361 research outputs found
Generating Classification Rules from Training Samples
In this paper, we describe an algorithm to extract classification rules from training samples using fuzzy membership functions. The algorithm includes steps for generating classification rules, eliminating duplicate and conflicting rules, and ranking extracted rules. We have developed software to implement the algorithm using MATLAB scripts. As an illustration, we have used the algorithm to classify pixels in two multispectral images representing areas in New Orleans and Alaska. For each scene, we randomly selected 10 per cent of the samples from our training set data for generating an optimized rule set and used the remaining 90 per cent of samples to validate the extracted rules. To validate extracted rules, we built a fuzzy inference system (FIS) using the extracted rules as a rule base and classified samples from the training set data. The results in terms of confusion matrices are presented in the paper
Construction Practices and its Effect on Bond Strength of Pavements
oai:ojs.pkp.sfu.ca:article/3Extensive study is carried out to ascertain the effects of various construction practices on the bond strength between different layers of the pavement. In this study various construction practices refers to curing time of the pavement, various equipments used during construction of pavement, surface treatment provided. In this study a review is also carried out of the research work carried out by various researchers for estimating the bond strength between the existing old hot mix asphalt (HMA) layer and the new hot mix asphalt (HMA) layer overlaid. Also a review is taken in study regarding various experiments conducted by the researchers on the bond strength of different layers. Normally milling provides a good strength and good bond at the interface between the old layer and new layer overlaid on it. It is also observed that curing time has least effect on the bond strength
Water Quality Retrieval from Landsat TM Imagery
In this paper, the utility of Landsat TM imagery for water quality studies in East Texas is investigated. Remote sensing has an important and effective role in water quality management. Remote sensing satellites measure the amount of solar radiation reflected by surface water and the reflectance of water depend upon the concentration and character of water quality parameters. Three water quality parameters namely the total suspended solids, chlorophyll-a, and turbidity are estimated in this study. In situ water quality parameter measurements from seven ground stations and the corresponding Landsat TM data were used to estimate the water quality parameters. Regression models are used to evaluate correlation between the water quality parameters and spectral reflectance values
Content-Based Image Retrieval Using Associative Memories
The rapid growth in the number of large-scale repositories has brought the need for efficient and effective content-based image retrieval (CBIR) systems. The state of the art in the CBIR systems is to search images in database that are “close” to the query image using some similarity measure. The current CBIR systems capture image features that represent properties such as color, texture, and/or shape of the objects in the query image and try to retrieve images from the database with similar features. In this paper, we propose a new architecture for a CBIR system. We try to mimic the human memory. We use generalized bi-directional associative memory (BAMg) to store and retrieve images from the database. We store and retrieve images based on association. We present three topologies of the generalized bi-directional associative memory that are similar to the local area network topologies: the bus, ring, and tree. We have developed software to implement the CBIR system. As an illustration, we have considered three sets of images. The results of our simulation are presented in the paper
A Novel Fuzzy Clustering Algorithm for Radial Basis Function Neural Network
A Fuzzy Radial basis function neural network (FRBFNN) classifier is proposed in the framework of Radial basis function neural network (RBFNN). This classifier is constructed using class-specific fuzzy clustering to form the clusters which represent the neurons i.e. fuzzy set hyperspheres (FSHs) in the hidden layer of FRBFNN. The creation of these FSHs is based on the maximum spread from inter-class information and intra-class fuzzy membership mechanism. The proposed approach is fast, independent of parameters, and shows good data visualization. The Least mean square training between the hidden layer to output layer in RBFNN is avoided, thus reduces the time complexity. The FRBFNN is trained quickly due to the fast converge of input data to form the FHSs in the hidden layer. The output is determined by the union operation of the FHSs outputs which are connected to the class nodes in the output layer. The performance of the proposed FRBFNN is compared with the other RBFNNs using ten benchmark datasets. The empirical findings demonstrate that the proposed FRBFNN is highly efficient classifier for pattern recognition
Knowledge Extraction from Survey Data Using Neural Networks
AbstractSurveys are an important tool for researchers. It is increasingly important to develop powerful means for analyzing such data and to extract knowledge that could help in decision-making. Survey attributes are typically discrete data measured on a Likert scale. The process of classification becomes complex if the number of survey attributes is large. Another major issue in Likert-Scale data is the uniqueness of tuples. A large number of unique tuples may result in a large number of patterns. The main focus of this paper is to propose an efficient knowledge extraction method that can extract knowledge in terms of rules. The proposed method consists of two phases. In the first phase, the network is trained and pruned. In the second phase, the decision tree is applied to extract rules from the trained network. Extracted rules are optimized to obtain a comprehensive and concise set of rules. In order to verify the effectiveness of the proposed method, it is applied to two sets of Likert scale survey data, and results show that the proposed method produces rule sets that are comparable with other knowledge extraction techniques in terms of the number of rules and accuracy
