As one of the most widely used storage devices today, hard drives are efficient and convenient, but their failure can cause significant losses. Therefore, early warning of hard drive failures, facilitating timely back...
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ISBN:
(数字)9798350363203
ISBN:
(纸本)9798350363210
As one of the most widely used storage devices today, hard drives are efficient and convenient, but their failure can cause significant losses. Therefore, early warning of hard drive failures, facilitating timely backup and transfer of stored content, is crucial to minimizing these losses. In recent years, research on hard drive failure prediction has been emerging continuously. However, many models suffer from short prediction times spans and low accuracy, which can lead to insufficient time for data backup. To address this issue, this paper proposes a Cross- Validation Accuracy Weighted Probability Ensemble model (CAWPE) to extend the failure detection cycle. By in-corporating a softmax function in the model's output layer, the interpretability of the model's output is enhanced. Experimental results show that the improved ensemble learning model significantly outperforms baseline models, including BP neural networks, random forests, and LSTM, on datasets of various hard drive models.
Remote sensing image instance segmentation aims to accurately separate independent objects and automatically identify land attributes. Recent advancements in large models have propelled self-supervised learning, espec...
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ISBN:
(数字)9798350363203
ISBN:
(纸本)9798350363210
Remote sensing image instance segmentation aims to accurately separate independent objects and automatically identify land attributes. Recent advancements in large models have propelled self-supervised learning, especially with the segment anything model (SAM). Although SAM can generate instance segmentation masks without additional training, its application to remote sensing images still faces challenges, such as a severe reliance on manual prior prompts, inaccuracies in detail segmentation, and insufficient generalization capability. Therefore, to enhance the generalization capability of SAM in the field of remote sensing images, we propose a SAM-based fine-tuning model called AFFE-SAM. This model combines adaptive low-rank (AdaLoRA) and variable adapter (MiMi-Adapter) fine-tuning methods. Additionally, it improves mask quality in the decoder by employing adaptive multi-scale feature enhancement. We conducted experiments on two datasets, obtaining mAP scores of 66.5 for the NWPU VHR-10 dataset and 67.1 for the SSDD dataset. The evaluation results validate the effectiveness of our method.
Detecting and classifying concept drift in data streams is essential for maintaining the accuracy and reliability of machine learning models deployed in dynamic environments. Traditional drift detection methods often ...
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ISBN:
(数字)9798350363203
ISBN:
(纸本)9798350363210
Detecting and classifying concept drift in data streams is essential for maintaining the accuracy and reliability of machine learning models deployed in dynamic environments. Traditional drift detection methods often apply a single windowing strategy across all drift types, limiting their ability to distinguish between sudden, gradual, incremental drifts. In this paper, we propose a novel approach that leverages tailored windowing strategies to enhance the performance of concept drift classifiers. Specifically, we apply sliding windows for gradual drift, tumbling windows for sudden drift, expanding windows for incremental drift, aligning each strategy with the characteristics of the respective drift type. By generating synthetic data streams using these customized windowing strategies, we train a drift classifier capable of both detecting and accurately classifying different types of drift in real-time data streams. Experimental results show that our approach significantly improves drift detection accuracy and classification performance compared to conventional windowing methods. These findings highlight the potential of tailored pre-training strategies in adaptive systems and offer a pathway for developing more robust and context-aware drift detection solutions.
Association football is a team sport played between two teams of 11 players, with approximately 250 million players in over 200 countries and dependencies, making it the world's most popular sport. Predicting the ...
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Medical image analysis has experienced different stages of development, especially with the emergence of deep learning. However, it is difficult to acquire large-scale, highquality labeled data to train the model when...
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Depression has become a serious problem in this current generation and the number of people affected by depression is increasing day by day. However, some of them manage to acknowledge that they are facing depression ...
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By constructing a knowledge graph in the field of breast cancer and combining the knowledge graph with case-based reasoning methods, a conceptual model of adjuvant diagnosis and treatment of breast cancer is construct...
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ISBN:
(数字)9798350363203
ISBN:
(纸本)9798350363210
By constructing a knowledge graph in the field of breast cancer and combining the knowledge graph with case-based reasoning methods, a conceptual model of adjuvant diagnosis and treatment of breast cancer is constructed, which provides a theoretical basis and implementation strategy for breast cancer diagnosis and treatment, Q&A and other related fields. Firstly, the data layer was constructed by combining the diagnosis and treatment data of breast diseases in specialized hospitals, related medical websites, and data in the field of breast cancer in the “Breast Cancer Diagnosis and Treatment Guidelines”. Secondly, combined with the ontology of breast cancer and empirical data ontology, the diagnosis and treatment pathway of breast cancer and the idea of evidence-based medicine were integrated to construct a model layer. Thirdly, through the OggDB database, the knowledge graph in the field of breast cancer is constructed, stored, displayed, and applied. Finally, the completed knowledge graph was combined with the case-based reasoning method to construct a conceptual model of adjuvant diagnosis and treatment of breast cancer. The knowledge graph aims to be applied to the conceptual model of assisted diagnosis and treatment of breast cancer based on case reasoning method, and serve the diagnosis and treatment process of breast diseases, so as to improve the efficiency of diagnosis and treatment of breast diseases.
