scholarly journals Leaf Diseases Detection of Medicinal Plants Based on Support Vector Machine Classification Algorithm

2021 ◽  
pp. 111-119
Author(s):  
Payal Bose ◽  
Shawni Dutta ◽  
Vishal Goyal ◽  
Samir K. Bandyopadhyay
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Graphical User Interface ◽  
Local Binary Pattern ◽  
Detection System ◽  
Vital Role ◽  
Automated System ◽  
Support Vector ◽  
Selection Processes ◽  
Healthy Area

On earth, plants play the most important part. Every organ of a plant plays a vital role in the ecological field as well as the medicinal field. But on the whole earth there are several species of plants are available. The different species of plants have different diseases. Therefore, it is required to identify the plants as well as their diseases correctly. It is difficult and also time consuming to identify the plants and their diseases manually. In this research an automatic disease detection system of plant is proposed. High-quality leaf images are used for training and testing. For detecting the healthy area and diseased area in a leaf, region-based and color-based region thresholding techniques are used. For feature selection Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) method were applied. Finally, for classification two-class and multi-class Support Vector Machine (SVM) were used. It is found that both feature selection processes with SVM give 99% accuracy. An user oriented graphical user interface is created for understanding the automated system.

scholarly journals Leaf Diseases Detection of Medicinal Plants based on Image Processing and Machine Learning Processes

2021 ◽  
Author(s):  
Payal Bose ◽  
SHAWNI DUTTA ◽  
Vishal Goyal ◽  
Samir K. Bandyopadhyay
Keyword(s):  
Feature Selection ◽  
Graphical User Interface ◽  
Local Binary Pattern ◽  
Detection System ◽  
Vital Role ◽  
Automated System ◽  
Support Vector ◽  
High Quality ◽  
System A ◽  
Selection Processes

: On earth, plants play the most important part. Every organ of a plant plays a vital role in the ecological field as well as the medicinal field. But on the whole earth there are several species of plants are available. Different plants have different diseases. Therefore it is needed to identify the plants and their diseases to prevent loss. Now to identify the plants and their diseases manually is very time consuming. In this research an automatic plant and their disease detection system is proposed. For experimental purposes, high-quality leaf images are accepted for training and testing. For detecting the healthy and diseased area in a leaf, region-based and color-based region thresholding techniques were used. For feature selection Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) method were applied. Finally for classification two-class and multi-class Support Vector Machine (SVM) was used. It is observed that both feature selection processes with SVM give 99% accuracy. Finally to understand the automated system a graphical user interface was created for all users.

Intrusion Detection System Based on Hybrid Feature Selection and Support Vector Machine (HFS-SVM)

2015 ◽  
Vol 781 ◽  
pp. 125-128 ◽  
Author(s):  
Yonchanok Khaokaew ◽  
Tanapat Anusas-Amornkul ◽  
Koonlachat Meesublak
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Motif Discovery ◽  
Detection System ◽  
Random Projection ◽  
Support Vector ◽  
Training Time ◽  
Detection Techniques ◽  
Projection Techniques

In recent years, anomaly based intrusion detection techniques are continuously developed and a support vector machine (SVM) is one of the technique. However, it requires training time and storage if there are lots of numbers of features. In this paper, a hybrid feature selection, using Correlation based on Feature Selection and Motif Discovery using Random Projection techniques, is proposed to reduce the number of features from 41 to 3 features with KDD'99 dataset. It is compared with a regular SVM technique with 41 features. The results show that the accuracy rate is also high at 98% and the training time is less than the regular SVM almost by half.

scholarly journals User Satisfaction Detection System for Smart Healthcare using Multimedia

2019 ◽  
Vol 8 (10) ◽  
pp. 3763-3766
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Healthcare System ◽  
User Satisfaction ◽  
Local Binary Pattern ◽  
Detection System ◽  
Support Vector ◽  
Health Issues ◽  
Smart Healthcare ◽  
Focus Point

