scholarly journals Ecg Signal based Arrhythmia Detection System using Optimized Hybrid Classifier

2019 ◽  
Vol 8 (9) ◽  
pp. 2207-2212
Keyword(s):  
Search Algorithm ◽  
Detection System ◽  
Well Being ◽  
Optimization Techniques ◽  
Feature Reduction ◽  
Support Vector ◽  
Severe Problem ◽  
Hybrid Classifier ◽  
Ecg Arrhythmia Classification ◽  
Ecg Arrhythmia

An essential diagnostic tool in identifying heart rhythm irregularities, known as arrhythmias, is the ECG (Electrocardiogram). Accurate identification of arrhythmias in clinical environments is critical to patient well-being, as both acute and chronic heart conditions are typically reflected in these measurements. This is known to be a severe problem even for human experts, due to variability between individuals and inevitable noise. In this research, we have proposed an effective ECG arrhythmia classification method using a hybrid classifier with SVM (Support vector machine) and ANN (Artificial neural network) which recently shows outstanding performance in the field of pattern recognition. Every ECG beat was transformed into two-dimensional data as input data for the hybrid classifier. Optimization of the proposed hybrid classifier includes various optimization techniques such as GA (Genetic algorithm) and CS (Cuckoo search) algorithm with an optimal objective function. Also, we have compared our proposed hybrid classifier with wellknown optimized ANN based ECG arrhythmia classification models. ECG recordings from the MIT-BIH arrhythmia database are used for the evaluation of the classifier. To precisely validate the hybrid classifier, cross-validation was performed at the evaluation, which involves every ECG recording as a test data with GA and with CS. The experimental results have successfully validated that the proposed hybrid classifier with the GA and CS has achieved excellent classification accuracy without any requirement of manual pre-processing of the ECG signals such as noise filtering, feature extraction, and feature reduction.

scholarly journals A support vector machine with the tabu search algorithm for freeway incident detection

2014 ◽  
Vol 24 (2) ◽  
pp. 397-404 ◽  
Author(s):  
Baozhen Yao ◽  
Ping Hu ◽  
Mingheng Zhang ◽  
Maoqing Jin
Keyword(s):  
Support Vector Machine ◽  
Tabu Search ◽  
Traffic Management ◽  
Search Algorithm ◽  
Detection System ◽  
Support Vector ◽  
Incident Detection ◽  
Tabu Search Algorithm ◽  
Proposed Model ◽  
Parameter Values

Abstract Automated Incident Detection (AID) is an important part of Advanced Traffic Management and Information Systems (ATMISs). An automated incident detection system can effectively provide information on an incident, which can help initiate the required measure to reduce the influence of the incident. To accurately detect incidents in expressways, a Support Vector Machine (SVM) is used in this paper. Since the selection of optimal parameters for the SVM can improve prediction accuracy, the tabu search algorithm is employed to optimize the SVM parameters. The proposed model is evaluated with data for two freeways in China. The results show that the tabu search algorithm can effectively provide better parameter values for the SVM, and SVM models outperform Artificial Neural Networks (ANNs) in freeway incident detection.

scholarly journals An Effective System of Intrusion Detection on Deep Neural Network by Hybrid Optimization in Cyber Security

2019 ◽  
Vol 9 (1) ◽  
pp. 1320-1327
Keyword(s):  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Search Algorithm ◽  
Detection System ◽  
Optimization Techniques ◽  
Hybrid Optimization ◽  
Experimental Result ◽  
Gray Wolf ◽  
High Detection

In present trends organizations are very much interested to protect data and prevent malware attack by using well flourished and excellent tools. Many algorithms are used for the intrusion detection system (IDS) and it has pros and cons. Here we proposed a novel method of intrusion detection using hybrid optimization techniques such as Gravity search algorithm with gray wolf optimization (GSGW). In this method the gray wolf technique has a leader for the continuous monitoring of the attacker and has a low false alarm rate and a high detection rate. The performance evaluation is done by the feature selection in NSL-KDD dataset. In the proposed method the experimental result reveals less false alarm rate, better accuracy and high Detection when compared to previous analysis.

scholarly journals Fatigue Detection on Face Image Using FaceNet Algorithm and K-Nearest Neighbor Classifier

