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

Exploring Consciousness in Artificial Intelligence

This paper in OSF delves into whether artificial intelligence (AI) systems could achieve consciousness, challenging assumptions like computational functionalism and substrate neutrality. It argues for biological naturalism, suggesting consciousness may depend on specific biological processes.

2 Jul 2024

How Do Machines Learn Language?

Natural Language Processing (NLP) has evolved from early rule-based systems to advanced neural networks and transformers. These technologies enable machines to understand, generate, and interact with human language across various applications.

2 Jul 2024

Integrating MALDI-TOF MS and ML for Rapid Milk Product Classification

This paper introduces a method combining matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with machine learning (ML) techniques to classify milk products efficiently. The study focused on differentiating organic milk (OM) from conventional milk (CM) using spectral data analysis

1 Jul 2024

SensorNet: Enhancing Fruit Contamination Detection with DL and Chemical Sensors

Published in Intelligent Systems with Applications, this study introduces SensorNet, a hybrid model combining deep learning (DL) with chemical sensor data to detect toxic additives in fruits like formaldehyde. SensorNet integrates convolutional layers for image analysis and sensor data preprocessing, achieving a high accuracy of 97.03% in distinguishing fresh from chemically treated fruits.

1 Jul 2024

Novel Watermarking Techniques for Identifying AI-Generated Text

Researchers presented advanced statistical tests and multi-bit watermarking to differentiate AI-generated text from natural text. With robust theoretical guarantees and low false-positive rates, the study compared watermark effectiveness using classical NLP benchmarks and developed sophisticated detection schemes.

28 Jun 2024

Flexible Image Editing with SceneDiffusion

Meta Research recently unveiled SceneDiffusion, an innovative method for flexible image layout rearrangement using diffusion models. This approach dynamically optimizes layered scene representations during sampling, enabling operations such as moving, resizing, cloning, and editing object appearances. SceneDiffusion requires no training and works swiftly with text-to-image diffusion models on diverse real-world images.

28 Jun 2024

AI and IoT Revolutionize Sports Training Analysis

Researchers have utilized AI and IoT voice devices to advance sports training feature recognition, employing sensors for real-time data transmission and analysis. This approach successfully identifies movement patterns and predicts athlete states, enhancing training effectiveness.

27 Jun 2024

Using Semantic Entropy to Detect Hallucinations

Researchers introduced an entropy-based uncertainty estimator to tackle false and unsubstantiated outputs in large language models (LLMs) like ChatGPT. This method detects confabulations by assessing meaning, improving LLM reliability in fields like law and medicine.

27 Jun 2024

Robust Watermarking for Generative Images

Researchers introduced a method combining image watermarking with latent diffusion models (LDM) to embed invisible signatures in generated images, enabling future detection and identification while addressing ethical concerns in generative image modeling.

26 Jun 2024

Enhanced Language Models for E-Learning

Researchers have developed an advanced method to augment large language models (LLMs) with domain-specific knowledge for E-learning, significantly improving their performance in generating accurate and contextually relevant content.

26 Jun 2024