Title: Feed forwarded neural network with learning-based tuna swarm optimisation (FFNN-LBTSO) for semen quality prediction systems

Authors: C. Shanthini; S. Silvia Priscila

Addresses: Department of Computer Science, Bharath Institute of Higher Education and Research, Chennai, 600 073, India ' Department of Computer Science, Bharath Institute of Higher Education and Research, Chennai, 600 073, India

Abstract: Now-a-days, some new diseases have come into existence due to lifestyle diseases. The major causes of the change in semen quality are environmental and lifestyle factors. One of the key tasks to assess the fertility potential of a male partner is semen analysis. Data-mining decision support systems can help identify this influence. Some seminal quality predictions were made. This research exploited unbalanced datasets with biased majority-class performance findings. Gradient descent local training is prone to local minima. Meta-heuristic algorithm optimisation permits local and global solution finding. The paper develops a neural network model to predict semen quality. This paper improves tuna swarm optimisation (TSO) using learning-based feed-forward neural networks (FFNN). To balance normal and atypical cases, SMOTE data balancing was used. Overflow produces minority class instances until the balance is reached. FFNN-LBTSO was tested for predictive power. Steps include data source and pre-processing, SMOTE, FFNN classification, and LBTSO for classifier weights and bias optimisation. UCI sperm prediction. Sensitivity, specificity, G-mean, and accuracy measure experimentation. Fertility-optimal semen was detected. Comparing SVM and ANN classifier results.

Keywords: FFNN; feed forward neural network; TSO; tuna swarm optimisation; SMOTE; synthetic minority oversampling technique; LBTSO; learning-based tuna swarm optimisation; ANN artificial neural network; machine learning (ML).

DOI: 10.1504/IJSSE.2025.149710

International Journal of System of Systems Engineering, 2025 Vol.15 No.5, pp.471 - 487

Received: 19 May 2023
Accepted: 23 Jul 2023
Published online: 11 Nov 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article