Estimation of different ripening stages of Fuji apples using image processing and spectroscopy based on the majority voting method

Color segmentation is one of the most thoroughly studied problems in agricultural applications of remote image capture systems, since it is the key step in several different tasks, such as crop harvesting, site specific spraying, and targeted disease control under natural light. This paper studies and compares five methods to segment plum fruit images under ambient conditions at 12 different light intensities, and an ensemble method combining them. In these methods, several color features in different color spaces are first extracted for each pixel, and then the most effective features are selected using a hybrid approach of artificial neural networks and the cultural algorithm (ANN-CA). The features selected among the 38 defined channels were the b* channel of L*a*b*, and the color purity index, C*, from L*C*h. Next, fruit/background segmentation is performed using five classifiers: artificial neural network-imperialist competitive algorithm (ANN-ICA); hybrid artificial neural network-harmony search (ANN-HS); support vector machines (SVM); k nearest neighbors (kNN); and linear discriminant analysis (LDA). In the ensemble method, the final class for each pixel is determined using the majority voting method. The experiments showed that the correct classification rate for the majority voting method excluding LDA was 98.59%, outperforming the results of the constituent methods.

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INTEGRATION OF MULTIPLE CLASSIFIERS FOR COMPUTERIZED DETECTION OF LUNG NODULES IN CT

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237215500404 ◽

2015 ◽

Vol 27 (04) ◽

pp. 1550040 ◽

Cited By ~ 1

Author(s):

Jorge Juan Suárez-Cuenca ◽

Wei Guo ◽

Qiang Li

Keyword(s):

Discriminant Analysis ◽

Ct Scan ◽

Majority Voting ◽

Classifier Combination ◽

Probability Method ◽

Multiple Classifiers ◽

Combination Methods ◽

Majority Voting Method ◽

Simple Logistic ◽

Voting Method

The purpose of this study was to investigate the usefulness of various classifier combination methods for improving the performance of a computer-aided diagnosis (CAD) system for pulmonary nodule detection in computed tomography (CT). We employed 85 CT scans with 110 nodules in the publicly available Lung Image Database Consortium (LIDC) dataset. We first applied our CAD scheme trained previously to the LIDC cases for identifying initial nodule candidates, and extracting 18 features for each nodule candidate. We used eight individual classifiers for false positives (FPs) reduction, including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), Naïve Bayes, simple logistic, artificial neural network (ANN) and support vector machines (SVMs) with three different kernels. Five classifier combination methods were then employed to integrate the outputs of the eight individual classifiers for improving detection performance. The five combination methods included two supervised (a likelihood ratio (LR) method and a probability method based on the output scores of the eight individual classifiers) and three unsupervised ones (the sum, the product and the majority voting of the output scores from the eight individual classifiers). Leave-one-case-out approach was employed to train and test individual classifiers and supervised combination methods. At a sensitivity of 80%, the numbers of FPs per CT scan for the eight individual classifiers were 6.1 for LDA, 19.9 for QDA, 10.8 for Naïve Bayes, 8.4 for simple logistic, 8.6 for ANN, 23.7 for SVM-dot, 17.0 for SVM-poly, and 23.4 for SVM-anova; the numbers of FPs per CT scan for the five combination methods were 3.3 for the majority voting method, 5.0 for the sum, 4.6 for the product, 65.7 for the LR and 3.9 for the probability method. Compared to the best individual classifier, the majority voting method reduced 45% of FPs at 80% sensitivity. The performance of our CAD can be improved by combining multiple classifiers. The majority voting method achieved higher performance levels than other combination methods and all individual classifiers.

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Designing a Fruit Identification Algorithm in Orchard Conditions to Develop Robots Using Video Processing and Majority Voting Based on Hybrid Artificial Neural Network

Applied Sciences ◽

10.3390/app10010383 ◽

2020 ◽

Vol 10 (1) ◽

pp. 383 ◽

Cited By ~ 2

Author(s):

Sajad Sabzi ◽

Razieh Pourdarbani ◽

Davood Kalantari ◽

Thomas Panagopoulos

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Video Processing ◽

Color Space ◽

Majority Voting ◽

Identification Algorithm ◽

Natural Conditions ◽

Artificial Neural ◽

Majority Voting Method ◽

Voting Method

The first step in identifying fruits on trees is to develop garden robots for different purposes such as fruit harvesting and spatial specific spraying. Due to the natural conditions of the fruit orchards and the unevenness of the various objects throughout it, usage of the controlled conditions is very difficult. As a result, these operations should be performed in natural conditions, both in light and in the background. Due to the dependency of other garden robot operations on the fruit identification stage, this step must be performed precisely. Therefore, the purpose of this paper was to design an identification algorithm in orchard conditions using a combination of video processing and majority voting based on different hybrid artificial neural networks. The different steps of designing this algorithm were: (1) Recording video of different plum orchards at different light intensities; (2) converting the videos produced into its frames; (3) extracting different color properties from pixels; (4) selecting effective properties from color extraction properties using hybrid artificial neural network-harmony search (ANN-HS); and (5) classification using majority voting based on three classifiers of artificial neural network-bees algorithm (ANN-BA), artificial neural network-biogeography-based optimization (ANN-BBO), and artificial neural network-firefly algorithm (ANN-FA). Most effective features selected by the hybrid ANN-HS consisted of the third channel in hue saturation lightness (HSL) color space, the second channel in lightness chroma hue (LCH) color space, the first channel in L*a*b* color space, and the first channel in hue saturation intensity (HSI). The results showed that the accuracy of the majority voting method in the best execution and in 500 executions was 98.01% and 97.20%, respectively. Based on different performance evaluation criteria of the classifiers, it was found that the majority voting method had a higher performance.

