Digital Commons Network^™

Missouri_Raw_Data_06.02.2026, Yasha Kahn, Lori Nichols, Panduka Nagahawatte

Missouri

No abstract provided.

Nevada_Raw_Data_06.02.2026, Yasha Kahn, Lori Nichols, Panduka Nagahawatte

Nevada

No abstract provided.

Illinois_Raw_Data_06.02.2026, Yasha Kahn, Lori Nichols, Panduka Nagahawatte

Illinois

No abstract provided.

Alaska_Raw_Data_06.02.2026, Yasha Kahn, Lori Nichols, Panduka Nagahawatte

Alaska

No abstract provided.

Ny_Buds.Csv, Yasha Kahn, Panduka Nagahawatte, Lori Nichols

CSVs

No abstract provided.

Concentrate_Mycotoxins_2.Ipynb, Yasha Kahn, Lori Nichols, Panduka Nagahawatte

Concentrates

No abstract provided.

Connecticut_Raw_Data_06.02.2026, Yasha Kahn, Lori Nichols, Panduka Nagahawatte

Connecticut

No abstract provided.

Arkansas_Raw_Data_06.02.2026, Yasha Kahn, Lori Nichols, Panduka Nagahawatte

Arkansas

No abstract provided.

California_Raw_Data_06.02.2026, Yasha Kahn, Lori Nichols, Panduka Nagahawatte

California

No abstract provided.

Model Data Of "Aerial Imagery Resolution Optimization And Machine Learning Model Comparison For Land Cover Classification", Jessica Suoja

Research Data Sets

Downloadable zip file contents:

• 1MeterModel.csv (CSV file, 8.5 KB)
• 1MeterPrediction.csv (CSV file, 81.4 MB)
• 2MeterModel.csv (CSV file, 9.6 KB)
• 2MeterPrediction.csv (CSV file, 20.8 MB)
• 5MeterModel.csv (CSV file, 9.1 KB)
• 5MeterPrediction.csv (CSV file, 2.4 MB)
• 50CentimeterModel.csv (CSV file, 9.7 KB)
• 50CentimeterPrediction.csv (CSV file, 333.3 MB)
• 75CentimeterModel.csv (CSV file, 9.6 KB)
• 75CentimeterPrediction.csv (CSV file, 147.0 MB)
• 1125MeterModel.csv (CSV file, 9.0 KB)
• 1125MeterPrediction.csv (CSV file, 65.8 MB)
• 125MeterModel.csv (CSV file, 9.6 KB)
• 125MeterPrediction.csv (CSV file, 53.1 MB)
• 1375MeterModel.csv (CSV file, 9.5 KB)
• 1375MeterPrediction.csv (CSV file, 44.1 MB)
• 150MeterModel.csv (CSV file, 9.0 KB)
• 150MeterPrediction.csv (CSV file, 37.0 MB)
• 175MeterModel.csv (CSV file, 9.6 KB)
• 175MeterPrediction.csv …

Model Data Of "Land Cover Classification Of A Desert Riparian Ecosystem Using A Basic Stacked Ensemble Modeling Approach", Jessica Suoja

Research Data Sets

Downloadable zip file contents:

• CARTscript.R (R script, 2.2 KB)
• ForModel.csv (CSV file, 9.5 KB) 
• ForPrediction.csv (CSV file, 44.1 MB)

Hawaii_Raw_Data_06.01.26, Yasha Kahn, Lori Nichols, Panduka Nagahawatte

Hawaii

No abstract provided.

Maryland_Raw_Data_06.01.2026, Yasha Kahn, Lori Nichols, Panduka Nagahawatte

Maryland

No abstract provided.

Ny_Concentrates.Csv, Yasha Kahn, Panduka Nagahawatte, Lori Nichols

CSVs

No abstract provided.

Missouri_Raw_Data_06.01.26, Yasha Kahn, Lori Nichols, Panduka Nagahawatte

Missouri

No abstract provided.

