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Articles 241 - 270 of 1161
Full-Text Articles in Computer Sciences
Arizona’S Experiential Learning Opportunities: Regional Security Operations Centers And Cybersecurity Clinics, Joshua Kipers, Paul Wagner, Robert J. Honomichl
Arizona’S Experiential Learning Opportunities: Regional Security Operations Centers And Cybersecurity Clinics, Joshua Kipers, Paul Wagner, Robert J. Honomichl
Journal of Cybersecurity Education, Research and Practice
The increasing frequency, sophistication, and economic impact of cybersecurity incidents have intensified the global demand for a skilled cybersecurity workforce. Traditional academic programs often fail to provide the applied experience necessary to prepare graduates for the rapidly evolving threat landscape. This paper examines Arizona’s innovative approaches to experiential cybersecurity education through the establishment of Regional Security Operations Centers (RSOCs) and the Arizona Cybersecurity Clinic. These initiatives integrate Kolb’s Experiential Learning Theory and the NICE Cybersecurity Workforce Framework to align academic preparation with real-world practice. The RSOCs, supported by the Arizona Department of Homeland Security, provide paid student internships focused on …
Static Malware Analysis For Incident Response: Developing A Tactical Aid With Ember, Joel Meoak, Shengjie Xu
Static Malware Analysis For Incident Response: Developing A Tactical Aid With Ember, Joel Meoak, Shengjie Xu
Journal of Cybersecurity Education, Research and Practice
Incident responders face a variety of challenges when identifying malware using existing solutions, particularly when rapid tactical decisions are needed. Traditional malware detection methods are often signature-based, limiting their effectiveness to previously known threats detected by anti-virus (AV) engines. Online analysis tools introduce confidentiality risks, potentially alerting adversaries that their actions are under scrutiny. While free sandbox environments offer useful capabilities, they often require substantial setup time and hardware resources that may not be available in the field. This research leverages the Elastic Malware Benchmark for Empowering Researchers (EMBER) dataset to develop a lightweight, portable tactical decision aid that enables …
Factors Influencing The Use Of Gis-Enabled Public E-Participation For Municipal Solid Waste Management, Irene Arinaitwe, Agnes Nakakawa, Gilbert Maiga
Factors Influencing The Use Of Gis-Enabled Public E-Participation For Municipal Solid Waste Management, Irene Arinaitwe, Agnes Nakakawa, Gilbert Maiga
The African Journal of Information Systems
Due to rapid global population growth and urbanization, approximately two billion metric tons of waste are generated annually. Municipal solid waste management has become a critical function for urban authorities. However, many urban authorities in low- and middle-income economies cannot provide efficient municipal solid waste management services because of suboptimal stakeholder participation in governance processes and inadequate information exchange. Therefore, this study sought to determine factors that influence the implementation of GIS-enabled public e-participation using Enhanced Adaptive Structuration Theory. A descriptive field study was conducted among staff of municipal authorities and residents in Uganda’s Kampala Metropolitan Area. Data were analyzed …
Experiential Learning: Innovative Approaches To Post-Secondary Cybersecurity Education, Brendan Bertone, Paul Wagner, Joshua Pauli
Experiential Learning: Innovative Approaches To Post-Secondary Cybersecurity Education, Brendan Bertone, Paul Wagner, Joshua Pauli
Journal of Cybersecurity Education, Research and Practice
The cybersecurity profession continues to face a significant shortfall of qualified professionals despite steady growth in degree programs. Employers consistently cite experience as the main barrier for entry-level cybersecurity hires. This paper argues that clinic-based experiential learning offers a scalable solution to that preparation gap. A systematic literature review spanning academic and professional literature was conducted to examine: (1) barriers to entry for aspiring cybersecurity professionals; (2) the effectiveness of experiential learning compared to traditional instruction; and (3) the viability and scalability of cybersecurity clinics. Screening emphasized workforce development, experiential pedagogy, and alignment with the NICE Cybersecurity Workforce Framework. Findings …
Information Security Awareness And Behavior Of Smartphone Users In The Ibadan Metropolis, Nigeria, Funmilola Olubunmi Omotayo
Information Security Awareness And Behavior Of Smartphone Users In The Ibadan Metropolis, Nigeria, Funmilola Olubunmi Omotayo
