Computer Sciences Commons^™

Towards Supporting Children's Metacomprehension During Web Search, Christine Dianne Pinney

Boise State University Theses and Dissertations

As children interact online with search engine result pages (SERPs), children’s comprehension monitoring skills are put to work. Children are known to struggle with utilizing online information as the text content can be misaligned with their reading abilities. Comprehension monitoring skills allow people to assess the comprehensibility of text and is measured by how well comprehension predictions align with performance on comprehension tests. Previous research has shown that augmenting standard SERPs with readability visual cues can be helpful for children searching online. While comprehension predictions are traditionally collected after reading, SERP information provides the opportunity to measure children's perceived comprehensibility …

Leveraging Machine Learning And Deep Learning Techniques For Voter Registration Fraud Detection, Nahid Anwar

Boise State University Theses and Dissertations

The primary objective of this research is to develop an advanced framework for detecting voter registration anomalies, with a specific focus on fraud detection, using the Idaho Voter Registration Election Dataset. The data set contains both anonymized real voter data and synthetically generated fraudulent instances, allowing for a comprehensive examination of potential vulnerabilities in voter registration systems. The real data was obtained from the Idaho Secretary of State's office. The initial part of the research involved data analysis and identification of misinformation and potential disinformation using statistical analysis and approximate string matching algorithms. Subsequently, we have created the aforementioned anonymized …

Modeling Language And Vision At Human Scales, Clayton Fields

Boise State University Theses and Dissertations

The impressive results that have recently been achieved in natural language processing and artificial intelligence have been primarily driven by the introduction of the transformer deep learning architecture, increasingly large models with many parameters and using enormous datasets. The size of models and their training data requirements present costly demands that freeze many researchers out of training with cutting edge models. Beyond these practical implications, current methods learn from text alone, without the rich array of sensory information that human beings use in learning language. This means that language models are often incapable of reasoning about the concrete world that …

Using Natural Language Processing And Machine Learning To Detect Online Radicalisation In The Maldivian Language, Dhivehi, Hussain Ibrahim, Ahmed Ibrahim, Michael N. Johnstone

Research outputs 2022 to 2026

Early detection of online radical content is important for intelligence services to combat radicalisation and terrorism. The motivation for this research was the lack of language tools in the detection of radicalisation in the Maldivian language, Dhivehi. This research applied Machine Learning and Natural Language Processing (NLP) to detect online radicalisation content in Dhivehi, with the incorporation of domain-specific knowledge. The research used Machine Learning to evaluate the most effective technique for detection of radicalisation text in Dhivehi and used interviews with Subject Matter Experts and self-deradicalised individuals to validate the results, add contextual information and improve recognition accuracy. The …

Overcoming Motor Imagery Bci Illiteracy: Adaptive Decoding And Knowledge Transfer In Eeg-Based Brain-Computer Interfaces, Zaid Shuqfa Shuqfa

Thesis/ Dissertation Defenses

Brain Computer Interface (BCI), Also known as brain-machine interface (BMI) is a mean of controlling machines without the need to activate peripheral nerves or muscles. It has received the attention of research for decades. Motor imagery-based BCI is a paradigm that is characterized by its user friendliness where users can generate control commands at their freewill, without waiting for a que from the BCI module. Motor imagery brain–computer interface (MI–BCI) has considerable potential in increasing the quality of the lives for people with mobility impairment and the healthy ones as well. Though, its diffusion in application still has many pitfalls …

Large Language Model Enabled Mental Health App Recommendations Using Structured Datasets, Kris Prasad, Md Abdullah Al Hafiz Khan

Symposium of Student Scholars

The increasing use of large language models (LLMs) in mental health support necessitates detailed evaluation of their recommendation capabilities. This study compares four modern LLMs—GPT-4o, Claude 3.5 Sonnet, and dataset-enhanced Gemma 2 and GPT-3.5-Turbo—in recommending mental health applications. We constructed a structured dataset of 55 mental health apps using RoBERTa-based sentiment analysis and keyword similarity scoring, focusing on depression, anxiety, ADHD, and insomnia. Standard LLMs demonstrated inconsistent accuracy and often relied on outdated or generic information. In contrast, our retrieval-augmented generation (RAG) pipeline enabled lower-cost models to achieve up to 55% higher accuracy than baseline models while recommending apps with …

