Balasubramanian Raman
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View article: A Decade of Prenatal Genetic Diagnostics: Insights From 1949 Cases at a Medical Genetics Facility in South India
A Decade of Prenatal Genetic Diagnostics: Insights From 1949 Cases at a Medical Genetics Facility in South India Open
Objective Our objective was to report on the performance of prenatal diagnostic procedures for genetic testing in a developing country. Method This retrospective study involved a review of medical records of women who underwent prenatal di…
View article: ASSOCIATION OF GLTSCR1, ERCC4, NBN, AND XRCC1 POLYMORPHISMS WITH GLIOMA AND MENINGIOMA RISK IN A TERTIARY CARE HOSPITAL
ASSOCIATION OF GLTSCR1, ERCC4, NBN, AND XRCC1 POLYMORPHISMS WITH GLIOMA AND MENINGIOMA RISK IN A TERTIARY CARE HOSPITAL Open
A hospital-based case-control study was systematically conducted over an 18-month period, from January 2023 to June 2024. The study cohort was recruited from the Department of Neuro surgery at King George Hospital,VisakhapatnamThe study in…
View article: Contraction, Criticality, and Capacity: A Dynamical-Systems Perspective on Echo-State Networks
Contraction, Criticality, and Capacity: A Dynamical-Systems Perspective on Echo-State Networks Open
Echo-State Networks (ESNs) distil a key neurobiological insight: richly recurrent but fixed circuitry combined with adaptive linear read-outs can transform temporal streams with remarkable efficiency. Yet fundamental questions about stabil…
View article: Turning Time Into Shapes: A Point‐Cloud Framework With Chaotic Signatures for Time Series
Turning Time Into Shapes: A Point‐Cloud Framework With Chaotic Signatures for Time Series Open
We propose a novel methodology for transforming financial time series into a geometric format via a sequence of point clouds, enabling richer modeling of nonstationary behavior. In this framework, volatility serves as a spatial directive t…
View article: Dynamics and Computational Principles of Echo State Networks: A Mathematical Perspective
Dynamics and Computational Principles of Echo State Networks: A Mathematical Perspective Open
Reservoir computing (RC) represents a class of state-space models (SSMs) characterized by a fixed state transition mechanism (the reservoir) and a flexible readout layer that maps from the state space. It is a paradigm of computational dyn…
View article: Real-time earthquake magnitude prediction using designed machine learning ensemble trained on real and CTGAN generated synthetic data
Real-time earthquake magnitude prediction using designed machine learning ensemble trained on real and CTGAN generated synthetic data Open
The earthquake early warning (EEW) system provides advance notice of potentially damaging ground shaking. In EEW, early estimation of magnitude is crucial for timely rescue operations. A set of thirty-four features is extracted using the p…
View article: Approximation-Aware Training for Efficient Neural Network Inference on MRAM Based CiM Architecture
Approximation-Aware Training for Efficient Neural Network Inference on MRAM Based CiM Architecture Open
Convolutional neural networks (CNNs), despite their broad applications, are constrained by high computational and memory requirements. Existing compression techniques often neglect approximation errors incurred during training. This work p…
View article: Biasing & Debiasing based Approach Towards Fair Knowledge Transfer for Equitable Skin Analysis
Biasing & Debiasing based Approach Towards Fair Knowledge Transfer for Equitable Skin Analysis Open
Deep learning models, particularly Convolutional Neural Networks (CNNs), have demonstrated exceptional performance in diagnosing skin diseases, often outperforming dermatologists. However, they have also unveiled biases linked to specific …
View article: Auto-WCEBleedGen Version V1 and V2: Challenge, Datasets and Evaluation
Auto-WCEBleedGen Version V1 and V2: Challenge, Datasets and Evaluation Open
In this document, we provide an overview of the Auto-WCEBleedGen Version V1 and V2. The challenge V1 was organized virtually by MISAHUB (Medical Imaging and Signal Analysis) in collaboration with the 8th International CVIP 2023 (Conference…
View article: SOFIM: Stochastic Optimization Using Regularized Fisher Information Matrix
SOFIM: Stochastic Optimization Using Regularized Fisher Information Matrix Open
This paper introduces a new stochastic optimization method based on the regularized Fisher information matrix (FIM), named SOFIM, which can efficiently utilize the FIM to approximate the Hessian matrix for finding Newton's gradient update …
View article: Synthesizing Sentiment-Controlled Feedback For Multimodal Text and Image Data
Synthesizing Sentiment-Controlled Feedback For Multimodal Text and Image Data Open
The ability to generate sentiment-controlled feedback in response to multimodal inputs comprising text and images addresses a critical gap in human-computer interaction. This capability allows systems to provide empathetic, accurate, and e…
