Faculty Publications
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Publications by NITK Faculty
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Item Comparative study of neural networks and K-means classification in web usage mining(2010) Raghavendra, P.S.; Chowdhury, S.R.; Kameswari, S.V.There are many models in literature and practice that analyse user behaviour based on user navigation data and use clustering algorithms to characterize their access patterns. The navigation patterns identified are expected to capture the user's interests. In this paper, we model user behaviour as a vector of the time he spends at each URL, and further classify a new user access pattern. The clustering and classification methods of k-means with non-Euclidean similarity measure, artificial neural networks, and artificial neural networks with standardised inputs were implemented and compared. Apart from identifying user behaviour, the model can also be used as a prediction system where we can identify deviational behaviour.Item Segmentation of intra-retinal cysts from optical coherence tomography images using a fully convolutional neural network model(Institute of Electrical and Electronics Engineers Inc., 2019) Girish, G.N.; Thakur, B.; Chowdhury, S.R.; Kothari, A.R.; Rajan, J.Optical coherence tomography (OCT) is an imaging modality that is used extensively for ophthalmic diagnosis, near-histological visualization, and quantification of retinal abnormalities such as cysts, exudates, retinal layer disorganization, etc. Intra-retinal cysts (IRCs) occur in several macular disorders such as, diabetic macular edema, retinal vascular disorders, age-related macular degeneration, and inflammatory disorders. Automated segmentation of IRCs poses challenges owing to variations in the acquisition system scan intensities, speckle noise, and imaging artifacts. Several segmentation methods have been proposed in the literature for IRC segmentation on vendor-specific OCT images that lack generalizability across imaging systems. In this paper, we propose a fully convolutional network (FCN) model for vendor-independent IRC segmentation. The proposed method counteracts image noise variabilities and trains FCN models on OCT sub-images from the OPTIMA cyst segmentation challenge dataset (with four different vendor-specific images, namely, Cirrus, Nidek, Spectralis, and Topcon). Further, optimal data augmentation and model hyperparametrization are shown to prevent over-fitting for IRC area segmentation. The proposed method is evaluated on the test dataset with a recall/precision rate of 0.66/0.79 across imaging vendors. The Dice correlation coefficient of the proposed method outperforms that of the published algorithms in the OPTIMA cyst segmentation challenge with a Dice rate of 0.71 across the vendors. © 2013 IEEE.Item Tunable dual color emission from the opposite faces of silicon nanoparticle embedded gel-glass(Elsevier B.V., 2023) Das, B.; Hossain, S.M.; Mohanraj, G.T.; Chowdhury, S.R.; Siddique, A.B.; Rahman, M.R.; Ray, M.A luminescent silicon nanoparticle embedded gel-glass, prepared by room temperature hydrolysis and reduction of aminosilane, exhibits intriguing dual photoluminescence (PL) from opposite faces of the glass. The face, which is excited with UV, exhibits excitation energy dependent blue-green emission. As the excitation energy is varied from 350 nm to 450 nm the PL peaks shift from 435 nm to 506 nm. The opposite surface, on the other hand emits nearly excitation independent green light – the PL peak shifts by ∼17 nm as the excitation energy is varied from 350 nm to 450 nm. The luminescent properties provide interesting insights into the light emission mechanism from nanostructured silicon. Spectral filtering by reabsorption and photon reabsorption-reemission in a size distributed nanoparticle system having different optical gaps play a combined role in the observed dual emission. We show that the dual emission can be tuned by simply varying the thickness of the glass. Such dual emission renders the luminescent glass amenable for several applications as a novel solid state display material. © 2023 Elsevier B.V.