Faculty Publications
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Publications by NITK Faculty
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Item Identifying Parking Lots from Satellite Images using Transfer Learning(Institute of Electrical and Electronics Engineers Inc., 2019) Kumar, S.; Thomas, E.; Horo, A.With the advent of digital image processing techniques and convolutional neural networks, the world has derived numerous benefits such as computerized photography, biological Image Processing, finger print and iris recognition, to name a few. Computer vision coupled with convolutional neural networks has attributed machines with a virtual intellectual ability to recognize and distinguish images based on several characteristics that may be impossible for the human eye to perceive. We have exploited this advancement in technology to particular use case of detecting number of empty and occupied parking slots from satellite images of parking lots. We have proposed a befitting sequence of classical image processing techniques and algorithms to perform pre-processing of satellite images of parking spaces. Moreover, we have proposed a Convolutional Neural Network model that takes as input these preprocessed images and identifies the empty and occupied parking slots with an accuracy of 97.73%. The potential benefits of using Neural Networks to realize the objective can be extended to open parking spaces of different configurations. This is due to the fact that establishing sensors over a large number of parking slots over a given open parking space can be a cumbersome and exorbitant task. The proposed model comprises of few convolutional layers and uses Rectified Linear Classification activation function. © 2019 IEEE.Item EffiCOVID-net: A highly efficient convolutional neural network for COVID-19 diagnosis using chest X-ray imaging(Academic Press Inc., 2025) Kumar, S.; Bhowmik, B.The global COVID-19 pandemic has drastically affected daily life, emphasizing the urgent need for early and accurate detection to provide adequate medical treatment, especially with limited antiviral options. Chest X-ray imaging has proven crucial for distinguishing COVID-19 from other respiratory conditions, providing an essential diagnostic tool. Deep learning (DL)-based models have proven highly effective in image diagnostics in recent years. Many of these models are computationally intensive and prone to overfitting, especially when trained on limited datasets. Additionally, conventional models often fail to capture multi-scale features, reducing diagnostic accuracy. This paper proposed a highly efficient convolutional neural network (CNN) called EffiCOVID-Net, incorporating diverse feature learning units. The proposed model consists of a bunch of EffiCOVID blocks that incorporate several layers of convolution containing (3×3) filters and recurrent connections to extract complex features while preserving spatial integrity. The performance of EffiCOVID-Net is rigorously evaluated using standard performance metrics on two publicly available COVID-19 chest X-ray datasets. Experimental results demonstrate that EffiCOVID-Net outperforms existing models, achieving 98.68% accuracy on the COVID-19 radiography dataset (D1), 98.55% on the curated chest X-ray dataset (D2), and 98.87% on the mixed dataset (DMix) in multi-class classification (COVID-19 vs. Normal vs. Pneumonia). For binary classification (COVID-19 vs. Normal), the model attains 99.06%, 99.78%, and 99.07% accuracy, respectively. Integrating Grad-CAM-based visualizations further enhances interpretability by highlighting critical regions influencing model predictions. EffiCOVID-Net's lightweight architecture ensures low computational overhead, making it suitable for deployment in resource-constrained clinical settings. A comparative analysis with existing methods highlights its superior accuracy, efficiency, and robustness performance. However, while the model enhances diagnostic workflows, it is best utilized as an assistive tool rather than a standalone diagnostic method. © 2025 Elsevier Inc.