2021 ◽  
Vol 7 (1) ◽  
pp. 22
Author(s):  
Faisal Dharma Adhinata ◽  
Diovianto Putra Rakhmadani ◽  
Danur Wijayanto
Keyword(s):  
Nearest Neighbor ◽  
Detection System ◽  
Well Being ◽  
Extraction Process ◽  
Polynomial Kernel ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Time Data ◽  
Multiclass Svm ◽  
Fatigue Detection

Background: The COVID-19 pandemic has made people spend more time on online meetings more than ever. The prolonged time looking at the monitor may cause fatigue, which can subsequently impact the mental and physical health. A fatigue detection system is needed to monitor the Internet users well-being. Previous research related to the fatigue detection system used a fuzzy system, but the accuracy was below 85%. In this research, machine learning is used to improve accuracy.Objective: This research examines the combination of the FaceNet algorithm with either k-nearest neighbor (K-NN) or multiclass support vector machine (SVM) to improve the accuracy.Methods: In this study, we used the UTA-RLDD dataset. The features used for fatigue detection come from the face, so the dataset is segmented using the Haar Cascades method, which is then resized. The feature extraction process uses FaceNet's pre-trained algorithm. The extracted features are classified into three classes—focused, unfocused, and fatigue—using the K-NN or multiclass SVM method.Results: The combination between the FaceNet algorithm and K-NN, with a value of  resulted in a better accuracy than the FaceNet algorithm with multiclass SVM with the polynomial kernel (at 94.68% and 89.87% respectively). The processing speed of both combinations of methods has allowed for real-time data processing.Conclusion: This research provides an overview of methods for early fatigue detection while working at the computer so that we can limit staring at the computer screen too long and switch places to maintain the health of our eyes. 

scholarly journals Multilevel Intrusion Detection System with Affinity Clustering and Ensemble SVM

2020 ◽  
pp. 522-529
Author(s):  
Sadhana Patidar ◽  
Priyanka Parihar ◽  
Chetan Agrawal
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Model Performance ◽  
Feature Reduction ◽  
Support Vector ◽  
Learning Tools ◽  
Security Issues ◽  
Proposed Model

Now-a-days with growing applications over internet increases the security issues over network. Many security applications are designed to cope with such security concerns but still it required more attention to improve speed as well accuracy. With advancement of technologies there is also evolution of new threats or attacks in network. So, it is required to design such detection system that can handle new threats in network. One of the network security tools is intrusion detection system which is used to detect malicious data packets. Machine learning tool is also used to improve efficiency of network-based intrusion detection system. In this paper, an intrusion detection system is proposed with an application of machine learning tools. The proposed model integrates feature reduction, affinity clustering and multilevel Ensemble Support Vector Machine. The proposed model performance is analyzed over two datasets i.e. NSL-KDD and UNSW-NB 15 dataset and achieved approx. 12% of efficiency over other existing work.

scholarly journals Intelligent security system detects the hidden objects in the smart grid

2020 ◽  
Vol 19 (1) ◽  
pp. 188
Author(s):  
Ammar Wisam Altaher ◽  
Abdullah Hasan Hussein
Keyword(s):  
Image Fusion ◽  
Search Algorithm ◽  
Detection System ◽  
Harmony Search ◽  
Hybrid Approach ◽  
Human Vision ◽  
Superior Performance ◽  
Support Vector ◽  
Accurate Information ◽  
Discrete Wavelet

<p>Monitoring the general public gathered in large numbers is one of the most challenging tasks faced by the law and order enforcement team. There is swiftly demand to that have inbuilt sensors which can detect the concealed weapon, from a standoff distance the system can locate the weapon with very high accuracy. Objects that are obscure and invisible from human vision can be seen vividly from enhanced artificial vision systems. Image Fusion is a computer vision technique that fuses images from multiple sensors to give accurate information. Image fusion using visual and infrared images has been employed for a safe, non-invasive standoff threat detection system. The fused imagery is further processed for specific identification of weapons. The unique approach to discover concealed weapon based on DWT in conjunction with Meta heuristic algorithm Harmony Search Algorithm and SVM classification is presented. It firstly uses the traditional discrete wavelet transform along with the hybrid Hoteling transform to obtain a fused imagery. Then a heuristic search algorithm is applied to search the best optimal harmony to generate the new principal components of the registered input images which is later classified using the K means support vector machines to build better classifiers for concealed weapon detection. Experimental results demonstrate the hybrid approach which shows the superior performance.</p>

scholarly journals Classification of Heart Rate Time Series Using Machine Learning Algorithms

