Digital Commons Network^™

The Role Of Ai In Addressing The United Nations Sustainable Development Goals (Sdgs): A Comprehensive Survey, Walid Abdullah, Muhammad Nawaz, Basma Nasir, Muhammad Javed Ramzan

Sustainable Machine Intelligence Journal

Artificial Intelligence (AI) emerges as a transformative tool in addressing global challenges, particularly in achieving the United Nations Sustainable Development Goals (SDGs). This paper provides a comprehensive survey of how AI technologies contribute to addressing global challenges across social, economic, and environmental domains. By enabling advanced data analysis, predictive modeling, and automation, AI offers innovative solutions across sectors such as poverty reduction, healthcare, quality education, sustainable cities, climate action, and the protection of life on land and below water. Special attention is given to practical, real-world examples where AI enables more efficient resource management, predictive analytics, environmental monitoring, and data-driven …

Enhanced Network Anomaly Detection Using Hybrid Deep Learning Network Based On Interactive Threshold, Maythem S. Derweesh, Sundos A. Hameed Alazawi, Anwar H. Al-Saleh

Baghdad Science Journal

In recent years, the growing use of the internet by both governments and private companies has led to a major increase in individual online activity. This expand lead to make the systems more effected to the threats and cyber attacks, and need more strong solutions to cyber security. Recently, deep learning (DL) and machine learning (ML) have become powerful tools in the cybersecurity field, especially for tasks such as detecting malware and filtering spam. This study present new multi layer method to detect the abnormal activities by busing advanced deep learning techniques. The proposed system work in tow main steps. …

Hate Speech Detection Using Optimized Feature Representation Via Spiral-Grey Wolf Optimizer-Based Machine Learning Approaches, Noor S. Farhan, Matheel E. Abdulmunim, Hasanen S. Abdullah

Journal of Soft Computing and Computer Applications

Hate speech detection is crucial as social media diversifies. This research present a lightweight, scalable system using traditional machine learning methods along with a new approach called Spiral-Grey Wolf Optimizer (S-GWO).

S-GWO effectively selects key features that consider both meaning and content from the Term Frequency Inverse Document Frequency (TF-IDF) space, leading to high-quality representation without excessive computing power.

The propoused system was tested on Arabic and another English datasets using six machine learning methods: SVM, RF, LR, KNN, NB, and SGD. It achieved 92% accuracy and F1 score on the Arabic dataset, while reaching 100% accuracy on the English …

Real-Time Production Of High-Resolution, Gap-Free, 3-Hourly Aod Over South Korea: A Machine Learning Approach Using Model Forecasts, Satellite Products, And Air Quality Data, Seoyeon Kim, Youjeong Youn, Menas Kafatos, Jaejin Kim, Wonsik Choi, Seung Hee Kim, Yangwon Lee

Institute for ECHO Articles and Research

Aerosol optical depth (AOD) is essential for air quality monitoring and climate research. However, satellite-based retrievals suffer from cloud-related data gaps, and reanalysis products are limited by coarse spatial resolution and substantial production latency. This study develops a real-time, gap-free, high-resolution (1.5 km) AOD retrieval system for South Korea. The system integrates Copernicus Atmosphere Monitoring Service (CAMS) forecasts, high-resolution meteorological fields, and ground-based air quality observations within a machine learning framework. Three models with varying training periods were systematically evaluated using cross-validation and independent validation with 2024 Aerosol Robotic Network (AERONET) data. The optimal model, trained on 2015–2023 data, achieved …

Quantitative Evaluation Of Tunnel Rock Mass Integrity Based On Mwd Technology, Zhang Kunmu, Peng Hao, Liang Ming, Han Yu, Song Guanxian

Journal of China & Foreign Highway

In tunnel construction,the quantitative evaluation of rock mass integrity heavily relies on information from the exposed face,and there are challenges when drilling data is used for integrity evaluation.To this end,this study introduced a novel method for quantitative evaluation of rock mass integrity during drilling,integrating numerical statistics with machine learning.A substantial dataset of digital drilling data was collected,covering three common types of rock mass integrity:relatively intact,relatively fractured,and fractured.Subsequently,a high-performance random forest model for the classification of rock mass integrity was developed through data preprocessing and hyperparameter optimization.The interpretability of the model ’s predictive results was enhanced using Shapley additive explanations (SHAP …

Advancing Skin Cancer Diagnostics: A Comparative Analysis Of Convolutional Neural Network Models For Skin Lesion Classification, Jesse Osuji, Tania Malik

