Databases and Information Systems Commons^™

A Gis Analysis Of The Relationship Between Ndvi Value, Slope, And Aspect Of The Terrain Of The Prairie Restoration Project, Basil Lund

2026 Symposium

The Prairie Restoration Project (PRP) encompasses 115 acres of land on the west side of the EWU campus. The goal of the PRP is to restore what was once a section of wheat farm back to its original Palouse prairie state. Here, I am using publicly available satellite data to examine some potential variables affecting the health of wheat plants planted in the years 2009 to 2017. I have used 4-band aerial imagery to create a map of NDVI values within the bounds of the PRP, which visualizes the near-infrared wavelengths that are reflected by healthy, chlorophyll-rich plants. I have …

Cellscout: Visual Analytics For Mining Biomarkers In Cell State Discovery, Rui Sheng, Zelin Zang, Jiachen Wang, Yan Luo, Zixin Chen, Yan Zhou, Shaolun Ruan, Huamin Qu

Research Collection School Of Computing and Information Systems

Cell state discovery is crucial for understanding biological systems and enhancing medical outcomes. A key aspect of this process is identifying distinct biomarkers that define specific cell states. However, difficulties arise from the co-discovery process of cell states and biomarkers: biologists often use dimensionality reduction to visualize cells in a two-dimensional space. Then they usually interpret visually clustered cells as distinct states, from which they seek to identify unique biomarkers. However, this assumption is often this assumption often fails to hold due to internal inconsistencies in a cluster, making the process trial-and-error and highly uncertain. Therefore, biologists urgently need effective …

Performance Enhancement For Rufa: Rapid Urban Forest Assessment, Nicholas Tan

Master's Theses

Urban forests are crucial to the livability and resilience of cities, offering critical ecosystem benefits such as air quality enhancement, temperature regulation, and biodiversity. Managing said urban forests is essential to ensure their sustainability and adaptability to rapidly changing environmental and climate conditions. The Rapid Urban Forest Assessment (RUFA) tool was developed to address the need for a standardized approach to evaluating and comparing urban and community forestry programs. By analyzing and aggregating tree-specific data across California, such as canopy cover, tree counts, and diversity scores, RUFA assigns a comprehensive urban forestry score for each city. This score allows for …

Damslnet: Dual-Attention Multi-Scale Lightweight Network For Plant Disease Classification, Linfan Deng, Juan Qin, Kun Li, Jinhua Zhu, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Accurately identifying crop diseases plays a crucial role in advancing intelligent and modern agricultural production. Deep learning techniques have performed robust performance in classifying plant disease images. However, current studies face the challenge that many plant disease datasets are generated in controlled environments, leading to reduced model performance in real-world agricultural settings. This paper aims to provide a lightweight model that can accurately classify plant diseases in natural environments. Specifically, this paper investigates the Dual-Attention Multi-Scale Lightweight Network (DAMSLNet), which combines dual-attention-based multi-scale feature extraction and deep information fusion, to classify plant diseases. At the front end, the model employs …

Bloom: Behavioral Learning And Outcome Observation In Microbes, Sean Sarwar Haque, Luke Compton Wharton, Ming Lin, Razvan Voicu

Symposium of Student Scholars

Understanding how pathogens respond to physical changes in their environment is crucial for developing effective treatments and preventative measures. Current research often relies on static models or experimental data that either fail to capture the dynamic interactions within cellular environments or are not generalizable to other types of pathogens. This project aims to address this gap by creating a comprehensive cell simulation that models pathogens and their response to chemical, physical, and physiological changes. The proposed solution is a simulation that integrates biological data and computational modeling to replicate the behavior of pathogens in real time as they are affected …

Leaf-Based Varietal Categorization Of Sweetpotato (Ipomoea Batatas L. Lam.), A Potentially Healthful Vegetable, Using Image Processing And K-Means Clustering, Shahidul Islam, Md Towfiqur Rahman, Md Hamidul Rahman, Abdul Momin

