scholarly journals Categorizing Diseases from Leaf Images Using a Hybrid Learning Model

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2073
Author(s):  
Devi N. ◽  
Leela Rani P. ◽  
Guru Gokul AR. ◽  
Raju Kannadasan ◽  
Mohammed H. Alsharif ◽  
...  
Keyword(s):  
Hybrid Model ◽  
Clustering Algorithm ◽  
Hybrid Learning ◽  
Critical Issue ◽  
Learning Model ◽  
Rapid Identification ◽  
Plant Diseases ◽  
Support Vector ◽  
Learning Machine ◽  
Occurrence Matrix

Plant diseases pose a severe threat to crop yield. This necessitates the rapid identification of diseases affecting various crops using modern technologies. Many researchers have developed solutions to the problem of identifying plant diseases, but it is still considered a critical issue due to the lack of infrastructure in many parts of the world. This paper focuses on detecting and classifying diseases present in the leaf images by adopting a hybrid learning model. The proposed hybrid model uses k-means clustering for detecting the disease area from the leaf and a Convolutional Neural Network (CNN) for classifying the type of disease based on comparison between sampled and testing images. The images of leaves under consideration may be symmetrical or asymmetrical in shape. In the proposed methodology, the images of various leaves from diseased plants were first pre-processed to filter out the noise present to get an enhanced image. This improved image enabled detection of minute disease-affected regions. The infected areas were then segmented using k-means clustering algorithm that locates only the infected (diseased) areas by masking the leaves’ green (healthy) regions. The grey level co-occurrence matrix (GLCM) methodology was used to fetch the necessary features from the affected portions. Since the number of fetched features was insufficient, more synthesized features were included, which were then given as input to CNN for training. Finally, the proposed hybrid model was trained and tested using the leaf disease dataset available in the UCI machine learning repository to examine the characteristics between trained and tested images. The hybrid model proposed in this paper can detect and classify different types of diseases affecting different plants with a mean classification accuracy of 92.6%. To illustrate the efficiency of the proposed hybrid model, a comparison was made against the following classification approaches viz., support vector machine, extreme learning machine-based classification, and CNN. The proposed hybrid model was found to be more effective than the other three.

MHLM Majority Voting Based Hybrid Learning Model for Multi-Document Summarization

2019 ◽  
Vol 9 (1) ◽  
pp. 67-81
Author(s):  
Suneetha S. ◽  
Venugopal Reddy A.
Keyword(s):  
Numerical Data ◽  
Hybrid Learning ◽  
Learning Model ◽  
Text Summarization ◽  
Majority Voting ◽  
Sentence Length ◽  
Support Vector ◽  
Data Set ◽  
Document Summarization ◽  
Multiple Documents

Text summarization from multiple documents is an active research area in the current scenario as the data in the World Wide Web (WWW) is found in abundance. The text summarization process is time-consuming and hectic for the users to retrieve the relevant contents from this mass collection of the data. Numerous techniques have been proposed to provide the relevant information to the users in the form of the summary. Accordingly, this article presents the majority voting based hybrid learning model (MHLM) for multi-document summarization. First, the multiple documents are subjected to pre-processing, and the features, such as title-based, sentence length, numerical data and TF-IDF features are extracted for all the individual sentences of the document. Then, the feature set is sent to the proposed MHLM classifier, which includes the Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Neural Network (NN) classifiers for evaluating the significance of the sentences present in the document. These classifiers provide the significance scores based on four features extracted from the sentences in the document. Then, the majority voting model decides the significant texts based on the significance scores and develops the summary for the user and thereby, reduces the redundancy, increasing the quality of the summary similar to the original document. The experiment performed with the DUC 2002 data set is used to analyze the effectiveness of the proposed MHLM that attains the precision and recall at a rate of 0.94, f-measure at a rate of 0.93, and ROUGE-1 at a rate of 0.6324.

scholarly journals Plasmodium Falciparum Identification in Thick Blood Preparations Using GLCM and Support Vector Machine (SVM)

