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- Data augmentation techniques are employed to enhance the variability of the training data (1)
- 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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- Ensemble (1)
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- Reducing overfitting and improving generalization. The preliminary outcomes of our proposed method demonstrate a promising accuracy level of 100% over the Large-scale fish dataset (1)
- Sleeptracking (1)
- The development of robust and efficient fish classification systems has become essential to preventing the rapid depletion of aquatic resources and building conservation strategies. A deep learning approach is proposed here for the automated classification of fish species from underwater images. The proposed methodology leverages state-of-the-art deep neural networks by applying the compact convolutional transformer (CCT) architecture (1)
- 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)
- This work proposes avenues for future research in the domain of fish classification. (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 is famous for faster training and lower computational cost. In CCT (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)
- With the potential for real-time deployment in aquatic monitoring systems. Furthermore (1)
Articles 1 - 5 of 5
Full-Text Articles in Computer Engineering
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 …
Classification Of Large Scale Fish Dataset By Deep Neural Networks, Priyanka Adapa
Classification Of Large Scale Fish Dataset By Deep Neural Networks, Priyanka Adapa
Electronic Theses, Projects, and Dissertations
The development of robust and efficient fish classification systems has become essential to preventing the rapid depletion of aquatic resources and building conservation strategies. A deep learning approach is proposed here for the automated classification of fish species from underwater images. The proposed methodology leverages state-of-the-art deep neural networks by applying the compact convolutional transformer (CCT) architecture, which is famous for faster training and lower computational cost. In CCT, data augmentation techniques are employed to enhance the variability of the training data, reducing overfitting and improving generalization. The preliminary outcomes of our proposed method demonstrate a promising accuracy level of …
Bridging The Gap Between Public Organizaions And Cybersecurity, Christopher Boutros
Bridging The Gap Between Public Organizaions And Cybersecurity, Christopher Boutros
Electronic Theses, Projects, and Dissertations
Cyberattacks are a major problem for public organizations across the nation, and unfortunately for them, the frequency of these attacks is constantly growing. This project used a case study approach to explore the types of cybersecurity public organization agencies face and how those crimes can be mitigated. The goal of this paper is to understand how public organization agencies have prepared for cyberattacks and discuss additional suggestions to improve their current systems with the current research available This research provides an analysis of current cyber security systems, new technologies that can be implemented, roadblocks public agencies face before and during …
Pillow Based Sleep Tracking Device Using Raspberry Pi, Venkatachalam Seviappan
Pillow Based Sleep Tracking Device Using Raspberry Pi, Venkatachalam Seviappan
Electronic Theses, Projects, and Dissertations
Almost half of all people have sleep interruptions at some point in their lives, making sleep disorders a common issue that affects a sizeable section of the population. Both their physical and emotional well-being may suffer as a result of this.Insomnia, which is a prevalent sleep disorder, is identified by symptoms including insufficient sleep duration and quality, trouble initiating sleep, multiple nighttime awakenings, early morning awakenings, and non-restorative sleep. It is essential to employ sleep monitoring systems to detect sleeping disorders as soon as possible for prompt diagnosis and treatment. To avoid sleep related health issues, there are plenty of …
Estimation On Gibbs Entropy For An Ensemble, Lekhya Sai Sake
Estimation On Gibbs Entropy For An Ensemble, Lekhya Sai Sake
Electronic Theses, Projects, and Dissertations
In this world of growing technology, any small improvement in the present scenario would create a revolution. One of the popular revolutions in the computer science field is parallel computing. A single parallel execution is not sufficient to see its non-deterministic features, as same execution with the same data at different time would end up with a different path. In order to see how non deterministic a parallel execution can extend up to, creates the need of the ensemble of executions. This project implements a program to estimate the Gibbs Entropy for an ensemble of parallel executions. The goal is …