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Sweetpotato (Ipomoea batatas Lam) leaves contain higher concentrations of phenolic compounds, flavonoids, and carotenoids that are remarkable in health promotion. However, the nutrient content in sweetpotato leaves varies from variety to variety, and leaf shape and color are the key identifying factors for the varietal classification of sweetpotatoes. So, detecting sweetpotato leaves is essential for the in-situ identification of sweetpotato varieties and for developing intelligent agricultural systems. This study aimed to create a leaf-shape-based varietal classification technique for sweetpotato using image processing techniques coupled with a K-means clustering algorithm. 38 leaf images (RGB) of two sweetpotato cultivars were collected …

In-Season Nitrogen Mmanagement: Leveraging Data Visualization For Precision Agriculture, Chathurika Harshani Narayana

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Effective nitrogen management is vital for sustainable agriculture, impacting both crop yield and environmental health. Traditional methods often use fixed application rates set before planting, which do not adapt to changing crop needs during the season. This can lead to over- or under-application, reducing efficiency and sustainability. While modern tools like sensors, satellites, and UAVs provide valuable real-time data on crop and field conditions, integrating and using this data to guide timely nitrogen decisions remains a major challenge. In-season nitrogen management offers a solution by allowing for dynamic adjustments to nitrogen applications, addressing crop needs as they arise. This approach …

Genomic Data Science Approaches For Understanding Human Diseases, Snehal Shah

All Dissertations

The intricate interplay of genetic predisposition, environmental influences, and lifestyle acts as the multifactorial landscape of diseases. Understanding this complexity presents a significant challenge. Molecular insights into disease mechanisms, particularly the interactions of DNA, RNA, and proteins with environmental and lifestyle factors, have revolutionized disease diagnosis, prognosis, and treatment. High-throughput technologies, such as next-generation sequencing, generate large amounts of molecular data, holding a wealth of knowledge. These datasets unveil the roles of genes and their interactions with various factors through analysis, shedding light on previously unknown molecular mechanisms underlying disease pathogenesis. Furthermore, they facilitate the discovery of biomarkers crucial for …

A Deep Learning Method To Predict Bacterial Adp-Ribosyltransferase Toxins, Dandan Zheng, Siyu Zhou, Lihong Chen, Guansong Pang, Jian Yang

Research Collection School Of Computing and Information Systems

Motivation: ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent virulence factors that orchestrate the manipulation of host cell functions to facilitate bacterial pathogenesis. Despite their pivotal role, the bioinformatic identification of novel bARTTs poses a formidable challenge due to limited verified data and the inherent sequence diversity among bARTT members. Results: We proposed a deep learning-based model, ARTNet, specifically engineered to predict bARTTs from bacterial genomes. Initially, we introduced an effective data augmentation method to address the issue of data scarcity …

Multithreaded Applications On The Heterogeneous Research Computing Environment., Sungbo Jung

Electronic Theses and Dissertations

Bioinformatics is a domain that has experienced rapid research growth in recent years, as evidenced by the increasing number of articles in biomedical databases such as PubMed, which adds over a million publications every year. However, this also poses a challenge for researchers who need to find relevant citations for their work. Therefore, developing efficient indexing and searching methods for text data is crucial for Bioinformatics. One key technique for information retrieval is document inversion, which involves creating an inverted index to enable efficient searching through vast collections of text or documents. This Ph.D. research aims to design the research …

Non-Binary Evaluation Of Next-Basket Food Recommendation, Yue Liu, Palakorn Achananuparp, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Next-basket recommendation (NBR) is a recommendation task that predicts a basket or a set of items a user is likely to adopt next based on his/her history of basket adoption sequences. It enables a wide range of novel applications and services from predicting next basket of items for grocery shopping to recommending food items a user is likely to consume together in the next meal. Even though much progress has been made in the algorithmic NBR research over the years, little research has been done to broaden knowledge about the evaluation of NBR methods, which is largely based on the …

