Computer Sciences Commons^™

A Neuronal Classification System For Plant Leaves Using Genetic Image Segmentation, Oluleye Babatunde, Leisa Armstrong, Dean Diepeveen, Jinsong Leng

Grain and Other Field Crops Research Articles

This paper demonstrates the use of radial basis networks (RBF), cellular neural networks (CNN)and genetic algorithm (GA) for automatic classification of plant leaves. A genetic neuronal system herein attempted to solve some of the inherent challenges facing current software being employed for plant leaf classification. The image segmentation module in this work was genetically optimized to bring salient features in the images of plants leaves used in this work. The combination of GA-based CNN with RBF in this work proved more efficient than the existing systems that use conventional edge operators such as Canny, LoG, Prewitt, and Sobel operators. The …

Object Recognition And Visual Search With A Physiologically Grounded Model Of Visual Attention, Frederik Beuth, Fred H. Hamker

MODVIS Workshop

Visual attention models can explain a rich set of physiological data (Reynolds & Heeger, 2009, Neuron), but can rarely link these findings to real-world tasks. Here, we would like to narrow this gap with a novel, physiologically grounded model of visual attention by demonstrating its objects recognition abilities in noisy scenes.

To base the model on physiological data, we used a recently developed microcircuit model of visual attention (Beuth & Hamker, in revision, Vision Res) which explains a large set of attention experiments, e.g. biased competition, modulation of contrast response functions, tuning curves, and surround suppression. Objects are represented by …

Modeling Visual Features To Recognize Biological Motion: A Developmental Approach, Giulio Sandini, Nicoletta Noceti, Alessia Vignolo, Alessandra Sciutti, Francesco Rea, Alessandro Verri, Francesca Odone

MODVIS Workshop

In this work we deal with the problem of designing and developing computational vision models – comparable to the early stages of the human development – using coarse low-level information.

More specifically, we consider a binary classification setting to characterize biological movements with respect to non-biological dynamic events. To this purpose, our model builds on top of the optical flow estimation, and abstract the representation to simulate the limited amount of visual information available at birth. We take inspiration from known biological motion regularities explained by the Two-Thirds Power Law, and design a motion representation that includes different low-level features, …

Trip: Tracking Rhythms In Plants, An Automated Leaf Movement Analysis Program For Circadian Period Estimation, Kathleen Greenham, Ping Lou, Sara E. Remsen, Hany Farid, C Robertson Mcclung

Dartmouth Scholarship

Background: A well characterized output of the circadian clock in plants is the daily rhythmic movement of leaves. This process has been used extensively in Arabidopsis to estimate circadian period in natural accessions as well as mutants with known defects in circadian clock function. Current methods for estimating circadian period by leaf movement involve manual steps throughout the analysis and are often limited to analyzing one leaf or cotyledon at a time.

Methods: In this study, we describe the development of TRiP (Tracking Rhythms in Plants), a new method for estimating circadian period using a motion estimation algorithm that can …

Domain Specific Document Retrieval Framework On Near Real-Time Social Health Data, Swapnil Soni

Kno.e.sis Publications

With the advent of web search and microblogging, the percentage of Online Health Information Seekers (OHIS) using these services to share and seek health information in real-time has increased exponentially. Recently, Twitter has emerged as one of the primary mediums for sharing and seeking of the latest information related to a variety of topics, including health information. Although Twitter is an excellent information source, the identification of useful information from the deluge of tweets is one of the major challenges. Twitter search is limited to keyword-based techniques to retrieve information for a given query and sometimes the results do not …

Analyzing The Social Media Footprint Of Street Gangs, Sanjaya Wijeratne, Derek Doran, Amit P. Sheth, Jack Dustin

