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

Redefining Chemical Language Models: Embracing Invalid Outputs

This study challenges the conventional view of generating invalid SMILES (simplified molecular-input line-entry system) as a limitation in chemical language models. Instead, researchers argue that generating invalid outputs serves as a self-corrective mechanism, enhancing model performance by filtering low-quality samples and facilitating exploration of chemical space.

9 Apr 2024

Optimizing Thermoelectric Properties with Bayesian Optimization

This study harnesses Bayesian optimization (BO) to enhance the thermoelectric properties of multicomponent III–V materials. Through iterative cycles of thin-film deposition, property measurements, and BO recommendations, the research achieves notable improvements in thermoelectric performance, underscoring the effectiveness of machine learning-driven optimization.

9 Apr 2024

Augmented Reality in Interior Design

Augmented reality (AR) emerges as a transformative tool in interior design, revolutionizing visualization and client engagement. Despite challenges like cost and privacy concerns, AR offers substantial benefits, fostering creativity, efficiency, and collaboration. With ongoing advancements and increased accessibility, the future holds immense potential for AR to reshape the interior design landscape, making it more accessible, efficient, and collaborative than ever before.

7 Apr 2024

Socioeconomic Dynamics in Ridesourcing Markets

Researchers delve into the complex interplay between socioeconomic inequality, platform profit, and societal welfare in ridesourcing markets through an agent-based simulation model. Their study reveals how disparities drive market dynamics, impacting both travelers and drivers, and highlights the need for nuanced consideration of these factors in shaping the future of ridesourcing economies.

7 Apr 2024

TCN-Attention-HAR Model: Advancing Human Activity Recognition

Researchers introduced the TCN-Attention-HAR model to enhance human activity recognition using wearable sensors, addressing challenges like insufficient feature extraction. Through experiments on real-world datasets, including WISDM and PAMAP2, the model showcased significant performance improvements, emphasizing its potential in accurately identifying human activities.

5 Apr 2024

Random Forest Models to Enhance Real Estate Tax Compliance

Researchers detail advancements in using Random Forest (RF) models to pinpoint tax risks within real estate, providing insights into discrepancies between declared values and model judgments. By meticulously selecting indicators and leveraging historical data, the RF model emerges as a robust tool for tax risk identification, offering decision support for tax authorities and promoting improved compliance levels within the real estate industry.

4 Apr 2024

Improved XFEL Pulse Characterization with Machine Learning

Researchers employ machine learning to enhance the prediction of attosecond two-colour pulses from X-ray free-electron lasers (XFELs), optimizing performance and potentially enhancing applications like time-resolved spectroscopy. Through dimensionality reduction and careful analysis, critical parameters, notably electron beam properties, are identified, leading to more accurate predictions and promising avenues for future XFEL research.

3 Apr 2024

Earth's Digital Doppelgangers: Computing Challenges Ahead

In Nature Computational Science, researchers highlight the transformative potential of digital twins for climate action, emphasizing the need for innovative computing solutions to enable effective human interaction.

2 Apr 2024

AI's Evolution in Content Creation

AI revolutionizes content creation by automating tasks, personalizing experiences, and expanding creative horizons across industries. Despite challenges in quality control and ethics, AI's potential to enhance human creativity and relevance is evident. Embracing opportunities while addressing challenges ensures AI remains a powerful ally in driving innovation and enriching content experiences in the digital age.

1 Apr 2024

Deep Learning for Real-time Safety Helmet Detection

Researchers in a Scientific Reports paper propose BiFEL-YOLOv5s, an advanced deep learning model, for real-time safety helmet detection in construction settings. By integrating innovative techniques like BiFPN, Focal-EIoU Loss, and Soft-NMS, the model achieves superior accuracy and recall rates while maintaining detection speed, offering a robust solution for safety monitoring in complex work environments.

1 Apr 2024