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

Using Artificial Intelligence to Track Student Performance

The integration of Artificial Intelligence (AI) in student performance tracking is reshaping education, automating grading, offering personalized insights, and enabling early intervention. This article explores the evolution from traditional AI methods to modern machine learning applications, emphasizing adaptive learning, predictive analytics, and natural language processing.

13 Nov 2023

The Role of Artificial Intelligence in Cybersecurity

Artificial Intelligence (AI) plays a crucial role in cybersecurity, enhancing threat identification, thwarting cyberattacks, and strengthening digital protection. From anomaly detection to behavioral analysis and encryption, AI contributes to proactive responses to evolving security challenges.

13 Nov 2023

Impact of Industrial Robot Applications on China's Global Value Chain Participation

This study delves into the intricate dynamics between industrial robot (IR) applications and China's global value chain participation (GVCP) from 2006 to 2014. Findings reveal that while IR applications facilitated import substitution, reducing backward participation, manufacturing localization led to a decline in China's opportunities to export intermediate inputs, impacting forward participation in global value chains.

12 Nov 2023

Digital Twin-Based Calibration System for Smoke Alarms

This paper addresses challenges in traditional smoke alarm calibration methods and presents a comprehensive framework for modeling and monitoring using a digital twin system. The proposed approach, validated in a real-world calibration scenario, improves calibration success rates and reduces defective product rates, showcasing the effectiveness of digital twin technology in manufacturing processes.

10 Nov 2023

AI and Explainable AI in Visual Quality Assurance: A Comprehensive Survey in Manufacturing

The paper published in the journal Electronics explores the crucial role of Artificial Intelligence (AI) and Explainable AI (XAI) in Visual Quality Assurance (VQA) within manufacturing. While AI-based Visual Quality Control (VQC) systems are prevalent in defect detection, the study advocates for broader applications of VQA practices and increased utilization of XAI to enhance transparency and interpretability, ultimately improving decision-making and quality assurance in the industry.

10 Nov 2023

DAIL: Enhancing In-Context Learning with Data Augmentation through Language Models

Researchers propose an innovative approach, Data Augmentation for In-Context Learning (DAIL), addressing a common challenge in In-Context Learning (ICL) where annotated demonstrations are scarce. Leveraging large language models' self-generated content familiarity, DAIL employs self-paraphrasing and majority voting, surpassing standard ICL techniques, especially in low-resource scenarios.

9 Nov 2023

Revolutionizing Urban Economic Competitiveness Analysis Using a CNN-based Approach

Researchers introduce a pioneering method for urban economic competitiveness analysis in China, addressing the limitations of traditional approaches. Leveraging convolutional neural networks (CNN) and a rich urban feature dataset, augmented using deep convolutional Generative Adversarial Networks (DCGAN), the model offers a comprehensive understanding of urban development, overcoming data scarcity challenges and outperforming traditional methods.

9 Nov 2023

Boosting Functional Test Evaluation with Camera-Based System and Machine Learning

Researchers have explored the feasibility of using a camera-based system in combination with machine learning, specifically the AdaBoost classifier, to assess the quality of functional tests. Their study, focusing on the Single Leg Squat Test and Step Down Test, demonstrated that this approach, supported by expert physiotherapist input, offers an efficient and cost-effective method for evaluating functional tests, with the potential to enhance the diagnosis and treatment of movement disorders and improve evaluation accuracy and reliability.

8 Nov 2023

DreamSmooth: Enhancing Model-Based Reinforcement Learning with Temporally Smoothed Rewards

Researchers have introduced DreamSmooth, an innovative approach to address the challenge of reward prediction in Model-Based Reinforcement Learning (MBRL). DreamSmooth focuses on predicting temporally smoothed rewards, offering significant improvements in sample efficiency and final performance, particularly in long-horizon, sparse-reward tasks, while maintaining competitive performance on standard benchmarks like the Deepmind Control Suite and Atari.