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Articles 3751 - 3780 of 291657

Full-Text Articles in Physical Sciences and Mathematics

A Sticky Situation: Mannose Glycosides, Morgan Romanski, Scott Hasty Apr 2026

A Sticky Situation: Mannose Glycosides, Morgan Romanski, Scott Hasty

2026 Student Academic Showcase

Mannose chemistry is notoriously difficult due to the electronic structure of the molecule. The dipole moments on the molecule provide stability, meaning adding and removing substituents come with many obstacles. This results in numerous byproducts, prevents reactions from going quickly, or prevents the reaction from occurring at all. In this project, the issue of producing a mannose molecule with the desired leaving group for glycosylations is investigated using two different methods of synthesis of attaching 2-mercaptopyridimine to a benzylated mannose on carbon 1. These two methods investigate and aim to solve the issue of epoxide formation and the mannose molecule …

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Complete Synthesis Of A Beta-Linked Disaccharide, Abby Dunn Apr 2026

Complete Synthesis Of A Beta-Linked Disaccharide, Abby Dunn

2026 Student Academic Showcase

The continual need to develop strategies and methods for oligosaccharide synthesis drives carbohydrate chemists to discover new glycosides. This push has led to the discovery of an attractive 6-methylpyrid-2-yl leaving group for chemical glycosylation. Presented are three syntheses of the molecules 2-mercapto-6-methylpyridine, glycosyl donor 6-methylpyrid-2-yl-2,3,4,6-O-acetyl-1-thio-β-D-glucopyranoside, and glycosyl acceptor methyl-2,3,4-O-benzyl-α-D-glucopyranoside. The 2-mercapto-6-methylpyridine molecule was affixed to the requisite sugar to produce the glycosyl donor, 6-methylpyrid-2-yl-2,3,4,6-O-acetyl-1-thio-β-D-glucopyranoside. This sugar was then coupled with methyl-2,3,4-O-benzyl-α-D-glucopyranoside using silver triflate (AgOTf) to obtain the targeted disaccharide.

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Theory And Simulations Of Delayed Stochastic And Deterministic Models Of Prion Diseases, Gangadhara Boregowda, Omar Sharif, Daniel Gutierrez Iii, Allegra Simmons, Laurent Pujo-Menjouet, Tamer Oraby, Michael R. Lindstrom Apr 2026

Theory And Simulations Of Delayed Stochastic And Deterministic Models Of Prion Diseases, Gangadhara Boregowda, Omar Sharif, Daniel Gutierrez Iii, Allegra Simmons, Laurent Pujo-Menjouet, Tamer Oraby, Michael R. Lindstrom

School of Mathematical & Statistical Sciences Faculty Publications

Neurodegenerative diseases (NDs), such as Alzheimer’s, Parkinson’s, and prion diseases, are characterized by the dynamical spread of toxic proteins through the brain. In prion diseases, cellular prion protein (PrPC), produced by neurons, misfolds into a toxic form, known as scrapie prion protein (PrPSc). PrPSc induces neuronal stress which ultimately leads to cell death. In this paper, we develop mathematical models for the progression of prion diseases, incorporating a cellular defense mechanism that introduces a delay term affecting protein translation and a volatility term accounting for unaccounted biological factors influencing the system. We also extend the model to capture the spatial …

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Re: Approval Letter For The Butte Priority Soils Operably Unit (Bpsou) Revised Draft Final Quarterly Operations And Maintenance Report: Butte Treatment Lagoon System (Btl) Second Quarter 2025 (Dated April 10, 2026), Emma Rott Apr 2026

Re: Approval Letter For The Butte Priority Soils Operably Unit (Bpsou) Revised Draft Final Quarterly Operations And Maintenance Report: Butte Treatment Lagoon System (Btl) Second Quarter 2025 (Dated April 10, 2026), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

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Mcgehee Blow Up And Collision Manifold Of Planar (2+2)-Body Problem, Nathaniel Scott Sill Apr 2026