View article: DeepMediX: A Deep Learning-Driven Resource-Efficient Medical Diagnosis Across the Spectrum
DeepMediX: A Deep Learning-Driven Resource-Efficient Medical Diagnosis Across the Spectrum Open
In the rapidly evolving landscape of medical imaging diagnostics, achieving high accuracy while preserving computational efficiency remains a formidable challenge. This work presents \texttt{DeepMediX}, a groundbreaking, resource-efficient…
View article: Vision Through the Veil: Differential Privacy in Federated Learning for Medical Image Classification
Vision Through the Veil: Differential Privacy in Federated Learning for Medical Image Classification Open
The proliferation of deep learning applications in healthcare calls for data aggregation across various institutions, a practice often associated with significant privacy concerns. This concern intensifies in medical image analysis, where …
View article: See Through the Fog: Curriculum Learning with Progressive Occlusion in Medical Imaging
See Through the Fog: Curriculum Learning with Progressive Occlusion in Medical Imaging Open
In recent years, deep learning models have revolutionized medical image interpretation, offering substantial improvements in diagnostic accuracy. However, these models often struggle with challenging images where critical features are part…
View article: Transcending Grids: Point Clouds and Surface Representations Powering Neurological Processing
Transcending Grids: Point Clouds and Surface Representations Powering Neurological Processing Open
In healthcare, accurately classifying medical images is vital, but conventional methods often hinge on medical data with a consistent grid structure, which may restrict their overall performance. Recent medical research has been focused on…
View article: Transfer learning based framework for image segmentation using medical images and Tversky similarity
Transfer learning based framework for image segmentation using medical images and Tversky similarity Open
Medical image segmentation and further processing are extremely difficult due to the variations in images produced by various medical imaging techniques. In addition, characteristics like color, size, shape, and the separation of the foreg…
View article: Interpretable Multimodal Emotion Recognition using Hybrid Fusion of Speech and Image Data
Interpretable Multimodal Emotion Recognition using Hybrid Fusion of Speech and Image Data Open
This paper proposes a multimodal emotion recognition system based on hybrid fusion that classifies the emotions depicted by speech utterances and corresponding images into discrete classes. A new interpretability technique has been develop…
View article: VISTANet: VIsual Spoken Textual Additive Net for Interpretable Multimodal Emotion Recognition
VISTANet: VIsual Spoken Textual Additive Net for Interpretable Multimodal Emotion Recognition Open
This paper proposes a multimodal emotion recognition system, VIsual Spoken Textual Additive Net (VISTANet), to classify emotions reflected by input containing image, speech, and text into discrete classes. A new interpretability technique,…
View article: Classification of COVID-19 from chest x-ray images using deep features and correlation coefficient
Classification of COVID-19 from chest x-ray images using deep features and correlation coefficient Open
COVID-19 is a viral disease that in the form of a pandemic has spread in the entire world, causing a severe impact on people's well being. In fighting against this deadly disease, a pivotal step can prove to be an effective screening and d…
View article: Affective Feedback Synthesis Towards Multimodal Text and Image Data
Affective Feedback Synthesis Towards Multimodal Text and Image Data Open
In this paper, we have defined a novel task of affective feedback synthesis that deals with generating feedback for input text & corresponding image in a similar way as humans respond towards the multimodal data. A feedback synthesis syste…
View article: P2SLR: A Privacy-Preserving Sign Language Recognition as-a-Cloud Service Using Deep Learning For Encrypted Gestures
P2SLR: A Privacy-Preserving Sign Language Recognition as-a-Cloud Service Using Deep Learning For Encrypted Gestures Open
Cloud-based services have revolutionized data storage and processing tasks. However, these services raise security concerns as service providers may misuse the user’s stored data. Privacy loss is particularly problematic for hearing and sp…
View article: P2SLR: A Privacy-Preserving Sign Language Recognition as-a-Cloud Service Using Deep Learning For Encrypted Gestures
P2SLR: A Privacy-Preserving Sign Language Recognition as-a-Cloud Service Using Deep Learning For Encrypted Gestures Open
Cloud-based services have revolutionized data storage and processing tasks. However, these services raise security concerns as service providers may misuse the user’s stored data. Privacy loss is particularly problematic for hearing and sp…