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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Moholkar, Kavita
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Electrical & Electronics Engineering

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Optimized convolutional neural network deep learning for Arabian handwritten text recognition Ritonga, Mahyudin; L. Bangare, Manoj; Manoj Bangare, Pushpa; L. Bangare, Sunil; Sachin Vanjire, Seema; Moholkar, Kavita; Kasat, Kishori; Rozak, Purnama
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.v14i2.7696

Abstract

In general, the term handwritten character recognition (HCR) refers to the process of recognizing handwritten characters in any form, whereas handwritten text recognition (HTR) refers to the process of reading scanned document images that include text lines and converting those text lines into editable text. The identification of recurring structures and configurations in data is the primary focus of the field of machine learning known as pattern recognition. Optical character recognition, often known as OCR, is a challenging issue to solve when it comes to the field of pattern recognition. This article presents machine learning enabled framework for accurate identification of Arabian handwriting. This framework has provisions for image processing, image segmentation, feature extraction and classification of handwritten images. Images are enhanced using contrast limited adaptive histogram equalization (CLAHE) algorithm. Image segmentation is performed by k-means algorithm. Classification is performed using convolutional neural network (CNN) VGG 16 and support vector machine (SVM) algorithm. Classification accuracy of CNN VGG 16 is 99.33%.
Enhanced deep auto encoder technique for brain tumor classification and detection Badashah, Syed Jahangir; Moholkar, Kavita; Bangare, Sunil L.; Gupta, Gaurav; T., Devi; Francis, Sammy; Hariram, Venkatesan; Omarov, Batyrkhan; Rane, Kantilal Pitambar; Raghuvanshi, Abhishek
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp2031-2040

Abstract

A brain tumor can develop due to uncontrolled proliferation of aberrant cells in brain tissue. Malignant tumor can influence the nearby brain tissues, potentially resulting in the person's death. Early diagnosis of a brain tumor is crucial for ensuring the survival of patients. This article introduces an improved method using a deep auto encoder for the classification and detection of brain tumor. Magnetic resonance imaging (MRI) images are obtained from the BraTS data sets. The images undergo preprocessing using an adaptive Wiener filter. Image preprocessing is essential for eliminating noise from the input MRI pictures, hence enhancing the accuracy of MRI image classification. The fuzzy C-means technique is used to accomplish image segmentation. The classification model comprises deep auto encoder, convolution neural network (CNN), and K-nearest neighbor techniques. The classification model is developed and evaluated using MRI image slices from the BraTS dataset. Accuracy of deep auto encoder is 98.81%. Accuracy of CNN is 95.50 and accuracy of K-nearest neighbor (KNN) technique is 91.30%.
Co-Authors Badashah, Syed Jahangir Bangare, Sunil L. Francis, Sammy Gupta, Gaurav Hariram, Venkatesan Kasat, Kishori L. Bangare, Manoj L. Bangare, Sunil Mahyudin Ritonga Manoj Bangare, Pushpa Omarov, Batyrkhan Purnama Rozak, Purnama Raghuvanshi, Abhishek Rane, Kantilal Pitambar Sachin Vanjire, Seema T., Devi