Data mining and machine learning methods for sustainable smart cities traffic classification: A survey

Shafiq, Muhammad; Tian, Zhihong; Bashir, Ali Kashif; Jolfaei, Alireza; Yu, Xiangzhan

doi:10.1016/j.scs.2020.102177

Cited by 182 publications

(96 citation statements)

References 100 publications

(135 reference statements)

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“…There are four classifier use in this research, which are Decision Tree, k-Nearest Neighbours and Naïve Bayes. These classifiers are usually used in classification (Shafiq et al, 2020).…”

Section: Classifiers Performancementioning

confidence: 99%

An Optimized DiscretizationApproach using k-Means Bat Algorithm

et.al¹

2021

TURCOMAT

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This study has proposed arelatively new discretization approach using k-means and Bat algorithm in preparation phase of classification problem. In essence, bat algorithm is applied to find the best search space solution. Eventually, the best search space solution is utilized to produce cluster centroid. The cluster centroid is very useful to determine appropriate breakpoint for discretization. The proposed discretization approach is applied in the experiments with continuous datasets. Decision Tree, k-Nearest Neighbours and Naïve Bayes classifiers are used in the experiments. The proposed discretization approach is evaluated against other existing approaches: K-Means algorithm, hybrid K-Means with Particle Swarm Optimization (PSO) and hybrid K-Means with Whale Optimization Algorithm (WOA).The classification performance is evaluated in terms of accuracy, recall, f-measure and receiver operating characteristic curve (ROC). To test the performance of the proposed algorithm, nine benchmark continuous datasets are used. The proposed algorithm show the better results compare to other approaches. The proposed algorithm performs better in discretization to solve classification problems.

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“…There are four classifier use in this research, which are Decision Tree, k-Nearest Neighbours and Naïve Bayes. These classifiers are usually used in classification (Shafiq et al, 2020).…”

Section: Classifiers Performancementioning

confidence: 99%

An Optimized DiscretizationApproach using k-Means Bat Algorithm

et.al¹

2021

TURCOMAT

View full text Add to dashboard Cite

show abstract

“…For example, streaming as a service is studied by many researchers [3][4][5], which can provide the sharing and simple processing capabilities for stream data. e idea of choosing suitable service or methods can be referred to in [6][7][8]. It is proposed to provide common functions for various data sources, which enable users to conveniently reuse these functions and form more complex functions through service composition.…”

Section: Introductionmentioning

confidence: 99%

An Anomaly Detection Algorithm Selection Service for IoT Stream Data Based on Tsfresh Tool and Genetic Algorithm

Yang

Abbasi

Mustafa

et al. 2021

Security and Communication Networks

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Anomaly detection algorithms (ADA) have been widely used as services in many maintenance monitoring platforms. However, there are numerous algorithms that could be applied to these fast changing stream data. Furthermore, in IoT stream data due to its dynamic nature, the phenomena of conception drift happened. Therefore, it is a challenging task to choose a suitable anomaly detection service (ADS) in real time. For accurate online anomalous data detection, this paper developed a service selection method to select and configure ADS at run-time. Initially, a time-series feature extractor (Tsfresh) and a genetic algorithm-based feature selection method are applied to swiftly extract dominant features which act as representation for the stream data patterns. Additionally, stream data and various efficient algorithms are collected as our historical data. A fast classification model based on XGBoost is trained to record stream data features to detect appropriate ADS dynamically at run-time. These methods help to choose suitable service and their respective configuration based on the patterns of stream data. The features used to describe and reflect time-series data’s intrinsic characteristics are the main success factor in our framework. Consequently, experiments are conducted to evaluate the effectiveness of features closed by genetic algorithm. Experimentations on both artificial and real datasets demonstrate that the accuracy of our proposed method outperforms various advanced approaches and can choose appropriate service in different scenarios efficiently.

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“…e key problem preventing attention mechanism and CapsNets from being applied in edge computing domain is that they both suffer from heavily computation and memory burdens. It is essential to consider the quantization techniques for deploying models on edge devices [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. is paper proposes the compressed wavelet tensor attention capsule network (CWTACapsNet) that integrates multilevel wavelet decomposition, tensor attention mechanism, and quantization techniques into the capsule network.…”

Section: Introductionmentioning

confidence: 99%

Compressed Wavelet Tensor Attention Capsule Network

Liu

Shan

Zhang

et al. 2021

Security and Communication Networks

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Texture classification plays an important role for various computer vision tasks. Depending upon the powerful feature extraction capability, convolutional neural network (CNN)-based texture classification methods have attracted extensive attention. However, there still exist many challenges, such as the extraction of multilevel texture features and the exploration of multidirectional relationships. To address the problem, this paper proposes the compressed wavelet tensor attention capsule network (CWTACapsNet), which integrates multiscale wavelet decomposition, tensor attention blocks, and quantization techniques into the framework of capsule neural network. Specifically, the multilevel wavelet decomposition is in charge of extracting multiscale spectral features in frequency domain; in addition, the tensor attention blocks explore the multidimensional dependencies of convolutional feature channels, and the quantization techniques make the computational storage complexities be suitable for edge computing requirements. The proposed CWTACapsNet provides an efficient way to explore spatial domain features, frequency domain features, and their dependencies which are useful for most texture classification tasks. Furthermore, CWTACapsNet benefits from quantization techniques and is suitable for edge computing applications. Experimental results on several texture datasets show that the proposed CWTACapsNet outperforms the state-of-the-art texture classification methods not only in accuracy but also in robustness.

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Data mining and machine learning methods for sustainable smart cities traffic classification: A survey

Cited by 182 publications

References 100 publications

An Optimized DiscretizationApproach using k-Means Bat Algorithm

An Optimized DiscretizationApproach using k-Means Bat Algorithm

An Anomaly Detection Algorithm Selection Service for IoT Stream Data Based on Tsfresh Tool and Genetic Algorithm

Compressed Wavelet Tensor Attention Capsule Network

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