Silpaja Chandrasekar

Ph.D. in Computer Science

Computer Science, Medical Image Analysis, Cancer Cell Detection

Dr. Silpaja Chandrasekar has a Ph.D. in Computer Science from Anna University, Chennai. Her research expertise lies in analyzing traffic parameters under challenging environmental conditions. Additionally, she has gained valuable exposure to diverse research areas, such as detection, tracking, classification, medical image analysis, cancer cell detection, chemistry, and Hamiltonian walks.

Dr. Silpaja commenced her career as a Programmer Analyst Trainee at CTS (Cognizant Technology Solutions). Later, she ventured into research and served as a Research Associate in the Department of Chemistry at IIT (Indian Institute of Technology) Madras. Her passion for research led her to become a full-time research scholar in the field. Additionally, Silpaja embraced the joy of teaching as an Assistant Professor at Sri Venkateswara College of Engineering, Chennai.

Silpaja’s research experience has honed her skills in writing manuscripts, news articles, and reviews. Moreover, she has actively participated as a peer reviewer for many journals. After taking a break to raise her two daughters, Silpaja is now eager to re-immerse herself in the realms of learning, research, and writing. She finds joy in cooking and spending quality time with her daughters. At present, she is a Tech writer in the field of Artificial Intelligence.

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Articles from Silpaja

Using Deep Learning and XAI to Enhance El Niño-Southern Oscillation Forecast

A groundbreaking study introduces the IGP-UHM AI v1.0 model, utilizing deep learning and XAI to enhance El Niño-Southern Oscillation (ENSO) prediction. The 2023–2024 forecast reveals sustained yet weakening EN conditions, emphasizing the model's credibility through Layerwise Relevance Propagation (LRP) explanations. The research underscores the need for ongoing refinement, human oversight, and raises crucial questions about ENSO predictability limits in the context of climate change.

7 Dec 2023

Deep Learning-Based Fault Monitoring in High-Voltage Circuit Breakers

Researchers propose an innovative fault monitoring approach for high-voltage circuit breakers, utilizing a specialized device and deep learning techniques. The unsupervised deep learning method showcases over 95% accuracy in fault diagnosis, outperforming traditional algorithms in feature extraction and computation speed. The study suggests a practical and efficient solution for real-time fault monitoring, holding promise for enhancing reliability in high-voltage systems.

6 Dec 2023

Efficient Aerodynamic Field Predictions in Heavy Vehicle Design: A CNN Approach

Researchers employ a Convolutional Neural Network (CNN) to predict velocity and pressure aerodynamic fields in heavy vehicles, showcasing substantial accuracy in comparison to Computational Fluid Dynamics (CFD) simulations. The CNN's efficiency, reducing computational time by four orders of magnitude, suggests promising prospects for cost-effective and efficient aerodynamic field predictions in vehicle design, addressing challenges associated with CFD tools.

6 Dec 2023

Revolutionizing Automatic Speech Translation with Enhanced Expressivity and Multilingual Capabilities

This paper introduces a groundbreaking series of models, including SeamlessM4Tv2, SeamlessExpressive, and SeamlessStreaming, designed to advance automatic speech translation. These models excel in preserving meaning, naturalness, and expressivity, catering to diverse linguistic contexts. Safety measures, including toxicity detection, gender bias evaluation, and watermarking, underscore the commitment to ethical deployment.

5 Dec 2023

MLpronto: Democratizing Machine Learning with a User-Friendly Interface

This paper introduces MLpronto, a user-friendly machine learning platform aimed at democratizing the field by providing accessibility without requiring programming skills. This web-based tool swiftly processes data, executes prevalent supervised machine learning algorithms, and generates corresponding programming code, catering to both novice users and those inclined towards programming.

5 Dec 2023

Using AI to Enhance Sales Forecasting

This article explores the profound impact of Artificial Intelligence (AI) on sales forecasting, emphasizing its role in leveraging advanced algorithms for deep data analysis, predictive modeling, demand forecasting, and customer segmentation. The applications of AI in sales forecasting, benefits such as enhanced accuracy and time efficiency, and the evolution from traditional to advanced AI methods are discussed.

4 Dec 2023

Advancing Healthcare Security Using AI

This article explores the pivotal role of Artificial Intelligence (AI) in fortifying healthcare security, emphasizing its significance in protecting sensitive patient data and ensuring compliance with regulations like HIPAA. Employing machine learning for anomaly detection, encryption techniques for data protection, and robust authentication methods, AI contributes to early threat detection, enhanced threat intelligence, and proactive security measures.

4 Dec 2023

A-Lab: Pioneering Autonomous Materials Discovery Through AI and Experimentation

Researchers introduce the A-Lab, an autonomous laboratory integrating AI, robotics, and historical data to synthesize 41 new compounds from 58 targets over 17 days. With a 71% success rate, the study underscores the impact of active learning, computational insights, and refined synthesis strategies in advancing materials discovery. The A-Lab's innovative approach advocates for the fusion of technology and experimental endeavors, marking a significant step towards autonomous materials research and development.

3 Dec 2023

Innovative Food Weight Estimation from Images Using Boosting Algorithms

Researchers unveil a pioneering method for accurately estimating food weight using advanced boosting regression algorithms trained on a vast Mediterranean cuisine image dataset. Achieving remarkable accuracy with a mean weight absolute error of 3.93 g, this innovative approach addresses challenges in dietary monitoring and offers a promising solution for diverse food types and shapes.

3 Dec 2023

Exploring Consumer-Grade Cameras for Precision Motion Analysis

Researchers investigate the feasibility of using action sport cameras (ASC) for motion analysis, comparing them with marker-based motion capture systems. The study assesses accuracy through different calibration methods, revealing slight differences in marker position and angular displacement between systems. Dynamic calibration for machine vision (MV) cameras and static calibration for ASC prove most accurate, showcasing the potential of consumer-grade setups for reliable motion analysis.

1 Dec 2023