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

The AI Revolution in Voice Commerce

This comprehensive article explores the evolution, applications, challenges, and prospects of artificial intelligence (AI) in voice commerce. From the introduction of virtual assistants to the integration of AI algorithms in voice-enabled platforms, the evolution of voice commerce has profoundly impacted consumer behavior and the retail landscape. The article delves into various applications of AI in voice commerce, such as voice-controlled shopping, personalized recommendations, and streamlined payment systems, while also addressing challenges like privacy concerns, security risks, and technical limitations.

19 Feb 2024

Understanding Dropout in AI

This paper extensively examines dropout, a crucial regularization method in training deep neural networks, covering its conceptual framework, practical implementation, theoretical foundations, effects on training dynamics, empirical evidence, and applications. Dropout addresses overfitting challenges by randomly deactivating neurons during training, fostering more resilient and generalized representations. It enhances model interpretability, adversarial robustness, transfer learning, and accelerates AI solution implementation.

19 Feb 2024

Lightweight Enhancements in YOLOv5 for Vehicle Detection

Researchers introduce a lightweight enhancement to the YOLOv5 algorithm for vehicle detection, integrating integrated perceptual attention (IPA) and multiscale spatial channel reconstruction (MSCCR) modules. The method reduces model parameters while boosting accuracy, making it optimal for intelligent traffic management systems. Experimental results showcase superior performance compared to existing algorithms, promising advancements in efficiency and functionality for vehicle detection in diverse traffic environments.

18 Feb 2024

Deep Learning Cleans Up Noise in X-ray Data Analysis

Researchers employed deep convolutional neural networks (CNNs) to denoise X-ray diffraction and resonant X-ray scattering data, overcoming challenges in structural analysis caused by experimental noise. By training CNNs with experimental data, they achieved remarkable accuracy in preserving structural features while removing noise, demonstrating the effectiveness of computational methods in advancing materials science research.

18 Feb 2024

Dynamic Node Selection for Federated Learning in Space-Air-Ground Information Networks

Researchers addressed challenges in Federated Learning (FL) within Space-Air-Ground Information Networks (SAGIN) by introducing the LCNSFL algorithm. LCNSFL, based on a Double Deep Q Network (DDQN), strategically selects nodes to minimize time and energy costs. Simulation results demonstrate LCNSFL's superiority over traditional methods, offering efficient convergence and resource utilization in dynamic network environments, essential for practical applications in SAGIN.

16 Feb 2024

Aesthetic Diffusion Model: Revolutionizing Interior Design with AI

Researchers introduced the Aesthetic Diffusion Model, utilizing AI to swiftly generate visually appealing interior designs based on text descriptions. By incorporating aesthetic scores, decoration styles, and spatial functionalities, the model streamlines the design process, enhancing efficiency and creativity while offering practical solutions for interior designers. Experimental results demonstrate the model's superiority, paving the way for future advancements in AI-driven interior design.

16 Feb 2024

AI Chatbots to Design Elementary English Lessons: Principles and Guidelines

This paper outlines ten principles for designing elementary English lessons using AI chatbots, addressing crucial aspects like media selection, motivation, feedback, and collaboration. Through a rigorous methodology involving expert validation and usability evaluation, the study offers practical guidelines to bridge the gap between theoretical insights and effective implementation, paving the way for enhanced language instruction and educational adaptability in diverse contexts.

16 Feb 2024

Enhanced MobileNetV2 for Precision Classification for Improving Agricultural Automation

Researchers unveil an upgraded version of MobileNetV2 tailored for agricultural product recognition, revolutionizing farming practices through precise identification and classification. By integrating novel Res-Inception and efficient multi-scale cross-space learning modules, the enhanced model exhibits substantial accuracy improvements, offering promising prospects for optimizing production efficiency and economic value in agriculture.

16 Feb 2024

Graph-Based Machine Learning for Advanced Cyber Threat Detection

Researchers introduce a novel approach to cybersecurity by extracting graph-based features from network traffic data and employing machine learning for early detection of cyber threats. Through experimentation and validation on the CIC-IDS2017 dataset, the method showcases superior performance compared to traditional connection analysis methods, indicating its potential for enhancing cybersecurity measures.

15 Feb 2024

Advancing Multi-Vehicle Dial-a-Ride Problems with Enhanced Deterministic Annealing Algorithm

This paper introduces an innovative Linearly Decreasing Deterministic Annealing (LD-DA) algorithm tailored for large-scale Multi-Vehicle Dial-a-Ride Problems (DARPs). By incorporating advanced local search techniques and a linearly decreasing annealing schedule, LD-DA ensures efficient exploration of feasible solution spaces, yielding competitive solutions at accelerated convergence rates. Extensive computational experiments demonstrate LD-DA's superiority over existing algorithms, highlighting its potential for real-world applications in transportation optimization.

15 Feb 2024