scholarly journals Characterizing Functional Connectivity Network Based on Multi-Domain Analysis for Epilepsy Classification

2020 ◽  
Vol 9 (3) ◽  
pp. 2379-2386
Keyword(s):  
Discriminant Analysis ◽  
Detection System ◽  
Performance Measure ◽  
Machine Learning Algorithms ◽  
Bayesian Optimization ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
The Brain

Epileptic is a neural disease exemplified through untypical concurrent signal discharge from the neurons present in the brain region. This abnormal brain functionality could be captured through electroencephalography (EEG) system. Generally the observed EEG signals are examined by the experienced neurologist, which may be time consuming when observing hours of EEG signal. Therefore, this proposed work provides a fully automatic epileptic seizure detection system by means of the multi-domain features along with various machine learning algorithms. Initially, the obtained EEG signals are processed to clear noise and artefacts. Subsequently, the pre-processed signals are segregated as 5 seconds epochs and for each epoch various features are extracted from frequency domain, time domain. Additionally entropy, correlation and graph theory approaches has been used for analysis the connectivity of the brain network. Subsequently, distinguishable features are chosen carefully in this regard from the immense feature set by virtue of multi-objective evolutionary method and convincingly, classification has been performed using support vector machine(SVM).A Bayesian optimization (BaO) algorithm was utilized to optimize the SVM's hyper-plane parameters. In addition, Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA),Random Forest Ensemble (RFE) and k-Nearest Neighbor Ensemble (k- NNE) was also used for comparing the proposed results. These obtained results validates by considering the performance of this work is competing along with state-of the-arts approaches. The proposed work is implemented on a CHB-MIT database .The obtained performance measure of the classifiers are 99.09%, 81.49%,80.90%,76.85% and 84.14 % in SVM , LDA, QDA, k- NNE and RFE respectively. Finally SVM with Bayesian Optimization (BaO) algorithm outperforms than other classifiers with accuracy, AUC, sensitivity and specificity, as 99.09%, 99.67%, 98.06% and 98.12%, respectively.

scholarly journals Vehicle Reidentification in a Connected Vehicle Environment using Machine Learning Algorithms

2018 ◽  
Vol 2672 (45) ◽  
pp. 160-172 ◽  
Author(s):  
Zuoyu Miao ◽  
K. Larry Head ◽  
Byungho Beak
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
Performance Measure ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Linear Discriminant

Deployment of connected vehicles will become possible for most American cities in the next 10 to 20 years. Connected vehicle (CV) applications (e.g., mobility, safety, environment) are constantly receiving vehicle data. The current ID protection mechanism assumes a vehicle’s ID changes every 5 minutes, so the topic of rematching vehicles is of interest in privacy protection and performance measure research. This paper explores the possibility of rematching connected vehicles’ IDs using popular machine learning techniques, including logistic regression (LR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), linear and nonlinear support vector machine (SVM) and nearest neighbor algorithms. An experiment is conducted using a microscopic traffic simulation model through a software-in-the-loop technique. The best average mismatching rate is 14%. To assess potential factors’ effects on matching accuracy, a Poisson mixed regression model is analyzed under the Bayesian inference framework. Findings are: different matching algorithms vary in matching performance and the linear SVM, the QDA and the LDA have the best accuracy results; traffic volume and market penetration rate have little impact on matching results; location and number of vehicles to be matched are considered significant. The results make the performance measurement of future CV applications feasible and also suggest that more secure mechanisms are needed to protect the public.

MULTIPLE CLASSIFICATION TECHNIQUES TOWARD A ROBUST AND RELIABLE P300 BCI SYSTEM

2020 ◽  
Vol 32 (02) ◽  
pp. 2050010
Author(s):  
Fatma EL-Zahraa M. Labib ◽  
Islam A. Fouad ◽  
Mai S. Mabrouk ◽  
Amr A. Sharawy
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Linear Discriminant ◽  
The Brain ◽  
Bci System

