1. The document presents a new approach for steganography detection using a combination of Fisher's linear discriminant function (FLD) and radial basis function neural network (RBF).
2. In the training phase, FLD is used to project high-dimensional image data onto a lower dimensional space, then an RBF network is trained to classify images as containing hidden data or not.
3. Experiments show the combined FLD-RBF method provides promising results for steganography detection compared to existing supervised methods, though extracting the hidden information remains challenging.
This document discusses various clustering techniques for image segmentation. It begins by defining clustering and image segmentation. It then describes four main clustering techniques - exclusive clustering (e.g. k-means), overlapping clustering (e.g. fuzzy c-means), hierarchical clustering, and probabilistic-D clustering. For each technique, it provides details on the clustering algorithm and steps. It concludes that fuzzy c-means is superior to other approaches for image segmentation efficiency but has high computational time, while probabilistic-D clustering aims to reduce this time.
This document summarizes a research paper that designed and implemented sphere decoding (SD) for multiple-input multiple-output (MIMO) systems using an FPGA. It used Newton's iterative method to calculate the matrix inverse as part of the SD algorithm, which reduces complexity compared to direct matrix inversion. The authors implemented SD for a 2x2 MIMO system with 4-QAM modulation. Simulation results showed that Newton's method converged after 7 iterations, and SD successfully calculated the minimum Euclidean distance vector.
This document summarizes recent convergence results for the fuzzy c-means clustering algorithm (FCM). It discusses both numerical convergence, referring to how well the algorithm attains the minima of an objective function, and stochastic convergence, referring to how accurately the minima represent the actual cluster structure in data. For numerical convergence, the document outlines global and local convergence theorems, showing FCM converges to minima or saddle points globally and linearly to local minima. For stochastic convergence, it discusses a consistency result showing the minima accurately represent cluster structure under certain statistical assumptions.
This document proposes a new method to remove the dependence of fuzzy c-means clustering on random initialization. The conventional fuzzy c-means algorithm's performance is highly dependent on the randomly initialized membership values used to select initial centroids. The proposed method uses an algorithm by Yuan et al. to determine initial centroids without randomization. These centroids are then used as inputs to the conventional fuzzy c-means algorithm. The performance of the proposed method is compared to conventional fuzzy c-means using partition coefficient and clustering entropy validity indices. Results show the proposed method produces more consistent and better performance by removing the effect of random initialization.
Designing a Minimum Distance classifier to Class Mean ClassifierDipesh Shome
This document describes designing and implementing a minimum distance to class mean classifier. It begins with an introduction to how minimum distance classification works by calculating the mean of each class and assigning unknown samples to the closest class mean. It then describes the experimental design which includes plotting training data, classifying test data using linear discriminant functions, drawing the decision boundary, and calculating accuracy. The implementation section provides code details for these steps. It finds an accuracy of 85% for classifying test data and concludes the classifier performs well for linear datasets but has lower accuracy for nonlinear problems due to only modeling linear decision boundaries.
Machine Learning Algorithms for Image Classification of Hand Digits and Face ...IRJET Journal
This document discusses machine learning algorithms for image classification using five different classification schemes. It summarizes the mathematical models behind each classification algorithm, including Nearest Class Centroid classifier, Nearest Sub-Class Centroid classifier, k-Nearest Neighbor classifier, Perceptron trained using Backpropagation, and Perceptron trained using Mean Squared Error. It also describes two datasets used in the experiments - the MNIST dataset of handwritten digits and the ORL face recognition dataset. The performance of the five classification schemes are compared on these datasets.
This document proposes a dynamic clustering algorithm using fuzzy c-means clustering. It begins with an introduction to fuzzy c-means clustering and its limitations when the chosen number of clusters is incorrect. It then proposes a dynamic clustering algorithm that starts with a fixed number of clusters but can automatically increase the number of clusters during iterations based on the data, improving purity. The algorithm is described and examples are provided to illustrate its effectiveness at forming clear clusters after iterations and determining when clustering has terminated.
In this experiment, I tried to implement Minimum
error rate classifier using the posterior probabilities which
uses Normal distribution to calculate likelihood probabilities to
classify given sample points
This document discusses various clustering techniques for image segmentation. It begins by defining clustering and image segmentation. It then describes four main clustering techniques - exclusive clustering (e.g. k-means), overlapping clustering (e.g. fuzzy c-means), hierarchical clustering, and probabilistic-D clustering. For each technique, it provides details on the clustering algorithm and steps. It concludes that fuzzy c-means is superior to other approaches for image segmentation efficiency but has high computational time, while probabilistic-D clustering aims to reduce this time.
This document summarizes a research paper that designed and implemented sphere decoding (SD) for multiple-input multiple-output (MIMO) systems using an FPGA. It used Newton's iterative method to calculate the matrix inverse as part of the SD algorithm, which reduces complexity compared to direct matrix inversion. The authors implemented SD for a 2x2 MIMO system with 4-QAM modulation. Simulation results showed that Newton's method converged after 7 iterations, and SD successfully calculated the minimum Euclidean distance vector.
This document summarizes recent convergence results for the fuzzy c-means clustering algorithm (FCM). It discusses both numerical convergence, referring to how well the algorithm attains the minima of an objective function, and stochastic convergence, referring to how accurately the minima represent the actual cluster structure in data. For numerical convergence, the document outlines global and local convergence theorems, showing FCM converges to minima or saddle points globally and linearly to local minima. For stochastic convergence, it discusses a consistency result showing the minima accurately represent cluster structure under certain statistical assumptions.
This document proposes a new method to remove the dependence of fuzzy c-means clustering on random initialization. The conventional fuzzy c-means algorithm's performance is highly dependent on the randomly initialized membership values used to select initial centroids. The proposed method uses an algorithm by Yuan et al. to determine initial centroids without randomization. These centroids are then used as inputs to the conventional fuzzy c-means algorithm. The performance of the proposed method is compared to conventional fuzzy c-means using partition coefficient and clustering entropy validity indices. Results show the proposed method produces more consistent and better performance by removing the effect of random initialization.
Designing a Minimum Distance classifier to Class Mean ClassifierDipesh Shome
This document describes designing and implementing a minimum distance to class mean classifier. It begins with an introduction to how minimum distance classification works by calculating the mean of each class and assigning unknown samples to the closest class mean. It then describes the experimental design which includes plotting training data, classifying test data using linear discriminant functions, drawing the decision boundary, and calculating accuracy. The implementation section provides code details for these steps. It finds an accuracy of 85% for classifying test data and concludes the classifier performs well for linear datasets but has lower accuracy for nonlinear problems due to only modeling linear decision boundaries.
Machine Learning Algorithms for Image Classification of Hand Digits and Face ...IRJET Journal
This document discusses machine learning algorithms for image classification using five different classification schemes. It summarizes the mathematical models behind each classification algorithm, including Nearest Class Centroid classifier, Nearest Sub-Class Centroid classifier, k-Nearest Neighbor classifier, Perceptron trained using Backpropagation, and Perceptron trained using Mean Squared Error. It also describes two datasets used in the experiments - the MNIST dataset of handwritten digits and the ORL face recognition dataset. The performance of the five classification schemes are compared on these datasets.
This document proposes a dynamic clustering algorithm using fuzzy c-means clustering. It begins with an introduction to fuzzy c-means clustering and its limitations when the chosen number of clusters is incorrect. It then proposes a dynamic clustering algorithm that starts with a fixed number of clusters but can automatically increase the number of clusters during iterations based on the data, improving purity. The algorithm is described and examples are provided to illustrate its effectiveness at forming clear clusters after iterations and determining when clustering has terminated.
In this experiment, I tried to implement Minimum
error rate classifier using the posterior probabilities which
uses Normal distribution to calculate likelihood probabilities to
classify given sample points
Implementation of K-Nearest Neighbor AlgorithmDipesh Shome
The document describes implementing the K-Nearest Neighbors (KNN) machine learning algorithm. It discusses taking input data, plotting sample points from the training data, implementing the KNN algorithm to classify test data points based on their distances to training points, and analyzing the results. Code is provided to perform the KNN implementation, including calculating distances, predicting classes, and plotting classified test points.
Optimising Data Using K-Means Clustering AlgorithmIJERA Editor
K-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori. The main idea is to define k centroids, one for each cluster. These centroids should be placed in a cunning way because of different location causes different result. So, the better choice is to place them as much as possible far away from each other.
This document presents a digital medical image cryptosystem based on an infinite-dimensional multi-scroll chaotic delay differential equation (DDE) for secure telemedicine applications. The cryptosystem performs an XOR operation between separated image planes and a shuffled chaotic attractor image from the DDE. The security keys are the initial condition and time constant in the DDE. The document analyzes the nonlinear dynamics of the DDE, including equilibrium points, waveforms, and a 3-scroll attractor. It evaluates the encryption and decryption of CT scan images using histograms, spectral density, key sensitivity, and correlation to demonstrate the cryptosystem's security.
