Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Image segmentation using encoder-decoder architecture and region consistency activation(Institute of Electrical and Electronics Engineers Inc., 2016) Naik, D.; Jaidhar, C.D.An Encoder-Decoder Neural Network Architecture is combined with a novel strategy to improve global label consistency, to come with an improved image segmentation model. Label Distribution predictions extracted from the SegNet Network is investigated and used in the project for image labeling. An algorithm called Region Consistency Activation (RCA) to improve the global label consistency is implemented. RCA is based on a novel transformation between Ultra metric Contour Map (UCM) and the Probability of Regions Consistency (PRC). These algorithms were rigorously tested on the CamVid dataset. Superior performances were achieved compared with the state-of-the-art methods on this dataset. © 2016 IEEE.Item Image Colorization Using GANs and Perceptual Loss(Institute of Electrical and Electronics Engineers Inc., 2020) Sankar, R.; Nair, A.; Abhinav, P.; Mothukuri, S.K.P.; Koolagudi, S.G.Image colorization is of great use for several applications, such as the restoration of old images, as well as enabling the storage of grayscale images, which take up less space, which can later be colorized. But this problem is hard since there exist many possible color combinations for a particular grayscale image. Recent developments have aimed to solve this problem using deep learning. But, for achieving good performance, they require highly processed inputs, along with additional elements, such as semantic maps. In this paper, an attempt has been made for generalizing the procedure of colorization using a conditional Deep Convolutional Generative Adversarial Network (DCGAN) by adding "Perceptual Loss". The network is trained over the CIFAR-100 dataset. The results of the proposed generative model with perceptual loss are compared with the existing state-of-the-art systems normal GAN model and U-Net Convolutional model. © 2020 IEEE.Item Overview of the HASOC Track at FIRE 2020: Hate Speech and Offensive Language Identification in Tamil, Malayalam, Hindi, English and German(Association for Computing Machinery, 2020) Mandl, T.; Modha, S.; Anand Kumar, M.; Chakravarthi, B.R.This paper presents the HASOC track and its two parts. HASOC is dedicated to evaluate technology for finding Offensive Language and Hate Speech. HASOC is creating test collections for languages with few resources and English for comparison. The first track within HASOC has continued work from 2019 and provided a testbed of Twitter posts for Hindi, German and English. The second track within HASOC has created test resources for Tamil and Malayalam in native and Latin script. Posts were extracted mainly from Youtube and Twitter. Both tracks have attracted much interest and over 40 research groups have participated as well as described their approaches in papers. In this overview, we present the tasks, the data and the main results. © 2020 ACM.Item VesselXnet - A lightweight and efficient encoder-decoder based model for Retinal Vessel Segmentation(Institute of Electrical and Electronics Engineers Inc., 2021) Narasimhadhan, A.V.; Putluru, S.P.R.; Merugu, V.R.One of the contemporary issues present in medical image segmentation is the segmentation of the Retinal blood vessels. This is because many diseases can be accurately identified from the vascular structure of the retina and hence can be treated early and diagnosed thoroughly. Manual segmentation is hectic, cumbersome, time consuming and also error-prone. Hence there is a need for automatic vessel segmentation which can be a better technological advancement in the medical field. Some of the segmentation methods which were proposed previously have problems of low segmentation accuracy, incomplete segmentation and a large model size. With the progress of deep learning and convolutional neural networks several U-net based architectures were extensively used for this task which offered reliable segmentation results. In this paper, we proposed a light weight U-net based architecture which provides comparable accuracy with much less total parameters. © 2021 IEEE.Item DeepOA: Clinical Decision Support System for Early Detection and Severity Grading of Knee Osteoarthritis(Institute of Electrical and Electronics Engineers Inc., 2021) Dalia, Y.; Bharath, A.; Mayya, V.; Kamath S․, S.S.Knee Osteoarthritis (OA) is a medical condition affecting the knee joint that causes pain due to the cartilage wear-And-Tear. The severity of the impairment is graded by experienced radiologists as per standardized grading systems like the Kellgren-Lawrence(KL) grading scheme. Early detection and classification of knee OA in a patient before it increases in severity can significantly aid in corrective measures and benefit humankind. In this work, we propose a DL model to automatically segment the knee region and predict onset of Knee OA with X-ray scans. A comparative study using an ensemble model consisting of a YOLOv5 object detection algorithm for knee joint segmentation is also proposed. Various classification models such as VGG16, Resnet etc., are experimented with for the KL grade classification. The detailed experiments are conducted to understand the need for the region of interest segmentation step in KL grade classification. The proposed Clinical Decision Support System (CDSS) can help the medical practitioners perform preemptive screening based on X-ray scans for detecting onset earlier and for enabling required treatment. © 2021 IEEE.Item Blindness (Diabetic Retinopathy) Severity Scale Detection(Institute of Electrical and Electronics Engineers Inc., 2021) Bygari, R.; Naik, R.; Uday Kumar, P.Diabetic retinopathy (DR) is a severe complication of diabetes that can cause permanent blindness. Timely diagnosis and treatment of DR are critical to avoid total loss of vision. Manual diagnosis is time consuming and error-prone. In this paper, we propose a novel deep learning based method for automatic screening of retinal fundus images to detect and classify DR based on the severity. The method uses a dual-path configuration of deep neural networks to achieve the objective. In