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- Emerging areas for future research that emerged from this study include the opportunity for training and testing using our model with a larger dataset and modifying different hyperparameters for further improvement. (1)
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- This Culminating Experience Project investigated how the densenet-161 model will perform on accident severity prediction compared to proposed methods. The research questions are: (Q1) What is the impact of usage of augmentation techniques on imbalanced datasets? (Q2) How will the hyper parameter tuning affect the model performance? (Q3) How effective is the proposed model compared to existing work? The findings are: Q1. The effectiveness of our model depends on the implementation of augmentation techniques that pay attention to handling imbalanced datasets. Our dataset poses a challenge due to distribution of classes in terms of accident severity. To address this challenge directly we utilize an augmentation process that involves applying transformations to the data. By applying these transformations our aim is to create a training set. This enables our model to grasp and capture the nuances of classes resulting in enhanced prediction accuracy and improved generalization abilities. Q2. Adjusting the settings of algorithms to enhance their performance is an aspect of machine learning known as fine tuning hyperparameters. In one of our experiments (1)
- We observed an improvement with accuracy reaching 92%. This increase of 4% is quite notable. Reflects an enhancement in performance. It's clear that the densenet-161 model excels at classifying data (1)
- We successfully increased our model's accuracy by 2% (1)
- We were able to unlock the hidden potential of our algorithm and enhance its ability to identify patterns and subtle details within the data. This entire process exemplifies how meticulous fine tuning of hyperparameters can lead to advancements in machine learning outcomes. Q3. The study findings show that the current work has achieved an accuracy rate of 88%. However (1)
- When we implemented the model (1)
- Which suggests its effectiveness in applications. This substantial boost in accuracy has ranging implications from improving the reliability of diagnoses to enhancing the precision of image recognition systems. These findings highlight the importance of utilizing models like densenet-161 to achieve levels of accuracy emphasizing their potential for profound advancements in fields reliant on precise data classification and analysis. The conclusions are: Q1. This method can help prevent bias in Favor of the majority class and balance the data. Q2. Hyper parameter tuning helps to improve accuracy. Q3. Densenet model able to achieve a 92% accuracy. Furthermore (1)
- Which was quite an improvement. Prior to tweaking the hyperparameters the model only achieved a 90% accuracy rate. This remarkable disparity truly emphasizes the impact that hyperparameter tuning can have on a model's performance. By making adjustments to these parameters (1)
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Articles 1 - 3 of 3
Full-Text Articles in Risk Analysis
Enhancing Accident Investigation Using Traffic Cctv Footage, Aksharapriya Peddi
Enhancing Accident Investigation Using Traffic Cctv Footage, Aksharapriya Peddi
Electronic Theses, Projects, and Dissertations
This Culminating Experience Project investigated how the densenet-161 model will perform on accident severity prediction compared to proposed methods. The research questions are: (Q1) What is the impact of usage of augmentation techniques on imbalanced datasets? (Q2) How will the hyper parameter tuning affect the model performance? (Q3) How effective is the proposed model compared to existing work? The findings are: Q1. The effectiveness of our model depends on the implementation of augmentation techniques that pay attention to handling imbalanced datasets. Our dataset poses a challenge due to distribution of classes in terms of accident severity. To address this challenge …
Analyzing The Impact Of Automation On Employment In Different Us Regions: A Data-Driven Approach, Thejaas Balasubramanian
Analyzing The Impact Of Automation On Employment In Different Us Regions: A Data-Driven Approach, Thejaas Balasubramanian
Electronic Theses, Projects, and Dissertations
Automation is transforming the US workforce with the increasing prevalence of technologies like robotics, artificial intelligence, and machine learning. As a result, it is essential to understand how this shift will impact the labor market and prepare for its effects. This culminating experience project aimed to examine the influence of computerization on jobs in the United States and answer the following research questions: Q1. What factors affect how likely different jobs will be automated? Q2. What are the possible effects of automation on the US workforce across states and industries? Q3. What are the meaningful predictors of the likelihood of …
Integration Of Blockchain Technology Into Automobiles To Prevent And Study The Causes Of Accidents, John Kim
Integration Of Blockchain Technology Into Automobiles To Prevent And Study The Causes Of Accidents, John Kim
Electronic Theses, Projects, and Dissertations
Automobile collisions occur daily. We now live in an information-driven world, one where technology is quickly evolving. Blockchain technology can change the automotive industry, the safety of the motoring public and its surrounding environment by incorporating this vast array of information. It can place safety and efficiency at the forefront to pedestrians, public establishments, and provide public agencies with pertinent information securely and efficiently. Other industries where Blockchain technology has been effective in are as follows: supply chain management, logistics, and banking. This paper reviews some statistical information regarding automobile collisions, Blockchain technology, Smart Contracts, Smart Cities; assesses the feasibility …