scholarly journals Image tampering detection using genetic algorithm

2019 ◽  
Vol 277 ◽  
pp. 02026
Author(s):  
Ritu Agarwal ◽  
Mallika Pant
Keyword(s):  
Genetic Algorithm ◽  
Digital Media ◽  
Digital Images ◽  
Search Algorithm ◽  
Recognition Rate ◽  
Difficult Problem ◽  
Optimal Number ◽  
Support Vector ◽  
Image Forgery ◽  
Selection Algorithms

As digital images become an indispensable source of information, the authentication of digital images has become crucial. Various techniques of forgery have come into existence, intrusive, and non-intrusive. Image forgery detection hence is becoming more challenging by the day, due to the unwavering advances in image processing. Therefore, image forensics is at the forefront of security applications aiming at restoring trust and acceptance in digital media by exposing counterfeiting methods. The proposed work compares between various feature selection algorithms for the detection of image forgery in tampered images. Several features are extracted from normal and spliced images using spatial grey level dependence method and many more. Support vector machine and Twin SVM has been used for classification. A very difficult problem in classification techniques is to pick features to distinguish between classes. Furthermore, The feature optimization problem is addressed using a genetic algorithm (GA) as a search method. At last, classical sequential methods and floating search algorithm are compared against the genetic approach in terms of the best recognition rate achieved and the optimal number of features.

scholarly journals Genetic Algorithm Approach to Find the Estimated Value of HMM parametersfor NS5 Methyltransferase Protein

2021 ◽  
Vol 14 (3) ◽  
pp. 1567-1578
Author(s):  
Nidhi Katiyar ◽  
Ravindra Nath ◽  
Shashwat Katiyar
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Search Algorithm ◽  
Specific Treatment ◽  
Machine Learning Techniques ◽  
Severe Dengue ◽  
Severe Illness ◽  
Support Vector ◽  
Learning Approaches ◽  
Observation Sequence

Dengue is the pandemic disease caused by Dengue virus (DENV), a mosquito-borne flavivirus. In recent years dengue has emerged as a foremost cause of severe illness and deaths in developing countries.About 400 million dengue infections occur worldwide each year.In general, dengue infections create only mild illness but infrequently expand into a lethal illness termed as severe dengue for which no specific treatment. The machine learning approach plays a significant role in bioinformatics and other fields of computer science.It exploitsapproaches like Hidden Markov Model (HMM), Genetic Algorithm (GA), Artificial Neural Network (ANN), and Support Vector Machine (SVM).The GA is a randomized search algorithm for solving the problem based on natural selection phenomena.Many machine learning techniques are based on HMM have been positively applied. In this work, We firstly used HMM parameters on the biological sequence,and after that, we catch the probability of the observation sequence of a mutated gene sequence. This study comparesboth methods, G.A. and HMM, to get the highest estimated value of the observation sequence. In this paper, we also discuss the applications ofGA in the bioinformatics field. In a further study, we will apply the other machine learning approaches to find the best result of protein studies.

scholarly journals Rolling bearing fault diagnosis based on MEEMD sample entropy and SSA-SVM

2021 ◽  
Vol 2125 (1) ◽  
pp. 012003
Author(s):  
Xuguang Li ◽  
Liyou Fu
Keyword(s):  
Fault Diagnosis ◽  
Search Algorithm ◽  
Recognition Rate ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Sample Entropy ◽  
Permutation Entropy ◽  
Support Vector ◽  
Svm Classifier ◽  
Kernel Parameter

Abstract The penalty parameter (c) and kernel parameter (g) contained in Support Vector Machine (SVM) cannot be adaptively selected according to actual samples, which results in low classification accuracy and slow convergence speed. A novel sparrow search algorithm was used to optimize the parameters of SVM classifier. Firstly, an improved ensemble empirical mode decomposition (MEEMD) method was used to decompose non-stationary and nonlinear vibration signals, and the eigenmode function (IMF) was obtained by removing abnormal signals from the original signals through permutation entropy, and the sample entropy was extracted. Finally, a fault diagnosis model based on SSA-SVM is constructed, and the high recognition rate and effectiveness of this method are proved by simulation and experimental data analysis.

