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 Smartphone Gaze Tracking through Machine Learning

Researchers present an open-source gaze-tracking solution for smartphones, using machine learning to achieve accurate eye tracking without the need for additional hardware. By utilizing convolutional neural networks and support vector regression, this approach achieves high levels of accuracy comparable to costly mobile trackers.

30 Aug 2023

Blockchain-Powered Traceability for Safer Grain and Oil Food Supply Chains

Researchers introduce a blockchain-based traceability model fortified with machine learning to ensure reliability in the intricate grain and oil food supply chain. By combining distributed ledger technology, IoT, and machine learning, the model enhances transparency, data accuracy, and recovery mechanisms, promising a safer and more transparent future for the grain and oil industry.

30 Aug 2023

Optimizing Factory Logistics with Reinforcement Learning: A Path to Industry 4.0 Efficiency

Researchers delve into the world of logistics automation, employing RL to enhance storage devices and logistics systems, with real-world implications for manufacturing efficiency. In this groundbreaking approach, using innovative reward signal calculations and AI-driven algorithms, they showcase efficiency gains of 30-100% and pave the way for a new era of unmanned factories and optimized production processes.

29 Aug 2023

Qualitative eXplainable Graphs: Unveiling Interpretability in Automated Driving

Researchers explore the innovative concept of Qualitative eXplainable Graphs (QXGs) for spatiotemporal reasoning in automated driving scenes. Learn how QXGs efficiently capture complex relationships, enhance transparency, and contribute to the trustworthy development of autonomous vehicles. This groundbreaking approach revolutionizes automated driving interpretation and sets a new standard for dependable AI systems.

29 Aug 2023

How AI Facial Recognition Works

Discover how artificial intelligence is transforming facial recognition by enhancing accuracy, adaptability, and real-time capabilities across various sectors. Explore the challenges and ethical considerations surrounding its implementation, while envisioning a future of personalized interactions, improved security, and responsible innovation.

28 Aug 2023

How Does AI Emotion Recognition Work?

Discover how AI's emotion recognition breakthroughs are reshaping industries, enhancing personalized experiences, and addressing challenges of cultural sensitivity and ethical considerations in an evolving human-machine interaction landscape.

28 Aug 2023

UniDoc: Advancing Multimodal Understanding through Unified Multimodal Instruct Tuning

This paper introduces UniDoc, a pioneering multimodal model designed to address the limitations of existing approaches in fully leveraging large language models (LLMs) for comprehensive text-rich image comprehension. Leveraging the interrelationships between tasks, UniDoc integrates text detection and recognition abilities, surpassing previous models and offering a unified methodology that enhances multimodal scenario understanding.

25 Aug 2023

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