Foodmask: Real-Time Food Instance Counting, Segmentation And Recognition, Huu-Thanh Nguyen, Yu Cao, Chong-Wah Ngo, Wing-Kwong Chan

Research Collection School Of Computing and Information Systems

Food computing has long been studied and deployed to several applications. Understanding a food image at the instance level, including recognition, counting and segmentation, is essential to quantifying nutrition and calorie consumption. Nevertheless, existing techniques are limited to either category-specific instance detection, which does not reflect precisely the instance size at the pixel level, or category-agnostic instance segmentation, which is insufficient for dish recognition. This paper presents a compact and fast multi-task network, namely FoodMask, for clustering-based food instance counting, segmentation and recognition. The network learns a semantic space simultaneously encoding food category distribution and instance height at pixel basis. …

A Computational Profile Of Invasive Lionfish In Belize: A New Insight On A Destructive Species, Joshua E. Balan

The Journal of Purdue Undergraduate Research

Since their discovery in the region in 2009, invasive Indonesian-native lionfish have been taking over the Belize Barrier Reef. As a result, populations of local species have dwindled as they are either eaten or outcompeted by the invaders. This has led to devastating losses ecologically and economically; massive industries in the local nations, such as fisheries and tourism, have suffered greatly. Attempting to combat this, local organizations, from nonprofits to ecotourism companies, have been manually spear-hunting them on scuba dives to cull the population. One such company, Reef Conservation Institute (ReefCI), operating out of Tom Owens Caye outside of Placencia, …

Creation Of A Digital Storage System For Genome Sequencing Metadata, Jacquelin W. Olexa

Undergraduate Theses, Professional Papers, and Capstone Artifacts

As the field of computational genomics continues to expand in both potential and application, it is now more imperative than ever to ensure that massive genetic sequencing datasets are properly stored in an accessible manner. This project sought to establish a practical, user-friendly, secure system for a genomics research lab (the Good Lab; thegoodlab.org) at the University of Montana. A MySQL database and connected web application was ruled the best configuration to maximize utility and accessibility for the lab’s researchers. Building the logical framework for the database, creating the server, and sourcing data occurred over several months. The dataset ranged …

Efficient Classification Of Very High Resolution Images, Mohammad I. Nouyed

Graduate Theses, Dissertations, and Problem Reports (ETD)

In recent decades, deep learning approaches have shown significant improvement in various image understanding tasks. However, analysis of high-resolution images remains a major challenge. In this work, we address the challenge of very high-resolution histopathological image (VHRHI) classification using a new information-theoretic discriminative patch selection approach. We show results on a high-resolution image dataset, namely, gigapixel whole slide tissue images for cancer tumors. Then we address how to efficiently classify challenging histopathology images, such as gigapixel whole-slide images for cancer diagnostics with image-level annotation. These ``weak labels'' are applied throughout the image but describe tumor regions of variable sizes and …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

A Review Of Threat Vectors To Dna Sequencing Pipelines, Tyler Rector

Cybersecurity Undergraduate Research Showcase

Bioinformatics is a steadily growing field that focuses on the intersection of biology with computer science. Tools and techniques developed within this field are quickly becoming fixtures in genomics, forensics, epidemiology, and bioengineering. The development and analysis of DNA sequencing and synthesis have enabled this significant rise in demand for bioinformatic tools. Notwithstanding, these bioinformatic tools have developed in a research context free of significant cybersecurity threats. With the significant growth of the field and the commercialization of genetic information, this is no longer the case. This paper examines the bioinformatic landscape through reviewing the biological and cybersecurity threats within …

Characterization And Estimation Of Musculoskeletal Pain Using Machine Learning, Boluwatife Faremi

Master's Theses

Traditional scales utilized for recording pain are known to be highly subjective and biased due to inaccuracies in recollecting actual pain intensities. As a result, machine learning (ML) models that are trained using these scores as ground truth are reported to have low performance for objective pain classification because of the huge disparity between what was felt in moments of pain and the scores recorded afterward.