Data And Code For: Combined Effects Of Forest Fire And Climate Warming On Seasonal Snow In The Western Oregon Cascades, Joseph D. Vigil

Environmental Science and Management Datasets

This dataset supports a study examining the combined effects of forest fire and climate warming on seasonal snow in the Breitenbush watershed of the western Oregon Cascades. The archive includes daily SnowModel snow water equivalent outputs for forested, current post-fire, mid-century, and end-century climate scenarios; field meteorological and manual snow observations; station metadata; modified SnowModel code used to represent post-fire snow-albedo decay; and R code used to calculate snow metrics and statistical comparisons reported in the associated manuscript. Derived results and documentation are included to support reproduction of the primary analyses.

Slm With Swarm Intelligence For Efficient Representation Of Medical Claims, Mohamed Ahmed Abo El-Enen, Ravi S. Sharma, Mustafa Abdulrazek, Amril Nazir, Reem Muhammad, Ahmed Talat Sahlol

All Works

Healthcare industry faces significant challenges due to fraudulent medical insurance claims, which result in substantial financial losses. We propose an automated system using domain-specific Small Language Models (SLMs) with a narrower scope and smaller parameter count than general-purpose Large Language Models (LLMs), combined with optimization algorithms to improve fraud detection. Our approach integrates numerical features, such as age and claim amount, with textual descriptions, including diagnoses and procedures, into a unified textual representation for each medical activity. This representation captures complex patterns, enhancing the model’s predictive ability. SLMs fine-tuned on medical corpora transform these textual inputs into fixed-dimensional numerical embeddings, …

The Deepfake Litmus Test: A Multimedia Authenticity Mechanism, Amna Alzaabi, Hessa Alqubaisi, Fatima Alzaabi, Richard Ikuesan

All Works

Deepfake technologies have made it increasingly difficult to distinguish authentic video content from manipulated media. This paper presents a forensic detection framework, referred to as the Litmus Test, which focuses on structural analysis of MP4 container files to detect signs of tampering. Unlike conventional AI-based approaches that operate as black boxes, this method examines the atomic composition of video containers to identify anomalies. The proposed method performs atom-level inspection of MP4 file hierarchies and structural markers to uncover anomalies indicative of synthetic manipulation. Evaluations using datasets such as CelebDF, UADFV, and DeeperForensics reveal that the framework can identify inconsistencies common …

Modelling Route-Level Interzonal Travel Time Using Gps Trajectories, Muhammad Faiq Ahmed, Tom Bellemans, Fatma Outay, Muhammad Ahmed, Afzal Ahmed, Feng Liu, Muhammad Adnan

All Works

Activity-based models (ABMs) require accurate travel-time estimates for accessibility calculations, yet many implementations rely on static routing outputs that fail to capture temporal congestion dynamics due to limited high-resolution data. This paper develops route-level travel-speed prediction models using GPS trajectory data from 48 vehicles in Flanders, Belgium. GPS trajectories are integrated with OpenStreetMap and land-use data through destination-based segmentation, in which trips from fixed origins are cumulatively segmented at zone crossings. To capture behavioural differences by trip length, separate Gamma regression models are estimated for short (≤5 km) and long (>5 km) trips using temporal, network, and spatial variables. …

Smart Health Care Application For Predicting Complications Risk In Type 2 Diabetes Management Using Personalized Digital Twins: A Focus On Early Intervention And Prevention Strategies, Haifaa Alkaabi, Ahed Abugabah

All Works

The study examined the application of Personalized Digital Twins (PDTs) to prevent complications during the management of Type 2 Diabetes, especially in early intervention and prevention plans. Based on a high-quality dataset related to the CDC Behavioral Risk Factor Surveillance System (BRFSS) data, we tested multiple predictive models such as the Random Forest, Gradient Boosting machines (GBM), and Extreme Gradient Boosting (XGBoost). We developed a composite risk indicator from established clinical risk factors (hypertension, dyslipidemia, elevated BMI) to stratify complication risk. The Random Forest model achieved 99% accuracy (AUC: 0.98) at the population-level risk classification. The GBM model was optimized …