A brain–computer interface (BCI) can be used for people with severe physical disabilities such as ALS or amyotrophic lateral sclerosis. BCI can allow these individuals to communicate again by creating a new communication channel directly from the brain to an output device. BCI technology can allow paralyzed people to share their intent with others, and thereby demonstrate that direct communication from the brain to the external world is possible and that it might serve useful functions. BCI systems include machine learning algorithms (MLAs). Their performance depends on the feature extraction and classification techniques employed. In this paper, we propose a system to exploit the P300 signal in the brain, a positive deflection in event-related potentials. The P300 signal can be incorporated into a spelling device. There are two benefits behind this kind of research. First of all, this work presents the research status and the advantages of communication via a BCI system, especially the P300 BCI system for disordered people, and the related literature review is presented. Secondly, the paper discusses the performance of different machine learning algorithms. Two different datasets are presented: the first dataset 2004 and the second dataset 2019. A preprocessing step is introduced to the subjects in both datasets first to extract the important features before applying the proposed machine learning methods: linear discriminant analysis (LDA I and LDA II), support vector machine (SVM I, SVM II, SVM III, and SVM IV), linear regression (LREG), Bayesian linear discriminant analysis (BLDA), and twin support vector machine (TSVM). By comparing the performance of the different machine learning systems, in the first dataset it is found that BLDA and SVMIV classifiers yield the highest performance for both subjects “A” and “B”. BLDA yields 98% and 66% for 15th and 5th sequences, respectively, whereas SVMIV yields 98% and 54.4% for 15th and 5th sequences, respectively. While in the second dataset, it is obvious that BLDA classifier yields the highest performance for both subjects “1” and “2”, it achieves 90.115%. The paper summarizes the P300 BCI system for the two introduced datasets. It discusses the proposed system, compares the classification methods performances, and considers some aspects for the future work to be handled. The results show high accuracy and less computational time which makes the system more applicable for online applications.

Introduction and Implementation of Machine Learning Algorithms in R

2019 ◽  
pp. 126-147
Author(s):  
S. R. Mani Sekhar ◽  
G. M. Siddesh
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Computer Science ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Linear Discriminant ◽  
The Given

Machine learning is one of the important areas in the field of computer science. It helps to provide an optimized solution for the real-world problems by using past knowledge or previous experience data. There are different types of machine learning algorithms present in computer science. This chapter provides the overview of some selected machine learning algorithms such as linear regression, linear discriminant analysis, support vector machine, naive Bayes classifier, neural networks, and decision trees. Each of these methods is illustrated in detail with an example and R code, which in turn assists the reader to generate their own solutions for the given problems.

A Review of Current Approaches of Brain Computer Interfaces

2012 ◽  
Vol 2 (2) ◽  
pp. 1-19
Author(s):  
Lochi Yu ◽  
Cristian Ureña
Keyword(s):  
Severe Disabilities ◽  
Extraction Methods ◽  
Support Vector ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
Computer Interfaces ◽  
Vector Machines ◽  
Different Types ◽  
Computational Resources ◽  
The Brain

Since the first recordings of brain electrical activity more than 100 years ago remarkable contributions have been done to understand the brain functionality and its interaction with environment. Regardless of the nature of the brain-computer interface BCI, a world of opportunities and possibilities has been opened not only for people with severe disabilities but also for those who are pursuing innovative human interfaces. Deeper understanding of the EEG signals along with refined technologies for its recording is helping to improve the performance of EEG based BCIs. Better processing and features extraction methods, like Independent Component Analysis (ICA) and Wavelet Transform (WT) respectively, are giving promising results that need to be explored. Different types of classifiers and combination of them have been used on EEG BCIs. Linear, neural and nonlinear Bayesian have been the most used classifiers providing accuracies ranges between 60% and 90%. Some demand more computational resources like Support Vector Machines (SVM) classifiers but give good generality. Linear Discriminant Analysis (LDA) classifiers provide poor generality but low computational resources, making them optimal for some real time BCIs. Better classifiers must be developed to tackle the large patterns variability across different subjects by using every available resource, method or technology.

scholarly journals Five Typical Stenches Detection Using an Electronic Nose

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2514 ◽  
Author(s):  
Wei Jiang ◽  
Daqi Gao
Keyword(s):  
Electronic Nose ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Odor Concentration ◽  
Linear Discriminant ◽  
Vector Machines ◽  
Components Analysis ◽  
Olfactory Organs

This paper deals with the classification of stenches, which can stimulate olfactory organs to discomfort people and pollute the environment. In China, the triangle odor bag method, which only depends on the state of the panelist, is widely used in determining odor concentration. In this paper, we propose a stenches detection system composed of an electronic nose and machine learning algorithms to discriminate five typical stenches. These five chemicals producing stenches are 2-phenylethyl alcohol, isovaleric acid, methylcyclopentanone, γ-undecalactone, and 2-methylindole. We will use random forest, support vector machines, backpropagation neural network, principal components analysis (PCA), and linear discriminant analysis (LDA) in this paper. The result shows that LDA (support vector machine (SVM)) has better performance in detecting the stenches considered in this paper.

