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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) CHIPSET (Journal on Computer Hardware, Signal Processing, Embedded System and Networking)
Narawade, Vaibhav
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Explainable zero-shot learning and transfer learning for real time Indian healthcare Saigaonkar, Swati; Narawade, Vaibhav
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i1.pp91-101

Abstract

Clinical note research is globally recognized, but work on real-time data, particularly from India, is still lagging. This study initiated by training models on medical information mart for intensive care (MIMIC) clinical notes, focusing on conditions like chronic kidney disease (CKD), myocardial infarction (MI), and asthma using the structured medical domain bidirectional encoder representations from transformers (SMDBERT) model. Subsequently, these models were applied to an Indian dataset obtained from two hospitals. The key difference between publicly available datasets and real-time data lies in the prevalence of certain diseases. For example, in a real-time setting, tuberculosis may exist, but the MIMIC dataset lacks corresponding clinical notes. Thus, an innovative approach was developed by combining a fine-tuned SMDBERT model with a customized zero-shot learning method to effectively analyze tuberculosis-related clinical notes. Another research gap is the lack of explainability because deep learning (DL) models are inherently black-box. To further strengthen the reliability of the models, local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP) explanations were projected along with narrative explanations which generated explanations in a natural language format. Thus, the research provides a significant contribution with ensemble technique of zero-shot learning and SMDBERT model with an accuracy of 0.92 as against the specialized models like scientific BERT (SCIBERT), biomedical BERT (BIOBERT) and clinical BioBERT.
Advancements in brain tumor classification: a survey of transfer learning techniques Jadhav, Snehal; Bharne, Smita; Narawade, Vaibhav
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp1002-1014

Abstract

This survey article presents a critical review of the state-of-the-art transfer learning (TL) methodologies applied in the field of brain tumor classification, with a special emphasis on their various contributions and associated performance metrics. We will discuss various pre-processing approaches, the underlying fine-tuning strategies, whether used purely or in an end-to-end training manner, and multi-modal applications. The current study specifically highlights the application of VGG16 and residual network (ResNet) methods for feature extraction, demonstrating that leveraging highorder features in magnetic resonance imaging (MRI) images can enhance accuracy while reducing training. We further analyze fine-tuning methods in relation to their role in optimizing model layers for small, domain-specific datasets, finding them particularly effective in enhancing performance on the small brain tumor dataset. It will look into end-to-end training, which means fine-tuning models that have already been trained on large datasets to make them better. It will also present multimodal TL as a way to use both MRI and computed tomography (CT) scan data to get better classification results. Comparing different pre-trained models can provide a better understanding of the strengths and weaknesses associated with the particular brain tumor classification task. This review aims to analyze the advancements in TL for medical image analysis and explore potential avenues for future research and development in this crucial field of medical diagnostics.
Marathi Speech Emotion recognition using Deep Learning techniques. Ketkar, Akhilesh; Mishra, Divyansh; Nirmal, Madhur; Mulla, Faizan; Narawade, Vaibhav
CHIPSET Vol. 5 No. 01 (2024): Journal on Computer Hardware, Signal Processing, Embedded System and Networkin
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/chipset.5.01.1-4.2024

Abstract

In the project, an emotion recognition system from speech is proposed using deep learning. The goal of this project is to classify a speech signal into one of the five emotions listed below: anger, boredom, fear, happiness, and sadness. Snippets below from numerous Marathi movies and TV shows were used to construct the dataset for Marathi language samples which include 20 audio samples for anger, 19 for boredom, 5 for fear, and 11 for happiness. The proposed system first processes a speech signal from the time domain to the frequency domain using Discrete Time Fourier Transform (DTFT). Then, data augmentation is performed which includes noise injection, stretching, shifting, and pitch scaling of the speech signal. Next, feature extraction is performed in which 5 features were selected, which include Mel Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate (ZCR), Chroma STFT, Mel Spectrogram, and Root mean square value. These features were then fed to a Convolutional Neural Network (CNN). The efficiency of the suggested system employing the CNNs is supported by experimental findings. This model’s accuracy on the test data is 80.33%, and its f1 values for anger, boredom, fear, happiness, and sadness are 0.85, 0.83, 0.50, 0.62, and 0.84, respectively.
Co-Authors Bharne, Smita Jadhav, Snehal Ketkar, Akhilesh Mishra, Divyansh Mulla, Faizan Nirmal, Madhur Saigaonkar, Swati