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has been cited by the following article:

• TITLE: Fusion of Activation Functions: An Alternative to Improving Prediction Accuracy in Artificial Neural Networks

  AUTHORS: Justice Awosonviri Akodia, Clement K. Dzidonu, David King Boison, Philip Kisembe

  KEYWORDS: Artificial Neural Networks, Container Dwell Time, Fusion of Activation Functions, Randomized Search CV Algorithm, Prediction Accuracy

  JOURNAL NAME: World Journal of Engineering and Technology, Vol.12 No.4, September 4, 2024

  ABSTRACT: The purpose of this study was to address the challenges in predicting and classifying accuracy in modeling Container Dwell Time (CDT) using Artificial Neural Networks (ANN). This objective was driven by the suboptimal outcomes reported in previous studies and sought to apply an innovative approach to improve these results. To achieve this, the study applied the Fusion of Activation Functions (FAFs) to a substantial dataset. This dataset included 307,594 container records from the Port of Tema from 2014 to 2022, encompassing both import and transit containers. The RandomizedSearchCV algorithm from Python’s Scikit-learn library was utilized in the methodological approach to yield the optimal activation function for prediction accuracy. The results indicated that “ajaLT”, a fusion of the Logistic and Hyperbolic Tangent Activation Functions, provided the best prediction accuracy, reaching a high of 82%. Despite these encouraging findings, it’s crucial to recognize the study’s limitations. While Fusion of Activation Functions is a promising method, further evaluation is necessary across different container types and port operations to ascertain the broader applicability and generalizability of these findings. The original value of this study lies in its innovative application of FAFs to CDT. Unlike previous studies, this research evaluates the method based on prediction accuracy rather than training time. It opens new avenues for machine learning engineers and researchers in applying FAFs to enhance prediction accuracy in CDT modeling, contributing to a previously underexplored area.