An Artificial Intelligence System for Apple Fruit Disease Classification Based on Support Vector Machine and Cockroach Swarm Optimization

2020 ◽  
pp. 137-147 ◽  
Author(s):  
Mohamed A. El-dosuky ◽  
Diego Oliva ◽  
Aboul Ella Hassanien
Keyword(s):  
Artificial Intelligence ◽  
Support Vector Machine ◽  
Apple Fruit ◽  
Disease Classification ◽  
Support Vector ◽  
Artificial Intelligence System ◽  
Swarm Optimization ◽  
Intelligence System ◽  
Fruit Disease

Automatic Fruit Disease Classification Using Images

2014 ◽  
pp. 82-100 ◽  
Author(s):  
Shiv Ram Dubey ◽  
Anand Singh Jalal
Keyword(s):  
Support Vector Machine ◽  
Apple Scab ◽  
Texture Features ◽  
Disease Classification ◽  
Economic Losses ◽  
Support Vector ◽  
Test Case ◽  
Agricultural Industry ◽  
Fruit Disease

Diseases in fruit cause devastating problems in economic losses and production in the agricultural industry worldwide. In this chapter, a method to detect and classify fruit diseases automatically is proposed and experimentally validated. The image processing-based proposed approach is composed of the following main steps: in the first step K-Means clustering technique is used for the defect segmentation, in the second step some color and texture features are extracted from the segmented defected part, and finally diseases are classified into one of the classes by using a multi-class Support Vector Machine. The authors have considered diseases of apple as a test case and evaluated the approach for three types of apple diseases, namely apple scab, apple blotch, and apple rot, along with normal apples. The experimental results express that the proposed solution can significantly support accurate detection and automatic classification of fruit diseases. The classification accuracy for the proposed approach is achieved up to 93% using textural information and multi-class support vector machine.

Artificial intelligence system based on the layout effect of both sides in volleyball matches

2020 ◽  
pp. 1-10
Author(s):  
Kai Zhao ◽  
Wei Jiang ◽  
Xinlong Jin ◽  
Xuming Xiao
Keyword(s):  
Artificial Intelligence ◽  
Moving Objects ◽  
Support Vector ◽  
Moving Target ◽  
Artificial Intelligence System ◽  
Fast Detection ◽  
Intelligence System ◽  
Detection And Tracking ◽  
Traditional Sports

The traditional sports match analysis mostly adopts the method of manual observation and recording, which is not only time-consuming and laborious but also has the defects of subjectivity and inaccuracy in the judgment results, resulting in the deviation of the match data analysis and statistical results. The purpose of this paper is to study an artificial intelligence system that can automatically analyze and evaluate the effect of both sides in volleyball matches. In this paper, the system is divided into two steps: detection and tracking of moving objects, recognition, and classification of players’ behaviors and movements. About moving target detection and tracking, this paper proposes a moving target fast detection framework based on a mixture of mainstream technologies and a MeanShift target tracking method based on Kalman filtering and adaptive target region size. For behavior and action recognition and classification, this paper proposes a classifier combining BP neural network and support vector machine. Experimental results show that the proposed algorithm and classifier are effective. By analyzing the performance of the proposed classifier, the classification accuracy is 98%.

scholarly journals An Explorative Analysis on the Machine-Vision-Based Disease Recognition of Three Available Fruits of Bangladesh

2021 ◽  
pp. 1-20
Author(s):  
Md. Tarek Habib ◽  
Md. Jueal Mia ◽  
Mohammad Shorif Uddin ◽  
Farruk Ahmed
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Machine Vision ◽  
Disease Classification ◽  
Support Vector ◽  
Classification Algorithms ◽  
Disease Detection ◽  
Automated Recognition ◽  
Segmentation Algorithms ◽  
Fruit Disease