In the present study, two devices were designed for gathering real-time, continuous in-session subjective pain scores and the recording of the autonomic nervous system (ANS) altered endodermal (EDA) activity. 24 participants were recruited to …

Development And Issues Of Biotech Seed Industry In China, Peijuan Chi, Hualing Xie, Ping Zhao, Fang Chen, Ning Wu, Zhixi Tian, Weicai Yang, Yanping Yang

Bulletin of Chinese Academy of Sciences (Chinese Version)

Biotech seed industry is a strategic core industry. Biotechnology combined with digital technology has promoted the seed industry into an intelligent era, and the breeding paradigm has changed from “experimental selection” to “computational selection”. Biotech seed industry has become a research and development intensive industry, and the market is highly concentrated, which is controlled by large multinational enterprises. The scientific and technological output of China and the United States is in the first echelon, and the number of papers and authorized patents ranks among the top two in the world. From the perspective of core competitiveness, the United States is …

Trace Dna Detection Using Diamond Dye: A Recovery Technique To Yield More Dna, Leah Davis

Master's Theses

This study aspires to find a new screening approach to trace DNA recovery techniques to yield a higher quantity of trace DNA from larger items of evidence. It takes the path of visualizing trace DNA on items of evidence with potential DNA so analysts can swab a more localized area rather than attempting to recover trace DNA through the general swabbing technique currently used for trace DNA recovery. The first and second parts consisted of observing trace DNA interaction with Diamond Dye on porous and non-porous surfaces.

The third part involved applying the Diamond Dye solution by spraying it onto …

Rattus Norvegicus As A Biological Detector Of Clandestine Remains And The Use Of Ultrasonic Vocalizations As A Locating Mechanism, Gabrielle M. Johnston

Master's Theses

In investigations, locating missing persons and clandestine remains are imperative. One way that first responder and police agencies can search for the remains is by using cadaver dogs as biological detectors. Cadaver dogs are typically used due to their olfactory sensitivity and ability to detect low concentrations of volatile organic compounds produced by biological remains. Cadaver dogs are typically chosen for their stamina, agility, and olfactory sensitivity. However, what is not taken into account often is the size of the animal and the expense of maintaining and training the animal. Cadaver dogs are typically large breeds that cannot fit in …

Invasive Buckthorn Mapping: A Uav-Based Approach Utilizing Machine Learning, Gis, And Remote Sensing Techniques In The Upper Peninsula Of Michigan, Vikranth Madeppa

Dissertations, Master's Theses and Master's Reports

An Invasive species is a species that is alien or non-native to the ecosystem which causes harm to economic, environmental, or human health (E.O. 13112 of Feb 3, 1999). Invasive species have posed a serious threat to ecosystems across the globe. These invasive species have impacts on the biodiversity and productivity of invaded forests. Remotely sensed data is a valuable resource for understanding and addressing issues related to invasive species. This study presents a novel approach for mapping the distribution of two invasive plant species, Common and Glossy Buckthorn, using unmanned aerial vehicles (UAVs), machine learning algorithms, geographic information systems …

The Rise Of Terrorism In Africa And What It Means For U.S. Policymakers, Bridget Mary Hughes

Master's Theses

This thesis utilizes three case studies to measure the rise of terrorism in Africa. This qualitative data leads the reader to one ultimate question: what does the rise in terrorism in Africa mean for the United States (U.S.) and U.S. policymakers, National Security academia, students in National Security, if anything at all? The answer is: if civilian and military compounds are being targeted by these violent extremists, as these three case studies demonstrate, it is my recommendation that U.S. policymakers cease sending troops to the African theater and, instead, allocate funding to the citizens and nonprofits of the countries whom …

Using Landsat Satellite Imagery To Estimate Groundcover In The Grainbelt Of Western Australia, Justin Laycock, Nick Middleton, Karen Holmes

