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- Data mining (2)
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- Bi-directional associative memories (1)
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- Computer security; information security; security assessment; software metrics (1)
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Articles 1 - 28 of 28
Full-Text Articles in Computer Sciences
Convolution Neural Networks For Phishing Detection, Arun D. Kulkarni
Convolution Neural Networks For Phishing Detection, Arun D. Kulkarni
Computer Science Faculty Publications and Presentations
Phishing is one of the significant threats in cyber security. Phishing is a form of social engineering that uses e-mails with malicious websites to solicitate personal information. Phishing e-mails are growing in alarming number. In this paper we propose a novel machine learning approach to classify phishing websites using Convolution Neural Networks (CNNs) that use URL based features. CNNs consist of a stack of convolution, pooling layers, and a fully connected layer. CNNs accept images as input and perform feature extraction and classification. Many CNN models are available today. To avoid vanishing gradient problem, recent CNNs use entropy loss function …
Multispectral Image Analysis Using Convolution Neural Networks, Arun D. Kulkarni
Multispectral Image Analysis Using Convolution Neural Networks, Arun D. Kulkarni
Computer Science Faculty Publications and Presentations
Machine learning (ML) techniques are used often to classify pixels in multispectral images. Recently, there is growing interest in using Convolution Neural Networks (CNNs) for classifying multispectral images. CNNs are preferred because of high performance, advances in hardware such as graphical processing units (GPUs), and availability of several CNN architectures. In CNN, units in the first hidden layer view only a small image window and learn low level features. Deeper layers learn more expressive features by combining low level features. In this paper, we propose a novel approach to classify pixels in a multispectral image using deep convolution neural networks …
Deep Convolution Neural Networks For Image Classification, Arun D. Kulkarni
Deep Convolution Neural Networks For Image Classification, Arun D. Kulkarni
Computer Science Faculty Publications and Presentations
Deep learning is a highly active area of research in machine learning community. Deep Convolutional Neural Networks (DCNNs) present a machine learning tool that enables the computer to learn from image samples and extract internal representations or properties underlying grouping or categories of the images. DCNNs have been used successfully for image classification, object recognition, image segmentation, and image retrieval tasks. DCNN models such as Alex Net, VGG Net, and Google Net have been used to classify large dataset having millions of images into thousand classes. In this paper, we present a brief review of DCNNs and results of our …
Texture Classification Using Angular And Radial Bins In Transformed Domain, Arun D. Kulkarni, Aavash Sthapit, Ashim Sedhain, Bishrut Bhattarai, Saurav Panthee
Texture Classification Using Angular And Radial Bins In Transformed Domain, Arun D. Kulkarni, Aavash Sthapit, Ashim Sedhain, Bishrut Bhattarai, Saurav Panthee
Computer Science Faculty Publications and Presentations
Texture is generally recognized as fundamental to perceptions. There is no precise definition or characterization available in practice. Texture recognition has many applications in areas such as medical image analysis, remote sensing, and robotic vision. Various approaches such as statistical, structural, and spectral have been suggested in the literature. In this paper we propose a method for texture feature extraction. We transform the image into a two-dimensional Discrete Cosine Transform (DCT) and extract features using the ring and wedge bins in the DCT plane. These features are based on texture properties such as coarseness, smoothness, graininess, and directivity of the …
Novel Technique To Analyze The Effects Of Cognitive And Non-Cognitive Predictors On Students Course Withdrawal In College, Mohammed Ali
Novel Technique To Analyze The Effects Of Cognitive And Non-Cognitive Predictors On Students Course Withdrawal In College, Mohammed Ali
Technology Faculty Publications and Presentations
A novel technique was applied to a college student database to identify the cognitive and non-cognitive factors that predict college students’ course withdrawal behaviors. Predictors such as high school grade point average (HSGPA), standardized test scores (ACT–American College Test or SAT-Scholastic Aptitude Test), number of credit hours enrolled, and age were analyzed in this study. Data mining software algorithms were used to study information about undergraduate students at a west-south-central state university in the United States. The study results revealed that two factors, number of enrolled credit hours, and a student’s age have the most effect on collegiate course withdrawal …
Ieee Access Special Section Editorial: Machine Learning Designs, Implementations And Techniques, Shadi A. Aljawarneh, Oguz Bayat, Juan A. Lara, Robert P. Schumaker
Ieee Access Special Section Editorial: Machine Learning Designs, Implementations And Techniques, Shadi A. Aljawarneh, Oguz Bayat, Juan A. Lara, Robert P. Schumaker
Computer Science Faculty Publications and Presentations
IEEE access special section editorial.
