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Articles 1 - 5 of 5
Full-Text Articles in Other Computer Engineering
Enhancing Information Architecture With Machine Learning For Digital Media Platforms, Taylor N. Mietzner
Enhancing Information Architecture With Machine Learning For Digital Media Platforms, Taylor N. Mietzner
Honors College Theses
Modern advancements in machine learning are transforming the technological landscape, including information architecture within user experience design. With the unparalleled amount of user data generated on online media platforms and applications, an adjustment in the design process to incorporate machine learning for categorizing the influx of semantic data while maintaining a user-centric structure is essential. Machine learning tools, such as the classification and recommendation system, need to be incorporated into the design for user experience and marketing success. There is a current gap between incorporating the backend modeling algorithms and the frontend information architecture system design together. The aim of …
Insights Into Cellular Evolution: Temporal Deep Learning Models And Analysis For Cell Image Classification, Xinran Zhao
Insights Into Cellular Evolution: Temporal Deep Learning Models And Analysis For Cell Image Classification, Xinran Zhao
Master's Theses
Understanding the temporal evolution of cells poses a significant challenge in developmental biology. This study embarks on a comparative analysis of various machine-learning techniques to classify cell colony images across different timestamps, thereby aiming to capture dynamic transitions of cellular states. By performing Transfer Learning with state-of-the-art classification networks, we achieve high accuracy in categorizing single-timestamp images. Furthermore, this research introduces the integration of temporal models, notably LSTM (Long Short Term Memory Network), R-Transformer (Recurrent Neural Network enhanced Transformer) and ViViT (Video Vision Transformer), to undertake this classification task to verify the effectiveness of incorporating temporal features into the classification …
Machine Learning For Electronic Structure Prediction, Shashank Pathrudkar
Machine Learning For Electronic Structure Prediction, Shashank Pathrudkar
Dissertations, Master's Theses and Master's Reports
Kohn-Sham density functional theory is the work horse of computational material science research. The core of Kohn-Sham density functional theory, the Kohn-Sham equations, output charge density, energy levels and wavefunctions. In principle, the electron density can be used to obtain several other properties of interest including total potential energy of the system, atomic forces, binding energies and electric constants. In this work we present machine learning models designed to bypass the Kohn-Sham equations by directly predicting electron density. Two distinct models were developed: one tailored to predict electron density for quasi one-dimensional materials under strain, while the other is applicable …
Stock Price Trend Prediction Using Emotion Analysis Of Financial Headlines With Distilled Llm Model, Rithesh H. Bhat
Stock Price Trend Prediction Using Emotion Analysis Of Financial Headlines With Distilled Llm Model, Rithesh H. Bhat
Computer Science and Engineering Theses
Capturing the volatility of stock prices helps individual traders, stock analysts, and institutions alike increase their returns in the stock market. Financial news headlines have been shown to have a significant effect on stock price mobility. Lately, many financial portals have restricted web scraping of stock prices and other related financial data of companies from their websites. In this study we demonstrate that emotion analysis of financial news headlines alone can be sufficient in predicting stock price movement, even in the absence of any financial data. We propose an approach that eliminates the need for web scraping of financial data. …
Exploring Machine Learning Techniques For Embedded Hardware, Neel R. Vora
Exploring Machine Learning Techniques For Embedded Hardware, Neel R. Vora
Computer Science and Engineering Theses
This thesis delves into the intricate symbiosis between machine learning (ML) methodologies and embedded hardware systems, with a primary focus on augmenting efficiency and real-time processing capabilities across diverse application domains. It confronts the formidable challenge of deploying sophisticated ML algorithms on resource-constrained embedded hardware, aiming not only to optimize performance but also to minimize energy consumption. Innovative strategies are explored to tailor ML models for streamlined execution on embedded platforms, with validation conducted across various real-world application domains. Notable contributions include the development of a deep-learning framework leveraging a variational autoencoder (VAE) for compressing physiological signals from wearables while …