Predicting Healthcare Service Quality Based On A Kalman-Optimized Bi-Lstm-Inspired Deep Learning Model, Mohammed K. Al-Khafaji, Eman S. Al-Shamery

Karbala International Journal of Modern Science

Health is one of the most important aspects of human well-being, and access to high-quality healthcare is essential for a good quality of life. Providing top-level health services at all times is crucial. However, the research in healthcare poses significant challenges due to the diversity and variations of medical practices across different hospitals. This paper aims to tackle the challenge of data missing and scattering during data collection. Then, the quality of services (QoS) offered by healthcare facilities will be analyzed and predicted from the patient's perspective. The model begins preprocessing data by data cleaning, handling missing values, and scattering …

The Role Of Individual Values In Bryant University's Sustainability Efforts, John Boccuzzi Iii

Honors Projects in Data Science

This research examines student, faculty, and staff perspectives on sustainability at Bryant University, with the goal of understanding how individual values align with the university's environmental initiatives. The objective is to assess perceptions of Bryant's current sustainability practices, explore how effectively these efforts are communicated across campus, and identify potential gaps between institutional action and community awareness. To achieve this, the study gathers qualitative data through an open-ended survey and applies sentiment analysis to interpret student attitudes toward sustainability. By analyzing these responses alongside Bryant's sustainability marketing efforts, this research will identify gaps between student engagement and institutional messaging The …

Artificial Intelligence In Orthopedic Medical Education: A Comprehensive Review Of Emerging Technologies And Their Applications, Kyle Sporn, Rahul Kumar, Phani Paladugu, Tejas Sekhar, Swapna Vaja, Tamer Hage, Ethan Waisberg, Chirag Gowda, Ram Jagadeesan, Nasif Zaman, Alireza Tavakkoli

SKMC Student Presentations and Publications

Integrating artificial intelligence (AI) and mixed reality (MR) into orthopedic education has transformed learning. This review examines AI-powered platforms like Microsoft HoloLens, Apple Vision Pro, and HTC Vive Pro, which enhance anatomical visualization, surgical simulation, and clinical decision-making. These technologies improve the spatial understanding of musculoskeletal structures, refine procedural skills with haptic feedback, and personalize learning through AI-driven adaptive algorithms. Generative AI tools like ChatGPT further support knowledge retention and provide evidence-based insights on orthopedic topics. AI-enabled platforms and generative AI tools help address challenges in standardizing orthopedic education. However, we still face many barriers that relate to standardizing data, …

Improving Image Quality In Electrical Capacitance Tomography Using Otsu Thresholding, Josiah Nombo

Tanzania Journal of Engineering and Technology (TJET)

Electrical Capacitance Tomography (ECT) is an imaging technique used in industrial process monitoring, particularly for monitoring and measuring the composition of multiphase flows. Despite its widespread application, the commonly used Linear Back Projection (LBP) algorithm often produces low-quality images due to its limited ability to handle high permittivity contrasts and nonlinearities. This study investigates the use of Otsu thresholding as a post-processing technique to enhance ECT image quality. By maximizing inter-class variance in the image histogram, Otsu thresholding improves contrast, clarity, and structural definition, enabling more effective segmentation of oil and gas components in multiphase flows. The proposed Otsu-based reconstruction …

Artificial Intelligence Applications For Grid-Connected Solar Inverters, Utkirjon Ubaydullaev, Sarvinoz Mirzaeva, Hasan Mustafoev

Chemical Technology, Control and Management

The increasing global demand for renewable energy has highlighted the importance of grid-connected solar inverters in ensuring efficient and stable power conversion. However, challenges such as fluctuations in solar energy generation, grid disturbances, and power quality issues necessitate advanced control strategies. The integration of artificial intelligence (AI) into solar inverters presents a transformative solution, enhancing performance, adaptability, and reliability in real-world applications.

