To address the challenge of how to improve the accuracy and efficiency of low-resource speech recognition, we chose the Tibetan U-Tsang dialect as the object of study and increased the diversity of training datathrou...
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A computational pragmatic model of conversational scalar implicature is established by using Bayes' theorem and its statistical methods, and a small man-machine dialogue program is implemented based on it. Firstly...
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this study investigates the privacy paradox, a phenomenon whereby individuals express concerns about privacy while actively engaging in activities that compromise their personal data. through a topic analysis of video...
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When semi-supervised learning (SSL) comes across imbalanced or long-tailed data, which is common in realistic applications such as autonomous driving, it will usually suffers from a severe deterministic bias which lea...
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this paper introduces a cutting-edge AI-empowered Unmanned Aerial Vehicle (UAV) system, enriched with stateof-the-art sensor technology, advanced image recognition algorithms, and autonomous navigation capabilities. T...
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ISBN:
(纸本)9798350386813;9798350386820
this paper introduces a cutting-edge AI-empowered Unmanned Aerial Vehicle (UAV) system, enriched with stateof-the-art sensor technology, advanced image recognition algorithms, and autonomous navigation capabilities. the system represents a transformative approach to search and rescue operations, offering unparalleled precision and rapid response times. Our methodology encompasses multifaceted data collection techniques, including surveys, interviews, datamining, Internet of things (IoT) sensors, and sophisticated video analytics. machinelearning and deep learning models are then applied to process and analyze this data, enabling real-time image recognition for precise target identification. the system's AI-driven autonomous navigation algorithms optimize mission planning, resulting in significantly reduced response times and heightened mission success rates. Extensive real-world tests and simulations validate the exceptional performance of the proposed AI-empowered UAV system. these tests underscore its capacity to expedite emergency response efforts in dynamic and challenging environments. In parallel, this paper addresses critical ethical considerations, ememphasizing responsible data handling practices, and robust security measures to ensure the system's integrity in sensitive contexts. As exemplified through a compelling case study of successful rescue operations, this technology represents a groundbreaking advancement in the field. By bridging the gap between cutting- edge technology and life-saving applications, it holds the potential to redefine the landscape of search and rescue missions, ushering in an era of heightened efficiency, precision, and impact.
Categorical data classification and clustering are essential to many fields, including patternrecognition, datamining, knowledge discovery, and machinelearning. It is crucial to understand how to provide categorica...
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In this article, we propose a machinelearning based fine-grained vehicle classification method VehiClassNet, which effectively solves the challenges of precise vehicle recognition and classification in intelligent tr...
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Withthe rapid development of highway network, overloading of freight vehicles has caused serious impact on road safety and infrastructure. In order to improve the data fusion accuracy of the overload control system, ...
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Heart is the human body's most vital organ. As a result, heart disease is considered as a serious condition, which may carry a significant long-term risk of death. However, the existing approaches cannot efficient...
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Lipreading, the ability to understand speech by watching a speaker's lip movements, has been a long-standing research area in the field of speech recognition. It is a useful technique for people who are deaf or ha...
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Lipreading, the ability to understand speech by watching a speaker's lip movements, has been a long-standing research area in the field of speech recognition. It is a useful technique for people who are deaf or hard of hearing, as well as for noisy environments where traditional speech recognition systems may not work effectively. the proposed system is a part of deep learning and computer vision fields. In the existing system, the main disadvantage is the model being unable to function on a wide set of vocabulary. the proposed system tackles this problem by having the model train on a large and sophisticated dataset. this project aims to develop a deep learning lipreading model that is capable of mapping a variable-length sequence of video frames to text. the model uses spatio-temporal convolutions to analyse and process spatio-temporal visual features. the model is trained by minimizing connectionist temporal classification loss which measures the difference between predicted and actual outputs.
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