Mcgehee Blow Up And Collision Manifold Of Planar (2+2)-Body Problem, Nathaniel Scott Sill

Theses and Dissertations

This thesis analyzes the dynamics of the Planar (2+2)-Body Problem which consists of two asteroids moving under the gravitational force of each other and of two larger primaries. We use the McGehee blow up technique to remove the singularities in the dynamics associated with a triple collision with one of the primaries by introducing a new set of variables. Additional variables are introduced to reduce the dimension of the problem. We then derive the dynamics for these new variables. We then use the energy relation that comes from the original Hamiltonian to describe the collision manifold which is pasted in …

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Synthesis, Structural Elucidation, In Vitro Antibacterial Activity Of Mononuclear Ag(I) Complex Derived From (E)-N-(4-Fluorophenyl)-1-(Pyridin-2-Yl) Methanimine And Triphenylphosphine Ancillary Ligand., Olufemi Stephen Odulaja, Saliu Alao Amolegbe, Muritala Adeniyi Olusola Apr 2026

Synthesis, Structural Elucidation, In Vitro Antibacterial Activity Of Mononuclear Ag(I) Complex Derived From (E)-N-(4-Fluorophenyl)-1-(Pyridin-2-Yl) Methanimine And Triphenylphosphine Ancillary Ligand., Olufemi Stephen Odulaja, Saliu Alao Amolegbe, Muritala Adeniyi Olusola

Tanzania Journal of Science

Chemo-therapeutic application of metal complexes to remediation and subjugation of emerging infectious diseases with a view to improving potency and efficacy of a variety of drugs is gaining more momentum, especially in the 21st century. This research work designed a new biologically active complex [AgL(PPh3)2]NO3,C, obtained from the reaction of Ag(I) nitrate with bidentate pyridinyl Schiff base ligand (E)-N-(4-fluorophenyl)-1-(pyridin-2-yl) methanimine L, with triphenylphosphine (PPh3) as co-ligand. Characterisation was done by FT-IR, UV-Vis, NMR, (TGA/DTA), X-ray crystallography, and elemental analysis. The combined effects of pyridinyl Schiff base ligand and PPh3 on the antibacterial activities against Staphylococcus aureus, Escherichia coli, Klebsiella pneumonia, …

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Optimization Of Pediatric Multidetector Ct Imaging Parameters Using A Machine Learning–Based Monte Carlo Simulation Model, Ali O. Masoud, Adithya Rajnaryanan, Khamis O. Amour, Ahmed M. Jusabani, Justin E Ngaile, Manoj Kumar, Mwingereza John Kumwenda Dr Apr 2026

Optimization Of Pediatric Multidetector Ct Imaging Parameters Using A Machine Learning–Based Monte Carlo Simulation Model, Ali O. Masoud, Adithya Rajnaryanan, Khamis O. Amour, Ahmed M. Jusabani, Justin E Ngaile, Manoj Kumar, Mwingereza John Kumwenda Dr

Tanzania Journal of Science

This study utilized Monte Carlo (MC) simulations to optimize radiation doses in pediatric multidetector computed tomography (MDCT) head scans by analyzing key parameters like tube current (mA), tube voltage (kV), pitch, and slice thickness. The findings indicate that reducing tube current significantly lowers the Computed Tomography Dose Index (CTDIvol) and Dose Length Product (DLP), effectively minimizing patient radiation exposure. Higher pitch values (0.7–0.9) further reduced radiation by decreasing beam overlap, while using a thinner slice thickness (0.6 mm) improved dose efficiency. A comparison highlighted the effectiveness of optimization: simulated parameters kVp 100, mAs 81, pitch 0.98 yielded a CTDIvol of …

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Differentiable Objectives For 3d Scene Relighting Via Gradient Descent On Olat Basis Coefficients, Anson Savage Apr 2026

Differentiable Objectives For 3d Scene Relighting Via Gradient Descent On Olat Basis Coefficients, Anson Savage

Theses and Dissertations

Designing effective lighting is an iterative and often time-consuming process. This work contributes to automatic lighting design research by presenting a render-engine agnostic optimization routine: gradient descent on RGB multipliers of one-light-at-a-time (OLAT) basis images. We compare several objective functions to accomplish lighting tasks and show that our method is capable of quickly and effectively exploring different lighting styles using either text prompts or reference images. We also present several datasets specific to lighting tasks and show that fine-tuning on these datasets can improve performance.