Colour Image Segmentation Using Soft Rough Fuzzy-C-Means and Multi Class SVM ijcisjournal
Color image segmentation algorithms in the literature segment an image on the basis of color, texture, and
also as a fusion of both color and texture. In this paper, a color image segmentation algorithm is proposed
by extracting both texture and color features and applying them to the One-Against-All Multi Class Support
Vector Machine classifier for segmentation. A novel Power Law Descriptor (PLD) is used for extracting
the textural features and homogeneity model is used for obtaining the color features. The Multi Class SVM
is trained using the samples obtained from Soft Rough Fuzzy-C-Means (SRFCM) clustering. Fuzzy set
based membership functions capably handle the problem of overlapping clusters. The lower and upper
approximation concepts of rough sets deal well with uncertainty, vagueness, and incompleteness in data.
Parameterization tools are not a prerequisite in defining Soft set theory. The goodness aspects of soft sets,
rough sets and fuzzy sets are incorporated in the proposed algorithm to achieve improved segmentation
performance. The Power Law Descriptor used for texture feature extraction has the advantage of being
dealt in the spatial domain thereby reducing computational complexity. The proposed algorithm is
comparable and achieved better performance compared with the state of the art algorithms found in the
literature.
The Positive Effects of Fuzzy C-Means Clustering on Supervised Learning Class...Waqas Tariq
Selection of inputs is one of the most substantial components of classification algorithms for data mining and pattern recognition problems since even the best classifier will perform badly if the inputs are not selected very well. Big data and computational complexity are main cause of bad performance and low accuracy for classical classifiers. In other words, the complexity of classifier method is inversely proportional with its classification efficiency. For this purpose, two hybrid classifiers have been developed by using both type-1 and type-2 fuzzy c-means clustering with cascaded a classifier. In this proposed classifier, a large number of data points are reduced by using fuzzy c-means clustering before applied to a classifier algorithm as inputs. The aim of this study is to investigate the effect of fuzzy clustering on well-known and useful classifiers such as artificial neural networks (ANN) and support vector machines (SVM). Then the role of positive effects of these proposed algorithms were investigated on applied different data sets.
The document proposes a novel Spatial Fuzzy C-Means (PET-SFCM) clustering algorithm to segment PET scan images of patients with neurodegenerative disorders like Alzheimer's disease. The algorithm incorporates spatial neighborhood information into the traditional Fuzzy C-Means algorithm. It was tested on real patient data sets and showed satisfactory results compared to conventional FCM and K-Means clustering algorithms. The PET-SFCM algorithm provides an effective way to segment PET images and analyze brain changes related to neurological conditions.
A COMPREHENSIVE ANALYSIS OF QUANTUM CLUSTERING : FINDING ALL THE POTENTIAL MI...IJDKP
Quantum clustering (QC), is a data clustering algorithm based on quantum mechanics which is
accomplished by substituting each point in a given dataset with a Gaussian. The width of the Gaussian is a
σ value, a hyper-parameter which can be manually defined and manipulated to suit the application.
Numerical methods are used to find all the minima of the quantum potential as they correspond to cluster
centers. Herein, we investigate the mathematical task of expressing and finding all the roots of the
exponential polynomial corresponding to the minima of a two-dimensional quantum potential. This is an
outstanding task because normally such expressions are impossible to solve analytically. However, we
prove that if the points are all included in a square region of size σ, there is only one minimum. This bound
is not only useful in the number of solutions to look for, by numerical means, it allows to to propose a new
numerical approach “per block”. This technique decreases the number of particles by approximating some
groups of particles to weighted particles. These findings are not only useful to the quantum clustering
problem but also for the exponential polynomials encountered in quantum chemistry, Solid-state Physics
and other applications.
The document summarizes statistical pattern recognition techniques. It is divided into 9 sections that cover topics like dimensionality reduction, classifiers, classifier combination, and unsupervised classification. The goal of pattern recognition is supervised or unsupervised classification of patterns based on features. Dimensionality reduction aims to reduce the number of features to address the curse of dimensionality when samples are limited. Multiple classifiers can be combined through techniques like stacking, bagging, and boosting. Unsupervised classification uses clustering algorithms to construct decision boundaries without labeled training data.
Incorporating Kalman Filter in the Optimization of Quantum Neural Network Par...Waqas Tariq
Kalman filter have been used for the estimation of instantaneous states of linear dynamic systems. It is a good tool for inferring of missing information from noisy measurement. The quantum neural network is another approach to the merging of fuzzy logic with the neural network and that by the investment of quantum mechanics theory in building the structure of neural network. The gradient descent algorithm has been used, widely, in training the neural network, but the problem of local minima is one of the disadvantages of this algorithm. This paper presents an algorithm to train the quantum neural network by using the extended kalman filter.
Behavior study of entropy in a digital image through an iterative algorithmijscmcj
Image segmentation is a critical step in computer vision tasks constituting an essential issue for pattern recognition and visual interpretation. In this paper, we study the behavior of entropy in digital images through an iterative algorithm of mean shift filtering. The order of a digital image in gray levels is defined. The behavior of Shannon entropy is analyzed and then compared, taking into account the number of iterations of our algorithm, with the maximum entropy that could be achieved under the same order. The use of equivalence classes it induced, which allow us to interpret entropy as a hyper-surface in real m dimensional space. The difference of the maximum entropy of order n and the entropy of the image is used to group the the iterations, in order to caractrizes the performance of the algorithm.
The document provides an overview of self-organizing maps (SOM). It defines SOM as an unsupervised learning technique that reduces the dimensions of data through the use of self-organizing neural networks. SOM is based on competitive learning where the closest neural network unit to the input vector (the best matching unit or BMU) is identified and adjusted along with neighboring units. The algorithm involves initializing weight vectors, presenting input vectors, identifying the BMU, and updating weights of the BMU and neighboring units. SOM can be used for applications like dimensionality reduction, clustering, and visualization.
This document discusses using fuzzy clustering to group real estate properties. It presents a case study clustering 46 real estate listings into 3 groups based on price, area, and region attributes. The fuzzy c-means clustering algorithm in MATLAB is used to assign membership levels and cluster centroids. The results identify 3 clusters - one for mid-priced properties in good regions and average areas, one for high-priced properties in excellent regions and large areas, and one for low-priced properties in poor regions and small areas. Graphs and tables show the clustered properties and centroids.
Soft computing is likely to play aprogressively important role in many applications including image enhancement. The paradigm for soft computing is the human mind. The soft computing critique has been particularly strong with fuzzy logic. The fuzzy logic is facts representationas a
rule for management of uncertainty. Inthis paperthe Multi-Dimensional optimized problem is addressed by discussing the optimal thresholding usingfuzzyentropyfor Image enhancement. This technique is compared with bi-level and multi-level thresholding and obtained optimal
thresholding values for different levels of speckle noisy and low contrasted images. The fuzzy entropy method has produced better results compared to bi-level and multi-level thresholding techniques.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
This document discusses using artificial neural networks for network intrusion detection. Specifically, it proposes a hybrid classification model that uses entropy-based feature selection to reduce the dataset, followed by four neural network techniques (RBFN, SOM, SMO, PART) for classification. It provides details on each neural network technique and the overall methodology, which uses 10-fold cross validation to evaluate performance based on standard criteria. The goal is to build an efficient intrusion detection system with low false alarms and high detection rates.
This document discusses tracking multiple objects in video using probabilistic distributions. It proposes using particle filters to represent object positions with random particles. The method initializes particles randomly, updates their positions each frame based on probabilistic distributions, and uses maximum likelihood estimation to compute the distribution parameters. It models object motion using a beta distribution and estimates the distribution's alpha and beta parameters from each frame to predict object positions. The results show this approach can effectively track multiple moving objects, especially when there are occlusions.
Anomaly Detection in Temporal data Using Kmeans Clustering with C5.0theijes
Anomaly detection is a challenging problem in Temporal data .In this paper we have proposed an algorithm using two different machine learning techniques Kmeans clustering and C5.0 decision tree , where Euclidean distance is used to find the closest cluster for the data set and then decision tree is built for each cluster using C5.0 decision tree technique and the rules of decision tree is used to classify each anomalous and normal instances in the dataset .The proposed algorithm gives impressive classification accuracy in the experimented result and describe the proposed system of kmeans and C5.0 decision tree
This document discusses principal component analysis (PCA) and its applications in image processing and facial recognition. PCA is a technique used to reduce the dimensionality of data while retaining as much information as possible. It works by transforming a set of correlated variables into a set of linearly uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. The document provides an example of applying PCA to a set of facial images to reduce them to their principal components for analysis and recognition.
