Faculty Publications
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Item Semantic Similarity and Paraphrase Identification for Malayalam Using Deep Autoencoders(Springer Science and Business Media Deutschland GmbH, 2021) Praveena, R.; Anand Kumar, M.; Padannayil, K.P.In this chapter, we deal with the sentence-level paraphrase identification for the Malayalam language. We use recursive autoencoder architecture for the unsupervised learning of phrase representations to extract features for paraphrase identification. Sentence’s features of varying lengths are converted to fixed-size representation using the convolution method of dynamic pooling. Initially, the Malayalam paraphrase identification system was designed to identify paraphrases and non-paraphrases alone and later extended to identify semi-equivalent paraphrases. Along with semantic features, conventional statistical features are further taken into account, resulting in improved system performance. The proposed system was implemented using word2vec embedding and obtained 77.67% accuracy for the two-class system and 66.07% for the three-class system. This chapter also discusses different experiments done for choosing the best parameters and embedding models. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Deep Learning for COVID-19(Springer Science and Business Media Deutschland GmbH, 2022) Bs, B.S.; Manoj Kumar, M.V.; Thomas, L.; Ajay Kumar, M.A.; Wu, D.; Annappa, B.; Hebbar, A.; Vishnu Srinivasa Murthy, Y.V.S.Ever since the outbreak in Wuhan, China, a variant of Coronavirus named “COVID 19” has taken human lives in millions all around the world. The detection of the infection is quite tedious since it takes 3–14 days for the symptoms to surface in patients. Early detection of the infection and prohibiting it would limit the spread to only to Local Transmission. Deep learning techniques can be used to gain insights on the early detection of infection on the medical image data such as Computed Tomography (CT images), Magnetic resonance Imaging (MRI images), and X-Ray images collected from the infected patients provided by the Medical institution or from the publicly available databases. The same techniques can be applied to do the analysis of infection rates and do predictions for the coming days. A wide range of open-source pre-trained models that are trained for general classification or segmentation is available for the proposed study. Using these models with the concept of transfer learning, obtained resultant models when applied to the medical image datasets would draw much more insights into the COVID-19 detection and prediction process. Innumerable works have been done by researchers all over the world on the publicly available COVID-19 datasets and were successful in deriving good results. Visualizing the results and presenting the summarized data of prediction in a cleaner, unambiguous way to the doctors would also facilitate the early detection and prevention of COVID-19 Infection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Convolutional Neural Network-Enabling Speech Command Recognition(Springer Science and Business Media Deutschland GmbH, 2023) Patra, A.; Pandey, C.; Palaniappan, K.; Sethy, P.K.The speech command recognition system based on deep image classification is the key that would tremendously promise to revolutionize research and development by overcoming the communication barrier between human and machine or computer. We are all aware of challenges in identifying the voice command in noise and variability in speed, pitch, and projection. This paper has developed an efficient and highly accurate speech command recognition for smart and effective speech processing applications like modern telecommunication. In particular, a novel convolutional neural network (CNN) is presented that works with a one-second audio clip consisting of one specific word including ten speech commands and other words labeled as “unknown,” and model implementations were operated in the noisy environment. The CNNs are structurally fully developed in such a way to recognize the speech commands with the utilization of deep learning (DL) for image classification concepts. Thus, this research used the concept of DL for image classification to translate the problem of speech command recognition into the image domain. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item An Efficient Infectious Disease Detection in Plants Using Deep Learning(Springer Science and Business Media Deutschland GmbH, 2024) Sunil, C.K.; Jaidhar, C.D.Over the past decade, agriculture has suffered reduced productivity from climate change and improper water, fertilizer, and pesticide use, fueling plant diseases. Pathogens pose the main threat, impacting crop yield and quality. Early detection and targeted treatments are crucial to improve both yield and quality. To address this, we have carried out deep learning-based approaches and published ours works in conferences and journSal. Those works are briefly discussed in the paper as follows: (i) Empirical work on different plant datasets is conducted to analyze the hyperparameters of the neural network. (ii) The research minimizes misclassifications by leveraging an ensemble-based strategy with AlexNet, ResNet, and VGGNet across seven plant leaf image datasets. The complexity of plant disease diagnosis in diverse conditions is tackled through a hybrid deep learning strategy, exemplified in the cardamom plant disease detection approach. (iii) An innovative deep learning-based approach is introduced to precise plant disease detection, crucial in the face of similar symptoms and imbalanced data. The proposed Multilevel Feature Fusion Network (MFFN) incorporates adaptive attention mechanisms, enhancing robustness by considering diverse network features. (iv) With cardamom plant disease classification utilizing U2-Net for background removal and EfficientNetV2 for classification, the network excels the performance on images with complex background, with this generated benchmark dataset with a complex background. This research work produced good results by achieving 99% accuracy on the tomato plant and 98.28% accuracy on the cardamom leaf dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item An efficient image retrieval system for remote sensing images using deep hashing network(Springer, 2020) Valaboju, S.; Venkatesan, M.Due to the huge increase in volumes of remote sensing images, there is a requirement for retrieval systems which maintain the retrieval accuracy and efficiency which requires better learning of features and the binary hash codes which better discriminate the images of different classes of images. The existing retrieval systems for remote sensing images use CNNs for feature learning which fails to preserve the spatial properties of an image which in turn affect the quality of binary hash code and the retrieval performance. This Paper tries to address the above goals by using (1) Extracting Hierarchical features of convolutional