Adaptive Multi-Agent Learning For Infrastructure-Aware Its: The Imer Data-Processing Approach, Mayssa Hamdani, Nafaa Jabeur, Ansar Yasar, Fatma Outay, Li Li

All Works

The performance of Intelligent Transportation Systems (ITS) critically depends on accurate and efficient road-condition monitoring. This paper presents IMER (Inspect–Map–Eliminate–Reduce), a novel AI-driven data-processing framework that extends the traditional Map-Reduce paradigm for infrastructure maintenance. IMER integrates confidence-based validation, redundancy elimination, and severity prioritization to enhance data quality and decision efficiency. Implemented within a multi-agent architecture, IMER enables autonomous agents to inspect, classify, and fuse multi-source road data in real time, supporting predictive and adaptive maintenance planning. Simulation results using augmented pothole datasets demonstrate a 39.9 % reduction in redundant reports and 39.8 % fewer false positives. These findings highlight IMER’s …

A Design Science Research Architecture For Xr-Based Pre-Visit Cultural Heritage Learning Applications, Mousa Al-Kfairy, Omar Alfandi, Saed Alrabaee

All Works

Pre-Visit preparation plays a critical role in shaping visitors’ learning and engagement in cultural heritage sites; however, existing approaches largely rely on static and passive materials that fail to foster meaningful understanding before the physical visit. Extended Reality (XR) technologies offer new opportunities to address this gap by enabling immersive, narrative-driven pre-visit learning experiences. This paper proposes a conceptual architecture for XR-based pre-visit cultural heritage learning applications, grounded in Design Science Research (DSR). Drawing on museum pedagogy, experiential learning, and XR interaction design, the study identifies key educational and technical requirements and translates them into a layered, modular system architecture. …

Mapping Llm Misuse In Computing Education: A Survey-Based Risk Analysis Of Faculty And Student Contexts, Noura Alzaabi, Mohamed El-Attar, Sarah Kohail, Mahmood Niazi

All Works

Large Language Models (LLMs) have become deeply embedded in computing higher education, yet the misuse risks they introduce for faculty and students remain insufficiently understood from a cybersecurity and data privacy perspective. This paper presents an empirical study in which a structured survey of 105 participants at a computing college was used to identify and systematically risk-score thirteen LLM misuse cases across faculty and student contexts. Using a Likelihood × Impact scoring model, the resulting taxonomy classifies misuse cases as Critical, High, or Medium severity, with over-reliance and skill atrophy, academic integrity violations, and research integrity risks emerging as the …

Evaluating Chatgpt-5 For Misuse Case Diagram Generation: An Empirical Evaluation, Alia Alzarooni, Yasser Khan, Hassan Alsayegh, Mohamed El-Attar, Rima Grati

All Works

Misuse case diagrams are a widely adopted technique in security requirements engineering, enabling analysts to model adversarial threats and derive countermeasures early in the software development lifecycle. However, manual construction of these diagrams is prone to incompleteness and subjectivity, requiring significant security expertise. Large language models (LLMs) such as ChatGPT present a promising opportunity to automate this process, yet their effectiveness for generating structured security modeling artifacts remains largely unexplored. This paper presents an exploratory study evaluating ChatGPT-5's ability to generate misuse case diagrams directly from textual security requirements, using 12 case studies of varying complexity spanning small, medium, and …

Aoi-Aware Agentic Federated Mixture-Of-Digital-Twin Experts For 6g Vehicular Edge Intelligence, Asadullah Tariq, Mohamed Adel Serhani, Ikbal Taleb, Shayma Alkobaisi, Tariq Qayyum, Irfan Ud Din

