The global impact of COVID-19 has been significant due to its rapid spread and high transmission rate. A large number of individuals have been exposed to this dangerous virus, emphasizing the importance of early detec...
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many educational institutions employ data mining to keep track of their students' records, particularly their academic achievements, which is more significant. To improve their achievements, students' academic...
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Cancer-related distress is the result of the psyche's reaction to the diagnosis given and to the various stressors in the development of the type of malignant disease. The spreading of the psychological distress a...
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Central management has used and maintained many portable medical devices(MDs). However, the central management lends medical staff the MDs only with managing by a ledger;therefore, missing or subletting may be caused....
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Multiple difficulties arise for video transmission in a wide-area millimeter wave (mm-wave) network due to the combined effects of network transmission link shortcomings and real-time limits of video data. The difficu...
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Congenital infections, disorders, or diseases occur when pregnant women get infected with an organism that further enters into their placenta and fetus after entering their bloodstream. Such conditions may affect newb...
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In the realm of software project management, Agile methodologies such as Scrum and Kanban have become quintessential for fostering adaptability and efficiency. "Nova," a novel project management tool, embodi...
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ISBN:
(数字)9798350376685
ISBN:
(纸本)9798350376692
In the realm of software project management, Agile methodologies such as Scrum and Kanban have become quintessential for fostering adaptability and efficiency. "Nova," a novel project management tool, embodies these methodologies to offer a user-friendly interface that supports the dynamic needs of modern software development teams. This paper explores Nova’s unique approach that integrates the foundational principles of Agile which are Scrum, and Kanban, thereby facilitating a seamless and effective management platform. Through a comparative analysis with traditional project management tools, this study evaluates how Nova enhances team collaboration, project tracking, and outcome predictability. Emphasizing its ease of use and adaptability, the research highlights Nova’s potential to improve project delivery times and overall team productivity.
Automobiles are required in everyday life to commute from destination to other. But, due to faulty road conditions, heavy traffic and lack of adequate driver skills, crash incidents happen almost every hour of the day...
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One of the pressing issues on user-generated platforms and social media is hate speech, a threat to online communities. Conventional detection methods, such as keyword matching or basic classification, often miss hate...
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ISBN:
(数字)9798331537555
ISBN:
(纸本)9798331537562
One of the pressing issues on user-generated platforms and social media is hate speech, a threat to online communities. Conventional detection methods, such as keyword matching or basic classification, often miss hate speech's subtleties and contextual dependencies. This paper introduces a pioneering deep learning approach, leveraging transformer architectures and attention mechanisms, to advance the state-of-the-art in hate speech detection. Our model's unique feature is its ability to focus on words that carry more contextual relevance, thereby interpreting the intended meaning of the words more accurately. The multi-head self-attention mechanism allows for differential weighting of various input parts, thereby enhancing the model's ability to distinguish between hostile and hateful language, even in cases of word similarity. We explain the architecture of our model with further explanation of the calculation involved in queries, keys, and values, followed by a description of the mathematical foundation of the attention mechanism. In fine-tuning the pre-trained transformer model, say BERT, from the hate speech dataset, our optimised model uses the Adam optimiser. We run experiments to check the performance of the proposed model by using accuracy, precision, recall, and F1-score to prove that our proposed model outperforms the base models like CNNs and RNNs. Our attention-based approach successfully highlights hate speech in subtle, context-sensitive ways. As this is one of the first studies where attention has been explored in the context of hate speech detection, it provides a good platform for further work on hate speech and multimodal inputs in many other languages. The results of our experiments indicate the promise of attention-based models as a powerful and scalable solution to the real-world challenges of hate speech detection.
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