Faculty Publications
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Item Mucormycosis, renowned as black fungus, is an uncommon and invasive fungal infection that commonly targets immunocompromised patients(Apple Academic Press, 2024) Arora, N.; Kakde, A.; Pandey, G.; Naaz, H.; Goyal, L.; Tiwari, S.; Elngar, A.A.A group of molds called Mucor causes mucormycosis. According to the Centre for Disease Control and Prevention (CDC), this type of infection has a mortality rate of 46%. According to a survey, mucormycosis has affected only 109 thousand people in India annually in the past which is 1% of the total population. But recently, the cases are increasing at a very high pace. It's presumed that uncontrolled diabetes and careless use of steroids are two main factors for the spread of mucormycosis in India. This fungus spread through the paranasal sinus into orbit affecting, the eyes,brain, and lungs. Therefore, it is vital to diagnose mucormycosis at the right point in time for timely treatment. Hence, this paper provides insight into mucormycosis occurrence in diabetic and COVID-19 patients and covers the types of disease (mucormycosis/COVID-19), and investigates how these molds are getting a chance to invade the body of immunocompromised patients. In addition, this paper addresses possible treatments and preventive measures © 2025 Apple Academic Press, Inc. All rights reserved.Item Enhancing Paediatric Healthcare: Deep Learning-Based Pneumonia Diagnosis from Children's Chest X-rays(Association for Computing Machinery, 2024) Patidar, M.; Pandey, G.; Koolagudi, S.G.; Karanth, K.S.; Chandra, V.Pneumonia is a severe disease in children and adults caused by lung infection. It is also the major cause of death in young children. Early diagnosis of pneumonia is essential as it can be life-threatening if not treated at the right time. In this paper, pneumonia detection in children using chest X-ray images has been done. The dataset considered for this work is the Kermany pneumonia chest X-ray dataset and a newly collected high-resolution dataset by us of children's Chest X-rays from Father Muller Hospital Mangalore, Karnataka. The dataset consists of chest X-ray images, that are preprocessed using an Auto-encoder before feeding them into the network. The proposed work includes a hybrid Ensemble approach for both datasets. The proposed approach uses three Well-known convolutional neural network (CNN) models for Ensemble. These models include MobilenetV2, ResNet152, and DenseNet169. These models were individually trained using transfer learning (as the models were Pre-trained on the ImageNet dataset) and fine-tuned. The results were compared with those of the proposed method. The Kermany pneumonia chest X-ray dataset results in the proposed Ensemble approach are as follows: We achieved 95.03% classification accuracy. The results of the proposed Ensemble approach on the Father Muller Hospital dataset are as follows: We achieved 82.60% classification accuracy. © 2024 Copyright held by the owner/author(s).Item Frame-Level Audio Hate Speech Detection for Kannada Language(Institute of Electrical and Electronics Engineers Inc., 2025) Gubbi, A.V.; Pandey, G.; Koolagudi, S.G.Detecting hate speech in audio has become increasingly challenging due to the increasing use of internet platforms and digital communication. Through this study, we develop an audio-based speech classifier to facilitate the detection of hate speech in the Kannada language. We present an approach to classifying hate speech at the frame level by extracting audio features such as Mel-Frequency Cepstral Coefficients (MFCCs), spectral bandwidth, spectral contrast, and chroma features. Furthermore, we present a custom Kannada hate speech dataset to address the scarcity of resources for hate speech studies in the Kannada language. We collected over 40 minutes of audio samples from YouTube and X (formerly Twitter). Our experiments show that an optimized XGBoost model achieved an accuracy of 73% on the custom dataset for frame-level classification. We also propose a cascading classifiers approach with two classifiers to exploit the locality of hate speech that improves the accuracy to 77%. Finally, we benchmark the proposed model against Logistic Regression, SVM, and XGBoost models. © 2025 IEEE.Item Time based Sentiment Analysis of Financial Headlines using Recurrent Neural Network(Institute of Electrical and Electronics Engineers Inc., 2025) Shashank, G.; Pandey, G.; Koolagudi, S.G.The sentiment of financial news headlines plays an important role in understanding market trends and investor behavior. This study proposes a Recurrent Neural Network (RNN)-based model for accurately classifying the sentiment of financial headlines into positive, neutral, and negative categories. Keeping in mind the time trend based behavior of the financial world, and the impact of certain keywords relevant only in the financial context, the RNN architecture captures the contextual nuances often overlooked by traditional methods. To address the domain-specific challenges of financial language and the inherent trends based on the time series based data, the model aims to incorporate embeddings that