SURE Journal: Science Undergraduate Research Experience Journal

For many years, skin cancer diagnosis and cancer research have been major focal points in the healthcare sector. Accurate classification of different types of skin lesions, including cancerous ones, is essential for proper diagnostics. Many machine learning (ML) techniques and models are used to solve various problems across various domains. One of such techniques is called neural networks. Convolutional Neural Networks (CNNs) are primarily used in computer vision-based problems, allowing computer systems to process and understand images. CNNs offer a potential tool for diagnosing skin lesions from images, occasionally demonstrating superior accuracy compared to trained dermatologists. This project explores various …

Pixel-Perfect Segmentation Of Solar Filaments, Jamie Harris

Undergraduate Research Symposium

The observation and classification of solar filaments has a drastic impact on the ability to predict solar-magnetic weather phenomena that threatens to put both satellite infrastructure and astronauts at risk. Using the Hɑ filter provided by the Global Oscillations Network Group (GONG), a network of six telescopes around the world dedicated to 24/7 surveillance of the sun, we are able to get images that clearly and prominently display filament activity. With the vast amount of images the GONG takes, it is not possible to manually analyze every image. Using the U-Net model for computer vision, we were able to train …

Studying Causal Inferences For Student Success In Higher Education, Prabesh Shrestha

Theses and Dissertations

Higher education institutions strive to support students in achieving their academic goals through targeted interventions and data-driven strategies. This study analyzed 19 variables, spanning student demographics, enrollment patterns, and institutional interventions, to identify predictors of students completing all enrolled courses in a semester. Using random forest, logistic regression, and bootstrap forest models, we assessed variable importance and derived causal estimates to determine their impact. Results from causal analysis indicate positive effects from session type, term load, referrals, kudos, flags, and scholarships, while negative impacts were associated with location, course delivery method, enrollment status, advisor contact, and registration interval. These findings …

Decoding The Chameleon Game, Tri Dang '25, Hieu Tran, Brian T. Howard, Sutthirut Charoenphon, Dat Nguyen '25

Student Research

The Chameleon game is a challenging word association activity where players are given a secret word and must respond with words relevant to that secret word. It requires strategic thinking and deduction. The Chameleon must cleverly guess the secret keyword in this game while avoiding suspicion. Our research aims to create an advanced artificial intelligence (AI) model that can play the Chameleon game from both perspectives: as the Chameleon and as a Human. This AI is designed to guess secret keywords based on the information provided by the players, choose the best strategies to avoid detection as the Chameleon, identify …

Ai-Optimized Resource Management In Next-Gen Wireless Networks, Fatemeh Lotfi

All Dissertations

Next-generation wireless networks must deliver highly adaptive, scalable, and intelligent connectivity to satisfy the heterogeneous demands of emerging services, including enhanced mobile broadband, massive machine-type communications, and ultra reliable low latency applications. The Open Radio Access Network (O-RAN) paradigm has emerged as a key enabler of this vision, introducing openness, virtualization, and artificial intelligence (AI)-driven control into the RAN ecosystem. O-RAN’s disaggregated architecture facilitates multi-vendor interoperability and empowers intelligent management through the RAN Intelligent Controller (RIC). However, achieving real-time, autonomous, and generalized optimization in such a dynamic environment remains a significant challenge due to its distributed nature, non-stationary traffic, and …

Exploring Synthetic Data Generation For Machine Learning And Statistical Analysis, Aditi S. Gajjar

Master's Theses

In the data-driven machine learning and statistical analysis world, real-world data availability is often constrained by privacy concerns, access restrictions, or resource limitations. To address these challenges, synthetic data provides a groundbreaking solution, enabling academics and data scientists to generate artificial datasets that mimic real-world data for model development and testing in a controlled, privacy-preserving environment. The syntheticDatasets package represents an R-based counterpart to Python's widely-used Scikit-learn dataset generators, adapted to meet the needs of statisticians, educators, and data scientists primarily working in the R programming ecosystem.

This thesis explores the design and implementation of the syntheticDatasets package, focusing on …

Applications Of Machine Learning In Gravitational-Wave Research With Current Interferometric Detectors, Elena Cuoco, Marco Cavaglià, Ik Siong Heng, David Keitel, Christopher Messenger

Physics Faculty Research & Creative Works

This article provides an overview of the current state of machine learning in gravitational-wave research with interferometric detectors. Such applications are often still in their early days but have reached sufficient popularity to warrant an assessment of their impact across various domains, including detector studies, noise and signal simulations, and the detection and interpretation of astrophysical signals. In detector studies, machine learning could be useful to optimize instruments like LIGO, Virgo, KAGRA, and future detectors. Algorithms could predict and help in mitigating environmental disturbances in real time, ensuring detectors operate at peak performance. Furthermore, machine-learning tools for characterizing and cleaning …

