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Full-Text Articles in Computer Engineering

Applications Of Supervised Machine Learning In Autism Spectrum Disorder Research: A Review, Kayleigh K. Hyde, Marlena N. Novack, Nicholas Lahaye, Chelsea Parlett-Pelleriti, Raymond Anden, Dennis R. Dixon, Erik Linstead Feb 2019

Applications Of Supervised Machine Learning In Autism Spectrum Disorder Research: A Review, Kayleigh K. Hyde, Marlena N. Novack, Nicholas Lahaye, Chelsea Parlett-Pelleriti, Raymond Anden, Dennis R. Dixon, Erik Linstead

Engineering Faculty Articles and Research

Autism spectrum disorder (ASD) research has yet to leverage "big data" on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as ...


Horse Racing Prediction Using Graph-Based Features., Mehmet Akif Gulum May 2018

Horse Racing Prediction Using Graph-Based Features., Mehmet Akif Gulum

Electronic Theses and Dissertations

This thesis presents an applied horse racing prediction using graph based features on a set of horse races data. We used artificial neural network and logistic regression models to train then test to prediction without graph based features and with graph based features. This thesis can be explained in 4 main parts. Collect data from a horse racing website held from 2015 to 2017. Train data to using predictive models and make a prediction. Create a global directed graph of horses and extract graph-based features (Core Part) . Add graph based features to basic features and train to using same predictive ...


Maintainability Analysis Of Mining Trucks With Data Analytics., Abdulgani Kahraman May 2018

Maintainability Analysis Of Mining Trucks With Data Analytics., Abdulgani Kahraman

Electronic Theses and Dissertations

The mining industry is one of the biggest industries in need of a large budget, and current changes in global economic challenges force the industry to reduce its production expenses. One of the biggest expenditures is maintenance. Thanks to the data mining techniques, available historical records of machines’ alarms and signals might be used to predict machine failures. This is crucial because repairing machines after failures is not as efficient as utilizing predictive maintenance. In this case study, the reasons for failures seem to be related to the order of signals or alarms, called events, which come from trucks. The ...


Mintbase V2.0: A Comprehensive Database For Trna-Derived Fragments That Includes Nuclear And Mitochondrial Fragments From All The Cancer Genome Atlas Projects., Venetia Pliatsika, Phillipe Loher, Rogan Magee, Aristeidis G. Telonis, Eric R. Londin, Megumi Shigematsu, Yohei Kirino, Isidore Rigoutsos Jan 2018

Mintbase V2.0: A Comprehensive Database For Trna-Derived Fragments That Includes Nuclear And Mitochondrial Fragments From All The Cancer Genome Atlas Projects., Venetia Pliatsika, Phillipe Loher, Rogan Magee, Aristeidis G. Telonis, Eric R. Londin, Megumi Shigematsu, Yohei Kirino, Isidore Rigoutsos

Computational Medicine Center Faculty Papers

MINTbase is a repository that comprises nuclear and mitochondrial tRNA-derived fragments ('tRFs') found in multiple human tissues. The original version of MINTbase comprised tRFs obtained from 768 transcriptomic datasets. We used our deterministic and exhaustive tRF mining pipeline to process all of The Cancer Genome Atlas datasets (TCGA). We identified 23 413 tRFs with abundance of ≥ 1.0 reads-per-million (RPM). To facilitate further studies of tRFs by the community, we just released version 2.0 of MINTbase that contains information about 26 531 distinct human tRFs from 11 719 human datasets as of October 2017. Key new elements include: the ...


Continuous User Authentication Via Random Forest, Ting-Wei Chang Jan 2018

Continuous User Authentication Via Random Forest, Ting-Wei Chang

Creative Components

No abstract provided.


