Developing A Hybrid Deep Neural Network For Business Performance Optimization

Abstract

Business optimization is a pressing problem for organizations to achieve competitive benefits in today's competitive market. However, traditional optimization techniques do not always reflect the complex and nonlinear relationships that occur with business systems, particularly in the presence of multiple variables. This research introduces a new hybrid deep neural network (DNN) model that combines Convolutional Neural Networks (CNNs) for feature extraction with Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, for sequential data modeling. The goal of the study is to improve business forecasting and decision-making by utilizing the hybrid DNN model to process historical data from different business processes, such as sales data, customer behavior, and market trends. The proposed model is evaluated using real-world data with various business performance indicators. The model effectively leverages CNNs to identify features and RNNs for time-series prediction to uncover unseen patterns and make more accurate predictions about business outcomes. The experimental results show that the hybrid DNN has higher accuracy (94%) than the conventional machine learning models in classification tasks and the lowest Mean Squared Error (MSE) of 0.032 and Root Mean Squared Error (RMSE) of 0.179 in regression tasks. The outcome demonstrates the potential of the hybrid model for efficient business functioning and in-depth analytics and optimization of business indicators. Overall, the study underscores the potential of deep learning to revolutionize business decision-making processes, particularly in high-dimensional data sets and complex environments. The research presented in this thesis brings a new, hybrid, advanced neural network model, which can be extended to other business applications and helps in the optimization of business models.