Virtual reality (VR) is an emerging technology with promising applications in organ visualization. Recognizing that this could be valuable in the field of medical education, we devised a pipeline that utilizes classic...
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This paper explores fashion line-ups based on GANs in a complex modern area of fashion recommendation and generation. The use of a fashion recommender system is of paramount importance in resolving such a subjective a...
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This research proposes an innovative method for categorizing phishing and legitimate websites based on content features extracted from their HTML structure. Leveraging data collection with the requests library and HTM...
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Traffic jams & drivers searching for parking can be a real, especially with more people living in cities and the rise in cars. This paper a smart parking guidance system that helps manage parking spots using the I...
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The advancement of applications based on various Deep Neural Network (DNN) architectures, such as Large Language Models (LLMs) has led to an increasing need for the efficient utilization of network resources to meet t...
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With the development of voice control technology, a new page has opened in the history of human-computer interaction. Voice assistants are one of the most important technologies in our daily lives because they enable ...
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Diabetes mellitus is a chronic metabolic disorder affecting millions worldwide. Early detection is crucial for effective management;however, current diagnostic methods often involve invasive procedures or are costly. ...
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Lung image compression and segmentation are essential diagnostic and monitoring techniques for a variety of respiratory ailments. We present a novel deep learning-based technique for segmenting and compressing lung im...
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With the advent of transfer learning approaches, Natural Language Processing (NLP) problems have experienced tremendous progress, as demonstrated by models such as Generative Pre-trained Transformers (GPT) and Bidirec...
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ISBN:
(纸本)9798350388800
With the advent of transfer learning approaches, Natural Language Processing (NLP) problems have experienced tremendous progress, as demonstrated by models such as Generative Pre-trained Transformers (GPT) and Bidirectional Encoder Representations from Transformers (BERT). The usefulness of such transfer learning strategies across a range of NLP tasks and domains is investigated in this work. The study uses a methodical methodology to assess BERT and GPT's performance on a wide range of tasks. In addition, the study evaluates the generalizability and flexibility of these models across a broad variety of disciplines, including social media, finance, legal, and biological literature. The study's methodology entails rigorous assessment utilizing task-specific standard metrics after pre-trained BERT and GPT models have been fine-tuned using task-specific datasets. To determine the relative benefits and drawbacks of transfer learning strategies in various contexts, comparative studies are carried out against baseline models and other cutting-edge methodologies. Additionally, the study looks at how the performance of BERT and GPT is affected by variables including task difficulty, dataset size, and domain specificity. The results provide a comprehensive understanding of the benefits and drawbacks of transfer learning strategies in a variety of NLP tasks and domains. While BERT performs admirably on tests requiring semantic comprehension and contextual knowledge, GPT is superior at producing text that is both cohesive and appropriate to the situation. Both models, however, show sensitivity to dataset features and idiosyncrasies unique to the domain, indicating the necessity for customized fine-tuning techniques for best results. All things considered, this study advances our knowledge of the usefulness and efficiency of transfer learning strategies and provides insightful information for academics and practitioners who want to use BERT, GPT, and related models in a variety
The research in this paper mostly focuses on the development of an integrated IoT and deep learning-based speech-driven smart vehicle. Which uses a speech recognition algorithm to enable control of the device with voi...
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