PCA is an unsupervised learning technique used to reduce the dimensionality of large data sets by transforming the data to a new set of variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. PCA is commonly used for applications like dimensionality reduction, data compression, and visualization. The document discusses PCA algorithms and applications of PCA in domains like face recognition, image compression, and noise filtering.
This document discusses improving data security for mobile devices using cloud computing storage. It proposes encrypting data stored in the cloud to address security issues. Mobile cloud computing integrates mobile networks and cloud computing to provide services for mobile users. However, storing large amounts of personal and enterprise data in the cloud raises security risks regarding data integrity, authentication, and access. The document reviews these risks and considers solutions like encryption and digital rights management to protect data stored in the cloud.
This document describes how to hack into a target machine using social engineering and SSH. It involves using Nmap to scan the target machine and find open ports, then using Hydra to brute force common username and password combinations to gain SSH access. Once logged in via SSH, the hacker can explore the system but does not have root privileges. The document provides steps to gain root access including viewing the /etc/passwd file to find the root username and attempt to su to gain root privileges on the target machine.
Implementation of K-Nearest Neighbor AlgorithmDipesh Shome
The document describes implementing the K-Nearest Neighbors (KNN) machine learning algorithm. It discusses taking input data, plotting sample points from the training data, implementing the KNN algorithm to classify test data points based on their distances to training points, and analyzing the results. Code is provided to perform the KNN implementation, including calculating distances, predicting classes, and plotting classified test points.
Optimising Data Using K-Means Clustering AlgorithmIJERA Editor
K-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori. The main idea is to define k centroids, one for each cluster. These centroids should be placed in a cunning way because of different location causes different result. So, the better choice is to place them as much as possible far away from each other.
This document presents a digital medical image cryptosystem based on an infinite-dimensional multi-scroll chaotic delay differential equation (DDE) for secure telemedicine applications. The cryptosystem performs an XOR operation between separated image planes and a shuffled chaotic attractor image from the DDE. The security keys are the initial condition and time constant in the DDE. The document analyzes the nonlinear dynamics of the DDE, including equilibrium points, waveforms, and a 3-scroll attractor. It evaluates the encryption and decryption of CT scan images using histograms, spectral density, key sensitivity, and correlation to demonstrate the cryptosystem's security.
Colour Image Segmentation Using Soft Rough Fuzzy-C-Means and Multi Class SVM ijcisjournal
Color image segmentation algorithms in the literature segment an image on the basis of color, texture, and
also as a fusion of both color and texture. In this paper, a color image segmentation algorithm is proposed
by extracting both texture and color features and applying them to the One-Against-All Multi Class Support
Vector Machine classifier for segmentation. A novel Power Law Descriptor (PLD) is used for extracting
the textural features and homogeneity model is used for obtaining the color features. The Multi Class SVM
is trained using the samples obtained from Soft Rough Fuzzy-C-Means (SRFCM) clustering. Fuzzy set
based membership functions capably handle the problem of overlapping clusters. The lower and upper
approximation concepts of rough sets deal well with uncertainty, vagueness, and incompleteness in data.
Parameterization tools are not a prerequisite in defining Soft set theory. The goodness aspects of soft sets,
rough sets and fuzzy sets are incorporated in the proposed algorithm to achieve improved segmentation
performance. The Power Law Descriptor used for texture feature extraction has the advantage of being
dealt in the spatial domain thereby reducing computational complexity. The proposed algorithm is
comparable and achieved better performance compared with the state of the art algorithms found in the
literature.
The Positive Effects of Fuzzy C-Means Clustering on Supervised Learning Class...Waqas Tariq
Selection of inputs is one of the most substantial components of classification algorithms for data mining and pattern recognition problems since even the best classifier will perform badly if the inputs are not selected very well. Big data and computational complexity are main cause of bad performance and low accuracy for classical classifiers. In other words, the complexity of classifier method is inversely proportional with its classification efficiency. For this purpose, two hybrid classifiers have been developed by using both type-1 and type-2 fuzzy c-means clustering with cascaded a classifier. In this proposed classifier, a large number of data points are reduced by using fuzzy c-means clustering before applied to a classifier algorithm as inputs. The aim of this study is to investigate the effect of fuzzy clustering on well-known and useful classifiers such as artificial neural networks (ANN) and support vector machines (SVM). Then the role of positive effects of these proposed algorithms were investigated on applied different data sets.
The document proposes a novel Spatial Fuzzy C-Means (PET-SFCM) clustering algorithm to segment PET scan images of patients with neurodegenerative disorders like Alzheimer's disease. The algorithm incorporates spatial neighborhood information into the traditional Fuzzy C-Means algorithm. It was tested on real patient data sets and showed satisfactory results compared to conventional FCM and K-Means clustering algorithms. The PET-SFCM algorithm provides an effective way to segment PET images and analyze brain changes related to neurological conditions.
A COMPREHENSIVE ANALYSIS OF QUANTUM CLUSTERING : FINDING ALL THE POTENTIAL MI...IJDKP
Quantum clustering (QC), is a data clustering algorithm based on quantum mechanics which is
accomplished by substituting each point in a given dataset with a Gaussian. The width of the Gaussian is a
σ value, a hyper-parameter which can be manually defined and manipulated to suit the application.
Numerical methods are used to find all the minima of the quantum potential as they correspond to cluster
centers. Herein, we investigate the mathematical task of expressing and finding all the roots of the
exponential polynomial corresponding to the minima of a two-dimensional quantum potential. This is an
outstanding task because normally such expressions are impossible to solve analytically. However, we
prove that if the points are all included in a square region of size σ, there is only one minimum. This bound
is not only useful in the number of solutions to look for, by numerical means, it allows to to propose a new
numerical approach “per block”. This technique decreases the number of particles by approximating some
groups of particles to weighted particles. These findings are not only useful to the quantum clustering
problem but also for the exponential polynomials encountered in quantum chemistry, Solid-state Physics
and other applications.
The document summarizes statistical pattern recognition techniques. It is divided into 9 sections that cover topics like dimensionality reduction, classifiers, classifier combination, and unsupervised classification. The goal of pattern recognition is supervised or unsupervised classification of patterns based on features. Dimensionality reduction aims to reduce the number of features to address the curse of dimensionality when samples are limited. Multiple classifiers can be combined through techniques like stacking, bagging, and boosting. Unsupervised classification uses clustering algorithms to construct decision boundaries without labeled training data.
Incorporating Kalman Filter in the Optimization of Quantum Neural Network Par...Waqas Tariq
Kalman filter have been used for the estimation of instantaneous states of linear dynamic systems. It is a good tool for inferring of missing information from noisy measurement. The quantum neural network is another approach to the merging of fuzzy logic with the neural network and that by the investment of quantum mechanics theory in building the structure of neural network. The gradient descent algorithm has been used, widely, in training the neural network, but the problem of local minima is one of the disadvantages of this algorithm. This paper presents an algorithm to train the quantum neural network by using the extended kalman filter.
Behavior study of entropy in a digital image through an iterative algorithmijscmcj
Image segmentation is a critical step in computer vision tasks constituting an essential issue for pattern recognition and visual interpretation. In this paper, we study the behavior of entropy in digital images through an iterative algorithm of mean shift filtering. The order of a digital image in gray levels is defined. The behavior of Shannon entropy is analyzed and then compared, taking into account the number of iterations of our algorithm, with the maximum entropy that could be achieved under the same order. The use of equivalence classes it induced, which allow us to interpret entropy as a hyper-surface in real m dimensional space. The difference of the maximum entropy of order n and the entropy of the image is used to group the the iterations, in order to caractrizes the performance of the algorithm.
The document provides an overview of self-organizing maps (SOM). It defines SOM as an unsupervised learning technique that reduces the dimensions of data through the use of self-organizing neural networks. SOM is based on competitive learning where the closest neural network unit to the input vector (the best matching unit or BMU) is identified and adjusted along with neighboring units. The algorithm involves initializing weight vectors, presenting input vectors, identifying the BMU, and updating weights of the BMU and neighboring units. SOM can be used for applications like dimensionality reduction, clustering, and visualization.
This document discusses using fuzzy clustering to group real estate properties. It presents a case study clustering 46 real estate listings into 3 groups based on price, area, and region attributes. The fuzzy c-means clustering algorithm in MATLAB is used to assign membership levels and cluster centroids. The results identify 3 clusters - one for mid-priced properties in good regions and average areas, one for high-priced properties in excellent regions and large areas, and one for low-priced properties in poor regions and small areas. Graphs and tables show the clustered properties and centroids.