Study of Pesticide Contaminated Navel Orange Recognition Using near Infrared Spectroscopy

2011 ◽  
Vol 186 ◽  
pp. 121-125 ◽  
Author(s):  
Long Xue ◽  
Jing Li ◽  
Mu Hua Liu ◽  
Xiao Wang ◽  
Chun Sheng Luo
Keyword(s):  
Genetic Algorithm ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Support Vector ◽  
Navel Orange ◽  
Set Up ◽  
Spectral Ranges

Based on Support Vector Machine (SVM) and genetic algorithm (GA), this paper intends to search for the characteristic spectral ranges and wavelengths of near infrared spectroscopy of navel oranges contaminated by different pesticides, and set up recognition models. The pesticides in the experiment were Lannate®L insecticide, fenvalerate and omethoate, and three different concentrations were given to each pesticide. Preparing ten groups of navel oranges, each group was sprayed with a different pesticide and the 10th group without pesticide spraying was used for comparison. Searching the whole spectral range through GA, 5 best spectral ranges (165 wavelengths) were obtained and the recognition rate reached 98.86%. Then based on the chosen spectral ranges, 85 feature wavelengths were extracted with continual GA-SVM optimization, and the recognition rate was 99.14%. Experiment results showed that the application of SVM combining with GA can not only improve recognition accuracy, but also simplify the model effectively

scholarly journals Semisupervised SVM Based on Cuckoo Search Algorithm and Its Application

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Ziping He ◽  
Kewen Xia ◽  
Wenjia Niu ◽  
Nelofar Aslam ◽  
Jingzhong Hou
Keyword(s):  
Search Algorithm ◽  
Recognition Rate ◽  
Model Performance ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Coordination Mechanism ◽  
Support Vector ◽  
Local Optimum ◽  
Improved Cuckoo Search Algorithm

Semisupervised support vector machine (S3VM) algorithm mainly depends on the predicted accuracy of unlabeled samples, if lots of misclassified unlabeled samples are added to the training will make the training model performance degrade. Thus, the cuckoo search algorithm (CS) is used to optimize the S3VM which also enhances the model performance of S3VM. Considering that the cuckoo search algorithm is limited to the local optimum problem, a new cuckoo search algorithm based on chaotic catfish effect optimization is proposed. First, use the chaotic mechanism with high randomness to initialize the nest for range expansion. Second, chaotic catfish nest is introduced into the effective competition coordination mechanism after falling into the local optimum, so that the candidate’s nest can jump out of the local optimal solution and accelerate the convergence ability. In the experiment, results show that the improved cuckoo search algorithm is effective and better than the particle swarm optimization (PSO) algorithm and the cuckoo search algorithm on the benchmark functions. In the end, the improved cuckoo search algorithm is used to optimize semisupervised SVM which is applied into oil layer recognition. Results show that this optimization model is superior to the semisupervised SVM in terms of recognition rate and time.

scholarly journals Action Recognition of Lower Limbs Based on Surface Electromyography Weighted Feature Method

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6147
Author(s):  
Jiashuai Wang ◽  
Dianguo Cao ◽  
Jinqiang Wang ◽  
Chengyu Liu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Action Recognition ◽  
Lower Limb ◽  
Surface Electromyography ◽  
Recognition Rate ◽  
Lower Limbs ◽  
Support Vector ◽  
Local Optimum ◽  
Improved Genetic Algorithm

To improve the recognition rate of lower limb actions based on surface electromyography (sEMG), an effective weighted feature method is proposed, and an improved genetic algorithm support vector machine (IGA-SVM) is designed in this paper. First, for the problem of high feature redundancy and low discrimination in the surface electromyography feature extraction process, the weighted feature method is proposed based on the correlation between muscles and actions. Second, to solve the problem of the genetic algorithm selection operator easily falling into a local optimum solution, the improved genetic algorithm-support vector machine is designed by championship with sorting method. Finally, the proposed method is used to recognize six types of lower limb actions designed, and the average recognition rate reaches 94.75%. Experimental results indicate that the proposed method has definite potentiality in lower limb action recognition.

scholarly journals Robust and Stable Gene Selection via Maximum-Minimum Correntropy Criterion

2015 ◽  
Author(s):  
Majid Mohammadi ◽  
Hossein Sharifi Noghabi ◽  
Ghosheh Abed Hodtani ◽  
Habib Rajabi Mashhadi
Keyword(s):  
Microarray Data ◽  
Gene Selection ◽  
Optimal Number ◽  
Support Vector ◽  
Stable Gene ◽  
Data Sets ◽  
Data Set ◽  
Selection Algorithms ◽  
Maximum Minimum ◽  
Selection Of

One of the central challenges in cancer research is identifying significant genes among thousands of others on a microarray. Since preventing outbreak and progression of cancer is the ultimate goal in bioinformatics and computational biology, detection of genes that are most involved is vital and crucial. In this article, we propose a Maximum-Minimum Correntropy Criterion (MMCC) approach for selection of biologically meaningful genes from microarray data sets which is stable, fast and robust against diverse noise and outliers and competitively accurate in comparison with other algorithms. Moreover, via an evolutionary optimization process, the optimal number of features for each data set is determined. Through broad experimental evaluation, MMCC is proved to be significantly better compared to other well-known gene selection algorithms for 25 commonly used microarray data sets. Surprisingly, high accuracy in classification by Support Vector Machine (SVM) is achieved by less than 10 genes selected by MMCC in all of the cases.