In hull construction, manually identifying and assessing pre-welding defects is a challenging and time-consuming task, with potential defects posing significant safety risks. Fortunately, with the advancement of deep ...
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ISBN:
(数字)9798350363203
ISBN:
(纸本)9798350363210
In hull construction, manually identifying and assessing pre-welding defects is a challenging and time-consuming task, with potential defects posing significant safety risks. Fortunately, with the advancement of deep learning, convolutional neural network (CNN)-based recognition models have been successfully applied to computer vision tasks. However, due to the diversity of interfering factors in pre-welding defect images, current models struggle to capture critical defect features. When using attention mechanisms to address this issue, attention shifts can occur, leading to lower model recognition accuracy. To address the issue of attention shift, this paper proposes a Dual-Branch Attention Enhancement module (DBAE). This module extends traditional attention mechanisms by introducing an additional branch focused on enhancing defect features, guiding the attention towards these critical features, and improving feature extraction. Experimental results demonstrate that the DBAE module has superior feature capture capabilities compared to other classic attention modules, with the most significant improvement reaching 7.25 %, consistently outperforming other attention mechanisms. Furthermore, the results confirm that DBAE is effective across various classic classification networks, exhibiting good generalizability.
In recent years, image inpainting has seen significant advancements, with the LaMa model., introduced in 2021, standing out for its improvements in large-area inpainting. Yet, challenges such as inpainting complex geo...
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ISBN:
(数字)9798350363203
ISBN:
(纸本)9798350363210
In recent years, image inpainting has seen significant advancements, with the LaMa model., introduced in 2021, standing out for its improvements in large-area inpainting. Yet, challenges such as inpainting complex geometries, processing high-resolution images swiftly, and achieving realism in filled areas persist. Existing solutions that incorporate diffusion models to enhance realism are hardware-intensive and unsuitable for high-resolution images. We propose a new image inpainting method, Downsampled Fast Fourier Convolution (DFFC), which addresses these challenges. Unlike LaMa's reliance on Fast Fourier Convolution (FFC) for early-stage global context capture without explicit downsampling, DFFC introduces a deep learning-driven downsampling module to reduce computational complexity and improve high-resolution image processing efficiency. This module allows for data volume reduction during processing while preserving global context, comprising: i) deep learning-based image downsampling, ii) an FFC-based inpainting architecture, iii) a high-perceptual-domain loss function, and iv) dynamic large-area mask training. Our method maintains the original model's performance, enhances processing speed, and reduces computational load.
Prolonged and frequent exposure to elevated blood glucose levels (hyperglycemia) significantly increases the likelihood of developing chronic complications, such as neuropathy, nephropathy, and cardiovascular disease,...
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ISBN:
(数字)9798350351552
ISBN:
(纸本)9798350351569
Prolonged and frequent exposure to elevated blood glucose levels (hyperglycemia) significantly increases the likelihood of developing chronic complications, such as neuropathy, nephropathy, and cardiovascular disease, along with acute symptoms like fatigue and blurry vision. While current technologies, such as continuous subcutaneous insulin infusion (CSII) and continuous glucose monitors (CGMs), can forecast adverse events like hypoglycemia and deliver small insulin doses to counteract hyperglycemia, progress in developing tailored AI-driven interventions remains limited, which poses a barrier to optimal diabetes care. To address this gap, we propose leveraging counterfactual explanations that guide patients in making targeted adjustments to their carbohydrate intake and insulin dosing to avoid abnormal glucose levels. We introduce GlyMan 4 , a novel method that generates counterfactual behavioral recommendations aimed at helping patients and caregivers make small, informed changes to prevent hyperglycemia, thus substantially reducing both its frequency and duration. Additionally, GlyMan incorporates user preferences into its intervention process and ensures more customized and patient-centered guidance. We rigorously evaluated GlyMan using real-world data from 21 type 1 diabetes (T1D) patients using automated insulin delivery (AID) systems. Results indicate that GlyMan surpasses existing methods, delivering 76.6% valid explanations and 86% effectiveness when assessed against historical data.
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