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Grp-02-199 Topological Constraints For Protein Folding, Charles Fanning Apr 2026

Grp-02-199 Topological Constraints For Protein Folding, Charles Fanning

C-Day Computing Showcase

We study whether topological loss-based constraints improve multidomain whole-chain protein structure prediction beyond the ColabFold baseline by better preserving the topologies of folded proteins. We benchmark against Wasserstein metrics with our own virtual persistence and RKHS semi-metric constraints as well as higher-order virtual persistence diagrams.

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Uc-158-205 Scrappyfin — Ind, Simulated Digital Wallet &​ Fraud Detection Platform, Logan Davis, Dante Galvan, Zahaira Jordan, Avery Bracey, Chris Pham Apr 2026

Uc-158-205 Scrappyfin — Ind, Simulated Digital Wallet &​ Fraud Detection Platform, Logan Davis, Dante Galvan, Zahaira Jordan, Avery Bracey, Chris Pham

C-Day Computing Showcase

ScrappyFin is an educational FinTech platform developed in partnership with The Home Depot to simulate a digital wallet system and demonstrate fraud detection techniques. Users can create virtual wallets and perform synthetic transactions in a controlled environment, enabling analysis of financial behavior without real risk. The platform combines rule-based logic with machine learning models to identify suspicious activity, such as unusual transaction amounts or patterns. An admin dashboard provides clear explanations for flagged transactions. ScrappyFin showcases how modern financial systems detect risk while serving as a practical learning tool for software engineering and data-driven applications.

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Uc-152-211 Ccse Capstone Meeting Intelligence Platform, Calvin Crose, Noah Enyart, Marcus Johnson, Jaeden Jones, Jonah Smith Apr 2026

Uc-152-211 Ccse Capstone Meeting Intelligence Platform, Calvin Crose, Noah Enyart, Marcus Johnson, Jaeden Jones, Jonah Smith

C-Day Computing Showcase

The CCSE Capstone Meeting Intelligence Platform is a web-based monitoring system designed to assist CCSE leadership in overseeing a large variety of capstone projects. Currently, the CCSE leadership handles the overseeing of projects manually, either with advisors sitting in on student-client meetings or by reviewing recordings, which can lead to problems where potential red flags are unidentified. To help manage this, the system processes Microsoft Teams meeting transcripts and analyzes them using a Large Language Model (LLM) combined with Retrieval Augmented Generation (RAG) techniques. It then will identify potential project risks like scope creep, conduct concerns, and deviations from project …

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Uc-167-222 Secu Horizon, Julian Duarte, Chance Boecker, Adam Martin, Rylan Collins, Oliver Haggard Apr 2026

Uc-167-222 Secu Horizon, Julian Duarte, Chance Boecker, Adam Martin, Rylan Collins, Oliver Haggard

C-Day Computing Showcase

Secu Horizon is a fast-paced, stealth-based 3d action platformer, where you run around in a dystopian city. The main mechanic of the game revolves around a knife projectile that the player throws and teleports to. The game will have a strong mix of smooth platforming, soft stealth sections, and bullet time combat, as the player throws around the knife to defeat enemies. The feeling that this game will invoke is that of being an unstoppable rebel ninja, in the cold dead of the night.