Distributed Knowledge Discovery For Diverse Data, Hossein Hamooni Jul 2017

Distributed Knowledge Discovery For Diverse Data, Hossein Hamooni

Computer Science ETDs

In the era of new technologies, computer scientists deal with massive data of size hundreds of terabytes. Smart cities, social networks, health care systems, large sensor networks, etc. are constantly generating new data. It is non-trivial to extract knowledge from big datasets because traditional data mining algorithms run impractically on such big datasets. However, distributed systems have come to aid this problem while introducing new challenges in designing scalable algorithms. The transition from traditional algorithms to the ones that can be run on a distributed platform should be done carefully. Researchers should design the modern distributed algorithms based on the ...


Development Of An Enhanced Generic Data Mining Life Cycle (Dmlc), Markus Hofmann, Brendan Tierney May 2017

Development Of An Enhanced Generic Data Mining Life Cycle (Dmlc), Markus Hofmann, Brendan Tierney

The ITB Journal

Data mining projects are complex and have a high failure rate. In order to improve project management and success rates of such projects a life cycle is vital to the overall success of the project. This paper reports on a research project that was concerned with the life cycle development for large scale data mining projects. The paper provides a detailed view of the design and development of a generic data mining life cycle called DMLC. The life cycle aims to support all members of data mining project teams as well as IT managers and academic researchers and may improve ...


Peeking Into The Other Half Of The Glass : Handling Polarization In Recommender Systems., Mahsa Badami May 2017

Peeking Into The Other Half Of The Glass : Handling Polarization In Recommender Systems., Mahsa Badami

Electronic Theses and Dissertations

This dissertation is about filtering and discovering information online while using recommender systems. In the first part of our research, we study the phenomenon of polarization and its impact on filtering and discovering information. Polarization is a social phenomenon, with serious consequences, in real-life, particularly on social media. Thus it is important to understand how machine learning algorithms, especially recommender systems, behave in polarized environments. We study polarization within the context of the users' interactions with a space of items and how this affects recommender systems. We first formalize the concept of polarization based on item ratings and then relate ...


Mining Sequences Of Developer Interactions In Visual Studio For Usage Smells, Kostadin Damevski, David C. Shepherd, Johannes Schneider, Lori Pollock Jan 2017

Mining Sequences Of Developer Interactions In Visual Studio For Usage Smells, Kostadin Damevski, David C. Shepherd, Johannes Schneider, Lori Pollock

Computer Science Publications

In this paper, we present a semi-automatic approach for mining a large-scale dataset of IDE interactions to extract usage smells, i.e., inefficient IDE usage patterns exhibited by developers in the field. The approach outlined in this paper first mines frequent IDE usage patterns, filtered via a set of thresholds and by the authors, that are subsequently supported (or disputed) using a developer survey, in order to form usage smells. In contrast with conventional mining of IDE usage data, our approach identifies time-ordered sequences of developer actions that are exhibited by many developers in the field. This pattern mining workflow ...


Prediction And Recommendations On The It Leaners' Learning Path As A Collective Intelligence Using A Data Mining Technique, Seong-Yong Hong, Juyun Cho, Yonghyun Hwang Oct 2016

Prediction And Recommendations On The It Leaners' Learning Path As A Collective Intelligence Using A Data Mining Technique, Seong-Yong Hong, Juyun Cho, Yonghyun Hwang

Journal of International Technology and Information Management

With the recent advances in computer technology along with pervasive internet accesses, data analytics is getting more attention than ever before. In addition, research areas on data analysis are diverging and integrating lots of different fields such as a business and social sector. Especially, recent researches focus on the data analysis for a better intelligent decision making and prediction system. This paper analyzes data collected from current IT learners who have already studied various IT subjects to find the IT learners’ learning patterns. The most popular learning patterns are identified through an association rule data mining using an arules package ...


Understanding The Relationships Between Sanitation And Health In Nicaragua And Honduras, Through Data Mining Tools, Ginevra Marina Lazerini, Josep Nualart, Sergio Ruiz-Cayuela, Maialen Urbina, Miquel Sànchez Marrè, Karina Gibert Jul 2016

Understanding The Relationships Between Sanitation And Health In Nicaragua And Honduras, Through Data Mining Tools, Ginevra Marina Lazerini, Josep Nualart, Sergio Ruiz-Cayuela, Maialen Urbina, Miquel Sànchez Marrè, Karina Gibert

International Congress on Environmental Modelling and Software

The aim of this work is to analyze water and sanitation supply data from Nicaragua and Honduras by using different data mining tools. The data has been provided by SIASAR (Rural Water and Sanitation Information System), which is a water and sanitation management and information platform created through the joint effort of different Central American Governments and the World Bank. In the study data from a survey performed in all the rural communities in Nicaragua and in a sample of the rural communities in Honduras from 2012 to 2015 is analyzed. Database contains 10206 communities described by 23 numerical variables ...


