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.

See less

Articles from Silpaja

Virtual Reality-based Safety Skills Training for Individuals in the Autism Spectrum

This review explores the impactful use of Virtual Reality (VR) technology in safety skills interventions for individuals with Autism Spectrum Disorder (ASD). Highlighting the effectiveness of VR training in diverse scenarios, the study underscores the need for personalized interventions and adaptive models, advocating cautious application in younger patients and calling for further research into tailored approaches for enhanced safety skill acquisition in the ASD population.

26 Jan 2024

Real-Time 3D Ultrasound Tracking of Microswimmers in Challenging Biomedical Spaces

Researchers unveil a cutting-edge 3D ultrasound tracking system for real-time monitoring of acoustically actuated microswimmers navigating intricate environments within the human body. With high reliability demonstrated through benchtop experiments, this innovation paves the way for advanced biomedical applications, showcasing significant progress in the clinical integration of microswimmer technology.

25 Jan 2024

Benchmarking CAN Intrusion Detection Systems with the ROAD Dataset

Researchers unveil the vulnerability of modern vehicles' controller area networks (CANs) to cyber threats and introduce a comprehensive guide to open CAN intrusion detection system (IDS) datasets. The ROAD dataset emerges as a benchmark, offering over 3.5 hours of realistic vehicle CAN data with diverse attacks, addressing the need for comparability and assessment of IDS approaches against subtle, advanced cyber threats in the automotive industry.

25 Jan 2024

Intelligent Systems and Machine Learning for Traffic Prediction on Suburban Roads

Researchers employ advanced intelligent systems to analyze extensive traffic data on northern Iranian suburban roads, revolutionizing traffic state prediction. By integrating principal component analysis, genetic algorithms, and cyclic features, coupled with machine learning models like LSTM and SVM, the study achieves a significant boost in prediction accuracy and efficiency, offering valuable insights for optimizing transportation management and paving the way for advancements in traffic prediction methodologies.

24 Jan 2024

Canine Cardiomegaly Detection: A Breakthrough in Deep Learning Diagnostics

Researchers unveil a regressive vision transformer (RVT) model to tackle the leading cause of death in dogs—cardiac disease. By integrating traditional diagnostic methods with advanced deep learning, the RVT model proves to be efficient, trustworthy, and superior, paving the way for enhanced canine cardiomegaly assessment and revolutionizing diagnostic accuracy in veterinary medicine.

24 Jan 2024

LGN Fusion Model for Accurate Protein-Ligand Binding Affinity Prediction in Drug Discovery

Researchers unveil LGN, a groundbreaking graph neural network (GNN)-based fusion model, addressing the limitations of existing protein-ligand binding affinity prediction methods. The study demonstrates the model's superiority, emphasizing the importance of incorporating ligand information and evaluating stability and performance for advancing drug discovery in computational biology.

23 Jan 2024

Using Deep Learning to Detect Black Pepper Leaf Disease

Scientists present a groundbreaking study published in Scientific Reports, introducing an intelligent transfer learning technique utilizing deep learning, particularly a convolutional neural network (CNN), to predict diseases in black pepper leaves. The research showcases the potential of advanced technologies in plant health monitoring, offering a comprehensive approach from dataset acquisition to the development of deep neural network models for early-stage leaf disease identification in agriculture.

23 Jan 2024

Weighted Dynamics in Neural Networks

Delve into the core of artificial intelligence (AI) as this exploration unravels the significance of weights in shaping the efficacy of machine learning (ML) models, particularly in neural networks. From the foundations of neural networks and weight initialization techniques to the challenges of vanishing and exploding gradients, discover the pivotal role weights play in AI development.

22 Jan 2024

Evolution of AI in Virtual Characters

Explore the fascinating journey of Artificial Intelligence (AI) in shaping virtual characters, from simple rule-based systems to emotionally intelligent entities. Witness the impact of Machine Learning (ML), Reinforcement Learning (RL), Natural Language Processing (NLP), and Deep Learning on the evolution of virtual characters in gaming, VR simulations, and beyond. Delve into the ethical considerations and challenges, anticipating a future where the boundaries between virtual and real-world interactions blur, promising transformative experiences with responsible AI integration.

22 Jan 2024

Introduction to Loss Function

Delve into the core of machine learning with a comprehensive exploration of loss functions. From understanding their significance in model training to exploring diverse types across classification, regression, and custom scenarios, this article provides insights into the challenges, considerations, and applications that shape the ML landscape. Whether dealing with image classification, natural language processing, or object detection, the strategic choice of loss functions emerges as a critical factor in optimizing model performance.

22 Jan 2024