Open Access. Powered by Scholars. Published by Universities.®

Computer Engineering Commons

Open Access. Powered by Scholars. Published by Universities.®

Articles 1 - 19 of 19

Full-Text Articles in Computer Engineering

Seasonal Warranty Prediction Based On Recurrent Event Data, Qianqian Shan, Yili Hong, William Q. Meeker Jr. Aug 2019

Seasonal Warranty Prediction Based On Recurrent Event Data, Qianqian Shan, Yili Hong, William Q. Meeker Jr.

William Q Meeker

Warranty return data from repairable systems, such as vehicles, usually result in recurrent event data. The non-homogeneous Poisson process (NHPP) model is used widely to describe such data. Seasonality in the repair frequencies and other variabilities, however, complicate the modeling of recurrent event data. Not much work has been done to address the seasonality, and this paper provides a general approach for the application of NHPP models with dynamic covariates to predict seasonal warranty returns. A hierarchical clustering method is used to stratify the population into groups that are more homogeneous than the than the overall population. The stratification facilitates ...


Seasonal Warranty Prediction Based On Recurrent Event Data, Qianqian Shan, Yili Hong, William Q. Meeker Jr. Nov 2018

Seasonal Warranty Prediction Based On Recurrent Event Data, Qianqian Shan, Yili Hong, William Q. Meeker Jr.

Statistics Preprints

Warranty return data from repairable systems, such as vehicles, usually result in recurrent event data. The non-homogeneous Poisson process (NHPP) model is used widely to describe such data. Seasonality in the repair frequencies and other variabilities, however, complicate the modeling of recurrent event data. Not much work has been done to address the seasonality, and this paper provides a general approach for the application of NHPP models with dynamic covariates to predict seasonal warranty returns. A hierarchical clustering method is used to stratify the population into groups that are more homogeneous than the than the overall population. The stratification facilitates ...


Machine Learning Techniques Implementation In Power Optimization, Data Processing, And Bio-Medical Applications, Khalid Khairullah Mezied Al-Jabery Jan 2018

Machine Learning Techniques Implementation In Power Optimization, Data Processing, And Bio-Medical Applications, Khalid Khairullah Mezied Al-Jabery

Doctoral Dissertations

"The rapid progress and development in machine-learning algorithms becomes a key factor in determining the future of humanity. These algorithms and techniques were utilized to solve a wide spectrum of problems extended from data mining and knowledge discovery to unsupervised learning and optimization. This dissertation consists of two study areas. The first area investigates the use of reinforcement learning and adaptive critic design algorithms in the field of power grid control. The second area in this dissertation, consisting of three papers, focuses on developing and applying clustering algorithms on biomedical data. The first paper presents a novel modelling approach for ...


Enhancing Informative Frame Filtering By Water And Bubble Detection In Colonoscopy Videos, Ashok Dahal, Junghwan Oh, Wallapak Tavanapong, Johnny S. Wong, Piet C. De Groen Jun 2017

Enhancing Informative Frame Filtering By Water And Bubble Detection In Colonoscopy Videos, Ashok Dahal, Junghwan Oh, Wallapak Tavanapong, Johnny S. Wong, Piet C. De Groen

Johnny Wong

Colonoscopy has contributed to a marked decline in the number of colorectal cancer related deaths. However, recent data suggest that there is a significant (4-12%) miss-rate for the detection of even large polyps and cancers. To address this, we have been investigating an ‘automated feedback system’ which informs the endoscopist of possible sub-optimal inspection during colonoscopy. A fundamental step of this system is to distinguish non-informative frames from informative ones. Existing methods for this cannot classify water/bubble frames as non-informative even though they do not carry any useful visual information of the colon mucosa. In this paper, we propose ...


