Effective implementation of machine learning algorithms using 3D colour texture feature for traffic sign detection for smart cities

2021 ◽  
Author(s):  
Manisha Vashisht ◽  
Brijesh Kumar
Keyword(s):  
Machine Learning ◽  
Smart Cities ◽  
Learning Algorithms ◽  
Texture Feature ◽  
Machine Learning Algorithms ◽  
Traffic Sign ◽  
Effective Implementation ◽  
Sign Detection ◽  
Traffic Sign Detection

Comparing Machine Learning Models for the Predictions of Speed in Smart Transportation Systems

2022 ◽  
pp. 34-46
Author(s):  
Amtul Waheed ◽  
Jana Shafi ◽  
Saritha V.
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Smart Cities ◽  
Learning Algorithms ◽  
High Capacity ◽  
Transportation Systems ◽  
Machine Learning Algorithms ◽  
Smart Transportation ◽  
Data Propagation ◽  
Machine Learning Models

In today's world of advanced technologies in IoT and ITS in smart cities scenarios, there are many different projections such as improved data propagation in smart roads and cooperative transportation networks, autonomous and continuously connected vehicles, and low latency applications in high capacity environments and heterogeneous connectivity and speed. This chapter presents the performance of the speed of vehicles on roadways employing machine learning methods. Input variable for each learning algorithm is the density that is measured as vehicle per mile and volume that is measured as vehicle per hour. And the result shows that the output variable is the speed that is measured as miles per hour represent the performance of each algorithm. The performance of machine learning algorithms is calculated by comparing the result of predictions made by different machine learning algorithms with true speed using the histogram. A result recommends that speed is varying according to the histogram.

scholarly journals Traffic Sign Classification

2021 ◽  
Vol 9 (9) ◽  
pp. 107-115
Author(s):  
Deepak R Sawalka
Keyword(s):  
Traffic Safety ◽  
Machine Learning Algorithms ◽  
Traffic Sign ◽  
The Public ◽  
Security Issues ◽  
Traffic Security ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Day By Day ◽  
Traffic Sign Classification

Abstract: The traffic signs engraved on the streets nowadays improve traffic security by advising the driver regarding speed limits or any further potential perils like profound thrilling streets, inescapable fix street works or any common intersections. With the quick improvement of economy and innovation in the cutting edge society, vehicles have become an imperative method for transportation in the day by day travel of individuals. Albeit the fame of autos has acquainted impressive comfort with individuals, it has additionally caused a various traffic security issues that can't be overlooked, for example, gridlock and successive street mishaps. Traffic security issues are to a great extent brought about by abstract reasons identified with the driver, like obliviousness, inappropriate driving activity and resistance with traffic rules, and keen vehicles have become a compelling way to wipe out these human components. Self-driving innovation can help, or even autonomously complete the driving activity, which is vital to free the human body and extensively lessen the rate of mishaps. Traffic sign identification and acknowledgment are significant in the advancement of astute vehicles, which straightforwardly influences the execution of driving practices. Traffic sign identification and grouping is of vital significance for the fate of independent vehicle innovation. We benchmark the commented on dataset with AI baselines Convolutional Neural Organizations (CNN). Computational strategies for AI (ML) have shown their importance for the projection of possible outcomes for educated choices. AI calculations have been applied for quite a while in numerous applications. An information driven methodology with higher precision as here can be extremely valuable for a proactive reaction from the public authority and residents. At long last, we propose a bunch of exploration openings and arrangement justification for additional useful applications. Keywords: Convolutional Neural Networks, Traffic sign detection, Traffic safety, Computational Methods, machine Learning Algorithms

Statistical Study of Machine Learning Algorithms Using Parametric and Non-Parametric Tests

2020 ◽  
Vol 11 (3) ◽  
pp. 80-105 ◽  
Author(s):  
Vijay M. Khadse ◽  
Parikshit Narendra Mahalle ◽  
Gitanjali R. Shinde
Keyword(s):  
Machine Learning ◽  
Optimal Algorithm ◽  
Smart Cities ◽  
Learning Algorithms ◽  
Performance Measure ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Statistical Validation ◽  
The Difference ◽  
Validation Of Results

The emerging area of the internet of things (IoT) generates a large amount of data from IoT applications such as health care, smart cities, etc. This data needs to be analyzed in order to derive useful inferences. Machine learning (ML) plays a significant role in analyzing such data. It becomes difficult to select optimal algorithm from the available set of algorithms/classifiers to obtain best results. The performance of algorithms differs when applied to datasets from different application domains. In learning, it is difficult to understand if the difference in performance is real or due to random variation in test data, training data, or internal randomness of the learning algorithms. This study takes into account these issues during a comparison of ML algorithms for binary and multivariate classification. It helps in providing guidelines for statistical validation of results. The results obtained show that the performance measure of accuracy for one algorithm differs by critical difference (CD) than others over binary and multivariate datasets obtained from different application domains.

