scholarly journals Melanoma Detection from Lesion Images using Optimized Features Selected by Metaheuristic Algorithms

2021 ◽  
Vol 16 (4) ◽  
pp. 0-0
Keyword(s):  
Metaheuristic Algorithms ◽  
Support Vector ◽  
Single Digit ◽  
Melanoma Detection ◽  
Supervised Classifiers ◽  
Accuracy Result ◽  
Feature Selector ◽  
Artificial Neural Network Ann ◽  
Ann Classifier ◽  
Better Than

This paper deals with a simple but efficient method for detection of deadly malignant melanoma with optimized hand-crafted feature sets selected by three alternative metaheuristic algorithms, namely Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Simulated Annealing (SA). Total 1898 number of features relating to lesion shapes, colors and textures are extracted from each of the 170 non-dermoscopy camera images of the popular MED-NODE dataset. This large feature set is then optimized and the number of features is reduced to up-to the range of single digit using metaheuristic algorithms as feature selector. Two well-known supervised classifiers, i.e. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are used to classify malignant and benign lesions. The best classification accuracy result found by this method is 87.69% with only 7 features selected by PSO using ANN classifier which is far better than the results found in the literature so far.

Brazilian Coins Recognition Using Histogram of Oriented Gradients Features

2019 ◽  
Vol 16 (10) ◽  
pp. 4170-4178
Author(s):  
Sheifali Gupta ◽  
Gurleen Kaur ◽  
Deepali Gupta ◽  
Udit Jindal
Keyword(s):  
Region Of Interest ◽  
Initial Step ◽  
Support Vector ◽  
Histogram Of Oriented Gradients ◽  
Lighting Conditions ◽  
Roi Extraction ◽  
Feature Values ◽  
Artificial Neural Network Ann ◽  
Ann Classifier

This paper tends to the issue of coin recognition when dealing with shading and reflection variations under the same lighting conditions. In order to approach the problem, a database containing Brazilian coin images (both front and reverse side of the coin) consisting of five different denominations have been used which is provided by the kaggle-diverse and largest data community in the world. This work focuses on an automatic image classification process for Brazilian coins. The imagebased classification of coins primarily incorporates three stages where the initial step is Region of Interest (ROI) extraction; the subsequent advance is extraction of features and classification. The first step of ROI extraction is accomplished by segmenting the coin region using the proposed segmentation method. In the second step i.e., feature extraction; Histogram of Oriented Gradients (HOG) features are extracted from the image. The image is converted to a vector containing feature values. The third step is where the extracted features are mapped to the class and are known as classification. Three classification algorithms i.e., Support Vector Machine (SVM), Artificial Neural Network (ANN) and K-Nearest Neighbour are compared for classification of five coin denominations. With the proposed segmentation methodology, the best classification accuracy of 92% is achieved in the case of ANN classifier.

scholarly journals Identification of Plant Leaf Disease using Machine Learning Techniques

2019 ◽  
Vol 8 (3) ◽  
pp. 6077-6081 ◽  
Keyword(s):  
Plant Disease ◽  
Digital Camera ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Leaf Sample ◽  
Learning Techniques ◽  
Artificial Neural Network Ann ◽  
Identification Techniques ◽  
Processing Techniques ◽  
Better Than

Plant disease identification and classification is major area of research as majority of people in India depend on agriculture for their main source of income and for food. Identification of the diseases in any crops is challenging since manual identification techniques being used in this are based on the experts advises which may not be efficient. Based on leaf features decisions about variety of diseases are taken. In this paper an automated framework is introduced which can be used to detect and classify the diseases in the leaf accurately. Leaf images are acquired by using digital camera. Pre-processing techniques, segmentation and feature extraction are performed on the acquired images. The features are passed on to the classifiers to classify the diseases. This work has been proposed to classify and distinguish the leaf sample based on its features. The proposed work is carried out with Artificial Neural Network (ANN), Support Vector Machine (SVM) and Naive Bayes classifiers to analyze the result. For given dataset ANN performed better than the other two classifiers

scholarly journals Fast Detection and Traceback-based Mitigation of Interest Flooding Attack

