All Research Papers Published In ICT Express

Research Article
4
10.1016/j.icte.2024.11.001

On the road to the metaverse: Point cloud video streaming: Perspectives and enablers

Feb 01, 2025
ICT Express
Patrick Enenche + 2 more +2

Research Article
10.1016/j.icte.2025.01.003

Adaptive beamforming scheme for coexistence of 5G base station and radar altimeter

Jan 01, 2025
ICT Express
Jiaqi Li + 1 more +1

Research Article
10.1016/j.icte.2024.09.005

DDS-P: Stochastic models based performance of IoT disaster detection systems across multiple geographic areas

Sep 01, 2024
ICT Express
Israel Araújo + 7 more +7

Research Article
6
10.1016/j.icte.2024.08.006

Deep neural network and trust management approach to secure smart transportation data in sustainable smart cities

Aug 19, 2024
ICT Express
Sohrab Khan + 5 more +5

Research Article
2
10.1016/j.icte.2024.08.002

Dynamic separate resource pool algorithm for vehicle-to-infrastructure communication

Aug 09, 2024
ICT Express
Jicheng Yin + 2 more +2

Front Matter
10.1016/s2405-9595(24)00097-3

Editorial Board

Aug 01, 2024
ICT Express

Research Article
11
10.1016/j.icte.2024.07.007

A hybrid approach of ConvLSTMBNN-DT and GPT-4 for real-time anomaly detection decision support in edge–cloud environments

Jul 31, 2024
ICT Express
Radityo Fajar Pamungkas + 3 more +3

Anomaly detection is a critical requirement across diverse domains to promptly identify abnormal behavior. Conventional approaches often face limitations with uninterpretable anomaly detection results, impeding efficient decision-making processes. This paper introduces a novel hybrid approach, the convolutional LSTM Bayesian neural network with nonparametric dynamic thresholding (ConvLSTMBNN-DT) for prediction-based anomaly detection. In addition, the model integrates fine-tuned generative pre-training version 4 (GPT-4) to provide human-interpretable explanations in edge–cloud environments. The proposed method demonstrates exceptional performance, achieving an average F1−score of 0.91 and an area under the receiver operating characteristic curve (AUC) of 0.86. Additionally, it effectively offers comprehensible decision-support explanations.

Research Article
10.1016/j.icte.2024.07.001

Learning to route and schedule links in reconfigurable networks

Jul 06, 2024
ICT Express
Xiangdong Yi + 1 more +1

Research Article
10.1016/j.icte.2024.07.003

Toward reliability-satisfied and hop-constrained backup paths for industrial internet demands

Jul 01, 2024
ICT Express
Huifen Huang + 1 more +1

Research Article
6
10.1016/j.icte.2024.06.001

Navigating the future of wireless networks: A multidimensional survey on semantic communications

Jun 06, 2024
ICT Express
Azharul Islam + 1 more +1