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

Applications and Challenges of AI in Voice Recognition

This comprehensive exploration delves into the pivotal role of Artificial Intelligence (AI) in voice recognition, uncovering techniques, applications, challenges, and future prospects. From feature extraction methods like Mel-Frequency Cepstral Coefficients (MFCCs) to classification approaches involving Gaussian Mixture Models (GMMs) and Deep Neural Networks (DNNs), the article navigates through the intricate landscape of AI-driven voice recognition.

20 Nov 2023

Decoding LSTM: Sequences and Dependencies

This article delves into the world of Long Short-Term Memory (LSTM) networks, tracing their origins to address challenges in handling sequential data. Exploring the intricate architecture of LSTMs, it dissects the roles of components like forget gates, input gates, cell states, and output gates. The piece highlights advantages such as long-term dependency handling, gradient stability, versatility, contextual understanding in NLP, and memory retention for time series forecasting.

20 Nov 2023

How does AI Help with Water Management?

This comprehensive article explores how artificial intelligence (AI) transforms water management by monitoring quality, predicting contamination, optimizing distribution, and conserving resources. It covers AI methods, implementation steps, and impacts across various water management domains, addressing challenges such as data accessibility, integration with existing infrastructure, financial constraints, regulatory compliance, and ethical considerations.

20 Nov 2023

Factuality Fine-Tuning: Mitigating Hallucinations in Large Pre-trained Language Models

Researchers address the issue of factual inaccuracies, or 'hallucinations,' in large pre-trained language models (LLMs) by proposing a practical fine-tuning approach. Leveraging recent Natural Language Processing (NLP) advancements and the direct preference optimization (DPO) algorithm, they enhance the factuality of LLMs, demonstrated through experiments on biographical and medical question-answering tasks.

17 Nov 2023

GraphCast: A Breakthrough in Weather Forecasting with Machine Learning

A groundbreaking machine learning weather prediction (MLWP) approach revolutionizing global medium-range weather forecasting. Unlike traditional numerical weather prediction systems, GraphCast leverages machine learning directly from reanalysis data, achieving unparalleled speed and accuracy in 10-day forecasts. With superior performance in severe weather event prediction, GraphCast signifies a crucial stride in precise and efficient weather forecasting, showcasing the potential of machine learning in modeling intricate dynamical systems.

16 Nov 2023

CHARTing the Course for Transparent Evaluation: Establishing Reporting Standards for AI-Driven Chatbots in Healthcare

The paper addresses concerns about the accuracy of AI-driven chatbots, focusing on large language models (LLMs) like ChatGPT, in providing clinical advice. The researchers propose the Chatbot Assessment Reporting Tool (CHART) as a collaborative effort to establish structured reporting standards, involving a diverse group of stakeholders, from statisticians to patient partners.

16 Nov 2023

A Fusion Module Approach for Enhancing Positioning Accuracy in Firefighting Robotics

This paper addresses challenges in positioning technology crucial for firefighting robots, introducing a Fusion Module based on the Blackboard architecture within the fire emergency reaction dispatching (FERD) system. This module leverages multiple sensors, employing strategies such as denoising, spatial alignment, confidence degree update, observation filtering, data fusion, and fusion decision to significantly improve positioning accuracy in complex indoor environments with multiple obstacles.

15 Nov 2023

Revolutionizing Quadruped Robot Locomotion: An Adaptive System Based on Double-Layered Central Pattern Generator

This paper introduces an innovative adaptive locomotion system for quadruped robots. The system, centered around a double-layered Central Pattern Generator (CPG) driven by gyroscope signals, enables seamless gait transitions and precise movement control. The research showcases the robot's ability to perform a regular trot pattern in complex environments, emphasizing its adaptability and effectiveness in diverse real-world scenarios.

15 Nov 2023

Energy-Efficient UAV Cluster Missions through Enhanced Task Allocation and Trajectory Planning

Researchers present a comprehensive strategy for optimizing Unmanned Aerial Vehicle (UAV) cluster tasks in three-dimensional space, focusing on complete area coverage. The proposed approach incorporates an enhanced Fuzzy C-clustering algorithm for task allocation and introduces a Particle Swarm Hybrid Ant Colony (PSOHAC) algorithm for trajectory planning.

14 Nov 2023

A Novel Algorithm for Accurate Void Size Extraction for Improving Pavement Safety

This paper introduces an innovative void size extraction algorithm for pavement safety assessments. Leveraging the continuous wavelet transform (CWT) method and ground-penetrating radar (GPR) signals, the algorithm effectively visualizes and accurately measures geometric parameters within void areas.

14 Nov 2023

Silpaja Chandrasekar

Articles from Silpaja

Applications and Challenges of AI in Voice Recognition

Decoding LSTM: Sequences and Dependencies

How does AI Help with Water Management?

Factuality Fine-Tuning: Mitigating Hallucinations in Large Pre-trained Language Models

GraphCast: A Breakthrough in Weather Forecasting with Machine Learning

CHARTing the Course for Transparent Evaluation: Establishing Reporting Standards for AI-Driven Chatbots in Healthcare

A Fusion Module Approach for Enhancing Positioning Accuracy in Firefighting Robotics

Revolutionizing Quadruped Robot Locomotion: An Adaptive System Based on Double-Layered Central Pattern Generator

Energy-Efficient UAV Cluster Missions through Enhanced Task Allocation and Trajectory Planning

A Novel Algorithm for Accurate Void Size Extraction for Improving Pavement Safety

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