The FIRE Summit 2024
Permanent URI for this collectionhttp://hdl.handle.net/1903/33513
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Item AI Bias in Facial Recognition Systems(2024) Metukuru, Akhil; Movva, Vineeth; Sun, Rick; Kramarczuk, KristinaItem Bias in AI: Skin Cancer Detection(2024) Zhang, Chris; Rodrigues, Rachel; Konka, Sanjana; Azobi, Gorgeous; Kramarczuk, KristinaItem Data Augmentations on Quantum Wasserstein Generative Adversarial Networks(2024-12-11) Lee, Joey; Lai, Devon; Banerjee, Ayan; Jabeen, ShabnamThe goal of this project is to explore Quantum Wasserstein Generative Adversarial Networks (QWGANs) and address its limitations by incorporating data augmentation techniques such as Elastic Transforms and Gaussian/Poisson Noise to simulate real-world imperfections, such as noise and distortions. With this we test the robustness of the QWGAN framework and compare QWGAN performance with such data modification techniques against one anotherItem Determining the Effect of the Removal of the Amino Acid Arginine on Viral Replication in E.coli(2024) Pattasseril, Nichelle; Plass, Julia; Etemad, Kasra; Mutziger, John; Alexandrian, Andre; O'Hara, JessicaItem Development of a Cell-Free Heme Biosensor(2024) Ly, Andrew; Savage, Emily; Bansal, Navya; Herbert, Xan; Spirito, CatherineItem DNA Aptamer Against Alzheimer’s β-Amyloid 42 Protein(2024) Alcoleas, Mariana; Abasi, Keon; Munyaneza, Joseph; Kothale, Siya; Spirito, CatherineItem DNA Aptamers against Parkinson's Disease Biomarker Alpha-Synuclein(2024) Cabrera Martin, Maria; Lynch, Margaret; Marin, Andrea; Saluja, Jasmine; Spirito, CatherineItem Effects of the aceE Gene on E. coli Growth and Bacteriophage Replication(2024) Lippert, Celia; Lynskey, Julie Anne; Lee, James; Dasgupta, Ruben; O'Hara JessicaItem EL33T H4X0R5?: Fearmongering and Biases in Cybercrime Journalism(2024) Zutchi, Aria; Hao, Emily; Le, Linh; Liu, Sydney; Kramarczuk, KristinaItem Fluorescent Cell-Free Biosensor to Detect Tuberculosis(2024) Amankwah, Amma; Connolly, Isabella; Kotoulek, Klara; Sembria, Maricka; Spirito, CatherineItem Hybrid Quantum Vision Transformers for Particle Image Classification(2024) Christopher, Darwin; Mahendran, Smithi; Shah, Saloni; Tanjore, Sid; Jabeen, ShabnamItem Impact on Removal of the fabF & fabH Genes on Bacteriophage Replication in Escherichia Coli(2024) Davis, Rose; Herbolsheimer, Sebastian; Schreiber, Samuel; Trieu, Amanda; O'Hara, JessicaItem Investigating Effects of sucA Gene Knockout on E. coli Growth & Phage Replication(2024) Rakee, Ruthu; Hollander, Brooke; Rudra, Avani; Tudor, Dennis; Nadeem, Hafsah; O'Hara, JessicaItem Investigating the Effects of tyrB on Escherichia Coli and Bacteriophage Replication(2024) Soni, Aarushi; King, Connor; Schankel, Kayla; Narine, Justin; Karanth, Ritvik; O'Hara, JessicaItem Ligation LAMP Assay Targeting miRNA-155 Biomarker for Diagnosing Acute Myeloid Leukemia(2024-12-06) Berdia, Jason; Buckmon, Logan; Vitievsky, Mark; Hebbar, Soma; Spirito, Catherine; Mcdonald, Cyan; Adane, YedidyaLigation LAMP Assay Targeting miRNA-155 Biomarker for Diagnosing Acute Myeloid Leukemia Mark Vitievsky, Logan Buckmon, Soma Hebbar, Jason Berdia Acute Myeloid Leukemia (AML) is a severely dangerous form of blood cancer with high mortality rates, in part due to limitations in current diagnostic methods, which are invasive, expensive, and time-consuming. This study uses a ligation LAMP (loop-mediated isothermal amplification) assay as a possible new diagnostic tool which targets the overexpression of the miRNA-155 biomarker, which is known for its role in immune suppression in AML. The Ligation-LAMP assay offers a rapid, cost-effective, and minimally invasive alternative to the traditional methods of diagnosis, such as bone marrow biopsies or blood counts. This project evaluates the sensitivity and specificity of the Ligation LAMP assay using both fluorescent and colorimetric