Improving Organizational Efficiency With Hybrid Neural Networks And Optimization Models

Abstract

In today's modern world, intelligent optimization systems and artificial intelligence (AI) are becoming more and more essential to the efficiency of the organization, the productivity of their workforce, and strategic decision-making. But traditional machine learning models struggle with nonlinear organizational relationships, sequential processes and workflows, and real-time resource optimization. This study introduces a hybrid deep neural network and long short-term memory (DNN-LSTM) model along with the Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA) techniques for enhanced prediction of the organizational efficiency and the managing of the organizations' operations. The proposed framework is based on the publicly available Productivity Prediction of Garment Employees dataset that can be found in the UCI Machine Learning Repository, which consists of about 1,197 operational records related to employee productivity and manufacturing performance. The dataset undergoes the preprocessing operations of normalization, feature encoding, and partitioning of the dataset prior to training of the model. The experimental evaluation is done by means of accuracy, precision, recall, F1-score, root mean square error (RMSE), and mean absolute error (MAE). The hybrid solution proposed achieved accuracy of 96.3%, precision of 95.8%, recall of 95.1% and F1 score of 95.4%, which is better than traditional ANN, CNN and single LSTM solutions. Moreover, the proposed model resulted in a low RMSE value (0.041) and a low MAE value (0.029), which means that there was a consistency in the prediction and low forecasting error. The incorporation of optimization algorithms greatly improved the convergence rate, tuning of the parameters, and stability of the model. The results validate the effectiveness and scalability of the proposed optimization-driven hybrid framework for intelligent prediction of organizational productivity, workflow prediction, and resource allocation in a modern enterprise environment.