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

Homomorphic Encryption and Dynamic Sparse Attention for Advancing Privacy in Dialogue Models

Researchers present a groundbreaking privacy-preserving dialogue model framework, integrating Fully Homomorphic Encryption (FHE) with dynamic sparse attention (DSA). This innovative approach enhances efficiency and accuracy in dialogue systems while prioritizing user privacy. Experimental analyses demonstrate significant improvements in precision, recall, accuracy, and latency, positioning the proposed framework as a powerful solution for secure natural language processing tasks in the information era.

13 Dec 2023

SCANN - Interpretable Deep Learning for Structure-Property Relationships

This paper introduces SCANN, an interpretable deep learning architecture with attention mechanisms tailored for comprehending material structures and predicting properties. Utilizing iterative learning and global attention scores, SCANN excels in capturing complex structure-property relationships, outperforming traditional methods. The study demonstrates SCANN's robust predictive capabilities across diverse datasets, emphasizing its interpretative capacity to unveil how material properties correlate with specific structural features, thereby guiding future advancements in material design and discovery.

12 Dec 2023

Innovative Optimization of Mega-Castings for Automotive Structures: A Multidisciplinary Approach

This scientific report explores the potential of mega-castings to replace steel sheets in automotive structures, offering cost efficiency and design flexibility. Researchers propose a novel two-phase optimization pipeline combining topology optimization, response-surface-based techniques, and machine learning to balance crash demands, castability, and structural goals. The approach outperforms traditional workflows, generating weight-optimized designs within shorter timeframes.

12 Dec 2023

Revolutionizing Security Analytics Using AI

This essay delves into the transformative role of Artificial Intelligence (AI) in Security Analytics, addressing the shift from traditional rule-based methods to AI-driven approaches. It explores the significance of this evolution in detecting and preventing cyber threats, emphasizing the proactive nature of AI-powered Security Analytics. The essay details methods enhancing cyber threat detection, applications in real-world scenarios across industries, and challenges facing AI in security.

11 Dec 2023

How is Data Labeling Used in AI?

This comprehensive exploration delves into the significance of data labeling in shaping artificial intelligence (AI). From enhancing model accuracy and training models to improving human-machine interaction, the article examines methodologies like manual labeling, semi-supervised labeling, and active learning. It also navigates the evolving landscape of data labeling, discussing advancements in automation, ethical considerations, and domain-specific solutions.

11 Dec 2023

Challenges and Solutions in AI-Driven Scientific Research

This paper delves into the transformative impact of machine learning (ML) in scientific research while highlighting critical challenges, particularly in COVID-19 diagnostics using AI-driven algorithms. The study underscores concerns about misleading claims, flawed methodologies, and the need for standardized guidelines to ensure credibility and reproducibility. It addresses issues such as data leakage, inadequate reporting, and overstatement of findings, emphasizing the importance of proper training and standardized methodologies in the rapidly evolving field of health-related ML.

10 Dec 2023

Enhancing End-User Robot Programming: A Comparative Study of Kinesthetic Teaching Approaches

This research explores end-user programming of collaborative robots through kinesthetic teaching. The study compares self-guided practice with curriculum-based training and finds no significant difference in programming proficiency. While both approaches offer insights into factors affecting success, the study underscores the need for refined learning interventions in end-user robot programming to enhance skills and perceptions effectively.

8 Dec 2023

Understanding Oklahoma's Earthquake Insurance Landscape: Influential Factors and Predictive Modeling

This research investigates the determinants of earthquake insurance uptake in Oklahoma post-2011 seismic events. Through supervised machine learning, it identifies influential factors including age, gender, ethnicity, political affiliation, tenure, housing status, education, income, earthquake experience, and environmental attitudes. The study emphasizes the significance of awareness and advanced machine learning tools for predictive modeling in managing environmental risks and advocates for informed disaster management strategies.

8 Dec 2023

FakeStack: A Deep Learning Approach for Robust Fake News Detection

This research introduces FakeStack, a powerful deep learning model combining BERT embeddings, Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) for accurate fake news detection. Trained on diverse datasets, FakeStack outperforms benchmarks and alternative models across multiple metrics, demonstrating its efficacy in combating false news impact on public opinion.

8 Dec 2023

Decoding TV Drama Success: Machine Learning Insights from Japanese Shows

Utilizing machine learning, a PLOS One study delves into the correlation between Japanese TV drama success and various metadata, including facial features extracted from posters. Analyzing 800 dramas from 2003 to 2020, the study reveals the impact of factors like genre, cast, and broadcast details on ratings, emphasizing the unexpected significance of facial information in predicting success.

7 Dec 2023