Zobrazeno 1 - 10 of 24 pro vyhledávání: '"Mahama, Yahaya"'
1
Akademický článek
Analysis of Work-Zone Crashes Using the Ordered Probit Model with Factor Analysis in Egypt
Autor: Kairan Zhang, Mohamed Hassan, Mahama Yahaya, Shupeng Yang
Publikováno v: Journal of Advanced Transportation, Vol 2018 (2018)
Work-zones, due to their nature, are predisposed to hazardous situations. This is a consequence of conducting construction work within the vicinity of, or near, vehicular traffic. The exposure to danger underscores the need for proper understanding o
Externí odkaz: https://doaj.org/article/16e0263b66d241ca8cf984e86c30f25f
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2
A Novel Feature Selection Method Based on Maximum Likelihood Logistic Regression for Imbalanced Learning in Software Defect Prediction
Autor: Mahama Yahaya, Tianrui Li, Kamal Bashir
Publikováno v: The International Arab Journal of Information Technology. 17:721-730
The most frequently used machine learning feature ranking approaches failed to present optimal feature subset for accurate prediction of defective software modules in out-of-sample data. Machine learning Feature Selection (FS) algorithms such as Chi-
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::4fa9cadd5aba09e4957594599560261b
https://doi.org/10.34028/iajit/17/5/5
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4
Akademický článek
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5
A machine-learning method for improving crash injury severity analysis: a case study of work zone crashes in Cairo, Egypt
Autor: Yue Su, Xiang Li, Chuanyun Fu, Mahama Yahaya, Wenbo Fan, Xinguo Jiang
Publikováno v: International Journal of Injury Control and Safety Promotion
International Journal of Injury Control and Safety Promotion, Taylor & Francis, 2020, pp.1-10. ⟨10.1080/17457300.2020.1746814⟩
The quality of vehicular collision data is crucial for studying the relationship between injury severity and collision factors. Misclassified injury severity data in the crash dataset, however, may cause inaccurate parameter estimates and consequentl
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e532f31a49010f49000fcadd713c15f
https://hal.archives-ouvertes.fr/hal-02537137
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6
Bayesian networks for imbalance data to investigate the contributing factors to fatal injury crashes on the Ghanaian highways
Autor: Yingfei Fan, Runhua Guo, Shiwei Xu, Kamal Bashir, Wenbo Fan, Xinguo Jiang, Mahama Yahaya
Publikováno v: Accident; analysis and prevention. 150
The crash data are often predominantly imbalanced, among which the fatal injury (or minority) crashes are significantly underrepresented relative to the non-fatal injury (or majority) ones. This unbalanced phenomenon poses a huge challenge to most of
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f6a669865aa780c4bc0dde307b73822e
https://pubmed.ncbi.nlm.nih.gov/33338913
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7
Akademický článek
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8
A Hybrid Data Preprocessing Technique based on Maximum Likelihood Logistic Regression with Filtering for Enhancing Software Defect Prediction
Autor: Kamal Bashir, Tayseer Ali, Mahama Yahaya, Ahmed Saad Hussein
Publikováno v: ISKE
Software Defect Prediction (SDP) is critical to ensure product reliability and customer satisfaction. Many studies conducted to predict defective modules in the software development process applied historical software defect data mined from online de
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ea64c9d26af5c3e7bcabdbf850019160
https://doi.org/10.1109/iske47853.2019.9170328
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9
Enhancing Crash Injury Severity Prediction on Imbalanced Crash Data by Sampling Technique with Variable Selection
Autor: Wenbo Fan, Kamal Bashir, Xinguo Jiang, Mahama Yahaya, Chuanyun Fu
Publikováno v: ITSC
The analysis of road crash data has long been used as a premise for influencing the road and automobile designs and guiding the implementation of various policies with the view to enhance the road safety. However, the crash data is associated with cl
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::422835567ca79b1ea1df9a8bfbb267ca
https://doi.org/10.1109/itsc.2019.8917223
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10
Ensemble-based model selection for imbalanced data to investigate the contributing factors to multiple fatality road crashes in Ghana
Autor: Xinguo Jiang, Mahama Yahaya, Kamal Bashir, Shiwei Xu, Runhua Guo, Caroline Matara
Publikováno v: Accident Analysis & Prevention. 151:105851
The study aims to identify relevant variables to improve the prediction performance of the crash injury severity (CIS) classification model. Unfortunately, the CIS database is invariably characterized by the class imbalance. For instance, the samples
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9eea6cd7b0c804efa2c01a67b6ebb308
https://doi.org/10.1016/j.aap.2020.105851
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