Flexible and scalable decentralized learning solutions are fundamentally important in the application of multi-agent systems. While several recent approaches introduce (ensembles of) kernel machines in the distributed...
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Considering the malaria disease-related moralities prevailing mainly in underdeveloped countries, early detection and treatment of malaria must be an essential strategy for lowering morbidity and fatality rates. Detec...
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This study proposes a machine learning- based diseases prediction algorithm to predict multiple diseases within a single prediction system. This algorithm analyzes an undisclosed patient datasets and applies various f...
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Internet of Things applications are on the rise within different areas of health and medical service. With this rapid rise of these applications, threat actors become more interested in targeting such devices. Within ...
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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
An uncountable number of computational resources are shared for various applications using a transformative technology called Cloud computing. It is an emerging technology that can offer scalable and on-demand resourc...
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In response to the traditional Dijkstra algorithm, the long search time of pheromone and the occasional redundant inflection points When the final path is obtained can lead to the inefficiency of the algorithm due to ...
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Reentrancy is a type of attack that can occur in smart contracts, enabling untrusted external code execution within the contract. This method exploits a vulnerability that allows an attacker to repeatedly invoke a fun...
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Breathing is one of the vital functions of a human. This is why the monitoring of breathing represents an important tool for diagnosing a medical condition in a patient, that is, for assessing health status. Apart fro...
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The detection of deepfake videos is essential due to the significant potential harm posed by manipulated media. Various deep learning techniques, including customized convolutional neural networks (CNNs), MobileNet, a...
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