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

AI-driven Fault Detection to Enhance Quality Control in Wiring Harness Manufacturing

This paper introduces a novel fault detection system for wiring harness manufacturing, leveraging an AI classification model and regional selective data scaling (RSDS) to overcome challenges such as limited labeled data and material variability. By integrating AI with RSDS and employing advanced data augmentation techniques, the system demonstrates exceptional accuracy and offers promising improvements over traditional fault detection methods. Although further research is needed to refine scalability and compatibility, this approach shows significant potential for enhancing manufacturing quality control practices.

14 Feb 2024

AndroidArena: Evaluating Large Language Models on Operating Systems

This paper presents AndroidArena, a benchmark environment for evaluating Large Language Models (LLMs) on operating systems, addressing challenges such as managing vast action spaces and coordinating inter-application tasks. By introducing adaptive metrics and identifying key capabilities essential for LLM success, the study highlights performance gaps and areas for improvement among state-of-the-art agents. The findings underscore the need for enhanced understanding, reasoning, exploration, and reflection abilities in LLM agents, paving the way for future investigations in the field.

14 Feb 2024

Policy Gradient Methods in Reinforcement Learning

This comprehensive article delves into the realm of reinforcement learning (RL) and policy gradient methods, elucidating their theoretical underpinnings, practical implementations, and diverse applications across real-world domains. It highlights the dynamic framework of RL for adaptive agents, the essence of policy gradient methods in optimizing decision-making policies, and their applications in robotics, finance, healthcare, and gaming.

13 Feb 2024

AI-Driven Character Evolution in Video Games

This article explores the integration of emotional intelligence (EI) into video game characters through artificial intelligence (AI) techniques. It discusses how AI algorithms enhance character development, focusing on emotion generation, natural language processing (NLP), facial and body animation, player-character interaction, and ethical considerations. By leveraging AI, developers create more immersive and emotionally resonant gaming experiences, shaping the future of interactive storytelling.

13 Feb 2024

UAVs and Machine Learning to Advance Antarctic Vegetation Monitoring

Researchers unveil a novel workflow employing deep learning and machine learning techniques to assess the vulnerability of East Antarctic vegetation to climate change. Utilizing high-resolution multispectral imagery from UAVs, XGBoost and U-Net classifiers demonstrate robust performance, highlighting the transformative potential of combining UAV technology and ML for non-invasive monitoring in polar ecosystems. Future research should focus on expanding training data and exploring other ML algorithms to enhance segmentation outcomes, furthering our understanding of Antarctic vegetation dynamics amid environmental challenges.

12 Feb 2024

Streamlining Coffee Grinder Maintenance with Remote Wear Classification

Researchers present a remote access server system leveraging image processing and deep learning to classify coffee grinder burr wear accurately. With over 96% accuracy, this mobile-friendly service streamlines assessment, benefiting both commercial coffee chains and enthusiasts, while its practicality and low cost suggest broader applications in machinery wear prediction.

12 Feb 2024

Transformative Healthcare: Constructing a Knowledge Graph with Machine Learning

Researchers introduce a pioneering system merging machine learning and knowledge graph technology to streamline medical diagnosis and treatment. Leveraging advanced methodologies like multiple levels refinement and knowledge distillation, the system empowers healthcare professionals with rapid and accurate solutions, offering a transformative tool for navigating complex medical research. Through iterative refinement and interactive exploration, this system provides comprehensive and relevant information, addressing key challenges in healthcare knowledge management.

9 Feb 2024

Tackling Biases in AI-driven Conservation with Ethical Safeguards

A study analyzing ChatGPT's responses on ecological restoration reveals biases towards Western academia and forest-centric approaches, neglecting indigenous knowledge and non-forest ecosystems. Urgent measures are proposed to ensure ethical AI practices, including transparency, decolonial formulations, and consideration of gender, race, and ethnicity in knowledge systems. Addressing data access and ownership issues is crucial for promoting inclusivity and transparency in embracing environmental justice perspectives.

9 Feb 2024

A Tailored Machine Learning Approach for Opioid Overdose Detection

Innovative research introduces a lightweight, interpretable machine-learning classifier to identify opioid overdoses in emergency medical services (EMS) records. By leveraging custom feature engineering methods and robust model architectures, this approach demonstrates superior performance, paving the way for enhanced opioid surveillance and targeted harm reduction initiatives at the local level.

8 Feb 2024

AI-Driven Fragment-Based Drug Discovery: Enhancing Molecular Encoding

Fragment-based drug discovery (FBDD) merges artificial intelligence with molecular biology, focusing on breaking down complex compounds into smaller fragments for drug development. Leveraging generative pre-trained transformers (GPT) models, researchers enhance molecular encoding and explore innovative methodologies. FBDD offers advantages in sensitivity and efficiency, albeit challenges in fragment selection persist.

8 Feb 2024