2021 ◽  
Vol 2 (1) ◽  
Keyword(s):  
Detection System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Heart Patients ◽  
Extraction Step ◽  
Ecg Arrhythmia ◽  
Electrocardiogram Ecg ◽  
The Given

An important diagnostic method for diagnosing abnormalities in the human heart is the electrocardiogram (ECG). A large number of heart patients increase the assignment of physicians. To reduce their assignment, an automatic computer detection system is needed. In this study, a computer system for classifying ECG signals is presented. The MIT-BIH, ECG arrhythmia database is used for analysis. After the ECG signal is noisy in the preprocessing stage, the data feature is extracted. In the feature extraction step, the decision tree is used and the support vector machine (SVM) is constructed to classify the ECG signal into two categories. It is normal or abnormal. The results show that the system classifies the given ECG signal with 90% sensitivity.

scholarly journals Performance Analysis of ECG Arrhythmia Classification based on Different SVM Methods

2020 ◽  
Vol 9 (12) ◽  
pp. 45-49
Keyword(s):  
Support Vector Machine ◽  
Decision Function ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Sets ◽  
Artery Disease ◽  
Ecg Arrhythmia Classification ◽  
The One ◽  
Ecg Arrhythmia ◽  
Ecg Data

Heart arrhythmias are the different types of heartbeats which are irregular in nature. In Tachycardia the heartbeat works too fast and in case of Bradycardia it works too slow. In the study of different cardiac conditions automatic detection of heart arrhythmia is done by the classification and feature extraction of Electrocardiogram(ECG) data. Various Support Vector Machine based methods are used to analyze and classify ECG signals for arrhythmia detection. There are several Support Vector Machine (SVM) methods used to classify the ECG data such as one against all, one against one and fuzzy decision function. This classification detects the existence of the arrhythmia and it helps the physicians to treat the heart patient with more accurate way. To train SVM, the MIT BIH Arrhythmia database is used which works with the heart disorder like sinus bradycardy, old inferior myocardial infarction, coronary artery disease, right bundle branch block. All three methods are implemented in proper way, and their rate of accuracy with SVM classifier is optimal when it is processed with the one-against-all method. The data sets of ECG arrhythmia are usually complex in nature, so for the SVM based classification one-against-all method has great impact and will fetch better result.

scholarly journals ELECTROCARDIOGRAM ARRHYTHMIA CLASSIFICATION SYSTEM USING SUPPORT VECTOR MACHINE BASED FUZZY LOGIC

2016 ◽  
Vol 9 (1) ◽  
pp. 35 ◽  
Author(s):  
Sugiyanto Sugiyanto ◽  
Tutuk Indriyani ◽  
Muhammad Heru Firmansyah
Keyword(s):  
Support Vector Machine ◽  
Fuzzy Logic ◽  
Support Vector ◽  
Ecg Signal ◽  
Average Value ◽  
Sensitivity Level ◽  
Accuracy Level ◽  
Baseline Wander ◽  
Ecg Arrhythmia Classification ◽  
Ecg Arrhythmia

Arrhythmia is a cardiovascular disease that can be diagnosed by doctors using an electrocardiogram (ECG). The information contained on the ECG is used by doctors to analyze the electrical activity of the heart and determine the type of arrhythmia suffered by the patient. In this study, ECG arrhythmia classification process was performed using Support Vector Machine based fuzzy logic. In the proposed method, fuzzy membership functions are used to cope with data that are not classifiable in the method of Support Vector Machine (SVM) one-against-one. An early stage of the data processing is the baseline wander removal process on the original ECG signal using Transformation Wavelet Discrete (TWD). Afterwards then the ECG signal is cleaned from the baseline wander segmented into units beat. The next stage is to look for six features of the beat. Every single beat is classified using SVM method based fuzzy logic. Results from this study show that ECG arrhythmia classification using proposed method (SVM based fuzzy logic) gives better results than original SVM method. ECG arrhythmia classification using SVM method based fuzzy logic forms an average value of accuracy level, sensitivity level, and specificity level of 93.5%, 93.5%, and 98.7% respectively. ECG arrhythmia classification using only SVM method forms an average value accuracy level, sensitivity level, and specificity level of 91.83%, 91.83%, and 98.36% respectively.

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