TEMPLATE MATCHING IN DIGITAL IMAGES USING A COMPACT GENETIC ALGORITHM WITH ELITISM AND MUTATION

2010 ◽  
Vol 19 (01) ◽  
pp. 91-106 ◽  
Author(s):  
RAFAEL R. DA SILVA ◽  
CARLOS R. ERIG LIMA ◽  
HEITOR S. LOPES
Keyword(s):  
Genetic Algorithm ◽  
Template Matching ◽  
Digital Images ◽  
Search Algorithm ◽  
Computational Cost ◽  
The Other ◽  
Reference Image ◽  
Memory Consumption ◽  
Compact Genetic Algorithm ◽  
Overall Performance

The emCGA is a new extension of the compact genetic algorithm (CGA) that includes elitism and a mutation operator. These improvements do not increase significantly the computational cost or the memory consumption and, on the other hand, increase the overall performance in comparison with other similar works. The emCGA is applied to the problem of object recognition in digital images. The objective is to find a reference image (template) in a landscape image, subject to distortions and degradation in quality. Two models for dealing with the images are proposed, both based on the intensity of light. Several experiments were done with reference and landscape images, under different situations. The emCGA was compared with an exhaustive search algorithm and another CGA proposed in the literature. The emCGA was found to be more efficient for this problem, when compared with the other algorithms. We also compared the two proposed models for the object. One of them is more suitable for images with rich details, and the other for images with low illumination level. Both models seem to perform equally in the presence of distortions. Overall, results suggested the efficiency of emCGA for template matching in images and encourages future developments.

GESTURE RECOGNITION SYSTEM FOR NIGERIAN TRIBAL GREETING POSTURES USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 5 (2) ◽  
pp. 609
Author(s):  
Segun Aina ◽  
Kofoworola V. Sholesi ◽  
Aderonke R. Lawal ◽  
Samuel D. Okegbile ◽  
Adeniran I. Oluwaranti
Keyword(s):  
Support Vector Machine ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
Human Interaction ◽  
Support Vector ◽  
System A ◽  
Extraction Algorithm ◽  
Gaussian Blur

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.

An Energy Efficient Routing Approach to Enhance Coverage for Application Specific Wireless Sensor Networks using Genetic Algorithm

2019 ◽  
Vol 13 ◽  
Author(s):  
Amandeep Kaur Sohal ◽  
Ajay Kumar Sharma ◽  
Neetu Sood
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Transmission ◽  
Energy Efficient ◽  
Search Algorithm ◽  
Cluster Formation ◽  
Green Computing ◽  
Wireless Sensor ◽  
Energy Efficient Routing

Background: An information gathering is a typical and important task in agriculture monitoring and military surveillance. In these applications, minimization of energy consumption and maximization of network lifetime have prime importance for green computing. As wireless sensor networks comprise of a large number of sensors with limited battery power and deployed at remote geographical locations for monitoring physical events, therefore it is imperative to have minimum consumption of energy during network coverage. The WSNs help in accurate monitoring of remote environment by collecting data intelligently from the individual sensors. Objective: The paper is motivated from green computing aspect of wireless sensor network and an Energy-efficient Weight-based Coverage Enhancing protocol using Genetic Algorithm (WCEGA) is presented. The WCEGA is designed to achieve continuously monitoring of remote areas for a longer time with least power consumption. Method: The cluster-based algorithm consists two phases: cluster formation and data transmission. In cluster formation, selection of cluster heads and cluster members areas based on energy and coverage efficient parameters. The governing parameters are residual energy, overlapping degree, node density and neighbor’s degree. The data transmission between CHs and sink is based on well-known evolution search algorithm i.e. Genetic Algorithm. Conclusion: The results of WCEGA are compared with other established protocols and shows significant improvement of full coverage and lifetime approximately 40% and 45% respectively.

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