An Analytical Assessment Of Sentiment Analysis Trends And Methods Through Systematic Review And Topic Modeling, Chelsey Hill, Fatima Irshaidat, Marina Johnson, Jorge Fresneda

Department of Information Management and Business Analytics Faculty Scholarship and Creative Works

Sentiment Analysis (SA) applies Artificial Intelligence (AI) and Machine Learning (ML) techniques to identify and interpret opinions, emotions, and sentiment polarity in text data. This study presents a systematic literature review of SA research published between 2012 and 2024 using a dataset of 14,482 journal articles indexed in Scopus. The primary objective of this research is to provide a data-driven overview of the evolution of SA research, offering insights into methodological evolutions, emerging application areas, and the growing influence of AI in the field. The research findings reveal a multidisciplinary growth of SA research, with increasing contributions from Health Sciences …

Marsanywhere: Dataset And Cross-View Diffusion Model For Satellite-To-Ground View Synthesis With Mars Data, Benjamin T. Hinchliff

Master's Theses

Satellite-to-ground view synthesis aims to create a realistic ground view image from a corresponding satellite view image. This is a well-studied problem for street level imagery, with good results being achieved by using modern image synthesis techniques such as diffusion models. However, despite the public availability of satellite and ground level imagery on Mars, these techniques have yet to be applied to the domain due to difficulties in collating and processing the data into a usable form. We address this deficiency by creating a dataset consisting of ground view panorama imagery from the Perseverance rover, along with associated satellite view …

Machine Learning-Based Detection Of Covert Data Exfiltration Via Electromagnetic Side-Channel Emissions From Computer Memory In Air-Gapped Systems, Oladapo Akintoye Ajike

All Theses

Air-gapped computer systems are physically isolated from unsecured networks. Though isolated, they remain vulnerable to covert data exfiltration via electromagnetic side channels and other covert-channel attacks. This research presents a comprehensive approach to detecting electromagnetic data exfiltration by establishing a controlled laboratory environment using low-cost, readily available hardware components. The study shows a proof-of-concept covert data transmission system that exploits electromagnetic emissions from computer memory access patterns through software-controlled Random Access Memory (RAM) operations.

The research methodology involved developing a C++ transmitter program that modulates CPU and memory-intensive operations to generate detectable electromagnetic signals at 100 MHz frequency, and implementing …

Integrating Semi-Supervised Learning And Ensemble Deep Learning For Low-Resource Deep Knowledge Tracing, Uchechukwu Melody Okechukwu

All Theses

As learning platforms scale, two obstacles prevent Deep Knowledge Tracing (DKT) from thriving in practice: the scarcity of labeled interactions and the opacity of predictions. We introduced a semi-supervised, ensemble DKT pipeline that trains diverse architectures, including Knowledge Proficiency Tracing (KPT), Exercise-Correlated KPT (EKPT), and Dynamic Key-Value Memory Networks (DKVMN) on a mixture of limited labeled and abundant unlabeled learner interactions, then aggregates predictions via majority voting to stabilize learning and reduce variance. Evaluated on benchmark datasets such as ASSISTments, the combined approach yields consistent gains in AUC, accuracy, and precision over strong supervised baselines while revealing influential interactions, surfacing …

Precision Agriculture In The Age Of Ai: A Systematic Review Of Machine Learning Methods For Crop Disease Detection, Munir Majdalawieh, Carla Martins, Mohammed Radi, Maher Alaraj, Shafaq Khan

All Works

Artificial Intelligence (AI) has become a critical tool in modern precision agriculture, particularly in the detection of plant diseases and pests. This study provides a comprehensive review of current AI methodologies applied to crop disease detection, with a focus on machine learning models, dataset availability, and performance metrics. Our findings indicate that Convolutional Neural Networks (CNNs) are the most widely used and cost-effective approach, while Vision Transformers (ViTs) exhibit superior accuracy but require significantly higher computational resources. We identify key research gaps, including the geographic bias in dataset origins, the trade-off between data quality and quantity, and the limited exploration …

Feature Extraction From Railroad Bearing Onboard Vibration Sensors Using Machine Learning Models, Diego Cantu

Theses and Dissertations

The University Transportation Center for Railway Safety (UTCRS) has developed an algorithm capable of identifying defective railroad bearings, determining damaged component(s) within, and quantifying severity of the defects. The defect-detection algorithm requires the operating speed as an input, which is not readily available in field operation of onboard sensors. Therefore, onboard sensors deployed in rail revenue service must rely on Global Positioning Systems (GPS) to obtain speed, which can be power-intensive and susceptible to signal interference. This study covers the development of a vibration-based model that extracts operating speed from wireless sensor data to enable fully autonomous onboard diagnostics. Signal …

