A study and identification of COVID-19 viruses using N-grams with Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine

Boujnouni, Mohamed El

doi:10.1109/iscv54655.2022.9806081

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…A study conducted by Sinsom Boonthong (2019) evaluated the effectiveness of seven mining methodologies in predicting data outcomes in classification, finding ANN, NB, and DT to be the top three. Consequently, our present study places these three methods at its core, with a particular emphasis on forecasting construction project cost management (Boujnouni, 2022;Katarya, 2020). Each of these methodologies extracts critical information from empirical data by detecting patterns, associations, and anomalies within the dataset (Liu et al, 2021).…”

Section: Data Mining Techniquementioning

confidence: 99%

Comparing data mining methods for predicting cost construction projects: A case study of cost management datasets from Thailand

Chaitongrat,

Janthachai,

Kusonkhum

et al. 2024

J. Infras. Policy. Dev.

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This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.

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Section: Data Mining Techniquementioning

confidence: 99%