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

Automation of Structural Health Monitoring for Civil Infrastructures Using AI and ML

This review explores the critical role of image-processing technologies in structural health monitoring (SHM) for civil infrastructures. It highlights the integration of artificial intelligence (AI) and machine learning (ML) to enhance SHM automation and accuracy. Various imaging modalities, including drones, thermography, LiDAR, and satellite imagery, are discussed for damage detection, crack identification, and deformation monitoring.

23 Apr 2024

Deep Learning for Computer-Assisted Interventions in Cataract Surgery

This study explores the transformative impact of deep learning (DL) techniques on computer-assisted interventions and post-operative surgical video analysis, focusing on cataract surgery. By leveraging large-scale datasets and annotations, researchers developed DL-powered methodologies for surgical scene understanding and phase recognition.

23 Apr 2024

Overview of Humanoid Robotics

Humanoid robotics, inspired by ancient myths and propelled by modern technology, is at the forefront of scientific and cultural advancement. From Pygmalion's statue to contemporary AI, humanoid robots reflect humanity's quest for understanding and replicating itself. Advancements in robotics, AI, and materials science are bringing humanoid robots closer to reality, with applications in healthcare, education, entertainment, and beyond.

23 Apr 2024

LCEFormer: Enhancing Remote Sensing Image Dehazing

Researchers introduce LCEFormer, a novel approach for remote sensing image dehazing, integrating CNN-based local context enrichment with transformer networks. Experiments on multiple datasets demonstrate state-of-the-art performance, surpassing existing methods in hazy scene restoration.

22 Apr 2024

Modular Time Representation in the Suprachiasmatic Nucleus Using ML

Using advanced imaging techniques and machine learning algorithms, researchers investigated the suprachiasmatic nucleus (SCN), the central circadian pacemaker in mammals. They discovered that collective calcium signals within the SCN correlated strongly with time of day, with distinct functional subtypes of neurons forming spatially organized modules. These modules exhibited ripple-like patterns representing specific time features, offering new insights into the systemic design principles of the biological clock.

20 Apr 2024

Automated Detection of Epiretinal Membranes in OCT Scans

Researchers developed a deep neural network (DNN) ensemble to automatically detect and classify epiretinal membranes (ERMs) in optical coherence tomography (OCT) scans of the macula. Leveraging over 11,000 images, the ensemble achieved high accuracy, particularly in identifying small ERMs, aided by techniques like mixup for data augmentation and t-stochastic neighborhood embeddings (t-SNE) for dimensional reduction.

20 Apr 2024

Traffic Coordination in VANETs with Probabilistic Manhattan Grid Topology

Researchers proposed a novel approach utilizing a modified Manhattan grid topology to address challenges in vehicular communication technology, focusing on India's dynamic traffic conditions. Their methodology integrated the fuzzy c-means (FCM) algorithm for jamming attacker detection, the modified fisheye state routing (MFSR) algorithm for efficient data exchange, and the particle swarm optimization (PSO) algorithm for precise attacker localization.

18 Apr 2024

Advancements in Human Action Recognition: A Deep Learning Perspective

The paper explores human action recognition (HAR) methods, emphasizing the transition to deep learning (DL) and computer vision (CV). It discusses the evolution of techniques, including the significance of large datasets and the emergence of HARNet, a DL architecture merging recurrent and convolutional neural networks (CNN).

18 Apr 2024

Machine Learning-based System for Analyzing Sexual Harassment in Middle Eastern Literature

Scholars utilized machine learning techniques to analyze instances of sexual harassment in Middle Eastern literature, employing lexicon-based sentiment analysis and deep learning architectures. The study identified physical and non-physical harassment occurrences, highlighting their prevalence in Anglophone novels set in the region.

17 Apr 2024

Predicting Preplaced Aggregate Concrete Strength with Machine Learning

Researchers evaluated 13 machine learning models to forecast compressive strength in preplaced aggregate concrete. Extreme gradient boosting (XGBoost) emerged as the most accurate, with sensitivity and SHAP analyses highlighting crucial factors like gravel and water-to-binder ratio.

17 Apr 2024