Publications

Non-Insulin-Dependent Diabetes Mellitus (NIDDM) is a chronic health condition caused by high blood sugar levels, and if not treated early, it can lead to serious complications i.e. blindness. Human Activity Recognition (HAR) offers potential for early NIDDM diagnosis, emerging as a key application for HAR technology. This research introduces DiabSense, a novel smartphone-dependent system for early staging of NIDDM to address the global challenge of undiagnosed diabetes. DiabSense incorporates HAR and Diabetic Retinopathy (DR) grading into a unified framework using advanced Graph Neural Networks (GNNs). HAR uses a comprehensive array of 23 human activities resembling Diabetes symptoms, and DR is already a prevalent complication of NIDDM. Graph Attention Network (GAT) in HAR achieved 98.32% accuracy on tri-axial accelerometer human activity sensor data, while Graph Convolutional Network (GCN) - Vision GNN in the Aptos 2019 retinal fundus dataset scored 84.48% for DR severity grading task, surpassing other state-of-the-art models. The trained GCN analyzed retinal images of four experimental human subjects for DR report generation, and GAT generated their average duration of daily activities over 30 days. The daily activities in non-diabetic periods of diabetic patients were measured and compared with the daily activities of the experimental subjects, which helped generate risk factors. Fusing risk factors with DR conditions enabled early diagnosis recommendations for the experimental subjects despite the absence of any apparent symptoms. The comparison of DiabSense system outcome with clinical diagnosis reports in the experimental subjects was conducted using the A1C test. The test results confirmed the accurate assessment of early diagnosis requirements for experimental subjects by the system. Overall, DiabSense exhibits significant potential for ensuring early NIDDM treatment, improving millions of lives worldwide.

Vision GNN (ViG) considers each pixel of a 2D image as a node, enabling richer structural representation and addresses CNNs’ limitations with irregular lesions present in fundus images.

GAT addresses the challenges of Long Short-Term Memory (LSTM) and Deep Bayesian Network (DBN) based models with efficient attention mechanisms that captures varying node importance and neighborhood sizes without full graph knowledge.

We aimed at augmenting, rather than replacing, clinicians during clinical decision making since clinicians are involved in the system during decision making. DiabSense reduces the need for expensive routine medical checkups and reliance on symptoms until the system alerts for an 'early diagnosis positive'. Since NIDDM symptoms are often absent in the early stages, undiagnosed patients gain an opportunity to begin medical treatment as soon as DiabSense detects a risk.

Diabetic Retinopathy (DR) is a serious eye condition that occurs due to high blood sugar levels in patients with Diabetes Mellitus. If left untreated, DR can potentially result in blindness. Using automated neural network-based methods to grade DR shows potential for early detection. However, the uneven and non-quadrilateral forms of DR lesions provide difficulties for traditional Convolutional Neural Network (CNN)-based architectures. To address this challenge and explore a novel algorithm architecture, this work delves into the usage of contrasting cluster assignments in retinal fundus images with the Swapping Assignments between multiple Views (SwAV) algorithm for DR grading. An ablation study was made where SwAV outperformed other CNN and Transformer-based models, independently and in ensemble configurations with an accuracy of 87.00% despite having fewer parameters and layers. The proposed approach outperforms existing state-of-the-art models regarding classification metrics, complexity, and prediction time. The findings offer great potential for medical practitioners, allowing for more accurate diagnosis of DR and earlier treatments to avoid visual loss.

Understanding and accurately predicting complex three-dimensional (3D) temperature distributions are critical in diverse domains, including climate science and industrial process optimization. This study presents a sophisticated framework employing a convolutional neural network (CNN)–based autoencoder (AE) architecture augmented with attention mechanisms for the efficient compression and reconstruction of 3D temperature distribution datasets. The framework integrates Singular Value Decomposition (SVD) analysis to ascertain the optimal latent space dimensionality, thereby ensuring a judicious balance between model complexity and reconstruction fidelity. Moreover, the autoencoder is trained using a customized loss function designed to prioritize higher temperature values, enhancing the reconstruction accuracy in critical regions—mathematically defined as regions where T > 675 °C. This ensures enhanced reconstruction accuracy in areas of significant thermal importance, which are critical for the accuracy of the model. Through systematic exploration of the latent space dimensionality and the relative weighting of non-zero temperature data points, optimal parameters are identified that maximize the coefficient of determination score. Empirical results indicate that optimal performance is achieved with a latent space size of six, incorporating a relative weight value of 4.5 for non-zero temperature data points and appropriate handling of zero-temperature data points. After evaluating the model for both zero and non-zero temperature data, the R² scores improved from 95.80% to 99.27%, demonstrating a significant enhancement in overall accuracy. This proposed methodology provides profound insights into the intrinsic structure of the data and offers highly accurate predictions for applications necessitating detailed spatial and temporal temperature analyses.