Resource management technical reports

Maintaining vegetative groundcover is an important component of sustainable agricultural systems and plays a critical function for soil and land conservation in Western Australia’s (WA) grainbelt (the south-west cropping region). This report describes how satellite imagery can be used to quantitatively and objectively estimate total vegetative groundcover, both in near real time and historically across large areas. We used the Landsat seasonal fractional groundcover products developed by the Joint Remote Sensing Research Program from the extensive archive of Landsat imagery. These products provide an estimate of the percentage of green vegetation, non-green vegetation and bare soil for each 30 m …

Farmer Adoption Of Advanced Technology In Agribusiness, Justin W. Belcher

USF Tampa Graduate Theses and Dissertations

Normally, family-owned farms are slow to adopt advanced technologies though these technologies can provide several benefits to the farm and have the potential to increase farm production volumes to help meet future population growth. The goal of this study was to document the factors that influence the adoption decision of advanced technologies by family-owned farms and what strategies can be used to motivate adoption. Case study research was conducted to gather data in a more structured way from family-owned farms typically excluded from past research for the purpose of comparing similarities across similar and dissimilar farms. For generalizing similarities, a …

Text Mining Policy Documents To Support Transboundary Integrated Ecosystem Assessment : The Case Of The South Mid-Atlantic Ridge, Debora Cristina Ferrari Ramalho

World Maritime University Dissertations

No abstract provided.

Development Of The Implementation Of Iot Monitoring System Based On Node-Red Technology, Anvar Kabulov, Inomjon Yarashov, Salamat Mirzataev

Karakalpak Scientific Journal

This article describes how to design and implement a process for storing environmental information in a database using the Internet of Things. The problems that need to be solved with the help of this IoT system are the growing demand for forecasts in the world, the demand of the world market for a new sustainable method of implementing the digitization environment through the Internet of Things. The design was implemented using Arduino, Node-Red and sensors, selected when choosing a component based on the required parameters and sent to the database for monitoring and processing. A study of previous work and …

Mapping Salt-Affected Land In The South-West Of Western Australia Using Satellite Remote Sensing, P A. Caccetta, John A. Simons, S Furby, Nicholas J. Wright, Richard J. George Dr

Natural resources published reports

Dryland salinity is a pervasive form of land degradation that has resulted from the clearing of about 17 M ha of native vegetation and the introduction of predominately cereal and pasture-based farming systems in the South-West of Western Australia. The change in water balance caused by clearing deep rooted endemic woodlands increased recharge and resulted in rising groundwater levels. After a lag period, the regolith began filling and groundwater approached the soil surface, evaporating and depositing stored salts in the rootzone of salt sensitive crops. Groundwater levels also rise and affect areas of remnant native vegetation, streams, wet-lands and rural …

Sibnet: Food Instance Counting And Segmentation, Huu-Thanh. Nguyen, Chong-Wah Ngo, Wing-Kwong Chan

Research Collection School Of Computing and Information Systems

Food computing has recently attracted considerable research attention due to its significance for health risk analysis. In the literature, the majority of research efforts are dedicated to food recognition. Relatively few works are conducted for food counting and segmentation, which are essential for portion size estimation. This paper presents a deep neural network, named SibNet, for simultaneous counting and extraction of food instances from an image. The problem is challenging due to varying size and shape of food as well as arbitrary viewing angle of camera, not to mention that food instances often occlude each other. SibNet is novel for …

An Automated Method To Enrich And Expand Consumer Health Vocabularies Using Glove Word Embeddings, Mohammed Ibrahim

Graduate Theses and Dissertations

Clear language makes communication easier between any two parties. However, a layman may have difficulty communicating with a professional due to not understanding the specialized terms common to the domain. In healthcare, it is rare to find a layman knowledgeable in medical jargon, which can lead to poor understanding of their condition and/or treatment. To bridge this gap, several professional vocabularies and ontologies have been created to map laymen medical terms to professional medical terms and vice versa. Many of the presented vocabularies are built manually or semi-automatically requiring large investments of time and human effort and consequently the slow …