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Full-Text Articles in Physical Sciences and Mathematics
Bm3d Image Denoising Using Learning-Based Adaptive Hard Thresholding, Farhan Bashar
Bm3d Image Denoising Using Learning-Based Adaptive Hard Thresholding, Farhan Bashar
Electronic Thesis and Dissertation Repository
Image denoising is an important pre-processing step in most imaging applications. Block Matching and 3D Filtering (BM3D) is considered to be the current state-of-art algorithm for additive image denoising. But this algorithm uses a fixed hard thresholding scheme to attenuate noise from a 3D block. Experiments show that this fixed hard thresholding deteriorates the performance of BM3D because it does not consider the context of corresponding blocks. In this thesis, we propose a learning based adaptive hard thresholding method to solve this issue. Also, BM3D algorithm requires as an input the value of the noise level in the input image. …
Capstone Projects Mining System For Insights And Recommendations, Melvrivk Aik Chun Goh, Swapna Gottipati, Venky Shankararaman
Capstone Projects Mining System For Insights And Recommendations, Melvrivk Aik Chun Goh, Swapna Gottipati, Venky Shankararaman
Research Collection School Of Computing and Information Systems
In this paper, we present a classification based system to discover knowledge and trends in higher education students’ projects. Essentially, the educational capstone projects provide an opportunity for students to apply what they have learned and prepare themselves for industry needs. Therefore mining such projects gives insights of students’ experiences as well as industry project requirements and trends. In particular, we mine capstone projects executed by Information Systems students to discover patterns and insights related to people, organization, domain, industry needs and time. We build a capstone projects mining system (CPMS) based on classification models that leverage text mining, natural …
Should I Follow This Fault Localization Tool's Output? Automated Prediction Of Fault Localization Effectiveness, Tien-Duy B. Le, David Lo, Ferdian Thung
Should I Follow This Fault Localization Tool's Output? Automated Prediction Of Fault Localization Effectiveness, Tien-Duy B. Le, David Lo, Ferdian Thung
Research Collection School Of Computing and Information Systems
Debugging is a crucial yet expensive activity to improve the reliability of software systems. To reduce debugging cost, various fault localization tools have been proposed. A spectrum-based fault localization tool often outputs an ordered list of program elements sorted based on their likelihood to be the root cause of a set of failures (i.e., their suspiciousness scores). Despite the many studies on fault localization, unfortunately, however, for many bugs, the root causes are often low in the ordered list. This potentially causes developers to distrust fault localization tools. Recently, Parnin and Orso highlight in their user study that many debuggers …
Active Semi-Supervised Approach For Checking App Behavior Against Its Description, Ma Siqi, Shaowei Wang, David Lo, Deng, Robert H., Cong Sun
Active Semi-Supervised Approach For Checking App Behavior Against Its Description, Ma Siqi, Shaowei Wang, David Lo, Deng, Robert H., Cong Sun
Research Collection School Of Computing and Information Systems
Mobile applications are popular in recent years. They are often allowed to access and modify users' sensitive data. However, many mobile applications are malwares that inappropriately use these sensitive data. To detect these malwares, Gorla et al. Propose CHABADA which compares app behaviors against its descriptions. Data about known malwares are not used in their work, which limits its effectiveness. In this work, we extend the work by Gorla et al. By proposing an active and semi-supervised approach for detecting malwares. Different from CHABADA, our approach will make use of both known benign and malicious apps to predict other malicious …
Sudden Cardiac Arrest Prediction Through Heart Rate Variability Analysis, Luke Joseph Plewa
Sudden Cardiac Arrest Prediction Through Heart Rate Variability Analysis, Luke Joseph Plewa
Master's Theses
The increase in popularity for wearable technologies (see: Apple Watch and Microsoft Band) has opened the door for an Internet of Things solution to healthcare. One of the most prevalent healthcare problems today is the poor survival rate of out-of hospital sudden cardiac arrests (9.5% on 360,000 cases in the USA in 2013). It has been proven that heart rate derived features can give an early indicator of sudden cardiac arrest, and that providing an early warning has the potential to save many lives. Many of these new wearable devices are capable of providing this warning through their heart rate …
Immunology Inspired Detection Of Data Theft From Autonomous Network Activity, Theodore O. Cochran
Immunology Inspired Detection Of Data Theft From Autonomous Network Activity, Theodore O. Cochran
CCE Theses and Dissertations
The threat of data theft posed by self-propagating, remotely controlled bot malware is increasing. Cyber criminals are motivated to steal sensitive data, such as user names, passwords, account numbers, and credit card numbers, because these items can be parlayed into cash. For anonymity and economy of scale, bot networks have become the cyber criminal’s weapon of choice. In 2010 a single botnet included over one million compromised host computers, and one of the largest botnets in 2011 was specifically designed to harvest financial data from its victims. Unfortunately, current intrusion detection methods are unable to effectively detect data extraction techniques …
Using Instance-Level Meta-Information To Facilitate A More Principled Approach To Machine Learning, Michael Reed Smith
Using Instance-Level Meta-Information To Facilitate A More Principled Approach To Machine Learning, Michael Reed Smith
Theses and Dissertations