Emotion plays a critical job ineffectively conveying one’s convictions and intentions. As an outcome, identification of emotion has turned into focus point of few studies recently. Patient observing models are getting to be significant in patient concern and can endow with helpful feedback related to health issues for caregivers and clinicians. In this work, patient fulfilment recognition framework is proposed that uses image frames extracted from the recorded visual-audio modality dataset. The images are treated with techniques such as Local Binary Pattern (LBP) which is a ocular descriptor. The proposed framework incorporates feature extraction from the images and then the Support Vector Machine (SVM) is applied for classification. The three distinct types of emotions are whether the patient is happy, sad or neutral and the same are detected based on the results. The result of such an analysis can be made use of by a group of analysts which include doctors, healthcare experts and system experts to improve smart healthcare system in steps. The reliability of information provided by such a system makes such upgradations more meaningful.

scholarly journals Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Mohammad Aljanabi ◽  
Mohd Arfian Ismail ◽  
Vitaly Mezhuyev
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Parameter Tuning ◽  
Feature Subset Selection ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Feature Subset

Many optimisation-based intrusion detection algorithms have been developed and are widely used for intrusion identification. This condition is attributed to the increasing number of audit data features and the decreasing performance of human-based smart intrusion detection systems regarding classification accuracy, false alarm rate, and classification time. Feature selection and classifier parameter tuning are important factors that affect the performance of any intrusion detection system. In this paper, an improved intrusion detection algorithm for multiclass classification was presented and discussed in detail. The proposed method combined the improved teaching-learning-based optimisation (ITLBO) algorithm, improved parallel JAYA (IPJAYA) algorithm, and support vector machine. ITLBO with supervised machine learning (ML) technique was used for feature subset selection (FSS). The selection of the least number of features without causing an effect on the result accuracy in FSS is a multiobjective optimisation problem. This work proposes ITLBO as an FSS mechanism, and its algorithm-specific, parameterless concept (no parameter tuning is required during optimisation) was explored. IPJAYA in this study was used to update the C and gamma parameters of the support vector machine (SVM). Several experiments were performed on the prominent intrusion ML dataset, where significant enhancements were observed with the suggested ITLBO-IPJAYA-SVM algorithm compared with the classical TLBO and JAYA algorithms.

scholarly journals Intrusion detection mechanism with machine learning process A case study with FMIFSSVM, FLCFSSVM, misuses SVM, anomaly SVM and Bayesian methods

2018 ◽  
Vol 7 (2.7) ◽  
pp. 277
Author(s):  
K V S S R Murthy ◽  
K V V Satyanarayana
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Bayesian Methods ◽  
Detection System ◽  
Knowledge Discovery In Databases ◽  
Support Vector ◽  
Data Set ◽  
Vast Number ◽  
Feature Based

Today, there is a far reaching of Internet benefits everywhere throughout the world, numerous sorts and vast number of security dangers are expanding. Since it isn't in fact possible to assemble a framework without any vulnerability, Intrusion Detection System (IDS), which can successfully distinguish Intrusion, gets to have pulled in consideration. Intrusion detection can be characterized as the way toward distinguishing irregular, unauthorized or unapproved action that objective is to target a system and its assets. IDS plays a very important role for analyzing the network passage, also it assumes a key part to analyze the system activity log and each log is portrayed by huge arrangement of highlights and it requires tremendous computational preparing force and time for the characterization procedure. This work proposes filter based feature selection methods to predict intrusion with Feature based Mutual Information Feature Selection Support Vector Machine (FMIFSSVM), Feature based Liner Correlation Feature Selection Support Vector Machine (FLCFSSVM), misuses SVM, anomaly SVM and Bayesian methods. The performances of these methods are considered by using the intrusion detection calculation data set called Knowledge Discovery in Databases (KDD) cup 99. Detection Rate (DR), False Alarm Rate (FAR) and Percentage of Successful Prediction (PSP) are the major performance measures studied in this work.

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