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作者机构:Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh 13100 Saudi Arabia Univ Azad Jammu & Kashmir Dept Comp Sci & Informat Technol Abdullah Campus Muzaffarabad Pakistan Univ Azad Jammu & Kashmir Athmuqam Dept Comp Sci & IT Neelum Campus Azad Kashmir Pakistan King Khalid Univ Coll Sci Art Mahayil Dept Comp Sci Abha Saudi Arabia Menoufia Univ Dept Comp Sci Fac Comp & Informat Menofia Governorat Menoufia Egypt Taibah Univ Coll Comp Sci & Engn Medina Saudi Arabia Umm Al Qura Univ Coll Comp & Informat Syst Dept Comp Sci Mecca Saudi Arabia Pakistan Inst Engn & Appl Sci Dept Comp & Informat Sci Islamabad Pakistan Prince Sattam Bin Abdulaziz Univ Dept Comp & Self Dev Preparatory Year Deanship Al Kharj Saudi Arabia
出 版 物:《APPLIED ARTIFICIAL INTELLIGENCE》 (应用人工智能)
年 卷 期:2022年第36卷第1期
页 面:Article: 2067647页
核心收录:
学科分类:0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术(可授工学、理学学位)]
基 金:Deanship of Scientific Research at King Khalid University [RGP 2/46/43] Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia [PNURSP2022R114] Deanship of Scientific Research at Umm Al-Qura University [22UQU4310373DSR22]
摘 要:Particulate matter is emitted from diverse sources and affect the human health very badly. Dust particles exposure from the stated environment can affect our heart and lungs very badly. The particle pollution exposure creates a variety of problems including nonfatal heart attacks, premature deaths in people with lung or heart disease, asthma, difficulty in breathing, etc. In this article, we developed an automated tool by computing multimodal features to capture the diverse dynamics of ambient particulate matter and then applied the Chi-square feature selection method to acquire the most relevant features. We also optimized parameters of robust machine learning algorithms to further improve the prediction performance such as Decision Tree, SVM with Linear and Regression, Naive Bayes (NB), Random Forest (RF), Ensemble Classifier, K-Nearest Neighbor, and XGBoost for classification. The classification results with and without feature selection methods yielded the highest detection performance with random forest, and GBM yielded 100% of accuracy and AUC. The results revealed that the proposed methodology is more robust to provide an efficient system that will detect the particulate matters automatically and will help the individuals to improve their lifestyle and comfort. The concerned department can monitor the individual s healthcare services and reduce the mortality risk.