scholarly journals New approaches for predicting outcomes in patients with atrial fibrillation

2020 ◽  
Vol 27 ◽  
pp. 28-32
Author(s):  
N. A. Novikova ◽  
M. Yu. Gilyarov ◽  
A. Yu. Suvorov ◽  
A. Yu. Kuchina
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Test Group ◽  
Learning Technologies ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Cardiovascular Risks ◽  
Linear Discriminant

Aim: we aimed to assess the capabilities of “machine learning” methods in predicting remote outcomes in patients with non-valvular atrial fi brillation (AF).Methods. From 2015 to 2016 234 patients with non-valvular AF were included in the study (median age 72 (65; 79) years; 50.0% men). During the median follow-up of 2.9 (2.7; 3.2) years 42 patients died, 9 patients had non-fatal acute cerebral circulatory disorders and 3 patients had non-fatal myocardial infarction (MI). These events in 52 subjects (22.2% from all patients included) were combined into a combined endpoint (death and a nonfatal cardiovascular accident at the stage of remote observation). The first 184 patients comprised a “training” group. The next 50 patients formed the “test” group. The following methods of «machine learning» were used in the analysis: classifi cation trees, linear discriminant analysis, the k-nearest neighbor method, support vectors method, neural network.Results. Long-term outcomes were influenced by age, known traditional risk factors for cardiovascular diseases, the presence of these diseases, changes in intracardiac hemodynamics and heart chambers as evaluated by echocardiography, the presence of concomitant anemia, advanced stages of chronic kidney disease, and the administration of drugs associated with a more severe cardiovascular disease progression (amiodarone, digoxin). The best prognosis was created using the model of linear discriminant analysis, the complex neural network model, and the support vector machine.Conclusion. Modern methods aimed at prognosis estimation seem to be of importance in cardiology. These methods include big data analysis and machine learning technologies. The methods require further evaluation and confirmation, and in the future they may allow correcting cardiovascular risks, using data from real clinical practice and evidence-based medicine at the same time.

scholarly journals NEW METHODS FOR PREDICTING OUTCOMES AND COMPLICATIONS IN PATIENTS WITH ATRIAL FIBRILLATION

2019 ◽  
Vol 26 (2(96)) ◽  
pp. 45-50
Author(s):  
N. A. Novikova ◽  
M. Yu. Gilyarov ◽  
A. Yu. Suvorov ◽  
A. Yu. Kuchina
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Atrial Fibrillation ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Test Group ◽  
Learning Technologies ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Linear Discriminant

Aim: assessment of the capabilities of “machine learning” methods in predicting remote outcomes in patients with non-valvular atrial fibrillation (AF).Methods. From 2015 to 2016 234 patients with non-valvular AF were included in the study (median age 72 (65; 79) years; 50.0% men). During the median follow-up of 2.9 (2.7; 3.2) years 42 patients died, 9 patients had non-fatal acute cerebral circulatory disorders and 3 patients had non-fatal myocardial infarction (MI). These events in 52 subjects (22.2% from all patients included) were combined into a combined endpoint (death and a nonfatal cardiovascular accident at the stage of remote observation). The first 184 patients comprised a “training” group. The next 50 patients formed the “test” group. The following methods of «machine learning» were used in the analysis: classification trees, linear discriminant analysis, the k-nearest neighbor method, support vectors method, neural network.Results. Long-term outcomes were influenced by age, known traditional risk factors for cardiovascular diseases, the presence of these diseases, changes in intracardiac hemodynamics and heart chambers as evaluated by echocardiography, the presence of concomitant anemia, advanced stages of chronic kidney disease, and the administration of drugs associated with a more severe cardiovascular disease progression (amiodarone, digoxin). The best prognosis was created using the model of linear discriminant analysis, the complex neural network model, and the support vector machine.Conclusion. Modern methods aimed at prognosis estimation seem to be of great potential for cardiology. These methods include big data analysis and machine learning technologies. The methods require further evaluation and con firmation, and in the future they may allow correcting cardiovascular risks, using data from real clinical practice and evidence-based medicine at the same time.

scholarly journals Fruit Classification Utilizing a Robotic Gripper with Integrated Sensors and Adaptive Grasping

