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.

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.

BREAST CANCER DETECTION USING RSFS-BASED FEATURE SELECTION ALGORITHMS IN THERMAL IMAGES

2021 ◽  
pp. 2150020
Author(s):  
Nazila Darabi ◽  
Abdalhossein Rezai ◽  
Seyedeh Shahrbanoo Falahieh Hamidpour
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Cancer Detection ◽  
Vital Role ◽  
Support Vector ◽  
Computer Aided Detection ◽  
K Nearest Neighbors ◽  
Cad System ◽  
Common Cancer ◽  
Selection Algorithms

Breast cancer is a common cancer in female. Accurate and early detection of breast cancer can play a vital role in treatment. This paper presents and evaluates a thermogram based Computer-Aided Detection (CAD) system for the detection of breast cancer. In this CAD system, the Random Subset Feature Selection (RSFS) algorithm and hybrid of minimum Redundancy Maximum Relevance (mRMR) algorithm and Genetic Algorithm (GA) with RSFS algorithm are utilized for feature selection. In addition, the Support Vector Machine (SVM) and k-Nearest Neighbors (kNN) algorithms are utilized as classifier algorithm. The proposed CAD system is verified using MATLAB 2017 and a dataset that is composed of breast images from 78 patients. The implementation results demonstrate that using RSFS algorithm for feature selection and kNN and SVM algorithms as classifier have accuracy of 85.36% and 75%, and sensitivity of 94.11% and 79.31%, respectively. In addition, using hybrid GA and RSFS algorithm for feature selection and kNN and SVM algorithms as classifier have accuracy of 83.87% and 69.56%, and sensitivity of 96% and 81.81%, respectively, and using hybrid mRMR and RSFS algorithms for feature selection and kNN and SVM algorithms as classifier have accuracy of 77.41% and 73.07%, and sensitivity of 98% and 72.72%, respectively.

scholarly journals Scrutinizing Attacks and Evaluating Performance Appraisal Parameters via Feature Selection in Intrusion Detection System

2021 ◽  
Author(s):  
Navroop Kaur ◽  
Meenakshi Bansal ◽  
Sukhwinder Singh S
Keyword(s):  
Feature Selection ◽  
Performance Evaluation ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Cyber Attacks ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Evaluation Parameters

Abstract In modern times the firewall and antivirus packages are not good enough to protect the organization from numerous cyber attacks. Computer IDS (Intrusion Detection System) is a crucial aspect that contributes to the success of an organization. IDS is a software application responsible for scanning organization networks for suspicious activities and policy rupturing. IDS ensures the secure and reliable functioning of the network within an organization. IDS underwent huge transformations since its origin to cope up with the advancing computer crimes. The primary motive of IDS has been to augment the competence of detecting the attacks without endangering the performance of the network. The research paper elaborates on different types and different functions performed by the IDS. The NSL KDD dataset has been considered for training and testing. The seven prominent classifiers LR (Logistic Regression), NB (Naïve Bayes), DT (Decision Tree), AB (AdaBoost), RF (Random Forest), kNN (k Nearest Neighbor), and SVM (Support Vector Machine) have been studied along with their pros and cons and the feature selection have been imposed to enhance the reading of performance evaluation parameters (Accuracy, Precision, Recall, and F1Score). The paper elaborates a detailed flowchart and algorithm depicting the procedure to perform feature selection using XGB (Extreme Gradient Booster) for four categories of attacks: DoS (Denial of Service), Probe, R2L (Remote to Local Attack), and U2R (User to Root Attack). The selected features have been ranked as per their occurrence. The implementation have been conducted at five different ratios of 60-40%, 70-30%, 90-10%, 50-50%, and 80-20%. Different classifiers scored best for different performance evaluation parameters at different ratios. NB scored with the best Accuracy and Recall values. DT and RF consistently performed with high accuracy. NB, SVM, and kNN achieved good F1Score.

scholarly journals Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 943 ◽  
Author(s):  
Sadegh Arefnezhad ◽  
Sajjad Samiee ◽  
Arno Eichberger ◽  
Ali Nahvi
Keyword(s):  
Feature Selection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Detection System ◽  
Steering Wheel ◽  
Support Vector ◽  
Inference System ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Neuro Fuzzy

This paper presents a novel feature selection method to design a non-invasive driver drowsiness detection system based on steering wheel data. The proposed feature selector can select the most related features to the drowsiness level to improve the classification accuracy. This method is based on the combination of the filter and wrapper feature selection algorithms using adaptive neuro-fuzzy inference system (ANFIS). In this method firstly, four different filter indexes are applied on extracted features from steering wheel data. After that, output values of each filter index are imported as inputs to a fuzzy inference system to determine the importance degree of each feature and select the most important features. Then, the selected features are imported to a support vector machine (SVM) for binary classification to classify the driving conditions in two classes of drowsy and awake. Finally, the classifier accuracy is exploited to adjust parameters of an adaptive fuzzy system using a particle swarm optimization (PSO) algorithm. The experimental data were collected from about 20.5 h of driving in the simulator. The results show that the drowsiness detection system is working with a high accuracy and also confirm that this method is more accurate than the recent available algorithms.

scholarly journals A Novel Intrusion Detection System for RPL Based IoT Networks with Bio-Inspired Feature Selection and Ensemble Classifier

