Faculty Publications
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Item User Interest Drift Identification Using Contextual Factors in Implicit Feedback-Based Recommender Systems(Springer Science and Business Media Deutschland GmbH, 2023) Chaitanya, V.S.; Deo, S.; Santhi Thilagam, P.S.The modeling of appropriate recommendations using the session interactions in the implicit feedback-based recommender systems necessitates the identification of user interest drift. But this identification is challenging due to the presence of unintentional interactions (noise) made by the user. Most of the existing literature focused on understanding the correlation between ongoing session interactions but did not explore the contextual factors, such as the time of occurrence of the session and the item’s popularity, that led the user to perform that specific interaction. This has resulted in the wrongful categorization of interactions between user interest drift and noise. To overcome these limitations, this work proposes a deep learning-based approach that uses both ongoing session information and contextual information. Depending on availability, this work also considers the user’s previous interactions to generate personalized recommendations. In comparison with the existing works, this work effectively identifies the user interest drift and generates the appropriate recommendations for the users. The proposed approach demonstrates superior performance over state-of-the-art baselines in terms of Recall and MRR, as evidenced by experimental results on benchmark datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.Item Estimating rock properties using sound levels produced during drilling(Elsevier BV, 2009) Vardhan, H.; Adhikari, G.R.; Govinda Raj, M.An attempt has been made in this paper to experimentally investigate the estimation of rock properties like compressive strength and abrasivity using sound levels produced during drilling. The investigation was carried out on a laboratory scale using small portable pneumatic drilling equipment used in hard rock drilling. For this purpose, a pneumatic drill setup was fabricated for drilling vertical holes. The compressive strength and the abrasivity of various rock samples collected from the field were determined in the laboratory. A set of test conditions were defined for measurement of sound level of the pneumatic drill. Also, with the help of the experimental setup, vertical drilling was carried out on the rock samples for varying thrust and air pressure values and the corresponding A-weighted equivalent continuous sound levels were measured. Results of this study indicate that sound level can be a promising tool in estimating rock properties during drilling. © 2008 Elsevier Ltd. All rights reserved.Item Efficient storage and transmission of digital fundus images with patient information using reversible watermarking technique and error control codes(2009) Nayak, J.; Subbanna Bhat, P.S.; Acharya, R.; Kumar, M.Handling of patient records is increasing overhead costs for most of the hospitals in this digital age. In most hospitals and health care centers, the patient text information and corresponding medical images are stored separately as different files. There is a possibility of mishandling the text file containing patient history. We are proposing a novel method for the compact storage and transmission of patient information with the medical images. In this technique, we are using a reversible watermarking technique to hide the patient information within the retinal fundus image. There is a possibility that these medical images, which carry patient information, can get corrupted by the noise during the storage or transmission. The safe recovery of patient information is important in this situation. So, to recover the maximum amount of text information in the noisy environment, the encrypted patient information is coded with error control coding (ECC) techniques. The performance of three types of ECC for various levels of salt & pepper (S & P) noise is tabulated for a specific example. The proposed system is more reliable even in a noisy environment and saves memory. © 2008 Springer Science+Business Media, LLC.Item Magnetic resonance image denoising using nonlocal maximum likelihood paradigm in DCT-framework(John Wiley and Sons Inc, 2015) Kumar, P.K.; Darshan, P.; Kumar, S.; Ravindra, R.; Rajan, J.; Saba, L.; Suri, J.S.The data acquired by magnetic resonance (MR) imaging system are inherently degraded by noise that has its origin in the thermal Brownian motion of electrons. Denoising can enhance the quality (by improving the SNR) of the acquired MR image, which is important for both visual analysis and other post processing operations. Recent works on maximum likelihood (ML) based denoising shows that ML methods are very effective in denoising MR images and has an edge over the other state-of-the-art methods for MRI denoising. Among the ML based approaches, the Nonlocal maximum likelihood (NLML) method is commonly used. In the conventional NLML method, the samples for the ML estimation of the unknown