Journal of Cybersecurity Education, Research and Practice
Today, there is a rapid increase in the number of people using the Internet via smartphones and relying on them for most of their daily activities. Consequently, smartphones are becoming the target of criminals for atrocious purposes. This study investigated the information security awareness and behavior of smartphone users in the Ibadan metropolis, Nigeria. The study adopted a descriptive survey design. Data was collected with a questionnaire from 400 respondents who were conveniently selected. Findings revealed that most smartphone users knew about the smartphone security features available on their phones. However, most also engaged in behaviors that threatened their information …
Towards Age-Inclusive Human Computer Interaction: A Study Of Text Message Adoption By Seniors, Sam Takavarasha, Varaidzo Mapepa
Towards Age-Inclusive Human Computer Interaction: A Study Of Text Message Adoption By Seniors, Sam Takavarasha, Varaidzo Mapepa
African Conference on Information Systems and Technology
Since mobile phones are increasingly becoming livelihoods-enablers to people in developing economies, inclusive interaction design are critical. This research on the age inclusivity of feature phones was motivated by some observation that elderly users had challenges with adoption of text messaging. We, therefore, hypothesized that elderly people had challenges with texting ‘cash’, ‘cheque’ or ‘visa’ on a feature phone. The paper investigates if the required agility and interface ergonomics were age discriminatory inhibitors of the adoption of text messaging given the diminishing dexterity and cognitive skills of seniors. Using Sen’s (1999) ‘heterogeneity of capabilities’ theory and Davis et al (1989)’s …
A Dual-Model Machine Learning Framework For Predictive Maintenance Of Industrial Digital Press Components, Richmond Darko, Emmanuel Adabor
A Dual-Model Machine Learning Framework For Predictive Maintenance Of Industrial Digital Press Components, Richmond Darko, Emmanuel Adabor
African Conference on Information Systems and Technology
This study presents a novel dual-model predictive maintenance framework designed to improve maintenance scheduling for components in industrial digital presses. The framework integrates two complementary approaches: a Threshold-Based Maintenance Approach (TBMA) for components operating within acceptable usage limits, and an Overdue Severity-Based Maintenance Approach (OSBMA) for those that have exceeded their expected lifespans or show signs of critical degradation. This study uses real-world operational data from a Konica Minolta C6000 press. It applies advanced machine learning models, including Gradient Boosting Machines and Random Forest for classification, and Generalized Additive Models (GAM) for Remaining Useful Life (RUL) prediction. The goal is …
The Integration Of Agile Methodologies In Devops Practices Within The Information Technology Industry, Ashley Hourigan, Ridewaan Hanslo
The Integration Of Agile Methodologies In Devops Practices Within The Information Technology Industry, Ashley Hourigan, Ridewaan Hanslo
African Conference on Information Systems and Technology
The demand for rapid software delivery in the Information Technology (IT) industry has significantly intensified, emphasising the need for faster software products and service releases with enhanced features to meet customer expectations. Agile methodologies are replacing traditional approaches such as Waterfall, where flexibility, iterative development and adaptation to change are favoured over rigid planning and execution. DevOps, a subsequent evolution from Agile, emphasises collaborative efforts in development and operations teams, focusing on continuous integration and deployment to deliver resilient and high-quality software products and services. This study aims to critically assess both Agile and DevOps practices in the IT industry …
Cybersecurity And Intention To Use Mobile Banking Applications, Ishmael Chikoo, Salah Kabanda
Cybersecurity And Intention To Use Mobile Banking Applications, Ishmael Chikoo, Salah Kabanda
African Conference on Information Systems and Technology
The adoption rate of mobile banking amongst consumers remains low, especially in developing countries where there is a knowledge gap in understanding why consumers do not engage in the frequent use of mobile banking applications. Given that most financial institutions see mobile banking as a strategy for their competitive advantage; it is important that they understand how best to address consumer’s fears brought about by cybersecurity threats. The purpose of this study is to investigate the perceived influence of cybersecurity on the user’s intentions to use mobile banking applications. Data collected from 90 participants was statistically analysed in Smart PLS …
