Plagiarism detectors are software or apps that are primarily intended for string-level comparisons between texts that raise the suspicion of being plagiarized and texts that have the potential to be original work. By ...
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Plagiarism detectors are software or apps that are primarily intended for string-level comparisons between texts that raise the suspicion of being plagiarized and texts that have the potential to be original work. By incorporating naturallanguageprocessing (NLP) methods into the currently in use detection methods, this study seeks to increase the accuracy of plagiarism detectors. A framework for plagiarism detectors was proposed, in which sets of naturallanguageprocessing (NLP) techniques were applied to several available documents deemed suspicious and authentic. The work was not limited to analyzing the given documents, but also involved understanding the structure of the text and accounting for text relations. Using the methods on a corpus of short paragraphs revealed the level of improvement that NLP techniques had, as well as their increased accuracy in identifying plagiarism.
Large-scale log files are vital for capturing critical information about system operations, network interactions, and user activities, making them essential in areas such as enterprise management, research, and cybers...
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ISBN:
(数字)9798331528829
ISBN:
(纸本)9798331528836
Large-scale log files are vital for capturing critical information about system operations, network interactions, and user activities, making them essential in areas such as enterprise management, research, and cybersecurity. However, as data volume grows exponentially, traditional text processingmethods struggle to efficiently parse these extensive logs, hindering accurate information extraction and analysis. This study focuses on leveraging existing naturallanguageprocessing techniques, including regular expressions, keyword extraction, and text clustering, to tackle the challenges of parsing large log files. We examine the specific issues posed by such data and introduce a comprehensive approach that incorporates log preprocessing with regular expressions, followed by key information extraction via advanced algorithms, and segmentation through text clustering methods. Our experimental results demonstrate an accuracy rate of 85%, highlighting the effectiveness and potential of our proposed solution for addressing the increasing complexity of log file analysis.
People cannot live without naturallanguage in their daily life, and naturallanguage is also an essential part of the heritage of human civilization. With the rapid development of information technology and the explo...
People cannot live without naturallanguage in their daily life, and naturallanguage is also an essential part of the heritage of human civilization. With the rapid development of information technology and the explosive growth of various data, naturallanguageprocessing (NLP) technology based on deep learning and other technologies in the field of artificial intelligence has emerged as the times require. With the rapid development of deep learning models in recent years, breakthroughs have been made in the field of naturallanguageprocessing. Based on recent research, this paper briefly introduces the development process of deep learning, the concepts of deep learning and NLP, the methods of deep learning used to solve the core problems of NLP, the application of the neural network model in naturallanguage modeling. This paper summarizes the current development achievements and forecasts its future development.
Acknowledging the scientific contribution of previous research work is necessary to ensure a smooth evolution in scientific fields. Citations play an important role in the ranking of authors, journals, institutions, a...
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This study investigates trends in health food research from 1975 to 2024 using text mining and generative AI. Analyzing 92,028 journal entries from Scopus revealed a significant increase in publications, with key topi...
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Dementia is a growing problem as our society ages, and detection methods are often invasive and expensive. Recent deep-learning techniques can offer a faster diagnosis and have shown promising results. However, they r...
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Dementia is a growing problem as our society ages, and detection methods are often invasive and expensive. Recent deep-learning techniques can offer a faster diagnosis and have shown promising results. However, they require large amounts of labelled data which is not easily available for the task of dementia detection. One effective solution to sparse data problems is data augmentation, though the exact methods need to be selected carefully. To date, there has been no empirical study of data augmentation on Alzheimer's disease (AD) datasets for NLP and speech processing. In this work, we investigate data augmentation techniques for the task of AD detection and perform an empirical evaluation of the different approaches on two kinds of models for both the text and audio domains. We use a transformer-based model for both domains, and SVM and Random Forest models for the text and audio domains, respectively. We generate additional samples using traditional as well as deep learning based methods and show that data augmentation improves performance for both the text- and audio-based models and that such results are comparable to state-of-the-art results on the popular ADReSS set, with carefully crafted architectures and features(1).
Summarizing legal case documents is a very daunting task as law practitioners have to traverse via a hundreds of case reports before determining the relevant case which may aid as a good resource material in an ongoin...
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Scientific Machine Reading Comprehension (SMRC) aims to understand scientific long text by providing answers for the given questions. Most existing methods trend to answer the question using Transformer-based models. ...
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This study evaluates the effectiveness of a Novel Recurrent Neural Network (RNN) against naturallanguageprocessing (NLP) methods for nutrient extraction from soaked and steamed nut water. Research Instruments and Me...
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ISBN:
(数字)9798350355468
ISBN:
(纸本)9798350355475
This study evaluates the effectiveness of a Novel Recurrent Neural Network (RNN) against naturallanguageprocessing (NLP) methods for nutrient extraction from soaked and steamed nut water. Research Instruments and Methodologies: Ten nutrients were retrieved utilizing advanced RNN and NLP approaches. The iteration value was estimated using G-power analysis with a power of 0.8 and a 95% confidence range. The findings indicated that the Novel RNN method attained an accuracy of 84.10%, surpassing the NLP approach's 71.48%. A statistical analysis indicated that the two approaches exhibited a substantial difference, evidenced by a two-tailed p-value of 0.000 (p < 0.05). The results indicate that the Novel RNN surpasses NLP methods in nutrient extraction accuracy, rendering it a more dependable option for this work.
This study provides a framework for identifying patients with Inflammatory Bowel Disease (IBD) on Twitter and learning from their personal experiences. First, we built a user classifier that distinguishes IBD patients...
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