Open Access
Issue
SHS Web Conf.
Volume 139, 2022
The 4th ETLTC International Conference on ICT Integration in Technical Education (ETLTC2022)
Article Number 03008
Number of page(s) 6
Section Topics in Computer Science
DOI https://doi.org/10.1051/shsconf/202213903008
Published online 13 May 2022
  1. Zhang, X.-D. A Matrix Algebra Approach to Artificial Intelligence; Springer, 2020; [CrossRef] [Google Scholar]
  2. Papatsimouli, M.; Lazaridis, L.; Kollias, K.-F.; Skordas, I.; Fragulis, G.F. Speak with Signs: Active Learning Platform for Greek Sign Language, English Sign Language, and Their Translation. 2020, doi:10.48550/arXiv.2012.11981. [Google Scholar]
  3. Lazaridis, L.; Papatsimouli, M.; Kollias, K.-F.; Sarigiannidis, P.; Fragulis, G.F. Hitboxes: A Survey About Collision Detection in Video Games. In Proceedings of the International Conference on Human-Computer Interaction; Springer, 2021; pp. 314–326. [Google Scholar]
  4. Wilkinson, J.; Arnold, K.F.; Murray, E.J.; van Smeden, M.; Carr, K.; Sippy, R.; de Kamps, M.; Beam, A.; Konigorski, S.; Lippert, C.; et al. Time to Reality Check the Promises of Machine Learning-Powered Precision Medicine. The Lancet Digital Health 2020, 2, e677–e680, doi:10.1016/S2589-7500(20)30200-4. [CrossRef] [Google Scholar]
  5. Hannun, A.Y.; Rajpurkar, P.; Haghpanahi, M.; Tison, G.H.; Bourn, C.; Turakhia, M.P.; Ng, A.Y. Cardiologist-Level Arrhythmia Detection and Classification in Ambulatory Electrocardiograms Using a Deep Neural Network. Nat Med 2019, 25, 65–69, doi:10.1038/s41591-018-0268-3. [CrossRef] [Google Scholar]
  6. Kollias, K.-F.; Syriopoulou-Delli, C.K.; Sarigiannidis, P.; Fragulis, G.F. The Contribution of Machine Learning and Eye-Tracking Technology in Autism Spectrum Disorder Research: A Systematic Review. Electronics 2021, 10, 2982. [CrossRef] [Google Scholar]
  7. Kollias, K.-F.; Syriopoulou-Delli, C.K.; Sarigiannidis, P.; Fragulis, G.F. The Contribution of Machine Learning and Eye-Tracking Technology in Autism Spectrum Disorder Research: A Review Study. In Proceedings of the 2021 10th International Conference on Modern Circuits and Systems Technologies (MOCAST); IEEE, 2021; pp. 1–4. [Google Scholar]
  8. Rajpurkar, P.; Irvin, J.; Ball, R.L.; Zhu, K.; Yang, B.; Mehta, H.; Duan, T.; Ding, D.; Bagul, A.; Langlotz, C.P.; et al. Deep Learning for Chest Radiograph Diagnosis: A Retrospective Comparison of the CheXNeXt Algorithm to Practicing Radiologists. PLOS Medicine 2018, 15, e1002686, doi:10.1371/journal.pmed.1002686. [CrossRef] [Google Scholar]
  9. Bien, N.; Rajpurkar, P.; Ball, R.L.; Irvin, J.; Park, A.; Jones, E.; Bereket, M.; Patel, B.N.; Yeom, K.W.; Shpanskaya, K.; et al. Deep-Learning-Assisted Diagnosis for Knee Magnetic Resonance Imaging: Development and Retrospective Validation of MRNet. PLOS Medicine 2018, 15, e1002699, doi:10.1371/journal.pmed.1002699. [CrossRef] [Google Scholar]
  10. Biau, G.; Scornet, E. A Random Forest Guided Tour. Test 2016, 25, 197–227. [CrossRef] [Google Scholar]
  11. Jiao, S.; Zou, Q.; Guo, H.; Shi, L. ITTCA-RF: A Random Forest Predictor for Tumor T Cell Antigens. Journal of translational medicine 2021, 19, 1–11. [Google Scholar]
  12. Watson, G.L.; Xiong, D.; Zhang, L.; Zoller, J.A.; Shamshoian, J.; Sundin, P.; Bufford, T.; Rimoin, A.W.; Suchard, M.A.; Ramirez, C.M. Fusing a Bayesian Case Velocity Model with Random Forest for Predicting COVID-19 in the US. Available at SSRN 3594606 2020. [Google Scholar]
