Digital Commons Network^™

Toward Privacy-Preserving Data Sharing - An Australian Healthcare Perspective, Kimley Foster, Nectarios Costadopoulos, Arash Mahboubi, Sabih Ur Rehman, Md Zahidul Islam

Research outputs 2022 to 2026

The rise of big data has brought increased urgency to the importance of privacy-preserving data sharing in healthcare. In Australia, health records exist in various databases; however data sharing is limited. While many consumers and healthcare professionals recognise the advantages of sharing data for research and health care services, misgivings about privacy and security persist. This study examined current perspectives on data sharing, investigating the trust level in privacy preserving data sharing tools and techniques among healthcare professionals and organisations, and their openness to adopting technology for secure data sharing. We incorporated participants from various healthcare professions across Australia. We …

Towards Robust Multimodal Land Use Classification: A Convolutional Embedded Transformer, Muhammad Zia Ur Rehman, Syed Mohammed Shamsul Islam, Anwaar Ulhaq, David Blake, Naeem Janjua

Research outputs 2022 to 2026

Multisource remote sensing data has gained significant attention in land use classification. However, effectively extracting both local and global features from various modalities and fusing them to leverage their complementary information remains a substantial challenge. In this paper, we address this by exploring the use of transformers for simultaneous local and global feature extraction while enabling cross-modality learning to improve the integration of complementary information from HSI and LiDAR data modalities. We propose a spatial feature enhancer module (SFEM) that efficiently captures features across spectral bands while preserving spatial integrity for downstream learning tasks. Building on this, we introduce a …

Joint 3d Beamforming-And-Trajectory Design For Uav-Satellite Uplink Covert Communication, Jihong Yu, Yuting Cai, Shihao Yan, Yun Li, Jingjing Wang, Jiahao Liu, Jianping An

Research outputs 2022 to 2026

In this paper,we study uplink covert communication in a space-air system,where an unmanned aerial vehicle (UAV) transmits sensitive data to a Geosynchronous Earth Orbit (GEO) satellite while preventing the transmission action from being discovered by a warden. We derive the optimal decision threshold of the warden. We investigate the 3-dimensional (3D) beamformer and 3D trajectory design for the transmitter UAV against this optimum warden to maximize the covert transmission rate in the presence of imperfect channel state information and uncertain noise. Due to the non-convex structure and dependence between beamforming vectors and locations of the transmitter UAV,we develop a decoupling …

Embodied Ai For Challenging Rearrangement Tasks In The Context Of Service And Assistive Robots, Mariia Khan

Theses: Doctorates and Masters

Embodied AI explores intelligent agents that learn through interaction with their environment, aiming to replicate human-like learning processes. Achieving this requires agents capable of understanding a scene via various sensors, reasoning about their actions, and reacting accordingly. These abilities are necessary for service domestic robots to assist humans in their day-to-day activities. Embodied AI tasks can include but are not limited to: visual exploration, visual navigation, instruction following and embodied question answering, which typically consider static (unchanging) environments, where objects do not move over time. This thesis addresses one of the most challenging Embodied AI tasks – visual room rearrangement, …

Anomaly Detection Of Seasonal Vessel Activity, Travis Rybicki

Theses: Doctorates and Masters

Monitoring maritime traffic is essential for ensuring the safety of vessels, safeguarding transported goods or persons, and preventing illicit or hazardous activity at sea. Increasingly, researchers have explored data-driven approaches to model expected vessel behaviour and detect deviations or anomalies. These anomalies—such as course deviations, unauthorised area entries, or unexpected operational patterns—can indicate emergencies, regulatory breaches, or unlawful intent. Data broadcast by vessels provides a valuable resource for such analyses; however, the inherent complexity and context-dependency of maritime behaviour present persistent modelling challenges. One critical yet underutilised factor in this context is seasonality. For certain vessel types, for example, fishing …

Automated Methods For Estimating Blood Alcohol Concentration Level From Facial Cues, Ensiyeh Keshtkaran

Theses: Doctorates and Masters

This thesis investigates different approaches for detecting alcohol intoxication in drivers by analysing facial video data. Tackling this issue necessitates the creation of a novel dataset to overcome the limitations of existing datasets. The dataset constructed in this study is the first to include RGB video recordings of individual faces at varying levels of alcohol intoxication during simulated driving, featuring 60 participants with BAC levels ranging from 0 to 0.165 g/100ml. The constructed dataset not only supports this thesis, but also offers the broader scientific community a valuable resource for further study and development.

