This paper presents a novel approach for dense scene text detection called DSSNet (Dense Script Spotter Network). The network leverages ResNet and FPN for feature extraction, employing multi-scale feature fusion and T...
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Traffic flow prediction is a key issue in urban traffic management, providing important support for urban planning and traffic diversion. In traffic flow prediction, LSTM (Long Short-Term Memory networks) and GRU (Gat...
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Speech Emotion Recognition (SER) is a critical area of research in Human-computer Interaction (HCI), aiming to develop systems capable of identifying and responding to human emotions conveyed through speech. This pape...
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We propose a quantum-weighted autoencoder network for compression computer-generated holograms. And the quantum-weighted autoencoder consists of embedding, entanglement, and measurement layers. Experimental results sh...
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The primary objective of this research is to develop and promote efficient and secure de-identification technology to address the application of sensitive personal information in healthcare big data. Considering the o...
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This research introduces DeepFakeGuard, a hybrid deep learning framework designed to detect fake profiles on social media platforms, addressing the growing threat of fraudulent accounts online. DeepFakeGuard integrate...
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This research develops an innovative AI-driven system for detecting oyster mushroom green mold disease using deep learning, Convolutional Neural Networks (CNNs), and IoT technologies. By implementing advanced image pr...
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Passive non-line-of-sight imaging (NLOS) has received extensive attention and research in recent years. However, the captured projected images only receive intensity information from light scattering, which contribute...
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This study investigates the impact of information and Communication Technology (ICT) skills on the employability of communication graduates, providing an in-depth analysis of the current job market in the telecommunic...
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The exponential growth of academic literature presents significant challenges for researchers attempting to find relevant information. Traditional keyword-based retrieval systems often fail to address issues such as s...
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ISBN:
(纸本)9783031785535;9783031785542
The exponential growth of academic literature presents significant challenges for researchers attempting to find relevant information. Traditional keyword-based retrieval systems often fail to address issues such as synonyms, homonyms, and semantic nuances, leading to suboptimal search results. This paper introduces a novel system called IntelliSMART (Intelligent Semantic Machine-Assisted research Tool), which leverages large language models (LLMs) and advanced semantic processing techniques to improve the retrieval of academic literature. Our approach integrates query rewriting, embedding generation, efficient indexing, and complex article retrieval mechanisms to provide highly accurate and contextually relevant results that align with the user's intent. The IntelliSMART system features a user-friendly front end that facilitates intuitive query input, along with a robust back end for handling user queries, generating embeddings, indexing extensive collections of academic papers, and efficiently retrieving the most relevant documents. The proposed system shows significant improvements over conventional methods, highlighting its potential to transform the search experience in academic research.
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