Intelligent Knowledge Exploration and Processing

Intelligent Knowledge Exploration and Processing

Ways of common Machine learning and Common algorithmic approaches in it

Document Type : Original Article

Author
zeinab mosazadeh
department computer.university bam.bam.iran
10.30508/kdip.2024.456229.1103
Abstract
Machine learning is a subfield of artificial intelligence (AI). The goal of machine learning in general is to understand the structure of data and fit that data into models that people can understand and use. Today, every technology user has benefited from machine learning. Facial recognition technology allows social media platforms to help users tag and share photos of their friends. Optical Character Recognition (OCR) technology converts text images into animated type. Recommendation engines, using machine learning, suggest what movies or TV shows to watch next, based on user preferences. Self-driving cars that rely on machine learning to navigate may soon be available to consumers. Machine learning is a constantly developing field. For this reason, there are considerations to keep in mind when working with machine learning methods or analyzing the impact of machine learning processes. In this article, common machine learning methods of supervised and unsupervised learning and common algorithmic approaches in machine learning. including k-nearest neighbor algorithm, decision tree learning and deep learning. His findings showed. With the expansion of the scope and fields of study and the use of machine learning in different fields, depending on the changing needs and complexity of different problems, different types of machine learning algorithms have emerged, each of which has its own efficiency and applications. . So you can choose the algorithm that best suits your business needs. In some cases, experts use a combination of these algorithms because one algorithm alone may not be able to solve a particular problem.
Keywords
Machine learning
k-nearest neighbor algorithm
decision tree learning
deep learning
1.    Anand, T., Singh, V., Bali, B., Sahoo, B. M., Shivhare, B. D., & Gupta, A. D. (2020, June). Survey paper: sentiment analysis for major government decisions. In 2020 International Conference on Intelligent Engineering and Management (ICIEM) (pp. 104-109). IEEE.
2.    Angelov, P., & Soares, E. (2020). Explainable-by-design approach for covid-19 classification via ct-scan
3.    Apostolopoulos, I. D., & Mpesiana, T. A. (2020). Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and engineering sciences in medicine, 43, 635-640
4.    Arif, M., Ajesh, F., Shamsudheen, S., Geman, O., Izdrui, D., & Vicoveanu, D. (2022). [Retracted] Brain Tumor Detection and Classification by MRI Using Biologically Inspired Orthogonal Wavelet Transform and Deep Learning Techniques. Journal of Healthcare Engineering, 2022(1), 2693621.
5.    Chen, R. C., Dewi, C., Huang, S. W., & Caraka, R. E. (2020). Selecting critical features for data classification based on machine learning methods. Journal of Big Data, 7(1), 52.
6.    Gao, X., Wen, J., & Zhang, C. (2019). An improved random forest algorithm for predicting employee turnover. Mathematical Problems in Engineering, 2019(1), 4140707.
7.    Gaye, B., Zhang, D., & Wulamu, A. (2021). Improvement of support vector machine algorithm in big data background. Mathematical Problems in Engineering, 2021(1), 5594899.
8.    Kaur, M., Kumar, V., Yadav, V., Singh, D., Kumar, N., & Das, N. N. (2021). Metaheuristic‐based Deep COVID‐19 Screening Model from Chest X‐Ray Images. Journal of healthcare engineering, 2021(1), 8829829.
9.    Lohani, B. P., & Thirunavukkarasan, M. (2021, November). A review: application of machine learning algorithm in medical diagnosis. In 2021 International Conference on Technological Advancements and Innovations (ICTAI) (pp. 378-381). IEEE.
10.Nassif, A. B., Shahin, I., Attili, I., Azzeh, M., & Shaalan, K. (2019). Speech recognition using deep neural networks: A systematic review. IEEE access, 7, 19143-19165.
11.Sahoo, B. M., Gupta, A. D., & Shivahare, B. D. (2018). Audio Visual EMG & GSR biofeedback analysis and spiritual methods for understanding human behaviour and psychosomatic disorders. Amity Journal of Computational Sciences, 2(1).
12.Schonlau M, Zou RY. The random forest algorithm for statisticallearning. The Stata Journal. 2020; 20(1):3-29. Doi:10.1177/1536867X20909688
13.Sethy, P. K., & Behera, S. K. (2020). Detection of coronavirus disease (covid-19) based on deep features.
14.Shivahare, B. D., & Gupta, S. K. (2021). Multi-level image segmentation using randomized spiral-based whale optimization algorithm. Recent Patents on Engineering, 15(5), 114-126.
15.Shivahare, B. D., & Gupta, S. K. (2016). Multilevel thresholding based image segmentation using whale optimization algorithm. image, 3, 4.
16.Shivahare, B. D., Singh, M., Gupta, A., Ranjan, S., Pareta, D., & Sahu, B. M. (2021, February). Survey Paper: Whale optimization algorithm and its variant applications. In 2021 International Conference on Innovative Practices in Technology and Management (ICIPTM) (pp. 77-82). IEEE.
17.Shivahare, B. D., Suman, S., Challapalli, S. S. N., Kaushik, P., Gupta, A. D., & Bibhu, V. (2022, February). Survey paper: Comparative study of machine learning techniques and its recent applications. In 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) (Vol. 2, pp. 449-454). IEEE.
18.Singh, D., Kumar, V., Yadav, V., & Kaur, M. (2021). Deep neural network-based screening model for COVID-19-infected patients using chest X-ray images. International Journal of Pattern Recognition and Artificial Intelligence, 35(03), 2151004.
19.Stamate, D., Smith, R., Tsygancov, R., Vorobev, R., Langham, J., Stahl, D., & Reeves, D. (2020). Applying deep learning to predicting dementia and mild cognitive impairment. In Artificial Intelligence Applications and Innovations: 16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, June 5–7, 2020, Proceedings, Part II 16 (pp. 308-319). Springer International Publishing.
20.Xie, W., She, Y., & Guo, Q. (2021). Research on multiple classification based on improved SVM algorithm for balanced binary decision tree. Scientific Programming, 2021(1), 5560465.
21.Wang, L. (2019, December). Research and implementation of machine learning classifier based on KNN. In IOP Conference Series: Materials Science and Engineering (Vol. 677, No. 5, p. 052038). IOP publishing.
Intelligent Knowledge Exploration and Processing
Volume 4, Issue 12
Spring 2024
Pages 76-87