Semantics-Based Summarization Of Entities In Knowledge Graphs, Kalpa Gunaratna Jan 2017

Semantics-Based Summarization Of Entities In Knowledge Graphs, Kalpa Gunaratna

Browse all Theses and Dissertations

The processing of structured and semi-structured content on the Web has been gaining attention with the rapid progress in the Linking Open Data project and the development of commercial knowledge graphs. Knowledge graphs capture domain-specific or encyclopedic knowledge in the form of a data layer and add rich and explicit semantics on top of the data layer to infer additional knowledge. The data layer of a knowledge graph represents entities and their descriptions. The semantic layer on top of the data layer is called the schema (ontology), where relationships of the entity descriptions, their classes, and the hierarchy of the ...


Optimizing Main Memory Usage In Modern Computing Systems To Improve Overall System Performance, Daniel Jose Campello Jun 2016

Optimizing Main Memory Usage In Modern Computing Systems To Improve Overall System Performance, Daniel Jose Campello

FIU Electronic Theses and Dissertations

Operating Systems use fast, CPU-addressable main memory to maintain an application’s temporary data as anonymous data and to cache copies of persistent data stored in slower block-based storage devices. However, the use of this faster memory comes at a high cost. Therefore, several techniques have been implemented to use main memory more efficiently in the literature. In this dissertation we introduce three distinct approaches to improve overall system performance by optimizing main memory usage.

First, DRAM and host-side caching of file system data are used for speeding up virtual machine performance in today’s virtualized data centers. The clustering ...


Efficient Algorithms For Clustering Polygonal Obstacles, Sabbir Kumar Manandhar May 2016

Efficient Algorithms For Clustering Polygonal Obstacles, Sabbir Kumar Manandhar

UNLV Theses, Dissertations, Professional Papers, and Capstones

Clustering a set of points in Euclidean space is a well-known problem having applications in pattern recognition, document image analysis, big-data analytics, and robotics. While there are a lot of research publications for clustering point objects, only a few articles have been reported for clustering a given distribution of obstacles. In this thesis we examine the development of efficient algorithms for clustering a given set of convex obstacles in the 2D plane. One of the methods presented in this work uses a Voronoi diagram to extract obstacle clusters. We also consider the implementation issues of point/obstacle clustering algorithms.


Neuron Clustering For Mitigating Catastrophic Forgetting In Supervised And Reinforcement Learning, Benjamin Frederick Goodrich Dec 2015

Neuron Clustering For Mitigating Catastrophic Forgetting In Supervised And Reinforcement Learning, Benjamin Frederick Goodrich

Doctoral Dissertations

Neural networks have had many great successes in recent years, particularly with the advent of deep learning and many novel training techniques. One issue that has affected neural networks and prevented them from performing well in more realistic online environments is that of catastrophic forgetting. Catastrophic forgetting affects supervised learning systems when input samples are temporally correlated or are non-stationary. However, most real-world problems are non-stationary in nature, resulting in prolonged periods of time separating inputs drawn from different regions of the input space.

Reinforcement learning represents a worst-case scenario when it comes to precipitating catastrophic forgetting in neural networks ...


Enhancing Informative Frame Filtering By Water And Bubble Detection In Colonoscopy Videos, Ashok Dahal, Junghwan Oh, Wallapak Tavanapong, Johnny S. Wong, Piet C. De Groen Jul 2015

Enhancing Informative Frame Filtering By Water And Bubble Detection In Colonoscopy Videos, Ashok Dahal, Junghwan Oh, Wallapak Tavanapong, Johnny S. Wong, Piet C. De Groen

Computer Science Conference Presentations, Posters and Proceedings

Colonoscopy has contributed to a marked decline in the number of colorectal cancer related deaths. However, recent data suggest that there is a significant (4-12%) miss-rate for the detection of even large polyps and cancers. To address this, we have been investigating an ‘automated feedback system’ which informs the endoscopist of possible sub-optimal inspection during colonoscopy. A fundamental step of this system is to distinguish non-informative frames from informative ones. Existing methods for this cannot classify water/bubble frames as non-informative even though they do not carry any useful visual information of the colon mucosa. In this paper, we propose ...


