Physical Sciences and Mathematics Commons^™

Enhancing Cybersecurity Through Autonomous Knowledge Graph Construction By Integrating Heterogeneous Data Sources, Hatoon Alharbi, Ali Hur, Hasan Alkahtani, Hafiz Farooq Ahmad

Research outputs 2022 to 2026

Cybersecurity plays a critical role in today’s modern human society, and leveraging knowledge graphs can enhance cybersecurity and privacy in the cyberspace. By harnessing the heterogeneous and vast amount of information on potential attacks, organizations can improve their ability to proactively detect and mitigate any threat or damage to their online valuable resources. Integrating critical cyberattack information into a knowledge graph offers a significant boost to cybersecurity, safeguarding cyberspace from malicious activities. This information can be obtained from structured and unstructured data, with a particular focus on extracting valuable insights from unstructured text through natural language processing (NLP). By storing …

Protocol For An Integrative Meta-Analysis Of The Application Of Machine Learning Algorithms In The Prediction Of Chronic Disease Risks And Outcomes, Ebenezer Afrifa-Yamoah, Emmanuel Peprah-Yamoah, Enoch Odame Anto, Victor Opoku-Yamoah, Eric Adua

Research outputs 2022 to 2026

Background: Precise risk prediction of chronic diseases is essential for effective preventive care and management. Machine learning (ML) is a promising avenue to enhance chronic disease risk prediction; however, a comprehensive assessment of ML performance across various chronic diseases, populations, and health settings is needed. Methods: This meta-analysis aims to synthesize evidence on the performance of ML techniques for predicting the risks and outcomes of chronic diseases. A literature search was conducted through PubMed, Web of Science, Scopus, Science Direct, Medline, and Embase. Studies applying ML techniques to predict chronic disease risks or outcomes and reporting performance metrics were included. …

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 …

Toward Embodied Navigation Through Vision And Language, Muraleekrishna Gopinathan

Theses: Doctorates and Masters

Embodied AI is a challenging but exciting field in which a robot learns to interact with human-living spaces to perform various tasks. This thesis studies the embodied navigation problem in which a robotic agent navigates in a previously unseen indoor environment based on a challenging task. In particular, the Vision-and-Language Navigation (VLN) task requires a robot to navigate based on a descriptive human-language instruction. This thesis aims to improve VLN agents on four key aspects - their understanding of the environment, training via additional data, correcting navigational errors, and predicting the layout of the environment for better planning.

First, we …

Machine Learning For Computer-Aided Diagnostics From Complex Medical Images, Afsah Saleem

Theses: Doctorates and Masters

Machine learning has significantly transformed medical image analysis in the current age of artificial intelligence offering vast potential in improving disease diagnosis and management. Cardiovascular diseases (CVDs) are among the leading cause of global mortality, emphasizing the need for early detection for effective intervention and prevention. Abdominal Aortic Calcification (AAC) is an early indicator and contributor to Atherosclerotic Cardiovascular Diseases (ASCVDs) and is commonly assessed through imaging modalities such as computed tomography (CT), X-rays, and Dual-energy X-ray Absorptiometry (DXA). Among these, lateral spine DXA scans, commonly used for osteoporosis screening, offer a cost-effective and low-radiation opportunity for opportunistic CVD risk …

Pygoscelis Penguins Prefer Different Oceanographic And Terrestrial Habitats During The Austral Summer, Katherine L. Gallagher, Rachel Herman, Katherine Walton, Michael S. Dinniman, Heather J. Lynch

OES Faculty Publications

Pygoscelis penguins interact with both terrestrial and marine environments, especially during the austral summer when they raise their chicks. Accessible ice-free ground spaces serve as locations for penguins to build colonies and successfully raise chicks while the surrounding marine environment serves as the foraging grounds for adult birds. Penguins must be able to navigate through the waters around their colonies to forage for Antarctic krill (Euphausia superba), their primary prey during the austral summer. Krill, whose distributions are driven partially by the surrounding physics and partially by their behaviors, must also be plentiful in the waters near the …

The Relative Contribution Of Deep And Shallow Benthic Sources To Iron Supply In The Ross Sea, With Specific Emphasis On The Ross Bank, Blair J.W. Greenan, Michael S. Dinniman, Dennis J. Mcgillicuddy Jr., Peter N. Sedwick, Stefanie L. Mack, Walker O. Smith Jr.

