A SVM Multi-Class Image Classification Method Based on DE and KNN in Smart City Management

Shu, Wanneng; Cai, Ken

doi:10.1109/access.2019.2941321

Cited by 19 publications

(11 citation statements)

References 28 publications

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“…The SVM [47], [48] is based on decision planes, which divide the boundaries of classes. These planes can separate a group of objects into different classes.…”

Section: ) Support Vector Machinementioning

confidence: 99%

“…In the classification phase, the unknown data are classified by the optimal plane (Equation 4) [47] to identify separable patterns, as shown in Figure 14.…”

Section: ) Support Vector Machinementioning

confidence: 99%

“…• SVM using autoencoder features: For the convolutional model, both the linear and RBF kernels among features of the encoder model gained high accuracy than using a polynomial kernel with quadratic. Thus, two degrees of the polynomial kernel may not be suitable, and this needs be adjusted the kernel to achieve acceptable accuracy in classification[47]. For the SVM with the dense model, both polynomial kernel with quadraticVOLUME 4, 2016 …”

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confidence: 99%

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Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Jaruenpunyasak

Duangsoithong

2021

IEEE Access

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Deep learning algorithms are employed in many applications, especially in medical fields such as gait analysis and human pose detection for rehabilitation. However, creating the desired model with deep learning algorithms requires high memory and computing costs, which is problematic because deep learning technologies must be run on low-power devices such as edge computing equipment. To deal with these problems, feature reduction methods reduce the memory and energy costs. This paper presents an empirical analysis of deep learning with feature reduction. The method classifies foot images for knee rehabilitation using convolutional and dense autoencoders. The obtained results are compared with those of conventional methods (histograms of oriented gradients and local binary pattern algorithms). The features were classified and compared using support vector machine, k-nearest neighbor, and multilayer perceptron methods. The experimental results demonstrate that the conventional method uses fewer features than the deep learning method with higher accuracy because its algorithm projects pixels onto the histogram. In addition, using fewer features in deep learning layers maintains high accuracy, which is beneficial for edge computing implementations.

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“…The SVM [47], [48] is based on decision planes, which divide the boundaries of classes. These planes can separate a group of objects into different classes.…”

Section: ) Support Vector Machinementioning

confidence: 99%

“…In the classification phase, the unknown data are classified by the optimal plane (Equation 4) [47] to identify separable patterns, as shown in Figure 14.…”

Section: ) Support Vector Machinementioning

confidence: 99%

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confidence: 99%

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Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Jaruenpunyasak

Duangsoithong

2021

IEEE Access

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“…Unlike the binary SVM classifier presented in Equation 23, T1-SVM and T2-SVM are required to work as non-binary classifiers and hence they need to be modified for the purpose of being able to make multiple classifications. We adopt a one-versus-all (OVA) [37,38] scheme to fulfill the multi-classification task. Without loss of generality, assuming that a four-class problem for the T1-SVM classification model is to be solved, i.e., P = 2, we need a series of four SVM classifiers, denoted as c 1 , c 2 , c 3 , and c 4 , respectively.…”

Section: Machine Learning Aided Spectrum Decision Makingmentioning

confidence: 99%

Geo-Location Information Aided Spectrum Sensing in Cellular Cognitive Radio Networks

Chen

Shen

Wang

et al. 2019

Sensors

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Apart from the received signal energy, auxiliary information plays an important role in remarkably ameliorating conventional spectrum sensing. In this paper, a novel spectrum sensing scheme aided by geolocation information is proposed. In the cellular cognitive radio network (CCRN), secondary user equipments (SUEs) first acquire their wireless fingerprints via either received signal strength (RSS) or time of arrival (TOA) estimation over the reference signals received from their surrounding base-stations (BSs) and then pinpoint their geographical locations through a wireless fingerprint (WFP) matching process in the wireless fingerprint database (WFPD). Driven by the WFPD, the SUEs can easily ascertain for themselves the white licensed frequency band (LFB) for opportunistic access. In view of the fact that the locations of the primary user (PU) transmitters in the CCRN are either readily known or practically unavailable, the SUEs can either search the WFPD directly or rely on the support vector machine (SVM) algorithm to determine the availability of the LFB. Additionally, in order to alleviate the deficiency of single SUE-based sensing, a joint prediction mechanism is proposed on the basis of cooperation of multiple SUEs that are geographically nearby. Simulations verify that the proposed scheme achieves higher detection probability and demands less energy consumption than the conventional spectrum sensing algorithms.

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“…Hence, expert features of ALE behaviors are difficult to be extracted, such as amplitude features, frequency features, time-frequency features, bispectrum features, and other higher-order spectral features. So some traditional machine learning methods based on expert features are useless for ALE behavior recognition, such as Support Vector Machine (SVM) [31], [32], decision tree [33], K Nearest Neighbor (KNN) [34], [35] and K-means [36]. If the unidimensional ALE signals are directly processed by those machine learning methods, the ALE behaviors of a radio station would not be recognized correctly.…”

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confidence: 99%

Recognizing Automatic Link Establishment Behaviors of a Short-Wave Radio Station by an Improved Unidimensional DenseNet

Chen

Lei

et al. 2020

IEEE Access

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It is difficult to recognize Automatic Link Establishment (ALE) behaviors of a shortwave radio station, if we do not acquire the radio station's communication protocol standard. A method is proposed to recognize different ALE behaviors by using an improved unidimensional DenseNet. In this work, we directly recognize ALE signals in physical layer without the radio station's communication protocol standard. Hence, we can avoid difficulties in demodulation, decryption and so on. Actually, the original DenseNet is used extensively in the field of computer vision, so the original DenseNet is firstly adapted for the unidimensional input. And then, two parallel dense blocks are used in our improved unidimensional DenseNet, which could improve the capability of network to extract ALE signals' deep features. The experimental results show that the proposed method is able to recognize different ALE behaviors of a shortwave radio station. And improved DenseNet has better recognition performance than simple DenseNet. The simple DenseNet only contains one dense block. Finally, the results of comparison experiments also show that some classic networks have worse performance in ALE behaviors recognition, such as LeNet-5, ResNet-34, and DenseNet-121. INDEX TERMS Recognition, unidimensional DenseNet, automatic link establishment, shortwave radio station, electronic countermeasure. I. INTRODUCTION The ALE behaviors of a shortwave radio station include Call, Handshake, Notification, Time Offset, Group Time Broadcast, Broadcast and Scanning Call. The Link Establishment (LE) behaviors recognition of a shortwave radio station is important and difficult in the field of electronic countermeasures. Mastering the ALE behaviors of enemy's radio station can effectively infer the communication intention and working status of the radio station in the wireless communication network. It provides a reference for further inferring the topological structure and tactical position of the shortwave radio station. As a reconnoiter in the field of electronic countermeasures, our goal is to recognize a radio station's ALE behaviors without the radio station's communication protocol (MIL-STD-188-141B) [1]. In this work, the purpose of ALE behaviors recognition is achieved by directly analyzing the signals in physical layer, avoiding The associate editor coordinating the review of this manuscript and approving it for publication was Miguel López-Benítez .

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A SVM Multi-Class Image Classification Method Based on DE and KNN in Smart City Management

Cited by 19 publications

References 28 publications

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Geo-Location Information Aided Spectrum Sensing in Cellular Cognitive Radio Networks

Recognizing Automatic Link Establishment Behaviors of a Short-Wave Radio Station by an Improved Unidimensional DenseNet

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