Dr. Sampath Lonka

Ph.D, Mathematics

Large Language Models (LLM), Natural Language Processing (NLP), Deep Learning, Data Science, Calculus

Dr. Sampath Lonka is a scientific writer based in Bangalore, India, with a strong academic background in Mathematics and extensive experience in content writing. He has a Ph.D. in Mathematics from the University of Hyderabad and is deeply passionate about teaching, writing, and research.

Sampath enjoys teaching Mathematics, Statistics, and AI to both undergraduate and postgraduate students. What sets him apart is his unique approach to teaching Mathematics through programming, making the subject more engaging and practical for students.

Sampath works in various fascinating research areas, including Natural Language Processing, Deep Learning, Topological Data Analysis, and AI for Mathematics. He has developed several impactful Computer Vision and Language Models that effectively tackle real-world problems. These research interests and work reflect Sampath's commitment to exploring the intersections of mathematics and cutting-edge technologies.

Beyond the professional realm, Sampath has diverse interests and enjoys reading captivating literature, embarking on adventurous travels, and experimenting with culinary delights in the kitchen.

Sampath's unwavering passion for mathematics, expertise in AI, and dedication to nurturing young minds comes from his unique blend of academic knowledge, research accomplishments, and teaching experience.

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Articles from Sampath

Machine Learning for Early Dropout Prediction in an Active Aging App

Researchers introduced the FERN model, a versatile neural encoder-decoder approach to earthquake rate forecasting. By leveraging artificial intelligence and deep learning algorithms, the FERN model overcomes the limitations of traditional earthquake prediction models like ETAS, demonstrating improved accuracy and short-term forecasting capabilities.

5 Aug 2023

Assessing AI's Role in Construction: A Hybrid Multi-Criteria Decision-Making Approach

Researchers conducted a comprehensive study to explore the utilization of AI tools in the construction sector. They employed a hybrid multi-criteria decision-making (MCDM) approach that integrated the Delphi method, TOPSIS, and ANP within a fuzzy context. The study highlights AI's significance in improving safety, sustainability, project planning, and construction processes, and provides valuable insights for decision-makers on the role of AI in the construction industry.

3 Aug 2023

Self-Supervised Learning for Robust Fault Diagnosis in Electrical Motors

Researchers present a novel framework for fault diagnosis of electrical motors using self-supervised learning and fine-tuning on a neural network-based backbone. The proposed model achieves high-performance fault diagnosis with minimal labeled data, addressing the limitations of traditional approaches and demonstrating scalability, expressivity, and generalizability for diverse fault diagnosis tasks.

3 Aug 2023

Hybrid Quantum Machine Learning for Cars: Navigating Natural Disasters Safely

This research paper introduces a novel approach using supervised hybrid quantum machine learning to improve emergency evacuation strategies for cars during natural disasters like earthquakes. The proposed method combines quantum and classical machine learning techniques, demonstrating superior accuracy and efficiency compared to conventional algorithms, and holds promise for real-world applications in dynamic environments.

2 Aug 2023

Sensitivity and Bias in Large Language Models: A Comparative Study of SDG Detection Models

This paper presents a comprehensive study comparing the effectiveness of specialized language models and the GPT-3.5 model in detecting Sustainable Development Goals (SDGs) within text data. The research highlights the challenges of bias and sensitivity in large language models and explores the trade-offs between broad coverage and precision. The study provides valuable insights for researchers and practitioners in choosing the appropriate model for specific tasks.

2 Aug 2023

TwinPort Integrates Digital Twin and Drones for Precise Ship Maneuvering

Researchers propose TwinPort, a cutting-edge architecture that combines digital twin technology and drone-assisted data collection to achieve precise ship maneuvering in congested ports. The approach incorporates a recommendation engine to optimize navigation during the docking process, leading to enhanced efficiency, reduced fuel consumption, and minimized environmental impact in smart seaports.

1 Aug 2023

Enhancing Facial Expression Recognition with the FT-CSAT Transformer

Researchers propose the Fine-Tuned Channel-Spatial Attention Transformer (FT-CSAT) model to address challenges in facial expression recognition (FER), such as facial occlusion and head pose changes. The model combines the CSWin Transformer with a channel-spatial attention module and fine-tuning techniques to achieve state-of-the-art accuracy on benchmark datasets, showcasing its robustness in handling FER challenges.

1 Aug 2023

Optimization Algorithms in Neural Networks

This comprehensive article explores the historical evolution of optimization, its importance, and its applications in various fields. It delves into the basic ingredients of optimization problems, the types of optimization algorithms, and their roles in deep learning, particularly in first-order and second-order techniques.

31 Jul 2023

Word Embeddings: Evolution, Applications, and Challenges

This article explores the evolution, applications, and challenges of word embeddings in natural language processing (NLP), highlighting their pivotal role in representing words as numerical vectors and their diverse use in tasks such as text classification, sentiment analysis, and biomedical text mining. It delves into the effectiveness of pre-trained models like Word2Vec, GloVe, and BERT, as well as multimodal embeddings that incorporate information from images, sounds, and knowledge to enhance text representations.

30 Jul 2023

AI Ethics: A Comprehensive Overview

This comprehensive article delves into the multifaceted realm of AI ethics, addressing its societal impact and the pressing questions raised by policymakers, researchers, and the public. It explores the foundational aspects of ethics, the pillars of AI ethics, ethical dilemmas posed by emerging technologies, and different approaches to effectively tackle ethical considerations in AI development and deployment.

29 Jul 2023