2021 ◽  
Author(s):  
Naveen Kumar ◽  
Shashank Srivast
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Learning Approaches ◽  
Previous Approach ◽  
State Information ◽  
Fast Detection ◽  
Flooding Attack ◽  
Artificial Neural Network Ann ◽  
Ann Classifier ◽  
Better Than

Abstract NDN Pending Interest Table (PIT) helps NDN by storing the state of a request within the router. This state information helps the router to redirect the data packet towards the requester. However, an attacker can send malicious requests, which could flood the PIT; this attack is known as Interest Flooding Attack (IFA). In our previous work, we have found the most relevant features needed to detect IFA and applied a few machine learning approaches for the offline detection of IFA. In this article, a trained Artificial Neural Network (ANN) classifier has been deployed on each NDN router for the online detection of IFA. A novel traceback-based mitigation is proposed, which is triggered after the detection. The proposed approach is found better than the previous approach in terms of the satisfaction ratio and throughput of the legitimate consumers.

Hardware/Software Co-Design of a Vision System for Automatic Classification of Date Fruits

2020 ◽  
Vol 11 (4) ◽  
pp. 21-40
Author(s):  
Lazhar Khriji ◽  
Ahmed Chiheb Ammari ◽  
Medhat Awadalla
Keyword(s):  
Nearest Neighbor ◽  
Vision System ◽  
Texture Feature ◽  
Automatic Classification ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural Network Ann ◽  
Ann Classifier ◽  
Accuracy Performance

This paper proposes a hardware/software (HW/SW) co-design of an automatic classification system of Khalas, Khunaizi, Fardh, Qash, Naghal, and Maan dates fruit varieties in Oman. Three artificial intelligence (AI) techniques are used for qualitative comparisons: artificial neural network (ANN), support vector machine (SVM), and K-nearest neighbor (KNN). The accuracy performance of all AI classifiers is characterized for multiple color, shape, size, and texture feature combinations and for different critical parameter settings of the classifiers. In total, 600 date samples (100 dates/variety) are selected and imaged each sample individually. The system starts with preprocessing and segmentation of the colored input images. A total of 19 features are extracted from each image for use in classification models. The ANN classifier is shown to outperform all other classifiers. 97.26% highest classification accuracy is achieved using a combination of 15 color and shape-size features.

scholarly journals SVM versus MAP on Accelerometer Data to Distinguish among Locomotor Activities Executed at Different Speeds

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Maurizio Schmid ◽  
Francesco Riganti-Fulginei ◽  
Ivan Bernabucci ◽  
Antonino Laudani ◽  
Daniele Bibbo ◽  
...  
Keyword(s):  
Inertial Sensor ◽  
Stair Climbing ◽  
Support Vector ◽  
Accelerometer Data ◽  
Sammon’S Mapping ◽  
Locomotor Activities ◽  
Artificial Neural Network Ann ◽  
Better Than ◽  
Bayes Approach

Two approaches to the classification of different locomotor activities performed at various speeds are here presented and evaluated: a maximum a posteriori (MAP) Bayes’ classification scheme and a Support Vector Machine (SVM) are applied on a 2D projection of 16 features extracted from accelerometer data. The locomotor activities (level walking, stair climbing, and stair descending) were recorded by an inertial sensor placed on the shank (preferred leg), performed in a natural indoor-outdoor scenario by 10 healthy young adults (age 25–35 yrs.). From each segmented activity epoch, sixteen features were chosen in the frequency and time domain. Dimension reduction was then performed through 2D Sammon’s mapping. An Artificial Neural Network (ANN) was trained to mimic Sammon’s mapping on the whole dataset. In the Bayes’ approach, the two features were then fed to a Bayes’ classifier that incorporates an update rule, while, in the SVM scheme, the ANN was considered as the kernel function of the classifier. Bayes’ approach performed slightly better than SVM on both the training set (91.4% versus 90.7%) and the testing set (84.2% versus 76.0%), favoring the proposed Bayes’ scheme as more suitable than the proposed SVM in distinguishing among the different monitored activities.