This review explores the role of AI techniques, including machine learning (ML), deep learning (DL), fuzzy logic, and reinforcement learning (RL), in optimizing key inverter functionalities such as maximum power point tracking (MPPT), fault detection, power quality enhancement, …

Development Of Fuzzy Ontology For Explainable Artificial Intelligence For Decision-Making In Fuzzy Environment, Pavel Kosov

Chemical Technology, Control and Management

In modern artificial intelligence systems, there is an acute need to understand the decision-making logic of "black box" algorithms. Our research proposes an innovative method for increasing the transparency of such systems through the formalization of fuzzy explanatory mechanisms. We have developed an extension of existing ontological approaches by introducing the concept of fuzziness into the structure of explanatory properties, which allows overcoming the fundamental limitations of traditional XAI methods. The proposed formalization is based on the theory of collective mental models and principles of fuzzy logic, providing a more accurate reflection of uncertainty and subjectivity in expert knowledge. Our …

A Critical Realist Erp Implementation In Zimbabwean Mining Industry Organisations, Jairos Mukwenha

Tanzania Journal of Engineering and Technology (TJET)

This research uses a critical realist framework to examine the factors influencing the success of enterprise resource planning (ERP) system implementation in Zimbabwean mining industry organisations. From the perspective of critical realism, the mining industry in Zimbabwe faces a complex interplay of opportunities and obstacles while implementing ERP systems. The deployment of ERP in mining firms is critically examined in this paper, emphasising how these systems might improve operational efficiency while considering Zimbabwe's particular socioeconomic circumstances. By exploring underlying structures, mechanisms, and outcomes, the research aims to identify critical challenges and opportunities and develop practical recommendations for improving ERP adoption …

Development Of A Microcontroller-Based Intelligent Traffic Light Control System For Vehicular Movement In T-Junctions, Frederick O. Ehiagwina

Tanzania Journal of Engineering and Technology (TJET)

This research is devoted to the issue of regulating traffic congestion in major cities using light-dependent resistors coupled with the PIC16F877A microcontroller. This study proposes an intelligent traffic control system for T-Junctions, utilizing sensing and control to optimize traffic flow through dynamic phase adjustments and congestion reduction, enabled by a microcontroller-based decision-making system. The proposed system reduces traffic congestion, automates control, and enhances safety, minimizing accidents and lowering infrastructure costs. Under simulated environment, it demonstrates an average response time of 50 ms and achieves 99% accuracy in displaying the correct countdown. Finally, the number of state transitions handled per minute …

An Efficient Blockchain-Based Privacy Preservation Scheme For Smart Grids, Mohamad Badra, Rouba Borghol

All Works

Smart grids have revolutionized electricity management and distribution, but they also generate and transmit vast amounts of consumer data, raising privacy concerns. In this paper, we propose a blockchain-based solution to preserve user's privacy in smart grids and to mitigates data forgery, profiling, and man-in-the-middle attacks. Moreover, our solution provides security services such as authentication and non-repudiation to prevent unauthorized access to sensitive data and ensure accountability and traceability. We validate our approach through testing and show that it is a simple, scalable, cost-effective solution with minimal computational processing overhead.

Design And Performance Analysis Of Fiber Bragg Grating Temperature Sensor For Industrial Processes Sensing Applications, Paul Stone Stone Brown Macheso S.B.

Tanzania Journal of Engineering and Technology (TJET)

The Fiber Bragg Grating (FBG) sensor has become a widespread sensing device because of its small size, passive design, immunity to electromagnetic interference, and direct ability to measure physical properties like temperature and strain. Recently, femtosecond infrared laser processing and regeneration techniques have resulted in the development of stable high-temperature gratings, which are a powerful tool in smart factories, an aspect of the fourth Industrial Revolution (4IR), and show promise for application in harsh environments like high pressure, high temperature, or ionizing radiation. The development of stable high-temperature gratings that can withstand harsh environmental factors like high temperatures, pressures, and …

Application Of Artificial Neural Network Models For Predicting Diesel And Petrol Prices In The Geographically Sparsed Regions In Tanzania, John M. Kafuku

Tanzania Journal of Engineering and Technology (TJET)

Fuel consumption in Tanzania, mainly diesel and petrol, accounts for 82 percent of the energy consumption in the country, with significant price volatility affecting market stability, availability of fuel, and investment decisions. This study uses an artificial neural network (ANN) with a backpropagating algorithm to predict fuel prices in four regions of Tanzania. Key input parameters include the currency inflation rate (CIR), the petrol fuel inventory (PFI), the diesel fuel inventory (DFI), and the fuel transport costs (FTC). The study selected the 6-10-10-2 ANN structures for Sumbawanga-Rukwa, Mpanda-Katavi, and Mbeya-Mbeya as well as 6-10-9-2 for the Songea-Ruvuma region. The results …

