EAI Endorsed Transactions on Scalable Information Systems
type: channel
description: EAI Endorsed Transactions on Scalable Information Systems is open access, a peer-reviewed scholarly journal focused on scalable distributed information systems, scalable, data mining, grid information systems, and more. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications. From 2024, the journal started to publish on a monthly frequency (12 issues per year). INDEXING: ESCI-WoS (IF: 1.3), Compendex, DOAJ, ProQuest, EBSCO, Crossref, Dimensions
publisher: European Alliance for Innovation (EAI)
language: en
publication name: EAI Endorsed Transactions on Scalable Information Systems
issn: 2032-9407
copyright: This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
items: Retraction Notice: The Quality Evaluation of Innovation and Entrepreneurship Education in Colleges and Universities in the Context of Big Data
items: A Decentralised Coordination Framework for Demand-Side Solar-Storage and Its Electricity Market Participation
items: Numerical Index Modulation Aided Multi-Carrier DCSK System: A Secure Transmission Scheme for Physical Layer Security
items: Risk-Aware Secure Routing Mechanism for Power Communication Networks Based on GNNs
items: Quantum Communication Networks for Secure Foundation Model Distribution Across Shanxi Power Grid Substations
items: Quantum Communication-Assisted Synchronization Framework for Distributed 3D Power Grid Models Across Multi-Regional Control Centers
items: Quantum Communication Technology for Optimizing 3D Cost Estimation and Secure Data Transmission in Substation Engineering
items: A Distributed and Secure Resource Allocation Method for Power Communication Networks Based on Policy Distillation
items: An Efficient Privacy-Preserving Secure Aggregation Scheme for Federated Learning with Input Verification and Dropout Resistance
items: EBTM: Web Vulnerability Attack Behavior Recognition Method Based on Feature Fusion
items: NAS-FD: Neural Architecture Search-Based Fraud Detection for Power Audit Data
items: Object Detection and Segmentation of Power Equipment in Infrared Images via Improved YOLOv8 and Prompt-Optimized SAM
items: Risk Early Warning for Police-Related Online Public Opinion Based on Deep Learning
items: Food Derived Biostimulants Technology Revealed and Retrieved by Natural Language Processing
items: Heterogeneous Distributed Computing-Based AI Video Generation: Real-Time Load Balancing and Intelligent Scheduling in New Media Art
items: Method for Analysing Information on Psychosomatic Issues Based on Service Recipient Dissatisfaction
items: Research on the Application of Large Language Model in Data Integration
items: A Method for Extracting Dissatisfaction Entities in the Pharmaceutical Sector Using Large Language Model
items: KADP-SQL: Knowledge-Augmented Generation and Dual-Path Validation in Text-to-SQL
items: An Artifact-Centric Process Mining Approach for Learning Style Analytics
items: System Design and Development for Industry-Education Integration in Art Universities Based on Generative Adversarial Networks and Attention Mechanism
items: Mitigating Latency in Chord-Based Routing under IPv6
items: A Review of Reference Architectures for the Next-Generation Internet of Things Systems
items: An Integrated Cybersecurity Defense Framework for Attack Intelligence Analysis, Counteraction, and Traceability in Complex Network Architectures
items: Ensuring Security in Multi-Tag Backscatter Communication Systems
items: Deep Learning Based Power Load Prediction in Smart Grid Networks
items: A Cholesky decomposition and fusion clustering based spectrum sensing method
items: On the Performance of RIS-Enhanced RSMA Networks with On-Off Control
items: Direction-of-Arrival Estimation for Deterministic Networks
items: Enhancing Stakeholder Analysis with AI: A Comparative Study of Productivity and Quality in the Educational Context

Retraction Notice: The Quality Evaluation of Innovation and Entrepreneurship Education in Colleges and Universities in the Context of Big Data
type: item
description: We, the Publisher, have retracted the following article: Fan et al. (2025). The Quality Evaluation of Innovation and Entrepreneurship Education in Colleges and Universities in the Context of Big Data. EAI Endorsed Scal Inf Syst. https://doi.org/10.4108/eetsis.7015 The article has been retracted due to authorship dispute. We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
subject: Retractions
  topic: section
creator: Xiaoxue Fan
creator: Ping Zhang
creator: Bingxin Zhang
rights: Copyright (c) 2026 Xiaoxue Fan, Ping Zhang, Bingxin Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-04
publication date: 2026-03-04
doi: 10.4108/eetsis.12091

