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

Medprompt: Elevating GPT-4 Performance in Medicine and Beyond through Intelligent Prompting Strategies

This article presents a groundbreaking study exploring Generative Pre-trained Transformer-4 (GPT-4) capabilities in specialized domains, with a focus on medicine. The innovative "Medprompt" strategy, incorporating dynamic few-shot, self-generated chain of thought, and choice shuffling ensemble techniques, significantly enhances GPT-4's performance, surpassing specialist models across diverse medical benchmarks.

1 Dec 2023

Cultural Transmission in AI Agents: A Breakthrough in Real-time Few-Shot Imitation Learning

This paper introduces a groundbreaking approach to instill cultural transmission as a foundational social skill in artificial intelligence (AI) agents. By employing neural networks and reinforcement learning in a 3D simulation environment, the study demonstrates how AI agents can imitate humans in novel situations without prior human data, paving the way for significant advancements in artificial general intelligence.

1 Dec 2023

Decoding Human-Machine Interactions: A Curated Dataset for Adaptive Human-Machine Interfaces

Researchers present a meticulously curated dataset of human-machine interactions, gathered through a specialized application with formally defined User Interfaces (UIs). This dataset aims to decode user behavior and advance adaptive Human-Machine Interfaces (HMIs), providing a valuable resource for professionals and data analysts engaged in HMI research and development.

30 Nov 2023

FollowNet: A Unified Benchmark for Advancing Car-Following Behavior Modeling

This paper introduces FollowNet, a pioneering initiative addressing challenges in modeling car-following behavior. With a unified benchmark dataset consolidating over 80K car-following events from diverse public driving datasets, FollowNet sets a standard for evaluating and comparing car-following models, overcoming format inconsistencies in existing datasets.

30 Nov 2023

Comparative Analysis of Deep Learning Methods for Radar-Based Human Activity Recognition

Researchers emphasize the growing significance of radar-based human activity recognition (HAR) in safety and surveillance, highlighting its advantages over vision-based sensing in challenging conditions. The study reviews classical Machine Learning (ML) and Deep Learning (DL) approaches, with DL's advantage in avoiding manual feature extraction and ML's robust empirical basis. A comparative study on benchmark datasets evaluates performance and computational efficiency, aiming to establish a standardized assessment framework for radar-based HAR techniques.

29 Nov 2023

Autonomous Nanorobots Achieve Powerful Thrust on Dry Surfaces Using Photothermal Shock

Researchers introduce a groundbreaking solution for nanorobotic motion limitations on solid surfaces by developing micronewton-thrust nanomotors utilizing a photothermal-shock technique. These nanorobots demonstrate exceptional thrust-to-weight ratios, enabling precise control on dry surfaces and interactions with micro/nano-objects. The autonomous nanorobots, equipped with machine vision and deep learning, showcase complex motions and functions, overcoming nanotribology challenges and expanding capabilities from fluids to dry surfaces.

29 Nov 2023

Intelligent Forecasting of Free Atmospheric Corrosion in Marine Steel Structure

Researchers present an intelligent framework, integrating a Group Method of Data Handling (GMDH) neural network and Shapley Additive Explanations (SHAP) analysis, to predict free atmospheric corrosion in marine steel structures. Leveraging historical sensor data, the framework demonstrates high forecasting accuracy, with optimal parameter selection enhancing performance. The SHAP analysis reveals the impact of environmental factors on corrosion, providing valuable insights into the dynamics of atmospheric corrosion in marine settings.

29 Nov 2023

Real-Time Table Tennis Ball Landing Point Detection Using Computer Vision

Researchers introduced an innovative method for real-time table tennis ball landing point determination, minimizing reliance on complex visual equipment. The approach, incorporating dynamic color thresholding, target area filtering, keyframe extraction, and advanced detection algorithms, significantly improved processing speed and accuracy. Tested on the Jetson Nano development board, the method showcased exceptional performance.

28 Nov 2023

Revolutionizing Dyslexia Detection: Unveiling the Power of Multi-Source Data and AI Models

Researchers present an innovative approach to dyslexia identification using a multi-source dataset incorporating eye movement, demographic, and non-verbal intelligence data. Experimenting with various AI models, including MLP, RF, GB, and KNN, the study demonstrates the efficacy of a fusion of demographic and fixation data in accurate dyslexia prediction. The insights gained, including the significance of IQ, age, and gender, pave the way for enhanced dyslexia detection, while challenges like data imbalance prompt considerations for future improvements.

28 Nov 2023

What are RNNs?

This comprehensive overview explores Recurrent Neural Networks (RNNs) as foundational tools in artificial intelligence and machine learning, particularly adept at handling sequential data. Delving into RNN architecture, advanced variations like LSTMs and GRUs, and their applications in diverse domains such as NLP, time series prediction, and speech-related tasks, the article highlights their strengths and impact. It addresses challenges faced by traditional RNNs, leading to the emergence of attention mechanisms and transformative models like Transformers, reshaping the landscape of sequence modeling.

27 Nov 2023

Silpaja Chandrasekar

Articles from Silpaja

Medprompt: Elevating GPT-4 Performance in Medicine and Beyond through Intelligent Prompting Strategies

Cultural Transmission in AI Agents: A Breakthrough in Real-time Few-Shot Imitation Learning

Decoding Human-Machine Interactions: A Curated Dataset for Adaptive Human-Machine Interfaces

FollowNet: A Unified Benchmark for Advancing Car-Following Behavior Modeling

Comparative Analysis of Deep Learning Methods for Radar-Based Human Activity Recognition

Autonomous Nanorobots Achieve Powerful Thrust on Dry Surfaces Using Photothermal Shock

Intelligent Forecasting of Free Atmospheric Corrosion in Marine Steel Structure

Real-Time Table Tennis Ball Landing Point Detection Using Computer Vision

Revolutionizing Dyslexia Detection: Unveiling the Power of Multi-Source Data and AI Models

What are RNNs?

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