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

Unraveling Causality and XAI: Perspectives and Potential

This article delves into the intricate relationship between causality and eXplainable Artificial Intelligence (XAI) from three perspectives. It examines the limitations of current XAI, explores how XAI can contribute to causal inquiry, and advocates for the integration of causality to enhance XAI.

21 Sep 2023

Enhancing Land Cover Mapping with Explainable AI and Google Earth Engine

Researchers have developed an open-source Python tool that integrates explainable artificial intelligence (XAI) with Google Earth Engine to improve land cover mapping and monitoring. The tool provides feature importance metrics and supports land cover classification and change detection workflows, making it a valuable resource for remote sensing applications with transparent machine learning.

20 Sep 2023

AI-Powered Workload Prediction for Railway Traffic Control Rooms

Researchers have developed a real-time machine learning framework, led by LightGBM, to predict and explain workload fluctuations in railway traffic control rooms, highlighting the importance of managing workload for employee well-being and operational performance. SHAP values provide insights into feature contributions, emphasizing the significance of teamwork dynamics.

20 Sep 2023

Enhancing Healthcare Resilience: A Novel Approach to Smart Skin Screening Apps

Researchers have introduced an innovative framework that combines system dynamics modeling, risk management, and resiliency concepts to assess the effectiveness of smartphone-based skin lesion screening applications. By analyzing various factors that affect these systems, the study provides valuable insights into improving skin health monitoring and risk management in healthcare, particularly in the context of skin cancer detection and prevention.

19 Sep 2023

Analyzing ChatGPT's Impact on Education: Insights from Twitter Conversations

This study examines the public's reactions and sentiments towards ChatGPT's role in education through Twitter data analysis. It reveals a complex interplay of positive and negative sentiments, highlighting the need for comprehensive exploration of AI's integration into education and the importance of considering diverse perspectives.

19 Sep 2023

How is AI used in Content Creation?

Artificial Intelligence (AI) revolutionizes content creation by automating generation, personalization, and distribution. This article explores AI applications, techniques, and challenges in intelligent content creation while highlighting the need for refining algorithms and addressing ethical considerations for the future.

18 Sep 2023

What is AI's Role in Health Insurance?

The integration of AI in health insurance brings significant improvements in risk assessment, claims processing, and customer service. It enhances efficiency, fraud detection, and personalization, but also faces challenges related to data privacy, bias, and regulation. The future holds promise for even more advanced AI applications in health insurance, benefiting both insurers and policyholders.

18 Sep 2023

Optimizing Seaport Parking with AI and Computer Vision: A Systematic Review

This paper explores the integration of artificial intelligence (AI) and computer vision (CV) technologies in addressing urban expansion challenges, particularly in optimizing container movement within seaports. Through a systematic review, it highlights the significant role of AI and CV in sustainable parking ecosystems, offering valuable insights for enhancing seaport management and smart city development.

17 Sep 2023

AI Chatbots Shine in Creativity, But Humans Still Hold Their Ground

A study comparing the creativity of AI chatbots and human participants in the Alternate Uses Task (AUT) reveals that chatbots consistently produce creative responses, often surpassing humans. However, the study underscores the unique complexity of human creativity, highlighting that while AI can excel, it still struggles to fully replicate or surpass the best human ideas.

17 Sep 2023

Bridging the Gap: Balancing Biology and Efficiency in Neural Networks for Robotics

Researchers delve into the delicate equilibrium between biological realism and computational efficiency in neural networks, with a focus on Spiking Neural Networks (SNNs) and their role in advancing robotics. They explore the potential of neuromorphic computing to harmonize the synthesis of artificial and natural cognition, ultimately contributing to explainable and adaptable robotic systems.

15 Sep 2023