Learning On The Relationships Between Respiratory Desease And The Use Of Traditional Stoves In Bangladesh Households, Camila Vergara, Iñigo Arregui, Alain Balaguer, Tamia Gómez, Carmen Sandoval, Miquel Sànchez Marrè, Karina Gibert Jul 2016

Learning On The Relationships Between Respiratory Desease And The Use Of Traditional Stoves In Bangladesh Households, Camila Vergara, Iñigo Arregui, Alain Balaguer, Tamia Gómez, Carmen Sandoval, Miquel Sànchez Marrè, Karina Gibert

International Congress on Environmental Modelling and Software

More than 4 million people die prematurely every year by deseases related to indoor air pollution produced by solid fuels used in cooking (WHO, 2016, Jones 1999), fifty thousand of them in Bangladesh (News Medical, 2012), being women and children the most affected. Risk of pneumonia is high due to the irritants, toxins and carcinogens realeased into air by the incomplete combustion of solid fuels (biomass) used in traditional stoves (WHO 2016), which produce PM10 (particulate matter, small enough (≤10μm) to get into lungs). An open data base from the World Bank (WHO, 2016) (Dasgutpa et al 2006) describing a ...


Socio Environmental Conflicts In Ecuador. The Use Of Preprocessing And Data Mining To Detect Influencing Factors On Violence And Crisis (1985 - 2016), Lina Pita Merino, Martí Rosas-Casals, Karina Gibert Jul 2016

Socio Environmental Conflicts In Ecuador. The Use Of Preprocessing And Data Mining To Detect Influencing Factors On Violence And Crisis (1985 - 2016), Lina Pita Merino, Martí Rosas-Casals, Karina Gibert

International Congress on Environmental Modelling and Software

The main concern regarding the spread of Socio Environmental Conflicts (SEC) is the constant increase of extractive activities to support the economic system. Conflicts originated in the clash of interests between the extractive industries and local populations is the more visible outcome, but the complexity of this phenomenon may not be that obvious. Among South American countries, the highest murder rates of environmental activists corresponded to Brazil, Peru and Colombia, three of the four Amazonian countries along with Ecuador (Global Witness, 2015). In addition, all of them have similar characteristics such as high levels of inequality and the presence of ...


Evaluation Of Classification And Ensemble Algorithms For Bank Customer Marketing Response Prediction, Olatunji Apampa Jan 2016

Evaluation Of Classification And Ensemble Algorithms For Bank Customer Marketing Response Prediction, Olatunji Apampa

Journal of International Technology and Information Management

This article attempts to improve the performance of classification algorithms used in the bank customer marketing response prediction of an unnamed Portuguese bank using the Random Forest ensemble. A thorough exploratory data analysis (EDA) was conducted on the data in order to ascertain the presence of anomalies such as outliers and extreme values. The EDA revealed that the bank data had 45, 211 instances and 17 features, with 11.7% positive responses. This was in addition to the detection of outliers and extreme values. Classification algorithms used for modelling the bank dataset include; Logistic Regression, Decision Tree, Naïve Bayes and ...


A Cloud-Based Framework For Smart Permit System For Buildings, Magdalini Eirinaki, Subhankar Dhar, Shishir Mathur Jan 2016

A Cloud-Based Framework For Smart Permit System For Buildings, Magdalini Eirinaki, Subhankar Dhar, Shishir Mathur

Faculty Publications

In this paper we propose a novel cloud-based platform for building permit system that is efficient, user-friendly, transparent, and has quick turn-around time for homeowners. Compared to the existing permit systems, the proposed smart city permit framework provides a pre-permitting decision workflow, and incorporates a data analytics and mining module that enables the continuous improvement of a) the end user experience, by analyzing explicit and implicit user feedback, and b) the permitting and urban planning process, allowing a gleaning of key insights for real estate development and city planning purposes, by analyzing how users interact with the system depending on ...


