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

FuXi-S2S: A Machine Learning Model to Forecast Weather

FuXi-S2S, a new machine learning model, improves weather forecasting by predicting global daily weather up to 42 days in advance. Trained on 72 years of data, it surpasses existing models in accuracy for precipitation and longwave radiation, enhancing subseasonal predictions.

8 Aug 2024

AI-Based Method for Accurate Assessment of Rock Strength

A recent study introduced an AI-based approach using transformer + UNet and ResNet-18 models for rock strength assessment and lithology identification in tunnel construction. The method showed high accuracy, reducing errors and enhancing safety and efficiency in geological engineering.

8 Aug 2024

Towards Eco-Friendly AI for a Cleaner Planet

AI's rapid advancement drives up energy demands, raising sustainability concerns. The article examines how energy-intensive AI models impact the environment through increased carbon emissions and e-waste. It discusses solutions like efficient hardware, improved data centers, and AI’s potential for optimizing energy use.

12 Aug 2024

Reviewing Drone Imagery for Infrastructure

A systematic tertiary study analyzed 57 secondary studies from 2018 to 2023 on using drone imagery for infrastructure management. The research identified key application areas, assessed trends, and highlighted challenges, providing a valuable reference for researchers and practitioners in the field.

7 Aug 2024

Enhancing Concrete Crack Detection with Computer Vision

Researchers developed a three-step computer vision framework using YOLOv8 and image processing techniques for efficient concrete crack detection and measurement. The method demonstrated high accuracy but faced challenges with small cracks, complex backgrounds, and pre-marked reference frames.

7 Aug 2024

ML Models Predict Wind-Solar Power Output

The study compared various machine-learning models for predicting wind-solar tower power output. While linear regression was inadequate, polynomial regression and deep neural networks (DNN) showed improved accuracy. The DNN model outperformed others, demonstrating high prediction accuracy and efficiency for renewable energy forecasting.

6 Aug 2024

Advanced AI Model Boosts Image Personalization

Imagine Yourself, an advanced AI model, revolutionizes personalized image generation by eliminating the need for individual tuning. This model surpasses previous limitations with a novel approach, achieving superior identity preservation, text fidelity, and visual quality, as confirmed by human evaluations.

6 Aug 2024

CYBERSECEVAL 3 Security Benchmark Evaluates Risks in LLMs

CYBERSECEVAL 3 introduces new security benchmarks to evaluate large language models like Llama 3, focusing on offensive security capabilities and risks. These benchmarks help assess and mitigate threats, advancing AI-driven cybersecurity for developers, end-users, and third-party applications.

5 Aug 2024

AI Dataset Boosts PCB Defect Detection

A new AI-powered dataset, DsPCBSD+, categorizes PCB surface defects into nine types, aiding deep learning-based detection for quality control. Comprising 20,276 annotated defects across 10,259 images, it addresses real-world variability and enhances the precision of AI-driven PCB inspections.

5 Aug 2024

AI-Powered Early Diagnosis of Strawberry Wilt

Researchers combined hyperspectral imagery with machine learning models to detect early Fusarium wilt in strawberries. The ANN model achieved the highest accuracy, predicting stress indicators like stomatal conductance and photosynthesis before visual symptoms, enhancing early disease detection and management.

4 Aug 2024