Kno.e.sis Publications

Gangs utilize social media as a way to maintain threatening virtual presences, to communicate about their activities, and to intimidate others. Such usage has gained the attention of many justice service agencies that wish to create better crime prevention and judicial services. However, these agencies use analysis methods that are labor intensive and only lead to basic, qualitative data interpretations. This paper presents the architecture of a modern platform to discover the structure, function, and operation of gangs through the lens of social media. Preliminary analysis of social media posts shared in the greater Chicago, IL region demonstrate the platform’s …

A Dynamic Behavioral Biometric Approach To Authenticate Users Employing Their Fingers To Interact With Touchscreen Devices, Arturo Ponce

CCAC Theses and Dissertations

The use of mobile devices has extended to all areas of human life and has changed the way people work and socialize. Mobile devices are susceptible to getting lost, stolen, or compromised. Several approaches have been adopted to protect the information stored on these devices. One of these approaches is user authentication. The two most popular methods of user authentication are knowledge based and token based methods but they present different kinds of problems.

Biometric authentication methods have emerged in recent years as a way to deal with these problems. They use an individual’s unique characteristics for identification and have …

Design, Programming, And User-Experience, Kaila G. Manca

Honors Scholar Theses

This thesis is a culmination of my individualized major in Human-Computer Interaction. As such, it showcases my knowledge of design, computer engineering, user-experience research, and puts into practice my background in psychology, com- munications, and neuroscience.

I provided full-service design and development for a web application to be used by the Digital Media and Design Department and their students.This process involved several iterations of user-experience research, testing, concepting, branding and strategy, ideation, and design. It lead to two products.

The first product is full-scale development and optimization of the web appli- cation.The web application adheres to best practices. It was …

Big Data And Smart Cities, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

Image Enhancement Of Cancerous Tissue In Mammography Images, Richard Thomas Richardson

CCAC Theses and Dissertations

This research presents a framework for enhancing and analyzing time-sequenced mammographic images for detection of cancerous tissue, specifically designed to assist radiologists and physicians with the detection of breast cancer. By using computer aided diagnosis (CAD) systems as a tool to help in the detection of breast cancer in computed tomography (CT) mammography images, previous CT mammography images will enhance the interpretation of the next series of images. The first stage of this dissertation applies image subtraction to images from the same patient over time. Image types are defined as temporal subtraction, dual-energy subtraction, and Digital Database for Screening Mammography …

Context-Driven Automatic Subgraph Creation For Literature-Based Discovery, Delroy H. Cameron, Ramakanth Kavuluru, Thomas Rindflesch, Amit P. Sheth, Krishnaprasad Thirunarayan, Olivier Bodenreider

Kno.e.sis Publications

Background: Literature-based discovery (LBD) is characterized by uncovering hidden associations in non-interacting scientific literature. Prior approaches to LBD include use of: 1) domain expertise and structured background knowledge to manually filter and explore the literature, 2) distributional statistics and graph-theoretic measures to rank interesting connections and 3) heuristics to help eliminate spurious connections. However, manual approaches to LBD are not scalable and purely distributional approaches may not be sufficient to obtain insights into the meaning of poorly understood associations. While several graph-based approaches have the potential to elucidate associations, their effectiveness has not been fully demonstrated. A considerable degree of …

Framework For Functional Tree Simulation Applied To 'Golden Delicious' Apple Trees, Marek Fiser

Open Access Theses

This research aims to develop a framework to model realistic and functional trees. Our modular framework is easily controllable and extensible by the user. The framework provides powerful features such as per-leaf light simulation and a source-sink based resources transport model. Our novel 3D reconstruction algorithm generates realistic tree models with a good 3D polygon topology. ^ The second part of this research is a functional model of a Golden Delicious apple tree. We use our simulation framework and data collected in Purdue Meigs farm over the year 2014 to create a realistic data-driven model. The model reacts on temperature …

