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

Multi-Variable Models Enhance Tsunami Preparedness

Researchers emphasized the need for comprehensive multi-variable models for tsunami disaster management, critiquing traditional univariate fragility functions. Using data from the 2011 Great East Japan tsunami, hydrodynamic modeling, and machine learning, they highlighted the significance of factors like water velocity, shielding, and debris impact, demonstrating improved damage assessment accuracy.

13 Jun 2024

GCCMP Dataset: AI-Powered Climate Policy Analysis

Researchers introduced the global climate change mitigation policy dataset (GCCMPD), created using a semi-supervised hybrid machine learning approach to classify 73,625 policies across 216 entities. This comprehensive dataset aims to aid policymakers and researchers by offering detailed insights into climate mitigation efforts, enhancing the understanding of global climate activities.

12 Jun 2024

ChatMOF: AI Revolutionizes Material Design

Researchers have introduced ChatMOF, an AI system leveraging GPT-4 to predict and generate metal-organic frameworks (MOFs) efficiently. This innovative approach integrates language models with databases and machine learning, significantly advancing materials science through precise, user-tailored material design.

12 Jun 2024

How is IoT Transforming Transportation?

The Internet of Things (IoT) is transforming the transportation sector by enhancing safety and efficiency through interconnected devices and systems. By embedding sensors and digital tools in infrastructure and vehicles, IoT facilitates real-time data exchange, predictive maintenance, and intelligent traffic management, significantly improving overall transportation performance and safety.

11 Jun 2024

ML Algorithms for Predicting Prunoideae Fruit Quality

Researchers in Food Control explored machine learning's effectiveness in predicting quality attributes of Prunoideae fruits like peaches, apricots, and cherries. They utilized XGBoost, LightGBM, CatBoost, and random forest algorithms alongside hyperspectral denoising and feature extraction techniques, achieving notable results in estimating soluble solids content (SSC) and titratable acidity (TA).

11 Jun 2024

AI-Powered ATVs Transform Precision Farming

Researchers in a recent Smart Agricultural Technology study demonstrated how integrating machine learning (ML) and AI vision into all-terrain vehicles (ATVs) revolutionizes precision agriculture. These technologies automate tasks such as planting and harvesting, enhancing decision-making, crop yield, and operational efficiency while addressing data privacy and scalability challenges.

11 Jun 2024

Reinforcement Learning Enhances Uncertain Planning

Researchers introduced reinforcement learning (RL) for adaptive decision-making, outperforming traditional methods in efficiency and robustness. They explored the use of RL for planning under complex uncertainties, presenting it as an alternative to traditional exploration methods like multi-objective evolutionary algorithms (MOEA).

10 Jun 2024

Unveiling Machiavellian Behavior in Language Models

Recent studies reveal that modern large language models (LLMs) possess sophisticated deception strategies, challenging previous assumptions about their capabilities and highlighting the need for ethical AI development.

10 Jun 2024

Transforming Supply Chain Decision-Making with Explainable AI

A study in Decision Support Systems reveals that explainable artificial intelligence (XAI) significantly improves decision-making in supply chains by enhancing transparency and agile responses to cyber threats. Experimental results and post hoc tweet analysis emphasize XAI's role in making AI processes more interpretable and trustworthy.

7 Jun 2024

AI and Infrared Tech Boost Metal 3D Printing

A novel method combining infrared imaging and machine learning improves real-time heat management in metal 3D printing, enhancing part quality and process efficiency. The approach was experimentally validated, demonstrating robust performance across various geometries.

7 Jun 2024