Generalized Detection Of Animal Behavior Using Accelerometers, Alexander J. Arrieta

Master's Theses

Animal mounted sensors are becoming increasingly used to passively monitor both domestic and wild animals. Advances in lightweight accelerometer and GPS technology have allowed many animals to be fitted with high accuracy sensors for extended periods of time. This leads to new opportunities to study animal behavior without direct observation. However, interpreting the raw data is difficult due to the high volume and missing context of the information. Machine learning techniques excel at extracting information from raw data streams and are excellent candidates for processing the sensor data. However, due to large variance in how different animals execute the same …

Predicting Newsworthiness Of California Bill Discussions, Venkata Kedarnath Chaturvedula

Master's Theses

Among various California bill hearings, it is crucial to identify which hearings are most likely to receive media coverage, as this highlights the importance of the bill and the hearing, while also underscoring its societal impact. Digital Democracy, established at Cal Poly’s Institute for Advanced Technology and Public Policy, provides users access to a distinctive dataset and information about state legislative committee hearings, hearing transcripts, assets, bill information, etc.

This thesis presents a machine learning framework for predicting whether California legislative bills are likely to receive media coverage. Drawing on a dataset of legislative ”tip sheets” provided by Digital Democracy …

Data-Driven Streamflow Forecasting In The Upper Colorado River Basin Using Spatio-Temporal Graph Networks, Akhila Akkala

All Graduate Theses and Dissertations, Fall 2023 to Present

Forecasting river flow is essential for managing water supplies, reducing flood risk, and supporting healthy ecosystems. In the Upper Colorado River Basin, much of the yearly water comes from melting snow. However, many traditional models struggle to capture how snowpack and river flow interact, especially across such a large and complex region.

This study uses a modern machine learning approach called a Spatio-Temporal Graph Neural Network (STGNN) to improve streamflow prediction. The model uses Snow Water Equivalent (SWE)—a measure of how much water is stored in the snowpack—along with river flow data. By treating each river gauge as part of …

Physics-Guided Strategies For Enhancing Neural Networks Trained With Limited Data, Jose Guadalupe Perez Zamora

Open Access Theses & Dissertations

Deep neural networks excel at a wide range of processing tasks across various disciplines. However, the quantity and quality of data significantly impact network performance. In specialized domains, high-quality datasets are often difficult to gather, interpret, and curate for effective learning. Few-shot learning techniques, including transfer learning, data augmentation, and meta-learning, have emerged to address these constraints.

We propose three physics-guided strategies for enhancing neural networks trained with limited data: (1) combining existing models like LSTMs with Physics-Informed Neural Networks through two-branch architectures that merge their outputs, (2) deriving custom physics-informed data augmentation algorithms to expand limited datasets, and (3) …

Llm-Driven Semantic Explanations For Soil Moisture Prediction Models, Bamory Ahmed Toru Koné, Khouloud Boukadi, Rima Grati, Emna Ben Abdallah, Massimo Mecella

All Works

Efficient soil moisture prediction is crucial for sustainable agricultural practices, especially in the face of climate change and increasing water scarcity. However, the adoption of machine learning (ML) models in this context is frequently limited by their lack of interpretability, particularly among non-expert users such as farmers. This study proposes a novel approach to soil moisture prediction that combines high predictive performance with enhanced explainability. We propose a framework that leverages large language models (LLMs) to generate textual explanations based on a proposed irrigation and soil moisture ontology, thus making the model's predictions more understandable to farmers. The ontology formalizes …

Bias Testing And Mitigation In Llm-Based Code Generation, Dong Huang, Jie M. Zhang, Qingwen Bu, Xiaofei Xie, Junjie Chen, Heming Cui

Research Collection School Of Computing and Information Systems

As the adoption of LLMs becomes more widespread in software coding ecosystems, a pressing issue has emerged: does the generated code contain social bias and unfairness, such as those related to age, gender, and race? This issue concerns the integrity, fairness, and ethical foundation of software applications that depend on the code generated by these models but are underexplored in the literature. This paper presents a novel bias testing framework that is specifically designed for code generation tasks. Based on this framework, we conduct an extensive empirical study on the biases in code generated by five widely studied LLMs (i.e., …

Is Complexity Virtuous?, Ryan Elmore, Jack Strauss

Business Information and Analytics: Faculty Scholarship

(Kelly et al., 2024) show that increasing complexity in linear models, with potentially thousands of predictors, is "virtuous". Their work contradicts the dogma of model selection, including the Principles of Parsimony and Occam's Razor. They find that when the number of predictors far exceeds the number of observations, the bias-variance trade-off breaks down, the variance declines, and the Sharpe ratio increases. In the context of ridge regression, we find that very high complexity coupled with large penalty terms (excessive shrinkage) generate forecasts that converge to a rolling window of past returns. For example, we show the past twelve-month moving average …

Introduction To Machine Learning And Machine Learning Systems, Raffi T. Khatchadourian Ph.D.