A Comprehensive Review On Gamification In Neurocybersecurity, Ms. Kritika
A Comprehensive Review On Gamification In Neurocybersecurity, Ms. Kritika
Journal of Cybersecurity Education, Research and Practice
The research investigates gamification methods as it applies to the emerging interdisciplinary domain that brings together cybersecurity with neuroscience and psychology. Neurocybersecurity implements neural concepts to protect digital systems from security threats while targeting the human vulnerabilities that present as the strongest points of attack. Several researchers have examined gamification techniques which incorporate game design elements to enhance cybersecurity training outcomes by improving user participation and knowledge retention and user conduct compliance. The research incorporates Self-Determination Theory along with Cognitive Load Theory to explain the design principles for efficient gamified interventions. The implementation of both cognitive performance improvement and neuroplasticity …
The Effectiveness Of Scenario-Based Cybersecurity Day Camps In Southern Rural Appalachia, Anna P. Rodgers-Stine, Tania Williams
The Effectiveness Of Scenario-Based Cybersecurity Day Camps In Southern Rural Appalachia, Anna P. Rodgers-Stine, Tania Williams
Journal of Cybersecurity Education, Research and Practice
As the emphasis on cybersecurity instruction in the K12 environment continues to expand, furthering access to cybersecurity education is paramount across the United States. While designated cybersecurity courses are not available in many schools, the implementation of cybersecurity camps may help to bridge the gap and increase student interest in and awareness of cybersecurity as a field. From 2021 through 2024, cybersecurity day camps were held in a region in rural southern Appalachia with the goal of increasing student interest and access to cybersecurity topics. Through the creation and implementation of these camps, it was found that scenario-based cybersecurity day …
Reverse Engineering Of Binary Programs Using Graph Attention Networks, Sai Nikhila Kanigiri
Reverse Engineering Of Binary Programs Using Graph Attention Networks, Sai Nikhila Kanigiri
Master's Theses
Understanding the functionality and behavior of binary code is essential for many software engineering tasks, including malware analysis, vulnerability detection, and program optimization. However, automating this process is challenging due to the complexity of machine code and the significant manual effort required from experienced software engineers. In this paper, we present BinGAT (Reverse Engineering of Binary Programs using Graph Attention Networks), a method for classifying binary programs into algorithmic categories using Graph Attention Neural Networks (GNNs) based on their Control-Flow Graphs (CFGs). Given a binary program, BinGAT extracts its CFG through static analysis and transforms the assembly instructions within each …
Optimizing Information Security In Cloud Environments: A Risk Management Approach And Guide For Enterprise Cloud Security, Joshua Olusegun Oyeniyi, Oluwashina Akinloye Oyeniran
Optimizing Information Security In Cloud Environments: A Risk Management Approach And Guide For Enterprise Cloud Security, Joshua Olusegun Oyeniyi, Oluwashina Akinloye Oyeniran
Journal of Cybersecurity Education, Research and Practice
In recent years, cloud computing has become increasingly integral to organizational operations due to its scalability, accessibility and cost effectiveness in managing data and resources. However, the rise in security threats and attacks on cloud environments necessitates having robust measures in place to protect data confidentiality, integrity and availability. This paper presents an optimized approach to cloud information security management by reviewing the current threat landscape, evaluating key risk management frameworks, and provided practical solutions for enhancing enterprise cloud security. The study examined three leading cloud security frameworks: the Cloud Controls Matrix (CCM) known for its cloud-specific controls, the NIST …
Large Language Model Enabled Mental Health App Recommendations Using Structured Datasets, Kris Prasad, Md Abdullah Al Hafiz Khan
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 …
Grp-082 Ready Cluster One: Optimizing Film Success With Data Science, Mohsin Md Abdul Karim, Joseph Richardson
Grp-082 Ready Cluster One: Optimizing Film Success With Data Science, Mohsin Md Abdul Karim, Joseph Richardson
C-Day Computing Showcase