  13. Torrey, L.; Shavlik, J. Transfer Learning Available online: https://www.igiglobal.com/chapter/transfer-learning/www.igiglobal.com/chapter/transfer-learning/36988 (accessed on 27 December 2021). [Google Scholar]
  14. UCI Machine Learning Repository Available online: https://archive.ics.uci.edu/ml/index.php (accessed on 27 December 2021). [Google Scholar]
  15. Akyol, K.; Gültepe, Y. A Study on Liver Disease Diagnosis Based on Assessing the Importance of Attributes. IJISA 2017, 9, 1–9, doi:10.5815/ijisa.2017.11.01. [CrossRef] [Google Scholar]
  16. Subbaiah, S.; Kavitha, M. Random Forest Algorithm for Predicting Chronic Diabetes Disease. Advancements in Applications of Microbiology and Bioinformatics in Pharmocology 2020, 08, 4–8. [Google Scholar]
  17. Pal, M.; Parija, S. Prediction of Heart Diseases Using Random Forest. J. Phys.: Conf. Ser. 2021, 1817, 012009, doi:10.1088/1742-6596/1817/1/012009. [CrossRef] [Google Scholar]
  18. Boinee, P.; Angelis, R.D.; Foresti, G.L. Meta Random Forests. World Academy of Science, Engineering and Technology 2008, 18, 1148–1157. [Google Scholar]
  19. Saqib, P.; Qamar, U.; Aslam, A.; Ahmad, A. Hybrid of Filters and Genetic Algorithm - Random Forests Based Wrapper Approach for Feature Selection and Prediction. In Proceedings of the Intelligent Computing; Arai, K., Bhatia, R., Kapoor, S., Eds.; Springer International Publishing: Cham, 2019; pp. 190–199. [CrossRef] [Google Scholar]
  20. Haque, Md.R.; Islam, Md.M.; Iqbal, H.; Reza, Md.S.; Hasan, Md.K. Performance Evaluation of Random Forests and Artificial Neural Networks for the Classification of Liver Disorder. In Proceedings of the 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2); February 2018; pp. 1–5. [Google Scholar]
  21. Benbelkacem, S.; Atmani, B. Random Forests for Diabetes Diagnosis. In Proceedings of the 2019 International Conference on Computer and Information Sciences (ICCIS); April 2019; pp. 1–4. [Google Scholar]
  22. Ali, J.; Khan, R.; Ahmad, N.; Maqsood, I. Random Forests and Decision Trees. IJCSI International Journal of Computer Science Issues 2012, 9, 272–278. [Google Scholar]
  23. UCI Machine Learning Repository: Liver Disorders Data Set Available online: https://archive.ics.uci.edu/ml/datasets/liver+disor ders (accessed on 27 December 2021). [Google Scholar]
  24. UCI Machine Learning Repository: Diabetes Data Set Available online: https://archive.ics.uci.edu/ml/datasets/diabetes (accessed on 27 December 2021). [Google Scholar]
  25. PIMA Indian Diabetes Prediction. Predicting the Onset of Diabetes | by Ishan Choudhary | Towards Data Science Available online: https://towardsdatascience.com/pima-indian-diabetes-prediction-7573698bd5fe (accessed on 27 December 2021). [Google Scholar]
  26. UCI Machine Learning Repository: Heart Disease Data Set Available online: https://archive.ics.uci.edu/ml/datasets/heart+disease (accessed on 27 December 2021). [Google Scholar]
  27. UCI Machine Learning Repository: Breast Cancer Wisconsin (Original) Data Set Available online: https://archive.ics.uci.edu/ml/datasets/breast+can cer+wisconsin+(original) (accessed on 27 December 2021). [Google Scholar]
  28. Sklearn.Ensemble.RandomForestClassifier Available online: https://scikitlearn/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html (accessed on 21 January 2022). [Google Scholar]
  29. Fragulis, G. F., Papatsimouli, M., Lazaridis, L., & Skordas, I. A. (2021). An Online Dynamic Examination System (ODES) based on open source software tools. Software Impacts, 7, 100046. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.