A Real-Time Internet Of Things (Iot) Based Affective Framework For Monitoring Emotions In Infants, Alhagie Sallah
A Real-Time Internet Of Things (Iot) Based Affective Framework For Monitoring Emotions In Infants, Alhagie Sallah
Electrical Engineering Theses
An increase in the number of working parents has led to a higher demand for remotely monitoring activities of babies through baby monitors. The baby monitors vary from simple audio and video monitoring frameworks to advance applications where we can integrate sensors for tracking vital signs such as heart rate, respiratory rate monitoring. The Internet of Things (IoT) is a network of devices where each device can is recognizable in the network. The IoT node is a sensor or device, which primarily functions as a data acquisition unit. The data acquired through the IoT nodes are wirelessly transmitted to the …
Developing Big Data Projects In Open University Engineering Courses: Lessons Learned, Juan A. Lara, Aurea Anguera De Sojo, Shadi Aljawarneh, Robert P. Schumaker, Bassam Al-Shargabi
Developing Big Data Projects In Open University Engineering Courses: Lessons Learned, Juan A. Lara, Aurea Anguera De Sojo, Shadi Aljawarneh, Robert P. Schumaker, Bassam Al-Shargabi
Computer Science Faculty Publications and Presentations
Big Data courses in which students are asked to carry out Big Data projects are becoming more frequent as a part of University Engineering curriculum. In these courses, instructors and students must face a series of special characteristics, difficulties and challenges that it is important to know about beforehand, so the lecturer can better plan the subject and manage the teaching methods in order to prevent students' academic dropout and low performance. The goal of this research is to approach this problem by sharing the lessons learned in the process of teaching e-learning courses where students are required to develop …
Cross-Validating Traffic Speed Measurements From Probe And Stationary Sensors Through State Reconstruction, Jia Li, Kenneth Perrine, Lidong Wu, C. Michael Walton
Cross-Validating Traffic Speed Measurements From Probe And Stationary Sensors Through State Reconstruction, Jia Li, Kenneth Perrine, Lidong Wu, C. Michael Walton
Computer Science Faculty Publications and Presentations
Traffic speed on freeways can be measured by two types of technologies, i.e. probe sensors and stationary sensors. Cross-validation is critical to ensure the consistency between heterogeneous measurements. A challenge lies in the mismatch of probe and stationary measurements in space and time, especially when one of them is relatively sparse. Towards filling the gap, this paper presents a cross-validation method based on traffic state reconstruction. The proposed method is computationally simple and robust. This makes it ready to be implemented for large data sets without complicated tuning. We present analytical formulation of the proposed method and an analysis of …
Generating Classification Rules From Training Samples, Arun D. Kulkarni
Generating Classification Rules From Training Samples, Arun D. Kulkarni
Computer Science Faculty Publications and Presentations
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 …
Multispectral Image Analysis Using Decision Trees, Arun D. Kulkarni, Anmol Shrestha
Multispectral Image Analysis Using Decision Trees, Arun D. Kulkarni, Anmol Shrestha
Computer Science Faculty Publications and Presentations
Many machine learning algorithms have been used to classify pixels in Landsat imagery. The maximum likelihood classifier is the widely-accepted classifier. Non-parametric methods of classification include neural networks and decision trees. In this research work, we implemented decision trees using the C4.5 algorithm to classify pixels of a scene from Juneau, Alaska area obtained with Landsat 8, Operation Land Imager (OLI). One of the concerns with decision trees is that they are often over fitted with training set data, which yields less accuracy in classifying unknown data. To study the effect of overfitting, we have considered noisy training set data …
Rapid Retrieval Of Lung Nodule Ct Images Based On Hashing And Pruning Methods, Lian Pan, Yan Qiang, Jie Yuan, Lidong Wu
Rapid Retrieval Of Lung Nodule Ct Images Based On Hashing And Pruning Methods, Lian Pan, Yan Qiang, Jie Yuan, Lidong Wu
Computer Science Faculty Publications and Presentations
The similarity-based retrieval of lung nodule computed tomography (CT) images is an important task in the computer-aided diagnosis of lung lesions. It can provide similar clinical cases for physicians and help them make reliable clinical diagnostic decisions. However, when handling large-scale lung images with a general-purpose computer, traditional image retrieval methods may not be efficient. In this paper, a new retrieval framework based on a hashing method for lung nodule CT images is proposed. This method can translate high-dimensional image features into a compact hash code, so the retrieval time and required memory space can be reduced greatly. Moreover, a …
A Framework For Measuring Security As A System Property In Cyberphysical Systems, Janusz Zalewski, Ingrid A. Buckley, Bogdan Czejdo, Steven Drager, Andrew J. Kornecki, Nary Subramanian
A Framework For Measuring Security As A System Property In Cyberphysical Systems, Janusz Zalewski, Ingrid A. Buckley, Bogdan Czejdo, Steven Drager, Andrew J. Kornecki, Nary Subramanian
Computer Science Faculty Publications and Presentations
This paper addresses the challenge of measuring security, understood as a system property, of cyberphysical systems, in the category of similar properties, such as safety and reliability. First, it attempts to define precisely what security, as a system property, really is. Then, an application context is presented, in terms of an attack surface in cyberphysical systems. Contemporary approaches related to the principles of measuring software properties are also discussed, with emphasis on building models. These concepts are illustrated in several case studies, based on previous work of the authors, to conduct experimental security measurements.