2017 ◽  
Vol 2 (1) ◽  
pp. 12-20 ◽  
Author(s):  
Farah Zakiyah Rahmanti ◽  
Novita Kurnia Ningrum ◽  
Septian Enggar Sukmana ◽  
Prajanto Wahyu Adi
Keyword(s):  
Support Vector Machine ◽  
Plasmodium Falciparum ◽  
Research Result ◽  
Texture Feature ◽  
Rapid Identification ◽  
Blood Film ◽  
Support Vector ◽  
Identification System ◽  
Risk Of Death ◽  
Occurrence Matrix

Malaria is one of the serious diseases that require rapid handling, otherwise it can lead to death. One of the causes of malaria parasites is plasmodium falciparum which can cause severe or fatal malaria. Handling a medical late can increase the risk of death. Therefore, it takes a rapid identification system with a high percentage of accuracy to reduce the risk of death. This research aims to build an identification system of plasmodium falciparum in thick blood film using Gray Level Co-occurrence Matrix (GLCM) and Support Vector Machine (SVM). The GLCM is used to get texture feature values such as contrast, correlations, energy, and homogeneity from images. Those values is processed and as an input of classification using SVM. The research result using SVM for accuracy value of  plasmodium falciparum identification can reach 93.33%.

scholarly journals Short-term electricity grid maximum demand forecasting with the ARIMAX-SVR Machine Learning Hybrid Model

2021 ◽  
Vol 28 (1) ◽  
pp. 22-30
Author(s):  
Hon Fung Chow
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Hybrid Model ◽  
Regression Models ◽  
Demand Forecasting ◽  
Learning Model ◽  
Support Vector ◽  
Linear Regression Models ◽  
Short Term ◽  
Machine Learning Model

This paper proposes and discusses the viability of a short-term grid maximum demand forecasting model combining autoregressive integrated moving average with regressors (ARIMAX) and support vector regression (SVR). Grid demand forecasting is essential to generation unit scheduling, maintenance planning and system security. Traditionally, grid demand is forecasted using multivariate linear regression models with parameters adjusted to past data. A disadvantage of the linear regression model is that the parameters require regular adjustment, otherwise the prediction accuracy will deteriorate over time. With recent advances in the field of machine learning and lower computational costs, the usage of machine learning in the power industry becomes increasingly practicable. The proposed model is a machine learning model that combines ARIMAX and SVR to exploit their respective effectiveness in predicting linear and non-linear data. In contrast to linear regression models, the machine learning model automatically updates itself when new data is included. The hybrid model is benchmarked against other forecasting models and demonstrated a marked improvement in accuracy, achieving RMSE of 67.7MW and MAPE of 1.32% in a seven-day forecast.

scholarly journals Intelligent Plant Disease Identification System Using Machine Learning

2020 ◽  
Vol 2 (1) ◽  
pp. 49
Author(s):  
Paramasivam Alagumariappan ◽  
Najumnissa Jamal Dewan ◽  
Gughan Narasimhan Muthukrishnan ◽  
Bhaskar K. Bojji Raju ◽  
Ramzan Ali Arshad Bilal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Real Time ◽  
Extreme Learning Machine ◽  
Plant Disease ◽  
Agricultural Sector ◽  
Plant Diseases ◽  
Support Vector ◽  
Identification System ◽  
Learning Machine

Agriculture is the backbone of every country in the world. In India, most of the rural population still depends on agriculture. The agricultural sector provides major employment in rural areas. Furthermore, it contributes a significant amount to India’s gross domestic product (GDP). Therefore, protecting and enhancing the agricultural sector helps in the development of India’s economy. In this work, a real-time decision support system integrated with a camera sensor module was designed and developed for identification of plant disease. Furthermore, the performance of three machine learning algorithms, such as Extreme Learning Machine (ELM) and Support Vector Machine (SVM) with linear and polynomial kernels was analyzed. Results demonstrate that the performance of the extreme learning machine is better when compared to the adopted support vector machine classifier. It is also observed that the sensitivity of the support vector machine with a polynomial kernel is better when compared to the other classifiers. This work appears to be of high social relevance, because the developed real-time hardware is capable of detecting different plant diseases.

