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

Precision Manufacturing with Machine Learning

Utilizing machine learning techniques, researchers enhanced additive manufacturing processes for β-Ti alloys, achieving precise predictions for layer height and grain size by considering nuanced parameters like laser power and scanning speed, thus advancing manufacturing efficiency and material properties.

29 Mar 2024

Enhanced Dynamic Graph Learning with TASER

Researchers introduce TASER, a temporal adaptive sampling method, enhancing accuracy and efficiency in dynamic graph neural networks (TGNNs). TASER dynamically adjusts minibatch and neighbor selection, leveraging GPU-based tools for improved performance, effectively denoising training samples and supporting neighbors while achieving significant speedups in training time.

28 Mar 2024

Tracing Model Contamination: Detecting LLM Radioactivity

Researchers explore methods for detecting traces of training data in large language models (LLMs), highlighting the efficacy of watermarking techniques over conventional methods like membership inference attacks. By illuminating key factors influencing radioactivity detection, the study contributes to understanding and mitigating risks associated with model contamination during fine-tuning processes.

27 Mar 2024

Eco-Friendly Plastic Discovery: Robotics and AI Revolutionize Material Design

Researchers leverage robotics and machine learning in a pioneering approach to accelerate the discovery of biodegradable plastic alternatives. By combining automated experimentation with predictive modeling, they develop eco-friendly substitutes mimicking traditional plastics, paving the way for sustainable material innovation.

27 Mar 2024

AI's Revolutionary Impact on Scientific Research

Artificial Intelligence (AI) is reshaping scientific research across diverse domains, accelerating discoveries and revolutionizing methodologies. From healthcare to astronomy, AI augments human capabilities, expedites processes, and unveils novel insights. Despite challenges in data quality and ethics, interdisciplinary collaboration and AI democratization promise to propel scientific exploration to unprecedented heights.

25 Mar 2024

DreamMotion: Innovating Video Editing Through Text-Driven Techniques

DreamMotion revolutionizes video editing by seamlessly integrating text-driven edits with space-time self-similarity alignment, preserving motion and structure. Its superior performance in both non-cascaded and cascaded frameworks marks a significant advancement, yet ethical concerns and challenges in handling substantial structural changes beckon further refinement.

25 Mar 2024

IABC-MLP Model Analysis for Enhancing Concrete Strength Prediction

In their study published in Scientific Reports, researchers introduced the IABC-MLP model for predicting concrete compressive strength. This innovative approach combines an improved artificial bee colony algorithm (IABC) with a multilayer perceptron (MLP) model, addressing issues like local optima and slow convergence. Comparative analyses demonstrated that IABC-MLP outperformed traditional methods and other heuristic algorithms in accuracy and convergence speed, showcasing its potential for real-world applications in concrete strength prediction.

22 Mar 2024

Genomic Prediction for Feed Efficiency in Cattle: A Comparative Analysis of Methods

Researchers examined various genomic prediction methods for feed efficiency (FE) traits in Nellore cattle. Machine learning (ML) techniques like multi-layer neural networks (MLNN) and support vector regression (SVR), alongside multi-trait genomic best linear unbiased prediction (MTGBLUP), surpassed traditional single-trait methods and Bayesian regression approaches. Through comprehensive data analysis, the study underscores SVR and MTGBLUP as effective tools for enhancing prediction accuracy in genomic selection studies for FE traits in Nellore cattle.

22 Mar 2024

MEMS Revolutionizing Wearable Technology

Researchers unveil a groundbreaking approach in wearable technology, integrating MEMS accelerometers with in-sensor computing for real-time gait pattern identification. Through innovative design and optimization, MEMS devices demonstrate robustness and competitive performance, offering significant energy savings potential and paving the way for cost-effective, versatile applications in healthcare and beyond.

21 Mar 2024

Digital Twin-based Roadheaders for Enhancing Coal Mining Efficiency

Researchers present a digital twin system for roadheaders in coal mining, integrating shape, performance, and control elements to enhance operational efficiency and safety. Utilizing numerical simulation, AI, and multi-source data fusion, the system enables real-time stress monitoring and adaptive adjustments, improving cutting parameters and preventing structural damage in challenging mining environments.

20 Mar 2024