Random Forest Algorithm For Land Cover Classification, Arun D. Kulkarni, Barrett Lowe
Random Forest Algorithm For Land Cover Classification, Arun D. Kulkarni, Barrett Lowe
Computer Science Faculty Publications and Presentations
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 …
Knowledge Extraction From Metacognitive Reading Strategies Data Using Induction Trees, Christopher Taylor, Arun D. Kulkarni, Kouider Mokhtari
Knowledge Extraction From Metacognitive Reading Strategies Data Using Induction Trees, Christopher Taylor, Arun D. Kulkarni, Kouider Mokhtari
Computer Science Faculty Publications and Presentations
The assessment of students’ metacognitive knowledge and skills about reading is critical in determining their ability to read academic texts and do so with comprehension. In this paper, we used induction trees to extract metacognitive knowledge about reading from a reading strategies dataset obtained from a group of 1636 undergraduate college students. Using a C4.5 algorithm, we constructed decision trees, which helped us classify participants into three groups based on their metacognitive strategy awareness levels consisting of global, problem-solving and support reading strategies. We extracted rules from these decision trees, and in order to evaluate accuracy of the extracted rules, …
The Random Forest Algorithm With Application To Multispectral Image Analysis, Barrett E. Lowe
The Random Forest Algorithm With Application To Multispectral Image Analysis, Barrett E. Lowe
Computer Science Theses
The need for computers to make educated decisions is growing. Various methods have been developed for decision making using observation vectors. Among these are supervised and unsupervised classifiers. Recently, there has been increased attention to ensemble learning--methods that generate many classifiers and aggregate their results. Breiman (2001) proposed Random Forests for classification and clustering. The Random Forest algorithm is ensemble learning using the decision tree principle. Input vectors are used to grow decision trees and build a forest. A classification decision is reached by sending an unknown input vector down each tree in the forest and taking the majority vote …
Multispectral Image Analysis Using Random Forest, Barrett Lowe, Arun Kulkarni
Multispectral Image Analysis Using Random Forest, Barrett Lowe, Arun Kulkarni
Computer Science Faculty Publications and Presentations
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 …
Knowledge Extraction From Survey Data Using Neural Networks, Imran Ahmed Khan
Knowledge Extraction From Survey Data Using Neural Networks, Imran Ahmed Khan
Computer Science Theses
Surveys are an important tool for researchers. Survey attributes are typically discrete data measured on a Likert scale. Collected responses from the survey contain an enormous amount of data. It is increasingly important to develop powerful means for clustering such data and knowledge extraction that could help in decision-making. The process of clustering 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 and that may increase the complexity of the knowledge extraction process. Also, …
Self-Configuring Neural Networks, Justin M. Anderson
Self-Configuring Neural Networks, Justin M. Anderson
Computer Science Theses
Neural Networks are an effective means of classifying data; however they are usually purpose built applications that are created for classifying a single data set. Programming a neural network can be a time consuming and sometimes error prone process. To alleviate both of these problems a self-configuring multilayer perceptron model was used to create and train neural networks. This application can take any training data set that is linearly or nonlinearly separable as input, then create the needed neural network structure and train itself, thus saving programmers' time and effort. The software has been tested with several data sets including …
Knowledge Extraction From Survey Data Using Neural Networks, Khan Imran, Arun Kulkarni
Knowledge Extraction From Survey Data Using Neural Networks, Khan Imran, Arun Kulkarni
Computer Science Faculty Publications and Presentations
Surveys 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. …
Ensure: A Time Sensitive Transport Protocol To Achieve Reliability Over Wireless In Petrochemical Plants, Leigh Sheneman
Ensure: A Time Sensitive Transport Protocol To Achieve Reliability Over Wireless In Petrochemical Plants, Leigh Sheneman
Computer Science Theses
As society becomes more reliant on the resources extracted in petroleum refinement the production demand for petrochemical plants increases. A key element is producing efficiently while maintaining safety through constant monitoring of equipment feedback. Currently, temperature and flow sensors are deployed at various points of production and 10/100 Ethernet cable is installed to connect them to a master control unit. This comes at a great monetary cost, not only at the time of implementation but also when repairs are required. The capability to provide plant wide wireless networks would both decrease investment cost and downtime needed for repairs. However, the …
