IF 4.6 3区计算机科学

ICT Express Pub Date : 2026-10-01 Epub Date: 2026-07-21 DOI: 10.1016/j.icte.2026.07.010

Jiaqian Wu, Bo Yang, Guangyu Xu, Legend Zhang, Junmin Lyu, Chao Liu

引用次数: 0

Probabilistic surface correlation for robust indoor localization using RSSI vector sequence 基于RSSI向量序列的概率面相关鲁棒室内定位

IF 4.6 3区计算机科学

ICT Express Pub Date : 2026-10-01 Epub Date: 2026-07-23 DOI: 10.1016/j.icte.2026.07.017

Beomju Shin

{"title":"Probabilistic surface correlation for robust indoor localization using RSSI vector sequence","authors":"Beomju Shin","doi":"10.1016/j.icte.2026.07.017","DOIUrl":"10.1016/j.icte.2026.07.017","url":null,"abstract":"<div><div>Accurate indoor localization remains a challenging task due to the inherently unstable nature of RSSI signals in complex environments. To address this challenge, we previously introduced the Surface Correlation (SC) technique, which estimates a user’s location by comparing the spatial distribution of accumulated RSSI vectors—referred to as the User RSSI Surface (URS)—with a pre-constructed radio map. SC captures the user’s signal pattern over space, improving localization accuracy. However, it relies on deterministic similarity calculations, which can be affected by noise and signal fluctuations commonly observed in indoor environments. In this study, we extend the SC framework by introducing a probabilistic formulation based on the Naive Bayes approach. We construct the URS using a sequence of BLE-based RSSI vectors synchronized with the user’s estimated trajectory and compute the posterior probability of each reference point (RP) given the observed URS. By modeling the conditional likelihood of RSSI values under each RP, our method accounts for the uncertainty inherent in wireless signal measurements and enables more robust location estimation. Experiments conducted on the L3 floor of KIST demonstrate that the proposed probabilistic SC significantly improves localization accuracy, particularly in environments with high RSSI variability, compared to the original SC method.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 5","pages":"Pages 1176-1182"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RealDA: Bridging the domain gap with realistic synthesized images for unsupervised domain adaptation RealDA：用真实的合成图像弥合领域差距，用于无监督的领域适应

IF 4.6 3区计算机科学

ICT Express Pub Date : 2026-10-01 Epub Date: 2026-07-20 DOI: 10.1016/j.icte.2026.07.008

Ngo-Kien Duong, Doanh C. Bui, Ba Hung Ngo, Tae Jong Choi

引用次数: 0

A robust cross-domain IDS using BiGRU-LSTM-attention for medical and industrial IoT security 使用BiGRU-LSTM-attention的稳健跨域IDS，用于医疗和工业物联网安全

IF 4.6 3区计算机科学

ICT Express Pub Date : 2026-10-01 Epub Date: 2025-09-06 DOI: 10.1016/j.icte.2025.08.011

Afrah Gueriani, Hamza Kheddar, Ahmed Cherif Mazari, Mohamed Chahine Ghanem

{"title":"A robust cross-domain IDS using BiGRU-LSTM-attention for medical and industrial IoT security","authors":"Afrah Gueriani, Hamza Kheddar, Ahmed Cherif Mazari, Mohamed Chahine Ghanem","doi":"10.1016/j.icte.2025.08.011","DOIUrl":"10.1016/j.icte.2025.08.011","url":null,"abstract":"<div><div>The increased Internet of Medical Things (IoMT) and the Industrial Internet of Things (IIoT) interconnectivity has introduced complex cybersecurity challenges, exposing sensitive data, patient safety, and industrial operations to advanced cyber threats. To mitigate these risks, this paper introduces a novel transformer-based intrusion detection system (IDS), termed BiGAT-ID—a hybrid model that combines bidirectional gated recurrent units (BiGRU), long short-term memory (LSTM) networks, and multi-head attention (MHA). The proposed architecture is designed to effectively capture bidirectional temporal dependencies, model sequential patterns, and enhance contextual feature representation. Extensive experiments on two benchmark datasets; CICIoMT2024 (medical IoT) and EdgeIIoTset (industrial IoT); demonstrate the model’s cross-domain robustness, achieving detection accuracies of 99.13% and 99.34%, respectively. Additionally, the model exhibits exceptional runtime efficiency, with inference times as low as 0.0002 seconds per instance in IoMT and 0.0001 seconds in IIoT scenarios. Coupled with a low false positive rate, BiGAT-ID proves to be a reliable and efficient IDS for deployment in real-world heterogeneous IoT environments.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 5","pages":"Pages 1075-1084"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comprehensive survey of false data injection attacks modeling and ML/DL-based detection in smart grids 智能电网中假数据注入攻击建模和基于ML/ dl的检测的综合研究

IF 4.6 3区计算机科学

ICT Express Pub Date : 2026-10-01 Epub Date: 2026-08-08 DOI: 10.1016/j.icte.2026.07.027

