ICT Express最新文献_第7页

Fusion of bidirectional neural networks decision for a high accuracy breast cancer detection based on DNA biomarker 基于DNA生物标志物的高精度乳腺癌检测的双向神经网络融合决策

IF 4.2 3区计算机科学

ICT Express Pub Date : 2026-06-01 Epub Date: 2026-02-24 DOI: 10.1016/j.icte.2026.02.007

Roslidar Roslidar , Qurrata A’yuni , M. Zulhamsyah , Fitri Arnia , Afnan Afnan , Essy Harnelly

引用次数: 0

Connectivity and stability analysis for UAVs in urban networks 城市网络中无人机的连通性和稳定性分析

IF 4.2 3区计算机科学

ICT Express Pub Date : 2026-06-01 Epub Date: 2026-02-27 DOI: 10.1016/j.icte.2026.02.011

Sooyeob Jung , Pansoo Kim , Song Noh , Junse Lee

引用次数: 0

A cross-dataset based zero-day intrusion detection system by integrating siamese network and reinforcement learning 基于连体网络和强化学习的跨数据集零日入侵检测系统

IF 4.2 3区计算机科学

ICT Express Pub Date : 2026-06-01 Epub Date: 2026-05-07 DOI: 10.1016/j.icte.2026.05.001

Md. Meheraj Hossain, Saumik Das Turja, Sibgatullah Tasnim, Md. Fahmid-Ul-Alam Juboraj, Muhammad Iqbal Hossain

引用次数: 0

Gradient Boosting Classifier with Zebra optimization algorithm for pregnancy risk prediction 基于斑马优化算法的梯度增强分类器妊娠风险预测

IF 4.2 3区计算机科学

ICT Express Pub Date : 2026-06-01 Epub Date: 2026-04-14 DOI: 10.1016/j.icte.2026.04.002

Proshenjit Sarker , Abdullah-Al Nahid , Md Abdus Samad , Kwonhue Choi

引用次数: 0

Advanced temporal attention mechanism based traffic prediction model for 5G and beyond cellular networks 基于时间注意机制的5G及以上蜂窝网络流量预测模型

IF 4.2 3区计算机科学

ICT Express Pub Date : 2026-06-01 Epub Date: 2025-12-12 DOI: 10.1016/j.icte.2025.12.004

Dharani Sabari Samudrala, Rajiv Senapati

{"title":"Advanced temporal attention mechanism based traffic prediction model for 5G and beyond cellular networks","authors":"Dharani Sabari Samudrala, Rajiv Senapati","doi":"10.1016/j.icte.2025.12.004","DOIUrl":"10.1016/j.icte.2025.12.004","url":null,"abstract":"<div><div>The performance of cellular networks depends heavily on accurate traffic prediction, as it is essential for smooth service delivery and effective resource management. However, the unpredictable and constantly changing patterns of network activity make this task highly challenging. Traditional forecasting approaches often fall short in representing these complex dependencies, leading to reduced prediction accuracy and inefficiency. To address this issue, the objective of this work is to design a prediction model that is both accurate and computationally efficient. In this paper, we have proposed a lightweight hybrid prediction model integrated with an Advanced Temporal model with Attention Mechanism (ATAM). The temporal attention mechanism enhances the model’s ability to focus on relevant sequential patterns, while the hybrid architecture ensures computational efficiency and scalability for real-time deployment. The model’s performance is evaluated through Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and paired t-test demonstrating significant improvements over traditional forecasting approaches. These findings highlight that combining temporal attention with lightweight architecture enhances predictive performance while maintaining efficiency. In conclusion, the ATAM-based framework offers a reliable solution for traffic prediction in 5G and beyond cellular networks. Further our proposed model can be extended to support adaptive resource allocation strategies, thereby enabling operators to optimize network quality of service while reducing computational overhead.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 3","pages":"Pages 537-544"},"PeriodicalIF":4.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148196074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image restoration using deep learning techniques: A comprehensive review 使用深度学习技术的图像恢复：全面回顾

IF 4.2 3区计算机科学

ICT Express Pub Date : 2026-06-01 Epub Date: 2026-03-05 DOI: 10.1016/j.icte.2026.03.002

Raksha Naravi Pai, Sheikh Mohammed Shuhood, Ajitha Shenoy, Poornalatha G.

引用次数: 0

From 2G to 6G: A comprehensive survey on the evolution and enhancement of AKA protocols for next-generation wireless networks 从2G到6G：下一代无线网络中AKA协议的演进和增强的综合调查

IF 4.2 3区计算机科学

ICT Express Pub Date : 2026-06-01 Epub Date: 2026-03-12 DOI: 10.1016/j.icte.2026.03.011

Antoine Hitayezu, Jui-Tang Wang, Tang Chen Chia

引用次数: 0

A hybrid mode intensity multiplexing method for OAM communications with crosstalk-bounded waists 具有串扰约束腰的OAM通信的混合模式强度复用方法

IF 4.2 3区计算机科学

ICT Express Pub Date : 2026-06-01 Epub Date: 2026-03-16 DOI: 10.1016/j.icte.2026.03.012

Young Jae Moon, Yeon Ho Chung

引用次数: 0

Improving data-free quantization with confidence-guided data synthesis 用信心引导的数据合成改进无数据量化

IF 4.2 3区计算机科学

ICT Express Pub Date : 2026-06-01 Epub Date: 2026-04-16 DOI: 10.1016/j.icte.2026.03.021

Deok-Woong Kim, Seung-Hwan Bae

引用次数: 0

FedUNet: A lightweight additive U-Net module for federated learning with heterogeneous models FedUNet：一个轻量级的附加U-Net模块，用于使用异构模型进行联邦学习

IF 4.2 3区计算机科学

ICT Express Pub Date : 2026-06-01 Epub Date: 2026-03-09 DOI: 10.1016/j.icte.2026.03.004

Kichang Lee , Beomseok Seo , JaeYeon Park

{"title":"FedUNet: A lightweight additive U-Net module for federated learning with heterogeneous models","authors":"Kichang Lee , Beomseok Seo , JaeYeon Park","doi":"10.1016/j.icte.2026.03.004","DOIUrl":"10.1016/j.icte.2026.03.004","url":null,"abstract":"<div><div>Federated learning (FL) enables decentralized model training without sharing data, but most existing methods assume homogeneous model architectures across clients, limiting applicability in heterogeneous real-world environments. We propose <strong><em>FedUNet</em></strong>, a lightweight and architecture-agnostic FL framework that introduces an additive U-Net-inspired module attached to each client’s backbone. By sharing only the compact bottleneck of the U-Net, <strong><em>FedUNet</em></strong> enables efficient knowledge transfer without architectural alignment, shared data, or costly distillation. The encoder–decoder structure with skip connections integrates multi-scale features into a shared latent space, promoting client-invariant representation alignment across heterogeneous models. Experimental results on CIFAR-10 and CIFAR-100 with VGG and ResNet backbones show that <strong><em>FedUNet</em></strong> achieves high accuracy with significantly reduced communication and computational overhead. Notably, a full U-Net attains 93.92% accuracy while requiring only 0.89 MB of communication per round, highlighting its practicality for resource-constrained FL systems.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 3","pages":"Pages 610-614"},"PeriodicalIF":4.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148196082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0