• Title/Summary/Keyword: Flask와 REST API

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Prediction of Sea Water Condition Changes using LSTM Algorithm for the Fish Farm (LSTM 알고리즘을 이용한 양식장 해수 상태 변화 예측)

  • Rijayanti, Rita;Hwang, Mintae
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.26 no.3
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    • pp.374-380
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    • 2022
  • This paper shows the results of a study that predicts changes in seawater conditions in sea farms using machine learning-based long short term memory (LSTM) algorithms. Hardware was implemented using dissolved oxygen, salinity, nitrogen ion concentration, and water temperature measurement sensors to collect seawater condition information from sea farms, and transferred to a cloud-based Firebase database using LoRa communication. Using the developed hardware, seawater condition information around fish farms in Tongyeong and Geoje was collected, and LSTM algorithms were applied to learning results using these actual datasets to obtain predictive results showing 87% accuracy. Flask and REST APIs were used to provide users with predictive results for each of the four parameters, including dissolved oxygen. These predictive results are expected to help fishermen reduce significant damage caused by fish group death by providing changes in sea conditions in advance.

LSTM Model-based Prediction of the Variations in Load Power Data from Industrial Manufacturing Machines

  • Rita, Rijayanti;Kyohong, Jin;Mintae, Hwang
    • Journal of information and communication convergence engineering
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    • v.20 no.4
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    • pp.295-302
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    • 2022
  • This paper contains the development of a smart power device designed to collect load power data from industrial manufacturing machines, predict future variations in load power data, and detect abnormal data in advance by applying a machine learning-based prediction algorithm. The proposed load power data prediction model is implemented using a Long Short-Term Memory (LSTM) algorithm with high accuracy and relatively low complexity. The Flask and REST API are used to provide prediction results to users in a graphical interface. In addition, we present the results of experiments conducted to evaluate the performance of the proposed approach, which show that our model exhibited the highest accuracy compared with Multilayer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM) models. Moreover, we expect our method's accuracy could be improved by further optimizing the hyperparameter values and training the model for a longer period of time using a larger amount of data.

Brainstorming using TextRank algorithms and Artificial Intelligence (TextRank 알고리즘 및 인공지능을 활용한 브레인스토밍)

  • Sang-Yeong Lee;Chang-Min Yoo;Gi-Beom Hong;Jun-Hyuk Oh;Il-young Moon
    • Journal of Practical Engineering Education
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    • v.15 no.2
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    • pp.509-517
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    • 2023
  • The reactive web service provides a related word recommendation system using the TextRank algorithm and a word-based idea generation service selected by the user. In the related word recommendation system, the method of weighting each word using the TextRank algorithm and the probability output method using SoftMax are discussed. The idea generation service discusses the idea generation method and the artificial intelligence reinforce-learning method using mini-GPT. The reactive web discusses the linkage process between React, Spring Boot, and Flask, and describes the overall operation method. When the user enters the desired topic, it provides the associated word. The user constructs a mind map by selecting a related word or adding a desired word. When a user selects a word to combine from a constructed mind-map, it provides newly generated ideas and related patents. This web service can share generated ideas with other users, and improves artificial intelligence by receiving user feedback as a horoscope.