Iot+Small Data: Transforming In-Store Shopping Analytics And Services, Meera Radhakrishnan, Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Rajesh Balan Jan 2016

Iot+Small Data: Transforming In-Store Shopping Analytics And Services, Meera Radhakrishnan, Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Rajesh Balan

Research Collection School Of Information Systems

We espouse a vision of small data-based immersive retail analytics, where a combination of sensor data, from personal wearable-devices and store-deployed sensors & IoT devices, is used to create real-time, individualized services for in-store shoppers. Key challenges include (a) appropriate joint mining of sensor & wearable data to capture a shopper’s product level interactions, and (b) judicious triggering of power-hungry wearable sensors (e.g., camera) to capture only relevant portions of a shopper’s in-store activities. To explore the feasibility of our vision, we conducted experiments with 5 smartwatch-wearing users who interacted with objects placed on cupboard racks in our lab ...


A Cloud-Based Framework For Smart Permit System For Buildings, Magdalini Eirinaki, Subhankar Dhar, Shishir Mathur Dec 2015

A Cloud-Based Framework For Smart Permit System For Buildings, Magdalini Eirinaki, Subhankar Dhar, Shishir Mathur

Magdalini Eirinaki

In this paper we propose a novel cloud-based platform for building permit system that is efficient, user-friendly, transparent, and has quick turn-around time for homeowners. Compared to the existing permit systems, the proposed smart city permit framework provides a pre-permitting decision workflow, and incorporates a data analytics and mining module that enables the continuous improvement of a) the end user experience, by analyzing explicit and implicit user feedback, and b) the permitting and urban planning process, allowing a gleaning of key insights for real estate development and city planning purposes, by analyzing how users interact with the system depending on ...


A Cloud-Based Framework For Smart Permit System For Buildings, Magdalini Eirinaki, Subhankar Dhar, Shishir Mathur Dec 2015

A Cloud-Based Framework For Smart Permit System For Buildings, Magdalini Eirinaki, Subhankar Dhar, Shishir Mathur

Shishir Mathur

In this paper we propose a novel cloud-based platform for building permit system that is efficient, user-friendly, transparent, and has quick turn-around time for homeowners. Compared to the existing permit systems, the proposed smart city permit framework provides a pre-permitting decision workflow, and incorporates a data analytics and mining module that enables the continuous improvement of a) the end user experience, by analyzing explicit and implicit user feedback, and b) the permitting and urban planning process, allowing a gleaning of key insights for real estate development and city planning purposes, by analyzing how users interact with the system depending on ...


A Cloud-Based Framework For Smart Permit System For Buildings, Magdalini Eirinaki, Subhankar Dhar, Shishir Mathur Dec 2015

A Cloud-Based Framework For Smart Permit System For Buildings, Magdalini Eirinaki, Subhankar Dhar, Shishir Mathur

Subhankar Dhar

In this paper we propose a novel cloud-based platform for building permit system that is efficient, user-friendly, transparent, and has quick turn-around time for homeowners. Compared to the existing permit systems, the proposed smart city permit framework provides a pre-permitting decision workflow, and incorporates a data analytics and mining module that enables the continuous improvement of a) the end user experience, by analyzing explicit and implicit user feedback, and b) the permitting and urban planning process, allowing a gleaning of key insights for real estate development and city planning purposes, by analyzing how users interact with the system depending on ...


