Robust Deep Learning Technique: U-Net Architecture for Pupil Segmentation

Gowroju, Swathi; Aarti,; Kumar, Sandeep

doi:10.1109/iemcon51383.2020.9284947

Cited by 29 publications

(4 citation statements)

References 25 publications

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“…Proposed system uses publicly available dataset from [15]. A biometric fingerprint database called Sokoto Coventry Fingerprint Dataset (SOCOFing) was created with academic research in mind.…”

Section: Methodsmentioning

confidence: 99%

Efficient Biometric Service Protocol for Cloud Services

2023

5th ISSE National Conference (INAC-05) on Systems Approach for Self-Reliance in Advanced Technologies

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Cloud services will be ones that can be accessed from a scattered cloud stockpiling worker rather than an on-site server. These measured systems are operated by an outsider and allow users online access to PC resources like systems administration or research. Cloud computing is used to store data on cloud workers and to distribute processing resources across the Internet. Due to the pooling of resources, security and data insurance have become a crucial area of concern in cloud computing. Cloud service providers use server farms that are affected by information spillage to store and keep customer data. It has been noted that several techniques have prioritised data protection while ignoring privacy in the execution. Authentication helps to safeguard and confirm a recipient's identity. We also offer a practical method for creating a session key between two interacting parties that makes use of two biometric models for secure message delivery. Finally, thorough tests and a comparison analysis were used to determine the validity and usefulness of the suggested remedy.

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Section: Methodsmentioning

confidence: 99%

Efficient Biometric Service Protocol for Cloud Services

2023

5th ISSE National Conference (INAC-05) on Systems Approach for Self-Reliance in Advanced Technologies

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“…One of the safe-to-use biometric traits is the eye. Eye not only authenticates a person but also hides much information needed in most applications [12][13][14][15]. One of the best applications is the result of this research paper.…”

Section: Introductionmentioning

confidence: 90%

“…ese other medical articles [10][11][12][13][14][15] studied the prediction through a pupillometer. Gowroju et al [16][17][18] proposed a method to segment the Pupil using a deep learning technique accurately.…”

Section: Literature Surveymentioning

confidence: 99%

Deep Neural Network for Accurate Age Group Prediction through Pupil Using the Optimized UNet Model

Gowroju

Kumar

Aarti

et al. 2022

Mathematical Problems in Engineering

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Predicting age automatically from the image is a difficult task and shortening the challenge to be more concise is also a challenging task. Nevertheless, the existing implementations using manually designed features using a wide variety of input features using benchmark datasets are unsatisfactory as they suffer from unknown subject information. It is challenging to judge CNN’s performance using such approaches. The proposed system performs the segmentation through UNet without using a dense layer to perform the segmentation and classification. The proposed system uses the skip connection to hold the loss at the max-pooling layer. Also, the morphological processing and probabilistic classification served as the proposed system’s novelty. The proposed method used three benchmark datasets, MMU, CASIA, and UBIRIS, to experiment with building a training model and tested using various optimization techniques to perform an accurate segmentation. To further test and improve the quality of the proposed method, we experimented with random images. The proposed system’s accuracy is 96% when experimented on random images of subjects collected purely for experimentation. Three optimizers, namely, Stochastic Gradient Descent, RMS Prop, and Adaptive Moment Optimizer, were experimented with in the proposed system to fit the system. The average accuracy we received using optimizers is 71.9, 84.3, and 96.0 for the loss value of 2.36, 2.30, and 1.82, respectively.

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“…Author Swati et al [7] in "Robust deep learning technique: U-net architecture for pupil segmentation. "proposed a novel CNN U-Net based model to perform the accurate segmentation of pupil.…”

Section: Iiliterature Surveymentioning

confidence: 99%

Identification of Software Clone Files Using Machine Learning

Swapna

2023

IJSREM

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One technique to reduce the likelihood of introducing a bug is to identify code clone files. Refactoring, often known as removal, is the process of making software more understandable and maintainable. When additional clones are discovered in software programs, we need a method to help developers with refactored code and to enhance software quality. This method must tell developers which clone files require reworking. To provide developers with recommendations on which files require code refactoring, our research proposes a novel learning technique that automatically extracts features from the identified code clones and trains models to present a novel technique to enhance classification performance by transforming outliers into Unknown clone sets. The Eclipse dataset, which comprises 213 Java software files, was utilized for this project. Support Vector Machine (SVM), Random Forest, Bagging, and K-Nearest Neighbors (KNN) are the algorithms that are employed. We compare these four categorization models and recommend the model with the highest accuracy. Our tool can be developed and applied to reduce system bugs. By recognizing software clone files and eliminating duplication code, our study can enhance clone maintenance. Also, the possibility of bad design for a system, difficulty in a system improvement or modification, and introduction of a new bug can be decreased by identifying and refactoring clones. KEYWORDS: Cloning, Refactoring, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision trees.

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Robust Deep Learning Technique: U-Net Architecture for Pupil Segmentation

Cited by 29 publications

References 25 publications

Efficient Biometric Service Protocol for Cloud Services

Efficient Biometric Service Protocol for Cloud Services

Deep Neural Network for Accurate Age Group Prediction through Pupil Using the Optimized UNet Model

Identification of Software Clone Files Using Machine Learning

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