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Uc-151-197 Nest: An Ai-Powered Transition Navigator For Aging-Out Foster Youth In Georgia, Stephen Sookra, Tylin Delaney, Brenden Bryant Apr 2026

Uc-151-197 Nest: An Ai-Powered Transition Navigator For Aging-Out Foster Youth In Georgia, Stephen Sookra, Tylin Delaney, Brenden Bryant

C-Day Computing Showcase

Each year, approximately 600–700 young people age out of the Georgia foster care system with no permanent family, no housing plan, and no clear guide beyond a 250‑page state transition PDF. The outcomes are severe: high rates of homelessness, low college completion, and unstable employment. Nest is an AI‑powered, mobile‑first web application that turns this overwhelming bureaucracy into a personalized 90‑day transition plan generated in under 60 seconds. Through a short conversational intake, the system collects a youth’s age, county, housing status, and education or work goals, then uses a deterministic rules engine to determine likely eligibility for key programs …

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Uc-149-185 Ai-Assisted Media Organization And Intake System, Meilun Wu, Nevaeh Branham, Isaiah Higgins, Claude Kangni, Fatima Ahmed Apr 2026

Uc-149-185 Ai-Assisted Media Organization And Intake System, Meilun Wu, Nevaeh Branham, Isaiah Higgins, Claude Kangni, Fatima Ahmed

C-Day Computing Showcase

This project includes the design and validation of a metadata-driven media intake and organization system implemented for the Office of the District Attorney, Cobb Judicial Circuit. The institution produces a considerable volume of photographs and video content via outreach programs and community interaction initiatives. Yet, the organization does not have an organized framework for managing media files, which results in inefficient file retrieval and data loss over time due to a lack of standardized organizational practices. In this regard, this project aims to develop a workflow-based system for the efficient organization of media files that will be powered by a …

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Uc-139-175 Agentic Debugger And Documenter, Olaoluwa Omodemi, Jade Le, Preston Dietz Apr 2026

Uc-139-175 Agentic Debugger And Documenter, Olaoluwa Omodemi, Jade Le, Preston Dietz

C-Day Computing Showcase

Modern software development teams routinely introduce subtle bugs — mutable default arguments, bare exception handlers, insecure eval()/exec() calls, resource leaks, hardcoded secrets — that escape manual review but accumulate into technical debt and security risk. Existing linters identify problems but leave remediation to the developer. This project investigates whether a coordinated multi-agent system, combining rule-based static analysis with generative LLM reasoning, can autonomously detect, fix, and document such issues with no developer involvement beyond providing the input file.

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Ur-160-200 Tortured Artist, Caitlin Tigani, Ben Scholl, Adam Tucker, Anaiya Tucker Apr 2026

Ur-160-200 Tortured Artist, Caitlin Tigani, Ben Scholl, Adam Tucker, Anaiya Tucker

C-Day Computing Showcase

You are a photographer that wants to move out, so you take pictures of your house to give to your real- estate agent. However, as you are developing the photos you hear a noise that makes you turn on the lights, ruining your photos. Now you must retake the photos before morning, but something around the house has changed. Rooms are no longer in the right place, items are moved around, doors are locked, and an entity is watching you. Will you find the secrets within the puzzles or be left tortured?

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Ur-147-188 Staged Multi-Modal Alzheimer Classification Using Uncertainty Quantification, Branden Chen, Ethan Litton, Long Doan Apr 2026

Ur-147-188 Staged Multi-Modal Alzheimer Classification Using Uncertainty Quantification, Branden Chen, Ethan Litton, Long Doan

C-Day Computing Showcase

Diagnosing Alzheimer’s disease often depends on costly neuroimaging techniques such as MRIs and PET scans, which are not always accessible and can place a significant financial burden on healthcare systems. Existing clinical workflows lack a reliable way to determine which patients truly require these advanced tests, resulting in either unnecessary imaging or delayed and inaccurate diagnoses. To address this challenge, we propose the Uncertainty-Driven Dual-view (UDD) model, a multi-stage framework that integrates low-cost clinical and structural data with uncertainty-aware learning. The model first generates predictions using accessible data and quantifies its confidence, referring only high-uncertainty cases for further evaluation with …

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Ur-084-219 Towards Bounding The Behavior Of Neural Networks, Emmanuel Nwankwo Apr 2026

