Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 6 , Issue 1 , PP: 27-34, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Noor abdulmuttaleb jaafar 1 * , Noor Razzaq Abbas 2 , Ammar Kadi 3 , Abdelhameed Ibrahim 4 , Abdelaziz A. Abdelhamid 5
Doi: https://doi.org/10.54216/JAIM.060103
The subset of manufactured insights (AI) known as machine learning starts in design acknowledgment, where information can be organized for human comprehension. For a long time, various applications utilizing machine learning have been created in healthcare, fund, military gear, and space investigation; presently, machine learning is a zone that's extending and progressing quickly. It utilizes information to optimize computer execution. AI is vital in combating modern coronaviruses in 2019 (COVID-19) -related matters and is used additionally in computer-assisted blend-making plans. Computer programs' settings are optimized based on preparing information or past encounters. It can moreover make future forecasts utilizing the information. With the assistance of machine learning, we are creating a numerical demonstration based on the data's measurements.
Numerous illustrations outline the viability of machine learning and counterfeit insights in this field. Counterfeit insights strategies can improve the consistency of forecasts and choices by making valuable calculations. AI is useful not for foreseeing people with COVID-19 but for assessing general wellbeing. It can screen the COVID-19 episode at different levels; in our paper, we use three machine learning calculations to analyze and predict. The leading precision was in XGP= 99%, but SVM and RF gave great precision at 98%.
Covid 19 , SVM , RF , XGB , Machine learning , Internet of things , Prediction
[1] Coronavirus Disease (COVID-19) - events as they happen. Library Catalog: www.who.int.
[2] Countries where Coronavirus has spread-Worldometer. Library Catalog: www.worldometers.info.
[3] COVID-19 situation reports. Library Catalog: www.who.int.
[4] Diagnosis of covid-19 and its clinical spectrum dataset. url=https://kaggle.com/einsteindata4u/covid19.
[5] WHO Director-General’s remarks at the media briefing on 2019-nCoV on 11 February 2020. Library Catalog: www.who.int.
[6] WHO EMRO | Questions and answers | COVID-19 | Health topics.
[7] Support Vector Machine Machine learning algorithm with example and code, January 2019. Library Catalog: www.codershood.info Section: Machine learning.
[8] Ali Al-Hazmi. Challenges presented by MERS corona virus, and SARS corona virus to global health. Saudi journal of biological sciences, 23(4):507–511, 2016. Publisher: Elsevier.
[9] Sina F Ardabili, Amir Mosavi, Pedram Ghamisi, Filip Ferdinand, Annamaria R Varkonyi-Koczy, Uwe Reuter, Timon Rabczuk, and Peter M Atkinson. Covid-19 outbreak prediction with machine learning. Available at SSRN 3580188, 2020.
[10] Ibrahim W, Abdullaev S, Alkattan H, Adelaja OA, Subhi AA. Development of a Model Using Data Mining Technique to Test, Predict and Obtain Knowledge from the Academics Results of Information Technology Students. Data. 2022; 7(5):67. https://doi.org/10.3390/data7050067
[11] Taiwo Oladipupo Ayodele. Types of machine learning algorithms. New advances in machine learning, 19–48, 2010.
[12] Doaa S. Khafaga, Hussein Alkattan, Alhumaima A. Subhi. (2022). Evaluating the Effect of Optimized Voting Using Hybrid Particle Swarm and Grey Wolf Algorithm on the Classification of the Zoo Dataset. Journal of Artificial Intelligence and Metaheuristics, 2 (1), 08-15.
[13] Hussein Alkattan, Sanjar Abdullaev, El-Sayed M. El-Kenawy. (2023). The «Climate in Weathers» Approach to Processing of Meteorological Series in Mesopotamia: Assessment of Climate Similarity and Climate Change using Data Mining. Journal of Intelligent Systems and Internet of Things, 10 (1), 48-65.
[14] Hetal Bhavsar and Amit Ganatra. A comparative study of training algorithms for supervised machine learning. International Journal of Soft Computing and Engineering (IJSCE), 2(4):2231–2307, 2012.
[15] Hussein Alkattan, S. K. Towfek, M. Y. Shams. (2023). Tapping into Knowledge: Ontological Data Mining Approach for Detecting Cardiovascular Disease Risk Causes Among Diabetes Patients. Journal of Artificial Intelligence and Metaheuristics, 4 (1), 08-15.
[16] Nanshan Chen, Min Zhou, Xuan Dong, Jieming Qu, Fengyun Gong, Yang Han, Yang Qiu, Jingli Wang, Ying Liu, Yuan Wei, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in wuhan, china: a descriptive study. The Lancet, 395(10223):507–513, 2020.
[17] Ahmed T. Alhasanه, Hussein Alkattan, Alhumaima Ali Subhi, El-Sayed M. El-Kenawy, Marwa M. Eid. (2023). A Comparative Analysis of Methods for Detecting and Diagnosing Breast Cancer Based on Data Mining. Journal of Artificial Intelligence and Metaheuristics, 4 (2), 08-17.
[18] Ehsan khodadadi, S. K. Towfek, Hussein Alkattan. (2023). Brain Tumor Classification Using Convolutional Neural Network and Feature Extraction. Fusion: Practice and Applications, 13 (2), 34-41.
[19] Ashok Kumar Dwivedi. Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Computing and Applications, 29(10):685–693, 2018.
[20] Taiwo Oladipupo Ayodele. Types of machine learning algorithms. New advances in machine learning, pages 19–48, 2010.
[21] Ya-Han Hu, Yi-Lien Lee, Ming-Feng Kang, and Pei-Ju Lee. Constructing inpatient pressure injury prediction models using machine learning techniques. Computers, Informatics, Nursing: CIN, 2020.
[22] Leslie Pack Kaelbling, Michael L Littman, and Andrew W Moore. Reinforcement learning: A survey. Journal of artificial intelligence research, 4:237–285, 1996.
