In this paper, the use of mathematical models to design sleep-wake and countermeasure schedules for improved performance is demonstrated. …”
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NoiseAugmentNet-HHO: Enhancing Histopathological Image Classification Through Noise Augmentation by Prem Purusottam Jena, Debahuti Mishra, Kaberi Das, Sashikala Mishra

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