Ur-084-219 Towards Bounding The Behavior Of Neural Networks, Emmanuel Nwankwo

C-Day Computing Showcase

Modern neural networks are typically considered black-box systems: while they are able to achieve state-of-the-art performance in many domains, it is difficult to elicit the reasons behind their decisions. From this, a sub-field of artificial intelligence called eXplainable Artificial Intelligence (XAI) arose to fill this gap. One approach to XAI is based on the symbolic compilation of a neural network's behavior to a logical formula. However, such approaches are limited in scalability, due to the fundamental difficulty of the problem. This research instead proposes an incremental and anytime approach to explaining the behavior of a neural network, for image recognition. …

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Grp-125-144 Mapping The Affordances Of Human-Ai Interaction: A Large-Scale Text Mining And Statistical Analysis Of Llm Usage Patterns, Anil Vallepu Apr 2026

Grp-125-144 Mapping The Affordances Of Human-Ai Interaction: A Large-Scale Text Mining And Statistical Analysis Of Llm Usage Patterns, Anil Vallepu

C-Day Computing Showcase

People increasingly communicate with AI for schoolwork, office tasks, and daily needs. This study investigates the affordances of human-AI interaction using modern text mining and statistical analysis on the WildChat-1M dataset of over 1.1 million real-world ChatGPT user conversation logs. We apply BERTopic to extract latent interaction topics, compute a probabilistic topic-document matrix P(T|D), and perform rigorous statistical testing including Welch’s T-test and ANOVA to compare affordance patterns between GPT-3.5 and GPT-4.0 Results reveal that creative writing, Coding, message drafting and many interesting topics are the dominant affordances, while a spatio-temporal trend analysis maps how interaction patterns evolve globally over …

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Grm-134-128 Neurovision: Mapping Brain Signals To Language And Visual Meaning, Siri Yellu Apr 2026

Grm-134-128 Neurovision: Mapping Brain Signals To Language And Visual Meaning, Siri Yellu

C-Day Computing Showcase

NeuroVision presents a unified framework for mapping electroencephalography (EEG) signals to language and visual meaning. Extracting semantic information from EEG remains a fundamental challenge due to its low signal-to-noise ratio, high dimensionality, and inter-subject variability. To address these challenges, we propose a multimodal representation learning framework that aligns EEG signals with both textual and visual embeddings through temporal modeling, spatial brain-region decomposition, and contrastive learning. The framework integrates self-supervised pretraining with supervised multimodal alignment to learn robust and transferable representations. Experimental results demonstrate BLEU-1 of 0.1106 and ROUGE-1 of 0.1493 for EEG-to-text generation, alongside a 52% improvement in retrieval performance …

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Grp-148-223 Uncertainty-Guided Conservative Propagation For Robust Coronary Artery Segmentation, Huan Huang, Chen Zhao Apr 2026

Grp-148-223 Uncertainty-Guided Conservative Propagation For Robust Coronary Artery Segmentation, Huan Huang, Chen Zhao

C-Day Computing Showcase

Coronary artery segmentation plays a key role in cardiovascular disease analysis, yet existing methods often produce fragmented and structurally inconsistent vessels in challenging regions. We propose an uncertainty-guided conservative propagation (UGCP) framework that improves segmentation reliability by allowing high-confidence regions to guide uncertain ones through controlled information propagation under a conservation principle. This mechanism enhances structural continuity while preventing unstable updates. Experiments on Coronary CT Angiography (CCTA) and Invasive Coronary Angiography (ICA) datasets demonstrate improved segmentation accuracy and topology preservation. Additional evaluations on other vascular datasets further suggest the generalizability of the proposed approach.