2021 ◽  
Author(s):  
Jayaprakash Pokala ◽  
B. Lalitha
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Ensemble Classifier ◽  
Support Vector ◽  
Routing Attacks ◽  
Salp Swarm Algorithm ◽  
Network Intrusion ◽  
Swarm Algorithm

Abstract Internet of Things (IoT) is the powerful latest trend that allows communications and networking of many sources over the internet. Routing protocol for low power and lossy networks (RPL) based IoT networks may be exposed to many routing attacks due to resource-constrained and open nature of the IoT nodes. Hence, there is a need for network intrusion detection system (NIDS) to protect RPL based IoT networks from routing attacks. The existing techniques for anomaly-based NIDS (ANIDS) subjects to high false alarm rate (FAR). Therefore, a novel bio-inspired voting ensemble classifier with feature selection technique is proposed in this paper to improve the performance of ANIDS for RPL based IoT networks. The proposed voting ensemble classifier combines the results of various base classifiers such as logistic Regression, support vector machine, decision tree, bidirectional long short-term memory and K-nearest neighbor to detect the attacks accurately based on majority voting rule. The optimized weights of base classifiers are obtained by using the feature selection method called simulated annealing based improved salp swarm algorithm (SA-ISSA), which is the hybridization of particle swarm optimization, opposition based learning and salp swarm algorithm. The experiments are performed with RPL-NIDDS17 dataset that contains seven types of attack instances. The performance of the proposed model is evaluated and compared with existing feature selection and classification techniques in terms of accuracy, attack detection rate (ADR), FAR and so on. The proposed ensemble classifier shows better performance with higher accuracy (96.4%), ADR (97.7%) and reduced FAR (3.6%).

scholarly journals An ensemble feature selection approach using hybrid kernel based SVM for network intrusion detection system

2021 ◽  
Vol 23 (1) ◽  
pp. 558
Author(s):  
Gaddam Venu Gopal ◽  
Gatram Rama Mohan Babu
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Network Intrusion Detection ◽  
Support Vector ◽  
Feature Subset ◽  
Network Intrusion ◽  
Feature Selection Approach ◽  
Hybrid Kernel

Feature selection is a process of identifying relevant feature subset that leads to the machine learning algorithm in a well-defined manner. In this paper, anovel ensemble feature selection approach that comprises of Relief  Attribute Evaluation and hybrid kernel-based support vector machine (HK-SVM) approach is proposed as a feature selection method for network intrusion detection system (NIDS). A Hybrid approach along with the combination of Gaussian and Polynomial methods is used as a kernel for support vector machine (SVM). The key issue is to select a feature subset that yields good accuracy at a minimal computational cost. The proposed approach is implemented and compared with classical SVM and simple kernel. Kyoto2006+, a bench mark intrusion detection dataset,is used for experimental evaluation and then observations are drawn.

Permission-based Android Malware Detection System Using Feature Selection with Genetic Algorithm

2019 ◽  
Vol 29 (02) ◽  
pp. 245-262 ◽  
Author(s):  
Oktay Yildiz ◽  
Ibrahim Alper Doğru
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Detection System ◽  
Malware Detection ◽  
Support Vector ◽  
Android Malware ◽  
Detection Systems ◽  
Android Malware Detection ◽  
Vector Machines ◽  
Accuracy Result

As the use of smartphones increases, Android, as a Linux-based open source mobile operating system (OS), has become the most popular mobile OS in time. Due to the widespread use of Android, malware developers mostly target Android devices and users. Malware detection systems to be developed for Android devices are important for this reason. Machine learning methods are being increasingly used for detection and analysis of Android malware. This study presents a method for detecting Android malware using feature selection with genetic algorithm (GA). Three different classifier methods with different feature subsets that were selected using GA were implemented for detecting and analyzing Android malware comparatively. A combination of Support Vector Machines and a GA yielded the best accuracy result of 98.45% with the 16 selected permissions using the dataset of 1740 samples consisting of 1119 malwares and 621 benign samples.

A Comparative Study on the Feature Selection Techniques for Intrusion Detection System

2019 ◽  
Vol 8 (1) ◽  
pp. 42-47
Author(s):  
D. Selvamani ◽  
V. Selvi
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Comparative Study ◽  
Intrusion Detection System ◽  
Detection System ◽  
Feature Selection Method ◽  
Support Vector ◽  
Network Intrusion ◽  
Chi Square Analysis ◽  
Feature Selection Techniques

The Intrusion Detection System (IDS) can be used broadly for securing the network. Intrusion detection systems (IDS) are typically positioned laterally through former protecting safety automation, like access control and verification, as a subsequent line of resistance that guards data classifications. Feature selection is employed to diminish the number of features in various applications where data has more than hundreds of attributes. Essential or relevant attribute recognition has converted a vital job to utilize data mining algorithms efficiently in today world situations. This article describes the comparative study on the Information Gain, Gain Ratio, Symmetrical Uncertainty, Chi-Square analysis feature selection techniques with different Classification methods like Artificial Neural Network, Naïve Bayes and Support Vector Machine. In this article, different performance metrics has utilized to choose the appropriate Feature Selection method for better data classification in IDS.

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