Volume 07, Rachel C. Lombardi, Ben Osterhout, Lindsay Graybill, Rebecca E. Dey, Skyler T. Carpenter, Emma Beckett, Jason Ware, Mollie Andrews, James Bates, Landon Cooper, Tiffani Jeffries, Maria Wheaton, Dallas Price, Laura Kahler, Sarah Charlton, Anna Bultrowicz, Emily Spittle, Erin Godwin, Eamon Brokenbrough

Incite: The Journal of Undergraduate Scholarship

Introduction from Interim Dean Dr. Jennifer Apperson

Spatial Analysis of Potential Risk Factors Associated with Addition of Atlantic Coast Pipeline Through Virginia by Rachel C. Lombardi

"Delicate Matters with No Speaking," "Hope and Nothing," "Mono Duality" by Ben Osterhout

"Connect" Graphic Design Senior Project by Lindsay Graybill

Phenolic Acids in Brassicaceae Plants: Ovipositional Stimulants or Deterrents for Cabbage White Butterfly, Pieris Rapae? by Rebecca E. Dey And Skyler T. Carpenter

"Abecedarian Cards" by Emma Beckett, Jason Ware, And Mollie Andrews

Helvetica: A Type Specimen Book by James Bates, Landon Cooper, Tiffani Jeffries, And Maria Wheaton

“Things Left Behind” by Dallas …

Computational Development For Secondary Structure Detection From Three-Dimensional Images Of Cryo-Electron Microscopy, Dong Si

Computer Science Theses & Dissertations

Electron cryo-microscopy (cryo-EM) as a cutting edge technology has carved a niche for itself in the study of large-scale protein complex. Although the protein backbone of complexes cannot be derived directly from the medium resolution (5-10 Å) of amino acids from three-dimensional (3D) density images, secondary structure elements (SSEs) such as alpha-helices and beta-sheets can still be detected. The accuracy of SSE detection from the volumetric protein density images is critical for ab initio backbone structure derivation in cryo-EM. So far it is challenging to detect the SSEs automatically and accurately from the density images at these resolutions. This dissertation …

The Hosoya Entropy Of A Graph, Abbe Mowshowitz, Matthias Dehmer

Publications and Research

This paper demonstrates properties of Hosoya entropy, a quantitative measure of graph complexity based on a decomposition of the vertices linked to partial Hosoya polynomials. Connections between the information content of a graph and Hosoya entropy are established, and the special case of Hosoya entropy of trees is investigated.

Smart Data - How You And I Will Exploit Big Data For Personalized Digital Health And Many Other Activities, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

On Using Synthetic Social Media Stimuli In An Emergency Preparedness Functional Exercise, Andrew Hampton, Shreyansh Bhatt, Gary Alan Smith, Jeremy S. Brunn, Hemant Purohit, Valerie L. Shalin, John M. Flach, Amit P. Sheth

Kno.e.sis Publications

This paper details the creation and use of a massive (over 32,000 messages) artificially constructed 'Twitter' microblog stream for a regional emergency preparedness functional exercise. By combining microblog conversion, manual production, and a control set, we created a web based information stream providing valid, misleading, and irrelevant information to public information officers (PIOs) representing hospitals, fire departments, the local Red Cross, and city and county government officials. PIOs searched, monitored, and (through conventional channels) verified potentially actionable information that could then be redistributed through a personalized screen name. Our case study of a key PIO reveals several capabilities that social …

Rice And Mouse Quantitative Phenotype Prediction In Genome-Wide Association Studies With Support Vector Regression, Abdulrhman Fahad M. Aljouie

Theses

Quantitative phenotypes prediction from genotype data is significant for pathogenesis, crop yields, and immunity tests. The scientific community conducted many studies to find unobserved quantitative phenotype high predictive ability models. Early genome-wide association studies (GWAS) focused on genetic variants that are associated with disease or phenotype, however, these variants manly covers small portion of the whole genetic variance, and therefore, the effectiveness of predictions obtained using this information may possibly be circumscribed [ 1 ].