scholarly journals Screening for Prediabetes Using Machine Learning Models

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Soo Beom Choi ◽  
Won Jae Kim ◽  
Tae Keun Yoo ◽  
Jee Soo Park ◽  
Jai Won Chung ◽  
...  
Keyword(s):  
External Validation ◽  
Systematic Evaluation ◽  
Support Vector ◽  
Ann Model ◽  
Internal Validation ◽  
Health And Nutrition ◽  
Svm Model ◽  
Score Model ◽  
Artificial Neural Network Ann ◽  
Better Than

The global prevalence of diabetes is rapidly increasing. Studies support the necessity of screening and interventions for prediabetes, which could result in serious complications and diabetes. This study aimed at developing an intelligence-based screening model for prediabetes. Data from the Korean National Health and Nutrition Examination Survey (KNHANES) were used, excluding subjects with diabetes. The KNHANES 2010 data(n=4685)were used for training and internal validation, while data from KNHANES 2011(n=4566)were used for external validation. We developed two models to screen for prediabetes using an artificial neural network (ANN) and support vector machine (SVM) and performed a systematic evaluation of the models using internal and external validation. We compared the performance of our models with that of a screening score model based on logistic regression analysis for prediabetes that had been developed previously. The SVM model showed the areas under the curve of 0.731 in the external datasets, which is higher than those of the ANN model (0.729) and the screening score model (0.712), respectively. The prescreening methods developed in this study performed better than the screening score model that had been developed previously and may be more effective method for prediabetes screening.

scholarly journals HRV Analysis and Ventricular Arrhythmia Classification using various Classifiers

2019 ◽  
Vol 8 (4) ◽  
pp. 6095-6100
Keyword(s):  
Random Forest ◽  
Ventricular Arrhythmias ◽  
Heart Diseases ◽  
Premature Ventricular Contraction ◽  
Support Vector ◽  
Svm Classifier ◽  
Ecg Signals ◽  
R Project ◽  
Artificial Neural Network Ann ◽  
Ann Classifier

Ventricular Arrhythmias are one of the fatal heart diseases, requires timely recognition. This paper deals with the classification of some of the ventricular arrhythmias as Premature Ventricular Contraction (PVC), Left Bundle Branch Block (LBBB) and Right Bundle Brach Block (RBBB) with some Normal (N) samples. A Support Vector Machine (SVM), Random Forest and Artificial Neural Network (ANN) classifier was trained and then tested with the help of online available MIT-BIH Arrhythmia Database. Signal processing, generation of Heart Rate Variability (HRV) signals from the available Electrocardiogram (ECG) signals and training and testing of ANN classifier was done in MATLAB environment, and the training and testing of SVM and Random Forest classifier was done in R project software. The SVM classifier was trained with the linear basis function and then with non-linear kernel based function to have better accuracy

Analysis of Slope Stability Based on Gaussian Process Regression

2012 ◽  
Vol 170-173 ◽  
pp. 1330-1333 ◽  
Author(s):  
Yan Zhang ◽  
Guo Shao Su ◽  
Liu Bin Yan
Keyword(s):  
Slope Stability ◽  
Gaussian Process ◽  
Stability Problem ◽  
Gaussian Process Regression ◽  
Engineering Problem ◽  
Support Vector ◽  
Test Study ◽  
Computation Process ◽  
Artificial Neural Network Ann ◽  
Better Than

Slope stability estimation is an engineering problem that involves several parameters. Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been successfully used in slope stability problem. However, there are some open issues for above-mentioned methods, which are very hard to overcome. For this reason, Gaussian Process Regression (GPR) which has a theoretical framework for obtaining the optimum hyperparameters self-adaptively has been used in slope stability problem. Without complicated mechanics computation process, through learning the empirical knowledge coming from real engineering, the complicated nonlinear mapping relationship between slope stability and its influencing factors was established easily using GPR. The results of test study indicate that the method is feasible, effective and simple to implement for slope stability evaluation. The results are better than previously published paper of ANN and SVM.

