Entity relationship extraction optimization based on entity recognition

Zhong, Yanru; He, Zhaorong; Zhao, Leixian; Jiang, Chaohao; Luo, Xiangfeng

doi:10.1117/12.2541712

Cited by 1 publication

(1 citation statement)

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“…For any work that involves the processing of time-series data, it is better to use a recurrent net (RNN) [ 148 ]. Supervised learning architectures are used for labeled data, such as using recursive neural tensor net (RNTN) and RNN for sentiment analysis [ 149 ], parsing [ 150 ], and entity/object recognition [ 151 ]. Deep belief networks (DBN) [ 152 , 153 ] and CNN [ 154 , 155 ] are used for images, objects [ 156 ], and speech recognition.…”

Section: Deep Learningmentioning

confidence: 99%

The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities

Nasif

Othman

Sani

2021

Sensors

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Networking is crucial for smart city projects nowadays, as it offers an environment where people and things are connected. This paper presents a chronology of factors on the development of smart cities, including IoT technologies as network infrastructure. Increasing IoT nodes leads to increasing data flow, which is a potential source of failure for IoT networks. The biggest challenge of IoT networks is that the IoT may have insufficient memory to handle all transaction data within the IoT network. We aim in this paper to propose a potential compression method for reducing IoT network data traffic. Therefore, we investigate various lossless compression algorithms, such as entropy or dictionary-based algorithms, and general compression methods to determine which algorithm or method adheres to the IoT specifications. Furthermore, this study conducts compression experiments using entropy (Huffman, Adaptive Huffman) and Dictionary (LZ77, LZ78) as well as five different types of datasets of the IoT data traffic. Though the above algorithms can alleviate the IoT data traffic, adaptive Huffman gave the best compression algorithm. Therefore, in this paper, we aim to propose a conceptual compression method for IoT data traffic by improving an adaptive Huffman based on deep learning concepts using weights, pruning, and pooling in the neural network. The proposed algorithm is believed to obtain a better compression ratio. Additionally, in this paper, we also discuss the challenges of applying the proposed algorithm to IoT data compression due to the limitations of IoT memory and IoT processor, which later it can be implemented in IoT networks.

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