As the capability for capturing and storing data increases and becomes more ubiquitous, an increasing number of organizations are looking to use machine learning techniques as a means of understanding and leveraging their data. However, the success of applying machine learning techniques depends on which learning algorithm is selected, the hyperparameters that are provided to the selected learning algorithm, and the data that is supplied to the learning algorithm. Even among machine learning experts, selecting an appropriate learning algorithm, setting its associated hyperparameters, and preprocessing the data can be a challenging task and is generally left to the expertise of …
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 …
Automatically Discovering The Number Of Clusters In Web Page Datasets, Zhongmei Yao
Automatically Discovering The Number Of Clusters In Web Page Datasets, Zhongmei Yao
Zhongmei Yao
Clustering is well-suited for Web mining by automatically organizing Web pages into categories, each of which contains Web pages having similar contents. However, one problem in clustering is the lack of general methods to automatically determine the number of categories or clusters. For the Web domain in particular, currently there is no such method suitable for Web page clustering. In an attempt to address this problem, we discover a constant factor that characterizes the Web domain, based on which we propose a new method for automatically determining the number of clusters in Web page data sets. We discover that the …
Feature Selection And Classification Methods For Decision Making: A Comparative Analysis, Osiris Villacampa
Feature Selection And Classification Methods For Decision Making: A Comparative Analysis, Osiris Villacampa
CCE Theses and Dissertations
The use of data mining methods in corporate decision making has been increasing in the past decades. Its popularity can be attributed to better utilizing data mining algorithms, increased performance in computers, and results which can be measured and applied for decision making. The effective use of data mining methods to analyze various types of data has shown great advantages in various application domains. While some data sets need little preparation to be mined, whereas others, in particular high-dimensional data sets, need to be preprocessed in order to be mined due to the complexity and inefficiency in mining high dimensional …
Novel Classification Of Slow Movement Objects In Urban Traffic Environments Using Wideband Pulse Doppler Radar, Berta Rodriguez Hervas
Novel Classification Of Slow Movement Objects In Urban Traffic Environments Using Wideband Pulse Doppler Radar, Berta Rodriguez Hervas
Open Access Theses & Dissertations
Every year thousands of people are involved in traffic accidents, some of which are fatal. An important percentage of these fatalities are caused by human error, which could be prevented by increasing the awareness of drivers and the autonomy of vehicles. Since driver assistance systems have the potential to positively impact tens of millions of people, the purpose of this research is to study the micro-Doppler characteristics of vulnerable urban traffic components, i.e. pedestrians and bicyclists, based on information obtained from radar backscatter, and to develop a classification technique that allows automatic target recognition with a vehicle integrated system. For …
A Comparative Study Of Two Different Fpga-Based Arrhythmia Classifier Architectures, Ahmet Turan Özdemi̇r, Kenan Danişman
A Comparative Study Of Two Different Fpga-Based Arrhythmia Classifier Architectures, Ahmet Turan Özdemi̇r, Kenan Danişman
Turkish Journal of Electrical Engineering and Computer Sciences
Early diagnosis of dangerous heart conditions is very important for the treatment of heart diseases and for the prevention of sudden cardiac death. Automatic electrocardiogram (ECG) arrhythmia classifiers are essential to timely diagnosis. However, most of the medical diagnosis systems proposed in the literature are software-based. This work focused on the hardware implementation of a mobile artificial neural network (ANN)-based arrhythmia classifier that is implemented on a field programmable gate array (FPGA) as a single chip solution, as an alternative to various software models of ANNs. Due to the parallel nature of ANNs, hardware implementation of ANNs needs a large …
Contrast Pattern Aided Regression And Classification, Vahid Taslimitehrani
Contrast Pattern Aided Regression And Classification, Vahid Taslimitehrani
Browse all Theses and Dissertations
Regression and classification techniques play an essential role in many data mining tasks and have broad applications. However, most of the state-of-the-art regression and classification techniques are often unable to adequately model the interactions among predictor variables in highly heterogeneous datasets. New techniques that can effectively model such complex and heterogeneous structures are needed to significantly improve prediction accuracy. In this dissertation, we propose a novel type of accurate and interpretable regression and classification models, named as Pattern Aided Regression (PXR) and Pattern Aided Classification (PXC) respectively. Both PXR and PXC rely on identifying regions in the data space where …
Intelligent Network Intrusion Detection Using An Evolutionary Computation Approach, Samaneh Rastegari
Intelligent Network Intrusion Detection Using An Evolutionary Computation Approach, Samaneh Rastegari
Theses: Doctorates and Masters
With the enormous growth of users' reliance on the Internet, the need for secure and reliable computer networks also increases. Availability of effective automatic tools for carrying out different types of network attacks raises the need for effective intrusion detection systems.
Generally, a comprehensive defence mechanism consists of three phases, namely, preparation, detection and reaction. In the preparation phase, network administrators aim to find and fix security vulnerabilities (e.g., insecure protocol and vulnerable computer systems or firewalls), that can be exploited to launch attacks. Although the preparation phase increases the level of security in a network, this will never completely …