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

Machine Learning Predicts Recovery in Endurance Athletes But Requires Personalized Strategies

Researchers used machine learning to predict recovery status and heart rate variability in endurance athletes, revealing significant variability in individual responses and emphasizing the need for personalized training approaches.

4 Sep 2024

MIT's AI Risk Repository Launches Database of 777 AI Risks

Researchers at MIT's AI Risk Repository created a comprehensive database categorizing 777 AI risks into two detailed taxonomies, providing a foundational tool for managing AI-related hazards.

4 Sep 2024

AI Predicts and Prevents Failures Across Industries

Artificial Intelligence transforms maintenance strategies by predicting failures, optimizing resource use, and improving operational efficiency across sectors.

3 Sep 2024

Optimizing Engine Coolants with TiO₂ Nanofluids

This study investigates the use of titanium dioxide (TiO₂) nanoparticles in engine coolants, revealing significant improvements in heat transfer efficiency. An artificial neural network identified the optimal concentration, demonstrating economic viability and potential sustainability benefits for enhanced engine cooling.

2 Sep 2024

Revolutionizing Gemstone Analysis with Deep Learning

The article presents a novel deep-learning model, GEMTELLIGENCE, which combines multiple data types to automate gemstone classification and detect treatments. The system achieves accuracy comparable to expert analysis, promising cost-effective and standardized solutions for the gemstone industry.

2 Sep 2024

Deep Reinforcement Learning Boosts Robotic Manipulation

A deep reinforcement learning model enhances robotic manipulation by integrating pushing and grasping, achieving an 87% success rate in cluttered environments.

30 Aug 2024

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