scholarly journals An Enhanced Rock Mineral Recognition Method Integrating a Deep Learning Model and Clustering Algorithm

Minerals ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 516 ◽  
Author(s):  
Chengzhao Liu ◽  
Mingchao Li ◽  
Ye Zhang ◽  
Shuai Han ◽  
Yueqin Zhu
Keyword(s):  
Deep Learning ◽  
Clustering Algorithm ◽  
Learning Model ◽  
Support Vector ◽  
Color Model ◽  
Comprehensive Model ◽  
Rock Mineral ◽  
Macro Scale ◽  
Deep Learning Model ◽  
Mineral Identification

Rock mineral recognition is a costly and time-consuming task when using traditional methods, during which physical and chemical properties are tested at micro- and macro-scale in the laboratory. As a solution, a comprehensive recognition model of 12 kinds of rock minerals can be utilized, based upon the deep learning and transfer learning algorithms. In the process, the texture features of images are extracted and a color model for rock mineral identification can also be established by the K-means algorithm. Finally, a comprehensive identification model is made by combining the deep learning model and color model. The test results of the comprehensive model reveal that color and texture are important features in rock mineral identification, and that deep learning methods can effectively improve identification accuracy. To prove that the comprehensive model could extract effective features of mineral images, we also established a support vector machine (SVM) model and a random forest (RF) model based on Histogram of Oriented Gradient (HOG) features. The comparison indicates that the comprehensive model has the best performance of all.

scholarly journals Leaf Image-based Plant Disease Identification using Color and Texture Features

2021 ◽  
Author(s):  
Nisar Ahmed ◽  
Hafiz Muhammad Shahzad Asif ◽  
Gulshan Saleem ◽  
Muhammad Usman Younus ◽  
Sadia Anwar ◽  
...  
Keyword(s):  
Plant Disease ◽  
Texture Features ◽  
Plant Diseases ◽  
Handheld Devices ◽  
Support Vector ◽  
Prototype System ◽  
Automated Identification ◽  
Disease Identification ◽  
Feature Based ◽  
Occurrence Matrix

Abstract Identification of plant disease is usually done through visual inspection or during laboratory examination which causes delays resulting in yield loss by the time identification is complete. On the other hand, complex deep learning models perform the task with reasonable performance but due to their large size and high computational requirements, they are not suited to mobile and handheld devices. Our proposed approach contributes automated identification of plant diseases which follows a sequence of steps involving pre-processing, segmentation of diseased leaf area, calculation of features based on the Gray-Level Co-occurrence Matrix (GLCM), feature selection and classification. In this study, six color features and twenty-two texture features have been calculated. Support vector machines is used to perform one-vs-one classification of plant disease. The proposed model of disease identification provides an accuracy of 98.79% with a standard deviation of 0.57 on 10-fold cross-validation. The accuracy on a self-collected dataset is 82.47% for disease identification and 91.40% for healthy and diseased classification. The reported performance measures are better or comparable to the existing approaches and highest among the feature-based methods, presenting it as the most suitable method to automated leaf-based plant disease identification. This prototype system can be extended by adding more disease categories or targeting specific crop or disease categories.

scholarly journals Enhancing Local Linear Models Using Functional Connectivity for Brain State Decoding

2013 ◽  
Vol 7 (3) ◽  
pp. 46-57
Author(s):  
Orhan Fırat ◽  
Mete Özay ◽  
Itır Önal ◽  
Ilke Öztekin ◽  
Fatoş T. Yarman Vural
Keyword(s):  
Functional Connectivity ◽  
Statistical Learning ◽  
Nearest Neighbor ◽  
Learning Model ◽  
Support Vector ◽  
Brain State ◽  
Neural Activation ◽  
K Nearest Neighbor ◽  
Semantic Categories ◽  
Learning Machine