Building on this, this thesis presents …

Gans And Synthetic Financial Data: Calculating Var*, David E. Allen, Leonard Mushunje, Shelton Peiris

Research outputs 2022 to 2026

Generative Adversarial Neural nets (GANs) are a new branch of machine learning techniques. A GAN learns to generate new data from the training data set. We examine the characteristics of the fake financial data using GANs trained on samples of daily S&P 500 and FTSE 100 index values. GANs feature two competing neural networks in a game theoretic context. The Generator net generates pseudo data that is presented to the discriminator net which then attempts to distinguish between the real and the fake data. This facilitates unsupervised learning on the dataset. The generative network generates data sets, while the discriminative …

Survey: A Study On Image Encryption Using Dna In Bioinformatics, Rana M. Zaki, Zaed S. Mahdi, Matheel E. Abdulmunim

Journal of Soft Computing and Computer Applications

One area of study between computer science and biology is bioinformatics, which deals with methods for collecting, processing, storing, and evaluating biological data. Sequences of RiboNucleic Acid (RNA), DeoxyriboNucleic Acid (DNA), and proteins make up biological data, which has a wide range of uses in domains such as feature extraction, data segmentation, data security, and more. In cryptography, DNA sequences are used as data carriers, enhancing the unique properties of biomolecules. This approach involves using DNA sequences to enhance the security of confidential data that must be transmitted over networks or stored securely. Several DNA-based security techniques have been developed, …

New Feature Selection Using Principal Component Analysis, Zaid Mundher Radeef, Soukaena Hassan Hashem, Ekhlas Khalaf Gbashi

Journal of Soft Computing and Computer Applications

Dimensionality reduction techniques streamline machine learning by reducing data complexity, improving model accuracy, and cutting computational costs. They remove noise and irrelevant features, making models faster and more efficient. These techniques also enhance data visualization and interpretation by condensing data into manageable, insightful dimensions. Ultimately, dimensionality reduction leads to simpler, more interpretable models without sacrificing critical information, making it a cornerstone of efficient data analysis and machine learning applications. Theoretically, feature extraction tends to create new features that encapsulate more information by combining multiple existing features, resulting in more concentrated and informative features. In contrast, feature selection involves choosing a …

Development Of A Hybrid Methodology Of Deep Learning And Machine Learning For Lung Nodule Detection In Medical Computed Tomography Images, Zaed S. Mahdi, Rana M. Zaki, Alaa Kadhim Farhan, Negar Majma

Journal of Soft Computing and Computer Applications

Deep learning and machine learning play an important role in the medical field, helping doctors make accurate, fast and effective diagnosis. Despite the progress achieved in the use of modern technologies in detecting cancerous nodes, current studies still suffer from some challenges and limitations that must be addressed to obtain high efficiency in identifying cancerous nodes. These challenges include using image pre-processing, combining deep learning and machine learning techniques, and constantly adapting to clinical changes, in order to address this. A hybrid methodology has been proposed for detecting cancerous nodules in the lung in medical Computed Tomography (CT) images. It …

Enhancing Image Classification Using A Convolutional Neural Network Model, Zena M. Saadi, Ahmed T. Sadiq, Omar Z. Akif, Marwa M. Eid

Journal of Soft Computing and Computer Applications

In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as …

Improved Rapidly-Exploring Random Tree Using Firefly Algorithm For Robot Path Planning, Dena Kadhim Muhsen, Firas Abdulrazzaq Raheem, Yuhanis Yusof, Ahmed T. Sadiq, Faiz Al Alawy

Journal of Soft Computing and Computer Applications

In robotics, efficient path planning makes robots work independently and move through changing environments over time. This study combines the Rapidly-exploring Random Tree (RRT) architecture with the Firefly Algorithm (FA) to make robot’s path-planning better. The proposed ERRT-FA, which stands for "Enhanced RRT with Firefly Algorithm", generates better routes using Firefly social habits. Plan routes using Firefly social habits can effectively aid in exploring configuration space. The role of the FA is to enhance the RRT algorithm by providing an optimized exploration of the search space, ultimately leading to optimizing the path found by the RRT algorithm and better paths …

Pushing The Boundaries Of Large Language Models: Innovations And Limitations In Nlp, Finance, And Mathematics, A M Muntasir Rahman