Bangladesh, being a densely populated country, hinges on agriculture for the security of finance and food to a large extent. Hence, both the fruits’ quantity and quality turn out to be very important, which can be degraded due to the attacks of various diseases. Automated fruit disease recognition can help fruit farmers, especially remote farmers, for whom adequate cultivation support is required. Two daunting problems, namely disease detection, and disease classification are raised by automated fruit disease recognition. In this research, we conduct an intense investigation of the applicability of automated recognition of the diseases of three available Bangladeshi local fruits, viz. guava, jackfruit, and papaya. After exerting four notable segmentation algorithms, [Formula: see text]-means clustering segmentation algorithm is selected to segregate the disease-contaminated parts from a fruit image. Then some discriminatory features are extracted from these disease-contaminated parts. Nine noteworthy classification algorithms are applied for disease classification to thoroughly get the measure of their merits. It is observed that random forest outperforms the eight other classifiers by disclosing an accuracy of 96.8% and 89.59% for guava and jackfruit, respectively, whereas support vector machine attains an accuracy of 94.9% for papaya, which can be claimed good as well as attractive for forthcoming research.

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

An Improved Intelligent Approach to Enhance the Sentiment Classifier for Knowledge Discovery Using Machine Learning

2020 ◽  
Vol 10 (4) ◽  
pp. 582-593
Author(s):  
Midde Venkateswarlu Naik ◽  
D. Vasumathi ◽  
A.P. Siva Kumar
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Global Warming ◽  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Sentiment Classification ◽  
Support Vector ◽  
Swarm Optimization

Aims: The proposed research work is on an evolutionary enhanced method for sentiment or emotion classification on unstructured review text in the big data field. The sentiment analysis plays a vital role for current generation of people for extracting valid decision points about any aspect such as movie ratings, education institute or politics ratings, etc. The proposed hybrid approach combined the optimal feature selection using Particle Swarm Optimization (PSO) and sentiment classification through Support Vector Machine (SVM). The current approach performance is evaluated with statistical measures, such as precision, recall, sensitivity, specificity, and was compared with the existing approaches. The earlier authors have achieved an accuracy of sentiment classifier in the English text up to 94% as of now. In the proposed scheme, an average accuracy of sentiment classifier on distinguishing datasets outperformed as 99% by tuning various parameters of SVM, such as constant c value and kernel gamma value in association with PSO optimization technique. The proposed method utilized three datasets, such as airline sentiment data, weather, and global warming datasets, that are publically available. The current experiment produced results that are trained and tested based on 10- Fold Cross-Validations (FCV) and confusion matrix for predicting sentiment classifier accuracy. Background: The sentiment analysis plays a vital role for current generation people for extracting valid decisions about any aspect such as movie rating, education institute or even politics ratings, etc. Sentiment Analysis (SA) or opinion mining has become fascinated scientifically as a research domain for the present environment. The key area is sentiment classification on semi-structured or unstructured data in distinguish languages, which has become a major research aspect. User-Generated Content [UGC] from distinguishing sources has been hiked significantly with rapid growth in a web environment. The huge user-generated data over social media provides substantial value for discovering hidden knowledge or correlations, patterns, and trends or sentiment extraction about any specific entity. SA is a computational analysis to determine the actual opinion of an entity which is expressed in terms of text. SA is also called as computation of emotional polarity expressed over social media as natural text in miscellaneous languages. Usually, the automatic superlative sentiment classifier model depends on feature selection and classification algorithms. Methods: The proposed work used Support vector machine as classification technique and particle swarm optimization technique as feature selection purpose. In this methodology, we tune various permutations and combination parameters in order to obtain expected desired results with kernel and without kernel technique for sentiment classification on three datasets, including airline, global warming, weather sentiment datasets, that are freely hosted for research practices. Results: In the proposed scheme, The proposed method has outperformed with 99.2% of average accuracy to classify the sentiment on different datasets, among other machine learning techniques. The attained high accuracy in classifying sentiment or opinion about review text proves superior effectiveness over existing sentiment classifiers. The current experiment produced results that are trained and tested based on 10- Fold Cross-Validations (FCV) and confusion matrix for predicting sentiment classifier accuracy. Conclusion: The objective of the research issue sentiment classifier accuracy has been hiked with the help of Kernel-based Support Vector Machine (SVM) based on parameter optimization. The optimal feature selection to classify sentiment or opinion towards review documents has been determined with the help of a particle swarm optimization approach. The proposed method utilized three datasets to simulate the results, such as airline sentiment data, weather sentiment data, and global warming data that are freely available datasets.

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