Recurrent Neural Network Analysis of Optometry Data

被引：0

作者：

Sharma, Avinash ^{[1
]}

Barua, Tarkeshwar ^{[2
]}

Saxena, Rini ^{[1
]}

Goswami, Manish ^{[3
]}

Goyel, Parul ^{[4
]}

机构：

[1] Chandigarh Grp Coll, Dept Comp Sci & Engn, Chandigarh Engn, Jhanjeri 140307, India

[2] Roorkeee Inst Technol, Dept Comp Sci & Engn, Roorkee, Uttar Pradesh, India

[3] Chandigarh Grp Coll, Chandigarh Pharm Coll, Jhanjeri 140307, India

[4] Maharishi Markandeshwar, Comp Sci & Engn MM Engn Coll, Ambala 1307, Haryana, India

来源：

2024 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND EMERGING COMMUNICATION TECHNOLOGIES, ICEC | 2024年

关键词：

Optometry; Optometry systems; Eye-tracking Data; Gaze Patterns; Time-series Data; Data Analysis; Predictive Analytics; Hyperparameter Tuning; Ophthalmology;

D O I：

10.1109/ICEC59683.2024.10837415

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This survey explores the potential of recurrent neural networks (RNNs) for analyse eye-trailling data, generally data obtained from Optometry systems. RNNs stand out at distinguishing patterns in consecutive data, making them appropriate for tasks like foretell future gaze, distinguishing areas of interest, and detecting anomalousness in gaze patterns. The abstract synopsis the process of probing Optometry data using RNNs. It highlights data pre-processing steps like cleaning, formatting, and normalization. The assortment of an LSTM network edifice and hyperparameter tuning for optimal execution are discussed. breeding and evaluation operation are explained[12], including using separate breeding, validation, and testing sets. Finally, the abstract mentions potential computer applications of the analysis, including human-computer interaction(HCI) (HCI) optimization and individual behavior understanding.

引用

页码：811 / 816

页数：6

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