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

Neural Networks Detect Helicopter Rotor Damage

Researchers used artificial neural networks (ANNs) to enhance damage detection in composite helicopter rotor blades. The study demonstrated high accuracy in load identification and damage localization, offering a promising approach for structural health monitoring in complex aerospace components.

30 Aug 2024

Machine Learning Unveils Satellite Salinity Bias Patterns

Researchers used an unsupervised machine learning model to classify biases in satellite sea surface salinity data, revealing significant biases in cold regions and strong current areas. The findings stress careful data interpretation and guide improvements in future satellite salinity measurements.

28 Aug 2024

FocusLLM Scales Context with Parallel Decoding

FocusLLM introduces a novel approach to extend the context length of language models using parallel decoding. By chunking and integrating local context, it efficiently handles long texts up to 400K tokens, enhancing performance in language modeling and long-context tasks.

28 Aug 2024

Lumos Enhances Multimodal AI with On-Device STR

Lumos, a multimodal AI system, integrates on-device scene text recognition (STR) to improve question answering capabilities. This innovation balances high-quality text recognition with optimized performance, advancing real-world applications for smart assistants.

28 Aug 2024

Machine Learning Cuts Costs in Solar Power Cooling

A machine learning framework optimized dry cooling system designs for supercritical CO2 Brayton cycle solar power plants, reducing lifetime cooling costs by 67%. The study highlights the potential for ML to accelerate cost-effective, sustainable energy solutions.

28 Aug 2024

AI Predicts Landslides with Enhanced Precision

Advancements in AI, particularly machine learning and deep learning, have significantly improved landslide prediction accuracy. By integrating diverse data sources and utilizing pattern recognition, AI offers real-time monitoring and better risk management, crucial for disaster preparedness.

27 Aug 2024

AI Optimizes School Fitness Evaluations

A multiplatform computer vision system was developed to assess schoolchildren's physical fitness using smartphones. This system demonstrated high accuracy in field and lab tests, providing a reliable and user-friendly tool for fitness evaluation in educational environments.

27 Aug 2024

Enhancing Maritime CV with Domain Knowledge

Researchers introduced innovative computer vision techniques to the maritime industry, incorporating ensemble learning and domain knowledge. These methods significantly improve detection accuracy and optimize video viewing on vessels, offering advancements for marine operations and communication.

27 Aug 2024

Reinforcement Learning Stabilizes Flow in Engineering

Researchers combined deep reinforcement learning with a CNN-based model to optimize flow control around square cylinders, reducing training time and improving accuracy. This method significantly enhances flow stability and offers promising applications in ocean engineering and aerodynamics.

26 Aug 2024

Language Models Detect Anomalies in Time Series

MIT researchers introduced SigLLM, using large language models for efficient anomaly detection in time-series data. Their approach, particularly the Detector method, offers a promising alternative to deep learning models, reducing complexity and cost in equipment monitoring.

26 Aug 2024