Network Analysis With Stochastic Grammars, Alan C. Lin Sep 2015

Network Analysis With Stochastic Grammars, Alan C. Lin

Theses and Dissertations

Digital forensics requires significant manual effort to identify items of evidentiary interest from the ever-increasing volume of data in modern computing systems. One of the tasks digital forensic examiners conduct is mentally extracting and constructing insights from unstructured sequences of events. This research assists examiners with the association and individualization analysis processes that make up this task with the development of a Stochastic Context -Free Grammars (SCFG) knowledge representation for digital forensics analysis of computer network traffic. SCFG is leveraged to provide context to the low-level data collected as evidence and to build behavior profiles. Upon discovering patterns, the analyst ...


Predicting Cross-Gaming Propensity Using E-Chaid Analysis, Eunju Suh, Matt Alhaery Jun 2015

Predicting Cross-Gaming Propensity Using E-Chaid Analysis, Eunju Suh, Matt Alhaery

UNLV Gaming Research & Review Journal

Cross-selling different types of games could provide an opportunity for casino operators to generate additional time and money spent on gaming from existing patrons. One way to identify the patrons who are likely to cross-play is mining individual players’ gaming data using predictive analytics. Hence, this study aims to predict casino patrons’ propensity to play both slots and table games, also known as cross-gaming, by applying a data-mining algorithm to patrons’ gaming data. The Exhaustive Chi-squared Automatic Interaction Detector (E-CHAID) method was employed to predict cross-gaming propensity. The E-CHAID models based on the gaming-related behavioral data produced actionable model accuracy ...


Engaging Developers In Open Source Software Projects: Harnessing Social And Technical Data Mining To Improve Software Development, Patrick Eric Carlson Jan 2015

Engaging Developers In Open Source Software Projects: Harnessing Social And Technical Data Mining To Improve Software Development, Patrick Eric Carlson

Graduate Theses and Dissertations

As software development has evolved, an increasing amount of collaboration and management is done online. Open source software, in particular, has benefited greatly from communication and collaboration on the Internet. As software projects increase in size, the codebase complexity and required communication between developers increases. The barriers of entry for development participation are not only technical in nature but involve understanding the changing dynamics of the community.

Social Technical Congruence (STC) attempts to understand and model the synergies between technical development and communication. Motivated by this theory, three algorithms were developed that leverage data from version control history and email ...


A Theory Of Name Resolution, Pierre Néron, Andrew Tolmach, Eelco Visser, Guido Wachsmuth Jan 2015

A Theory Of Name Resolution, Pierre Néron, Andrew Tolmach, Eelco Visser, Guido Wachsmuth

Computer Science Faculty Publications and Presentations

We describe a language-independent theory for name binding and resolution, suitable for programming languages with complex scoping rules including both lexical scoping and modules. We formulate name resolution as a two-stage problem. First a language-independent scope graph is constructed using language-specific rules from an abstract syntax tree. Then references in the scope graph are resolved to corresponding declarations using a language-independent resolution process. We introduce a resolution calculus as a concise, declarative, and language- independent specification of name resolution. We develop a resolution algorithm that is sound and complete with respect to the calculus. Based on the resolution calculus we ...


Where Are We In Wastewater Treatment Plants Data Management? A Review And A Proposal, Manel Poch, Joaquim Comas, José Porro, Manel Garrido-Baserba, Lluis Corominas, Maite Pijuan Jun 2014

Where Are We In Wastewater Treatment Plants Data Management? A Review And A Proposal, Manel Poch, Joaquim Comas, José Porro, Manel Garrido-Baserba, Lluis Corominas, Maite Pijuan

International Congress on Environmental Modelling and Software

Wastewater treatment plants (WWTP) are comprised of complex processes that need to be optimally managed. To attain that, in the last years an impressive effort has been made to incorporate monitoring devices able to provide from several hundred to more than ten thousand signals. With the aim to take benefit of those data, different data mining techniques have been applied to transform them into information and knowledge in order to help WWTP's managers. Furthermore, several mathematical models have been developed intending to simulate process behaviour including biomass and pollutants transformation. However, it is recognized that this it is not ...


