Engineering Commons^™

A Bidirectional Deep Lstm Machine Learning Method For Flight Delay Modelling And Analysis, Desmond B. Bisandu, Irene Moulitsas

National Training Aircraft Symposium (NTAS)

Flight delays can be prevented by providing a reference point from an accurate prediction model because predicting flight delays is a problem with a specific space. Only a few algorithms consider predicted classes' mutual correlation during flight delay classification or prediction modelling tasks. None of these existing methods works for all scenarios. Therefore, the need to investigate the performance of more models in solving the problem of flight delay is vast and rapidly increasing. This paper presents the development and evaluation of LSTM and BiLSTM models by comparing them for a flight delay prediction. The LSTM does the feature extraction …

Deep Learning Model For Integrated Estimation Of Wheelchair And Human Poses Using Camera Images, Shimpei Aihara, Takara Sakai, Ryusei Shibata, Toshiaki Matsubara, Ryosuke Mizukami, Yudai Yoshida, Akira Shionoya

International Sports Engineering Association – Engineering of Sport

No abstract provided.

Image Based Recognition Of The Monuments In Prizren, Bertan Karahoda, Annea Futko

UBT International Conference

Image classification application has recently been covering a high number of research fields. In the other hand as the performance of the mobile devices is being updated day by day, the implementation of image recognition algorithms in them, is not only being trendy but very helpful in everyday tasks. With the automatic monument recognition, visiting a city is easy and fun. This application recognizes the captured monument, gives useful information and describes that particular landmark.

In this paper there are used four historical monuments of the city of Prizren, Kosovo and the aim is to classify these four famous monuments. …

Real-Time Non-Contact Road Defect Detection Using Inexpensive Sensors, Zhao Xing Lim, Mohammad Jahanshahi, Tarutal Ghosh Mondal, Da Cheng, Shutao Wang, Mohammad K. Sweidan, Aanis Ahmad, Omar Hesham Abouhussein, Xi Chen

The Summer Undergraduate Research Fellowship (SURF) Symposium

Road defects such as potholes, humps, and road cracks have become one of the main concerns for road and traffic safety worldwide. Pavement defect detection is crucial to ensure road safety. However, current solutions to this problem are either too time-consuming or too expensive to be employed large-scale. We propose a novel approach which has the ability to autonomously detect potholes in real-time using cost-effective sensors. Inexpensive sensors are mounted on a vehicle and a deep learning algorithm is used to identify road defects. The detection system is paired with a GPS and positional sensors to map the location of …