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Enhanced Accuracy of Heart Disease Prediction using Machine Learning and Recurrent Neural Networks Ensemble Majority Voting Method

International Journal of Advanced Computer Science and Applications ◽

10.14569/ijacsa.2020.0110369 ◽

2020 ◽

Vol 11 (3) ◽

Author(s):

Irfan Javid ◽

Ahmed Khalaf ◽

Rozaida Ghazali

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Heart Disease ◽

Recurrent Neural Networks ◽

Majority Voting ◽

Disease Prediction ◽

Majority Voting Method ◽

Neural Networks Ensemble ◽

Voting Method

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Kernel selection in multi-class support vector machines and its consequence to the number of ties in majority voting method

Artificial Intelligence Review ◽

10.1007/s10462-011-9281-3 ◽

2011 ◽

Vol 40 (3) ◽

pp. 213-230 ◽

Cited By ~ 13

Author(s):

Henry Joutsijoki ◽

Martti Juhola

Keyword(s):

Support Vector Machines ◽

Majority Voting ◽

Support Vector ◽

Kernel Selection ◽

Vector Machines ◽

Majority Voting Method ◽

Voting Method

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Research on QoS Classification of Network Encrypted Traffic Behavior Based on Machine Learning

Electronics ◽

10.3390/electronics10121376 ◽

2021 ◽

Vol 10 (12) ◽

pp. 1376

Author(s):

Yung-Fa Huang ◽

Chuan-Bi Lin ◽

Chien-Min Chung ◽

Ching-Mu Chen

Keyword(s):

Machine Learning ◽

Majority Voting ◽

Deep Packet Inspection ◽

Traffic Classification ◽

K 13 ◽

Packet Inspection ◽

Majority Voting Method ◽

Encrypted Traffic ◽

Traffic Behavior

In recent years, privacy awareness is concerned due to many Internet services have chosen to use encrypted agreements. In order to improve the quality of service (QoS), the network encrypted traffic behaviors are classified based on machine learning discussed in this paper. However, the traditional traffic classification methods, such as IP/ASN (Autonomous System Number) analysis, Port-based and deep packet inspection, etc., can classify traffic behavior, but cannot effectively handle encrypted traffic. Thus, this paper proposed a hybrid traffic classification (HTC) method based on machine learning and combined with IP/ASN analysis with deep packet inspection. Moreover, the majority voting method was also used to quickly classify different QoS traffic accurately. Experimental results show that the proposed HTC method can effectively classify different encrypted traffic. The classification accuracy can be further improved by 10% with majority voting as K = 13. Especially when the networking data are using the same protocol, the proposed HTC can effectively classify the traffic data with different behaviors with the differentiated services code point (DSCP) mark.

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Protein Sequence Classification with Improved Extreme Learning Machine Algorithms

BioMed Research International ◽

10.1155/2014/103054 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 51

Author(s):

Jiuwen Cao ◽

Lianglin Xiong

Keyword(s):

Extreme Learning Machine ◽

Protein Sequence ◽

Protein Sequences ◽

Activation Function ◽

Majority Voting ◽

Training Algorithms ◽

Sequence Classification ◽

Protein Sequence Classification ◽

Learning Machine ◽

Majority Voting Method

Precisely classifying a protein sequence from a large biological protein sequences database plays an important role for developing competitive pharmacological products. Comparing the unseen sequence with all the identified protein sequences and returning the category index with the highest similarity scored protein, conventional methods are usually time-consuming. Therefore, it is urgent and necessary to build an efficient protein sequence classification system. In this paper, we study the performance of protein sequence classification using SLFNs. The recent efficient extreme learning machine (ELM) and its invariants are utilized as the training algorithms. The optimal pruned ELM is first employed for protein sequence classification in this paper. To further enhance the performance, the ensemble based SLFNs structure is constructed where multiple SLFNs with the same number of hidden nodes and the same activation function are used as ensembles. For each ensemble, the same training algorithm is adopted. The final category index is derived using the majority voting method. Two approaches, namely, the basic ELM and the OP-ELM, are adopted for the ensemble based SLFNs. The performance is analyzed and compared with several existing methods using datasets obtained from the Protein Information Resource center. The experimental results show the priority of the proposed algorithms.

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Determination of Apple, Lemon, and Banana Ripening Stages Using Electronic Nose and Image Processing

10.1007/978-981-16-4149-7_70 ◽

2021 ◽

pp. 755-769

Author(s):

S. Suthagar ◽

K. S. Tamilselvan ◽

M. Priyadharshini ◽

B. Nihila

Keyword(s):

Image Processing ◽

Electronic Nose ◽

Ripening Stages

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