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

Understanding Human Perception and Trust in AI

Human perception and trust in AI are influenced by factors such as transparency, explainability, reliability, and cultural norms. Transparency in AI decision-making processes fosters trust, while personalization and user control enhance reliability.

16 Apr 2024

Integrated Machine Learning for Enhancing Footing Stability Prediction in Shallow Foundations

Researchers introduced two novel predictive models employing metaheuristic algorithms, Backtracking Search Algorithm (BSA) and Equilibrium Optimizer (EO), combined with artificial neural networks (ANNs) to assess the bearing capacity of footings on two-layered soil masses. Both BSA-ANN and EO-ANN models demonstrated improved prediction accuracy over conventional ANN models, with EO exhibiting superior performance.

16 Apr 2024

Autonomous Electrochemical Platform for Mechanistic Investigation

Researchers present an autonomous electrochemical platform for investigating molecular electrochemistry mechanisms. Utilizing artificial intelligence, the platform autonomously identifies electrochemical mechanisms, designs experimental conditions, and extracts kinetic information.

15 Apr 2024

Emergency Communication with UAVs: ISATR Algorithm Optimization

Researchers propose the ISATR algorithm to optimize trajectory planning and resource allocation for UAV-assisted emergency communication. By employing cellular automata for user trajectory prediction and iterative scheduling, the algorithm effectively enhances communication quality in disaster scenarios like earthquakes.

15 Apr 2024

Airborne Particulate Matter Pollution Monitoring in Surface Mines Using IoT and Machine Learning

This study introduced an innovative approach to address airborne particulate matter (PM) pollution in surface mines using Internet of Things (IoT) technology and machine learning (ML) techniques. The study highlighted PM 1.0 as the predominant pollutant and employed a real-time monitoring system to track PM concentrations in ball clay surface mine sites.

12 Apr 2024

Multimodal Dataset for Driver Monitoring in Assisted Driving Automation

Researchers introduced the manD 1.0 dataset, designed for driver monitoring in automated driving scenarios. This dataset, comprising data from 50 participants navigating through various driving scenarios in a simulator, includes environmental, vehicle, and driver state information, such as physiology and gaze.

12 Apr 2024

Predicting Lithium-Ion Battery Remaining Useful Life Using SDAE-Transformer Fusion Model

Researchers introduced a novel fusion model for predicting lithium-ion battery Remaining Useful Life (RUL), integrating Stacked Denoising Autoencoder (SDAE) and transformer capabilities. This model outperformed others in accuracy and robustness, offering a promising direction for battery life prediction research, crucial for battery management systems and predictive maintenance strategies.

11 Apr 2024

Insights into Smart Home Adoption Among China's Elderly

This study in PLOS ONE delves into smart home device adoption among elderly individuals in China, employing the Technology Acceptance Model (TAM) alongside other variables. Analyzing 236 questionnaires, the study identifies crucial factors influencing adoption, offering valuable insights for designing tailored smart home technologies to enhance elderly well-being and autonomy.

11 Apr 2024

Using AI and Satellite Imagery to Predict Citizen Deprivation Perception

Researchers leverage AI and earth observation techniques to predict citizen perceptions of deprivation in Nairobi's slums. Combining satellite imagery and citizen science, their methodology accurately forecasts deprivation, offering policymakers invaluable insights for targeted interventions aligned with Sustainable Development Goal 11, potentially benefiting millions worldwide.

10 Apr 2024

Emotion Perception: CNN Insights into Human Brain

Researchers explored how convolutional neural networks (CNNs) model the human brain's ability to perceive emotions from visual stimuli. They found that CNNs exhibit emotion selectivity akin to the human visual system, with deeper layers showing increased sensitivity, affirming their potential in understanding neural processes underlying emotion perception.

10 Apr 2024