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

Feature Extraction in Machine Learning

Feature extraction is a vital process in computer vision, signal processing, and machine learning, condensing raw data into meaningful representations for improved analysis. It plays a crucial role in reducing dimensionality, eliminating noise, and enhancing the performance of machine learning models. Despite challenges like the curse of dimensionality and subjectivity in feature selection, advancements, and innovative techniques promise an exciting future for feature extraction, reinforcing its indispensable role in transforming diverse data into actionable insights.

27 Dec 2023

What is Backpropagation?

This piece elucidates the pivotal role of backpropagation in neural networks, detailing its mechanics and significance in refining predictive capabilities. Drawing parallels with the brain's neural connections, it navigates through forward propagation, emphasizing backpropagation's iterative weight adjustments driven by error calculations. The chain rule in calculus emerges as the linchpin, enabling precise gradient computation crucial for the network's evolution.

25 Dec 2023

One Year of ChatGPT: A Boon or Burden in the Generative AI Era?

Chat Generative Pre-Trained Transformer (ChatGPT) revolutionized scientific research in 2022, offering invaluable support in writing, coding, and brainstorming. Despite its positive impact, concerns about misuse, biases, and transparency challenges persist. The dual nature of ChatGPT as a boon and potential hazard necessitates ethical frameworks, transparency, and ongoing evaluation for responsible integration in scientific exploration.

22 Dec 2023

AI Revolutionizing Vehicle Safety Technology

The integration of artificial intelligence (AI) into vehicle safety systems represents a transformative leap in the automotive landscape. This paradigm shift, evident in Advanced Driver-Assistance Systems (ADAS) and autonomous vehicles, enables vehicles to analyze real-time data, predict hazards, and make split-second decisions, enhancing road safety.

22 Dec 2023

AI-Based Elderly and Visually Impaired Human Activity Monitoring

This paper unveils the Elderly and Visually Impaired Human Activity Monitoring (EV-HAM) system, a pioneering solution utilizing artificial intelligence, digital twins, and Wi-Sense for accurate activity recognition. Employing Deep Hybrid Convolutional Neural Networks on Wi-Fi Channel State Information data, the system achieves a remarkable 99% accuracy in identifying micro-Doppler fingerprints of activities, presenting a revolutionary advancement in elderly and visually impaired care through continuous monitoring and crisis intervention.

20 Dec 2023

Using Machine Learning to Combat Fake News

This paper emphasizes the crucial role of machine learning (ML) in detecting and combating fake news amid the proliferation of misinformation on social media. The study reviews various ML techniques, including deep learning, natural language processing (NLP), ensemble learning, transfer learning, and graph-based approaches, highlighting their strengths and limitations in fake news detection. The researchers advocate for a multifaceted strategy, combining different techniques and optimizing computational strategies to address the complex challenges of identifying misinformation in the digital age.

20 Dec 2023

AI Unravels Extreme Precipitation Dynamics: CNN-Based Analysis

Researchers unveil a pioneering approach using a convolutional neural network (CNN) to analyze extreme precipitation patterns' link to climate shifts. This CNN-based method, trained with data from 10,000 precipitation stations, overcomes limitations of traditional analyses, providing high-resolution maps and nuanced insights into the sensitivity of extreme precipitation to climate change for North America, Europe, Australia, and New Zealand.

19 Dec 2023

YOLOX Breakthrough: Targeting Weeds in Paddy Fields

Researchers introduce an innovative weed detection solution for rice fields. Utilizing YOLOX technology, particularly the YOLOX-tiny model, the approach outshines competitors, promising accurate herbicide application by agricultural robots during the vulnerable rice seedling stage. The breakthrough addresses challenges in weed control, marking a significant advancement in precision agriculture.

19 Dec 2023

AI's Transformative Impact on Visual Search

The marriage of Artificial Intelligence (AI) and visual search technology has reshaped digital interaction, democratizing access to information across diverse industries. Fueled by the evolution of AI, particularly with Convolutional Neural Networks (CNNs), visual search transcends traditional boundaries, offering feature-rich capabilities. From retail innovations to personalized experiences, AI's multifaceted role in feature extraction, image recognition, and contextual understanding drives the seamless integration of visual search, promising a future where image exploration becomes the norm in our digital landscape.

19 Dec 2023

How is AI Used in Traffic Flow Optimization?

The surge in urban population and traffic challenges necessitate a paradigm shift in traffic flow optimization. Artificial Intelligence (AI) emerges as the transformative force, employing data-driven strategies. From AI-driven traffic signal control to predictive analytics and autonomous vehicles, this evolution promises efficient, safe, and sustainable urban mobility. Challenges, including data privacy and infrastructure upgrades, must be addressed for equitable access and unbiased solutions.

19 Dec 2023