Fuzzy Adaptive Resonance Theory: Applications And Extensions, Clayton Parker Smith Jan 2015

Fuzzy Adaptive Resonance Theory: Applications And Extensions, Clayton Parker Smith

Masters Theses

"Adaptive Resonance Theory, ART, is a powerful clustering tool for learning arbitrary patterns in a self-organizing manner. In this research, two papers are presented that examine the extensibility and applications of ART. The first paper examines a means to boost ART performance by assigning each cluster a vigilance value, instead of a single value for the whole ART module. A Particle Swarm Optimization technique is used to search for desirable vigilance values. In the second paper, it is shown how ART, and clustering in general, can be a useful tool in preprocessing time series data. Clustering quantization attempts to meaningfully ...


Hot Zone Identification: Analyzing Effects Of Data Sampling On Spam Clustering, Rasib Khan, Mainul Mizan, Ragib Hasan, Alan Sprague Jan 2014

Hot Zone Identification: Analyzing Effects Of Data Sampling On Spam Clustering, Rasib Khan, Mainul Mizan, Ragib Hasan, Alan Sprague

Journal of Digital Forensics, Security and Law

Email is the most common and comparatively the most efficient means of exchanging information in today's world. However, given the widespread use of emails in all sectors, they have been the target of spammers since the beginning. Filtering spam emails has now led to critical actions such as forensic activities based on mining spam email. The data mine for spam emails at the University of Alabama at Birmingham is considered to be one of the most prominent resources for mining and identifying spam sources. It is a widely researched repository used by researchers from different global organizations. The usual ...


Online Multi-Stage Deep Architectures For Feature Extraction And Object Recognition, Derek Christopher Rose Aug 2013

Online Multi-Stage Deep Architectures For Feature Extraction And Object Recognition, Derek Christopher Rose

Doctoral Dissertations

Multi-stage visual architectures have recently found success in achieving high classification accuracies over image datasets with large variations in pose, lighting, and scale. Inspired by techniques currently at the forefront of deep learning, such architectures are typically composed of one or more layers of preprocessing, feature encoding, and pooling to extract features from raw images. Training these components traditionally relies on large sets of patches that are extracted from a potentially large image dataset. In this context, high-dimensional feature space representations are often helpful for obtaining the best classification performances and providing a higher degree of invariance to object transformations ...


Prevention And Detection Of Intrusions In Wireless Sensor Networks, Ismail Butun Jan 2013

Prevention And Detection Of Intrusions In Wireless Sensor Networks, Ismail Butun

Graduate Theses and Dissertations

Wireless Sensor Networks (WSNs) continue to grow as one of the most exciting and challenging research areas of engineering. They are characterized by severely constrained computational and energy

resources and also restricted by the ad-hoc network operational

environment. They pose unique challenges, due to limited power

supplies, low transmission bandwidth, small memory sizes and limited energy. Therefore, security techniques used in traditional networks cannot be directly adopted. So, new ideas and approaches are needed, in order to increase the overall security of the network. Security applications in such resource constrained WSNs with minimum overhead provides significant challenges, and is the ...


Clustering Spam Domains And Destination Websites: Digital Forensics With Data Mining, Chun Wei, Alan Sprague, Gary Warner, Anthony Skjellum Jan 2010

Clustering Spam Domains And Destination Websites: Digital Forensics With Data Mining, Chun Wei, Alan Sprague, Gary Warner, Anthony Skjellum

Journal of Digital Forensics, Security and Law

Spam related cyber crimes have become a serious threat to society. Current spam research mainly aims to detect spam more effectively. We believe the identification and disruption of the supporting infrastructure used by spammers is a more effective way of stopping spam than filtering. The termination of spam hosts will greatly reduce the profit a spammer can generate and thwart his ability to send more spam. This research proposes an algorithm for clustering spam domains extracted from spam emails based on the hosting IP addresses and tracing the IP addresses over a period of time. The results show that many ...