Assessing Metacognitive Skills Using Adaptive Neural Networks, Anderson Justin, Kouider Mokhtari, Arun Kulkarni
Assessing Metacognitive Skills Using Adaptive Neural Networks, Anderson Justin, Kouider Mokhtari, Arun Kulkarni
Computer Science Faculty Publications and Presentations
The assessment of student's levels of metacognitive knowledge and skills is critical in determining their ability to effectively perform complex cognitive tasks such as solving mathematics or reading comprehension problems. In this paper, we use an adaptive multiplayer perceptron model to categorize participants based on their metacognitive awareness and perceived use of reading strategies while reading. Eight hundred and sixty-five middle school students participated in the study. All participants completed a 30-item instrument- the Metacognitive Awareness-of-Reading Strategies Inventory (MARSI). We used adaptive multi-layer perceptron models to classify participants into three groups based on their metacognitive strategy awareness levels using thirteen …
Water Quality Retrieval From Landsat Tm Imagery, Arun D. Kulkarni
Water Quality Retrieval From Landsat Tm Imagery, Arun D. Kulkarni
Computer Science Faculty Publications and Presentations
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 …
Association-Based Image Retrieval For Automatic Target Recognition., Arun D. Kulkarni, H. Gunturu, S. Dalta
Association-Based Image Retrieval For Automatic Target Recognition., Arun D. Kulkarni, H. Gunturu, S. Dalta
Computer Science Faculty Publications and Presentations
Model-based automatic target recognition (ATR)systems deal with recognizing three dimensional objects from two dimensional images. In order to recognizeand identify objects the ATRsystem must have one or more stored models. Multiple two dimensional views of each three dimensional objectthat may appear in the universe it deals withare stored in the database. During recognition, two dimensional view of atarget object is used a query image and the search is carried out to identify the corresponding three dimensional object. Stages of a model-based ATR system include preprocessing, segmentation, feature extraction, and searching thedatabase. One of the most important problems in a model-based …
Association-Based Image Retrieval, Arun D. Kulkarni, H. Gunturu, S. Dalta
Association-Based Image Retrieval, Arun D. Kulkarni, H. Gunturu, S. Dalta
Computer Science Faculty Publications and Presentations
With advances in the computer technology and the World Wide Web there has been an explosion in the amount and complexity of multimedia data that are generated, stored, transmitted, analyzed, and accessed. In order to extract useful information from this huge amount of data, many content-based image retrieval (CBIR) systems have been developed in the last decade. A typical CBIR system captures image features that represent image properties such as color, texture, or shape of objects in the query image and try to retrieve images from the database with similar features. Recent advances in CBIR systems include relevance feedback based …
Content-Based Image Retrieval Using Associative Memories, Arun D. Kulkarni
Content-Based Image Retrieval Using Associative Memories, Arun D. Kulkarni
Computer Science Faculty Publications and Presentations
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 …
Fuzzy Neural Network Models For Multispectral Image Analysis, Arun D. Kulkarni, Sara Mccaslin
Fuzzy Neural Network Models For Multispectral Image Analysis, Arun D. Kulkarni, Sara Mccaslin
Computer Science Faculty Publications and Presentations
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 …
Discovery Of Functional And Approximate Functional Dependencies In Relational Databases, Ronald S. King, James J. Legendre
Discovery Of Functional And Approximate Functional Dependencies In Relational Databases, Ronald S. King, James J. Legendre
Computer Science Faculty Publications and Presentations
This study develops the foundation for a simple, yet efficient method for uncovering functional and approximate functional dependencies in relational databases. The technique is based upon the mathematical theory of partitions defined over a relation's row identifiers. Using a levelwise algorithm the minimal non-trivial functional dependencies can be found using computations conducted on integers. Therefore, the required operations on partitions are both simple and fast. Additionally, the row identifiers provide the added advantage of nominally identifying the exceptions to approximate functional dependencies, which can be used effectively in practical data mining applications.