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All Journal Bulletin of Electrical Engineering and Informatics
Lalar, Sachin
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Electrical & Electronics Engineering

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CLAHE-AlexNet optimized deep learning model for accurate detection of diabetic retinopathy G., Swetha; Gupta, Gaurav; Rane, Kantilal Pitambar; Ghag, Omkar M.; Korde, Sachin K.; Lalar, Sachin; Omarov, Batyrkhan; Raghuvanshi, Abhishek
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.7854

Abstract

Diabetic retinopathy (DR) is a disease that affects the blood vessels that are located in the retina. Loss of vision due to diabetes is a common consequence of the illness and a key factor in the progression of vision loss and blindness. Both ophthalmology and diabetes research have become more dependent on computer vision and image processing techniques in recent years. Fundus photography, also known as a fundus image, is a method that may be used to capture an image of the back of a person's eye. This article presents optimized deep learning model for diagnostic marking in retinal fundus images towards accurate detection of retinopathy. For experimental work, 500 images were selected from available open source Kaggle data set. 400 images were used to train deep learning model and remaining 100 images were used to validate the model. Images were enhanced using the contrast limited adaptive histogram equalization (CLAHE) algorithm. Pre trained convolutional neural network (CNN) models-AlexNet, VGG16, GoogleNet, and ResNet are used for classification and prediction of images. Accuracy, specificity, precision and F1-score of AlexNet is better than VGG16, ResNet-50, and GoogleNet. Sensitivity of ResNet-50 is higher than other pre trained CNN models.
An efficient course recommendation system for higher education students using machine learning techniques M. Arcinas, Myla; Meenakshi, Meenakshi; S. Bahalkar, Pranjali; Bhaturkar, Deepali; Lalar, Sachin; Pitambar Rane, Kantilal; Garg, Shaifali; Omarov, Batyrkhan; Raghuvanshi, Abhishek
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.7711

Abstract

Education institutions and teachers are in desperate need of automated, non-intrusive means of getting student feedback that would allow them to better understand the learning cycle and assess the success of course design. Students would benefit from a framework that intelligently guides their actions and provides exercises or resources to support and enhance their learning. The recommender system framework is a software agent that learns the user's preferences through a variety of channels and then utilizes that knowledge to provide product suggestions. A recommendation engine considers all potential user interests as background information, uses that knowledge to produce convincing recommendations, and then returns those ideas to the user. This article presents a feature selection and machine learning based course recommendation system for higher education students. principal component analysis (PCA) algorithm is used for feature selection. AdaBoost, k nearest neighbour (KNN), and Naïve Bayes algorithms are used to classify and predict student data. It is found that the AdaBoost algorithm is having better accuracy and F1 score for course recommendation to students. PCA AdaBoost is achieving an accuracy of 99.5%.
Co-Authors Bhaturkar, Deepali G., Swetha Garg, Shaifali Ghag, Omkar M. Gupta, Gaurav Korde, Sachin K. M. Arcinas, Myla Meenakshi, Meenakshi Omarov, Batyrkhan Pitambar Rane, Kantilal Raghuvanshi, Abhishek Rane, Kantilal Pitambar S. Bahalkar, Pranjali