Reeling In Big Phish With A Deep Md5 Net, Brad Wardman, Gary Warner, Heather Mccalley, Sarah Turner, Anthony Skjellum Jan 2010

Reeling In Big Phish With A Deep Md5 Net, Brad Wardman, Gary Warner, Heather Mccalley, Sarah Turner, Anthony Skjellum

Journal of Digital Forensics, Security and Law

Phishing continues to grow as phishers discover new exploits and attack vectors for hosting malicious content; the traditional response using takedowns and blacklists does not appear to impede phishers significantly. A handful of law enforcement projects — for example the FBI's Digital PhishNet and the Internet Crime and Complaint Center (ic3.gov) — have demonstrated that they can collect phishing data in substantial volumes, but these collections have not yet resulted in a significant decline in criminal phishing activity. In this paper, a new system is demonstrated for prioritizing investigative resources to help reduce the time and effort expended examining this ...


Hierarchical Routing In Manets Using Simple Clustering, Adam Carnine Jan 2009

Hierarchical Routing In Manets Using Simple Clustering, Adam Carnine

UNLV Theses, Dissertations, Professional Papers, and Capstones

This thesis presents both a review of current MANET routing protocols and a new MANET routing algorithm. The routing protocols reviewed include representative samples from the three primary forms of routing found in MANETS: proactive routing, reactive routing and hybrid routing. Secure algorithms are given special treatment in the review. In addition several protocol enhancements are discussed.

The proposed routing protocol is designed to support networks of a medium size, containing over 200 nodes but less than 3,000 nodes. The design is intentionally simple to allow ease of implementation in comparison with other MANET protocols that provide similar functionality.


Supporting Protocols For Structuring And Intelligent Information Dissemination In Vehicular Ad Hoc Networks, Filip Cuckov Jan 2009

Supporting Protocols For Structuring And Intelligent Information Dissemination In Vehicular Ad Hoc Networks, Filip Cuckov

Electrical & Computer Engineering Theses & Disssertations

The goal of this dissertation is the presentation of supporting protocols for structuring and intelligent data dissemination in vehicular ad hoc networks (VANETs). The protocols are intended to first introduce a structure in VANETs, and thus promote the spatial reuse of network resources. Segmenting a flat VANET in multiple cluster structures allows for more efficient use of the available bandwidth, which can effectively increase the capacity of the network. The cluster structures can also improve the scalability of the underlying communication protocols. The structuring and maintenance of the network introduces additional overhead. The aim is to provide a mechanism for ...


Summaritive Digest For Large Document Repositories With Application To E-Rulemaking, Lijun Chen Jan 2007

Summaritive Digest For Large Document Repositories With Application To E-Rulemaking, Lijun Chen

Browse all Theses and Dissertations

Large document repositories need to be organized and summarized to make them more accessible and understandable. Such needs exist in many applications, including web search, e-rulemaking (electronic rulemaking) and document archiving. Even though much has been done in the areas of document clustering and summarization, there are still many new challenges and issues that need to be addressed as the repositories become larger, more prevalent and dynamic. In this dissertation, we investigate more informative ways to organize and summarize large document repositories, especially e-rulemaking feedback repositories (ERFRs), so that the large repositories can be managed and digested more efficiently and ...


Clustering And Hybrid Routing In Mobile Ad Hoc Networks, Lan Wang Apr 2005

Clustering And Hybrid Routing In Mobile Ad Hoc Networks, Lan Wang

Computer Science Theses & Dissertations

This dissertation focuses on clustering and hybrid routing in Mobile Ad Hoc Networks (MANET). Specifically, we study two different network-layer virtual infrastructures proposed for MANET: the explicit cluster infrastructure and the implicit zone infrastructure. In the first part of the dissertation, we propose a novel clustering scheme based on a number of properties of diameter-2 graphs to provide a general-purpose virtual infrastructure for MANET. Compared to virtual infrastructures with central nodes, our virtual infrastructure is more symmetric and stable, but still light-weight. In our clustering scheme, cluster initialization naturally blends into cluster maintenance, showing the unity between these two operations ...