A Decentralised Coordination Framework for Demand-Side Solar-Storage and Its Electricity Market Participation
type: item
description: The rapid proliferation of rooftop photovoltaics and behind-the-meter battery storage is creating systemic risks in distribution networks. This includes local network constraint violations, insufficient outage resilience, and enlarged cybersecurity attack surfaces, leaving millions of demand-side distributed energy resources (DERs) to operate without coordinated oversight. Existing solutions (dynamic operating envelopes, virtual power plants, peer-to-peer trading, and single-household energy management systems) each address only partial aspects of this challenge. Based on multi-agent reinforcement learning, this paper proposes a Decentralised Coordination Framework (DCF) that organises the energy dispatch into a three-tier hierarchical architecture. Specifically, it has a user layer executing Proximal Policy Optimisation (PPO) for local dispatch, a feeder layer in which a dynamically elected L1 leader applies MADDPG to coordinate community flexibility via a Virtual Aggregation Unit (VAU); and a cross-feeder layer where an L2 leader manages inter-community balancing and market interfaces. It integrates a directed acyclic graph (DAG)-based verifiable execution ledger, Paillier homomorphic encryption, and LLM-based anomaly detection to enhance security. Potential market participation pathway and revenue distribution mechanism are proposed to align with the Australian National Electricity Market. The DCF provides a scalable, market-ready foundation for commercial demandside DER deployment under high renewable penetration.
subject: Research articles
  topic: section
subject: Distributed energy resources
  topic: keywords
subject: Multi-agent reinforcement learning
  topic: keywords
subject: Decentralised coordination
  topic: keywords
subject: Electricity market participation
  topic: keywords
creator: Bo Li
creator: Yong-Feng Ge
creator: Yuan Miao
creator: Ruoxuan Cui
creator: Xin Li
creator: Kate Wang
creator: Hua Wang
rights: Copyright (c) 2026 Bo Li, Yong-Feng Ge, Yuan Miao, Ruoxuan Cui, Xin Li, Kate Wang, Hua Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-19
publication date: 2026-03-19
doi: 10.4108/eetsis.12071

Numerical Index Modulation Aided Multi-Carrier DCSK System: A Secure Transmission Scheme for Physical Layer Security
type: item
description: This paper presents a novel Numerical Index Modulation-assisted Differential Chaos Shift Keying (NIMDCSK) system, architected on a multi-carrier framework where numerous information-bearing subcarriers are supported by a single common reference subcarrier. The proposed system introduces an additional dimension for data transmission by embedding information bits into the dynamic allocation of signal energy across subcarriers. This mechanism not only boosts the transmission rate but also significantly enhances physical layer security. Specifically, the NIM-DCSK scheme obfuscates its transmission characteristics through pseudo-random energy allocation patterns, where deliberately varying power levels create a complex and non-stationary signal signature. This fluctuating energy distribution effectively masks the underlying data structure, making it extremely difficult for unauthorized eavesdroppers to perform feature analysis or achieve successful decoding. Compared to conventional DCSK systems, comprehensive analyses demonstrate that the NIM-DCSK system achieves substantial gains in both energy efficiency (EE) and spectral efficiency (SE), alongside improved bit error rate (BER) performance under various channel conditions. These advantages are realized without a commensurate increase in structural complexity. The inherent security features, including inherent resistance to passive interception and a reduced vulnerability to statistical analysis, are key benefits. Consequently, this work facilitates the integration of index modulation with chaos-based communications, ensuring high-rate transmissions coupled with robust security. The proposed system is, therefore, highly suitable for applications demanding reliable and secure wireless links, such as secure sensor networks, tactical military communications, and confidential IoT deployments.
subject: Research articles
  topic: section
subject: Multi-carrier differential chaos shift keying
  topic: keywords
subject: numerical index modulation
  topic: keywords
subject: physical layer security
  topic: keywords
creator: Wang Chen
creator: Chao Duan
rights: Copyright (c) 2026 Wang Chen, Chao Duan https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-16
publication date: 2026-03-16
doi: 10.4108/eetsis.12043