are fine-tuned on financial text along with a capacity to capture time based context. Experiments conducted on a dataset of financial stocks for a period from 2003 to 2020 help demonstrate the effectiveness of the proposed RNN compared to other benchmark methods. It provides a result with 97% accuracy and accurately captures the context of verbal and time based sentiment context. © 2025 IEEE.Item Non-Invasive Detection of Anemia Using Deep Learning on Conjunctival Images(Institute of Electrical and Electronics Engineers Inc., 2025) Kedar, D.S.; Pandey, G.; Koolagudi, S.G.Anemia, characterized by low levels of red blood cells or hemoglobin, affects millions worldwide, significantly affecting public health. Traditional diagnostic methods, while effective, are invasive, costly, and inaccessible in resource-constrained settings. This paper proposes a non-invasive approach for anemia detection using conjunctival images analyzed through deep learning techniques. The proposed methodology involves capturing high-resolution conjunctival images, pre-processing them, and using a customized Convolutional Neural Network (CNN) for feature extraction and classification. The results achieved by the customized CNN fine-tuned with a batch size of 16 give an Accuracy of 96%, Precision of 95%, Recall of 96%, and ROC-AUC score of 0.99. The customized CNN outperformed the other models for this work, such as Random Forest, XGBoost, SVM, ResNet50, and MobileNetV2. This work highlights the potential for non-invasive diagnostic tools to improve accessibility and efficiency in healthcare, particularly for underserved populations. The findings endorse integrating deep learning in healthcare as a transformative approach to address global challenges such as anemia. © 2025 IEEE.Item Rare Sound Event Detection Using Multi-resolution Cochleagram Features and CRNN with Attention Mechanism(Birkhauser, 2025) Pandey, G.; Koolagudi, S.G.Acoustic event detection (AED) or sound event detection (SED) is a problem that focuses on automatically detecting acoustic events in an audio recording along with their onset and offset times. Rare acoustic event detection in AED is a challenging problem. Rare AED aims to detect rare but significant sound events in an audio signal. Traditional methods used for SED often struggle to accurately detect rare sound events due to their infrequent occurrence and diverse characteristics. This paper introduces novel features named as multi-resolution cochleagrams (MRCGs) for rare SED tasks. Different cochleagrams with different resolutions are extracted from the audio recording and stacked to get the MRCG feature vector. The equivalent rectangular bandwidth (ERB) scale used in the cochleagram simulates the human auditory filter. The classifier used is a convolutional recurrent neural network (CRNN) embedded with an attention module. This work considers the Task 2 DCASE 2017 dataset for detecting rare sound events. Results show that the proposed MRCG and CRNN with attention combination improves the performance. The proposed method achieved an average error rate of 0.11 and an average F1 score of 94.3%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.Item Rare sound event detection using superlets and a convolutional TDPANet(Springer Science and Business Media Deutschland GmbH, 2025) Pandey, G.; Koolagudi, S.G.Rare Sound Event Detection (RSED) focuses on identifying infrequent but significant sound events in audio recordings with precise onset and offset times. It is crucial for applications like surveillance, healthcare, and environmental monitoring. An essential component in RSED systems is extracting effective time-frequency representation as input features. These features capture short, transient acoustic events in an audio input recording, even in noisy and complex environments. Most existing approaches to this RSED problem rely on input features as time-frequency representations, such as the Mel spectrogram, Constant-Q Transform (CQT), and Continuous Wavelet Transform (CWT). However, these approaches often suffer from resolution trade-offs between frequency and time. This trade-off limits their ability to precisely capture the fine-grained details needed to detect these events in complex acoustic environments. To overcome these limitations, we introduce superlets, a novel time-frequency representation that offers super-resolution in both time and frequency domains. To process the high-resolution Superlet features, we have also proposed a Convolutional Temporal Dilated Pyramid Attention Network (TDPANet). This novel neural network architecture incorporates convolutional feature extraction, dilated temporal modeling, multi-scale temporal pooling, and temporal attention mechanisms to enhance event detection accuracy. We evaluate our method on the DCASE 2017 Task 2 rare sound event dataset, which includes isolated sound events and real-world acoustic scenes. Experimental results show that our proposed method significantly outperforms state-of-the-art techniques, achieving an Error Rate (ER) of 0.15 and an F1-score of 92.3%, demonstrating its effectiveness in detecting rare sound events. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.