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

Predicting Copper Prices with Cutting-Edge Neural Networks

Researchers developed a 1D-CNN model to accurately predict global copper prices using data from 1991-2023. This CNN outperforms traditional methods, offering dependable forecasts until 2027, proving valuable for policymakers in managing price volatility and strategic decision-making.

4 Aug 2024

Computer Vision Technique for Accurate Plant Heights

A scaleless monocular vision method accurately measures plant heights by converting color images to binary data. Achieving high precision within 2–3 meters and minimal error, this non-contact technique demonstrates potential for reliable plant height measurement under varied lighting conditions.

1 Aug 2024

Variational Autoencoders Improve Energy Minimization Stability

Researchers developed a deep learning-based approach using variational autoencoders (VAEs) to address instabilities in energy minimization within density functional theory. VAEs improved accuracy and stability in density profiles, demonstrating effective performance in both 1D and 3D models with successful transfer learning.

1 Aug 2024

Unraveling the Mysteries of Machine Learning

Machine learning's progress brings accuracy but also opaque decision-making. Understanding these "black boxes" is essential in sectors like healthcare, finance, and autonomous driving. Interpretability fosters trust, ensures accountability, and promotes ethical use, balancing model complexity with transparency for effective application.

31 Jul 2024

Predicting Concrete Strength with Machine Learning Models

Researchers developed a machine-learning model to predict concrete compressive strength using 228 samples and six algorithms. The XGBoost model delivered the highest accuracy, aligning predictions with conventional theory and demonstrating the potential of ML in concrete strength forecasting.

30 Jul 2024

Llama 3: Meta's New AI Model Rivals GPT-4

Meta's Llama 3, a 405B parameter transformer with a 128K token context window, matches GPT-4 in performance across various tasks. With integrated image, video, and speech capabilities, it emphasizes data quality and efficiency, though further development is needed for widespread release.

30 Jul 2024

Automating Railway Track Design with Machine Learning

Researchers developed an automated method for recommending sublayer and form layer thicknesses in railway tracks using cone penetration test (CPT) data. Leveraging machine learning algorithms, the study achieved high accuracy with a random forest classifier fine-tuned via Bayesian optimization.

30 Jul 2024

Novel Backdoor Attacks on Large Language Models

Researchers introduced an adaptive backdoor attack method to steal private data from pre-trained large language models (LLMs). This method, tested on models like GPT-3.5-turbo, achieved a 92.5% success rate. By injecting triggers during model customization and activating them during inference, attackers can extract sensitive information, underscoring the need for advanced security measures.

30 Jul 2024

Predicting Building Damage with AI Models

Researchers developed and compared convolutional neural network (CNN) and support vector machine (SVM) models to predict damage intensity in masonry buildings on mining terrains. Both models achieved high accuracy, with the CNN model outperforming in precision and F1 score. The study highlights CNN's effectiveness despite its higher data preparation needs, suggesting its potential for automated damage prediction.

29 Jul 2024

AI Enhances Building Efficiency and Cuts Emissions

Researchers evaluated AI's potential to reduce energy consumption and emissions in US medium office buildings. Their study demonstrated that AI could significantly enhance building energy efficiency, achieving up to a 21% reduction in energy use and substantial carbon emissions reductions by 2050, particularly when combined with supportive energy policies and low-carbon power sources.

29 Jul 2024