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Gc-172-139 Detection Of Sms Spam Using Transformer Bert Model, Nathan Bonner, Zachary Kandell, Michael Hayes, David Quintanilla, Leon Greenberg Apr 2026

Gc-172-139 Detection Of Sms Spam Using Transformer Bert Model, Nathan Bonner, Zachary Kandell, Michael Hayes, David Quintanilla, Leon Greenberg

C-Day Computing Showcase

This project evaluates automated SMS spam classification by comparing traditional machine learning against modern transformer architectures. We built a Bidirectional LSTM (BiLSTM) baseline using TF-IDF feature extraction and NearMiss-1 undersampling to handle severe class imbalances. We then compared this against a fine-tuned Hugging Face Sentence-BERT model. Preliminary results show Sentence-BERT significantly outperformed the BiLSTM baseline (99.01% vs. 95.65% accuracy). These findings demonstrate that transformer-based embeddings offer a highly accurate, scalable solution for spam mitigation without relying on aggressive data undersampling.

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Gc-168-220 Active Directory To Cloud Security Data Pipeline, Michael Butler, Shahiba Shamshad, Mounia Touil, Koko Afantchao Apr 2026

Gc-168-220 Active Directory To Cloud Security Data Pipeline, Michael Butler, Shahiba Shamshad, Mounia Touil, Koko Afantchao

C-Day Computing Showcase

This project builds an automated pipeline that extracts identity and asset data from on-prem Active Directory, stages it in Google BigQuery, and securely sends normalized data to Lucid through Google Cloud Run. Using PowerShell scripts, the system collects users, groups, computers, DNS, DHCP, and related metadata without changing the source environment. BigQuery serves as the staging layer for validation and processing, while Cloud Run transforms and transfers the latest data to Lucid for visualization. The goal is to provide a repeatable, traceable, and reliable workflow that improves visibility into identity relationships for security investigations, auditing, and validation in a controlled …

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Grp-120-138 Wall-E: Wide-Area Aerial Live Learning For Emergency Disaster Evaluation, Shiva Shrestha Apr 2026

Grp-120-138 Wall-E: Wide-Area Aerial Live Learning For Emergency Disaster Evaluation, Shiva Shrestha

C-Day Computing Showcase

WALL-E is an AI-powered, custom-built quadcopter that surveys disaster zones in real time, classifying building damage and generating a GPS-tagged damage map with no internet required. The system uses an RGB camera for live AI inference and a FLIR thermal camera for additional situational awareness. A custom YOLO based model runs entirely onboard the Jetson Orin Nano, logging every detection via GPS for immediate command use. Future work will expand detection capabilities to include human presence identification.

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Gc-173-232 A Surrogate Accountability Framework For Agentic Ai Systems, Crystal Tubbs Apr 2026

Gc-173-232 A Surrogate Accountability Framework For Agentic Ai Systems, Crystal Tubbs

C-Day Computing Showcase

Agentic AI systems introduce new accountability challenges because autonomous agents can act, adapt, and execute decisions without continuous human oversight. This research develops the Surrogate Accountability Framework (SAF), an architectural approach for embedding external oversight, traceability, and control directly within agentic workflows. To operationalize SAF, a working system, Chrysalis, was designed and implemented as a real time governance layer that monitors agent behavior, evaluates decision pressure, and enforces constraints through validation and intervention mechanisms. By shifting accountability from post hoc evaluation to continuous system level enforcement, this approach reduces the risk of compounding errors and enables interpretable, actionable control signals. …

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Grm-06-150 Comparative Analysis Of Deep Learning Architectures For Inpatient Mortality Prediction Using Time-Series Vital Signs, Jonathan Meurer, Andrew Philip John, Pranay Udhaya, Nyah Robinson, Dhruv Shrivastva Apr 2026

Grm-06-150 Comparative Analysis Of Deep Learning Architectures For Inpatient Mortality Prediction Using Time-Series Vital Signs, Jonathan Meurer, Andrew Philip John, Pranay Udhaya, Nyah Robinson, Dhruv Shrivastva

C-Day Computing Showcase

This project evaluates deep learning architectures for predicting inpatient mortality using time-series vital signs derived from the MIMIC-III dataset. A baseline Long Short-Term Memory (LSTM) model was reproduced and extended with Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN), and Transformer architectures. Vital signs including heart rate, respiratory rate, temperature, and systolic blood pressure were processed into fixed-length time windows for model input. Results indicate that GRU achieved the highest performance, while Transformer models underperformed due to dataset limitations. This study demonstrates the impact of model architecture on clinical time-series prediction tasks.

