Deep Learning-Based Retrieval of Biotechnological Research Information: Enhancing Access to Educational Resources

Abstract

This paper presents the development of a deep learning-based model designed to enhance the retrieval and management of biotechnological research information. The model leverages advanced signal processing techniques, originally developed for language recognition, to effectively handle the complex data structures typical in biotechnological studies. Our research details the architectural and functional design of a system built on a browser/server (B/S) architecture using the MVC model, with Vue.js for the frontend and Spring Boot for the backend. The system is engineered to support a structured, diverse, and intelligent educational resource management plan tailored for biotechnology research environments. By incorporating large data analytics and intelligent processing, the system analyses behavioral and personality traits of researchers to provide personalized resource recommendations. This not only aids in categorizing and ranking relevant scientific documents but also addresses the challenges posed by the rapid expansion of biotechnological data. Through deep extraction techniques that utilize basic information about researchers and their specific learning contexts, the system models biotechnological research behaviors and experiences, potentially revitalizing researchers' engagement with ongoing studies.