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

AI-Powered Satellites: Boosting Connectivity and Beyond

The integration of artificial intelligence (AI) with satellite technology has ushered in a new era of enhanced connectivity, efficiency, and capabilities across various sectors, from telecommunications to agriculture and disaster management.

25 Jun 2024

Secret Strategies Enhance Game AI Performance

Researchers introduced a private agent leveraging private deliberation and deception, achieving higher long-term payoffs in multi-player games than its public counterpart. Utilizing the partially observable stochastic game framework, in-context learning, and chain-of-thought prompting, this study highlights advanced communication strategies' potential to improve AI performance in competitive and cooperative scenarios.

25 Jun 2024

Evaluating Text-to-Image Generative Models: A Global Perspective

Researchers found that current automated metrics inadequately capture the diverse human preferences necessary for evaluating text-to-image generative models across different regions like Africa, Europe, and Southeast Asia.

25 Jun 2024

Automated 3D Scene Virtualization Boosts VR Experiences

Researchers introduced the Virtual Experience Toolkit (VET) in the journal Sensors, utilizing deep learning and computer vision for automated 3D scene virtualization in VR environments. VET employs advanced techniques like BundleFusion for reconstruction, semantic segmentation with O-CNN, and CAD retrieval via ScanNotate to enhance realism and immersion.

24 Jun 2024

Using ML to Optimize Titanium Dioxide Photocatalytic Degradation Rates

Researchers explored 13 machine learning models to predict the efficacy of titanium dioxide (TiO2) in degrading air pollutants. Models like XG Boost, decision tree, and lasso regression demonstrated high accuracy, with XG Boost notably excelling with low mean absolute error and root mean squared error.

24 Jun 2024

ML-based Prediction of Explosion Shockwaves

Researchers developed two physics-informed machine learning (PIML) models to predict the peak overpressure of ground-reflected explosion shockwaves, significantly improving accuracy over traditional methods. This innovation aids in structural design and explosion hazard assessment.

21 Jun 2024

Deep Learning Probes Cultural Ecosystem Services

Researchers used AI models to analyze Flickr images from global protected areas, identifying cultural ecosystem services (CES) activities. Their study reveals distinct regional patterns and underscores the value of social media data for conservation management.

21 Jun 2024

Neural Network Automates Mussel Larvae Monitoring

Researchers developed a neural network (NN) architecture based on You Only Look Once (YOLO) to automate the detection, classification, and quantification of mussel larvae from microscopic water samples.

20 Jun 2024

Drought Prediction with Deep Learning

Researchers reviewed deep learning (DL) techniques for drought prediction, highlighting the dominance of the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), and normalized difference vegetation index (NDVI). The study emphasizes the need for more research in America and Africa, suggesting opportunities for developing countries.

20 Jun 2024

Deep Learning Enhances Urban Building Mapping

Researchers used a novel AI method combining RGB orthophotos and digital surface models to improve building footprint extraction from aerial and satellite images, achieving higher accuracy and efficiency.

19 Jun 2024