This study presents a novel approach to predicting and optimizing screenplay investments by combining graph theory and finite mixture modeling (FMM) techniques. We construct a k-partite graph representing movies, genres, subgenres, production companies, directors, actors, and directors of photography, to explore the interconnectedness between these entities. Using FMM, we identify clusters within budget tiers, enabling a deeper understanding of how similar films perform based on their creative team and production characteristics. By balancing profit potential with risk-adjusted profit, the model suggests the most viable budget tiers for unproduced screenplays. This approach incorporates confidence intervals and evaluates the accuracy of budget …
Gc-074 Real-Time Object Detection, Rohit Malik
Gc-074 Real-Time Object Detection, Rohit Malik
C-Day Computing Showcase
This project explores the implementation of real-time object detection using the You Only Look Once (YOLO) architecture. Leveraging its speed and accuracy, we developed a system capable of identifying and localizing multiple objects within live video streams. Our implementation focused on optimizing YOLO's performance for real-time applications, specifically addressing the trade-off between speed and accuracy. We employed a pre-trained YOLO model and fine-tuned it on a custom dataset tailored to specific object classes. This fine-tuning process aimed to enhance the model's ability to recognize objects in our target environment. The system was implemented using Python and the OpenCV library, enabling …
Gc-079 Nibbleai, Ryan Deem, Zeynep Birgili, Austin Cook
Gc-079 Nibbleai, Ryan Deem, Zeynep Birgili, Austin Cook
C-Day Computing Showcase
Ever looked into your fridge or pantry and wondered, “What can I make with this?” NibbleAI is a mobile app designed to solve exactly that. Using artificial intelligence, the app identifies ingredients from user-uploaded images and suggests recipes based on what’s available. Built with React Native and powered by a DenseNet169 model for image recognition, NibbleAI seamlessly analyzes photos and returns curated recipe ideas — all within a few taps. This intuitive approach helps users reduce food waste, save time, and get creative with the ingredients they already have.
Grm-076 Assessing The Performance Of Intelligent Agents In Visual Food Recognition Relative To Manual Data Entry, El Arbi Belfarsi, Henry Ekeocha
Grm-076 Assessing The Performance Of Intelligent Agents In Visual Food Recognition Relative To Manual Data Entry, El Arbi Belfarsi, Henry Ekeocha
C-Day Computing Showcase
Accurate dietary assessment remains a critical yet time-consuming task in health and nutrition monitoring. This study benchmarks the macronutrient estimation capabilities of three intelligent vision agents: GPT Vision, Claude, and Gemini against manually logged food data. We unify two distinct datasets: MenuMatch, annotated by a professional nutritionist, and CGMacros, populated through user entries on MyFitnessPal. After flattening and cleaning both datasets, we first assess each model’s performance in calorie estimation. GPT Vision outperforms the others with the lowest percentage error 13.83% and is subsequently used to benchmark the macro estimations of Claude and Gemini. While Claude shows higher carbohydrate and …
Grm-109 Quantum Machine Learning For Science And Engineering Research, Andrew Polisetty
Grm-109 Quantum Machine Learning For Science And Engineering Research, Andrew Polisetty
C-Day Computing Showcase
This research project aims to understand and explore the practical applications of Quantum Machine Learning (QML) in solving real-world challenges. By comparing classical machine learning models such as Support Vector Machines (SVM), Neural Networks, Logistic Regression, and Naive Bayes, with their quantum counterparts. Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), Quantum Logistic Regression (QLR), Quantum Deep Neural Networks (QDNN), and Hybrid Quantum Models, we gain hands-on experience in advanced machine learning techniques. The project cover diverse domains including cybersecurity, healthcare, industrial engineering, energy management, and supply chain optimization. Each part of project involves working with real-world datasets, preprocessing, …
Grp-021 Shap-Explainable Image-To-Topology Regression, Charles Fanning
Grp-021 Shap-Explainable Image-To-Topology Regression, Charles Fanning
C-Day Computing Showcase
We evaluated whether deep regression models predicting vectorized topological features (in the form of persistence landscapes) actually learn the underlying persistent homology of the image. A DenseNet-121 is trained to regress 300-dimensional persistence landscapes from grayscale scene images. Using SHAP, we evaluate the contribution of pixels in the original images to the persistence landscapes. Across all six classes, SHAP-feature overlap is consistently lower than the baseline, implying that DenseNet may not be truly learning the underlying persistent homology.
