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

Optimizing Seaport Parking with AI and Computer Vision: A Systematic Review

This paper explores the integration of artificial intelligence (AI) and computer vision (CV) technologies in addressing urban expansion challenges, particularly in optimizing container movement within seaports. Through a systematic review, it highlights the significant role of AI and CV in sustainable parking ecosystems, offering valuable insights for enhancing seaport management and smart city development.

17 Sep 2023

AI Chatbots Shine in Creativity, But Humans Still Hold Their Ground

A study comparing the creativity of AI chatbots and human participants in the Alternate Uses Task (AUT) reveals that chatbots consistently produce creative responses, often surpassing humans. However, the study underscores the unique complexity of human creativity, highlighting that while AI can excel, it still struggles to fully replicate or surpass the best human ideas.

17 Sep 2023

Bridging the Gap: Balancing Biology and Efficiency in Neural Networks for Robotics

Researchers delve into the delicate equilibrium between biological realism and computational efficiency in neural networks, with a focus on Spiking Neural Networks (SNNs) and their role in advancing robotics. They explore the potential of neuromorphic computing to harmonize the synthesis of artificial and natural cognition, ultimately contributing to explainable and adaptable robotic systems.

15 Sep 2023

Revolutionizing Maritime Safety: AI-Driven Ship Detection and Tracking

Researchers have developed a cutting-edge ship detection and tracking model for inland waterways, addressing data scarcity issues. Leveraging few-shot learning and innovative transfer learning techniques, this model achieves remarkable accuracy, promising advancements in maritime safety and monitoring systems.

15 Sep 2023

Precision Ventilation Predictions Using Machine Learning: A Leap Forward in ICU Care

Researchers have developed two advanced machine learning models for predicting the duration of invasive and non-invasive mechanical ventilation in ICU patients. These models outperformed existing methods, providing valuable tools for enhancing patient care, optimizing resource allocation, and benchmarking clinical practices in critical care settings.

15 Sep 2023

Securing the Web: Deep Learning-Powered Malware Detection

Researchers have developed a robust web-based malware detection system that utilizes deep learning, specifically a 1D-CNN architecture, to classify malware within portable executable (PE) files. This innovative approach not only showcases impressive accuracy but also bridges the gap between advanced malware detection technology and user accessibility through a user-friendly web interface.

14 Sep 2023

Creating Fair and Inclusive AI-Generated Images with ITI-GEN1

Researchers have introduced ITI-GEN1, an innovative approach that leverages reference images and inclusive tokens to generate inclusive images without extensive model retraining. This versatile and efficient method enhances inclusiveness and fine-grained attribute control in AI-generated images, offering a valuable tool for addressing challenges in image generation while ensuring fairness and representation.

14 Sep 2023

Revolutionizing Mobile Network Traffic Prediction: CSTCN-Transformer Unveiled

Researchers have introduced a groundbreaking deep-learning model called the Convolutional Block Attention Module (CBAM) Spatio-Temporal Convolution Network-Transformer (CSTCN) to accurately predict mobile network traffic. By integrating temporal convolutional networks, attention mechanisms, and Transformers, the CSTCN-Transformer outperforms traditional models, offering potential benefits for resource allocation and network service quality enhancement.

13 Sep 2023

Bridging the Gap: Visualizing and Enhancing Medical Text AI's Decision-Making

Researchers have developed a novel approach that combines ResNet-based deep learning with Grad-CAM visualization to enhance the accuracy and interpretability of medical text processing. This innovative method provides valuable insights into AI model decision-making processes, making it a promising tool for improving healthcare diagnostics and decision support systems.

13 Sep 2023

Battling Hallucinations in Language Models: The DoLa Approach

Researchers have introduced a novel decoding strategy called Decoding by Contrasting Layers (DoLa) to tackle the problem of hallucinations in large language models (LLMs). By dynamically selecting and contrasting layers within LLMs, DoLa significantly improves the truthfulness and factual accuracy of generated content, offering potential benefits in various natural language processing tasks.

13 Sep 2023