Nagaraju Brahamanpally
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Nagaraju Brahamanpally has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Amitabh Mishra
Department of Cybersecurity and Information Technology, University of West Florida (UWF), Pensacola, FL 32514, USA
2 shared publications - Lucas S. Liberman
Department of Computer Science, Hall Marcus College of Science and Engineering, University of West Florida, Pensacola, FL 32514, USA
1 shared publication
Latest Publications
- Open Access
A Comparative Performance Analysis of Locally Deployed Large Language Models Through a Retrieval-Augmented Generation Educational Assistant Application for Textual Data Extraction
Background: Rapid advancements in large language models (LLMs) have significantly enhanced Retrieval-Augmented Generation (RAG) techniques, leading to more accurate and context-aware information retrieval systems. Methods: This article presents the creation of a RAG-based chatbot tailored for university course catalogs, aimed at answering queries related to course details and other essential academic information, and investigates its performance by testing it on several locally deployed large language models. By leveraging multiple LLM architectures, we evaluate performance of the models under test in terms of context length, embedding size, computational efficiency, and relevance of responses. Results: The experimental analysis obtained by this research, which builds on recent comparative studies, reveals that while larger models achieve higher relevance scores, they incur greater response times than smaller, more efficient models. Conclusions: The findings underscore the importance of balancing accuracy and efficiency for real-time educational applications. Overall, this work contributes to the field by offering insights into optimal RAG configurations and practical guidelines for deploying AI-powered educational assistants.
Open AccessA Comparative Performance Analysis of Locally Deployed Large Language Models Through a Retrieval-Augmented Generation Educational Assistant Application for Textual Data Extraction
Background: Rapid advancements in large language models (LLMs) have significantly enhanced Retrieval-Augmented Generation (RAG) techniques, leading to more accurate and context-aware information retrieval systems. Methods: This article presents the creation of a RAG-based chatbot tailored for university course catalogs, aimed at answering queries related to course details and other essential academic information, and investigates its performance by testing it on several locally deployed large language models. By leveraging multiple LLM architectures, we evaluate performance of the models under test in terms of context length, embedding size, computational efficiency, and relevance of responses. Results: The experimental analysis obtained by this research, which builds on recent comparative studies, reveals that while larger models achieve higher relevance scores, they incur greater response times than smaller, more efficient models. Conclusions: The findings underscore the importance of balancing accuracy and efficiency for real-time educational applications. Overall, this work contributes to the field by offering insights into optimal RAG configurations and practical guidelines for deploying AI-powered educational assistants.
- Open Access
An Optimal, Power Efficient, Internet of Medical Things Framework for Monitoring of Physiological Data Using Regression Models
The sensors used in the Internet of Medical Things (IoMT) network run on batteries and need to be replaced, replenished or should use energy harvesting for continuous power needs. Additionally, there are mechanisms for better utilization of battery power for network longevity. IoMT networks pose a unique challenge with respect to sensor power replenishment as the sensors could be embedded inside the subject. A possible solution could be to reduce the amount of sensor data transmission and recreate the signal at the receiving end. This article builds upon previous physiological monitoring studies by applying new decision tree-based regression models to calculate the accuracy of reproducing data from two sets of physiological signals transmitted over cellular networks. These regression analyses are then executed over three different iteration varieties to assess the effect that the number of decision trees has on the efficiency of the regression model in question. The results indicate much lower errors as compared to other approaches indicating significant saving on the battery power and improvement in network longevity.
Open AccessAn Optimal, Power Efficient, Internet of Medical Things Framework for Monitoring of Physiological Data Using Regression Models
The sensors used in the Internet of Medical Things (IoMT) network run on batteries and need to be replaced, replenished or should use energy harvesting for continuous power needs. Additionally, there are mechanisms for better utilization of battery power for network longevity. IoMT networks pose a unique challenge with respect to sensor power replenishment as the sensors could be embedded inside the subject. A possible solution could be to reduce the amount of sensor data transmission and recreate the signal at the receiving end. This article builds upon previous physiological monitoring studies by applying new decision tree-based regression models to calculate the accuracy of reproducing data from two sets of physiological signals transmitted over cellular networks. These regression analyses are then executed over three different iteration varieties to assess the effect that the number of decision trees has on the efficiency of the regression model in question. The results indicate much lower errors as compared to other approaches indicating significant saving on the battery power and improvement in network longevity.