OES Faculty Publications

Summer primary productivity in the Ross Sea is limited by the availability of dissolved iron (DFe) in the euphotic zone. Previous studies have suggested that benthic sources dominate the supply of DFe to the upper water column in the southern Ross Sea polynya. The purpose of this study is to investigate whether these benthic sources are derived from shallow banks or deeper areas of the continental shelf. Our study combines analysis of in situ observations near Ross Bank with a physical ocean model simulation in an overlapping summertime period to investigate the vertical supply of DFe. Two passive tracer dyes …

Sandy Subterranean Estuaries Minimize Groundwater Nitrogen Pollution Impacts On Coastal Waters, Xiaogang Chen, Isaac R. Santos, Jinzhou Du, Bochao Xu, Joseph J. Tamborski, Ding He, Neven Cukrov, Christian J. Sanders, Jianan Liu, Peiyuan Zhu, Yan Zhang, Ling Li

OES Faculty Publications

Widespread anthropogenic activities pollute groundwater that eventually seeps out to the coastal ocean. Here, we resolve nutrient transformations and fluxes in 11 sandy subterranean estuaries (STEs) with contrasting nutrient sources and development trajectories. Coastal groundwater nitrogen pollution stems from sewage discharge and land use change. Anthropogenically derived groundwater nutrient fluxes with high N/P ratios (∼170) accounted for 22%–61% of riverine inputs into China's coastal waters, providing an additional source of nutrients that can fuel coastal eutrophication and algal blooms. Sandy STEs remarkably attenuated ∼84% of nitrogen pollution, minimizing the impact of submarine groundwater discharge (SGD) on coastal water quality. Hence, …

The Δ¹³C Signature Of Dissolved Organic And Inorganic Carbon Reveals Complex Carbon Transformations Within A Salt Marsh, Meagan J. Eagle, Kevin D. Kroeger, John W. Pohlman, Joseph J. Tamborski, Zhaohui Aleck Wang, T. W. Brooks, Jennifer O. Keefe Suttles, Adrian Mann

OES Faculty Publications

Coastal wetlands have high rates of atmospheric CO₂ uptake, which is subsequently respired back to the atmosphere, stored as organic matter within flooded, anoxic soils, or exported to the coastal ocean. Transformation of fixed carbon occurs through a variety of subsurface aerobic and anaerobic microbial processes, and results in a large inventory of dissolved carbon. Carbon source and the roles of aerobic respiration, sulfate reduction, and methane cycling were evaluated within salt marsh peat and the underlying sandy subterranean estuary. There is a large increase in dissolved inorganic carbon (DIC, 7,350 ± 3,900 μmol L^-¹), dissolved organic carbon …

Dust Deposition To The Sargasso Sea: A Comparison Of Estimates Using Aluminum In The Surface Ocean Versus Aerosols And Rainwater, T. E. Williams, P. N. Sedwick, B. M. Sohst, K. N. Buck, S. Caprara, R. J. Johnson, D. C. Ohnemus, J. A. Resing, L. E. Sofen, A. Tagliabue, B. S. Twining

OES Faculty Publications

As a major component of mineral aerosol that partially dissolves in seawater, aluminum (Al) has been used to estimate time-averaged dust fluxes to the ocean, based on dissolved aluminum (DAl) concentrations and an assumed replacement time of DAl (tau DAl) in the surface mixed layer, along with the fractional solubility (Als) and mass abundance of Al in dust. We apply this method in the Bermuda region using seasonal water-column DAl concentrations, and compare our results with fluxes estimated from Al measured in Bermuda aerosols and rain. Inventories of DAl over the maximum mixed-layer depth of 200 m and empirically derived …

From Wind To Whales: Potential Hydrodynamic Impacts Of Offshore Wind Energy On Nantucket Shoals Regional Ecology, Eileen E. Hofmann, Jeffrey R. Carpenter, Qin J. Chen, Josh T. Kohut, Richard L. Merrick, Erin L. Meyer-Gutbrod, Douglas P. Nowacek, Kaustubha Raghukumar, Nicholas R. Record, Kelly Oskvig

OES Faculty Publications

The National Academy of Sciences, Engineering, and Medicine convened a committee in June 2023 to assess the potential hydrodynamic and ecological impacts from offshore wind energy development in the Nantucket Shoals region, with particular attention to impacts on the critically endangered North Atlantic right whale (Eubalaena glacialis) that forages on zooplankton aggregations in the region. The assessment suggested that the effects of offshore wind energy development will be difficult to distinguish from the effects of natural variability and climate change in this region. The Consensus Study Report recommendations highlight observational and modeling studies that will advance understanding of …

High Antarctic Coastal Productivity In Polynyas Revealed By Considering Remote Sensing Ice-Adjacency Effects, Hilde Oliver, Jessica S. Turner, Alexandre Castagna, Henry Houskeeper, Heidi Dierssen