Dissertations

Large Language Models (LLMs) have emerged as transformative tools across a spectrum of domains, yet their practical deployment reveals a blend of remarkable potential and notable limitations. This research explores innovative methodologies to extend the capabilities of LLMs while addressing critical challenges in their evaluation and application. By leveraging rule-based approaches, the in-context learning capabilities of LLMs, and human-in-the-loop validation across three focused studies, this research introduces robust strategies for dataset synthesis, model enhancement, and model assessment in three distinct domains: natural language processing, financial sentiment analysis, and mathematical reasoning

The first study proposes an efficient data augmentation framework, EASE, …

First-Principles Study Of Ferroelectric Properties And Co2 Reduction Reaction Capabilities In Two-Dimensional Monolayers And Heterostructures, Mo Li

Dissertations

Two-dimensional (2D) materials hold significant potential for CO₂ reduction reactions (CO₂RR) due to their high surface-to-volume ratio. However, achieving high selectivity for desired products and overcoming limitations posed by scaling relationships remain challenging. Recent studies suggest that ferroelectric (FE) materials with switchable out-of-plane polarization (OOP) can effectively tune the adsorption behavior, thermodynamics, and kinetics of CO₂RR, offering promising solutions to these challenges. Using density functional theory (DFT) and the Berry phase approach, this work expands the family of 2D ferroelectrics by theoretically identifying Y₂CO₂, Y₂CS₂, and Sc …

Ensemble Learning Models For Large-Scale Time Series Forecasting In Supply Chain, Minjuan Zhang

Dissertations

Machine learning and AI techniques are transforming supply chain forecasting, driven by the expanding availability of data assets. These advanced methods offer powerful opportunities to optimize management processes, reduce operational costs, and enhance strategic decision-making, which is crucial for enterprise success. However, conventional statistical approaches, such as Autoregressive Integrated Moving Average Models (ARIMA), dynamic regression, and Unobserved Component Models (UCMs)—which have long dominated time series forecasting—often fall short in accuracy and scalability. These traditional models face limitations in batch processing, handling large-scale data, addressing uncertainty-induced disruptions, and synchronizing demand-supply scenarios.

To address these challenges, a novel class of AI-powered ensemble …

Machine Learning Methods For Pattern Recognition Analysis Of Genomic And Molecular Data, Kuang Du

Dissertations

While immune therapies achieve remarkable success in treating various cancers, only a subset of patients achieves a durable clinical response, and many exhibit innate or acquired resistance. Precision medicine aims to tailor treatments to individual patients based on specific biological markers, ensuring that each patient receives the therapy most likely to be effective. Predictive biomarkers and gene signatures offer potential for more personalized treatment strategies by identifying patients likely to benefit. Recent studies suggest that gene signatures, comprising sets of genes, hold predictive value for certain clinical variables. Typically derived from biological expert knowledge, these signatures demonstrate substantial predictive potential, …

Knowledge Diffusion In Networks Of Artificial Learners, Ehsan Beikihassan

Dissertations

The dissertation draws inspiration from the topic of peer learning in the social sciences and the study of information dissemination and knowledge diffusion in network science. In particular, it introduces and studies a setting involving a population or network of artificial learners, with the objective of optimizing aggregate performance measures under constraints on training resources. In this context, natural knowledge diffusion processes in networks of interacting artificial learners are studied. The term "natural" refers to processes that emulate human peer learning, where the internal state and learning processes of students remain largely opaque, and the main degree of freedom lies …

Interactive Visualization Workflows For Mitigating Analytical Uncertainty, Kaustav Bhattacharjee

Dissertations

This dissertation takes a process-centric and stakeholder-first perspective for handling analytical uncertainty: the form of uncertainty that confronts data analysts' insight-generation processes in high-consequence decision-making scenarios. The cost of an incorrect decision when data is used for movie recommendations as opposed to when personal data is used to drive insights or when data-driven modeling is used to drive real-time decisions for maintaining the health of a grid are vastly different in terms of consequences. This dissertation looks at analytical uncertainty in two real-world scenarios: i) how sensitive information leakage can be prevented during the open data release process with data …

Crowd-Sourced Learning For Computer Graphics Applications, Yunhao Zhang

Dissertations

Computer Graphics (CG) revolves around virtual content creation using computational methods, spanning applications from games to visual effects. Typically, the creation of CG content is led by expert practitioners who guide computational algorithms towards satisfactory results. Thus, creating CG content often requires manual iterations encompassing algorithm design, parameter tuning, and aesthetic feedback. This work investigates how to leverage crowd-sourcing to streamline such creation processes, focusing on animation and simulation. In animation, a novel crowd-sourcing framework is proposed for combat animation, enabling users to analyze motion similarities, and retrieve matching motions using novel crowd-sourced motion features. Such features enable quantifying previously …

