Carotid endarterectomy (CEA) is a surgical treatment for carotid artery stenosis for stroke prevention. Although CEA has been shown to be effective in patients with severe plaque stenosis, predicting long-term outcome...
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Credit rating is crucial in the fast-changing 21st-century banking industry to determine creditworthiness. Traditional credit score systems may not be able to handle today's complex money habits because they are f...
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Credit rating is crucial in the fast-changing 21st-century banking industry to determine creditworthiness. Traditional credit score systems may not be able to handle today's complex money habits because they are focused on statistics and prior data. This research advises adding management, human resources, and organizational factors to machine learning credit evaluations in addition to financial data. Structure of the research describes different machine learning types. Logistic regression, decision trees, random forests, gradient boosting, and neural networks. The algorithms are trained using this dataset's financial metrics, management practices, HR indicators, and organizational procedures. Feature engineering strategies pull data from various sources to get a full picture of someone's reputation. The research argues that machine learning models should be transparent, especially in the highly regulated banking business. Using LIME and SHAP values helps make credit scoring determinations more dependable and understandable. Credit scoring will be more precise, and financial institutions will understand credit risk aspects better. Banks can improve loan selections, portfolio performance, and risk by adding management, human resources, and organizational data to financial data. This research helps financial organizations analyze credit risk in the age of machine learning and big data, resulting in more accurate credit score models.
In this paper, HTTP status codes are used as custom metrics within the HPA as the experimental scenario. By integrating the Random Forest classification algorithm from machine learning, attacks are assessed and predic...
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Continual event extraction is a practical task in natural language processing that requires models to learn quickly from new event types and data sources without forgetting pre-existing knowledge. It is important sinc...
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Accurate detection and classification of electrocardiogram (ECG) signals is required for cardiac condition diagnosis and timely medical intervention. However, capturing the complex temporal dependencies in ECG data is...
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Neuromorphic computing is a cutting-edge field of research that focuses on designing and developing computer systems and hardware architectures inspired by the structure and functioning of the human brain. The main ob...
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Various advancements in the machine learning methods helps in early identification of eye diseases by using an automated system. It has significant advantages over the manual detection. This article offers a thorough ...
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In recent years, virtual reality (VR) is gaining popularity amongst educators and learners. If a picture is worth a thousand words, a VR session is worth a trillion words. VR technology completely immerses users with ...
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This paper presents a parameter-efficient prompt tuning method, named PPT, to adapt a large multi-modal model for 3D point cloud understanding. Existing strategies are quite expensive in computation and storage, and d...
ISBN:
(纸本)9798350384581;9798350384574
This paper presents a parameter-efficient prompt tuning method, named PPT, to adapt a large multi-modal model for 3D point cloud understanding. Existing strategies are quite expensive in computation and storage, and depend on time-consuming prompt engineering. We address the problems from three aspects. Firstly, a PromptLearner module is devised to replace hand-crafted prompts with learnable contexts to automate the prompt tuning process. Then, we lock the pre-trained backbone instead of adopting the full fine-tuning paradigm to substantially improve the parameter efficiency. Finally, a lightweight PointAdapter module is arranged near target tasks to enhance prompt tuning for 3D point cloud understanding. Comprehensive experiments are conducted to demonstrate the superior parameter and data efficiency of the proposed method. Meanwhile, we obtain new records on 4 public datasets and multiple 3D tasks, i.e., point cloud recognition, few-shot learning, and part segmentation. The implementation is available at https://***/auniquesun/PPT.
Digital filter design plays a crucial role in signal processing applications, aiming to enhance, extract, or suppress specific components of a signal. Soft computing techniques have emerged as effective methods for de...
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