In this article, A novel dual-beam extended interaction oscillator(EIO) with frequency of 0.14THz is designed. Compared with the traditional single-cavity EIO, this design connects the two single-cavity structures wit...
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Food demand is expected to grow substantially as a result of major factors such as population. It necessitates that food manufacturers streamline their supply chain to accommodate shorter product life cycles. To manag...
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A novel method has been proposed to design and develop a Triangular Hut-shaped photovoltaic panel with rotating mechanism using modified maximum power point tracking (MPPT). In this, irradiance for the Primary panel c...
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Load dispatch is an indispensable part of power system operation. Economic Dispatch (ED) and Combined Economic Emission Dispatch (CEED) are the standard complex contained benchmark used for accessing the potential of ...
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The power management system (PMS) plays a critical role in microgrid (MG) management, ensuring stable and reliable operation while minimizing energy costs by effectively managing local generation resources. This paper...
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Gene Name Entity Recognition (NER) plays a crucial role in the realm of biomedical text mining by focusing on the identification and extraction of gene references from scientific literature. Recent advancements in the...
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ISBN:
(纸本)9798350337488
Gene Name Entity Recognition (NER) plays a crucial role in the realm of biomedical text mining by focusing on the identification and extraction of gene references from scientific literature. Recent advancements in the field-particularly the emergence of pre-trained transformer-based language models like BioBERT-have shown significant promise in the domain of biomedical NER. However, these models are often trained on existing, publicly available datasets, which may not fully capture the nuances of the domain or adequately cover less-studied genes. This study places its primary emphasis on fine-tuning BioBERT specifically for gene NER tasks. To address the limitations associated with current publicly available datasets, we have meticulously crafted a custom dataset. This dataset is thoughtfully constructed through the systematic collection and detailed annotation of a diverse range of biomedical literature from specialized sources. It intentionally includes genes that have been extensively researched, as well as those that have received limited attention in existing corpora. The fine-tuning process involves initializing the BioBERT model with pre-trained weights and then training it on our custom dataset using a sequence tagging approach. To enhance the model's performance, we systematically explore various techniques, including data augmentation, entity-level features, and attention mechanisms. Additionally, we conduct rigorous hyperparameter optimization to maximize the model's accuracy, precision, and recall in gene mention recognition. We thoroughly evaluate the performance of the fine-tuned BioBERT model through a comprehensive set of cross-validation experiments. The results highlight the effectiveness of our tailored dataset in enhancing BioBERT's performance in gene NER. The fine-tuned model achieves impressive F1 scores, precision, and recall, specifically 0.96, 0.95, and 0.98, surpassing previous models when it comes to recognizing the dvu (Desulfovibrio vul
The co-rotating vortex pair is a well-known test problem for validating aeroacoustic computational frameworks. Both the flow field and the radiated acoustic far-field can be determined analytically, which allows for t...
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In this work, we propose a simple yet novel prob-abilistic model for a renewable smart-grid and electric vehi-cle (EV) ecosystem supported by cellular vehicle-to-grid (C- V2G) infrastructure. Our stochastic model acco...
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We present a Multi Lingual Sync model for generating lip-synced videos in multiple languages. The model consists of Lingua Speak for translation and Wav2Lip for lip synchronization. The workflow involves extracting au...
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A neurological condition called Parkinson's disease (PD) impairs motion and creates tremors. Although it is challenging to identify PD in its early stages, research has revealed that 90% of those who have the cond...
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