Smt. Swaroopa Shastri

Work place: Department of Computer Science and Engineering (MCA), Visvesvaraya Technological University Kalaburagi, Karnataka



Research Interests: Artificial Intelligence, Computational Learning Theory, Image Compression, Image Manipulation, Image Processing


Ms. Swaroopa Shastri working as an Assistant Professor in the Department of Computer Science and Engineering (MCA) at Visvesvaraya Technological University, Centre for PG Studies, Kalaburagi. She is in the field of Computer Appications at VTU’s, CPGS, Kalaburagi, Karnataka, India. She is in teaching profession for more than 10 years. She has presented/published 20 papers in National and International Journals and Conference. Her main area of interest includes Machine Learning, Image Processing and Artificial Intelligence.

Author Articles
An Efficient Approach for Text-to-Speech Conversion Using Machine Learning and Image Processing Technique

By Smt. Swaroopa Shastri Shashank Vishwakarma

DOI:, Pub. Date: 8 Aug. 2023

This study explores the conversion of English to Hindi, first to text, and subsequently to speech. The first part of the implementation is the text recognition from images, in which two approaches are used for text character recognition: a maximally stable extensible region (MSER) and grayscale conversion the second part of the paper deals with the geometric filtering in combination with stroke width transform (SWT). Subsequently, letter/alphabets are grouped to detect text sequences, which are then fragmented into words. Finally, a 96 percent accurate spell check is performed using naive Bayes and decision tree algorithms, followed by the use of optical character recognition (OCR) to digitize. The word Give our text-to-speech synthesizer (TTS) the recognized text to convert it to Hindi language using the text-to-speech model. Based on aspects such speech speed, sound quality, pronunciation, and clarity.

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Detection and Classification of Alzheimer’s Disease by Employing CNN

By Smt. Swaroopa Shastri Ambresh Bhadrashetty Supriya Kulkarni

DOI:, Pub. Date: 8 Apr. 2023

Alzheimer’s illness is an ailment of mind which results in mental confusion, forgetfulness and many other mental problems. It effects physical health of a person too. When treating a patient with Alzheimer's disease, a proper diagnosis is crucial, especially into earlier phases of condition as when patients are informed of the risk of the disease, they can take preventative steps before irreparable brain damage occurs. The majority of machine detection techniques are constrained by congenital (present at birth) data, however numerous recent studies have used computers for Alzheimer's disease diagnosis. The first stages of Alzheimer's disease can be diagnosed, but illness itself cannot be predicted since prediction is only helpful before it really manifests. Alzheimer’s has high risk symptoms that effects both physical and mental health of a patient. Risks include confusion, concentration difficulties and much more, so with such symptoms it becomes important to detect this disease at its early stages. Significance of detecting this disease is the patient gets a better chance of treatment and medication. Hence our research helps to detect the disease at its early stages. Particularly when used with brain MRI scans, deep learning has emerged as a popular tool for the early identification of AD. Here we are using a 12- layer CNN that has the layers four convolutional, two pooling, two flatten, one dense and three activation functions. As CNN is well-known for pattern detection and image processing, here, accuracy of our model is 97.80%.

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