The avoidance of mortality in lung cancer is highly dependent on finding defects in the lungs early, to initiate effective treatments in time. Most often, lung disorders are diagnosed and treated using chest radiograp...
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In recent years, computer vision has made significant strides in enabling machines to perform a wide range of tasks, from image classification and segmentation to image generation and video analysis. It is a rapidly e...
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ISBN:
(数字)9781510661936
ISBN:
(纸本)9781510661929;9781510661936
In recent years, computer vision has made significant strides in enabling machines to perform a wide range of tasks, from image classification and segmentation to image generation and video analysis. It is a rapidly evolving field that aims to enable machines to interpret and understand visual information from the environment. One key task in computer vision is image classification, where algorithms identify and categorize objects in images based on their visual features. image classification has a wide range of applications, from image search and recommendation systems to autonomous driving and medical diagnosis. However, recent research has highlighted the presence of bias in image classification algorithms, particularly with respect to human-sensitive attributes such as gender, race, and ethnicity. Some examples are computer programmers being predicted better in the context of men in images compared to women, and the accuracy of the algorithm being better on greyscale images compared to colored images. This discrepancy in identifying objects is developed through correlation the algorithm learns from the objects in context known as contextual bias. This bias can result in inaccurate decisions, with potential consequences in areas such as hiring, healthcare, and security. In this paper, we conduct an empirical study to investigate bias in the image classification domain based on sensitive attribute gender using deep convolutional neuralnetworks (CNN) through transfer learning and minimize bias within the image context using data augmentation to improve overall model performance. In addition, cross-data generalization experiments are conducted to evaluate model robustness across popular open-source image datasets.
Handwritten language detection plays a crucial role in various applications, such as document analysis, translation services, and forensic document examination. This research focuses on the development and evaluation ...
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In this paper, we explore the intersection of technology and cultural preservation by developing a self-supervised learning framework for the classification of musical symbols in historical manuscripts. Optical Music ...
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This study investigates medical image classification employing Convolutional neuralnetworks (CNNs), Support Vector machine (SVM) and Genetic Algorithm (GA) focusing on hyperparameter optimization for the task of Brai...
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In previous work, a concept for a novel flexible part feeding system based on aerodynamic feeding was presented. In contrast to conventional part feeding systems, such as vibratory bowl feeders, aerodynamic part feedi...
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General Adversarial networks (GANs) have emerged as a powerful framework for generating reliable and transformative synthetic data in areas such as image generation, image and text synthesis, and data augmentation. Th...
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Hyperspectral image (HSI) classification is valuable in remote sensing due to its rich spectral and spatial information. In the last decade, deep learning methods, especially Convolutional neuralnetworks (CNNs), have...
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ISBN:
(纸本)9798350350920
Hyperspectral image (HSI) classification is valuable in remote sensing due to its rich spectral and spatial information. In the last decade, deep learning methods, especially Convolutional neuralnetworks (CNNs), have revolutionized HSI classification by extracting intangible semantic features and maintaining the spatial structure during feature extraction. However, the efficacy of these techniques can be constrained by the limited availability of labeled samples in HSI data. To address the issue of small-sample HSI classification, a Lightweight Multiscale Feature Fusion Network (L-MFFN) is introduced. The Multiscale Feature Extraction Module (MFEM) and the enhanced Spectral-Spatial Attention Module (SSAM) are designed and combined in L-MFFN, optimizing the use of deep and shallow features. This integration improves the extraction and fusion of multiscale spectral-spatial features, enhancing classification performance. The proposed model demonstrates state-of-the-art performance across two HSI datasets and stands out in situations with limited labeled samples, highlighting its capability to effectively tackle the challenge of small-sample HSI classification.
Second-order optimization algorithms have garnered significant interest in deep learning due to their ability to leverage curvature information, leading to faster convergence and enhanced generalization compared to fi...
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In terms of music genre classification, neuralnetworks and machinelearning models have their respective advantages. This paper aims to compare the performance and feature extraction capability between neural network...
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