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

Optimized Sampling Enhances Landslide Prediction Accuracy

Researchers confirmed that partition-based sampling significantly improves landslide prediction models in Henan Province. The II-BPNN model, which utilized partition-based random sampling, outperformed other models in accuracy, recall, and specificity, showcasing the benefits of this approach for enhanced landslide susceptibility mapping.

21 Jul 2024

Predictive Model for Tea Anthracnose in Yunnan

Researchers developed a rapid, non-destructive prediction model for tea anthracnose in Yunnan using IoT climate data and advanced statistical techniques. The LASSO-COX-NOMOGRAM model demonstrated high accuracy, outperforming traditional methods, and revealed significant correlations between 21 environmental factors and disease severity, enhancing early detection and eco-friendly management practices in tea production.

21 Jul 2024

AI Revolution in Smart City Transport

A comprehensive review identifies key trends in applying machine learning and deep learning to intelligent transportation systems, highlighting significant advancements and future research directions.

19 Jul 2024

Deep Learning Reveals Regulatory Impacts on European Banking

Researchers utilized deep learning techniques to detect anomalies in the European banking sector, finding significant correlations between European Banking Authority events and banking anomalies.

19 Jul 2024

AI Framework Optimizes Warehouse Layouts

Researchers introduced an AI-driven framework for automated warehouse layout generation using constrained beam search. This method optimizes layouts for storage capacity, accessibility, and throughput, demonstrating significant improvements over traditional manual designs and validating its effectiveness in real-world applications.

18 Jul 2024

3D Printing and Machine Learning: Eco-Friendly Polymer Revolution

Researchers explored the integration of 3D printing and machine learning (ML) with biodegradable polymers, highlighting advancements in material preparation, design, and post-processing for sustainable manufacturing.

18 Jul 2024

MetaUrban: A New Frontier for AI in Urban Environments

Researchers introduced MetaUrban, an advanced simulation platform designed for AI systems in urban environments. It generates diverse, interactive urban scenes for point-to-point navigation and social interaction tasks, leveraging reinforcement and imitation learning to enhance the reliability of mobile agents like delivery robots and robotic canines.

17 Jul 2024

Novel Dataset Enhances Cotton Content Detection in Textiles

Researchers detailed the impact of computer vision in textile manufacturing, focusing on identifying fabric imperfections and measuring cotton composition. They introduced a dataset of 1300 fabric images, expanded to 27,300 through augmentation, covering cotton percentages from 30% to 99%. This dataset aids in training machine learning models, streamlining traditionally labor-intensive cotton content assessments, and enhancing automation in the textile industry.

17 Jul 2024

Transformative Role of AI in Space Operations

Artificial Intelligence (AI) is transforming industries worldwide, including space exploration, where it enhances mission efficiency, decision-making, and innovation. This article explores AI's pivotal role in space missions, data analysis, and future trends, highlighting its transformative impact on understanding and exploring the cosmos.

16 Jul 2024

Meta's 3DGen Revolutionizes Text-to-3D Generation

Meta's new 3DGen pipeline enables rapid, high-fidelity text-to-3D asset generation by integrating AssetGen for 3D shapes and TextureGen for detailed textures. Evaluations show 3DGen significantly outperforms industry standards in both speed and quality, particularly excelling with complex prompts.

16 Jul 2024