scholarly journals Machine Learning and Deep Learning Models for Traffic Flow Prediction: A Survey

2020 ◽  
Author(s):  
Ayele Gobezie ◽  
Marta Sintayehu Fufa
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Traffic Flow ◽  
Series Data ◽  
Intelligent Transport System ◽  
Learning Approaches ◽  
Traffic Flow Prediction ◽  
Traffic Regulation ◽  
Flow Prediction ◽  
The Impact

Abstract Traffic congestion is one of the problems for cities around the world due to the rapid increasing of vehicles in urbanization. Traffic flow prediction is of a great importance for Intelligent Transport System (ITS) which helps to optimize the traffic regulation of a transportation in the city. Nowadays, several researches have been studied so far on traffic flow prediction, accurate prediction has not yet been exploited by most of existing studies due to the impact of inability to effectively deal with spatial temporal features of the times series data. Traffic information in transportation system will also be affected by different factors. In this research we intended to study various models for Traffic flow prediction on the basis machine learning and deep learning approaches. Factors affecting the performance of traffic flow prediction intensity are studied as well. Benchmark performance evaluation metrics are also reviewed. Generally, this manuscript covers relevant methods and approaches, review the state-of-art works with respect to different traffic flow prediction technique help researchers in exploring future directions so as to realize robust traffic flow prediction.

scholarly journals Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Yuhan Jia ◽  
Jianping Wu ◽  
Ming Xu
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Short Term Memory ◽  
Complex Model ◽  
Urban Traffic ◽  
Environmental Data ◽  
Traffic Modeling ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
The Impact

Accurate traffic flow prediction is increasingly essential for successful traffic modeling, operation, and management. Traditional data driven traffic flow prediction approaches have largely assumed restrictive (shallow) model architectures and do not leverage the large amount of environmental data available. Inspired by deep learning methods with more complex model architectures and effective data mining capabilities, this paper introduces the deep belief network (DBN) and long short-term memory (LSTM) to predict urban traffic flow considering the impact of rainfall. The rainfall-integrated DBN and LSTM can learn the features of traffic flow under various rainfall scenarios. Experimental results indicate that, with the consideration of additional rainfall factor, the deep learning predictors have better accuracy than existing predictors and also yield improvements over the original deep learning models without rainfall input. Furthermore, the LSTM can outperform the DBN to capture the time series characteristics of traffic flow data.

scholarly journals A Review of Computer-Aided Expert Systems for Breast Cancer Diagnosis

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2764
Author(s):  
Xin Yu Liew ◽  
Nazia Hameed ◽  
Jeremie Clos
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Deep Learning ◽  
Main Process ◽  
Learning Approaches ◽  
Learning Methods ◽  
Advantages And Disadvantages ◽  
Computer Aided ◽  
Conventional Methods ◽  
The Impact

A computer-aided diagnosis (CAD) expert system is a powerful tool to efficiently assist a pathologist in achieving an early diagnosis of breast cancer. This process identifies the presence of cancer in breast tissue samples and the distinct type of cancer stages. In a standard CAD system, the main process involves image pre-processing, segmentation, feature extraction, feature selection, classification, and performance evaluation. In this review paper, we reviewed the existing state-of-the-art machine learning approaches applied at each stage involving conventional methods and deep learning methods, the comparisons within methods, and we provide technical details with advantages and disadvantages. The aims are to investigate the impact of CAD systems using histopathology images, investigate deep learning methods that outperform conventional methods, and provide a summary for future researchers to analyse and improve the existing techniques used. Lastly, we will discuss the research gaps of existing machine learning approaches for implementation and propose future direction guidelines for upcoming researchers.

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