Hassam Ishfaq, Nermish Mushtaq, Muhammad Asim Saleem, Qi Huang, Waqas Amin

{"title":"A comprehensive survey of false data injection attacks modeling and ML/DL-based detection in smart grids","authors":"Hassam Ishfaq, Nermish Mushtaq, Muhammad Asim Saleem, Qi Huang, Waqas Amin","doi":"10.1016/j.icte.2026.07.027","DOIUrl":"10.1016/j.icte.2026.07.027","url":null,"abstract":"<div><div>False data injection attacks (FDIAs) represent one of the most significant cyber–physical threats to modern smart grids, as they can corrupt measurement data used in state estimation while evading conventional residual-based detection. This paper provides a structured review of FDIA modeling and detection methodologies for cyber–physical power systems, covering both conceptual and mathematical formulations with attention to adversarial knowledge, measurement topology, and stealth bypass conditions. In this review Detection methods are organized into four families: statistical models, classical machine learning, feedforward deep learning, and recurrent neural networks. A comparative analysis evaluates their detection accuracy, computational cost, temporal modeling capacity, and real-time feasibility. Results show that architectures capturing temporal dependencies particularly LSTM and GRU consistently outperform statistical baselines and static classifiers. GRU offers a favorable trade-off between model complexity and performance. The paper further identifies concrete research gaps, including multi-attack detection, cross-topology generalization, physics-informed learning, explainable AI, privacy-preserving training, and quantum-enhanced approaches.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 5","pages":"Pages 1222-1240"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mamba-powered Moiré pattern suppression: A two-stage deep learning framework for high fidelity restoration mamba驱动的moir<s:1>模式抑制：用于高保真度恢复的两阶段深度学习框架

IF 4.6 3区计算机科学

ICT Express Pub Date : 2026-10-01 Epub Date: 2025-11-29 DOI: 10.1016/j.icte.2025.11.020

Chia-Hung Yeh, Chen Lo

{"title":"Mamba-powered Moiré pattern suppression: A two-stage deep learning framework for high fidelity restoration","authors":"Chia-Hung Yeh, Chen Lo","doi":"10.1016/j.icte.2025.11.020","DOIUrl":"10.1016/j.icte.2025.11.020","url":null,"abstract":"<div><div>The moiré effect remains a persistent challenge in image processing, introducing complex artifacts that degrade visual quality, particularly during image digitization and display. Although CNNs and Transformers have shown promise in moiré removal, CNNs lack the ability to model long-range dependencies, while Transformers are computationally expensive for high-resolution images. To overcome these challenges, we propose a novel two-stage moiré pattern removal framework based on the Mamba architecture. In the first stage, a Mamba-based detection network precisely identifies moiré-affected regions, leveraging its efficient long-range dependency modeling to capture intricate pattern distributions. In the second stage, a restoration network incorporates the detected moiré information as a reference to guide the removal process, leading to more targeted and effective artifact suppression. By explicitly separating detection from restoration, our method enhances both accuracy and computational efficiency. Extensive experiments on publicly available datasets demonstrate that our approach significantly outperforms state-of-the-art methods in both quantitative and qualitative evaluations, producing clearer and more detailed image reconstructions. This innovative framework establishes a new paradigm for moiré pattern removal, offering a scalable and high-performance solution for real-world applications.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 5","pages":"Pages 1112-1117"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adversarial-aware federated learning with dynamic client weighting for healthcare record security 针对医疗记录安全性的具有动态客户机权重的对抗性感知联邦学习

IF 4.6 3区计算机科学

ICT Express Pub Date : 2026-10-01 Epub Date: 2026-03-14 DOI: 10.1016/j.icte.2026.03.010

Hamad Naeem, Amjad Alsirhani, Faeiz M. Alserhani

{"title":"Adversarial-aware federated learning with dynamic client weighting for healthcare record security","authors":"Hamad Naeem, Amjad Alsirhani, Faeiz M. Alserhani","doi":"10.1016/j.icte.2026.03.010","DOIUrl":"10.1016/j.icte.2026.03.010","url":null,"abstract":"<div><div>Achieving broad generalization in healthcare AI necessitates multi-institutional data, but strict data privacy laws keep clinical records confined within hospitals. Federated Learning (FL) enables collaboration without sharing raw data, offering a path to secure, scalable clinical prediction. Recent challenges include the growing exposure of electronic health records to adversarial manipulation, and the presence of non-IID client data with uneven sample sizes that degrade global model performance. Many current FL methods still use uniform or fixed aggregation rules that ignore client quality, making them sensitive to data imbalance and attack-induced noise. We present an attack-aware FL pipeline that starts with hospital side preprocessing and Deep Particle Swarm Optimization (DPSO) feature selection to obtain a compact, discriminative attribute set. Clients train a temporal Deep Convolutional Neural Network (DCNN) model round wise, logging per-round accuracy and effective sample count; robustness is probed with Projected Gradient Descent (PGD) adversarial perturbations. The server performs dynamic aggregation using a normalized (accuracy × samples) client score to weight each parameter tensor during averaging. Updated global weights are redistributed to hospitals and the cycle repeats to convergence, yielding a privacy preserving, quality aware global predictor. The method is evaluated on the UC Irvine Heart Disease dataset under two settings: the original clean data and an adversarially perturbed variant. It attains 94.44 % global accuracy on the UCL Heart Disease dataset and 97.36 % on the UCL Breast Cancer dataset. The proposed approach facilitates a lightweight, edge-deployable intrusion detection system for healthcare, providing real-time and privacy-preserving protection through federated learning.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 5","pages":"Pages 1124-1129"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From text to multimodality: A comprehensive survey of LLMs and MLLMs across diverse domains 从文本到多模态：跨不同领域的法学硕士和法学硕士的综合调查

