This paper delves into the core problem of supermarket staff scheduling optimization, especially in terms of reducing human resource costs and increasing corporate profits. Genetic algorithm is adopted as the solution...
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In Facial Expression Recognition (FER), existing models often encounter challenges such as high parameter counts and significant computational demands, making real-time application on resource-constrained devices diff...
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Currently, meta-learning is the mainstream approach to solving the problem of scarce data in few-shot text classification. Still, challenges remain, such as embedding vectors not being compact enough, suboptimal meta-...
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In the traditional training and teaching process of badminton, coaches usually use the traditional way to teach, and this type of teaching method is prone to many problems such as high subjectivity and lack of data su...
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Mechanized detection of sarcasm in textual data is a challenging task in NLP, as it involves recognizing the opposite of what is actually meant. This has become important today since many artificial intelligence tasks...
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Aiming at the current problem of single effect and same style for all kinds of style migration, the article develops a style migration system based on VGG-19 model. The system is developed with Flask as the back-end f...
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In today's society, the amount of information we need to process daily from sources such as news, videos, and literature is relatively high. The primary strategy to decrease the workload is to use effective summar...
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ISBN:
(纸本)9783031777370;9783031777387
In today's society, the amount of information we need to process daily from sources such as news, videos, and literature is relatively high. The primary strategy to decrease the workload is to use effective summarization techniques, either through extractive (where the summary is made up of extracts from the source itself) or abstractive methods. Traditional summarization models often rely on extensive humanannotated data, which is usually quite costly. This research proposes an approach leveraging transformer models to optimize and affordably augment small datasets, enhancing the performance of summarization models. Using sentence clustering and pre-trained models on tasks such as summarization or paraphrasing, we explore whether such an approach can yield better results across various summarization datasets that target different formats, such as video conference transcripts and news articles.
The Hadoop framework has emerged as a pivotal component within the big data ecosystem, owing to its proficiency in managing large-scale, diverse datasets with robust fault tolerance and scalability capabilities. Howev...
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Alzheimer's disease is a fatal brain disorder that impacts predominantly the elderly. The early identification of Alzheimer's illness requires the use of efficient automated methods. For the categorization of ...
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Traditional decision support systems (DSS) show obvious limitations in dealing with increasingly complex and dynamic decision-making scenarios. By integrating graph neural networks (GNNs) and expert systems (ESs), thi...
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