the occurrence of local outliers produced by the K-means algorithm remains challenging since they affect the clustering performance due to misclassification. While current algorithms can identify the local outliers in...
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A robust communication infrastructure is the backbone of power system efficiency and reliability advancements, enabling real-time data exchange and control. the smart grid, a complex network of digital devices, relies...
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this paper proposes a descent reward (DR) system to enhance agent's navigation and landing capabilities in the LunarLander-v2 environment provided by OpenAI. the DR system is evaluated with Heuristics Control, Lin...
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the method of behavioral energy-loaded testing considered in this paper are based on Petri nets, extended by temperature and volt/ampere characteristics, and have the features of a comprehensive consideration of behav...
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Blockchain is the most cutting-edge technology nowadays. Most organizations are trying to achieve decentralization using blockchain technology. But storing information in such a system incurs some charges due to the c...
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Skin cancer is considered to be the most common type of cancer worldwide. Nonetheless, the corresponding death rate can be considerably reduced with early detection and classification. Massive efforts have been made i...
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ISBN:
(纸本)9798350385434;9798350385427
Skin cancer is considered to be the most common type of cancer worldwide. Nonetheless, the corresponding death rate can be considerably reduced with early detection and classification. Massive efforts have been made in recent years to build machine learning algorithms that can aid in the early identification of skin cancer. the three most prevalent forms of skin lesions - melanoma (MEL), squamous cell carcinoma (SCC), and basal cell carcinoma (BCC) - are the subject of our paper's effort on the accurate classification of these types of cancer. To achieve this, YOLO, version 7 (v7), a convolution neural network (CNN) architecture, is implemented through transfer learning. After completing data augmentation, the results obtained by YOLO, with a total of 2792 training samples, demonstrate superior performance in comparison to previously published research works in the literature. In terms of accuracy, sensitivity, and specificity, the average values are 89.65 %, 85 %, and 91.90 %, respectively.
Fine-grained visual classification (FGVC) is a challenging problem in computer vision, focused on distinguishing visually similar subcategories within broader categories. this paper introduces MSAFNet, an innovative f...
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Single image super-resolution (SISR) models are able to enhance the visual quality of underwater images and contribute to a better understanding of underwater environments. the integration of these models in Autonomou...
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Millions of individuals globally experience spinal ailments. this stresses the importance of swift detection for achieving better treatment results. Traditional diagnosis techniques can be lengthy. they might lack pre...
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Withthe development and spread of IoT technology, the number of devices connected to the Internet is increasing. Some data generated by IoT devices include spatio-temporal data (STD) that depends on the location and ...
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ISBN:
(纸本)9798350326970
Withthe development and spread of IoT technology, the number of devices connected to the Internet is increasing. Some data generated by IoT devices include spatio-temporal data (STD) that depends on the location and time of data generation. therefore, we have proposed the STD retention system (STD-RS) using vehicles as a network infrastructure for local production and consumption of STD. this paper proposes a transmission control method that can mitigate the fluctuation of RSS due to the influence of obstacles in the real environment and evaluates its effectiveness through experiments on actual devices.
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