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

AI Unveils Psychological Traits: Analyzing Social Media Behavior on VK

In a study published in Scientific Reports, advanced AI techniques dissected the social media activity of 1358 VK users, unveiling correlations between behavior and personality traits. Through meticulous analysis of 753,252 posts and reposts alongside Big Five traits and intelligence assessments, the research highlighted the influence of emotional tone and engagement metrics on psychological attributes, advocating for behavior-based diagnostic models in the digital realm.

12 Mar 2024

ChatGPT-4: Human-like Behavior in AI Chatbots

In a groundbreaking study, researchers detailed how ChatGPT-4 chatbots exhibited remarkably human-like behavioral and personality traits in Turing test scenarios and classic behavioral games. Through interactive sessions and comprehensive analyses, the study unveiled ChatGPT-4's tendencies towards altruism, fairness, trust, cooperation, and risk aversion, offering profound insights into the adaptability and responsiveness of AI in diverse scenarios.

12 Mar 2024

KNOWAGENT: Enhancing Planning Abilities in Language Models

Researchers proposed KNOWAGENT, integrating action knowledge into large language models to improve planning accuracy. Through iterative self-learning and leveraging external knowledge, KNOWAGENT mitigates planning hallucinations, demonstrating superior performance in complex tasks like HotpotQA and ALFWorld, albeit facing challenges in reasoning and memory constraints.

11 Mar 2024

Harnessing Machine Learning for Climate Change Resilience

Machine learning (ML) emerges as a pivotal tool in addressing climate change, offering insights into complex environmental data and guiding policy decisions. Despite challenges in data quality and interpretability, ML empowers communities, fosters scientific innovation, and promotes climate justice. Through interdisciplinary collaboration and investment, ML promises to propel climate resilience and sustainability for future generations.

11 Mar 2024

Fostering Social Connections: Virtual Reality Villages for Elderly Well-being

Researchers introduced "Cap de Ballon," a virtual village designed to combat social isolation among older individuals. Leveraging virtual reality (VR), artificial intelligence (AI), and visual analysis, the collaborative effort resulted in a comprehensive methodology to develop immersive VR environments, fostering communication, creativity, and positive emotions among seniors. The study's findings underscored seniors' enthusiasm for VR engagement, highlighting the potential of virtual villages to enhance social and emotional well-being in the elderly population.

8 Mar 2024

Machine Learning Models for Classification of Migraine Headaches

In a recent paper published in Scientific Reports, researchers addressed the challenges of accurately diagnosing migraine headaches using machine learning (ML) techniques. Leveraging state-of-the-art ML algorithms such as support vector machine (SVM), k-nearest neighbors (KNN), random forest (RF), decision tree (DST), and deep neural networks (DNN), the study demonstrated remarkable effectiveness in classifying seven different types of migraines.

7 Mar 2024

Liquid Lens-Based Camera and EEPMD-Net for 3D Scene Capture and Reconstruction

Delve into the cutting-edge realm of holography with a liquid lens-based camera and the innovative EEPMD-Net, as unveiled in Light: Science & Applications. This groundbreaking fusion enables rapid and high-fidelity 3D scene acquisition and holographic reconstruction, offering unprecedented realism and potential applications across diverse fields from entertainment to scientific visualization.

6 Mar 2024

Photon-Powered Intelligence: Unveiling L2ONN for Lifelong Learning

This paper presents the groundbreaking lifelong learning optical neural network (L2ONN), offering efficient and scalable AI systems through photonic computing. L2ONN's innovative architecture harnesses sparse photonic connections and parallel processing, surpassing traditional electronic models in efficiency, capacity, and lifelong learning capabilities, with implications for various applications from vision classification to medical diagnosis.

5 Mar 2024

An Overview of AI Accelerators

This comprehensive overview delves into the realm of AI accelerators, including GPUs, FPGAs, and ASICs, highlighting their unique strengths and applications across industries like healthcare, autonomous vehicles, finance, and natural language processing. Despite challenges in hardware-software co-design and interoperability, AI accelerators offer significant advantages in performance, energy efficiency, and scalability, driving innovation and transformative advancements in AI-driven technologies.

4 Mar 2024

Novel Algorithm for Accurate Weather Monitoring and Enhancing Highway Safety

Researchers presented an innovative algorithm combining frequency and spatial domain techniques to monitor severe weather conditions on highways effectively. Utilizing image processing methods, the algorithm accurately identified rainy days and assessed rainfall intensity, demonstrating its potential to enhance road traffic safety by distinguishing between weather conditions. While successful in daytime monitoring, limitations exist for nighttime data, highlighting areas for future research to address and improve the model's capabilities.

4 Mar 2024