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

ML-based Graph Model Enhances Stock Trend Prediction

The Laplacian correlation graph (LOG) significantly improves stock trend prediction by modeling price correlations. Experimental results show superior accuracy and returns, highlighting LOG's potential in real-world investment strategies.

6 Jun 2024

Carbon-Neutral Mobility Hub Planning Using AI

Researchers introduced a novel AI-based, three-tier scheme for identifying and assessing carbon-neutral mobility hubs, focusing on optimal site selection, suitability analysis, and traffic impact evaluation. The study demonstrated significant reductions in vehicle travel and substantial cost savings, promoting sustainable urban transportation planning.

5 Jun 2024

UAVs and Computer Vision Enhance Remote Runway Inspections

Researchers developed an automated system utilizing UAVs and deep learning to monitor and maintain remote gravel runways in Northern Canada. This system accurately detects defects and evaluates runway smoothness, proving more effective and reliable than traditional manual methods in harsh and isolated environments.

5 Jun 2024

Predicting Salicylic Acid Solubility Using Machine Learning

Researchers in Digital Chemical Engineering applied six machine learning algorithms to predict the solubility of salicylic acid in 13 solvents, achieving high accuracy. The random forest (RF) algorithm outperformed others with the lowest total error, showcasing the efficacy of ML in pharmaceutical applications.

4 Jun 2024

Permafrost Embankment Thermal Prediction via Deep Learning

A novel approach integrates deep learning with geotechnical knowledge to predict the stochastic thermal regime of permafrost embankments. Validated against real data, this method enhances accuracy and reduces computational costs, proving effective for diverse environmental conditions.

4 Jun 2024

The Transformative Role of AI in Music

AI is reshaping music composition, production, and distribution, enabling new creative possibilities and efficiencies. AI platforms like Amper Music and AIVA generate compositions, while tools like LANDR and Melodrive enhance sound quality and offer novel arrangements. AI empowers artists, fosters collaboration, and drives innovation, transforming traditional roles in the music industry.

4 Jun 2024

AI and NLP Transform Software Requirements Engineering

Researchers reviewed the integration of NLP in software requirements engineering (SRE) from 1991 to 2023, highlighting advancements in machine learning and deep learning. The study found that AI technologies significantly enhance the accuracy and efficiency of SRE tasks, despite challenges in integrating these technologies into existing workflows.

3 Jun 2024

CNNs Optimize Waste Management in Smart Cities

Researchers evaluated deep learning models for waste classification in smart cities, with ResNeXt-101 emerging as the top performer. The study suggests a federated learning framework to enhance trash detection across diverse environments, leveraging multiple CNN models for improved efficiency in waste management.

3 Jun 2024

CIMNet Advances Crop Disease Detection

Researchers introduced CIMNet, a novel network for crop disease image recognition, excelling in noisy environments. Featuring a non-local attention module and multi-scale critical information fusion, CIMNet outperformed traditional models in accuracy and applicability, significantly enhancing crop disease detection and improving agricultural productivity.

31 May 2024

Clustering Swap Prediction for Image-Text Pre-Training

Researchers introduced Clus, a novel clustering swap prediction strategy for learning an image-text embedding space, which leverages distillation learning to achieve state-of-the-art performance in tasks like image-text retrieval and visual question answering.

31 May 2024