DNA Barcodes, which are particular fragments derived from brief sections of DNA (such as mitochondrial, nuclear, and plastid sequences), can be used to identify organisms from the major life kingdoms. In addition to s...
DNA Barcodes, which are particular fragments derived from brief sections of DNA (such as mitochondrial, nuclear, and plastid sequences), can be used to identify organisms from the major life kingdoms. In addition to supporting conventional taxonomic techniques, DNA barcoding is a potent tool that advances our knowledge of species diversity and their ecological functions. On a variety of organisms, the use of this approach for species categorization has been successful. In this paper, we examine how DNA barcoding has been used to classify species based on DNA barcodes as well as other related research that has been done over the years on the subject. After experimenting with a number of deep learning models, we have propose a Variational Auto Encoder + Feed Forward Neural Network workflow for classifiying species using DNA barcodes. The models have been assessed on the basis of performance factors including accuracy, recall, and precision. COI, rbcL, matK, and ITS are the specific gene sections that have been identified as barcodes. For both simulated and real datasets, the model can attain an average accuracy of greater than 95 percent. This DNA barcoding approach has the ability to simplify DNA barcode-based species identification and serve as a tool for species categorization.
Objective: This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their performance. Materials and Methods: We evalu...
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Accurate identification and categorization of suicidal events can yield better suicide precautions, reducing operational burden, and improving care quality in high-acuity psychiatric settings. Pre-trained language mod...
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Rare diseases (RDs) are collectively common and affect 300 million people worldwide. Accurate phenotyping is critical for informing diagnosis and treatment, but RD phenotypes are often embedded in unstructured text an...
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Self-tracking can help personalize self-management interventionsfor chronic conditions like type 2 diabetes (T2D), but refecting onpersonal data requires motivation and literacy. Machine learning(ML) methods can ident...
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Objective Adverse event (AE) extraction following COVID-19 vaccines from text data is crucial for monitoring and analyzing the safety profiles of immunizations, identifying potential risks and ensuring the safe use of...
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Objective Adverse event (AE) extraction following COVID-19 vaccines from text data is crucial for monitoring and analyzing the safety profiles of immunizations, identifying potential risks and ensuring the safe use of these products. Traditional deep learning models are adept at learning intricate feature representations and dependencies in sequential data, but often require extensive labeled data. In contrast, large language models (LLMs) excel in understanding contextual information, but exhibit unstable performance on named entity recognition (NER) tasks, possibly due to their broad but unspecific training. This study aims to evaluate the effectiveness of LLMs and traditional deep learning models in AE extraction, and to assess the impact of ensembling these models on performance. Methods In this study, we utilized reports and posts from the Vaccine Adverse Event Reporting System (VAERS) (n=621), Twitter (n=9,133), and Reddit (n=131) as our corpora. Our goal was to extract three types of entities: vaccine, shot, and adverse event (ae). We explored and fine-tuned (except GPT-4) multiple LLMs, including GPT-2, GPT-3.5, GPT-4, Llama-2 7b, and Llama-2 13b, as well as traditional deep learning models like Recurrent neural network (RNN) and Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT). To enhance performance, we created ensembles of the three models with the best performance. For evaluation, we used strict and relaxed F1 scores to evaluate the performance for each entity type, and micro-average F1 was used to assess the overall performance. Results The ensemble model achieved the highest performance in "vaccine," "shot," and "ae," with strict F1-scores of 0.878, 0.930, and 0.925, respectively, along with a micro-average score of 0.903. These results underscore the significance of fine-tuning models for specific tasks and demonstrate the effectiveness of ensemble methods in enhancing performance. Conclusion In conclusion,
Rice production in northern Thailand, particularly in the Mae Na Ruea Sub-district, has been significantly affected by climate change, with increasing temperatures and decreasing water availability leading to reduced ...
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ISBN:
(数字)9798331504243
ISBN:
(纸本)9798331504250
Rice production in northern Thailand, particularly in the Mae Na Ruea Sub-district, has been significantly affected by climate change, with increasing temperatures and decreasing water availability leading to reduced yields. This study aims to determine the optimal rice planting periods to maximize yields under changing climatic conditions by using the Decision Support System for Agrotechnology Transfer (DSSAT) model. The research utilizes the CERES-Rice module within DSSAT to simulate rice growth and yields across 15 planting periods from mid-April to late July 2023. Weather, soil, and crop management data were input into the model to predict yields under various environmental conditions. The results indicate that mid-May (W5) is the optimal planting time, with a predicted yield of 4294 kg/ha, 41.95% higher than the average. In contrast, early May (W3) showed the lowest yield at 1889 kg/ha. The simulated yield for weeks (W9) and (W10) were 3,018 kg/ha and 2,987 kg/ha, respectively, which were only 0.23% and 1.26% lower than the average. This demonstrates the model's a high degree of precision. The study highlights the significant impact of planting time on yield outcomes, emphasizing the need for adaptive strategies to cope with climate variability. However, the model's limitations, including underestimation of early-season drought stress and lack of consideration for factors such as irrigation and pest pressures, suggest the need for further research to enhance prediction accuracy and address long-term environmental variability.
This study addresses the challenges of achieving proper clothing fit for children by developing a touchless and costeffective multi-output Neural Network-based prediction model using Key Anthropometric Variables (KAVs...
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ISBN:
(数字)9798350354447
ISBN:
(纸本)9798350354454
This study addresses the challenges of achieving proper clothing fit for children by developing a touchless and costeffective multi-output Neural Network-based prediction model using Key Anthropometric Variables (KAVs). The emergence of COVID-19 highlighted the impracticality and health risks of traditional manual anthropometric measurements and trying on clothes for the appropriate clothing sizes. The study focuses on Malaysian children aged 4-12 years, using a dataset of 1,629 children to develop and test the model. The Backpropagation Neural Network (BPNN) model, incorporating both upper and lower body measurements, demonstrated robust performance, particularly for lower body variables. However, predicting head girth remained challenging due to its unique growth patterns. The model's performance was evaluated using Mean Square Error and the Coefficient of Determination, with results indicating high accuracy and reliability. Despite some limitations, including the need for a more representative sample and the use of manual data collection, the study successfully developed a model that can assist in reducing infection risks. This research highlights the importance of innovative, contactless measurement solutions to ensure accurate clothing fit and safety for children, which are still lacking in Malaysia. It aims to inspire further research in the field of children's anthropometric measurements.
Medical practice in the intensive care unit is based on the assumption that physiological systems such as the human glucose-insulin system are predictable. We demonstrate that delay within the glucose-insulin system c...
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