Cascade saccade machine learning network with hierarchical classes for traffic sign detection

2021 ◽  
Vol 67 ◽  
pp. 102700
Author(s):  
Zhanwen Liu ◽  
Mingyuan Qi ◽  
Chao Shen ◽  
Yong Fang ◽  
Xiangmo Zhao
Keyword(s):  
Machine Learning ◽  
Traffic Sign ◽  
Hierarchical Classes ◽  
Learning Network ◽  
Sign Detection ◽  
Traffic Sign Detection

scholarly journals A Novel Smart City-Based Framework on Perspectives for Application of Machine Learning in Combating COVID-19

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Absalom E. Ezugwu ◽  
Ibrahim Abaker Targio Hashem ◽  
Olaide N. Oyelade ◽  
Mubarak Almutari ◽  
Mohammed A. Al-Garadi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Smart Cities ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Learning Framework ◽  
National Healthcare ◽  
Machine Learning Approach ◽  
The Internet Of Things ◽  
Analyze Data

The spread of COVID-19 worldwide continues despite multidimensional efforts to curtail its spread and provide treatment. Efforts to contain the COVID-19 pandemic have triggered partial or full lockdowns across the globe. This paper presents a novel framework that intelligently combines machine learning models and the Internet of Things (IoT) technology specifically to combat COVID-19 in smart cities. The purpose of the study is to promote the interoperability of machine learning algorithms with IoT technology by interacting with a population and its environment to curtail the COVID-19 pandemic. Furthermore, the study also investigates and discusses some solution frameworks, which can generate, capture, store, and analyze data using machine learning algorithms. These algorithms can detect, prevent, and trace the spread of COVID-19 and provide a better understanding of the disease in smart cities. Similarly, the study outlined case studies on the application of machine learning to help fight against COVID-19 in hospitals worldwide. The framework proposed in the study is a comprehensive presentation on the major components needed to integrate the machine learning approach with other AI-based solutions. Finally, the machine learning framework presented in this study has the potential to help national healthcare systems in curtailing the COVID-19 pandemic in smart cities. In addition, the proposed framework is poised as a pointer for generating research interests that would yield outcomes capable of been integrated to form an improved framework.

scholarly journals Prediction of Air Pollution in Smart Cities Using Machine Learning Techniques

2021 ◽  
Vol 9 (12) ◽  
pp. 273-277
Author(s):  
Mrs. Gowri G
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Predictive Model ◽  
Regression Models ◽  
Smart Cities ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Temporal Data ◽  
Spatio Temporal

Abstract: Air-pollution is one of the main threats for developed societies. According to the World Health Organization (WHO), pollution is the main cause of deaths among children aged under five years. Smart cities are called to play a decisive role to increase such pollution in real-time. The increase in air pollution due to fossil fuel consumption as well as its ill effects on the climate has made air pollution forecasting an important research area in today’s times. Deployment of the Internet of things (IoT) based sensors has considerably changed the dynamics of predicting air quality. prediction of spatio-temporal data has been one of the major challenges in creating a good predictive model. There are many different approaches which have been used to create an accurate predictive model. Primitive predictive machine learning algorithms like simple linear regression have failed to produce accurate results primarily due to lack of computing power but also due to lack of optimization techniques. A recent development in deep learning as well as improvements in computing resources has increased the accuracy of predicting time series data. However, with large spatio-temporal data sets spanning over years. Employing regression models on the entire data can cause per date predictions to be corrupted. In this work, we look at dealing with pre-processing the times series. However, pre-processing involves a similarity measure, we explore the use of Dynamic Time Warping (DTW). K-means is then used to classify the spatio-temporal pollution data over a period of 16 years from 2000 to 2016. Here Mean Absolute error (MAE) and Root Mean Square Error (RMSE) have been used as evaluation criteria for the comparison of regression models. Keywords: Spatio-temporal data, Primitive predictive machine learning algorithms, regression models

Sign in / Sign up

Export Citation Format

Share Document