Research on Cucumber Downy Mildew Detection System based on SVM Classification Algorithm

In the assistive research area, human–computer interface (HCI) technology is used to help people with disabilities by conveying their intentions and thoughts to the outside world. Many HCI systems based on eye movement have been proposed to assist people with disabilities. However, due to the complexity of the necessary algorithms and the difficulty of hardware implementation, there are few general-purpose designs that consider practicality and stability in real life. Therefore, to solve these limitations and problems, an HCI system based on electrooculography (EOG) is proposed in this study. The proposed classification algorithm provides eye-state detection, including the fixation, saccade, and blinking states. Moreover, this algorithm can distinguish among ten kinds of saccade movements (i.e., up, down, left, right, farther left, farther right, up-left, down-left, up-right, and down-right). In addition, we developed an HCI system based on an eye-movement classification algorithm. This system provides an eye-dialing interface that can be used to improve the lives of people with disabilities. The results illustrate the good performance of the proposed classification algorithm. Moreover, the EOG-based system, which can detect ten different eye-movement features, can be utilized in real-life applications.

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Obscure Image Classification and Restoration using Support Vector Machines

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d8913.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 8231-8236

Keyword(s):

Kernel Estimation ◽

Classification Algorithm ◽

Support Vector ◽

Svm Classification ◽

Vector Machines ◽

Blurred Image ◽

Simulation Results ◽

Blurred Images ◽

Identification And Characterization ◽

Restoration Model

A restoration and classification computation for blurred image which depends on obscure identification and characterization is proposed in this paper. Initially, new obscure location calculation is proposed to recognize the Gaussian, Motion and Defocus based blurred locales in the image. The degradation-restoration model referred with pre-processing followed by binarization and features extraction/classification algorithm applied on obscure images. At this point, support vector machine (SVM) classification algorithm is proposed to cluster the blurred images. Once the obscure class of the locales is affirmed, the structure of the obscure kernels of the blurred images are affirmed. At that point, the obscure kernel estimation techniques are embraced to appraise the obscure kernels. At last, the blurred locales are re-established utilizing nonblind image deblurring calculation and supplant the blurred images with the restored images. The simulation results demonstrate that the proposed calculation performs well

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On the diurnal cycle of cucumber downy mildew and on the effect of light upon sporulation

Japanese Journal of Phytopathology ◽

10.3186/jjphytopath.24.109 ◽

1959 ◽

Vol 24 (2) ◽

pp. 109-113 ◽

Cited By ~ 2

Author(s):

Toshihiro KAJIWARA ◽

Yoshito IWATA

Keyword(s):

Downy Mildew ◽

Diurnal Cycle ◽

Cucumber Downy Mildew ◽

Effect Of Light

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Pure FPGA Implementation of an HOG Based Real-Time Pedestrian Detection System

Sensors ◽

10.3390/s18041174 ◽

2018 ◽

Vol 18 (4) ◽

pp. 1174 ◽

Cited By ~ 14

Author(s):

Jian Luo ◽

Chang Lin

Keyword(s):

Real Time ◽

Detection System ◽

Pedestrian Detection ◽

Image Sensor ◽

Frame Rate ◽

External Memory ◽

Support Vector ◽

Svm Classification ◽

Prior Art ◽

Memory Modules

In this study, we propose a real-time pedestrian detection system using a FPGA with a digital image sensor. Comparing with some prior works, the proposed implementation realizes both the histogram of oriented gradients (HOG) and the trained support vector machine (SVM) classification on a FPGA. Moreover, the implementation does not use any external memory or processors to assist the implementation. Although the implementation implements both the HOG algorithm and the SVM classification in hardware without using any external memory modules and processors, the proposed implementation’s resource utilization of the FPGA is lower than most of the prior art. The main reasons resulting in the lower resource usage are: (1) simplification in the Getting Bin sub-module; (2) distributed writing and two shift registers in the Cell Histogram Generation sub-module; (3) reuse of each sum of the cell histogram in the Block Histogram Normalization sub-module; and (4) regarding a window of the SVM classification as 105 blocks of the SVM classification. Moreover, compared to Dalal and Triggs’s pure software HOG implementation, the proposed implementation‘s average detection rate is just about 4.05% less, but can achieve a much higher frame rate.

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A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing

Neurocomputing ◽

10.1016/j.neucom.2018.05.002 ◽

2018 ◽

Vol 313 ◽

pp. 47-64 ◽

Cited By ~ 101

Author(s):

Xiaoan Yan ◽

Minping Jia

Keyword(s):

Fault Diagnosis ◽

Rolling Bearing ◽

Classification Algorithm ◽

Svm Classification

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A Reduction SVM Classification Algorithm Based on Adaptive AP Clustering Granulation

Intelligent Computing Methodologies - Lecture Notes in Computer Science ◽

10.1007/978-3-319-09339-0_15 ◽

2014 ◽

pp. 144-153

Author(s):

Xiuxi Wei

Keyword(s):

Classification Algorithm ◽

Svm Classification ◽

Ap Clustering

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Credit Card Fraud Detection through Parenclitic Network Analysis

Complexity ◽

10.1155/2018/5764370 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 10

Author(s):

Massimiliano Zanin ◽

Miguel Romance ◽

Santiago Moral ◽

Regino Criado

Keyword(s):

Data Mining ◽

Complex Network ◽

Credit Card ◽

Reference Group ◽

Detection System ◽

Fraud Detection ◽

Data Representation ◽

Classification Algorithm ◽

Data Set ◽

Economic Implications

The detection of frauds in credit card transactions is a major topic in financial research, of profound economic implications. While this has hitherto been tackled through data analysis techniques, the resemblances between this and other problems, like the design of recommendation systems and of diagnostic/prognostic medical tools, suggest that a complex network approach may yield important benefits. In this paper we present a first hybrid data mining/complex network classification algorithm, able to detect illegal instances in a real card transaction data set. It is based on a recently proposed network reconstruction algorithm that allows creating representations of the deviation of one instance from a reference group. We show how the inclusion of features extracted from the network data representation improves the score obtained by a standard, neural network-based classification algorithm and additionally how this combined approach can outperform a commercial fraud detection system in specific operation niches. Beyond these specific results, this contribution represents a new example on how complex networks and data mining can be integrated as complementary tools, with the former providing a view to data beyond the capabilities of the latter.

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