Uc-040 Security Lookup Interface Project, Alhasan Mohsen, Alejandro Albarran, Ethan Lan, Jerrat Jester
Uc-040 Security Lookup Interface Project, Alhasan Mohsen, Alejandro Albarran, Ethan Lan, Jerrat Jester
C-Day Computing Showcase
The "Security Lookup Interface" capstone project aims to create a streamlined tool for COX's cybersecurity team, enabling analysts to efficiently perform IP address and hostname lookups while providing actionable, data-driven insights to enhance security investigations. The project will develop a user-friendly interface that simplifies the lookup process, allowing cybersecurity analysts to quickly retrieve relevant data and make informed decisions during security investigations. One of the key features of the tool is its seamless integration with both internal APIs and external resources. This integration will ensure that analysts have quick and easy access to valuable information, minimizing manual effort and enabling …
Uc-049 From Forecast To Fortune: Portfolio Optimization And Prediction, Nia Taylor
Uc-049 From Forecast To Fortune: Portfolio Optimization And Prediction, Nia Taylor
C-Day Computing Showcase
This project explores the intersection of time series forecasting and portfolio optimization to support data-driven investment strategies. Historical price data from 30 individual stocks was analyzed using two forecasting models: ARIMA and Prophet. Each model’s performance was evaluated using key accuracy metrics, including Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Mean Directional Accuracy (MDA). Results showed that ARIMA performed better on error-based metrics, while Prophet excelled at predicting directional trends. In parallel, historical return data was used to construct optimized portfolios using Modern Portfolio Theory. Two strategies were implemented: one minimizing overall volatility and another maximizing …
Ur-001 Large Language Model Enabled Mental Health App Recommendations Using Structured Datasets, Kris Prasad
Ur-001 Large Language Model Enabled Mental Health App Recommendations Using Structured Datasets, Kris Prasad
C-Day Computing Showcase
The increasing use of large language models (LLMs) in mental health support neces-sitates detailed evaluation of their recommendation capabilities. This study compares four modern LLMs—GPT-4o, Claude 3.5 Sonnet, dataset-enhanced Gemma 2, and dataset-enhanced 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 emonstrated inconsistent accuracy and often relied on outdated or generic information. In contrast, our retrieval-augmented generation (RAG) pipeline enabled lower-cost models to achieve 100% accuracy, compared to baseline models (GPT-4o at 45% and Claude at …
Uc-116 Robot Tactics, John Anderson
Uc-116 Robot Tactics, John Anderson
C-Day Computing Showcase
Robot Tactics is a first person strategic shooter made in Unity where the player takes control of an agent who fights off bodyguards who are chasing him while using Robots to detour them
Ur-094 Aistudy: Using Ai To Study Ai, Mason Pederson
Ur-094 Aistudy: Using Ai To Study Ai, Mason Pederson
C-Day Computing Showcase
Interactive AI studying tool or AIStudy is a flask-based web-app which enables users to quickly search, save, and study scientific papers. AIStudy streamlines the literature review process by utilizing large language models (LLMs) allowing for users to engage with research in a creative and interactive way. To begin with a user searches up papers using the arXiv API and PyMuPDF for scraping the contents. These are saved to a user database managed by SQL Alchemy. The user can then ask a chatbot about one or more papers at a time through Ollama’s API in order to produce Retrieval-Augmented Generated (RAG) …
Gc-008 Medivault: An Ai-Powered Secure Medical Image Sharing Platform, Henry Ekeocha, Reginald Calixte, Dhruv Patel