standards to detect varying concentrations of miRNA-155. Early findings show that higher levels of miRNA-155 correlate to faster fluorescence and colorimetric changes, in turn validating the assay’s potential for earlier AML detection. Future enhancements include the implementation of machine learning algorithms to hone in on the diagnostic thresholds, testing patient samples to address variability, and improving assay reliability. This project represents a step forward in the development of accessible and relatively inexpensive diagnostic tools for AML, providing the possibility of earlier diagnosis and improved patient livelihoods.Item Ligation Loop-Mediated Isothermal Amplification for the Detection of Prostate Cancer(2024-12-06) Hanieh, Yanar; Amanuel, Betiel; Negussie, Alex; Spirito, CatherineItem Ligation Loop-Mediated Isothermal Amplification for the Detection of Prostate Cancer(2024-12-06) Hanieh, Yanar; Amanuel, Betiel; Negussie, Alex; Spirito, CatherineItem One-Pot Ligation LAMP Assay to Detect miRNA-222: A Glioma Biomarker(2024) Pallavajjala, Roshni; Adane, Yedidya; Fernandes, Catarina; Kaiser, Jillian; Patel, Khushi; Spirito, CatherineMany cancer diagnostic methods are invasive, time-consuming, and expensive. Delayed cancer diagnosis can lower patient survival rates. PCR-based techniques that detect miRNA biomarkers in blood have been utilized as early screening tools for various cancers. As an alternative to PCR, we designed and optimized an isothermal amplification technique, Ligation Loop-Mediated Isothermal Amplification (Ligation LAMP) assay, to detect miR-222, an established biomarker that is found in elevated levels in the bloodstream of early-stage glioma patients. We designed colorimetric and fluorescent Ligation-LAMP assays and demonstrated their specificity and sensitivity in detecting miR-222. We are working on implementing our assay into a One-Pot system, using Thermally Responsive Alkane Partitions (TRAPs) and a strand displacement assay utilizing magnetic beads. We found that the Ligation LAMP assay is sensitive and specific to glioma biomarker miR-222 and different probe lengths for Strand Displacement did not have a significant impact on ligation. With these results, we can further improve the One-Pot assay to be more point-of-care.Item Quantum Optimization for Solving NP-Hard Problems(2024) Dayal, Arnav; Kalidindi, Raghava; Kosuru, Sohan; Moosavi, Miles; Jabeen, ShabnabThe University of Maryland has a lot of resources that it seeks to ensure every student has easy access to, ranging from facilities like Wi-fi to basic safety measures such as streetlights. Ensuring these resources are properly distributed amongst campus can grow to be expensive considering the University’s 1,339-acre estate. This optimization algorithm aims to minimize the resources necessary to ensure the entirety of any given area is fully encompassed by whatever facility the user desires. Quantum optimization is the ideal way to accomplish this task as classical optimizers are unable to provide as efficient of a solution due to the risk of getting trapped in local minima and the significantly weaker processing ability. The poorer performance of the classical optimizer is demonstrated in our results.Item Quantum-Enhanced Forecasting: Leveraging Quantum Gramian Angular Field And CNNs for Stock Return Predictions(2024) Bapanapalli, Abhinav; Mahmud, Ateef; Nadig, Chiraag; Thakker, Krish; Jabeen, ShabnamPredicting stock price movements is a complex challenge faced by many traders and analysts. Our research leverages Quantum Gramian Angular Field (QGAF) transformations combined with Convolutional Neural Networks (CNNs) to classify stock price trends as "up" or "down." By transforming 1D time-series stock data into 2D images, we enable CNNs to extract features more effectively, showcasing the potential of quantum machine learning in financial forecasting.