Edge learning (EL) is an end-to-edge collaborative learning paradigm enabling devices to participate in model training and data analysis, opening countless opportunities for edge intelligence. As a promising EL framew...
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Coconut tree diseases are a serious risk to agricultural yield, particularly in developing countries where conventional farming practices restrict early diagnosis and intervention. Current disease identification metho...
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T- Data classification and extraction are fundamental tasks in the field of computer vision and data analysis. This abstract presents an overview of these concepts along with the utilization of Python and OpenCV, a po...
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Billions of people worldwide are affected by vision impairment majorly caused due to age-related degradation and refractive errors. Diabetic Retinopathy(DR) and Macular Hole(MH) are among the most prevalent senescent ...
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A new stochastic coordinate descent deep learning architectures optimization is proposed for Automated Diabetic Retinopathy Detection and Classification from different data sets and convolution networks. Initially, th...
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The evolution of bone marrow morphology is necessary in Acute Mye-loid Leukemia(AML)*** takes an enormous number of times to ana-lyze with the standardization and inter-observer ***,we proposed a novel AML detection m...
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The evolution of bone marrow morphology is necessary in Acute Mye-loid Leukemia(AML)*** takes an enormous number of times to ana-lyze with the standardization and inter-observer ***,we proposed a novel AML detection model using a Deep Convolutional Neural Network(D-CNN).The proposed Faster R-CNN(Faster Region-Based CNN)models are trained with Morphological *** proposed Faster R-CNN model is trained using the augmented *** overcoming the Imbalanced Data problem,data augmentation techniques are *** Faster R-CNN performance was com-pared with existing transfer learning *** results show that the Faster R-CNN performance was significant than other *** number of images in each class is *** example,the Neutrophil(segmented)class consists of 8,486 images,and Lymphocyte(atypical)class consists of eleven *** dataset is used to train the CNN for single-cell morphology classifi*** proposed work implies the high-class performance server called Nvidia Tesla V100 GPU(Graphics processing unit).
Although Convolutional Neural Networks(CNNs)have achieved remarkable success in image classification,most CNNs use image datasets in the Red-Green-Blue(RGB)color space(one of the most commonly used color spaces).The e...
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Although Convolutional Neural Networks(CNNs)have achieved remarkable success in image classification,most CNNs use image datasets in the Red-Green-Blue(RGB)color space(one of the most commonly used color spaces).The existing literature regarding the influence of color space use on the performance of CNNs is *** paper explores the impact of different color spaces on image classification using *** compare the performance of five CNN models with different convolution operations and numbers of layers on four image datasets,each converted to nine color *** find that color space selection can significantly affect classification accuracy,and that some classes are more sensitive to color space changes than *** color spaces may have different expression abilities for different image features,such as brightness,saturation,hue,*** leverage the complementary information from different color spaces,we propose a pseudo-Siamese network that fuses two color spaces without modifying the network *** experiments show that our proposed model can outperform the single-color-space models on most *** also find that our method is simple,flexible,and compatible with any CNN and image dataset.
Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detecti...
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Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.
As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management *** has become a promi...
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As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management *** has become a promising solution to this problem due to its powerful modeling capability,which has become a consensus in academia and ***,because of the data-dependence and inexplicability of AI models and the openness of electromagnetic space,the physical layer digital communication signals identification model is threatened by adversarial *** examples pose a common threat to AI models,where well-designed and slight perturbations added to input data can cause wrong ***,the security of AI models for the digital communication signals identification is the premise of its efficient and credible *** this paper,we first launch adversarial attacks on the end-to-end AI model for automatic modulation classifi-cation,and then we explain and present three defense mechanisms based on the adversarial *** we present more detailed adversarial indicators to evaluate attack and defense ***,a demonstration verification system is developed to show that the adversarial attack is a real threat to the digital communication signals identification model,which should be paid more attention in future research.
Wind field forecasting is crucial for human activities, but numerical weather prediction still has room to improve accuracy. In this paper, we formalize wind field forecast correction as a spatiotemporal sequence pred...
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