Soft computing is likely to play aprogressively important role in many applications including image enhancement. The paradigm for soft computing is the human mind. The soft computing critique has been particularly strong with fuzzy logic. The fuzzy logic is facts representationas a
rule for management of uncertainty. Inthis paperthe Multi-Dimensional optimized problem is addressed by discussing the optimal thresholding usingfuzzyentropyfor Image enhancement. This technique is compared with bi-level and multi-level thresholding and obtained optimal
thresholding values for different levels of speckle noisy and low contrasted images. The fuzzy entropy method has produced better results compared to bi-level and multi-level thresholding techniques.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
This document discusses using artificial neural networks for network intrusion detection. Specifically, it proposes a hybrid classification model that uses entropy-based feature selection to reduce the dataset, followed by four neural network techniques (RBFN, SOM, SMO, PART) for classification. It provides details on each neural network technique and the overall methodology, which uses 10-fold cross validation to evaluate performance based on standard criteria. The goal is to build an efficient intrusion detection system with low false alarms and high detection rates.
This document discusses tracking multiple objects in video using probabilistic distributions. It proposes using particle filters to represent object positions with random particles. The method initializes particles randomly, updates their positions each frame based on probabilistic distributions, and uses maximum likelihood estimation to compute the distribution parameters. It models object motion using a beta distribution and estimates the distribution's alpha and beta parameters from each frame to predict object positions. The results show this approach can effectively track multiple moving objects, especially when there are occlusions.
Anomaly Detection in Temporal data Using Kmeans Clustering with C5.0theijes
Anomaly detection is a challenging problem in Temporal data .In this paper we have proposed an algorithm using two different machine learning techniques Kmeans clustering and C5.0 decision tree , where Euclidean distance is used to find the closest cluster for the data set and then decision tree is built for each cluster using C5.0 decision tree technique and the rules of decision tree is used to classify each anomalous and normal instances in the dataset .The proposed algorithm gives impressive classification accuracy in the experimented result and describe the proposed system of kmeans and C5.0 decision tree
This document discusses principal component analysis (PCA) and its applications in image processing and facial recognition. PCA is a technique used to reduce the dimensionality of data while retaining as much information as possible. It works by transforming a set of correlated variables into a set of linearly uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. The document provides an example of applying PCA to a set of facial images to reduce them to their principal components for analysis and recognition.
PCA is an unsupervised learning technique used to reduce the dimensionality of large data sets by transforming the data to a new set of variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. PCA is commonly used for applications like dimensionality reduction, data compression, and visualization. The document discusses PCA algorithms and applications of PCA in domains like face recognition, image compression, and noise filtering.
This document discusses improving data security for mobile devices using cloud computing storage. It proposes encrypting data stored in the cloud to address security issues. Mobile cloud computing integrates mobile networks and cloud computing to provide services for mobile users. However, storing large amounts of personal and enterprise data in the cloud raises security risks regarding data integrity, authentication, and access. The document reviews these risks and considers solutions like encryption and digital rights management to protect data stored in the cloud.
This document describes how to hack into a target machine using social engineering and SSH. It involves using Nmap to scan the target machine and find open ports, then using Hydra to brute force common username and password combinations to gain SSH access. Once logged in via SSH, the hacker can explore the system but does not have root privileges. The document provides steps to gain root access including viewing the /etc/passwd file to find the root username and attempt to su to gain root privileges on the target machine.
This document discusses the use of an adaptive decision feedback equalizer (ADFE) to mitigate pulse dispersion in optical communication channels. It begins by describing different sources of dispersion in optical fibers. Then it proposes using a fractional spaced decision feedback equalizer (FSDFE) integrated with activity detection guidance (ADG) and tap decoupling (TD) to improve performance. Simulation results show the FSDFE can estimate the channel impulse response and minimize differences between the input and output. Adding ADG and TD further improves convergence rate, detection of inactive taps, and asymptotic performance. The ADFE is an effective technique for equalization and mitigating dispersion in optical links.
This document summarizes a study on using a fuzzy total margin based support vector machine (FTM-SVM) approach to handle class imbalance in machine learning classification problems. It discusses how traditional SVM classifiers can overfit to the majority class in imbalanced data sets. The proposed FTM-SVM method aims to address this issue by incorporating a total margin algorithm, different cost functions, and fuzzy membership functions to reduce the effect of outliers and noise on the minority class. The paper evaluates the FTM-SVM approach on artificial and imbalanced data sets, finding it achieves higher performance measures than some existing class imbalance learning methods.
This document describes the design and implementation of a printed rectangular monopole antenna for wireless networks. It aims to create a broadband antenna for frequencies like Bluetooth, Wi-Fi, and WiMAX between 2.4-2.4835 GHz. The antenna is printed on a PCB with a rectangular patch and ground plane. It is fed using a microstrip line. The design achieves a bandwidth of 4.1-4.26 GHz through optimization of parameters like patch size and feed length. Both software simulation and hardware implementation are conducted, with the hardware results showing slightly reduced bandwidth compared to simulation. The antenna demonstrates good performance for broadband wireless applications.
This document summarizes research on topology control techniques in wireless sensor networks. It first discusses how topology control aims to reduce energy consumption while maintaining network connectivity by regulating nodes' transmission power. It then reviews several existing topology control algorithms proposed in other papers. These algorithms distribute transmission power control to maximize network lifetime. Finally, the document concludes that many topology control algorithms have been developed to achieve energy efficient routing, but implementing them on real-world testbeds poses challenges.
This document summarizes research on evaluating WiMAX network performance using vertical handoff. It describes the setup used, which includes 8 base stations to test handoff as a mobile station moves between cells. Graphs show the mobile station's throughput drops slightly during handoff, with maximum delay of 0.025 seconds. Vertical handoff between WiMAX and WLAN networks is also tested, with the document observing a smooth handoff between the networks as the mobile nodes move between their coverage areas.
This document discusses a computational fluid dynamics (CFD) analysis of a shell and tube heat exchanger with different baffle inclinations. The study aims to determine the optimal baffle inclination angle and mass flow rate. It analyzes heat transfer characteristics for baffle inclinations of 0, 10 and 20 degrees. The results indicate that a helical baffle configuration forces fluid rotation, increasing heat transfer rates and coefficients more than a segmental baffle design. Overall, the CFD simulation allows determination of outlet temperatures, pressure drops, and optimal design parameters for improved heat exchanger performance.
This document summarizes the design optimization and analysis of an impeller for a centrifugal compressor. It begins with background on centrifugal compressors and their applications. The aim is then stated as developing a methodology to design a centrifugal compressor impeller accounting for real fluid effects. A computer program is developed based on jet-wake theory to estimate impeller dimensions. The methodology is validated by comparing results to an existing impeller design, showing encouraging accuracy. The method is then applied to design an impeller for an air conditioning system using R-12 as the refrigerant at 18,000 rpm. Key design parameters are examined at varying speeds to select optimal values.
This document discusses India's smart cities initiative and the role of public-private partnerships. It notes that India's urban population is growing rapidly and current infrastructure cannot support this growth. The government plans to build 100 smart cities to address issues like pollution, congestion, and resource scarcity. Public-private partnerships are seen as key to providing the large investments needed, estimated at over $10 billion per city. PPPs can help develop smart infrastructure, healthcare, mobility, technology and energy systems. The document analyzes how PPPs can ensure quality infrastructure and services to enable smart city development in India.
The document summarizes a study on identification marks among three population groups in Daman and Diu, India. It found that moles, scars, and tattoos were the most common identification marks. Moles were observed on the face most frequently across all groups. Scars were found primarily on knees. Tattoos were located mainly on the forearm. Psychological perceptions of marks varied, with some females viewing moles or scars on the face negatively. The study concludes that analyzing identification marks and their locations among different groups can help with personal identification. Expanding such research may further forensic anthropology goals.
This study examined the tensile behavior of ferrocement composite panels with varying numbers of wire mesh layers and inclusion of steel fibers. 36 panels were cast and tested under direct tension. Panels were divided into groups based on number of mesh layers (1 to 6 layers) and use of steel fibers. Testing found that ultimate load, elongation and tensile strength increased with additional mesh layers due to higher reinforcement volume fraction. Panels with steel fibers exhibited 10-17% higher strength than non-fiber panels. Failure occurred through cracking perpendicular to the load direction. The study concluded that ferrocement properties directly correlate to the number of reinforcing mesh layers.
The document discusses using support vector machines (SVM) and various lexical, semantic, and syntactic features for question classification. It aims to develop a state-of-the-art machine learning based question classifier. Various features are discussed, including lexical features like n-grams and stemming, syntactic features like question headwords, and semantic features derived from named entity recognition, WordNet senses, and semantic word lists. SVM is used as the classifier to take advantage of its good performance for text classification tasks. The results show that combining these feature types can achieve accurate question classification.