the first step, a modified UNet++ based retinal vessel segmentation is used to create a fundus image that emphasises elements like haemorrhages, cotton wool spots, and exudates that are vital to identify the DR stages. Subsequently, two convolutional neural networks (CNN) classifiers take the original image and the newly created fundus image respectively as inputs and identify the severity of DR on a scale of 0 to 4. These two scores are then passed through a shallow neural network classifier (ANN) to predict the final DR stage. The public datasets STARE, DRIVE, CHASE DB1, and APTOS are used for training and evaluation. Our method achieves an accuracy of 94.80% and Quadratic Weighted Kappa (QWK) score of 0.9254, and outperform many state-of-the-art methods. © 2021 IEEE.Item Findings of Shared Task on Offensive Language Identification in Tamil and Malayalam(Association for Computing Machinery, 2021) Kumaresan, P.K.; Premjith; Sakuntharaj, R.; Thavareesan, S.; Subalalitha, S.; Anand Kumar, M.; Chakravarthi, B.R.; Mccrae, J.P.We present the results of HASOC-Dravidian-CodeMix shared task1 held at FIRE 2021, a track on offensive language identification for Dravidian languages in Code-Mixed Text in this paper. This paper will detail the task, its organisation, and the submitted systems. The identification of offensive language was viewed as a classification task. For this, 16 teams participated in identifying offensive language from Tamil-English code mixed data, 11 teams for Malayalam-English code mixed data and 14 teams for Tamil data. The teams detected offensive language using various machine learning and deep learning classification models. This paper has analysed those benchmark systems to find out how well they accommodate a code-mixed scenario in Dravidian languages, focusing on Tamil and Malayalam. © 2021 Owner/Author.Item Fusing Conventional and Deep Learning Features for Hyperspectral Image Change Detection(Institute of Electrical and Electronics Engineers Inc., 2022) Bobate, N.; Yadav, P.P.; Narasimhadhan, A.V.In the field of remote sensing technology Change Detection (CD) is one of the major areas of research. Changes that have occurred on the earth's surface over time can be detected with this tool. Hyperspectral Image (HSI) data with high spectral resolution can help in identifying subtle changes than the typical multispectral image (MSI), and CD technology has benefitted immensely with the applications of HSI. Traditional CD techniques that used MSI as their input data are challenging to implement on HSI due to the high dimensionality of hyperspectral data. Furthermore, HSI data is affected by a lot of distortion and redundancy, contaminating the spectral-only information for CD purposes. CD accuracy can be improved by extracting the useful features of HSI. In Change Detection algorithms, the initial step is to extract features. Traditionally it is done using arithmetic operation, image transformation, and statistical methods. While some advanced strategies for extracting features are utilizing convolutional neural networks (CNNs) using the deep learning method. In this work, we aimed to integrate the conventional features with CNN extracted features to boost the overall ac-curacy of popular DL-based CD techniques. Spectral matching algorithms are used for extracting conventional features. In addition, appropriate changes are made to the recent deep learning architectures called Three-Directions Spectral-Spatial Convolution neural network (TDSSC) and General End-To-End Neural Network (GETNET), to fuse the conventional features. Farmland, River and USA data sets are used for experimentation. The proposed approach proves to be useful in improving the performance of DL-based CD techniques. © 2022 IEEE.Item FedPruNet: Federated Learning Using Pruning Neural Network(Institute of Electrical and Electronics Engineers Inc., 2022) Gowtham, L.; Annappa, A.; Sachin, D.N.Federated Learning (FL) is a distributed form of training the machine learning and deep learning models on the data spread over heterogeneous edge devices. The global model at the server learns by aggregating local models sent by the edge devices, maintaining data privacy, and lowering communication costs by just communicating model updates. The edge devices on which the model gets trained usually will have limitations towards power resource, storage, computations to train the model. This paper address the computation overhead issue on the edge devices by presenting a new method named FedPruNet, which trains the model in edge devices using the neural network model pruning method. The proposed method successfully reduced the computation overhead on edge devices by pruning the model. Experimental results show that for the fixed number of communication rounds, the model parameters are pruned up to 41.35% and 65% on MNIST and CIFAR-10 datasets, respectively, without compromising the accuracy compared to training FL edge devices without pruning. © 2022 IEEE.Item Epidemic Outbreak Prediction with Ensemble of Deep Learning Models(Institute of Electrical and Electronics Engineers Inc., 2022) Vasudev, R.; Dahikar, P.; Jain, A.; Patil, N.The ability of today's technology has proved it's significance and dire need in the world yet again, with COVID-19 being a global pandemic. Various techniques are being incorporated and researches being conducted everyday in order to mitigate this pandemic. Forecasting of COVID-19 cases is one such task in machine learning which is being researched intensively to develop reliable forecasting models.In the proposed work, we have forecasted the number of COVID-19 confirmed,recovered and death cases globally using time series data with machine learning and deep learning ensemble models. The purpose of this study is to prove that ensemble of several week learners that we have developed can result in a better performing model. Deep learning models always tend to perform better than machine learning and traditional linear models due to their non-linearity. Our study concludes that deep learning ensemble model achieves better performance than the machine learning ensemble (Random forest) and the individual base learners used in ensemble model itself in COVID-19 forecasting. © 2022 IEEE.