Abstract Pipe failure prediction models are essential for informing proactive management decisions. This study aims to establish a reliable model returning the probability of pipe using gradient boosted tree model, and specific segmentation grouping pipes on 1 km grid that associates localised characteristics. The is applied an extensive UK network with approximately 40,000 pipeline 14-year his...

This paper proposes a classification approach for flight delays using Bidirectional Long Short-Term Memory (BiLSTM) and (LSTM) models. Flight are major issue in the airline industry, causing inconvenience to passengers financial losses airlines. The BiLSTM LSTM models, powerful deep learning techniques, have shown promising results task. In this study, we collected dataset from United States (U...

Road network extraction from remotely sensed imagery has become a powerful tool for updating geospatial databases, owing to the success of convolutional neural (CNN) based deep learning semantic segmentation techniques combined with high-resolution that modern remote sensing provides. However, most CNN approaches cannot obtain high precision maps rich details when processing imagery. In this st...

21. Bennett DH, Evans DW: Correlation of left ventricular mass determined by echocardiography with vectorcardiographic and electrocardiographic voltage measurements. Br Heart J 36: 981, 1974 22. McFarland TM, Mohsin A, Goldstein S, Pickard SD, Stein PD: Echocardiographic diagnosis of left ventricular hypertrophy. Circulation 57: 1140, 1978 23. Savage DD, Drayer JIM, Henry WL, Mathews EC Jr, War...

The prediction of Parkinson’s disease is most important and challenging problem for biomedical engineering researchers and doctors. The symptoms of disease are investigated in middle and late middle age. In this paper, minimum redundancy maximum relevance feature selection algorithms is used to select the most important feature among all the features to predict the Parkinson diseases. Here, it ...