This document proposes a model for effectively gathering requirements from multiple sites within an organization. Requirements gathering is an important but challenging part of software development, made more difficult when requirements must be collected from different organizational units/sites. The proposed model is an iterative process that involves collecting requirements from each site for a given module, checking for contradictions or ambiguities, validating the practicality of requirements, and reconciling any issues found before moving to the next module. This process continues until all requirements from all sites have been gathered in a consistent, unambiguous manner.
This document summarizes a proposed architecture for remote patient monitoring using wireless sensor networks. The architecture allows virtual groups to be formed between patients, nurses, and doctors to enable remote analysis of patient data collected by wireless body area networks (WBANs). The patient data is transmitted through an underlying environmental sensor network to members of the virtual group. The proposed architecture addresses challenges of power supply for body sensor networks and quality of service guarantees.
This document summarizes research on utilizing fly ash to treat domestic wastewater. Fly ash was collected and characterized, then used as a filter media in two containers with thicknesses of 5 cm and 10 cm. Domestic wastewater was treated by passing it through the fly ash filters. Testing showed the 10 cm thick fly ash filter reduced biochemical oxygen demand by 71.48%, chemical oxygen demand by 66.59%, and total solids by 69.02% compared to untreated wastewater. The research concludes that fly ash is effective at removing various impurities from domestic wastewater and is a low-cost option for small-scale wastewater treatment.
This document summarizes an innovative routing algorithm called AntHocNet for mobile ad hoc networks. AntHocNet combines aspects of ant colony optimization and information bootstrapping to address the challenges of routing in dynamic mobile networks. Key elements of AntHocNet include the use of both reactive and proactive routing components, combining ant-based path sampling with a lightweight bootstrapping process to update routing information, and using a composite pheromone metric to guide path selection. The document evaluates the performance impacts of these different design components through simulation studies.
This document analyzes the performance of a diesel engine fueled with blends of biodiesel derived from Cashew Nut Shell Liquid (CNSL) and ethanol. Experiments were conducted with diesel and blends containing 10%, 15%, 20% CNSL, as well as blends with 5% and 10% ethanol added to the 15% CNSL blend. Performance parameters like brake thermal efficiency, fuel consumption, emissions were measured and compared across fuel blends and to diesel. Results showed the 15% CNSL blend performed better than other blends, while adding ethanol reduced performance due to its lower energy content. This research evaluates CNSL biodiesel and its blends as potential alternatives to conventional diesel
The document compares the design of circular and square water tanks using the working stress method and limit state method. It was found that:
1) The limit state method requires less steel than the working stress method for both circular and square tank designs.
2) A circular tank design is more economical than a square tank design due to requiring less steel.
3) The limit state method results in a more rational and economical design compared to the traditional working stress method.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
The document summarizes research on using artificial neural networks for restoring digitized paintings. Specifically, it reviews using a Median Radial Basis Function neural network to separate cracks from brush strokes that have been misidentified as cracks. The MRBF network uses hue and saturation values to classify pixels identified by a top-hat transform as either cracks or brush strokes. It was tested on three images and able to separate correctly identified cracks from brush strokes. The paper concludes the MRBF methodology can be applied to virtually restore digitized paintings by separating real cracks from misidentified brush strokes.
Expert system design for elastic scattering neutrons optical model using bpnnijcsa
In present paper, a proposed expert system is designed to obtain a trained formulae for the optical model
parameters used in elastic scattering neutrons of light nuclei for (7Li), at energy range between [(1) to
(20)] MeV. A simple algorithm has used to design this expert system, while a multi-layer backwardpropagation
neural network (BPNN) is applied for training and testing the data used in this model. This
group of formulae may get a simple expert system occurring from governing formulae model, and predicts
the critical parameters usually resulted from the complicated computer coding methods. This expert system
may use in nuclear reactions yields in both fission and fusion nature who gives more closely results to the
real model.
In this paper generation of binary sequences derived from chaotic sequences defined over Z4 is proposed.
The six chaotic map equations considered in this paper are Logistic map, Tent Map, Cubic Map, Quadratic
Map and Bernoulli Map. Using these chaotic map equations, sequences over Z4 are generated which are
converted to binary sequences using polynomial mapping. Segments of sequences of different lengths are
tested for cross correlation and linear complexity properties. It is found that some segments of different
length of these sequences have good cross correlation and linear complexity properties. The Bit Error Rate
performance in DS-CDMA communication systems using these binary sequences is found to be better than
Gold sequences and Kasami sequences.
This summary provides an overview of the key points from the document:
1) The document presents the use of General Regression Neural Networks (GRNN) to predict propagation path loss in an urban environment based on measurements taken in Kavala, Greece.
2) Two neural network models are studied - one for path loss prediction and another using error control. Their performance is compared to measured path loss values based on error metrics.
3) For line-of-sight predictions, the GRNN model achieves better performance than empirical models due to using multiple input parameters and generalization. For non-line-of-sight, a third GRNN model including street orientation has the lowest error rates.
COLOR IMAGE ENCRYPTION BASED ON MULTIPLE CHAOTIC SYSTEMSIJNSA Journal
This document proposes a novel color image encryption scheme based on multiple chaotic systems. The scheme utilizes the ergodic properties of chaotic systems to perform pixel permutation and applies a substitution operation to achieve diffusion. In the permutation stage, two generalized Arnold maps are used to generate hybrid chaotic sequences to permute pixel positions. In the diffusion stage, four pseudo-random gray value sequences generated by another generalized Arnold map are used to diffuse the permuted image via bitwise XOR operations. Security analysis shows the scheme has a large key space and is highly secure against statistical attacks, differential attacks, and chosen/known plaintext attacks.
COLOR IMAGE ENCRYPTION BASED ON MULTIPLE CHAOTIC SYSTEMSIJNSA Journal
This paper proposed a novel color image encryption scheme based on multiple chaotic systems. The ergodicity property of chaotic system is utilized to perform the permutation process; a substitution
operation is applied to achieve the diffusion effect. In permutation stage, the 3D color plain-image matrix
is converted to a 2D image matrix, then two generalized Arnold maps are employed to generate hybrid chaotic sequences which are dependent on the plain-image’s content. The generated chaotic sequences are then applied to perform the permutation process. The encryption’s key streams not only depend on the
cipher keys but also depend on plain-image and therefore can resist chosen-plaintext attack as well as
known-plaintext attack. In the diffusion stage, four pseudo-random gray value sequences are generated by another generalized Arnold map. The gray value sequences are applied to perform the diffusion process by bitxoring operation with the permuted image row-by-row or column-by-column to improve the encryption rate. The security and performance analysis have been performed, including key space analysis, histogram analysis, correlation analysis, information entropy analysis, key sensitivity analysis, differential analysis etc. The experimental results show that the proposed image encryption scheme is highly secure thanks to its large key space and efficient permutation-substitution operation, and therefore it is suitable for practical image and video encryption.
COLOR IMAGE ENCRYPTION BASED ON MULTIPLE CHAOTIC SYSTEMSIJNSA Journal
This paper proposed a novel color image encryption scheme based on multiple chaotic systems. The ergodicity property of chaotic system is utilized to perform the permutation process; a substitution operation is applied to achieve the diffusion effect. In permutation stage, the 3D color plain-image matrix is converted to a 2D image matrix, then two generalized Arnold maps are employed to generate hybrid chaotic sequences which are dependent on the plain-image’s content. The generated chaotic sequences are then applied to perform the permutation process. The encryption’s key streams not only depend on the cipher keys but also depend on plain-image and therefore can resist chosen-plaintext attack as well as
known-plaintext attack. In the diffusion stage, four pseudo-random gray value sequences are generated by
another generalized Arnold map. The gray value sequences are applied to perform the diffusion process by bitxoring operation with the permuted image row-by-row or column-by-column to improve the encryption rate. The security and performance analysis have been performed, including key space analysis, histogram analysis, correlation analysis, information entropy analysis, key sensitivity analysis, differential analysis
etc. The experimental results show that the proposed image encryption scheme is highly secure thanks to its
large key space and efficient permutation-substitution operation, and therefore it is suitable for practical image and video encryption.
The document analyzes the use of the Tent map as a source of pseudorandom bits for generating binary codes. It evaluates the Tent map's period length, discrimination value, and merit factor. The Tent map is proposed as an alternative to traditional low-complexity pseudorandom bit generators. Different window functions are applied to the binary codes generated from the Tent map to reduce side lobes and improve performance. Results show discrimination increases with sequence length and some window functions perform better than others at different lengths.
