We describe a method where a utility can anonymize network device configurations and upload them to a remote service provider who analyzes connectivity (oblivious to the anonymization) and returns the results in anony...
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The mission of Digital Twin is to disseminate authoritative academic works in an effort to advance the state of the art in digital twin and inspire originality in the creation of effective, resilient, and sustainable ...
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The article highlights the advantages of using the Grafana platform using the example of measurements involving particulate matter, volatile organic compounds, temperature, humidity, pressure, and wind speed. The intu...
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The emergence of bias in deep neural models represents a significant reliability concern, which may lead to overoptimistic results on seen data while compromising the model's ability to generalize effectively on u...
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ISBN:
(数字)9798350308365
ISBN:
(纸本)9798350308372
The emergence of bias in deep neural models represents a significant reliability concern, which may lead to overoptimistic results on seen data while compromising the model's ability to generalize effectively on unseen datasets. Recent studies conducted on The Cancer Genome Atlas (TCGA), which is a publicly available repository of histopathology images, reveal that the TCGA cancerous features extracted by deep neural networks surprisingly are able to discriminate slides based on their origin sites. This finding undoubtedly indicates the existence of site-specific patterns embedded in the extracted features learned by deep networks rather than focusing on histomorphologic patterns. Consequently, this biased behavior raises concerns about the reliability of these networks. This observation motivates us to conduct a series of experiments in which we present two distinct evolutionary feature selection strategies, each differentiated by its objective function evaluation. The primary goal is to select the features with a minimized foot-print of data source signatures, thereby ensuring a more accurate and site-independent cancer classification. We have conducted nine comprehensive independent experiments across nine cancer types, employing each evolutionary strategy. The comparison between results obtained through evolutionary strategies and the original feature sets highlights the substantial impact of feature selection methods on bias reduction while maintaining accuracy in cancer-type discrimination. Furthermore, the comparison of the two strategies with each other demonstrates the intricate nature of bias and its integration with cancerous features during the training process.
In everyday life, various exposures at work or at home can lead to skin conditions such as allergies or infections. Skin lesions serve as essential indications, alerting to future issues and requiring immediate care a...
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As a respiratory syndrome correlated with some cardiovascular diseases, obstructive sleep apnea (OSA) not only destroys the quality of our sleep, but also induces a variety of major chronic diseases such as heart dise...
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Recent advances in 5G wireless technologies calls for larger bandwidth, which motivates design engineers and researchers to explore a higher frequency spectrum than the existing one spectrum of below 6 GHz. Millimeter...
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An examination is a useful tool for assessing students' knowledge. Evaluation of exams is a difficult and time-consuming process. The automatic examination of answer scripts makes this task easier for teachers, re...
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IoT plays a crucial role in transforming the agricultural industry by offering diverse use cases starting from crop monitoring and precision farming to yield optimization. The fundamental reason why the agricultural c...
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This work offers a methodical approach to detecting road signs using CNN, deep learning, and algorithms. As a one-unit regression problem, the object detection problem is defined as follows: probability classes and st...
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