Abstract

This study uses the Convolutional Neural Network (CNN) method with the AlexNet model to classify eye diseases based on medical images. The dataset includes labeled images of three types of eye diseases: cataract, glaucoma, and diabetic retinopathy. The experimental results show that the model achieved an accuracy of 75.18%, which indicates that CNN with the AlexNet architecture can classify eye diseases quite well. This research shows that deep learning can be used to help doctors or health professionals in diagnosing eye diseases through automatic image analysis. Although the accuracy still needs to be improved, this study can serve as a reference for developing an automated diagnostic system in the future. Further research is expected to increase accuracy, expand the dataset, and apply other deep learning techniques to improve the performance of eye disease detection.

Keywords: Classification of Eye Diseases, Convolutional Neural Network, Alexnet, Deep Learning

Citation

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Links

• Publisher Page: https://ijconsist.org/index.php/ijconsist/article/view/160
• Google Scholar: https://scholar.google.com/scholar?cluster=9341093393877158497

Abstract

Software requirements specification (SRS) is an essential part of software development. SRS has two features: functional requirements (FR) and non-functional requirements (NFR). Functional requirements define the needs that are directly in contact with stakeholders. Non-functional requirements describe how the software provides the means to carry out functional requirements. Non-functional requirements are often mixed with functional requirements. This study compares four primarily used machine learning methods for classifying functional and nonfunctional requirements. The contribution of our research is to use the PROMISE and SecReq (ePurse) dataset, then classify them by comparing the FastText+SVM, FastText+CNN, SVM, and CNN classification methods. CNN outperformed other methods on both datasets. The accuracy obtained by CNN on the PROMISE dataset is 99% and on the Seqreq dataset is 94%.

Keywords: CNN; FastText; Requirements Classification; Software Requirements; SVM

Citation

Haque MA, Rahman MA, Siddik MS. Non-Functional Requirements Classification with Feature Extraction and Machine Learning: An Empirical Study. In: 1st International Conference on Advances in Science, Engineering and Robotics Technology 2019 (ICASERT 2019) IEEE; 2019. p. 1–5.

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Links

• Publisher Page: https://iptek.its.ac.id/index.php/jts/article/view/13005
• Google Scholar: https://scholar.google.com/scholar?cluster=1908119983044836934