Path Loss Prediction by Robust Regression Methodsijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
New Approach of Preprocessing For Numeral RecognitionIJERA Editor
The present paper proposes a new approach of preprocessing for handwritten, printed and isolated numeral
characters. The new approach reduces the size of the input image of each numeral by discarding the redundant
information. This method reduces also the number of features of the attribute vector provided by the extraction
features method. Numeral recognition is carried out in this work through k nearest neighbors and multilayer
perceptron techniques. The simulations have obtained a good rate of recognition in fewer running time.
Improvement of Anomaly Detection Algorithms in Hyperspectral Images Using Dis...sipij
Recently anomaly detection (AD) has become an important application for target detection in hyperspectral remotely sensed images. In many applications, in addition to high accuracy of detection we need a fast and reliable algorithm as well. This paper presents a novel method to improve the performance of current AD algorithms. The proposed method first calculates Discrete Wavelet Transform (DWT) of every pixel vector of image using Daubechies4 wavelet. Then, AD algorithm performs on four bands of “Wavelet transform” matrix which are the approximation of main image. In this research some benchmark AD algorithms including Local RX, DWRX and DWEST have been implemented on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral datasets. Experimental results demonstrate significant improvement of runtime in proposed method. In addition, this method improves the accuracy of AD algorithms because of DWT’s power in extracting approximation coefficients of signal, which contain the main behaviour of signal, and abandon the redundant information in hyperspectral image data.
Blind Image Seperation Using Forward Difference Method (FDM)sipij
In this paper, blind image separation is performed, exploiting the property of sparseness to represent images. A new sparse representation called forward difference method is proposed. It is known that most of the independent component analysis (ICA) basis functions, extracted from images are sparse and gives unreliable sparseness measure. In the proposed method, the image mixture is first transformed to sparse images. These images are divided into blocks and for each block the sparseness measure ε0 norm is applied. The block having the most sparseness is considered to determine the separation matrix. The efficiency of the proposed method is compared with other sparse representation functions.
This document proposes using machine learning techniques to predict COVID-19 infections based on chest x-ray images. Specifically, it involves using discrete wavelet transform to extract space-frequency features from chest x-rays, reducing the dimensionality of features using Shannon entropy, and then training standard machine learning classifiers like logistic regression, support vector machine, decision tree, and convolutional neural network on the extracted features to classify images as COVID-19 positive or negative. The document provides background on the proposed techniques of discrete wavelet transform, entropy, and various machine learning models.
A Fuzzy Interactive BI-objective Model for SVM to Identify the Best Compromis...ijfls
This document summarizes a research paper that proposes a fuzzy bi-objective support vector machine (SVM) model to identify infected COVID-19 patients. The model uses SVM classification with two objectives - maximizing margin between classes and minimizing misclassification errors. An α-cut transforms the fuzzy model into a classical bi-objective problem solved using weighting methods. This generates multiple efficient solutions. An interactive process then identifies the best compromise based on minimizing the number of support vectors in each class. The model constructs a utility function to measure COVID-19 infection levels based on the SVM classification.
A FUZZY INTERACTIVE BI-OBJECTIVE MODEL FOR SVM TO IDENTIFY THE BEST COMPROMIS...ijfls
A support vector machine (SVM) learns the decision surface from two different classes of the input points. In several applications, some of the input points are misclassified and each is not fully allocated to either of these two groups. In this paper a bi-objective quadratic programming model with fuzzy parameters is utilized and different feature quality measures are optimized simultaneously. An α-cut is defined to transform the fuzzy model to a family of classical bi-objective quadratic programming problems. The weighting method is used to optimize each of these problems. For the proposed fuzzy bi-objective quadratic programming model, a major contribution will be added by obtaining different effective support vectors due to changes in weighting values. The experimental results, show the effectiveness of the α-cut with the weighting parameters on reducing the misclassification between two classes of the input points. An interactive procedure will be added to identify the best compromise solution from the generated efficient solutions. The main contribution of this paper includes constructing a utility function for measuring the degree of infection with coronavirus disease (COVID-19).
This document summarizes research on using particle swarm optimization to reconstruct microwave images of two-dimensional dielectric scatterers. It formulates the inverse scattering problem as an optimization problem to find the dielectric parameter distribution that minimizes the difference between measured and simulated scattered field data. Numerical results show that a particle swarm optimization approach can accurately reconstruct the shape and dielectric properties of a test cylindrical scatterer, with lower background reconstruction error than a genetic algorithm approach. The research demonstrates that particle swarm optimization is a suitable technique for high-dimensional microwave imaging problems.
This document introduces an R package called PSF that implements a Pattern Sequence based Forecasting (PSF) algorithm for univariate time series forecasting. The PSF algorithm clusters time series data and then predicts future values based on identifying repeating patterns of clusters. The PSF package contains functions that perform the main steps of the PSF algorithm, including selecting the optimal number of clusters, selecting the optimal window size, and making predictions for a given window size and number of clusters. The package aims to promote and simplify the use of the PSF algorithm for time series forecasting.
The document compares using Euclidean distance versus Mahalanobis distance for supervised self-organizing maps in a rail defect classification application. It finds that using Mahalanobis distance, which accounts for variance differences across input dimensions, leads to better classification results, especially when using a partitioning approach that separates the multi-class problem into binary sub-problems. Specifically, the Mahalanobis distance improved classification rates from 92.8% to 94.1% for a global classifier, and from 90% to 95% for a partitioning classifier.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
This document compares self-organizing feature maps (SOFM) with k-means clustering and artificial neural networks for pattern recognition and feature map creation. SOFM uses competitive learning to organize input vectors into clusters without supervision, mapping similar inputs close together on the map. K-means aims to partition inputs into a predefined number of clusters by minimizing within-cluster variation. Artificial neural networks implement classification in three phases - self-organizing feature map learning, followed by supervised learning phases. The document discusses algorithms, architectures, and training approaches for each method.
This document summarizes a research paper that examines pricing strategy in a two-stage supply chain consisting of a supplier and retailer. The supplier offers a credit period to the retailer, who then offers credit to customers. A mathematical model is formulated to maximize total profit for the integrated supply chain system. The model considers three cases based on the relative lengths of the credit periods offered at each stage. Equations are developed to represent the profit functions for the supplier, retailer and overall system in each case. The goal is to determine the optimal selling price that maximizes total integrated profit.
The document discusses melanoma skin cancer detection using a computer-aided diagnosis system based on dermoscopic images. It begins with an introduction to skin cancer and melanoma. It then reviews existing literature on automated melanoma detection systems that use techniques like image preprocessing, segmentation, feature extraction and classification. Features extracted in other studies include asymmetry, border irregularity, color, diameter and texture-based features. The proposed system collects dermoscopic images and performs preprocessing, segmentation, extracts 9 features based on the ABCD rule, and classifies images using a neural network classifier to detect melanoma. It aims to develop an automated diagnosis system to eliminate invasive biopsy procedures.
This document summarizes various techniques for image segmentation that have been studied and proposed in previous research. It discusses edge-based, threshold-based, region-based, clustering-based, and other common segmentation methods. It also reviews applications of segmentation in medical imaging, plant disease detection, and other fields. While no single technique can segment all images perfectly, hybrid and adaptive methods combining multiple approaches may provide better results. Overall, image segmentation remains an important but challenging task in digital image processing and computer vision.
This document presents a test for detecting a single upper outlier in a sample from a Johnson SB distribution when the parameters of the distribution are unknown. The test statistic proposed is based on maximum likelihood estimates of the four parameters (location, scale, and two shape) of the Johnson SB distribution. Critical values of the test statistic are obtained through simulation for different sample sizes. The performance of the test is investigated through simulation, showing it performs well at detecting outliers when the contaminant observation represents a large shift from the original distribution parameters. An example application to census data is also provided.
This document summarizes a research paper that proposes a portable device called the "Disha Device" to improve women's safety. The device has features like live location tracking, audio/video recording, automatic messaging to emergency contacts, a buzzer, flashlight, and pepper spray. It is designed using an Arduino microcontroller connected to GPS and GSM modules. When the button is pressed, it sends an alert message with the woman's location, sets off an alarm, activates the flashlight and pepper spray for self-defense. The goal is to provide women a compact, one-click safety system to help them escape dangerous situations or call for help with just a single press of a button.
- The document describes a study that constructed physical fitness norms for female students attending social welfare schools in Andhra Pradesh, India.
- Researchers tested 339 students in classes 6-10 on speed, strength, agility and flexibility tests. Tests included 50m run, bend and reach, medicine ball throw, broad jump, shuttle run, and vertical jump.
- The results showed that 9th class students had the best average time for the 50m run. 10th class students had the highest flexibility on average. Strength and performance generally improved with increased class level.
