KaydedilenlerDerginizi ekleyin

Dergiler / Journal of Soft Computing and Artificial Intelligence / 2023 / Cilt: 4 Sayı: 1

Comparative Analysis of Globalisation Techniques for Medical Document Classification

Bekir PARLAK
Salih Berkan AYDEMİR
Sayfa
7–14
Yayın Tarihi
2023-06-25
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Classifiers are MNB, DT, and SVM.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Medical document classification is one of the important topics of text mining.
📊
Temel Bulgular & Veri:To observe how successful local feature selection methods in the field of text classification affect the classification performance on these two equivalent data sets by applying different globalisation techniques.
💡
Sonuç & Katkı:Classifiers are MNB, DT, and SVM.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Medical documents · Text Classification · Feature selection

Abstract

Medical document classification is one of the important topics of text mining. Globalisation techniques play a major role in text classification. It is also known that globalisation techniques play an important role in text classification. Our aim in the study is to conduct a detailed analysis on two data sets with English and Turkish content by using medical text summaries of Turkish articles. These datasets consist of Turkish and English text summaries of the same articles. To observe how successful local feature selection methods in the field of text classification affect the classification performance on these two equivalent data sets by applying different globalisation techniques. The feature selection methods used are CHI2, MI, OR, WLLR. Globalisation techniques are SUM, AVG, MAX. Classifiers are MNB, DT, and SVM.

🔖

Bu Makaleye Atıf Yap

PARLAK, B., AYDEMİR, S. B. (2023). Comparative Analysis of Globalisation Techniques for Medical Document Classification. Journal of Soft Computing and Artificial Intelligence, Cilt: 4 Sayı: 1, 7–14. https://ajindex.com/articles/952507

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (1)

  1. P. Slipenchuk and A. Epishkina, “Practical User and Entity Behavior Analytics Methods for Fraud Detection Systems in Online Banking: A Survey,” in Biologically Inspired Cognitive Architectures 2019, Cham, 2020, pp. 83–93. doi: 10.1007/978-3-030-25719-4_11. E. T. Anumol, “Use of Machine Learning Algorithms with SIEM for Attack Prediction,” in Intelligent Computing, Communication and Devices, New Delhi, 2015, pp. 231–235. doi: 10.1007/978-81-322-2012-1_24. T. Laue, T. Klecker, C. Kleiner, and K.-O. Detken, “A SIEM Architecture for Advanced Anomaly Detection,” vol. 6, no. 1, p. 17, 2022. S. Asanger and A. Hutchison, “Experiences and Challenges in Enhancing Security Information and Event Management Capability Using Unsupervised Anomaly Detection,” in 2013 International Conference on Availability, Reliability and Security, Sep. 2013, pp. 654–661. doi: 10.1109/ARES.2013.86. M. Goldstein, S. Asanger, M. Reif, and A. Hutchison, “Enhancing Security Event Management Systems with Unsupervised Anomaly Detection:,” in Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods, Barcelona, Spain, 2013, pp. 530–538. doi: 10.5220/0004230105300538. A. Lukashin, M. Popov, A. Bolshakov, and Y. Nikolashin, “Scalable Data Processing Approach and Anomaly Detection Method for User and Entity Behavior Analytics Platform,” in Intelligent Distributed Computing XIII, Cham, 2020, pp. 344–349. doi: 10.1007/978-3-030-32258-8_40. Z. Tian, C. Luo, H. Lu, S. Su, Y. Sun, and M. Zhang, “User and Entity Behavior Analysis under Urban Big Data,” ACMIMS Trans. Data Sci., vol. 1, no. 3, p. 16:1-16:19, Sep. 2020, doi: 10.1145/3374749. D. C. Le and A. N. Zincir-Heywood, “Evaluating Insider Threat Detection Workflow Using Supervised and Unsupervised Learning,” in 2018 IEEE Security and Privacy Workshops (SPW), May 2018, pp. 270–275. doi: 10.1109/SPW.2018.00043. B. Sharma, P. Pokharel, and B. Joshi, “User Behavior Analytics for Anomaly Detection Using LSTM Autoencoder - Insider Threat Detection,” in Proceedings of the 11th International Conference on Advances in Information Technology, New York, NY, USA, Jul. 2020, pp. 1–9. doi: 10.1145/3406601.3406610. T. Al-Shehari and R. A. Alsowail, “An Insider Data Leakage Detection Using One-Hot Encoding, Synthetic Minority Oversampling and Machine Learning Techniques,” Entropy, vol. 23, no. 10, Art. no. 10, Oct. 2021, doi: 10.3390/e23101258. M. Dosh, “Detecting insider threat within institutions using CERT dataset and diffe