Open Access
| Issue |
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
|
|
|---|---|---|
| Article Number | 00133 | |
| Number of page(s) | 24 | |
| DOI | https://doi.org/10.1051/bioconf/20249700133 | |
| Published online | 05 April 2024 | |
- “Indian Institute of Technology Madrras,” CS6464: Concepts In Statistical Learning Theory, January - May 2023. [Online]. Available: http://www.cse.iitm.ac.in/~vplab/statistical_learning_theory.html. [Google Scholar]
- J.P. Mueller and L. Massaron, Machine Learning For Dummies, Hoboken, New Jersey: John Wiley & Sons, Inc, 2016. [Google Scholar]
- “javatpoint.com,” Machine Learning, [Online]. Available: https://www.javatpoint.com/machine-learning. [Accessed 6 feb 2023]. [Google Scholar]
- O. Theobald, Machine Learning For Absolute Beginners, London, UK: Scatterplot press, 2017. [Google Scholar]
- S. Theodoridis, Machine Learning A Bayesian and Optimization Perspective, Elsevier Ltd, 2020. [Google Scholar]
- R. Wolff, “ MonkeyLearn Blog,” 5 Types of Classification Algorithms in Machine Learning, 26 August 2020. [Online]. Available: https://monkeylearn.com/blog/classification-algorithms/. [Google Scholar]
- R. Praba, G. Darshan, K.T. Roshanraj. and P.B. Surya Prakash, “Study On Machine Learning Algorithms,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 7, no. 4, pp. 67–72, 2021. [Google Scholar]
- C. Nasa and Suman, “Evaluation of Different Classification Techniques for WEB Data,” International Journal of Computer Applications, vol. 52, no. 9, p. 0975–8887, 2012. [Google Scholar]
- N.H. Ali, M. Emaduldeen and A.E. Ali, “Learning Evolution: a Survey,” Iraqi Journal of Science, vol. 62, no. 12, pp. 4978–4987, 2021. [CrossRef] [Google Scholar]
- A. Ram and Meenakshi, “Short Review on Machine Learning andits Application,” SPECIALUSIS UGDYMAS / SPECIAL EDUCATION, vol. 1, no. 43, pp. 9894–9902, 2022. [Google Scholar]
- S. Bansal, “ANALYTIXLABS,” MACHINE LEARNING What is Classification Algorithm in Machine Learning? With Examples, 25 JANUARY 2023. [Online]. Available: https://www.analytixlabs.co.in/blog/classification-in-machine-learning/. [Google Scholar]
- J. Brownlee, “Machine Learning Mastery,” 4 Types of Classification Tasks in Machine Learning, 19 Aug 2020. [Online]. Available: https://machinelearningmastery.com/types-of-classification-in-machinelearning/. [Google Scholar]
- S.N. Dhage and C.K. Raina, “A review on Machine Learning Techniques,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 4, no. 3, pp. 395–399, 2016. [Google Scholar]
- V. Rastogi, S. Satija, P.K. Sharma and S. Singh, “MACHINE LEARNING ALGORITHMS:OVERVIEW,” International Journal of Advanced Research in Engineering and Technology (IJARET), vol. 11, no. 9, pp. 512–517, 2020. [Google Scholar]
- P. Ariwala, “Maruti Techlabs,” 9 Real-World Problems that can be Solved by Machine Learning, 16 Oct 2023. [Online]. Available: https://marutitech.com/problems-solved-machine-learning/. [Google Scholar]
- M. Waseem, “Edureka Blog,” How To Implement Classification In Machine Learning?, 2 Aug 2023. [Online]. Available: https://www.edureka.co/blog/classification-in-machine-learning/. [Google Scholar]
- M. Yuvalı, B. Yaman and Ö. Tosun, “Classification Comparison of Machine Learning Algorithms Using Two Independent, C.A.D Datasets,” Mathematics, vol. 10, no. 3, p. 311, 2022. [CrossRef] [Google Scholar]
- R.V.K. Reddy and U.R. Babu, “A Review on Classification Techniques in Machine Learning,” International Journal of advance research in science and engineering, vol. 7, no. 3, pp. 40–47, 2018. [Google Scholar]
- D. Michie, D.J. Spiegelhalter and C.C. Taylor, Machine Learning, Neural and Statistical Classification, 1994. [Google Scholar]
- P.I. Exchange, “PI.EXCHANGE,” Understanding Classification in Machine Learning, 16 Jan 2023. [Online]. Available: https://www.pi.exchange/blog/understanding-classification-in-machinelearning. [Google Scholar]
