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 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.

Wavelet-Based Multiresolution Bispectral Analysis for Detection and Classification of Helicopter Drive-Shaft Problems

2017 ◽  
Vol 140 (6) ◽  
Author(s):  
Mohammed A. Hassan ◽  
Michael R. Habib ◽  
Rania A. Abul Seoud ◽  
Abdel M. Bayoumi
Keyword(s):  
Search Algorithm ◽  
Drive Shaft ◽  
Superior Performance ◽  
Support Vector ◽  
Classification Algorithms ◽  
Test Bed ◽  
K Nearest Neighbor ◽  
Safety Level ◽  
Rotor Drive ◽  
Minimum Number

Condition monitoring and fault diagnostics in rotorcraft have significant effect on improving safety level and reducing operational and maintenance costs. In this paper, a new method is proposed for fault detection and diagnoses of AH-64D (Apache helicopter) tail rotor drive-shaft problems. The proposed method depends on decomposing signal into different frequency ranges using mother wavelet. The most informative part of the vibration signal is then determined by calculating Shannon entropy of each part. Bispectrum is calculated for this part to investigate quadratic nonlinearities in this segment. Then, search algorithm is used to extract minimum number of indicative features from the bispectrum, which are then fed to classification algorithms. In order to quantitatively evaluate the proposed method, six classification algorithms are compared against each other such as fine K-nearest neighbor (KNN), cubic KNN, quadratic discriminant analysis, linear support vector machine (SVM), Gaussian SVM, and neural network. Comparison criteria include accuracy, precision, sensitivity, F score, true alarm, recall, and error classification accuracy (ECA). The proposed method is verified using real-world vibration data collected from a dedicated AH-64D helicopter tail rotor drive train (TRDT) research test bed. The proposed algorithm proves its ability in finding minimum number of indicative features and classifying the shaft faults with superior performance.

scholarly journals Comparison of Different Classifiers and the Majority Voting Rule for the Detection of Plum Fruits in Garden Conditions

2019 ◽  
Vol 11 (21) ◽  
pp. 2546 ◽  
Author(s):  
Razieh Pourdarbani ◽  
Sajad Sabzi ◽  
Mario Hernández-Hernández ◽  
José Luis Hernández-Hernández ◽  
Ginés García-Mateos ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Harmony Search ◽  
Hybrid Approach ◽  
Majority Voting ◽  
Ensemble Method ◽  
Support Vector ◽  
Artificial Neural ◽  
Majority Voting Method ◽  
Voting Method

Color segmentation is one of the most thoroughly studied problems in agricultural applications of remote image capture systems, since it is the key step in several different tasks, such as crop harvesting, site specific spraying, and targeted disease control under natural light. This paper studies and compares five methods to segment plum fruit images under ambient conditions at 12 different light intensities, and an ensemble method combining them. In these methods, several color features in different color spaces are first extracted for each pixel, and then the most effective features are selected using a hybrid approach of artificial neural networks and the cultural algorithm (ANN-CA). The features selected among the 38 defined channels were the b* channel of L*a*b*, and the color purity index, C*, from L*C*h. Next, fruit/background segmentation is performed using five classifiers: artificial neural network-imperialist competitive algorithm (ANN-ICA); hybrid artificial neural network-harmony search (ANN-HS); support vector machines (SVM); k nearest neighbors (kNN); and linear discriminant analysis (LDA). In the ensemble method, the final class for each pixel is determined using the majority voting method. The experiments showed that the correct classification rate for the majority voting method excluding LDA was 98.59%, outperforming the results of the constituent methods.

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.

Privacy Protection of Patient Medical Images using Digital Watermarking Technique for E-healthcare System

2019 ◽  
Vol 15 (8) ◽  
pp. 802-809
Author(s):  
Asokan Sivaprakash ◽  
Samuel Nadar Edward Rajan ◽  
Sundaramoorthy Selvaperumal
Keyword(s):  
Health Care ◽  
Digital Watermarking ◽  
Privacy Protection ◽  
Medical Images ◽  
Search Algorithm ◽  
Research Work ◽  
Cuckoo Search ◽  
Superior Performance ◽  
Discrete Wavelet ◽  
Medical Systems

Background: Privacy protection has been a critical issue in the delivery of medical images for telemedicine, e-health care and other remote medical systems. Objective: The aim of this proposed work is to implement a secure, reversible, digital watermarking technique for the transmission of medical data remotely in health care systems. Methods: In this research work, we employed a novel optimized digital watermarking scheme using discrete wavelet transform and singular value decomposition using cuckoo search algorithm based on Lévy flight for embedding watermark into the grayscale medical images of the patient. The performance of our proposed algorithm is evaluated on four different 256 × 256 grayscale host medical images and a 32 × 32 binary logo image. Results: The performance of the proposed scheme in terms of peak signal to noise ratio was remarkably high, with an average of 55.022dB compared to other methods. Conclusion: Experimental results reveal that the proposed method is capable of achieving superior performance compared to some of the state-of-art schemes in terms of robustness, security and high embedding capacity which is required in the field of telemedicine and e-health care system.

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