Connecting multiple aerial vehicles to a rigid central platform through passive spherical joints holds the potential to construct a fully-actuated aerial platform. The integration of multiple vehicles enhances efficie...
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Currently, practically all competitors and mental wellbeing individuals keep an eye upon that physical characteristic of their bones for a variety of reasons, including for athletes to boost performance and everyday i...
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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
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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In the contemporary financial landscape, managing and hedging risk remains an indispensable aspect of portfolio management. Value at Risk (VaR) is a widely accepted measure for quantifying and understanding the downsi...
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As a remote game originating from the roots of Nepal, Baghchal has witnessed limited exploration and consequently, the strategic aspect of the game remains underdeveloped. The game can be characterized as a heterogene...
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This manuscript addresses the challenges in developing Automotive High-Performance Computing (HPC) systems for Future Smart Cars and Unmanned Mobile Vehicles (UMVs). We introduce a novel Unified Machine Vision (UMV) n...
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Swar is a decentralized music platform, using blockchain and machine learning to change music industry fundamentals, where artists and users will reap the most benefits. Unlike other platforms, Swar offers transparent...
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It's safe to say that the proliferation of synthetically generated media, particularly Deepfakes, has raised significant concerns regarding privacy, security, and integrity of digital content. There is a lot of re...
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Cloud Computing introduced high latency, mobility, bandwidth, etc., which Fog Computing addressed. Fog Computing streamlines remote IoT device and sensor management. However, Fog-Cloud has security and privacy issues....
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