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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Zahid, M., Habib, M. F., Ijaz, M., Ameer, I., Ullah, I., Ahmed, T., & He, Z. (2024). Factors affecting injury severity in motorcycle crashes: Different age groups analysis using Catboost and SHAP techniques. Traffic Injury Prevention, 10 pages. External link
Wang, C., Ijaz, M., Chen, F., Song, D., Hou, M., Zhang, Y., Cheng, J., & Zahid, M. (2023). Differences in single-vehicle motorcycle crashes caused by distraction and overspeed behaviors: considering temporal shifts and unobserved heterogeneity in prediction. International Journal of Injury Control and Safety Promotion, 30(3), 375-391. External link
Iqbal, M., Zhang, D., Khan, M. I., Zahid, M., & Jalal, F. E. (2023). Effects of Rebar Size and Volume Fraction of Glass Fibers on Tensile Strength Retention of GFRP Rebars in Alkaline Environment via RSM and SHAP Analyses. Journal of Materials in Civil Engineering, 35(9), 15 pages. External link
Ullah, I., Liu, K., Yamamoto, T., Zahid, M., & Jamal, A. (2023). Modeling of machine learning with SHAP approach for electric vehicle charging station choice behavior prediction. Travel Behaviour and Society, 31, 78-92. External link
Wang, C., Ijaz, M., Chen, F., Easa, S. M., Zhang, Y., Cheng, J., & Zahid, M. (2023). Temporal assessment of injury severities of two types of pedestrian-vehicle crashes using unobserved-heterogeneity models. Journal of Transportation Safety & Security, 50 pages. External link
Shah, S. F. A., Chen, B., Zahid, M., & Ahmad, M. R. (2022). Compressive strength prediction of one-part alkali activated material enabled by interpretable machine learning. Construction and Building Materials, 360, 129534 (10 pages). External link
Wang, C., Ijaz, M., Chen, F., Zhang, Y., Cheng, J., & Zahid, M. (2022). Evaluating gender differences in injury severities of non-helmet wearing motorcyclists: Accommodating temporal shifts and unobserved heterogeneity. Analytic Methods in Accident Research, 36, 100249 (24 pages). External link
Jamal, A., Ijaz, M., Almosageah, M., Al-Ahmadi, H. M., Zahid, M., Ullah, I., & Al Mamlook, R. E. (2022). Implementing the Maximum Likelihood Method for Critical Gap Estimation under Heterogeneous Traffic Conditions. Sustainability, 14(23), 15888 (13 pages). External link
Amin, M. N., Salami, B. A., Zahid, M., Iqbal, M., Khan, K., Abu-Arab, A. M., Alabdullah, A. A., & Jalal, F. E. (2022). Investigating the Bond Strength of FRP Laminates with Concrete Using LIGHT GBM and SHAPASH Analysis. Polymers, 14(21), 16 pages. External link
Ullah, I., Liu, K., Yamamoto, T., Zahid, M., & Jamal, A. (2022). Prediction of electric vehicle charging duration time using ensemble machine learning algorithm and Shapley additive explanations. International Journal of Energy Research, 46(11), 15211-15230. External link
Abdulalim Alabdullah, A., Iqbal, M., Zahid, M., Khan, K., Nasir Amin, M., & Jalal, F. E. (2022). Prediction of rapid chloride penetration resistance of metakaolin based high strength concrete using light GBM and XGBoost models by incorporating SHAP analysis. Construction and Building Materials, 345, 13 pages. External link