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Grm-05-158 Spark Mllib Vs Python Frameworks, Austin D Klein Apr 2026

Grm-05-158 Spark Mllib Vs Python Frameworks, Austin D Klein

C-Day Computing Showcase

The original study evaluated Apache Spark MLlib on large datasets showing that it was able to outperform Weka in speed while also achieving similar accuracy scores. Our research builds upon this work by extending the analysis to compare Apache Spark MLlib with PyTorch. We will measure training time, speed, and accuracy to compare the results of each approach on similar hardware specifications as the original test. Original tests show promise, though we have only implemented one of the datasets. We will continue to implement the following datasets and improve metrics.

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Grm-179-195 A Retrieval-Augmented Generation (Rag) System For Bible Question Answering Using Scriptural Text And Commentary, Maryam Koya, Pragya Mishra Apr 2026

Grm-179-195 A Retrieval-Augmented Generation (Rag) System For Bible Question Answering Using Scriptural Text And Commentary, Maryam Koya, Pragya Mishra

C-Day Computing Showcase

This project develops and evaluates a question-answering (QA) system designed to address theological and interpretive questions about the New Testament. It uses a Retrieval-Augmented Generation (RAG) framework that integrates a pretrained large language model with a structured knowledge base consisting of public-domain Berean Standard Bible (BSB) New Testament and New Testament commentaries. User queries are embedded to retrieve semantically relevant passages, which are then supplied as contextual input for answer generation. The system is evaluated based on retrieval quality, answer faithfulness, and comparison to ground truth. Performance is benchmarked against a baseline BM25 keyword retrieval system without commentary, demonstrating that …

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Grp-07-163 Smishguard: An Ai-Powered Framework For Sms Phishing Detection And Alert System For Vulnerable Users, Jiban Krisna Das Apr 2026

Grp-07-163 Smishguard: An Ai-Powered Framework For Sms Phishing Detection And Alert System For Vulnerable Users, Jiban Krisna Das

C-Day Computing Showcase

This research introduces SMISH-GUARD, a multi-layer framework for adaptive SMS phishing (smishing) detection that integrates language-aware semantic modeling, graph-theoretic campaign reasoning, and cost-sensitive decision calibration within a unified architecture. The framework integrates dual transformer encoders for multilingual semantic understanding with a heterogeneous temporal graph layer that captures relational attack signals such as shared URLs, sender reuse, and campaign propagation patterns. A cost-sensitive decision optimization module is further incorporated to translate probabilistic model outputs into risk-aware alert policies that explicitly balance false-positive inconvenience against the higher societal and financial cost of missed smishing attacks. The study evaluates four integrated datasets comprising …

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Gc-140-126 Machine Learning Models For Solar Power Output Prediction: A Comparative Study With Adaptive Pso-Based Random Forest Tuning, Hasitha Mahabaduge Apr 2026

Gc-140-126 Machine Learning Models For Solar Power Output Prediction: A Comparative Study With Adaptive Pso-Based Random Forest Tuning, Hasitha Mahabaduge

C-Day Computing Showcase

Accurate prediction of solar power output is essential for energy scheduling, grid reliability, and efficient integration of renewable resources. Because photovoltaic generation is governed by changing atmospheric conditions, forecasting output is inherently a nonlinear learning problem. This study evaluates four machine learning models — Linear Regression, Random Forest, Multi-Layer Perceptron (MLP), and an Adaptive Particle Swarm Optimization-tuned Random Forest (Adaptive PSO-RF) — using irradiance, temperature, humidity, wind speed, cloud cover, and time-derived features drawn from a dataset of 6,738 observations. Random Forest achieved the strongest overall performance, with an RMSE of 1,816.24 and an R² of 0.9533. The Adaptive PSO-RF …

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