Pre-Processing Techniques Applied To Automatic Taxon Identification On Fish Otoliths, Ramon Reig-Bolaño, Pere Marti-Puig Jun 2014

Pre-Processing Techniques Applied To Automatic Taxon Identification On Fish Otoliths, Ramon Reig-Bolaño, Pere Marti-Puig

International Congress on Environmental Modelling and Software

This paper analyzes the characteristics of a rotation-invariant Feature space to be used in a classifier of fish otoliths, it is compared to two other Feature spaces, one with raw data and another with transformed data (using the Elliptic Fourier Descriptors EFD). Otoliths are found in the inner ear of fish. Their shape can be analyzed to determine sex, age, populations and species, and thus they can provide necessary and relevant information for ecological studies. The Automatic Taxon Identifier (ATI) is used to classify fish otoliths directly from a query image and is implemented on-line in a Public Database. This ...


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 ...


Educational Data Mining: An Advance For Intelligent Systems In Education, Ryan Baker Dec 2013

Educational Data Mining: An Advance For Intelligent Systems In Education, Ryan Baker

Ryan S.J.d. Baker

Computer-based technologies have transformed the way we live, work, socialize, play, and learn. Today, the use of data collected through these technologies is supporting a second-round of transformation in all of these areas. Over the last decades, the methods of data mining and analytics have transformed field after field. Scientific fields such as physics, biology, and climate science have leveraged these methods to manage and make discoveries in previously unimaginably large datasets. The first journal devoted to data mining and analytics methods in biology, Computers in Biology and Medicine, began publication as long ago as the 1970s. In the mid-1990s ...


Population Validity For Educational Data Mining Models: A Case Study In Affect Detection, Ryan Baker, Jaclyn Ocumpaugh, Sujith Gowda, Neil Heffermnan, Cristina Heffernan Dec 2013

Population Validity For Educational Data Mining Models: A Case Study In Affect Detection, Ryan Baker, Jaclyn Ocumpaugh, Sujith Gowda, Neil Heffermnan, Cristina Heffernan

Ryan S.J.d. Baker

ICT-enhanced research methods such as educational data mining (EDM) have allowed researchers to effectively model a broad range of constructs pertaining to the student, moving from traditional assessments of knowledge to assessment of engagement, meta-cognition, strategy, and affect. The automated detection of these constructs allows EDM researchers to develop intervention strategies that can be implemented either by the software or the teacher. It also allows for secondary analyses of the construct, where the detectors are applied to a data set that is much larger than one that could be analyzed by more traditional methods. However, in many cases, the data ...


Rank Based Anomaly Detection Algorithms, Huaming Huang May 2013

Rank Based Anomaly Detection Algorithms, Huaming Huang

Electrical Engineering and Computer Science - Dissertations

Anomaly or outlier detection problems are of considerable importance, arising frequently in diverse real-world applications such as finance and cyber-security. Several algorithms have been formulated for such problems, usually based on formulating a problem-dependent heuristic or distance metric. This dissertation proposes anomaly detection algorithms that exploit the notion of ``rank," expressing relative outlierness of different points in the relevant space, and exploiting asymmetry in nearest neighbor relations between points: a data point is ``more anomalous" if it is not the nearest neighbor of its nearest neighbors. Although rank is computed using distance, it is a more robust and higher level ...


A Knowledge-Based Clinical Toxicology Consultant For Diagnosing Multiple Exposures, Joel D. Schipper, Douglas D. Dankel Ii, A. Antonio Arroyo, Jay L. Schauben May 2013

A Knowledge-Based Clinical Toxicology Consultant For Diagnosing Multiple Exposures, Joel D. Schipper, Douglas D. Dankel Ii, A. Antonio Arroyo, Jay L. Schauben

Publications

Objective: This paper presents continued research toward the development of a knowledge-based system for the diagnosis of human toxic exposures. In particular, this research focuses on the challenging task of diagnosing exposures to multiple toxins. Although only 10% of toxic exposures in the United States involve multiple toxins, multiple exposures account for more than half of all toxin-related fatalities. Using simple medical mathematics, we seek to produce a practical decision support system capable of supplying useful information to aid in the diagnosis of complex cases involving multiple unknown substances.

Methods: The system is automatically trained using data mining techniques to ...