Pest diagnosis system based on deep learning using collective intelligence

Lee, Hoo-Young; Kim, Yejin; Park, Koo-Rack

doi:10.1177/0020720919833052

Cited by 9 publications

(7 citation statements)

References 17 publications

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“…The flow of fault detection for rolling bearings combined with wavelet packet dispersion entropy and AFSA-SVM is as follows. 11 1. The vibration signals of four working conditions with length N are collected by the sensor equipment.…”

Section: Fault Detection Based On Wpde and Afsa-svmmentioning

confidence: 99%

RETRACTED: Rolling bearing fault detection approach based on improved dispersion entropy and AFSA optimized SVM

Liu

Shen

Yang

2020

International Journal of Electrical Engineering & Education

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The support vector machine (SVM) does not have a fixed parameter selection method and the manual selection of parameters is difficult to determine the validity, which affects the accuracy of recognition. simultaneously, The existing coarse-grained approach cannot effectively analyze the high-frequency components of time series. In view of the shortcomings of the above method, we put forward a new technique of rolling bearings for fault detection, which combines wavelet packet dispersion entropy (WPDE) and artificial fish swarm algorithm (AFSA) optimize support vector machines (AFSA-SVM). First of all, wavelet packet is devoted to decompose the original vibration signal into components of different frequency bands. Secondly, the dispersion entropy (DE) are calculated for each of the obtained frequency band components to acquire more comprehensive and complete fault information. Afterward, Input feature samples into the SVM model for training, and AFSA is used to optimize the parameters of SVM to obtain the optimal value so as to establish the best classification model. Finally, the prepared test set is input into AFSA-SVM for fault classification. The achievement of bearing detection experiments show that this approach can accurately and quickly identify fault types.

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Section: Fault Detection Based On Wpde and Afsa-svmmentioning

confidence: 99%

RETRACTED: Rolling bearing fault detection approach based on improved dispersion entropy and AFSA optimized SVM

Liu

Shen

Yang

2020

International Journal of Electrical Engineering & Education

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“…T E D networks. Lee et al 48 used deep learning approach to build a pest diagnostic system with large datasets. Gao et al 49 concentrated on community outlier detection approaches to identify local and global anomalies.…”

Section: R E T R a Cmentioning

confidence: 99%

RETRACTED: BMADSN: Big data multi-community anomaly detection in social networks

Mohanasundaram

2019

International Journal of Electrical Engineering & Education

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In today's world, most of the people are using social networks for day-to-day activities. The most frequently used social sites are Facebook, Twitter, Google+, etc. These popular social networks are used by some of the users for abnormal or illegal activities. It is very important and necessary to identify and avoid such illegal activities without harming anyone in the society. In recent decades, social networks are becoming a popular research area for most researchers. Many authors are doing research on social network datasets and proposing various anomaly detection mechanisms to identify anomalous activities in both static and dynamic growing social networks. Various anomaly detection techniques are proposed by the authors to investigate malicious activities in social networks. In general, the process of identifying anomaly activities of the users in the given dataset is called anomaly detection. The anomaly detection in social networks is the process of investigating whether the users of the given social networks are involved in illegal activities or not. In this work, we proposed a most elegant approach to identify the anomalous or outlier users in the given social network. The proposed approach is considering the users participated in multiple communities of social networks. The designed algorithms are implemented and tested in a big data environment three node cluster using open source Hadoop ecosystem tools. Algorithm1 is used to investigate the nodes/users who participated in multiple communities of the given social network’s dataset. Algorithm2 takes the set of users participated in multiple communities and apply graph metrics such as degree and community score to predict the users involved in the anomalous activity.

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“…The major aim of automated process is to introduce a low-cost and highaccuracy solution for disease detection using digital signal processing methods. 4 Recently, computer vision and digital signal processing mechanisms have gained attraction for the detection of plant leaf diseases. These techniques include several stages such as pre-processing of image, segmentation, extraction of features and classification of disease class.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: A Hybrid Approach for Plant Leaf Disease Detection and Classification Using Digital Image Processing Methods

Rao

Kulkarni

2020

International Journal of Electrical Engineering & Education

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Detection of plant leaf disease has been considered an interesting research field which is helpful to improve the crop and fruit yield. Computer vision and machine learning based approaches have gained huge attraction in digital image processing field. Several visual computing based techniques have been presented in the past for early prediction of plant leaf diseases. However, detection accuracy is still considered as a challenging task. Hence, in order to overcome this issue, we introduce a novel hybrid approach carried out in three forms. During the first phase, image enhancement and image conversion scheme are incorporated, which helps to overcome the low-illumination and noise related issues. In the next phase, a combined feature extraction technique is developed by using GLCM, Complex Gabor filter, Curvelet and image moments. Finally, a Neuro-Fuzzy Logic classifier is trained with the extracted features. The proposed approach is implemented using MATLAB simulation tool where PlantVillage Database is considered for analysis. The average detection accuracy has been obtained as more than 90% for 2 test cases which shows that the proposed combination of feature extraction and image pre-processing process is able to obtain improved classification accuracy. This work is useful for the students of UG/PG programme to carry out Project-based learning.

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Pest diagnosis system based on deep learning using collective intelligence

Cited by 9 publications

References 17 publications

RETRACTED: Rolling bearing fault detection approach based on improved dispersion entropy and AFSA optimized SVM

RETRACTED: Rolling bearing fault detection approach based on improved dispersion entropy and AFSA optimized SVM

RETRACTED: BMADSN: Big data multi-community anomaly detection in social networks

RETRACTED: A Hybrid Approach for Plant Leaf Disease Detection and Classification Using Digital Image Processing Methods

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