Many factors affect the digital transformation of any country, such as government policies and regulations, ICT (Information and Communication technology) development, development of infrastructure, and citizens' ...
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The study aimed to identify Cloud service evaluation metrics and evaluate the quality of cloud services in Governmental organizations, Non-Government Organizations, and Companies in Ethiopia. It found that the cloud s...
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This paper presents a new methodology that utilizes computer vision techniques for the automatic detection and classification of stages in wildflower growth. Wild plant development monitoring is essential for ecologic...
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We designed a large language model evaluation system based on open-ended questions. The system accomplished multidimensional evaluation of LLMs using open-ended questions, and it presented evaluation results with eval...
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This study centers on the development of a narrative-driven, science fiction-themed animated video using Vyond's Go AI-powered platform. The objective is to enhance conceptual understanding and cognitive engagemen...
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Games are made for entertainment purposes and have become more relevant over time. Thanks to advances in technology, game development has become more efficient thanks to game engines such as Unity. With this technolog...
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Non-invasive brain-computer interface technology has been developed for detecting human mental states with high performances. Detection of the pilots' mental states is particularly critical because their abnormal ...
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ISBN:
(纸本)9781665464444
Non-invasive brain-computer interface technology has been developed for detecting human mental states with high performances. Detection of the pilots' mental states is particularly critical because their abnormal mental states could cause catastrophic accidents. In this study, we presented the feasibility of classifying distraction levels (namely, normal state, low distraction, and high distraction) by applying the deep learning method. To the best of our knowledge, this study is the first attempt to classify distraction levels under a flight environment. We proposed a model for classifying distraction levels. A total of ten pilots conducted the experiment in a simulated flight environment. The grand-average accuracy was 0.8437 (+/- 0.0287) for classifying distraction levels across all subjects. Hence, we believe that it will contribute significantly to autonomous driving or flight based on artificial intelligence technology in the future.
The development of computer vision technology in recent days, enhanced the human-computer interface (HCI) systems in a broad spectrum. The recent developments of human computer interfacing such as Augmented reality ap...
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As a cutting-edge development trend in the field of visualization, cloud rendering technology is currently dominated by game engine manufacturers. Cloud rendering technology based on WebGL engine has not yet been full...
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With the rapid development of computer vision technology, athlete posture recognition based on image analysis has become a research hotspot in the field of sports technology. This study proposes a new fusion boundary ...
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ISBN:
(纸本)9798400718144
With the rapid development of computer vision technology, athlete posture recognition based on image analysis has become a research hotspot in the field of sports technology. This study proposes a new fusion boundary filtering algorithm specifically for the posture recognition of volleyball players, aiming to improve the accuracy and real-time performance of posture recognition. Firstly, the preliminary posture information of athletes is extracted through a deep learning framework, and then this information is finely processed using boundary filtering technology to effectively filter out background noise and non-target interference. Compared with traditional posture recognition algorithms, this algorithm improves the recognition accuracy by 15% in complex backgrounds and also has significant advantages in real-time performance. Additionally, this study explores the adaptability of the algorithm in different lighting and multi-person scenarios, validating its potential application in actual sports competitions. Experimental results demonstrate that the fusion boundary filtering algorithm is an effective posture recognition technology for volleyball players, providing a theoretical basis and experimental evidence for further technological development and application.
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