The authors propose a statistical learning model for classifying cognitive processes based on distributed patterns of neural activation in the brain, acquired via functional magnetic resonance imaging (fMRI). In the proposed learning machine, local meshes are formed around each voxel. The distance between voxels in the mesh is determined by using functional neighborhood concept. In order to define functional neighborhood, the similarities between the time series recorded for voxels are measured and functional connectivity matrices are constructed. Then, the local mesh for each voxel is formed by including the functionally closest neighboring voxels in the mesh. The relationship between the voxels within a mesh is estimated by using a linear regression model. These relationship vectors, called Functional Connectivity aware Mesh Arc Descriptors (FC-MAD) are then used to train a statistical learning machine. The proposed method was tested on a recognition memory experiment, including data pertaining to encoding and retrieval of words belonging to ten different semantic categories. Two popular classifiers, namely k-Nearest Neighbor and Support Vector Machine, are trained in order to predict the semantic category of the item being retrieved, based on activation patterns during encoding. The classification performance of the Functional Mesh Learning model, which range in 62-68% is superior to the classical multi-voxel pattern analysis (MVPA) methods, which range in 40-48%, for ten semantic categories.

scholarly journals Hybrid Model-Based Learning Learning in Welcome Era Industrial Revolution 4.0

2019 ◽  
Vol 1 (1) ◽  
pp. 46
Author(s):  
Siti Nurul Hidayah
Keyword(s):  
Big Data ◽  
Hybrid Model ◽  
Industrial Revolution ◽  
Hybrid Learning ◽  
Learning Model ◽  
Model Based

Penelitian ini bertujuan untuk memberikan solusi dan informasi kepada dunia pendidikan mengenai model pembelajaran yang efektif dan interaktif dalam menyongsong masa revolusi industri 4.0. Fokus penelitian ini adalah dengan adanya model pembelajaran berbasis hybrid learning. Model pembelajaran yang menggabungkan atau menyatukan antara pembelajaran secara  online dengan pembelajaran secara bertatap muka dengan peserta didik, karena dalam masa revolusi industri 4.0 ditandai dengan kemajuan teknologi informasi sebagai media utama dalam kehidupannya. Metode dalam penelitian ini yaitu dengan menggunakan pendekatan kualitatif deskriptif dengan teknik pengambilan data melalui studi pustaka. Hasil penelitian ini menunjukkan bahwa model pembelajaran berbasis Hybrid Learning  efektif digunakan dalam pengajaran di kelas pada dunia pendidikan di masa revolusi 4.0 ini, karena metode ini adalah metode yang menggabungkan 2 cara yaitu pengajaran dengan online dan tatap muka, yang mana dalam era revolusi industri 4.0 lebih mengedepankan teknologi atau era big data. Maka dari itu guru maupun dosen perlu berinovasi dalam menyongsong masa revolusi industri 4.0 dengan model pembelajaran yang efektif dan interaktif seperti Hybrid Learning.

scholarly journals A Novel Image Classification Approach for Maize Diseases Recognition

2020 ◽  
Vol 13 (3) ◽  
pp. 331-339
Author(s):  
Yuchen Wei ◽  
Lisheng Wei ◽  
Tao Ji ◽  
Huosheng Hu
Keyword(s):  
Image Classification ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Color Space ◽  
Support Vector ◽  
Classification Approach ◽  
Large Scale Data ◽  
Lab Color Space ◽  
Occurrence Matrix ◽  
Future Work

Background: The spot, streak and rust are the most common diseases in maize, all of which require effective methods to recognize, diagnose and handle. This paper presents a novel image classification approach to the high accuracy recognition of these maize diseases. Methods: Firstly, the k-means clustering algorithm is deployed in LAB color space to reduce the influence of image noise and irrelevant background, so that the area of maize diseases could be effectively extracted. Then the statistic pattern recognition method and gray level co-occurrence matrix (GLCM) method are jointly used to segment the maize disease leaf images for accurately obtaining their texture, shape and color features. Finally, Support Vector Machine (SVM) classification method is used to identify three diseases. Results: Numerical results clearly demonstrate the feasibility and effectiveness of the proposed method. Conclusion: Our future work will focus on the investigation of how to use the new classification methods in dimensional and large scale data to improve the recognizing performance and how to use other supervised feature selection methods to improve the accuracy further.

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