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jintao Zhang ◽  
Shuang Lai ◽  
Huahua Yu ◽  
Erjie Wang ◽  
Xizhe Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information Acquisition ◽  
Fruits And Vegetables ◽  
Nearest Neighbor ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Tactile Sensing ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Agricultural Robots

As the core component of agricultural robots, robotic grippers are widely used for plucking, picking, and harvesting fruits and vegetables. Secure grasping is a severe challenge in agricultural applications because of the variation in the shape and hardness of agricultural products during maturation, as well as their variety and delicacy. In this study, a fruit identification method utilizing an adaptive gripper with tactile sensing and machine learning algorithms is reported. An adaptive robotic gripper is designed and manufactured to perform adaptive grasping. A tactile sensing information acquisition circuit is built, and force and bending sensors are integrated into the robotic gripper to measure the contact force distribution on the contact surface and the deformation of the soft fingers. A robotic manipulator platform is developed to collect the tactile sensing data in the grasping process. The performance of the random forest (RF), k-nearest neighbor (KNN), support vector classification (SVC), naive Bayes (NB), linear discriminant analysis (LDA), and ridge regression (RR) classifiers in identifying and classifying five types of fruits using the adaptive gripper is evaluated and compared. The RF classifier achieves the highest accuracy of 98%, while the accuracies of the other classifiers vary from 74% to 97%. The experiment illustrates that efficient and accurate fruit identification can be realized with the adaptive gripper and machine learning classifiers, and that the proposed method can provide a reference for controlling the grasping force and planning the robotic motion in the plucking, picking, and harvesting of fruits and vegetables.

scholarly journals Classification model for accuracy and intrusion detection using machine learning approach

2021 ◽  
Vol 7 ◽  
pp. e437
Author(s):  
Arushi Agarwal ◽  
Purushottam Sharma ◽  
Mohammed Alshehri ◽  
Ahmed A. Mohamed ◽  
Osama Alfarraj
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Nearest Neighbor ◽  
Performance Metrics ◽  
Detection System ◽  
Confusion Matrix ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
K Nearest Neighbor

In today’s cyber world, the demand for the internet is increasing day by day, increasing the concern of network security. The aim of an Intrusion Detection System (IDS) is to provide approaches against many fast-growing network attacks (e.g., DDoS attack, Ransomware attack, Botnet attack, etc.), as it blocks the harmful activities occurring in the network system. In this work, three different classification machine learning algorithms—Naïve Bayes (NB), Support Vector Machine (SVM), and K-nearest neighbor (KNN)—were used to detect the accuracy and reducing the processing time of an algorithm on the UNSW-NB15 dataset and to find the best-suited algorithm which can efficiently learn the pattern of the suspicious network activities. The data gathered from the feature set comparison was then applied as input to IDS as data feeds to train the system for future intrusion behavior prediction and analysis using the best-fit algorithm chosen from the above three algorithms based on the performance metrics found. Also, the classification reports (Precision, Recall, and F1-score) and confusion matrix were generated and compared to finalize the support-validation status found throughout the testing phase of the model used in this approach.

Linear Discriminant Analysis for Prediction of Group Membership: A User-Friendly Primer

2019 ◽  
Vol 2 (3) ◽  
pp. 250-263 ◽  
Author(s):  
Peter Boedeker ◽  
Nathan T. Kearns
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Best Practice ◽  
Nearest Neighbor ◽  
Multinomial Logistic Regression ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Prior Specification

In psychology, researchers are often interested in the predictive classification of individuals. Various models exist for such a purpose, but which model is considered a best practice is conditional on attributes of the data. Under certain conditions, linear discriminant analysis (LDA) has been shown to perform better than other predictive methods, such as logistic regression, multinomial logistic regression, random forests, support-vector machines, and the K-nearest neighbor algorithm. The purpose of this Tutorial is to provide researchers who already have a basic level of statistical training with a general overview of LDA and an example of its implementation and interpretation. Decisions that must be made when conducting an LDA (e.g., prior specification, choice of cross-validation procedures) and methods of evaluating case classification (posterior probability, typicality probability) and overall classification (hit rate, Huberty’s I index) are discussed. LDA for prediction is described from a modern Bayesian perspective, as opposed to its original derivation. A step-by-step example of implementing and interpreting LDA results is provided. All analyses were conducted in R, and the script is provided; the data are available online.

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