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- Data augmentation techniques are employed to enhance the variability of the training data (1)
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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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- 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)
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- 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)
Articles 1 - 6 of 6
Full-Text Articles in Systems Science
An Analysis Of Security Risks Posed By Text-Based Generative Ai And Corporate Security Weaknesses Leading To Data Leaks, Tashya Rakshana Byreddy
An Analysis Of Security Risks Posed By Text-Based Generative Ai And Corporate Security Weaknesses Leading To Data Leaks, Tashya Rakshana Byreddy
Electronic Theses, Projects, and Dissertations
ABSTRACT
Generative AI (GenAI) has become a fundamental part of modern life, influencing how we work, learn, and interact with technology. This project focuses specifically on text-based GenAI, which is widely used for tasks such as information gathering, code improvement, and content creation. Despite its benefits, it presents significant security risks that are often underestimated by users. This project investigates these risks and the corporate security gaps that lead to unintentional data leaks. The project also provides a brief overview of Large Language Models (LLMs), which are based on the deep learning technique known as Transformer architecture, used for performing …
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 …
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 …
Accounting And Financial Statements Auto Analysis System, Zhen Jia
Accounting And Financial Statements Auto Analysis System, Zhen Jia
Electronic Theses, Projects, and Dissertations
This project was motivated by the need to revolutionize the generation of financial statements and financial analysis process thus speeding up business decision making. The research questions were: 1) How can machine learning increase the speed of financial statement preparation and automate financial statements analysis? 2) How can businesses balance the benefits of automating financial analysis with potential concerns around privacy, data security, and bias? 3) Can the Java J2EE framework provide a reliable running environment for machine learning?
The findings were: 1) Machine learning can significantly increase the accuracy and speed of financial analysis. Using machine learning algorithms, financial …
The Observation Of Smart Camera Security, Shun-Hsin Wang
The Observation Of Smart Camera Security, Shun-Hsin Wang
Electronic Theses, Projects, and Dissertations
At present, as the Internet of Thing (IoT) is increasingly widely used in human life, how to protect IoT devices from Malware attack has become an inevitable problem. This project is an analysis of two malwares and how they impact the Internet of Thing (IoT), especially the smart cameras common in people’s life and used in the enterprise. The analysis looks at the vulnerabilities of smart cameras and how Mirai malware and Persirai malware take advantage to these vulnerabilities to attack smart cameras within the network through the simulation process. Through the simulation, although both malwares were implemented using different …
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 …