Development of Advanced Deep Learning Algorithms for Domain-Specific Challenges

Abstract

Deep learning has made significant advancements since the 1940s, utilizing large amounts of data and computational power to mimic human cognitive processes. However, current models often struggle with scalability and specificity for real-world applications in various fields. This research aims to tackle some of these challenges by developing advanced deep-learning algorithms to improve predictive analytics and decision-making across diverse domains. The entire thesis comprises four parts, each focusing on a specific challenge of deep learning. The first part focuses on improving traffic safety through geospatial intelligence. It involves refining spatial clustering techniques for urban network analysis and developing a temporal convolution model to learn spatiotemporal patterns in recent traffic accident trends across the United States. This study aims to enhance the accuracy and efficiency of traffic hotspot prediction, thereby improving traffic safety measures. The second part extends to environmental sciences, using deep learning models to predict the Leaf Area Index (LAI), a critical measure of vegetation density. Accurately forecasting LAI using globally collected data from 1982 to 2016 can help anticipate vegetation growth patterns, optimize agricultural productivity, and develop targeted conservation strategies to mitigate the adverse impacts of climate change on biodiversity and ecosystem services. The third part of our research addresses the pressing global food security challenge. We integrate cutting-edge deep learning techniques with spatiotemporal features of remote sensing data to forecast crop yields for three major crops in the United States. This project involves employing Gaussian processes to provide a probabilistic framework that captures uncertainty and enhances the prediction reliability of deep learning models. This segment emphasizes the pivotal role of sophisticated analytical techniques in boosting agricultural productivity and sustainability by providing more reliable and timely predictions of crop harvests for farmers. The fourth part of our research delves into an exploration of optimizing Graph Neural Networks (GNNs) through an innovative pruning algorithm called Iterative Gradient Rank Pruning, inspired by the Lottery Ticket Hypothesis. This approach aims to significantly reduce computational demands while maintaining robustness for large-scale graph data analysis. The integration of each research component plays a crucial role in establishing a comprehensive strategy that utilizes the power of deep learning to effectively address specific challenges. This study underscores the immense potential of deep learning to drive progress and innovation across a diverse range of domains and industries. Additionally, this dissertation outlines studies that enhance the privacy, security, and efficiency of location-based alert systems by using advanced encryption techniques.