Multispectral Image Analysis Using Random Forest
Classical methods for classification of pixels in multispectral images include supervised classifiers such as the maximum-likelihood classifier, neural network classifiers, fuzzy neural networks, support vector machines, and decision trees. Recently, there has been an increase of interest in ensemble learning – a method that generates many classifiers and aggregates their results. Breiman proposed Random Forestin 2001 for classification and clustering. Random Forest grows many decision trees for classification. To classify a new object, the input vector is run through each decision tree in the forest. Each tree gives a classification. The forest chooses the classification having the most votes. Random Forest provides a robust algorithm for classifying large datasets. The potential of Random Forest is not been explored in analyzing multispectral satellite images. To evaluate the performance of Random Forest, we classified multispectral images using various classifiers such as the maximum likelihood classifier, neural network, support vector machine (SVM), and Random Forest and compare their results
Calculation of tunneling current across Trapezoidal potential barrier in a Scanning Tunneling Microscope
The Planar Model of the Electrode-Vacuum-Electrode configuration for STM in
which electrode surfaces are assumed to be infinite parallel planes, with
atomic size separation and vacuum between them, is used to calculate tunneling
current densities for both low and high bias voltages. Non WKB, Airy function
solutions for the Schr\"odinger Equation for the trapezoidal barrier in the
tunneling region are used to calculate the tunneling probability. Pauli
blocking effects are found to cancel in the calculation of the net
(Forward-Reverse) current density. Temperature dependent Fermi Factors for each
electrode are introduced and the calculation involves integration over the
electron energies. Thus the energy of the tunneling electrons is not limited to
the Fermi energy in this calculation. In order to convert the current densities
obtained in the planar model to tunneling currents the tip and sample surfaces
cannot be treated as infinite plane surfaces. Instead the tip and the sample
surfaces are modelled as confocal hyperboloids, and the tip sample distance is
replaced by the length of the line of force (field line). The current is found
by integrating the current density over a finite area of the tip. The
calculated tunnel currents for a few electrode pairs at room temperature are
plotted for several values of bias voltage and tip sample distances. The effect
of the curvature of the tip is also studied by repeating the calculations for
various tip radii. The dependence of tunneling current on electrode
temperatures is also studied. Some estimate of lateral resolution and its
dependence on bias voltage and tip radius is also presented.Comment: 14 pages,19 figure
Fuzzy Neural Network Models For Multispectral Image Analysis
Fuzzy neural networks (FNNs) provide a new approach for classification of multispectral data and to extract and optimize classification rules. Neural networks deal with issues on a numeric level, whereas fuzzy logic deals with them on a semantic or linguistic level. FNNs synthesize fuzzy logic and neural networks. Recently, there has been growing interest in the research community not only to understand how FNNs arrive at particular decisions but how to decode information stored in the form of connection strengths in the network. In this paper, we propose fuzzy neural network models for classification of pixels in multispectral images and to extract fuzzy classification rules. During the training phase, the connection strengths are updated. After training, classification rules are extracted by backtracking along the weighted paths through the FNN. The extracted rules are then optimized using a fuzzy associative memory (FAM) bank. The data mining system described above is useful in many practical applications such as mapping, monitoring and managing our planet’s resources and health, climate change impacts and assessments, environmental change detection and military reconnaissance
Random Forest Algorithm for Land Cover Classification
Since the launch of the first land observation satellite Landsat-1 in 1972, many machine learning algorithms have been used to classify pixels in Thematic Mapper (TM) imagery. Classification methods range from parametric supervised classification algorithms such as maximum likelihood, unsupervised algorithms such as ISODAT and k-means clustering to machine learning algorithms such as artificial neural, decision trees, support vector machines, and ensembles classifiers. Various ensemble classification algorithms have been proposed in recent years. Most widely used ensemble classification algorithm is Random Forest. The Random Forest classifier uses bootstrap aggregating for form an ensemble of classification and induction tree like tree classifiers.
A few researchers have used Random Forest for land cover analysis. However, the potential of Random Forest has not yet been fully explored by the remote sensing community. In this paper we compare classification accuracy of Random Forest with other commonly used algorithms such as the maximum likelihood, minimum distance, decision tree, neural network, and support vector machine classifiers
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