Instead, this study shows prediction ability from whole genome single nucleotide polymorphisms (SNPs) data of 1940 genotyped stoke mouse with - 12k SNPs, and 413 …

Cancer Risk Prediction With Next Generation Sequencing Data Using Machine Learning, Nihir Patel

Theses

The use of computational biology for next generation sequencing (NGS) analysis is rapidly increasing in genomics research. However, the effectiveness of NGS data to predict disease abundance is yet unclear. This research investigates the problem in the whole exome NGS data of the chronic lymphocytic leukemia (CLL) available at dbGaP. Initially, raw reads from samples are aligned to the human reference genome using burrows wheeler aligner. From the samples, structural variants, namely, Single Nucleotide Polymorphism (SNP) and Insertion Deletion (INDEL) are identified and are filtered using SAMtools as well as with Genome Analyzer Tool Kit (GATK). Subsequently, the variants are …

Mapping Legume Roots Can Determine Best Performing Crops, Frédérique C. Guinel

Clear Language Summaries

Legume plants have a mutually beneficial relationship with bacteria. These beneficial bacteria live in nodules found on plant roots. Until recently there was no way to systematically measure the roots the nodules are found on and to place accurately the nodules on these roots. The research team created software and a method to measure multiple root parameters which were not available previously. This allows different plant varieties to be compared with precise measurements of the root structure in order to figure out which plants will grow better.

Gender-Based Violence In 140 Characters Or Fewer: A #Bigdata Case Study Of Twitter, Hemant Purohit, Tanvi Banerjee, Andrew Hampton, Valerie L. Shalin, Nayanesh Bhandutia, Amit P. Sheth

Kno.e.sis Publications

Public institutions are increasingly reliant on data from social media sites to measure public attitude and provide timely public engagement. Such reliance includes the exploration of public views on important social issues such as gender-based violence (GBV). In this study, we examine big (social) data consisting of nearly fourteen million tweets collected from Twitter over a period of ten months to analyze public opinion regarding GBV, highlighting the nature of tweeting practices by geographical location and gender. We demonstrate the utility of Computational Social Science to mine insight from the corpus while accounting for the influence of both transient events …

Knowledge Enabled Approach To Predict The Location Of Twitter Users, Revathy Krishnamurthy, Pavan Kapanipathi, Amit P. Sheth, Krishnaprasad Thirunarayan

Kno.e.sis Publications

Knowledge bases have been used to improve performance in applications ranging from web search and event detection to entity recognition and disambiguation. More recently, knowledge bases have been used to analyze social data. A key challenge in social data analysis has been the identification of the geographic location of online users in a social network such as Twitter. Existing approaches to predict the location of users, based on their tweets, rely solely on social media features or probabilistic language models. These approaches are supervised and require large training dataset of geo-tagged tweets to build their models. As most Twitter users …

Mining Behavior Of Citizen Sensor Communities To Improve Cooperation With Organizational Actors, Hemant Purohit

Kno.e.sis Publications

Web 2.0 (social media) provides a natural platform for dynamic emergence of citizen (as) sensor communities, where the citizens generate content for sharing information and engaging in discussions. Such a citizen sensor community (CSC) has stated or implied goals that are helpful in the work of formal organizations, such as an emergency management unit, for prioritizing their response needs. This research addresses questions related to design of a cooperative system of organizations and citizens in CSC. Prior research by social scientists in a limited offline and online environment has provided a foundation for research on cooperative behavior challenges, including ‘ …

Value Oriented Big Data Processing With Applications, Krishnaprasad Thirunarayan

Kno.e.sis Publications

We discuss the nature of Big Data and address the role of semantics in analyzing and processing Big Data that arises in the context of Physical-Cyber-Social Systems. To handle Volume, we advocate semantic perception that can convert low-level observational data to higher-level abstractions more suitable for decision- making. To handle Variety, we resort to semantic models and annotations of data so that intelligent processing can be done independent of heterogeneity of data formats and media. To handle Velocity, we seek to use continuous semantics capability to dynamically create event or situation specific models and recognize relevant new concepts, entities and …