A Fuzzy Based Framework For Sustainable Technology Selection In Small-Scale Gold Mining Operations, John M. Kafuku

Tanzania Journal of Engineering and Technology (TJET)

Small-scale gold mining (SSGM) operations in Tanzania has been operating inefficiently due to inadequate mining processing technologies, poor working tools, lack of enough capital, and insufficient electricity. Despite the efforts made by different stakeholders in boosting the sustainability of SSGM yet the sector has not reached the expected goal. This paper proposes a framework for appropriate technology selection to help small scale gold miners in evaluating various gold mineral processing technologies. The framework utilizes the fuzzy logic set theory for technology evaluation and selection. The developed framework for technology selection upon validation provided results that technology adequacy of more than …

Synthetic Inertia Provision For Load Frequency Control In Networks With High Penetration Of Renewable Energy Sources, Paulina Mkoi

Tanzania Journal of Engineering and Technology (TJET)

The integration of renewable energy sources (RESs) such as solar photovoltaic (PV) and wind energy has become a promising solution as the world shifts toward clean energy. Solar PV and wind resources are increasingly replacing conventional synchronous generators, leading to reduced system inertia and increased vulnerability to frequency instability during disturbances. To address this challenge, this study proposes a novel synthetic inertia provision strategy using a battery energy storage system (BESS) integrated alongside solar PV. The proposed method dynamically compensates for the loss of inertia by considering the variability of solar PV output due to changes in irradiance and temperature. …

Infusing Aboriginal Perspectives In Cyber Education, John Shannahan, Mohiuddin Ahmed

Research outputs 2022 to 2026

While human factors are important in cyber security, the discipline has largely not explored incorporating indigenous perspectives—or, more specifically, in an Australian context, Aboriginal perspectives—in its curricula. In this paper, we introduce a promising approach for aligning Aboriginal perspectives with the needs of cyber security graduates and incorporating diverse perspectives into cyber degrees. The approach advocates for the centrality of good curriculum design fundamentals: backward design, constructive alignment, and student outcomes. The paper ends by reflecting on challenges and lessons from the first implementation and review of the material. It provides recommendations for other cyber practitioners exploring ways of incorporating …

College Market, Krysta L. Ray, Sijan Panday, Janniebeth Melendez, Gavin Kent

ATU Scholars Symposium

Each year, according to planetaid.org, college students generate 640 million pounds of waste with items like clothes, furniture, books, and other belongings to avoid the hassle of moving them, contributing to unnecessary waste. Current marketplace applications are overloaded with listings, making it difficult for students to buy and sell items efficiently. For example, 250 million sellers worldwide user Facebook Marketplace. To address this, College Market offers a solution that reduces waste while facilitating seamless connections between students selling unwanted goods and those seeking affordable, local items. College Market is a secure, campus-centered application designed to address both environmental and economic …

Generating More Equitable Fair Use, Jacqueline Kessel

Pepperdine Law Review

From advancing healthcare and education to threatening democratic systems, generative artificial intelligence (AI) has demonstrated a capacity to positively and negatively impact society. And these benefits and consequences are not shared equitably. Copyright law, however, stands as a powerful mechanism in monitoring AI system development. Several complaints have charged generative AI system developers with copyright infringement, alleging that (1) ingesting copyrighted works as training data infringes the copyright owner’s exclusive right to reproduce works in copies and (2) generating AI outputs infringes the exclusive right to prepare derivative works because the outputs are based upon the works on which the …

Meet Atu Intro, Caleb Urbani, Darlene Matamoros, Dena Paw, Jean Caballero

ATU Scholars Symposium

Meet ATU is our app designed to improve a student’s network and campus experience. Specifically made for students at Arkansas Tech University, Meet ATU allows students to connect and engage with their classmates easily. Students can add classes to their profiles using a course reference number. Students can also personalize their profiles to help them find other students to connect with within their enrolled classes. Meet ATU encourages students to communicate and collaborate with others through the messaging page. This would help students grow their network. The app will also include a leaderboard page that tracks points earned through various …

Self-Supervised Learning And Its Applications In Medical Image Analysis, Siladittya Manna

Doctoral Theses

Self-supervised learning (SSL) enables learning robust representations from unlabeled data and it consists of two stages: pretext and downstream. The representations learnt in the pretext task are transferred to the downstream task. Self-supervised learning has appli- cations in various domains, such as computer vision tasks, natural language processing, speech and audio processing, etc. In transfer learning scenarios, due to differences in the data distribution of the source and the target data, the hierarchical co-adaptation of the representations is destroyed, and hence proper fine-tuning is required to achieve satisfactory performance. With self-supervised pre-training, it is possible to learn repre- sentations aligned …

Analysis And Monitoring Of A Robotics Curriculum: Are Simnow Modules Valuable?, Jacob Applegarth, Ibrahim Baida, Anthony Iacco, Ngan Nguyen, Nathan Novotny

Posters

No abstract provided.

