Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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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.