IF 4.6 3区计算机科学

ICT Express Pub Date : 2026-10-01 Epub Date: 2026-07-31 DOI: 10.1016/j.icte.2026.07.023

Anbukkarasi S, Hemalatha S, Arunkumar B, Shanmugavalli V, Kokila S, Yashaswini K A

{"title":"From text to multimodality: A comprehensive survey of LLMs and MLLMs across diverse domains","authors":"Anbukkarasi S, Hemalatha S, Arunkumar B, Shanmugavalli V, Kokila S, Yashaswini K A","doi":"10.1016/j.icte.2026.07.023","DOIUrl":"10.1016/j.icte.2026.07.023","url":null,"abstract":"<div><div>The rapid development of Artificial Intelligence has made it possible for advanced language learning systems, including Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs), to be created. These models go beyond the traditional text-based processing by incorporating different modalities like video, audio, text creating more accurate prediction systems. Here, we aim to give a thorough look at multimodal models focusing on their use and challenges in different real-world domains including healthcare, education, industry, governance, security, assistive systems, scientific research, and creative media. We break down model architecture, multimodal fusion techniques, training methods, and specific adaptations for domains that improve understanding, decision-making, and interactive capabilities. On top of that, we discuss major benchmark datasets, performance evaluation methods, and real-world examples with the help of which we point out the present capabilities and practical limitations. We engage with major challenges such as data inconsistency, model alignment scalability computational cost, privacy issues, bias, and explainability. Besides, this article explores instructional-tuned multimodal systems, lightweight domain adaptations, retrieval-augmented multimodal reasoning, and human-in-the-loop learning models that are gaining ground. We finish by listing prospective research directions that lead to trustworthy, energy-efficient, context-aware, and ethically aligned multimodal AI systems that connect artificial and augmented intelligence. This article is a thorough guide for researchers, professionals, and policymakers in pushing forward multimodal models to strong and socially responsible usage across different domains.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 5","pages":"Pages 1189-1209"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bi-LSTM-Transformer hybrid network for V2V optical wireless ISAC systems 用于V2V光无线ISAC系统的Bi-LSTM-Transformer混合网络

IF 4.6 3区计算机科学

ICT Express Pub Date : 2026-10-01 Epub Date: 2026-04-09 DOI: 10.1016/j.icte.2026.04.001

Genxue Zhou, Minghua Cao, Qing Yang, Yue Zhang, Xia Zhang, Huiqin Wang

{"title":"Bi-LSTM-Transformer hybrid network for V2V optical wireless ISAC systems","authors":"Genxue Zhou, Minghua Cao, Qing Yang, Yue Zhang, Xia Zhang, Huiqin Wang","doi":"10.1016/j.icte.2026.04.001","DOIUrl":"10.1016/j.icte.2026.04.001","url":null,"abstract":"<div><div>With the growing integration of Integrated Sensing and Communication (ISAC) with Optical Wireless in intelligent transportation systems—particularly in Vehicle-to-Vehicle (V2V) communication for autonomous driving, traditional signal processing techniques encounter substantial challenges in detecting communication and LiDAR signals. This is particularly evident in high-noise environments, where these methods struggle to effectively suppress interference, leading to elevated bit error rates (BER) and diminished accuracy in ranging and velocity measurements. To tackle this issue, we introduce a hybrid neural network architecture that integrates Bidirectional Long Short-Term Memory (Bi-LSTM) and Transformer for signal detection. Simulation results demonstrate that, at a BER of 10<sup>-3</sup>, the proposed algorithm achieves a Signal-to-Noise Ratio (SNR) gain of approximately 6.9 dB under the conditions of a 780 nm wavelength and moderate turbulence at the communication receiver. At the LiDAR receiver, under an SNR of 12 dB, the Root Mean Square Errors (RMSE) of distance and velocity are reduced by approximately 93.6% and 53.5%, respectively. Furthermore, the ambiguity function displays a more concentrated main lobe and superior suppression of side lobes, thus validating the algorithm's efficacy in enhancing distance resolution and Doppler tolerance.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 5","pages":"Pages 1090-1098"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recoverable Autoencoder-Based Intrusion Detection with Trust Level Driven Slice Access Control in 5G/B5G networks 5G/B5G网络中基于可恢复自编码器的信任水平驱动分片访问控制入侵检测

IF 4.6 3区计算机科学

ICT Express Pub Date : 2026-10-01 Epub Date: 2026-09-16 DOI: 10.1016/j.icte.2026.09.008

Li-Yu Yang, Jyun-Wei Chen, Li-Der Chou

引用次数: 0

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