A Computer Vision-Based Method for Classification of Red Meat Quality After Nitrosamine Appendage

2021 ◽  
pp. 2150005
Author(s):  
Monika Arora ◽  
Parthasarathi Mangipudi
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Food Samples ◽  
Red Meat ◽  
Support Vector ◽  
Svm Classifier ◽  
Deep Convolutional Neural Networks ◽  
Improved Performance ◽  
Artificial Neural Network Ann ◽  
Ann Classifier

Nitrosamine is a carcinogenic chemical used as a preservative in red meat whose identification is an ordeal. This paper presents a computer vision-based non-destructive method for identifying quality disparities between preservative treated and untreated (control) red meat. To access the discrepancy in the quality of red meat, both traditional machine learning and deep learning-based methods have been used. Support vector machine (SVM) classifier and artificial neural network (ANN) models have been used to detect the presence of nitrosamine in test samples. The paper also made use of different pre-trained deep convolutional neural networks (DCNN) with transfer learning approach such as ResNet-34, ResNet-50, ResNet-101, VGG-16, VGG-19, AlexNet and MobileNetv2 to examine the presence of nitrosamine in the food samples. While the ANN classifier performed better in comparison to the SVM classifier, the highest testing accuracy and F1-score were obtained using the deep learning model, ResNet-101 with 95.45% and 96.54%, respectively. The experimental results demonstrate an improved performance in comparison to the existing methods; indicating the feasibility of the proposed work for food quality control in real-time applications.

scholarly journals Comparison of Image Texture Based Supervised Learning Classifiers for Strawberry Powdery Mildew Detection

2019 ◽  
Vol 1 (3) ◽  
pp. 434-452 ◽  
Author(s):  
Chang ◽  
Mahmud ◽  
Shin ◽  
Nguyen-Quang ◽  
Price ◽  
...  
Keyword(s):  
Powdery Mildew ◽  
Image Data ◽  
Absolute Error ◽  
Detection Algorithm ◽  
Image Texture ◽  
Support Vector ◽  
Disease Detection ◽  
K Nearest Neighbors ◽  
Supervised Classifiers ◽  
Ann Classifier

Strawberry is an important fruit crop in Canada but powdery mildew (PM) results in about 30–70% yield loss. Detection of PM through an image texture-based system is beneficial, as it identifies the symptoms at an earlier stage and reduces labour intensive manual monitoring of crop fields. This paper presents an image texture-based disease detection algorithm using supervised classifiers. Three sites were selected to collect the leaf image data in Great Village, Nova Scotia, Canada. Images were taken under an artificial cloud condition with a Digital Single Lens Reflex (DSLR) camera as red-green-blue (RGB) raw data throughout 2017–2018 summer. Three supervised classifiers, including artificial neural networks (ANN), support vector machine (SVM), and k-nearest neighbors (kNN) were evaluated for disease detection. A total of 40 textural features were extracted using a colour co-occurrence matrix (CCM). The collected feature data were normalized, then used for training and internal, external and cross-validations of developed classifiers. Results of this study revealed that the highest overall classification accuracy was 93.81% using the ANN classifier and lowest overall accuracy was 78.80% using the kNN classifier. Results identified the ANN classifier disease detection having a lower Root Mean Square Error (RMSE) = 0.004 and Mean Absolute Error (MAE) = 0.003 values with 99.99% of accuracy during internal validation and 87.41%, 88.95% and 95.04% of accuracies during external validations with three different fields. Overall results demonstrated that an image texture-based ANN classifier was able to classify PM disease more accurately at early stages of disease development.

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