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Updated: Jun 16, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Optimizing network bandwidth slicing identification: NADAM-enhanced CNN and VAE data preprocessing for enhanced

Md Fahim Ul Islam¹, Shahriar Hossain¹, Md Golam Rabiul Alam¹

¹Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh.

|October 21, 2025

Summary

This summary is machine-generated.

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Future communication networks use network slicing (NS) for diverse Quality of Service (QoS) needs. Our AI system improves bandwidth allocation accuracy, outperforming other methods for efficient network management.

Area of Science:

Computer Science
Telecommunications Engineering
Artificial Intelligence

Background:

Future communication networks depend on network slicing (NS) to create virtual networks on shared infrastructure.
Meeting diverse Quality of Service (QoS) requirements for applications like IoT and low-latency communication is crucial.
Intelligent algorithms, especially AI and deep learning, are vital for optimizing NS resource allocation and management.

Purpose of the Study:

To propose an Interpretable Network Bandwidth Slicing Identification (INBSI) system for efficient network slicing.
To enhance resource allocation and dynamic network slice management in next-generation networks.
To provide insights into AI's role in optimizing network management through explainable AI (XAI).

Main Methods:

Related Experiment Videos

Last Updated: Jun 16, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Developed an INBSI system using a modified Convolutional Neural Network (CNN) with Nesterov-accelerated Adaptive Moment Estimation (NADAM) optimization.
Employed a Variational Autoencoder (VAE) for data preprocessing and validity assessment.
Utilized Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) for model interpretability.

Main Results:

The proposed INBSI system achieved a peak accuracy of 84% in the system environment.
This performance surpassed alternative methods such as k-nearest neighbors (76%), Random Forest (69%), and Gaussian Naive Bayes (55%).
XAI techniques provided insights into input feature impacts on network slicing.

Conclusions:

AI-driven solutions, particularly the proposed INBSI system, show significant potential for optimizing network slicing.
The system offers a pathway for operators to enhance resource allocation and future network management.
Interpretable AI models are key to understanding and trusting AI in critical network functions.