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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Rane, Kantilal Pitambar
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Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

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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.
Optimized convolutional neural network enabled technique for sentiment analysis from social media data Veena, Chinta; Sultanpure, Kavita A.; Meenakshi, Meenakshi; Bangare, Sunil L.; Raskar, Punam Sunil; Sadashiv Kulkarni, Shriram; Arcinas, Myla M.; Rane, Kantilal Pitambar
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.v14i4.7712

Abstract

Sentiment analysis is an area of computational linguistics that studies natural language processing. The most significant subtasks are gathering people's thoughts and organizing them into groups to determine how they feel. The primary purpose of sentiment analysis is to determine whether the individual who created a piece of material has a positive or negative opinion about a subject. It has been claimed that sentiment analysis and social media mining have contributed to the recent success of both private sector and the government. Emotional analysis has applications in practically every aspect of modern life, from individuals to corporations, telecommunications to medical, and economics to politics. This article describes an improved sentiment analysis model based on gray level co-occurrence matrix (GLCM) texture feature extraction and a convolutional neural network (CNN). This model was created using tweets. First, texture characteristics are extracted from the input data set using the GLCM technique. This feature extraction improves categorization accuracy. CNNs are used to classify objects. It outperforms both the support vector machine and the AdaBoost algorithms in terms of accuracy. CNN has achieved an accuracy of 98.5% for sentiment analysis task.
Co-Authors Arcinas, Myla M. Badashah, Syed Jahangir Bangare, Jyoti L. Bangare, Sunil L. Chikmurge, Diptee Francis, Sammy G., Swetha Ghag, Omkar M. Gupta, Gaurav Hariram, Venkatesan Jawarneh, Malik Kaliyaperumal, Karthikeyan Kasat, Kishori Korde, Sachin K. Lalar, Sachin Meenakshi, Meenakshi Moholkar, Kavita Omarov, Batyrkhan Raghuvanshi, Abhishek Raskar, Punam Sunil Sadashiv Kulkarni, Shriram Sultanpure, Kavita A. T., Devi Veena, Chinta Veluri, Ravi Kishore