Extracting City Traffic Events From Social Streams, Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, Amit P. Sheth

Kno.e.sis Publications

Cities are composed of complex systems with physical, cyber, and social components. Current works on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present …

Understanding Social Effects In Online Networks, Huda Alhazmi, Swapna S. Gokhale, Derek Doran

Kno.e.sis Publications

Understanding the motives behind people’s interactions online can offer sound bases to predict how a social network may evolve and also support a host of applications. We hypothesize that three offline social factors, namely, stature, relationship strength, and egocentricity may also play an important role in driving users’ interactions online. Therefore, we study the influence of these three social factors in online interactions by analyzing the transitivity in triads or three-way relationships among users. Analyzing transitivity through the lens of triad census for four popular social networks, namely, Facebook, Twitter, YouTube and Slashdot, we find that: (i) users’ interactions are …

Semantic Gateway As A Service Architecture For Iot Interoperability, Pratikkumar Desai, Amit P. Sheth, Pramod Anantharam

Kno.e.sis Publications

The Internet of Things (IoT) is set to occupy a substantial component of future Internet. The IoT connects sensors and devices that record physical observations to applications and services of the Internet. As a successor to technologies such as RFID and Wireless Sensor Networks (WSN), the IoT has stumbled into vertical silos of proprietary systems, providing little or no interoperability with similar systems. As the IoT represents future state of the Internet, an intelligent and scalable architecture is required to provide connectivity between these silos, enabling discovery of physical sensors and interpretation of messages between things. This paper proposes a …

Community Chairs As A Catalyst For Campus Collaboration In Stem, Neal Grandgenett, David Boocker, Hesham Ali, Angela M. Hodge, Brian Dorn, Christine E. Cutucache

Biology Faculty Publications

Strong collaborative partnerships are critical to the ongoing success of any urban or metropolitan university in its efforts to build the science, technology, engineering, and mathematics (STEM) career pathways so critical to our nation. At the University of Nebraska at Omaha, we have established a faculty leadership structure of "community chairs" that work across colleges to support campus priorities. This paper describes UNO’s STEM community chair model, including selected initiatives, impacts, and challenges to date.

Knowledge-Driven Personalized Contextual Mhealth Service For Asthma Management In Children, Pramod Anantharam, Tanvi Banerjee, Amit P. Sheth, Krishnaprasad Thirunarayan, Surendra Marupudi, Vaikunth Sridharan

Kno.e.sis Publications

Wide adoption of smartphones and availability of low-cost sensors has resulted in seamless and continuous monitoring of physiology, environment, and public health notifications. However, personalized digital health and patient empowerment can become a reality only if the complex multisensory and multimodal data is processed within the patient context. Contextual processing of patient data along with personalized medical knowledge can lead to actionable information for better and timely decisions. We present a system called kHealth capable of aggregating multisensory and multimodal data from sensors (passive sensing) and answers to questionnaire (active sensing) from patients with asthma. We present our preliminary data …

Using Ehrs For Heart Failure Therapy Recommendation Using Multidimensional Patient Similarity Analytics, Maryam Panahiazar, Vahid Taslimitehrani, Naveen L. Pereira, Jyotishman Pathak

Kno.e.sis Publications

Electronic Health Records (EHRs) contain a wealth of information about an individual patient’s diagnosis, treatment and health outcomes. This information can be leveraged effectively to identify patients who are similar to each for disease diagnosis and prognosis. In recent years, several machine learning methods 1 have been proposed to assessing patient similarity, although the techniques have primarily focused on the use of patient diagnoses data from EHRs for the learning task. In this study, we develop a multidimensional patient similarity assessment technique that leverages multiple types of information from the EHR and predicts a medication plan for each new patient …