Open Educational Resources

Lecture slides introducing machine learning and machine learning systems for an undergraduate software engineering course. Topics include what machine learning is and how it differs from traditional programming, foundation models, the major types of learning (supervised, unsupervised, reinforcement, and others), and applications across domains. Using a food-delivery time-prediction case study, the deck walks through a typical ML pipeline—data collection and cleaning, feature engineering, model training, and evaluation—and covers evaluation methods (precision and recall, confusion matrices, error measures) along with underfitting versus overfitting and the realities of learning and evaluation in production. Based on "Machine Learning in Production/AI Engineering" by Christian …

Advances In Artificial Intelligence For Glioblastoma Radiotherapy Planning And Treatment, Reid Master, Nesha Rubin, James Sampson, Kamlesh K Yadav, Shruti Pandita, Aria Sabbagh, Anika Krishnan, Patrick J Silva, Kenneth S Ramos, Vincent Gregoire, Nikos Paragios, Sunil Krishnan, Tej K Pandita

The Brown Foundation: Institute of Molecular Medicine

Glioblastoma is an aggressive central nervous system tumor characterized by diffuse infiltration. Despite substantial advances in oncology, survival outcomes have shown little improvement over the past three decades. Radiotherapy remains a cornerstone of treatment; however, it faces several challenges, including considerable inter-observer variability in clinical target volume delineation, dose constraints associated with adjacent organs at risk, and the persistently poor prognosis of affected patients. Recent advances in artificial intelligence, particularly deep learning, have shown promise in automating radiation therapy mapping to improve consistency, accuracy, and efficiency. This narrative review explores current auto segmentation frameworks, dose mapping, and biologically informed radiotherapy …

Fish-Spec: Fast Identification System For Handheld Spectroscopy And Species Classification, Mitchell Sueker, Nicholas Mackinnon, Gregory Bearman, Amanda Tabb, Diane Kim, Rosalee S. Hellberg, Alireza Akhbardeh, Hamid Reza Marateb, Jianwei Qin, Moon Kim, Fartash Vasefi, Hossein Kashani Zadeh

Food Science Faculty Articles and Research

Accurate fish species identification is critical to prevent mislabeling and fraud in the seafood industry. We present a handheld multi-mode point spectroscopy system that combines fluorescence (365 and 395 nm excitation) and reflectance measurements in the visible to near-infrared (∼350–900 nm) and short-wave infrared (∼900–1700 nm) regions for rapid, non-destructive classification of fish fillets. Tissue spectra were acquired at 25 positions on 68 fillets from 11 species, in both frozen and thawed states. Feature-level fusion across all four modes enabled higher classification accuracy than any single mode alone. A global machine-learning model classified all species with 85 ± 2.8 %, …

Field Canals Improvement Projects Duration Prediction: A Comparative Analysis Of Machine Learning Models, Hania Ghouse, Ukaegbu Chinonso Ishmael, Edgar Dario Obando-Paredes, Hashem Shafik Shakir, Ali Al-Bayaty

Electrical and Computer Engineering Faculty Publications and Presentations

There are several essential elements in project construction management to be studied appropriately, and priority to these elements, such as cost and duration, is predominantly interesting to be investigated. In this research, the duration of field canal improvement projects (DFCIP) was predicted using two relatively new machine learning (ML) models - the Multivariate Adaptive Regression Spline (MARS) and Extreme Learning Machine (ELM). The targeted DFCIP was calculated using other dependent parameters, such as the length of the pipe, years of construction, the geographical zone of the network, the supplied area with water, and finally the actual cost of the field …

Archaeological Predictive Meta-Modeling In Pre-Columbian Mexico, Peter Stamm

Electronic Theses and Dissertations

Archaeological Predictive Modeling stands firmly as an important tool for Archaeologists to predict undiscovered sites from civilizations all across the globe. While powerful, this methodology is not without its own set of qualms. Striking a balance between pure a data-driven approach while also observing leading expert theories can be a complicated task. Going further, deciding on the specific domain of features to emphasize or overlook can be a challenge within itself, as one misstep can drastically change the output of model, sometimes for the worst. In addition, creating models that can expose their reasoning process can be rather difficult to …