A methodology for evaluating information security (IS) for a distributedcomputer network (DCN) of a university (hereinafter referred to as UDCN) has been proposed. A mathematical model for calculating the UDCN vulner...
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Autonomous mobile agent systems can accomplish multiple tasks in an asynchronous and dynamic manner in platforms where there is excessive network load, network interruption and in slow networks. However, the decisions...
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The rapid evolution of mobile networks presents challenges for devices with limited computing power. Mobile or multi-access edge computing (MEC) addresses this by providing computing resources in proximity to end devi...
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Deep neural networks have achieved state-of-the-art performance on numerous applications in the medical field, with use-cases ranging from automation of mundane tasks to diagnosis of life-threatening diseases. Despite...
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ISBN:
(数字)9781665467704
ISBN:
(纸本)9781665467704
Deep neural networks have achieved state-of-the-art performance on numerous applications in the medical field, with use-cases ranging from automation of mundane tasks to diagnosis of life-threatening diseases. Despite these achievements, deep neural networks are considered "black boxes" due to their complex structure and general lack of transparency in their decision-making process. These attributes make it challenging to incorporate deep learning into existing clinical workflows as decisions often need more support than blind faith in a statistical model. This paper presents an investigation of uncertainty estimation for the detection of colon polyps using deep convolutional neural networks (CNNs). We experiment with two different approaches to measure uncertainty, Monte Carlo (MC) dropout and deep ensembles, and discuss the advantages and disadvantages of both methods in terms of computational efficiency and performance gain. Furthermore, we apply the two uncertainty methods to two different state-of-the-art CNN-based polyp segmentation architectures. The uncertainty is visualized as heatmaps on the input images and can be used to make more informed decisions on whether or not to trust a model's predictions. The results show that the predictive uncertainties provide a comparison between different models' predictions which can be interpreted as contrastive explanations where the values are largely influenced by the degree of independence between the models in the ensemble. We also reveal that MC dropout is shown to lack at providing contrastive uncertainty values due to the high correlation between the models' in the ensemble.
The demand for faster data processing and trans-mission on mobile devices and computers is increasing. There-fore, there is a growing interest in using photonic crystals to create advanced all-optical processors and c...
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Self-checkout systems have revolutionized retail by offering speedy, convenient checkout. However, this ease of use has opened the door to increased fraud, with dishonest customers exploiting vulnerabilities. Traditio...
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作者：
Lu, LiLu, TongSali, AduwatiSchool of Microelectronics and Data Science
Anhui University of Technology Maanshan243002 China
Department of Computer and Communication Systems Engineering Faculty of Engineering Selangor Serdang43400 Malaysia
Universiti Putra Malaysia Selangor Serdang43400 Malaysia
The rise of cloud-based disaster recovery systems has reshaped disaster recovery practices. Integrated into the Hadoop framework with core components like Hadoop distributed File System (HDFS), MapReduce, and Yet Anot...
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Globally, the greatest concerns of farmers are plant diseases. A considerable loss of yield has an immediate impact on the economy. Machine learning models exhibited capabilities to detect plant diseases. To enhance t...
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The importance of simulation in motor-sport racing has steadily increased. Fine-tuning these simulators is complex and traditionally manual, relying on engineers' expertise. This study develops a virtual driver an...
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Next generation 6G wireless envisions a much higher data rate and a lower latency compared to 5G wireless networks. Directional antennas with narrow beams across high mmWave frequencies hold the key to achieving such ...
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