Surveying The Role Of Visual Analytics In Human-Machine Teaming, Naga Datha Saikiran Battula

Theses

Humans and machines both possess their unique capabilities and have their strengths and weaknesses, which can be complementary to one another and allow them to achieve a common goal. Teaming in the modern era involves text prompts, voice commands, gesture recognition, touch interfaces, and the latest visualization techniques that allow parties/agents to interact. Communication through visualization plays a vital role in allowing robust insights to be gained through a glance. Using visualization as a medium between humans and machines can increase the communication bandwidth. Human-machine teaming has witnessed much progress, with many theories and practical examples emerging. In the report, …

Automated Segmentation Of The Ulnar Nerve In Mri Using Deep Learning Techniques, Akhil Nagulapalli

Theses

Cubital Tunnel Syndrome (CuTS), a condition caused by compression of the ulnar nerve, results in numbness, tingling, pain, and even muscle atrophy, affecting fine motor skills and diminishing patient quality of life. Accurate diagnosis of CuTS is challenging, as current diagnostic methods—including clinical exams, nerve conduction studies, and unaided MRI—often lack the precision to reliably identify the nerve and detect compression in its early stages. Deep learning-based segmentation offers a promising solution, enabling precise and automated identification of nerve structures in MRI images, which could significantly improve diagnostic accuracy and support timely intervention.

A novel deep learning model for segmenting …

Shalom In Social Media Marketing Shalom In Social Media Marketing, Jill R. Risner, Thomas Betts

University Faculty Publications and Creative Works

This paper will examine social media and the ways in which it currently does and does not contribute to shalom through an examination of both the platforms themselves as well as the content shared through them. As Christ’s ambassadors in the world and the primary funders of social media, marketers have power and an obligation to consider social media’s impact on shalom in the world and to use it in ways that contribute to shalom. This paper will provide several recommendations of how marketers can do this including posting content that intentionally contributes to shalom, engaging on platforms that support …

Ai-Assisted Academia: Unveiling Doctoral Students' Perspectives On Dissertation In Practice Innovation, Jennifer J. Lesh, Jévaughn J. Lancaster

Faculty and Staff Publications & Presentations

This action research study explores 73 doctoral students' perceptions of using Generative Artificial Intelligence (GAI) throughout their research journey in one educational doctorate (Ed.D) program. The first phase employed surveys, while the second incorporated semi-structured focus group interviews based on the survey data from a diverse sample of students across educational disciplines currently enrolled in the university's educational leadership doctoral program. In the study's first phase, the survey quantified educators' familiarity with, attitudes towards, perceived challenges, ethical considerations, and benefits of using GAI in doctoral research. The exploration of GAI in this practitioner-inspired doctoral program has uncovered essential insights into …

Impact Of Urban Development On Uv Exposure: A Clustering And Machine Learning Assessment, Taufik Roni Sahroni Mr., Verdi Yasin, Lulut Alfaris, Reza Ariefka, Ruben Cornelius Siagian, Mohammad Alfin Karim, Nana Rahdiana, Ade Suhara

Journal of Environmental Science and Sustainable Development

The relocation of Indonesia's capital city is anticipated to promote inclusive economic growth while embracing cultural diversity. However, this transition may affect ultraviolet (UV) radiation exposure patterns. The study investigated variations in UV exposure in the IKN region, focusing on urban development factors such as land use and population density that affect public health, sun protection, and skin cancer prevention. The research hypothesized that UV radiation is significantly correlated with these factors. UV Index data from 2010-2023, a hierarchical clustering method, identifies complex data patterns without determining the number of clusters. XGBoost, a machine learning model, was used for handling …

Cropsync: Ai-Powered Sustainable Crop Management, Ziad Doughan, Ibrahim Mneimneh, Zouheir Nakouzi, Noor Al Khaib, Samer Damaj, Jamal Chaaban, Hamza Mrad, Sari Itani

