The Application Method of Machine Learning for Analyzing User Transaction Tendency in Big Data environments

최도현,; 박중오,; Choi, Do-Hyeon; Park, Jung-Oh

doi:10.6109/jkiice.2015.19.10.2232

Cited by 5 publications

(1 citation statement)

References 2 publications

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“…IoT data has time-series characteristics because it periodically collects data from sensors. Using this characteristic, Recurrent Neural Network such as LSTM is used [16][17][18][19][20][21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Improving data quality using a deep learning network

Hwang

Lee

Jung

2020

IJEECS

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<p>IoT data is collected in real time and is treated as highly reliable data because of its high precision. However, it often exhibits incomplete values for reasons such as sensor aging and failure, poor operating environment, and communication problems. The characteristics of IoT data transmitted with high precision and time series are suitable to use LSTM, which is one kind of RNN. In this paper, when applying LSTM to data quality improvement in IoT environment where data are collected simultaneously from several sensors, it is suggested that it is effective to construct LSTM individually for each sensor accuracy.</p>

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Section: Introductionmentioning

confidence: 99%

Improving data quality using a deep learning network

Hwang

Lee

Jung

2020

IJEECS

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A SNS Data-driven Comparative Analysis on Changes of Attitudes toward Artificial Intelligence

Yun¹,

Yang²,

Lim³

2016

Journal of Digital Convergence

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AI (Artificial Intelligence) has attracted interest as a key element for technological advancement in various fields. In Korea, internet companies are leading the development of AI business technology. Active government funding plans for AI technology has also drawn interest. But not everyone is optimistic about AI. Both positive and negative opinions coexist about AI. However, attempts on analyzing people's opinions about AI in a quantitative way was scarce. In this study, we used text mining on SNS (Social Networking Service) to collect opinions about AI. And then we performed a comparative analysis about whether people view it as a positive thing or a negative thing and performed a comparative analysis to recognize popular key-words. Based on the results, it was confirmed that the change of key-words and negative posts have increased through time. And through these results, we were able to predict trend about AI.

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Implemented of non-destructive intelligent fruit Brix(sugar content) automatic measurement system

LeeDukKyu¹,

Seob²

2020

KSS

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The Application Method of Machine Learning for Analyzing User Transaction Tendency in Big Data environments

Cited by 5 publications

References 2 publications

Improving data quality using a deep learning network

Improving data quality using a deep learning network

A SNS Data-driven Comparative Analysis on Changes of Attitudes toward Artificial Intelligence

Implemented of non-destructive intelligent fruit Brix(sugar content) automatic measurement system

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