- F.Y. Osisanwo, J.T. Akinsola, J.O. Hinmikaiye, O. Olakanmi and J. Akinjobi, “Supervised Machine Learning Algorithms: Classification and Comparison,” International Journal of Computer Trends and Technology (IJCTT), vol. 48, no. 3, pp. 128–138, 2017. [CrossRef] [Google Scholar]
- S. Ray, “A Quick Review of Machine Learning Algorithms,” in 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (Com-IT-Con), India, 14th -16th Feb 2019, India, 2019. [Google Scholar]
- M.A. Kashmoola, M.K. Ahmed and N.Y.A. Alsaleem, “Network Traffic Prediction Based on Boosting Learning,” Iraqi Journal of Science, vol. 63, no. 9, pp. 4047–4056, 2022. [CrossRef] [Google Scholar]
- F.H. Fadel and S.F. Behadili, “A Comparative Study for Supervised Learning Algorithms to Analyze Sentiment Tweets,” Iraqi Journal of Science, vol. 63, no. 6, pp. 2712–2724, 2022. [CrossRef] [Google Scholar]
- C. Shelke, “Machine Leaning and it’s Various Algorithms- A Study,” Journal of Emerging Technologies and Innovative Research (JETIR), vol. 8, no. 5, pp. 258–265, 2021. [Google Scholar]
- S.D. Jadhav and H.P. Channe, “Comparative Study of K-NN, Naive Bayes and Decision Tree Classification Techniques,” International Journal of Science and Research (IJSR), vol. 5, no. 1, pp. 1842–1845, 2016. [Google Scholar]
- N. Kalcheva, M. Todorova and G. Marinova, “NAIVE BAYES CLASSIFIER, DECISION TREE AND ADABOOST ENSEMBLE ALGORITHM -ADVANTAGES AND DISADVANTAGES,” in 6th International Scientific Conference, E.R.AZ - Knowledge Based Sustainable Development, Belgrade, Serbia, 2020. [Google Scholar]
- T. 365 Team, “The 365 Team,” Introduction to Decision Trees: Why Should You Use Them?, 12 Apr 2023. [Online]. Available: https://365datascience.com/tutorials/machine-learning-tutorials/decisiontrees/. [Google Scholar]
- ValianceSolutions, “data science central,” Improving Predictions with Ensemble Model, 8 Oct 2016. [Online]. Available: https://www.datasciencecentral.com/improving-predictions-withensemble-model/. [Google Scholar]
- Spotfire, “spotfire,” What is a random forest?, [Online]. Available: https://www.spotfire.com/glossary/what-is-a-random-forest. [Google Scholar]
- T.F. Beginner, “Tutorial For Beginner,” Linear Regression vs Logistic Regression in Machine Learning, [Online]. Available: https://tutorialforbeginner.com/linear-regression-vs-logistic-regressionin-machine-learning. [Google Scholar]
- K.S.S. Kumar, “DOC493: Intelligent Data Analysis and Probabilistic Inference Lecture 15.,” Imperial College., 2015. [Google Scholar]
- IBM, “IBM,” What is the k-nearest neighbors algorithm?, [Online]. Available: https://www.ibm.com/topics/knn. [Google Scholar]
- A.A. Soofi and A. Awan, “Classification Techniques in Machine Learning: Applications and Issues,” Journal of Basic & Applied Sciences, vol. 13, pp. 459–465, 2017. [CrossRef] [Google Scholar]
- W.Z. Cárdenas, M. Zumbado and T. Zelaya, “McCulloch-Pitts Artificial Neuron and Rosenblatt's Perceptron: An abstract specification in Z,” Revista Technology, vol. 5, pp. 16–29, 2020. [Google Scholar]
- J.T. Point, “java T point,” Artificial Neural Network Tutorial, [Online]. Available: https://www.javatpoint.com/artificial-neural-network. [Google Scholar]
- Wikipedia, “Wikipedia,” Biological neuron models, [Online]. Available: https://en.wikipedia.org/wiki/Biological_neuron_model. [Google Scholar]
- J.D. Pineda-Jaramillo, “A review of Machine Learning (ML) algorithms used for modeling travel mode choice,” DYNA, vol. 86, no. 211, pp. 2–41, 2019. [Google Scholar]
- J. Djuris, I. Kurcubic and S. Ibric, “Review of machine learning algorithms application in pharmaceutical technology,” Arhiv za farmaciju, vol. 71, no. 4, pp. 302–317, 2021. [CrossRef] [Google Scholar]
- R. Quiza and J.P. Davim, “Computational methods and optimization,” in Machining of hard materials, Verlag, Springer, 2011, pp. 177–208. [CrossRef] [Google Scholar]
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