This document summarizes research on downdraft gasification of biomass. It discusses how downdraft gasifiers effectively convert solid biomass into a combustible producer gas. The gasification process involves pyrolysis and reactions between hot char and gases that produce CO, H2, and CH4. Downdraft gasifiers are well-suited for biomass gasification due to their simple design and ability to manage the gasification process with low tar production. The document also reviews previous studies on gasifier configuration upgrades and their impact on performance, and the principles of downdraft gasifier operation.
This document summarizes the design and manufacturing of a twin spindle drilling attachment. Key points:
- The attachment allows a drilling machine to simultaneously drill two holes in a single setting, improving productivity over a single spindle setup.
- It uses a sun and planet gear arrangement to transmit power from the main spindle to two drilling spindles.
- Components like gears, shafts, and housing were designed using Creo software and manufactured. Drill chucks, bearings, and bits were purchased.
- The attachment was assembled and installed on a vertical drilling machine. It is aimed at improving productivity in mass production applications by combining two drilling operations into one setup.
The document presents a comparative study of different gantry girder profiles for various crane capacities and gantry spans. Bending moments, shear forces, and section properties are calculated and tabulated for 'I'-section with top and bottom plates, symmetrical plate girder, 'I'-section with 'C'-section top flange, plate girder with rolled 'C'-section top flange, and unsymmetrical plate girder sections. Graphs of steel weight required per meter length are presented. The 'I'-section with 'C'-section top flange profile is found to be optimized for biaxial bending but rolled sections may not be available for all spans.
This document summarizes research on analyzing the first ply failure of laminated composite skew plates under concentrated load using finite element analysis. It first describes how a finite element model was developed using shell elements to analyze skew plates of varying skew angles, laminations, and boundary conditions. Three failure criteria (maximum stress, maximum strain, Tsai-Wu) were used to evaluate first ply failure loads. The minimum load from the criteria was taken as the governing failure load. The research aims to determine the effects of various parameters on first ply failure loads and validate the numerical approach through benchmark problems.
This document summarizes a study that investigated the larvicidal effects of Aegle marmelos (bael tree) leaf extracts on Aedes aegypti mosquitoes. Specifically, it assessed the efficacy of methanol extracts from A. marmelos leaves in killing A. aegypti larvae (at the third instar stage) and altering their midgut proteins. The study found that the leaf extract achieved 50% larval mortality (LC50) at a concentration of 49 ppm. Proteomic analysis of larval midguts revealed changes in protein expression levels after exposure to the extract, suggesting its bioactive compounds can disrupt the midgut. The aim is to identify specific inhibitor proteins in the midg
This document presents a system for classifying electrocardiogram (ECG) signals using a convolutional neural network (CNN). The system first preprocesses raw ECG data by removing noise and segmenting the signals. It then uses a CNN to extract features directly from the ECG data and classify arrhythmias without requiring complex feature engineering. The CNN architecture contains 11 convolutional layers and is optimized using techniques like batch normalization and dropout. The system was tested on ECG datasets and achieved classification accuracy of over 93%, demonstrating its effectiveness at automated ECG classification.
This document presents a new algorithm for extracting and summarizing news from online newspapers. The algorithm first extracts news related to the topic using keyword matching. It then distinguishes different types of news about the same topic. A term frequency-based summarization method is used to generate summaries. Sentences are scored based on term frequency and the highest scoring sentences are selected for the summary. The algorithm was evaluated on news datasets from various newspapers and showed good performance in intrinsic evaluation metrics like precision, recall and F-score. Thus, the proposed method can effectively extract and summarize online news for a given keyword or topic.
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A NEW APPROACH OF STEGANOGRAPHY USING RADIAL
BASIS FUNCTION NEURAL NETWORK
Debashish Rout ,Bijaya Kumar Khamari,Tusharkanta Samal and Ashok Kumar Bhoi
Department of Computer Science and Engineering ,VSSUT, Burla, India134
Department of Mechanical Engineering ,VSSUT, Burla, India2
1234
debashish.rout1@gmail.com,bijayaa.khamari@gmail.com,samaltushar1@gmail.com,ashokbhoiserc@gmail.com
Abstract - Steganographic tools and techniques are becoming more potential and widespread. Illegal use
of steganography poses serious challenges to the law enforcement agencies. Limited work has been
carried out on supervised steganalysis using neural network as a classifier. We present a combined
method of identifying the presence of covert information in a carrier image using fisher’s linear
discriminant (FLD) function followed by the radial basis function (RBF). Experiments show promising
results when compared to the existing supervised steganalysis methods, but arranging the retrieved
information is still a challenging problem.
Keywords - Steganography, carrier image, covert image.
I. INTRODUCTION
Steganography is a type of hidden communication that literally means “covered writing”. The message is
out in the open, often for all to see, but goes undetected because the very existence of the message is secret [12,
20, 21]. Steganalysis could be described as a method to prevent steganography. There are other attacks on
steganography. Attacking the end hosts of the steganography algorithm by searching for security credentials is
not steganalysis. Digital forensics encompasses more methods than solely steganalysis to attack steganography.
The target for digital forensics is detection of steganography. The objective of steganalysis is “detecting
messages hidden using steganography”. Steganalysis is about separating covermessages from stego-messages.
In this work, passive steganalysis is focused.
Most of the present literature on steganalysis follows either a parametric model [28, 24, 26] or a blind
model [32, 27, 22, 23, 35, 33]. A general steganalysis method that can attack steganography blindly, detect
hidden data without knowing embedding methods, will be more useful in practical applications. A framework
for steganalysis based on supervised learning has been done in [34]. The framework was further developed and
tested. Limited work has been carried out on supervised steganalysis, using neural networks as a classifier [29,
30]. Fishers’ linear discriminant function (FLD) as a classifier show impressive results in [31]. The present
neural network based steganalytic work is implemented by combining the radial basis function neural network
with fishers’ linear discriminant function.
II. METHODOLOGY
Machine learning theory based steganalysis assume no statistical information about the stego image, host
image and the secret message. This work falls under the category of supervised learning employing two phase
strategies: a) training phase and b) testing phase. In training phase, original carriers are supplied to neural
classifier to learn the nature of the images. RBF takes the role of neural classifier in this work. By training the
classifier for a specific embedding algorithm a reasonably accurate detection can be achieved. RBF neural
classifier in this work learns a model by averaging over the multiple examples which include both stego and
non-stego images. In testing phase, unknown images are supplied to the trained classifier to decide whether
secret information is present or not. The flowcharts of both the phases are given below in fig. 1:
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Fig.1(a): Training Phase
Fig. 1(b): Testing phase
2.1 Fisher’s Linear Discriminant Function
The process of changing the dimensions of a vector is called transformation. The transformation of a set of
n-dimensional real vectors onto a plane is called a mapping operation. The result of this operation is a planar
display. The main advantage of the planar display is that the distribution of the original patterns of higher
dimensions (more than two dimensions) can be seen on a two dimensional graph. The mapping operation can be
linear or non-linear. R.A. Fisher developed a linear classification algorithm [1] and a method for constructing a
classifier on the optimal discriminant plane, with minimum distance criterion for multi-class classification with
small number of patterns [16]. The method of considering the number of patterns and feature size [4], and the
relations between discriminant analysis and multilayer perceptrons [17] has been addressed earlier. A linear
mapping is used to map an n-dimensional vector space onto a two dimensional space. Some of the linear
mapping algorithms are principal component mapping [5], generalized declustering mapping [2, 3, 8, 9], least
squared error mapping [11] and projection pursuit mapping [6]. In this work, the generalized declustering
optimal discriminant plane is used. The mapping of the original pattern ‘X’ onto a new vector ‘Y’ on a plane is
done by a matrix transformation, which is given by
Y=AX (1)
where
(2)
and φ1 and φ 2 are the discriminant vectors (also called projection vectors).
An overview of different mapping techniques [14, 15] is addressed earlier. The vectors φ1 and φ2 are
obtained by optimizing a given criterion. The plane formed by the discriminant vectors is the optimal vectors
which are the optimal discriminant planes. This plane gives the highest possible classification for the new
patterns.
The steps involved in the linear mappings are:
Step 1: Computation of the discriminant vectors φ1 and φ2: this is specific for a particular linear mapping
algorithm.
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Step 2: Computation of the planar images of the original data points: this is for all linear mapping algorithms.
1) Computation of discriminant vectors φ1 and φ2
The criterion to evaluate the classification performance is given by:
(3)
Where
Sb the between class matrix, and
Sw the within class matrix which is non-singular.