true pixel are chosen in a nonlocal fashion based on the intensity similarity of the pixel neighborhoods. Euclidean distance is generally used to measure this similarity. It has been recently shown that computing similarity measure is more robust in discrete cosine transform (DCT) subspace, compared with Euclidean image subspace. Motivated by this observation, we integrated DCT into NLML to produce an improved MRI filtration process. Other than improving the SNR, the time complexity of the conventional NLML can also be significantly reduced through the proposed approach. On synthetic MR brain image, an average improvement of 5% in PSNR and 86%reduction in execution time is achieved with a search window size of 91 × 91 after incorporating the improvements in the existing NLML method. On an experimental kiwi fruit image an improvement of 10% in PSNR is achieved. We did experiments on both simulated and real data sets to validate and to demonstrate the effectiveness of the proposed method. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 256-264, 2015 © 2015 Wiley Periodicals, Inc.Item Segmentation of intima media complex from carotid ultrasound images using wind driven optimization technique(Elsevier Ltd, 2018) Yamanakkanavar, Y.; Madipalli, P.; Rajan, J.; Kumar, P.K.; Narasimhadhan, A.V.Cardiovascular diseases are the third leading cause of death worldwide. The primitive indication of the possible onset of a cardiovascular disease is atherosclerosis, which is the accumulation of plaque on the arterial wall. The intima-media thickness (IMT) of the common carotid artery is an early marker of the development of cardiovascular disease. The computation of the IMT and the delineation of the carotid plaque are significant predictors for the clinical diagnosis of the risk of stroke. For a robust diagnosis, carotid ultrasound images must be free from speckle noise. To address this problem, we use state-of-the-art despeckling and enhancement methods in this work. Many edge-based methods for IMT estimation have been proposed to overcome the limitations of manual segmentation. In this paper, we present a fully automated region-of-interest (ROI) extraction and a threshold-based segmentation of the intima media complex (IMC) using a wind driven optimization (WDO) technique. A quantitative evaluation is carried out on 90 carotid ultrasound images of two different datasets. The obtained results are compared with those of state-of-the-art techniques such as a model-based approach, a dynamic programming method, and a snake segmentation method. The experimental analysis shows that the proposed method is robust in measuring the IMT in carotid ultrasound images. © 2017 Elsevier LtdItem Stack generalized deep ensemble learning for retinal layer segmentation in Optical Coherence Tomography images(Elsevier Sp. z o.o., 2020) Anoop, B.N.; Pavan, R.; Girish, G.N.; Kothari, A.R.; Rajan, J.Segmentation of retinal layers is a vital and important step in computerized processing and the study of retinal Optical Coherence Tomography (OCT) images. However, automatic segmentation of retinal layers is challenging due to the presence of noise, widely varying reflectivity of image components, variations in morphology and alignment of layers in the presence of retinal diseases. In this paper, we propose a Fully Convolutional Network (FCN) termed as DelNet based on a deep ensemble learning approach to selectively segment retinal layers from OCT scans. The proposed model is tested on a publicly available DUKE DME dataset. Comparative analysis with other state-of-the-art methods on a benchmark dataset shows that the performance of DelNet is superior to other methods. © 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of SciencesItem A retinex based non-local total generalized variation framework for OCT image restoration(Elsevier Ltd, 2022) Smitha, A.; Febin, I.P.; Padikkal, P.A retinex driven non-local total generalized variational (TGV) model is proposed in this paper to restore and enhance speckled images. The combined first and second-order TGV controlled by a balancing parameter are used to improve the enhancement and restoration process. The distribution of the speckle is estimated from input images using detailed statistical analysis. The model is designed to handle speckle-noise following a Gamma distribution, as analyzed later in this paper. The non-local TGV model is shown to restore images without causing any visual artefacts, unlike the normal total variation (TV) model. Moreover, a retinex framework shows a remarkable improvement to the contrast features of the data without distorting the natural image characteristics as quantified visually and statistically in the experimental section of this work. A fast numerical approximation based on the Split-Bregman scheme is employed to improve the efficiency of the model in terms of computation. The proposed model is verified to have despeckled and enhanced the Optical Coherence Tomography (OCT) data to a greater extent compared to the state-of-the-art models as observable from the results shown in this paper. © 2021 Elsevier Ltd