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

Enhancing Non-Surgical Root Canal Treatment Prognoses with Machine Learning

Researchers delve into the integration of machine learning (ML) to refine prognostic accuracy in non-surgical root canal treatments (NSRCT). By utilizing advanced ML models like Random Forest (RF) and K Nearest Neighbours (KNN), traditional clinical prognostic approaches are enhanced, resulting in improved sensitivity and accuracy in predicting NSRCT outcomes.

25 Aug 2023

Text-Aided Dynamic Avatar Synthesis: Transforming Text Descriptions into Lifelike 3D Avatars

This article introduces TADA, a groundbreaking approach that leverages a 2D diffusion model and a parametric body model to generate expressive 3D avatars from textual descriptions. By ensuring harmony between geometry and texture, TADA enables realistic animation with semantic consistency. The method's effectiveness is demonstrated through qualitative and quantitative evaluations, showcasing its ability to create detailed and lifelike digital characters.

24 Aug 2023

AI and Remote Sensing Synergy: Transforming Earth Sciences through Data Analysis

Researchers explore the integration of AI and remote sensing, revolutionizing data analysis in Earth sciences. By exploring AI techniques such as deep learning, self-attention methods, and real-time object detection, the study unveils a wide range of applications from land cover mapping to economic activity monitoring. The paper showcases how AI-driven remote sensing holds the potential to reshape our understanding of Earth's processes and address pressing environmental challenges.

24 Aug 2023

The Metaverse's Impact on AI News Anchors: Unveiling User Engagement Factors

This study dives into the metaverse's influence on the interaction between humans and AI, specifically focusing on AI news anchors. Employing an expectation confirmation theory-based model, researchers explore the factors driving users' intention to watch news from AI anchors. The findings highlight the pivotal roles of perceived intelligence, satisfaction, and trust, shedding light on insights crucial for commercializing AI news anchors.

24 Aug 2023

Efficient Text-to-Video Generation and Editing Using Simple Diffusion Adapter

Researchers have introduced an innovative approach to bridge the gap between Text-to-Image (T2I) AI technology and the lagging development of Text-to-Video (T2V) models. They propose a "Simple Diffusion Adapter" (SimDA) that efficiently adapts a strong T2I model for T2V tasks, incorporating lightweight spatial and temporal adapters.

23 Aug 2023

Optimizing Electric Vehicle Operation in Multimicrogrids through Coordinated Control

Researchers introduce an innovative approach to optimize the operation of electric vehicles (EVs) within multimicrogrids (MMGs) in a paper published in the journal Sustainability. Their novel method involves a four-level control hierarchy, encompassing microsource controllers, unit controllers, microgrid controllers, and a top-level multimicrogrid controller.

23 Aug 2023

LightSpaN: Advancing Traffic Monitoring Through IoT and AI

Researchers present LightSpaN, a streamlined Convolutional Neural Network (CNN)-based solution for swift and accurate vehicle identification in intelligent traffic monitoring systems powered by the Internet of Things (IoT). This innovative approach outperforms existing methods with an average accuracy of 99.9% for emergency vehicles, contributing to reduced waiting and travel times.

22 Aug 2023

Elevating SIoT Service Recommendations: A Context-Aware Framework

Researchers introduce an innovative context-aware framework to enhance service recommendations within the Social Internet of Things (SIoT) paradigm. By combining review-based and engagement-based learning, this framework offers a robust approach for addressing the challenges of dynamic context and user preferences in SIoT service recommendations.

22 Aug 2023

Unveiling Microplastic-Organic Pollutant Interactions: A Hybrid ML Approach

Researchers present an innovative study in the journal Water Research, where they employ a hybrid machine learning (ML) approach to decipher the intricate interplay between microplastics (MPs) and organic pollutants (OPs) in freshwater systems. Their novel model, combining genetic algorithms and support vector machines, exhibits remarkable predictive power.

22 Aug 2023

Designing the Future: Big Data and AI Revolutionize Product Innovation

This paper explores how the fusion of big data and artificial intelligence (AI) is reshaping product design in response to heightened consumer preferences for customized experiences. The study highlights how these innovative methods are breaking traditional design constraints, providing insights into user preferences, and fostering automation and intelligence in the design process, ultimately driving more competitive and intelligent product innovations.

22 Aug 2023