Unmanned Aerial Vehicles (UAVs) are airborne nodes that are controlled remotely from ground stations. They have been used in a variety of applications in recent years, including disaster management, military operation...
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Brain tumor identification and categorization serve a precarious role in early diagnosis and treatment planning. This study suggests a new deep learning (DL)-based structure for the automatic detection and categorizat...
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Face recognition under occlusion presents a persistent challenge in computer vision, primarily due to difficulties in capturing and effectively integrating visible and obscured facial features. This paper introduces a...
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Brain–machine interfaces (BMIs) offer significant promise for enabling paralyzed individuals to control external devices using their brain signals. One challenge is that during the online brain control (BC) process, ...
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Promoting maternal health is significant as GDM is a severe health concern for both infants and mothers. As opposed to the conventional systems based primarily on clinical diagnosis, this study attempts to develop a s...
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Social media platforms, such as YouTube, generate an extensive amount of unstructured data, offering valuable insights into user behavior, engagement patterns, and preferences. This project focuses on predictive analy...
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Recently, with the emergence of many image editing tools (photoshop, Topaz studio, etc.), the authenticity of images has been severely challenged. However, the performance of some existing traditional feature extracti...
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The AI-Driven Health chat assistant is an innovative healthcare solution that seamlessly integrates technology and care, enabling users to have natural language conversations about symptoms, treatments, and general he...
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Analyzing the social interactions and texts on Twitter can provide valuable insights into users' behavior, opinions, and even their geographical locations. Location inference of users within a social network finds...
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This paper proposes a Multi –Task Learning Model approach to detect Sarcasm and to analyze Sentiment within the given text. Sarcasm detection is important for enhancing natural language understanding particularly in ...
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ISBN:
(数字)9798331521349
ISBN:
(纸本)9798331521356
This paper proposes a Multi –Task Learning Model approach to detect Sarcasm and to analyze Sentiment within the given text. Sarcasm detection is important for enhancing natural language understanding particularly in customer reviews and social media where sarcastic sentences are mostly used. This system uses BERT (Bi-directional Encoder Representations from Transformers) to processes both tasks concurrently. It uses separate layers to handle each task individually. The text processing pipeline includes techniques such as text normalization, tokenization, and lemmatization and then followed by encoding using BERT’s tokenizer before using it for model training. BERT can understand the complex patterns of language which helps in easily recognize the small clues present in sarcastic statements. The architecture of the model incorporates specific layers for sarcasm and sentiment classification which optimizes prediction with loss functions for each task. If there is imbalance in class distributions class weighting is applied and model evaluation is performed using K-Fold cross validation to ensure robustness. To measure the model effectiveness performance metrics such as ROC-AUC curves, confusion matrix and detailed classification reports are used. Therefore, including MTL and BERT improves the performance and effectiveness in sarcasm detection and sentiment analysis.
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