All Works

Digital twin-enabled vehicular edge intelligence is expected to become a fundamental service paradigm for sixth-generation (6G) intelligent transportation systems. However, the performance of such systems depends not only on model accuracy, but also on the freshness of digital twin states, timeliness of inference, privacy-preserving model training, and efficient use of heterogeneous edge resources. Existing DT-assisted federated learning and edge mixture-of-experts solutions optimize digital twin synchronization, distributed learning, and sparse inference largely independently, without allowing digital twin states to actively govern expert specialization, expert refreshing, and distributed orchestration. Nevertheless, the joint problem of how digital twins should guide federated expert specialization, …

Drone Authentication System Using Radio Frequency Fingerprinting, Jamila Muhsen Alnuaimi, Shamma Ghaleb Almansoori, Noura Ahmed Alrumeithi, Richard Ikuesan

All Works

The widespread integration of unmanned aerial vehicles (UAVs) across domains such as logistics, surveillance, and emergency response has introduced critical security challenges, particularly unauthorized access, identity spoofing, and drone cloning. Traditional software-based authentication methods, including GPS tracking and encryption, have proven inadequate against advanced cyber-physical threats. This paper proposes a secure and automated drone authentication framework based on Radio Frequency (RF) fingerprinting, leveraging intrinsic hardware-level signal imperfections to generate unique and unclonable drone identities. Using Random Forest classifiers, the system captures, preprocesses, and analyses RF features to distinguish between authorized and unauthorized UAVs. Validation with real-world RF datasets demonstrates high …

Human Factors In Visual Attention: Gender Differences In Engagement With Male-Oriented Ads, Mohamed Basel Almourad, Emad Bataineh, Zelal Wattar, Mohammed Hussain

All Works

In today's competitive market, it is increasingly important to understand how visual design shapes customer behaviour. This study examines the decision drivers influencing female customers' purchase choices when buying male-oriented products as gifts, identifying the visual components of advertisements that attract them by analysing the relationship between purchase intention and visual attention. Results show that participants with higher purchase intent focused more on product imagery and branding, indicating that visual appeal, perceived quality, and brand familiarity significantly guide their decisions, with brand awareness speeding up decision-making by reducing the need for repeated visual checks. Conversely, those with low purchase intent …

Towards A Context-Aware Driving Assistance System (Ca-Das): Advancing Intelligent Vehicular Safety Through Multimodal Context Integration, Fatma Outay, Siham Farrag, Anjum Zameer, Ansar Yassar

All Works

Driving-related behavioural factors are responsible for 90% of traffic collisions. The rapid growth of urbanization and the complexity of the traffic conditions demand a smart, efficient, and flexible transportation system. The advancement of transportation through technologies such as the Internet of Things (IoT) and AI have reshaped the way that drivers interact with their vehicles and the surrounding environment. In this paper, we propose a comprehensive Context-Aware Driving Assistance System (CA-DAS) that employs sensor fusion, semantic context modelling, along with a machine-learning-based approach to provide personalised and proactive driving assistance across dynamic scenarios. The proposed CA-ADS was developed using a …

From Image To Insight: Evaluating Llm Accuracy In Understanding Uml Use Case Diagrams With Claude, Mohamed El-Attar, Yasser Khan, Mahmood Niazi, Sajjad Mahmood, Mohammad Alshayeb

All Works

UML use case diagrams are a prominent artefact of requirements engineering, capturing the functional scope of a software system in terms of actors, use cases, and their stereotyped relationships. The emergence of multimodal large language models with image understanding capabilities raises the question of whether such models can reliably extract structured construct-level information from use case diagram images. This paper reports an empirical evaluation of Claude on the task of counting 14 notational construct types from a corpus of 78 computer-generated UML use case diagrams, assessed against manually verified ground truth annotations. Results reveal a strongly differentiated accuracy profile: Claude …

Samuel Johnson Dictionary, 1755 Edition, Beth Rapp Young

Research Data and Datasets

This dataset contains digitized page images from the 1755 first edition of A Dictionary of the English Language, contributed by the University of Florida and Indiana University as part of a digital humanities project led by Beth Rapp Young. The collection provides foundational primary source material for the study of 18th-century English lexicography and supports research in textual analysis, transcription, and historical linguistics.