Supervised learning algorithms have accomplished significant advancements in the field of medical image classification. However, these methods highly rely on large labelled datasets and it is costly to obtain image da...
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This study delves into the realm of part-of-speech (POS) tagging within Malaysia's multilingual context. We investigate the efficacy of CRF-QTAG, semi-supervised CRF models and rule-based systems within Bahasa Roj...
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Game developers constantly strive to make the player's experience more immersive by constantly innovating new ways to increase the level of interactivity of the player with the game. Different tools and methods of...
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In an era marked by growing complexity and interconnectivity, addressing crime is paramount for the well-being of communities worldwide. This study utilises Los Angeles Crime Data to employ clustering algorithms which...
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This paper presents a comprehensive approach to detect oral disease using image detection method. Oral disease is usually checked with the presence of dentist, however, with the trend of dentist growing slower and slo...
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The Social Distance Monitoring (SDM) system is a technology-driven solution that aims to enforce and maintain social distancing rules. During epidemics such as COVID-19, practising physical distancing is crucial to pr...
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The rapid development of Internet of Things(IoT)technology has led to a significant increase in the computational task load of Terminal Devices(TDs).TDs reduce response latency and energy consumption with the support ...
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The rapid development of Internet of Things(IoT)technology has led to a significant increase in the computational task load of Terminal Devices(TDs).TDs reduce response latency and energy consumption with the support of task-offloading in Multi-access Edge computing(MEC).However,existing task-offloading optimization methods typically assume that MEC’s computing resources are unlimited,and there is a lack of research on the optimization of task-offloading when MEC resources are *** addition,existing solutions only decide whether to accept the offloaded task request based on the single decision result of the current time slot,but lack support for multiple retry in subsequent time *** is resulting in TD missing potential offloading opportunities in the *** fill this gap,we propose a Two-Stage Offloading Decision-making Framework(TSODF)with request holding and dynamic *** Short-Term Memory(LSTM)-based task-offloading request prediction and MEC resource release estimation are integrated to infer the probability of a request being accepted in the subsequent time *** framework learns optimized decision-making experiences continuously to increase the success rate of task offloading based on deep learning *** results show that TSODF reduces total TD’s energy consumption and delay for task execution and improves task offloading rate and system resource utilization compared to the benchmark method.
Human Activity Recognition (HAR) from video data has gained significant attention due to its wide-ranging applications in fields like security surveillance, healthcare, and human-computer interaction. Recognizing and ...
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Multiword Expression (MWE) extraction has been popular research in Natural Language Processing (NLP) area. This research aims to extract different MWE types, including Chinese (ZH), English (EN), Chinese-English (ZH-E...
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Natural Language Processing (NLP) plays a critical role in deciphering unstructured text data. This review paper explores the integration of Latent Dirichlet Allocation (LDA) and Bidirectional Encoder Representations ...
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