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zenodoJournal article2026-07-28

Adaptive Intercell Interference Management in Heterogeneous Networks Using Enhanced Feedforward Neural Network: A Drive-Test-Based Simulation Study

Onu Kingsley Eyiogwu, Ekwueme Uchechukwu Theo, Obisike Kio Chima

The densification of Long-Term Evolution (LTE) heterogeneous networks (HetNets) enhances spectral efficiency but exacerbates intercell interference (ICI), especially at cell edges. This study proposes an Enhanced Feedforward Neural Network (eFFNN) for adaptive interference mitigation using 12,847 drive-test samples from Port Harcourt. The model integrates advanced techniques such as batch normalization, dropout, and dynamic loss weighting, and is deployed within a MATLAB/Simulink-based LTE HetNet simulation. Results show significant performance gains, including 68% reduction in packet loss, 36.8% improvement in throughput (85.48 Mbps), and 26–32% latency reduction. The model achieves high predictive accuracy with an R² of 0.9969, demonstrating strong effectiveness.

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zenodoJournal article2026-07-28

Cosmic Ray Modulation in the Heliosphere: Physical Mechanisms, Solar Cycle Variability, Numerical Modeling, Artificial Intelligence, and Space Weather Applications

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zenodoJournal article2026-01-28

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Accurate modeling of crude oil and brine interfacial tension via robust machine learning approaches

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zenodoJournal article2026-07-28

A BIBLIOMETRIC ANALYSIS OF GLOBAL RESEARCH ON DATA USE IN SOCIAL SERVICES (1950–2026): EVIDENCE FROM THE WEB OF SCIENCE DATABASE USING VOSVIEWER

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zenodoJournal article2026-07-28

Enhancing Depression Detection Accuracy in Northwest Nigerian Adults Using Ensemble Learning Technique

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Abstract — Millions of individuals all over the world suffer from depression, a serious and common mental illness. In Northwest Nigeria, depression is still not well recognized because of stigma, the use of multiple languages, including English, Hausa, and Fulfulde, and a l…

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