Faculty Publications
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Item A Deep Ensemble Learning-Based CNN Architecture for Multiclass Retinal Fluid Segmentation in OCT Images(Institute of Electrical and Electronics Engineers Inc., 2023) Rahil, M.; Anoop, B.N.; Girish, G.N.; Kothari, A.R.; Koolagudi, S.G.; Rajan, J.Retinal Fluids (fluid collections) develop because of the accumulation of fluid in the retina, which may be caused by several retinal disorders, and can lead to loss of vision. Optical coherence tomography (OCT) provides non-invasive cross-sectional images of the retina and enables the visualization of different retinal abnormalities. The identification and segmentation of retinal cysts from OCT scans is gaining immense attention since the manual analysis of OCT data is time consuming and requires an experienced ophthalmologist. Identification and categorization of the retinal cysts aids in establishing the pathophysiology of various retinal diseases, such as macular edema, diabetic macular edema, and age-related macular degeneration. Hence, an automatic algorithm for the segmentation and detection of retinal cysts would be of great value to the ophthalmologists. In this study, we have proposed a convolutional neural network-based deep ensemble architecture that can segment the three different types of retinal cysts from the retinal OCT images. The quantitative and qualitative performance of the model was evaluated using the publicly available RETOUCH challenge dataset. The proposed model outperformed the state-of-the-art methods, with an overall improvement of 1.8%. © 2013 IEEE.Item Bi-level Acoustic Scene Classification Using Lightweight Deep Learning Model(Birkhauser, 2024) Spoorthy, V.; Koolagudi, S.G.Identifying a scene based on the environment in which the related audio is recorded is known as acoustic scene classification (ASC). In this paper, a bi-level light-weight Convolutional Neural Network (CNN)-based model is presented to perform ASC. The proposed approach performs classification in two levels. The scenes are classified into three broad categories in the first level as indoor, outdoor, and transportation scenes. The three classes are further categorized into individual scenes in the second level. The proposed approach is implemented using three features: log Mel band energies, harmonic spectrograms and percussive spectrograms. To perform the classification, three CNN classifiers, namely, MobileNetV2, Squeeze-and-Excitation Net (SENet), and a combination of these two architectures, known as SE-MobileNet are used. The proposed combined model encashes the advantages of both MobileNetV2 and SENet architectures. Extensive experiments are conducted on DCASE 2020 (IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events) Task 1B development and DCASE 2016 ASC datasets. The proposed SE-MobileNet model resulted in a classification accuracy of 96.9% and 86.6% for the first and second levels, respectively, on DCASE 2020 dataset, and 97.6% and 88.4%, respectively, on DCASE 2016 dataset. The proposed model is reported to be better in terms of both complexity and accuracy as compared to the state-of-the-art low-complexity ASC systems. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item Latent fingerprint segmentation using multi-scale attention U-Net(Inderscience Publishers, 2024) Akhila, P.; Koolagudi, S.G.Latent fingerprints are the fingerprints lifted from crime scene surfaces. Segmentation of latent fingerprints from the background is an important preprocessing task which is challenging due to the poor quality of the fingerprints. Though fingerprint segmentation approaches based on their orientation and frequency are reported in the literature, they could not adequately address the problem. We propose a latent fingerprint segmentation model based on the U-Net attention network in this work. We added the Atrous Spatial Pyramid Pooling (ASPP) layer to the network to facilitate multi-scale fingerprint segmentation. Our approach could effectively segment the latent fingerprint region from the background and even detect occluded and partial fingerprints with simple network architecture. To evaluate the performance, we have compared our results with the manual ground truth using NIST SD27A dataset. Our segmentation model has improved matching accuracy on the NIST SD27A dataset. © 2024 Inderscience Enterprises Ltd.Item InDS: Intelligent DRL Strategy for Effective Virtual Network Embedding of an Online Virtual Network Requests(Institute of Electrical and Electronics Engineers Inc., 2024) Keerthan Kumar, T.G.K.; Addya, S.K.; Koolagudi, S.G.Network virtualization is a demanding feature in the evolution of future Internet architectures. It enables on-demand virtualized resource provision for heterogeneous Virtual Network Requests (VNRs) from diverse end users over the underlying substrate network. However, network virtualization provides various benefits such as service separation, improved Quality of Service, security, and more prominent resource usage. It also introduces significant research challenges. One of the major such issues is allocating substrate network resources to VNR components such as virtual machines and virtual links, also named as the virtual network embedding, and it is proven to be mathbb {N}mathbb {P} -hard. To address the virtual network embedding problem, most of the existing works are 1) Single-objective, 2) They failed to address dynamic and time-varying network states 3) They neglected network-specific features. All these limitations hinder the performance of existing approaches. This work introduces an embedding framework called Intelligent Deep Reinforcement Learning (DRL) Strategy for effective virtual network embedding of an online VNRs (InDS). The proposed InDS uses an actor-critic model based on DRL architecture and Graph Convolutional Networks (GCNs). The GCN effectively captures dependencies between the VNRs and substrate network environment nodes by extracting both network and system-specific features. In DRL, the asynchronous advantage actor-critic agents can learn policies from these features during the training to decide which virtual machines to embed on which servers over time. The actor-critic helps in efficiently learning optimal policies in complex environments. The suggested reward function considers multiple objectives and guides the learning process effectively. Evaluation of simulation results shows the effectiveness of InDS in achieving optimal resource allocation and addressing diverse objectives, including minimizing congestion, maximizing acceptance, and revenue-to-cost ratios. The performance of InDS exhibits superiority in achieving 28% of the acceptance ratio and 45% of the revenue-to-cost ratio by effectively managing the network congestion compared to other existing baseline works. © 2013 IEEE.Item MICAnet: A Deep Convolutional Neural Network for mineral identification on Martian surface(Elsevier B.V., 2024) Kumari, P.; Soor, S.; Shetty, A.; Koolagudi, S.G.Mineral identification plays a vital role in understanding the diversity and past habitability of the Martian surface. Mineral mapping by the traditional manual method is time-consuming and the unavailability of ground truth data limited the research on building supervised learning models. To address this issue an augmentation process is already proposed in the literature that generates training data replicating the spectra in the MICA (Minerals Identified in CRISM Analysis) spectral library while preserving absorption signatures and introducing variability. This study introduces MICAnet, a specialized Deep Convolutional Neural Network (DCNN) architecture for mineral identification using the CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) hyperspectral data. MICAnet is inspired by the Inception-v3 and InceptionResNet-v1 architectures, but it is tailored with 1-dimensional convolutions for processing the spectra at the pixel level of a hyperspectral image. To the best of the authors’ knowledge, this is the first DCNN architecture solely dedicated to mineral identification on the Martian surface. The model is evaluated by its matching with a TRDR (Targeted Reduced Data Record) dataset obtained using a hierarchical Bayesian model. The results demonstrate an impressive f-score of at least .77 among different mineral groups in the MICA library, which is on par with or better than the unsupervised models previously applied to this objective. © 2024