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Your search keyword '"Devi, V. Brindha"' showing total 11 results
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11 results on '"Devi, V. Brindha"'

4. Medical Image Segmentation Using Grey Wolf-Based U-Net with Bi-Directional Convolutional LSTM.

Author

Tamilmani, G., Phaneendra Varma, CH, Devi, V. Brindha, and Ramesh Babu, G

Subjects
DEEP learning, IMAGE segmentation, CONVOLUTIONAL neural networks, DIAGNOSTIC imaging, OPTIMIZATION algorithms, EVOLUTIONARY computation
Abstract

In recent years, deep learning-based networks have been able to achieve state-of-the-art performance in medical image segmentation. U-Net, one of the currently available networks, has proven to be effective when applied to the segmentation of medical images. A Convolutional Neural Network's (CNN) performance is heavily dependent on the network's architecture and associated parameters. There are many layers and parameters that need to be set up in order to manually create a CNN, making it a complex procedure. Designing a network is made more difficult by using a variety of connections to increase the network's complexity. Evolutionary computation can be used to set the parameters of CNN and/or organize the CNN layers as an optimization strategy. This paper proposes an automatic evolutionary method for detecting an optimal network topology and its parameters for the segmentation of clinical image using Grey Wolf Optimization algorithm. Also, Bi-Directional LSTM integrated in the skip connection to extract dense feature characteristics of image by combining feature maps extracted from encoded and previous decoded path in nonlinear way (MIS-GW-U-Net-BiDCLSTM) is proposed. The experimental results demonstrate that the proposed method attains 98.49% accuracy with minimal parameters, which is much better than that of the other methods. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Intelligent Tourist System For The 21st Century

Author

G Sriram, Devi V Brindha, M Meenaloshini, and R Subhashini

Subjects
History, Economy, Business, Tourism, Computer Science Applications, Education
Abstract

Travel Industry is always at the forefront of technology adoption. Most of our researchers and town planners have preferred the method of machine learning approach for tour recommendation models. Some traditional methods have achieved certain levels of success in tourism research, but sometimes artificial neural network (ANN) and regression analysis techniques give better results. Our objective is to investigate the different ways in which the machine learning models can be applied in tourism prediction problems and to show the performance of machine learning methods. In this paper we have an intelligent tourist system using ML/AI that has been modeled based on various features using Sentiment analysis and can be performed over these reviews which will be helpful to find tourist place popularity. Based on sentiment analysis results, tourists can easily decide their own tour destination to be visited. We have categorized the places based on their preference like the most visited or least visited family trip or friend’s trip and so on. The dataset was collected from various tourism review websites. We performed a comparative study of feature extraction algorithms that is Count Vectorization, TFIDF Vectorization, along with classification algorithms like Naive Bayes, Support Vector Machine, and Random Forest algorithm. With these results, a recommendation system has been built which would map an individual user’s interests to the highest rated tourist places and generates a tour plan satisfying the user’s needs.

Published
2021

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