International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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A Study on the Classification of Variables Affecting Smartphone Addiction in Decision Tree Environment Using Python Program

  • Received : 2022.10.05
  • Accepted : 2022.10.11
  • Published : 2022.12.31
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Abstract

Since the launch of AI, technology development to implement complete and sophisticated AI functions has continued. In efforts to develop technologies for complete automation, Machine Learning techniques and deep learning techniques are mainly used. These techniques deal with supervised learning, unsupervised learning, and reinforcement learning as internal technical elements, and use the Big-data Analysis method again to set the cornerstone for decision-making. In addition, established decision-making is being improved through subsequent repetition and renewal of decision-making standards. In other words, big data analysis, which enables data classification and recognition/recognition, is important enough to be called a key technical element of AI function. Therefore, big data analysis itself is important and requires sophisticated analysis. In this study, among various tools that can analyze big data, we will use a Python program to find out what variables can affect addiction according to smartphone use in a decision tree environment. We the Python program checks whether data classification by decision tree shows the same performance as other tools, and sees if it can give reliability to decision-making about the addictiveness of smartphone use. Through the results of this study, it can be seen that there is no problem in performing big data analysis using any of the various statistical tools such as Python and R when analyzing big data.

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References

  1. Suchul Lee and Mihyun Ko, "Exploring the Key Technologies on Next Production Innovation", Journal of the Korea Convergence Society, Vol. 9, No. 9, pp. 199-207, 2018. DOI: https://doi.org/10.15207/JKCS.2018.9.9.199
  2. T. M. Mitchell, "The discipline of machine learning(Vol. 9)", Carnegie Mellon University, Shcool of Computer Science, MachineLearning Department, 2006. DOI: https://www.researchgate.net/publication/268201693_The_Discipline_of_Machine_Learning
  3. Min, Meekyung, "Classification of Seoul Metro Stations Based on Boarding/ Alighting Patterns Using Machine Learning Clustering," The Journal of The Institute of Internet, Broadcasting and Communication (IIBC), Vol. 18, No. 4, pp. 13-18, 2018(8). DOI: https://doi.org/10.7236/JIIBC.2018.18.4.13.
  4. Jihyun Lee, Jiyoung Woo, Ah Reum Kang, Young-Seob Jeong, Woohyun Jung, Misoon Lee and Sang Hyun Kim, "Comparative Analysis on Machine Learning and Deep Learning to Predict Post-Induction Hypotension", 2020 by the authors. Licensee MDPI, Basel, Switzerland, Sensors 2020, 20(16), 4575, 2020. DOI: https://doi.org/10.3390/s20164575
  5. Joerg Evermann, Jana-Rebecca Rehse and Peter Fettke, "Predicting process behaviour using deep learning", Decision Support Systems, Vol. 100, pp. 129-140, 2017(8) DOI: https://doi.org/10.1016/j.dss.2017.04.003
  6. Hyung-Woo, Lee, "Development of Supervised Machine Learning based Catalog Entry Classification and Recommendation System", Journal of Internet Computing and Services, Vol. 20, Issue 1, pp. 57-65, 2019. DOI: http://doi.org/10.7472/jksii.2019.20.1.57
  7. Se Hoon Jung, Jong Chan Kim, Kim Cheeyong, Kang Soo You and Chun Bo Sim, "A Study on Classification Evaluation Prediction Model by Cluster for Accuracy Measurement of Unsupervised Learning Data", Journal of Korea Multimedia Society Vol. 21, No. 7, pp. 779-786, 2018(7). DOI: https://doi.org/10.9717/kmms.2018.21.7.779
  8. Hayoung Eom, Jeonghwan Kim, Seungyun Ji and Heeyoul Choi, "Autonomous Parking Simulator for Reinforcement Learning", Journal of Digital Contents Society, Vol. 21, No. 2, pp. 381-386, 2020(2) DOI: DOI : 10.9728/dcs.2020.21.2.381
  9. Kim, Pan Jun, "An Analytical Study on Automatic Classification of Domestic Journal articles Using Random Forest", Journal of the Korean Society for information Management , Vol. 36. Issue. 2, pp. 57-77, 2019(6). DOI: https://doi.org/10.3743/KOSIM.2019.36.2.057.
  10. Yang, Jae-Wan, Lee, Young-Doo and Koo, In-Soo, "Sensor Fault Detection Scheme based on Deep Learning and Support Vector Machine", The Journal of The Institute of Internet, Broadcasting and Communication (IIBC), Vol. 18, No. 2, pp. 185-195, 2018(4). DOI:http://doi.org/10.7236/JIIBC.2018,18.2.185.
  11. Kyoungho Choi and Jin Ah Yoo, "A reviews on the social network analysis using R", Journal of KIISE, JOK:software and application, Vol. 6, No. 1, 2015. DOI: http://dx.doi.org/10.15207/JKCS.2015.6.1.077
  12. Youngseok Lee, "Python-based Software Education Model for Non-Computer Majors", Journal of the Korea Convergence Society, Vol. 9, No. 3, pp. 73-78, 2018. DOI: https://doi.org/10.15207/JKCS.2018.9.3.073
  13. Y. J. Kim, J. W. Ryu, W. M. Song and M. W. Kim, "Fire Probability Prediction Based on Weather Information Using Decision Tree", Journal of KIISE, JOK:software and application, Vol. 40, No. 11, 2013(11). DOI: http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE02283313
  14. K. N. Lee and H. C. Lee, "A Study on the Combined Decision Tree(C4.5) and Neural Network Algorithm for Classification of Mobile Telecommunication Customer", Korea Intelligent Information Systems Society, Vol. 9, No. 1, pp. 139-155, 2003(6). DOI: http://jiisonline.evehost.co.kr/files/DLA/8-9-1.pdf
  15. T. B. Yoon and J. H. Lee, "Design of Heuristic Decision Tree (HDT) Using Human Knowledge", Korean Institute of Intelligent Systems, Vol. 19, No. 1, pp. 161-164, 2009(4) DOI: https://doi.org/10.5391/JKIS.2009.19.4.525.