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

IoT-Driven Smart Farming System to Transform Agriculture

Researchers present a groundbreaking integrated agricultural system utilizing IoT-equipped sensors and AI models for precise rainfall prediction and fruit health monitoring. The innovative approach combines CNN, LSTM, and attention mechanisms, demonstrating high accuracy and user-friendly interfaces through web applications, heralding a transformative era in data-driven agriculture.

10 Jan 2024

Fungal Fortune Telling: AI Predicts Macrofungal Fruitbody Occurrences

In this groundbreaking study, researchers deploy artificial neural networks (ANN) to forecast the presence of macrofungal fruitbodies in Western Hungary. Focusing on Amanita and Russula species, the study reveals the significance of species-specific meteorological parameters in enhancing accuracy, marking a pioneering step in AI-driven predictions for ecological studies.

10 Jan 2024

The Role of AI in Transportation

Delve into the transformative realm of artificial intelligence (AI) within transportation, revolutionizing efficiency, safety, and sustainability. From dynamic route optimization to traffic management and ethical considerations, this exploration unveils the profound impact of AI, steering the future of transportation into a realm defined by intelligence, safety, and eco-friendliness.

9 Jan 2024

Batch Normalization in Deep Learning

This in-depth exploration elucidates the pivotal role of Batch Normalization in deep learning, addressing challenges related to internal covariate shifts. By standardizing inputs within each layer, Batch Normalization enhances stability, mitigates gradient-related issues, and expedites convergence rates during training. The article delves into the mechanics, advantages, and adaptability of Batch Normalization, establishing it as a cornerstone for crafting robust and high-performing neural network models across diverse architectures.

9 Jan 2024

Enhancing MRI Safety: Deep Learning for Motion Artifact Detection

Researchers introduce a groundbreaking deep learning method, published in Medical Physics, to detect and measure motion artifacts in undersampled brain MRI scans. The approach, utilizing synthetic motion-corrupted data and a convolutional neural network, offers a potential safety measure for AI-based approaches, providing real-time alerts and insights for improved MRI reconstruction methods.

8 Jan 2024

Advancing Biomedical Research: The Integration of AI/ML in Predictive Analysis

This paper explores the dynamic integration of artificial intelligence/machine learning (AI/ML) in biomedical research, emphasizing its pivotal role in predictive analysis across diverse domains. While acknowledging transformative potential, the paper highlights challenges such as inclusivity, synergy between computational models and human expertise, and standardization of clinical data, presenting them as opportunities for innovation in a transformative era for human health optimization through AI/ML in biomedical research.

5 Jan 2024

Extensive ML Dataset for Advancing Stomatal Research in Hardwood Trees

This paper presents an extensive dataset of approximately 11,000 stomatal images from temperate hardwood trees, covering 17 common species and 55 genotypes. The dataset, validated for accuracy and machine learning model training, enables high-throughput analysis of stomatal characteristics, exploration of diversity among tree types, and the creation of new indices for stomatal measurements, offering valuable insights for ecologists, plant biologists, and ecophysiologists.

5 Jan 2024

Leveraging Machine Learning Techniques for Forest Cover Assessment

Researchers address critical forest cover shortage, utilizing Sentinel-2 satellite imagery and sophisticated algorithms. Artificial Neural Networks (ANN) and Random Forest (RF) algorithms showcase exceptional accuracy, achieving 97.75% and 96.98% overall accuracy, respectively, highlighting their potential in precise land cover classification. The study's success recommends integrating hyperspectral satellite imagery for enhanced accuracy and explores the possibilities of deep learning algorithms for further advancements in forest cover assessment.

4 Jan 2024

GNN-Based Edge Ranking for Rapid Post-Disaster Recovery

This work presents a novel Graph Neural Network (GNN) method for swiftly identifying critical road segments post-disaster, aiding efficient recovery and resilience planning. Overcoming computational challenges, the GNN-based edge ranking framework proves effective in large-scale networks, offering accuracy and adaptability. This approach showcases versatility, enabling real-time analysis and facilitating proactive measures for reinforcing critical infrastructure against future disruptions.

4 Jan 2024

Machine Learning-Based Pedestrian Crossing Decision Models to Increase Pedestrian Safety

Researchers focus on improving pedestrian safety within intelligent cities using AI, specifically support vector machine (SVM). Leveraging machine learning and authentic pedestrian behavior data, the SVM model outperforms others in predicting crossing probabilities and speeds, demonstrating its potential for enhancing road traffic safety and integrating with intelligent traffic simulations. The study emphasizes the significance of SVM in accurately predicting real-time pedestrian behaviors, contributing to refined decision models for safer road designs.

3 Jan 2024