Master’s Thesis from the year 2017 in the subject Computer Sciences – Industry 4.0, grade: 5.0/5.0, , course: Computer Science and Engineering, language: English, abstract: Accident data analysis is one of the prime interests in the present era. Analysis of accident is very essential because it can expose the relationship between the different types of attributes that commit to an accident. Road, traffic and airplane accident data have different nature in comparison to other real world data as accidents are uncertain. Analyzing diverse accident dataset can provide the information about the contribution of these attributes which can be utilized to deteriorate the accident rate. Nowadays, Data mining is a popular technique for examining the accident dataset. In this study, Association rule mining, different classification, and clustering techniques have been implemented on the dataset of the road, traffic accidents, and an airplane crash. Achieved result illustrated accuracy at a better level and found many different hidden circumstances that would be helpful to deteriorate accident ratio in near future.
A propos de l’auteur
Prayag Tiwari is working as a Research Fellow at University of Padova, Italy. His research areas focus on Machine Learning, Deep Learning, Io T, Image Processing, Data Warehousing etc. He received his MS from NUST MISIS, Moscow. He worked also as a Research Assistant in NUST MISIS as well he has Teaching and Industrial work experience. He has several publications in Journal, Book Series and Conferences of IEEE, Springer, Elsevier, Wiley, IGI-Global etc..
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