This research proposes a hybrid approach for a procedural level generation for our 2D platformer game. The method integrates manual level creation and procedural algorithm to generate a complete level for 2D rouge-lik...
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Ensuring color accuracy is essential when processing underwater images of coral reefs. These images often suffer from color distortion and blurring due to absorption and scattering. Current trends in underwater color ...
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Career site pages contain valuable information for job seekers, but effectively classifying these pages into relevant categories remains a challenge. This study aims to develop a classification system for career websi...
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Pineapple (Ananas Comocus L) thrive in tropical and subtropical regions. Pineapples are characterized by their unique traits, including a firm texture and richness in Vitamin C. Pineapple belong to the Non-Climacteric...
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Understanding customer is crucial for marketing strategies and increasing customer satisfaction in today's business environment. One method to fulfill of marketing strategies is segment customer based on their pur...
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The role of automatic generated playlists and recommendations in music streaming services has played a significant role in driving the steady growth of the music industry since 2015. With the evolving landscape of mus...
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Facial expressions play a vital role in human communication, enabling us to convey a wide range of emotions such as happiness, anger, and sadness. Human-computer Interaction (HCI) is a rapidly growing and highly appea...
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The current study aims to implement a data sharing system that uses the blockchain to ensure data privacy and security-preserving during profile corresponding. The system also utilized a bloom filter to authenticate k...
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Big Data Analytics implementation in healthcare can provide an end-to-end solution with better information value insights. This paper will highlight expert opinion in verifying the quality factors in big data analytic...
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In Aspect-based Sentiment Analysis (ABSA), accurately determining the sentiment polarity of specific aspects within text requires a nuanced understanding of linguistic elements, including syntax. Traditional ABSA appr...
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In Aspect-based Sentiment Analysis (ABSA), accurately determining the sentiment polarity of specific aspects within text requires a nuanced understanding of linguistic elements, including syntax. Traditional ABSA approaches, particularly those leveraging attention mechanisms, have shown effectiveness but often fall short in integrating crucial syntax information. Moreover, while some methods employ Graph Neural Networks (GNNs) to extract syntax information, they face significant limitations, such as information loss due to pooling operations. Addressing these challenges, our study proposes a novel ABSA framework that bypasses the constraints of GNNs by directly incorporating syntax-aware insights into the analysis process. Our approach, the Syntax-Informed Attention Mechanism Vector (SIAMV), integrates syntactic distances obtained from dependency trees and part-of-speech (POS) tags into the attention vectors, ensuring a deeper focus on linguistically relevant elements. This not only substantially enhances ABSA accuracy by enriching the attention mechanism but also maintains the integrity of sequential information, a task managed by adopting Long Short-Term Memory (LSTM) networks. The LSTM’s inputs, consisting of syntactic distance, POS tags, and the sentence itself, are processed to generate a syntax vector. This vector is then combined with the attention vector, offering a robust model that adeptly captures the nuances of language. Moreover, the sequential processing capability of LSTM ensures minimal information loss across the text by preserving the context and dependencies inherent in the sentence structure, unlike traditional pooling methods. Our experimental findings demonstrate that this innovative combination of SIAMV and LSTM significantly outperforms existing GNN-based ABSA models in accuracy, thereby setting a new standard for sentiment analysis research. By overcoming the traditional reliance on GNNs and their pooling-induced information loss, our method
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