Identifying Thematic Intersections between Ecology, Maintenance, and Sustainability: A Neural Network-Based Approach

This paper presents a novel methodological framework designed to quantitatively assess the thematic overlap among three key fields—ecology, maintenance, and sustainability—through automated analysis of research articles. The central research question is: To what extent is it possible for a neural network to accurately quantify the amount of thematic overlap between these disciplines? To address this question, a representative sample of 20 research articles was collected, with an equal number for each discipline. The documents were preprocessed by converting the text to lower case, removing extraneous characters, and tokenizing into segments. The most important features were then selected using keyword frequency analysis and dimensionality reduction of data, thus keeping important linguistic patterns without relying solely on manually compiled keyword lists. These numerical representations were used as inputs to a feedforward neural network implemented in MATLAB. The net, with two hidden layers (using the tangent sigmoid activation function) and a linear output layer for the three domains, produces probability scores indicating the extent to which ecology, maintenance, and sustainability characteristics are exhibited by each article. Theme overlap is quantitatively measured by the so-obtained probabilities, revealing strong as well as subtle interrelations between the disciplines. A baseline comparison to standard classification techniques is given. In general, this system offers a replicable model of interdisciplinarity text analysis and suggests productive directions for future research and applied work.