(4)
(5)
where
P(ωi) a priori the probability of the ith
pattern, generally, p ((ωi) = 1/m
mi the mean of each feature of the ith
class patterns, (i=1.2…,m),
mo the global mean of a feature of all the patterns in all the classes,
X {xi, I=1, 2,…L} the n-dimensional patterns of each class,
L the total number of patterns.
Eq.(3) states that the distance between the class centers should be maximum. The discriminant vector j1 that
maximizes ‘J’ in Eq. (3) is found as a solution of the eigenvalue problem given by:
(6)
where
λml the greatest non-zero eigenvalue of(Sb Sw
–1
)
φ1 eigenvalue corresponding to λml
The reason for choosing the eigenvector with maximum eigenvalue is that the Euclidean distance of this
vector will be the maximum, when compared with that of the other eigenvectors of Eq.(6). Another discriminant
vector j2 is obtained, by using the same criterion of Eq.(3). The discriminant vector j2 should also satisfy the
condition given by:
(7)
Eq.(7) indicates that the solution obtained is geometrically independent and the vectors φ1 and φ2 are
perpendicular to each other. Whenever the patterns are perpendicular to each other, it means, that there is
absolutely no redundancy, or repetition of a pattern. The discriminant vector φ2 is found as a solution of the
eigenvalue problem, which is given by:
(8)
where
λm2 the greatest non-zero eigen value of Qp SbSw
-1
, and
Qp the projection matrix which is given by
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(9)
where
I an identity matrix
The eigenvector corresponding to the maximum eigenvalue of Eq. (8) is the discriminant vector φ2. In
Eq.(6) and Eq. (8), SW should be non-singular. The SW matrix should be non-singular, even for a more general
discriminating analysis and multi-orthonormal vectors [7, 18, 19]. If the determinant of SW is zero, then singular
value decomposition (SVD) on SW has to be done. On using SVD [10, 13], SW is decomposed into three
matrices U, W and V. The matrices U and W are unitary matrices, and V is a diagonal matrix with non-negative
diagonal elements arranged in the decreasing order. A small value of 10-5
to 10-8
is to be added to the diagonal
elements of V matrix, whose value is zero. This process is called perturbation. After perturbing the V matrix, the
matrix Sw
1
is calculated by:
(10)
where
SW
1
the non-singular matrix which has to be considered in the place of Sw.
Minimum perturbed value should be considered, which is just sufficient to make Sw
1
nonsingular. As per Eq.(7),
when the values of φ1 and φ2 are innerproducted, the resultant value should be zero. In reality, the
innerproducted value will not be zero. This is due to floating point operations.
2) Computation of two-dimensional vector from the original n-dimensional input patterns
The two-dimensional vector set yi is obtained by:
(11)
The vector set yi is obtained by projecting the original pattern ‘X’ onto the space, spanned by φ1 and φ2 by
using Eq.(11). The values of ui and vi can be plotted in a two-dimensional graph, to know the distribution of the
original patterns.
2.2 Radial Basis Function
A radial basis function (RBF) is a real-valued function whose value depends only on the distance from the
origin. If a function ‘h’ satisfies the property h(x)=h(||x||), then it is a radial function. Their characteristic feature
is that their response decreases (or increases) monotonically with distance from a central point. The centre, the
distance scale, and the precise shape of the radial function are parameters of the model, all fixed if it is linear
[25].
A typical radial function is the Gaussian which, in the case of a scalar input, is
h(x)=exp((-(x-c)2
)/(r2
)) (12)
Its parameters are its centre c and its radius r.
A Gaussian RBF monotonically decreases with distance from the centre. In contrast, a multiquadric RBF
which, in the case of scalar input, monotonically increases with distance from the centre. Gaussian-like RBFs
are local (give a significant response only in a neighbourhood near the centre) and are more commonly used
than multiquadric-type RBFs which have a global response. Radial functions are simply a class of functions. In
principle, they could be employed in any sort of model (linear or nonlinear) and any sort of network (single-
layer or multi-layer). RBF networks have traditionally been associated with radial functions in a single-layer
network. In the Figure 2, the input layer carries the outputs of FLD function. The distance between these values
and centre values are found and summed to form linear combination before the neurons of the hidden layer.
These neurons are said to contain the radial basis function with exponential form. The outputs of the RBF
activation function is further processed according to specific requirements.
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Fig. 2: Radial Basis Function Network
III. IMPLEMENTATION
a) Training
1. Decide number of cover images.
2. Read each Image.
3. Calculate the principal component vector by
Z=Z * Z T
where
Z denotes the intensities of image
4. Find eigenvector of the Z matrix by applying eigen process.
5. Calculate the φ1 and φ2 vectors.
φ1 = eigenvector ( Sb * Sw
-1
)
Sb = Σ( PCVi – M0 ) ( PCVi – M0 )T
/ N
where:
PCVi ( i = 1,2,3 )
PCV1, Principal component vector1
PCV2, Principal component vector2
PCV3, Principal component vector3
M0 = Average of (PCV1 + PCV2 + PCV3)
Sw = (Σ( PCVi – Mi ) ( PCVi – Mi )T
) / N
where:
Mi ( i = 1, 2, 3)
M1, average of PCV1
M2, average of PCV2
M3, average of PCV3
6. Calculate φ2 vector.
φ2 = eigenvector (Q Sb Sw
-1
)
Q = I – ((φ1* φ1
-1
* Sw
-1
) / (φ1
t
* Sw
-1
* Phi_ φ1) )
7. Transfer for N dimensional vector into 2 dimensional vector.
U = φ1 * PCVi ( 1 , 2 , 3 )
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V = φ2* PCVi ( 1 , 2 , 3 )
8. Apply RBF.
No. of Input = 2
No. of Patterns = 15
No. of Centre = 2
Calculate RBF as
RBF = exp (-X)
Calculate Matrix as
G = RBF
A = GT
* G
Calculate
B = A-1
Calculate
E = B * GT
9. Calculate the final weight.
F = E * D
10. Store the final weights in a File.
b) Testing
1. Read steganographed image.
2. Calculate the principal component vector.
Z=Z * Z T
3. Find eigenvector of the Z matrix by applying eigen process.
4. Calculate RBF as.
RBF = exp ( -X )
G = RBF
A = GT
* G
B = A-1
E = B * GT
5. Calculate.
F = E * D
6. Classify the pixel as containing information or not.
IV. RESULTS AND DISCUSSION
The simulation of steganalysis has been implemented using MATLAB 7®. Sample sets of images
considered are gray and true color images. The different sets of cover images considered in the simulation are
given in Figure 3. The information image is given in Figure 4. Encryption technique has not been considered
during the simulation. The different ways the secret information scattered in the cover images are given in
Figure 5.
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Fig. 3: Cover images
Fig. 4: Information Image
Fig. 5: Distribution of information image in cover image
In this simulation, the information is embedded using least significant bit (LSB), discrete cosine
transformation (DCT) separately. In certain cases, 50% of the information image is embedded using LSB and
remaining 50% of the information is embedded using DCT. In the entire simulation, the size of the information
image is considered to 1/8 size of the original image (Table
I). The outputs of the FLD (Figure 6), RBF (Figure 7), and combined method FLD with RBF (Figure 8) are
shown. The projection vectors are given in Table II.
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Table I: SIMULATION ENVIRONMENT USED
Table II: PROJECTION VECTORS
These vectors obtained after finding out the Sw and Sb matrices considering 30 steganographed images
using the images given in Figure 3 and Figure 4. Figure 7 and figure 8 are obtained by setting a detection
threshold value of 2. Any output greater than a threshold is considered as the pixel containing the information.
The threshold value is different for different method.
Fig. 6: Steganalysis using FLD
Fig. 7: Steganalysis using RBF
V. CONCLUSION
Steganalysis has been implemented using FLD, RBF and combination of FLD and RBF algorithms. The
outputs of the algorithms for one steganographed image have been presented. Secret information is getting
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retrieved by the proposed algorithms with various degrees of accuracies. It can be noticed that the combined
method FLDRBF is much promising in detecting the presence of hidden information. The cover images chosen
for the simulation are standard images. The percentage of identifying the hidden information is more than 95%,
but arranging the retrieved information is still a challenging problem. The information can be well arranged in a
meaningful way by using a set of association rules.
Fig. 8: Steganalysis using FLDRBF
VI. ACKNOWLEDGEMENT
We express our sincere thanks to Mr. Ch Gandhi, GEQD, Hyderabad for his valuable support in improving
the quality of the paper. We also express our heartful thanks to Mr. P. Krishna Sastry and Mr. M. Krishna for
their technical support in doing experiments at computer forensic division, Hyderabad. Appreciation also goes
to Ms. S. Rajeswari, Ms. G. Sarada, Ms. Uma Devi, and Ms. Chennamma and for their help in literature survey
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