One Size Doesn’T Fit All: Towards Design And Evaluation Of Developmentally Appropriate Parental Control Tool, Prakriti Dumaru, Mahdi Nasrullah Al-Ameen

Computer Science Student Research

As children progress through developmental stages, they undergo substantial biological, cognitive, and social changes, creating unique needs for online safety across different age groups (e.g., young children, tweens, teens). The existing parental control tools fail to account for these differences, leaving a notable gap in the literature on parental mediation. To this end, we conducted 10 focus group sessions with a total of 20 parents to understand their preferences for age-appropriate design components that promote self-regulation and open communication, followed by an ideation workshop with four UX design experts to translate these preferences into customized features. We then evaluated these …

Exploring The Impacts Of An Adaptive Haptic Heartbeat Within A Socially Assistive Robot, Jade Thompson

Honors Theses

This research investigates the therapeutic effects of an adaptive haptic heartbeat within Therabot, a stuffed robotic dog. A simulated haptic heartbeat that adjusts its own speed based on user heart rate was developed for integration within Therabot. A user study evaluated the effects of various heartbeat behaviors on user experiences with Therabot, with respect to improvements in self-reported state anxiety, physiological improvements, and perceptions of the robot. A relationship was found between improvements in self-reported state anxiety and positive opinions of Therabot, regardless of condition. Additionally, differences were found between conditions with respect to improved aspects of state anxiety, with …

Autonomous Intelligence In Fashion: A Comprehensive Analysis Of Agentic Ai Across The Fashion Ecosystem, Andrew Burnstine

Faculty and Staff Publications & Presentations

The fashion industry is undergoing a paradigm shift with the emergence of agentic artificial intelligence (AI), a sophisticated class of intelligent systems exhibiting autonomous decision-making, continuous learning, and adaptive action with minimal human intervention. Moving beyond traditional AI applications in fashion focused on predictive analytics, generative tools, and supervised automation, agentic AI introduces a transformative paradigm wherein intelligent agents proactively navigate the complexities inherent in design, manufacturing, supply chain optimization, and consumer personalization. This paper presents a comprehensive exploration of the evolving role of agentic AI across the multifaceted fashion ecosystem, offering an in-depth analysis of its technological underpinnings, operational …

Fact-Based Counter Narrative Generation To Combat Hate Speech, Brian Wilk, Homaira Huda Shomee, Suman Kalyan Maity, Sourav Medya

Computer Science Faculty Research & Creative Works

Online hatred has become an increasingly pervasive issue, affecting individuals and communities across various digital platforms. To combat hate speech in such platforms, counter narratives (CNs) are regarded as an effective method. In recent years, there has been growing interest in using generative AI tools to construct CNs. However, most of the generative models produce generic responses to hate speech and can hallucinate, reducing their effectiveness. To address the above limitations, we propose a counter narrative generation method that enhances CNs by providing non-aggressive, fact-based narratives with relevant background knowledge from two distinct sources, including a web search module. Furthermore, …

The Attitudes And Perspectives Of Laboratory Professionals On The Use Of Machine Learning Combined With Maldi For Viral Identification: A Qualitative Study, Grace Johnson

Honors Projects

The use of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with machine learning (ML) has been proposed by numerous studies as a novel approach for viral identification. However, the development and implementation of this instrumentation is still in its early stages, and laboratory professionals' perspectives on its feasibility, accuracy, implementation, and effect on current laboratory operating procedures remain underexplored.

This study aimed to investigate laboratory professionals’ attitudes and opinions regarding the use of MALDI-TOF-MS coupled with machine learning for viral identification, focusing on perceived benefits, barriers, and factors that would affect participants’ opinions on implementation.

A qualitative descriptive research …