Publications by Avery Holloman
Crowd Movement Prediction Modeling Pedestrian Dynamics Using Agent-Based Simulation
Crowd Movement Prediction Modeling Pedestrian Dynamics Using Agent-Based Simulations In my recent analysis focused on predicting crowd movement in urban environments, I utilized agent-based simulations to model pedestrian dynamics effectively. My aim was to enhance emergency response strategies and improve urban planning by anticipating crowd b...
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Spatial Analysis of Traffic Accident Clusters in San Francisco
Spatial Analysis of Traffic Accident Clusters in San Francisco In my recent project, I embarked on a fascinating journey to analyze spatial patterns of traffic accidents in San Francisco using advanced statistical tools and geographical data handling techniques. My primary aim was to identify clusters of accidents, which could potentially info...
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K-means Clustering
K-means Clustering When I looked at the results from the K-means clustering of my data with K values of 2, 3, and 4, I noticed some interesting patterns and distributions. I particularly focused on how well each cluster was defined and how much overlap there was between clusters in different scenarios. For K=2, the division was quite clear, spl...
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Analyzing Fish Migration Patterns Using K-means Clustering
Analyzing Fish Migration Patterns Using K-means Clustering When I embarked on the project to analyze fish migration patterns using K-means clustering, my main objective was to pinpoint common routes and crucial gathering spots for fish populations during their migrations. This analysis is pivotal as it sheds light on the environmental influenc...
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Hierarchical Clustering Analysis of Simulated Carbon Sequestration Data
My Analysis of Carbon Sequestration Patterns Using Hierarchical Clustering In my recent study on carbon sequestration, I was driven by the need to understand how different regions contribute to carbon storage. My main goal was to map out the effectiveness of various ecosystems or forest types in sequestering carbon, which is pivotal for craftin...
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Matrix Completion for Aircraft Component Stress
Matrix Completion for Aircraft Component Stress In my recent work on aircraft component stress, I employed matrix completion techniques to address missing data issues, a common challenge in sensor-derived datasets. This approach, particularly using soft imputation, is pivotal because it allows me to fill in gaps that occur due to sensor failure...
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Analyzing Simulated Data on Coral Reef Health
Analyzing Simulated Data on Coral Reef Health In my study on the impact of ocean acidification on coral reefs, I simulated a large dataset to analyze how increased CO2 levels influence various health indicators of coral ecosystems. This simulated data includes key variables such as calcium carbonate levels, algae cover, fish diversity, coral bl...
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Advantages and Disadvantages of Decision Trees in Gene Expression Analysis
Advantages and Disadvantages of Decision Trees in Gene Expression Analysis In my exploration of statistical learning methods for gene expression analysis, I’ve delved into various techniques, including decision trees. Decision trees have been particularly intriguing due to their simplicity and direct approach to modeling complex biological da...
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Bagging and Random Forests Performance on IoT Sensor Data
Bagging and Random Forests Performance on IoT Sensor Data I’m focusing on leveraging IoT sensor data to optimize energy consumption in urban areas. This approach mirrors some concepts from ensemble methods like bagging and random forests, known for their robustness in predictive accuracy, which is essential when dealing with complex urban en...
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Principal Components Analysis in Crime Pattern Analysis
When I analyzed the PCA plot of the simulated crime data, I noticed clear patterns related to urbanization and crime rates. The First Principal Component (PC1) captured the majority of the variance in the data, and I saw it was heavily influenced by variables like UrbanPop, AssaultRate, and RapeRate. This told me that urban areas are strongly ...
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