OES Faculty Publications

Ocean color-based estimates of Antarctic net primary productivity (NPP) have indicated low nearshore productivity in ice-adjacent waters, contrasting with coupled physical–biogeochemical models. To understand this discrepancy, we assessed satellite records of polynya NPP by comparing field data with two satellite imagery datasets derived using different processing schemes. Our results indicate historical underestimation of chlorophyll a for imagery obtained using default atmospheric correction processing within approximately 100 km of ice-covered coastlines due to adjacency effects. Using radiative transfer modeling, we find that biases in ocean color polynya observations due to adjacency effects correspond to the high albedo of ice and snow. …

Dynamics Of Water Mass Exchange Across The Central Ross Sea Slope, Yuanjie Chen, Zhaoru Zhang, Pasquale Castagno, Xianxian Han, Michael S. Dinniman, Zhiqiang Liu, Chuning Wang

OES Faculty Publications

The central Ross Sea slope (CRSS) is a critical region for water mass exchange, significantly influencing the physical and biological processes of the Ross Sea shelf and the global overturning circulation. This study utilizes a high-resolution ocean-ice shelf-sea ice coupled model to investigate the mechanisms driving this exchange, involving the onshore transport of circumpolar deep water (CDW) and the offshore transport of dense shelf water (DSW). Combining numerical simulations and mooring observations, this work reveals high-frequency oscillations with a periodicity of ∼32 hr in CDW transport induced by Topographic Rossby Waves and DSW transport induced by cyclonic eddies. These findings …

Elucidating The Role Of Marine Benthic Carbon In A Changing World, Cristina Schultz, Jessica Y. Luo, Damian C. Brady, Robinson W. Fulwiler, Matthew H. Long, Colleen M. Petrik, Jeremy M. Testa, Heather M. Benway, David Burdige, Marta M. Cecchetto, Isa Elegbede, Natalya Evans, Alexandra Frenzel, Kayla Gillen, Lisa C. Herbert, Heidi K. Hirsh, Gennadi Lessin, Lisa Levin, Kanchan Maiti, Sairah Malkin, Sarah L. Mincks, Stanley Nmor, Anh Pham, James Pinckney, Christophe Rabouille, Shaily Rahman, Subhadeep Rakshit, Nicholas E. Ray, Dalton K. Sasaki, Samantha A. Siedlecki, Christopher Somes, Aron Stubbins, Olivier Sulpis, Cleuza Trevisan, Yiyang Xu, Hang Yin

OES Faculty Publications

The ocean plays a major role in controlling atmospheric carbon at decadal to millennial timescales, with benthic carbon representing the only geologic-scale storage of oceanic carbon. Despite its importance, detailed benthic ocean observations are limited and representation of the benthic carbon cycle in ocean and Earth system models (ESMs) is mostly empirical with little prognostic capacity, which hinders our ability to properly understand the long-term evolution of the carbon cycle and climate change-related feedbacks. The Benthic Ecosystem and Carbon Synthesis (BECS) working group, with the support of the US Ocean Carbon & Biogeochemistry Program (OCB), identified key challenges limiting our …

Common Era Time-Transgressive Forcing Of Caribbean Water Balance, Peter J. Van Hengstum, Shawna N. Little, Richard M. Sullivan, Jeffrey P. Donnelly, Tyler S. Winkler, Anne E. Tamalavage, Patricia A. Beddows, Patricia L. Fall, Jiabi Du, Kaustubh Thirumalai, Nancy A. Albury, Sloan Coats

OES Faculty Publications

Tropical North Atlantic sea surface temperatures and low-latitude rainfall covary, but prehistoric subtropical rainfall records are often misaligned. Here, a submarine groundwater discharge record from the northern Bahamas archives regional water balance in the northeastern Atlantic Warm Pool. We compare the reconstruction to the tropical North Atlantic seasonal temperature gradient, which can help inform how northeastern Caribbean rainy seasons are influenced by the Atlantic Warm Pool. A positive water balance in the northern Bahamas aligned with a ~0.9 °C seasonal temperature gradient from 0 to 950 CE, with both covarying on multi-decadal timescales. Aridity began at ~950 CE when a …

Embscu, Mariia Khan, Jumana Abu-Khalaf, David Suter, Bodo Rosenhahn, Yue Qiu, Yuren Cong

Research Datasets

This dataset was created for the evaluation of the EmbSCU method, suitable for solving the Scene Change Understanding (SCU) task. The SCU task involves predicting a changed location, describing a change, and generating language instructions for the robotic agent to revert a change. Current datasets, related to scene change understanding, can be divided into scene change detection (SCD) and image difference captioning (IDC) datasets. Unlike existing approaches, EmbSCU facilitates simultaneous change detection, description and language-based rearrangement instruction generation for the agent to revert changes. Although the EmbSCU dataset is simulated, it is highly complex, incorporating 104 unique indoor Ai2Thor rooms. …