Gc-008 Medivault: An Ai-Powered Secure Medical Image Sharing Platform, Henry Ekeocha, Reginald Calixte, Dhruv Patel
C-Day Computing Showcase
MediVault is a secure, cloud-native platform that empowers patients and healthcare providers to upload, view, and share medical images like X-rays and MRIs with confidence. Built using Next.js and Node.js, and deployed on AWS Free Tier (S3, RDS, KMS), the system implements role-based access, two-factor authentication, and end-to-end encryption to ensure privacy and HIPAA compliance. MediVault features an intuitive interface and integrates AI-enhanced automation to streamline metadata tagging and detect potential anomalies in scans. Designed for scalability, usability, and compliance, this project showcases real-world expertise in full-stack cloud development and healthcare cybersecurity.
Gc-025 Secure Medical Image Sharing Platform – Medshare, Toyese Adenuga, Richard Lateu
Gc-025 Secure Medical Image Sharing Platform – Medshare, Toyese Adenuga, Richard Lateu
C-Day Computing Showcase
The Secure Medical Image Sharing Platform is a cloud-based solution that ensures secure upload, management, and sharing of medical images, adhering to HIPAA and GDPR. It utilizes advanced encryption protocols, role-based access control (RBAC), and audit trails to safeguard patient data. The platform's user-friendly interface facilitates seamless interaction between patients and healthcare providers, enabling the use of AI tools for diagnostic support.
Gc-058 Personalized Wellness Recommendations, Varshini Yaganti
Gc-058 Personalized Wellness Recommendations, Varshini Yaganti
C-Day Computing Showcase
A health recommendation system using machine learning, built on Hadoop, Spark, and HDFS, represents a significant advancement in personalized healthcare. This project aims to leverage big data technologies to process and analyze vast amounts of medical data across a distributed computing environment, utilizing at least three virtual machines. The background of this project lies in the increasing prevalence of chronic diseases and the growing volume of health-related data collected by healthcare providers. The motivation for this project stems from several key factors. Firstly, traditional healthcare systems often struggle to provide personalized recommendations due to the sheer volume and complexity of …
Gc-123 Deep Learning-Based Skin Cancer Detection, Andrew Polisetty, Akshay Krishna Varma Buddharaju, Siri Yellu
Gc-123 Deep Learning-Based Skin Cancer Detection, Andrew Polisetty, Akshay Krishna Varma Buddharaju, Siri Yellu
C-Day Computing Showcase
Skin cancer is increasingly becoming a severe health problem globally today, but early detection is essential to enhance survival rates. Nonetheless, conventional diagnosis relies largely on visual examinations by dermatologists, which can be subjective and time-consuming. This research examines the application of deep learning for the automation of skin cancer detection based on dermoscopic images from the HAM10000 dataset. The models VGG19, DenseNet121 and ResNet152 will be trained and evaluated, with class mbalance addressed using data augmentation strategies. The outputs will demonstrate the applicability of deep learning to improve skin cancer diagnosis. Classification optimization using ensemble modeling and its improved …
Grm-011 Non-Invasive Convolution-Based Coronary Artery Blood Pressure Prediction, Rene Lisasi
Grm-011 Non-Invasive Convolution-Based Coronary Artery Blood Pressure Prediction, Rene Lisasi
C-Day Computing Showcase
The primary objective of this proposal is to develop an innovative technique for determining the functional significance of coronary artery lesions in patients with coronary artery disease (CAD) and evaluate its utility for clinical decision-making using coronary computed tomography angiography (CCTA).