neural network and using them to sequential learning to better learn the features preserving spatial and semantic properties. (2) Use lossless triplet loss with two more loss functions to generate the binary hash codes which better discriminate the images of different classes. The proposed architecture consists of three phases: (1) Fine-tuning a pre-trained model. (2) Extracting the hierarchical features of convolutional neural network. (3) Using those features to train the deep learning-based hashing network. Experiments are conducted on a publicly available dataset UCMD and show that when hierarchial convolutional features are considered there is a significant improvement in performance. © Springer Nature Singapore Pte Ltd 2020.Item A TFD Approach to Stock Price Prediction(Springer, 2020) Chanduka, B.; Bhat, S.S.; Rajput, N.; Mohan, B.R.Accurate stock price predictions can help investors take correct decisions about the selling/purchase of stocks. With improvements in data analysis and deep learning algorithms, a variety of approaches has been tried for predicting stock prices. In this paper, we deal with the prediction of stock prices for automobile companies using a novel TFD—Time Series, Financial Ratios, and Deep Learning approach. We then study the results over multiple activation functions for multiple companies and reinforce the viability of the proposed algorithm. © 2020, Springer Nature Singapore Pte Ltd.Item Skeleton based Human Action Recognition for Smart City Application using Deep Learning(Institute of Electrical and Electronics Engineers Inc., 2020) Rashmi, M.; Guddeti, R.M.R.These days the Human Action Recognition (HAR) is playing a vital role in several applications such as surveillance systems, gaming, robotics, and so on. Interpreting the actions performed by a person from the video is one of the essential tasks of intelligent surveillance systems in the smart city, smart building, etc. Human action can be recognized either by using models such as depth, skeleton, or combinations of these models. In this paper, we propose the human action recognition system based on the 3D skeleton model. Since the role of different joints varies while performing the action, in the proposed work, we use the most informative distance and the angle between joints in the skeleton model as a feature set. Further, we propose a deep learning framework for human action recognition based on these features. We performed experiments using MSRAction3D, a publicly available dataset for 3D HAR, and the results demonstrated that the proposed framework obtained the accuracies of 95.83%, 92.9%, and 98.63% on three subsets of the dataset AS1, AS2, and AS3, respectively, using the protocols of [19]. © 2020 IEEE.Item Retinal-Layer Segmentation Using Dilated Convolutions(Springer Science and Business Media Deutschland GmbH, 2020) Guru Pradeep Reddy, T.; Ashritha, K.S.; Prajwala, T.M.; Girish, G.N.; Kothari, A.R.; Koolagudi, S.G.; Rajan, J.Visualization and analysis of Spectral Domain Optical Coherence Tomography (SD-OCT) cross-sectional scans has gained a lot of importance in the diagnosis of several retinal abnormalities. Quantitative analytic techniques like retinal thickness and volumetric analysis are performed on cross-sectional images of the retina for early diagnosis and prognosis of retinal diseases. However, segmentation of retinal layers from OCT images is a complicated task on account of certain factors like speckle noise, low image contrast and low signal-to-noise ratio amongst many others. Owing to the importance of retinal layer segmentation in diagnosing ophthalmic diseases, manual segmentation techniques have been proposed and adopted in clinical practice. Nonetheless, manual segmentations suffer from erroneous boundary detection issues. This paper thus proposes a fully automated semantic segmentation technique that uses an encoder–decoder architecture to accurately segment the prominent retinal layers. © 2020, Springer Nature Singapore Pte Ltd.Item Leveraging deep learning approaches for patient case similarity evaluation(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2021) Naganure, N.; Ashwin Nayak, U.; Kamath S․, S.One of the fundamental problems in Health Informatics is evaluating the clinical similarity between two patients for treatment recommendation. Retrieving clinical records of existing patients who are potentially similar to a newly arrived patient could help a physician in faster diagnosis and recommending informed treatment options, especially in the case of areas where specialist medical care is scarce. In Western countries, patient records are extensively stored in the form of Electronic Health Records (EHR), which are created manually by human experts, which can take a lot of time and is a cost-intensive process. In developing countries like India, patient records are increasingly being stored in digital formats and often contain diverse, heterogeneous, unstructured reports of patients. These can be potentially utilized for designing patient similarity assessment and recommendation systems. In this paper, we propose a patient similarity evaluation framework built on two supervised learning models—Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU). Our method creates an optimal patient representation for existing patients by aggregating reports collected over the duration of treatment, to overcome the loss of temporal information, for which a cohort of 16,723 patients across 8 disease categories was used. Both the models (CNN and GRU) learn by passing through the records of a patient chronologically and achieve an accuracy of 97.60 and 93.62%, respectively, on standard EHR dataset like MIMIC-III. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.Item Clustering Enhanced Encoder–Decoder Approach to Dimensionality Reduction and Encryption(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2021) Mukesh, B.R.; Madhumitha, N.; Aditya, N.P.; Vivek, S.; Anand Kumar, M.Dimensionality reduction refers to reducing the number of attributes that are being considered, by producing a set of principal variables. It can be divided into feature selection and feature extraction. Dimensionality reduction serves as one of the preliminary challenges in storage management and is useful for effective transmission over the Internet. In this paper, we propose a deep learning approach using encoder–decoder networks for effective (almost-lossless) compression and encryption. The neural network essentially encrypts data into an encoded format which can only be decrypted using the corresponding decoders. Clustering is essential to reduce the variation in the dataset to ensure overfit. Using clustering resulted in a net gain of 1% over the standard encoder architecture over three MNIST datasets. The compression ratio achieved was 24.6:1. The usage of image datasets is for visualization only and the proposed pipeline could be applied for textual and visual data as well. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.