M3t, Mariia Khan, Jumana Abu-Khalaf, David Suter, Bodo Rosenhahn, Yue Qiu, Yuren Cong

Research Datasets

For embodied agents, such as robots, tracking objects in their surroundings through visual observation is essential — a task, referred to as Visual Object Tracking (VOT). For instance, during a rearrangement task, a robot may need to track objects, as part of the scene change understanding process, to accurately restore them to their original states. Classic Multiple Object Tracking (MOT) datasets typically focus on tracking moving, single-class object instances in a video from a fixed viewpoint, limiting their applicability to embodied AI tasks. In embodied AI tasks, objects belong to multiple classes, are often static, and are observed from continuously …

Saom, Mariia Khan

Research Datasets

The SAOM dataset is created for the evaluation of the whole-object semantic segmentation in embodied AI indoor environments. The SAOM dataset is tailored for segmentation in dynamic embodied environments, focusing on interactable objects. It includes 54 object classes, all of which are either `pickupable’, `openable’, or `receptacles`. Unlike static-object datasets, the objects in SAOM can undergo transformations, such as being opened, closed, or moved.

The Future Of Shipping At The Biodiversity-Climate Nexus : Round Table Report, Mary S. Wisz, Fabio Berzaghi, Mikaela Gomez, Maud C. Quinzin, Renis Auma Ojwala, Emily Nuckolls

Reports

This report presents the outcomes of a cross-disciplinary Round Table hosted by the World Maritime University (WMU) from May 13-15, 2024, in Malmö, Sweden. The event brought together 51 experts from academia, industry, NGOs, and international organizations, including the IMO, to explore shipping’s role at the biodiversity-climate nexus. The Round Table had a particular focus on the impacts of shipping that are currently unregulated or under-regulated, and opportunities to address biodiversity loss and pollution as the sector decarbonizes. The report highlights the need to apply comprehensive, ecosystem-based approaches to effectively manage shipping's impacts, including cumulative impacts, on the environment, people, …

The Arctic Ocean Region : Biodiversity, Governance And Protective Measures, Antonios Andreadakis, Graeme Chesters, Dimitrios Dalaklis, 90 North Foundation

Reports

EXECUTIVE SUMMARY

• The Arctic Ocean region stands at the forefront of global environmental transformation, serving as a harbinger of climate change and a frontier for emergent geopolitical and economic interests.

• The Arctic region's economic potential, particularly in shipping and resource extraction, appears to offer shorter trade routes, reduced fuel consumption, and increased access to high-value resources. However, this analysis largely ignores the complex geo-physical, environmental and geographical limitations of the region.

• The fragility of Arctic Ocean ecosystems – characterized by slow ecological recovery, specialised biodiversity, and extreme environmental conditions – renders them especially vulnerable to anthropogenic disturbances. …

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 …

Learning Paradigms For Rhythm Detection And Generation Using Mathematical Models, Biophysical And Artificial Neural Networks, Prianka Bose

Dissertations

Humans possess an inherent ability to recognize evenly-spaced rhythms, known as isochronous rhythms, owing to the brain's predisposition to entrain to external auditory stimuli with regular temporal intervals. The central focus of this research is to understand how the brain learns and retains rhythmic time intervals in the context of music. This dissertation studies rhythm detection and generation through mathematical models, biophysical networks, and artificial neural networks, addressing both isochronous and non-isochronous patterns.

A primary focus of the thesis is on isochronous rhythms. In particular, given a perturbation to an isochronous rhythm such as a tempo change or phase shift …

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 …

Determination Of Electrochemical Parameters For Predicting Reaction Mechanism And Algorithmic Approaches To Pain Assessment, Huize Xue

Dissertations

This dissertation introduces novel advancements in electrochemical kinetics and pain assessment, structured into two main parts. The first part focuses on the comprehensive analysis of the kinetic and mechanistic aspects of electrochemical reactions, utilizing a combination of experimental techniques and simulation methods. A new software tool, Envismetrics, was developed using Python to facilitate the analysis of complex electrochemical data, including cyclic voltammetry (CV), chronoamperometry (CA), and hydrodynamic voltammetry (HDV). The software was rigorously tested and validated with well-characterized redox systems such as the ferricyanide/ferrocyanide couple, dimethylamine borane (DMAB), and Per- and Polyfluoroalkyl Substances (PFAS). It was successfully used to determine …

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 …