BAU Journal - Science and Technology

CropSync is a smart agriculture system that uses AI and IoT technologies to enable sustain- able crop management and precision farming. The system aims to address the challenges faced by the agriculture sector, such as increasing food production to meet global population demands while minimizing environmental impact. CropSync integrates sensors, cameras, and cloud-based analytics to provide farmers with real-time insights and recommendations for optimizing crop cul- tivation. The system upholds engineering professional and ethical standards, considering broader social, environmental, and economic implications. From a social perspective, CropSync improves food security and enhances farmers’ livelihoods through increased productivity and efficient re- …

Optimizing Vgg16 Deep Learning Model With Enhanced Hunger Games Search For Logo Classification, Mohammed Hussain, Thaer Thaher, Mohamed Basel Almourad, Majdi Mafarja

All Works

Accurate classification of logos is a challenging task in image recognition due to variations in logo size, orientation, and background complexity. Deep learning models, such as VGG16, have demonstrated promising results in handling such tasks. However, their performance is highly dependent on optimal hyperparameter settings, whose fine-tuning is both labor-intensive and time-consuming. Swarm intelligence algorithms have been widely adopted to solve many highly nonlinear, multimodal problems and have succeeded significantly. The Hunger Games Search (HGS) is a recent swarm intelligence algorithm that has shown good performance across various applications. However, the standard HGS still faces limitations, such as restricted population …

Agfi-Gan: An Attention-Guided And Feature-Integrated Watermarking Model Based On Generative Adversarial Network Framework For Secure And Auditable Medical Imaging Application, Xinyun Liu, Ronghua Xu, Chen Zhao

Michigan Tech Publications

With the rapid digitization of healthcare, the secure transmission of medical images has become a critical concern, especially given the increasing prevalence of cyber threats and data privacy breaches. Medical images are frequently transmitted via the Internet and cloud platforms, making them susceptible to unauthorized access, tampering, and theft. While traditional cryptographic techniques play a vital role, they are often insufficient to fully ensure the integrity and confidentiality of these sensitive images. In this paper, we present AGFI-GAN, a robust and secure framework for medical image watermarking that leverages attention-guided and Feature-Integrated mechanisms within a Generative Adversarial Network (GAN). Specifically, …

Text-To-Text Generative Approach For Enhanced Complex Word Identification, Patrycja Śliwiak, Syed Afaq Ali Shah

Research outputs 2022 to 2026

This paper presents a novel approach for solving the Complex Word Identification (CWI) task using the text-to-text generative model. The CWI task involves identifying complex words in text, which is a challenging Natural Language Processing task. To our knowledge, it is a first attempt to address CWI problem into text-to-text context. In this work, we propose a new methodology that leverages the power of the Transformer model to evaluate complexity of words in binary and probabilistic settings. We also propose a novel CWI dataset, which consists of 62,200 phrases, both complex and simple. We train and fine-tune our proposed model …

Muramyl Peptide Blend Ameliorates Intestinal Inflammation And Barrier Integrity In Caco-2 Cells, Dmytro M. Masiuk, Victor S. Nedzvetsky, Giyasettin Baydas

Karbala International Journal of Modern Science

Intestinal barrier function depends on epithelial adhesion, which restricts permeability and microbial invasion from the internal environment. Impairment of barrier integrity and gut function is closely linked to pro-inflammatory changes. Inflammation is often the primary factor that provokes gut function disorders. The anti-inflammatory potential of postbiotics has been reported in recent years. Muramyl peptides (MPs) are small signaling molecules that stimulate intracellular pathogen receptors and can regulate cell responses. However, the molecular mechanisms of MPs' effects on intestinal cells remain unknown. The study of MPs treatment on lipopolysaccharide (LPS)-challenged Caco-2 intestinal cells aimed to investigate the postbiotic effects on intestinal …

Explainable Artifacts For Synthetic Western Blot Source Attribution, João Phillipe Cardenuto, Sara Mandelli, Daniel Moreira, Paolo Bestagini, Edward J. Delp, Anderson Rocha

Computer Science: Faculty Publications and Other Works

Recent advancements in artificial intelligence have enabled generative models to produce synthetic scientific images that are indistinguishable from pristine ones, posing a challenge even for expert scientists habituated to working with such content. When exploited by organizations known as paper mills, which systematically generate fraudulent articles, these technologies can significantly contribute to the spread of misinformation about ungrounded science, potentially undermining trust in scientific research. While previous studies have explored black-box solutions, such as Convolutional Neural Networks, for identifying synthetic content, only some have addressed the challenge of generalizing across different models and providing insight into the artifacts in synthetic …