Risk-Aware Secure Routing Mechanism for Power Communication Networks Based on GNNs
type: item
description: INTRODUCTION: As digital transformation progresses, power communication networks become exposed to increasingly complex security threats. Moreover, traditional routing algorithms cannot perceive or respond to dynamic security threats, making reliable data transmission difficult to achieve under network attack conditions. OBJECTIVES: Therefore, this study proposes a risk-aware secure routing (RASR) mechanism for power communication networks based on graph neural networks (GNNs). METHODS: The mechanism first introduces an autoencoder-graph neural network (AGNN) architecture for determining critical nodes, thus establishing a scoring prediction model customized to the structure of power communication backbone networks. It then integrates the essential scores of the nodes, historical failure rates, and traffic load factors to devise a path node risk quantification model. Finally, it incorporates risk quantification results into routing policies to enable proactive routing avoidance and thus bypass high-risk nodes. RESULTS: Experimental results show that the RASR algorithm effectively meets the differentiated service requirements of heterogeneous traffic—including delay-sensitive, bandwidth-sensitive, and reliability-sensitive traffic. Compared with traditional routing algorithms, it demonstrates greater stability and fault tolerance under high-risk attack scenarios while eliminating the need for redundant backup strategies, thereby markedly reducing resource overhead. CONCLUSION: Therefore, the proposed mechanism offers important theoretical support and a novel technical approach for establishing secure, reliable power communication networks.
subject: Research articles
  topic: section
subject: Cyberattack
  topic: keywords
subject: Power Communication Network
  topic: keywords
subject: Key Node Identification
  topic: keywords
subject: Risk-Aware
  topic: keywords
subject: Secure Routing
  topic: keywords
creator: Yanjun Zhao
creator: Yue Zhang
creator: Xiaowei Zhao
creator: Liyu Liu
creator: Xiang Wang
creator: Kaiyue An
rights: Copyright (c) 2026 Yanjun Zhao, Yue Zhang, Xiaowei Zhao, Liyu Liu, Xiang Wang, Kaiyue An https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-16
publication date: 2026-03-16
doi: 10.4108/eetsis.12042

Quantum Communication Networks for Secure Foundation Model Distribution Across Shanxi Power Grid Substations
type: item
description: INTRODUCTION: Foundation models deployed in power grid infrastructure face escalating security threats from advancing quantum computing capabilities, which can compromise classical encryption protecting multi-gigabyte model parameters during distributed transmission and storage across geographically dispersed substations. OBJECTIVES: This research develops and validates a quantum-secured communication framework integrating quantum key distribution with hierarchical encryption mechanisms specifically designed for protecting foundation model parameters in operational power grid environments. METHODS: A dual-channel quantum communication architecture was deployed across five substations in Shanxi Province spanning 580 kilometers, implementing BB84 protocol with decoy state techniques for quantum key generation, dynamic key-data mapping algorithms for parameter encryption, and Ceph-based distributed storage with blockchain audit trails. The system underwent 30-day continuous operational validation protecting a 500-million-parameter Transformer model under real-world conditions including temperature variations (-5°C to 35°C), grid maintenance events, and concurrent SCADA traffic. RESULTS: The framework achieved 99.2% system availability with distance-dependent quantum key distribution rates ranging from 4.5 kbps (50 km) to 0.5 kbps (180 km), quantum bit error rates maintained between 3.2-11.4% within operational thresholds, hierarchical AES encryption throughput of 85 MB/s for model parameters, and storage system performance delivering 8,500-10,800 read IOPS with 1.05 ms average latency. CONCLUSION: This work validates the practical viability of quantum communication networks for securing distributed foundation models in critical power infrastructure, demonstrating information-theoretic security under operational network conditions while establishing integration protocols between quantum key distribution channels and encrypted data transmission pathways for large-scale A…
subject: Research articles
  topic: section
subject: Quantum Communication Networks
  topic: keywords
subject: Foundation Model Security
  topic: keywords
subject: Power Grid Cybersecurity
  topic: keywords
subject: Encrypted Transmission Protocols
  topic: keywords
subject: Quantum Key Distribution
  topic: keywords
creator: Jiwu Liu
creator: Kai Xue
creator: Yachen Wang
creator: Chunguang Ren
creator: Xiaojian Zhang
creator: Kai Han
rights: Copyright (c) 2026 Jiwu Liu, Kai Xue, Yachen Wang, Chunguang Ren, Xiaojian Zhang, Kai Han https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-16
publication date: 2026-03-16
doi: 10.4108/eetsis.12041

Quantum Communication-Assisted Synchronization Framework for Distributed 3D Power Grid Models Across Multi-Regional Control Centers
type: item
description: INTRODUCTION: The synchronization of three-dimensional digital twin models across geographically distributed power grid control centers represents a critical challenge in modern power system operations. Traditional synchronization methods including Network Time Protocol (NTP) and Precision Time Protocol (PTP) achieve only millisecond-level accuracy, which proves fundamentally insufficient for monitoring and responding to critical power grid transients that occur on sub-millisecond timescales. The proliferation of renewable energy sources and distributed generation resources further intensifies synchronization requirements. OBJECTIVES: This research develops a comprehensive quantum communication-assisted synchronization framework designed to achieve unprecedented temporal accuracy for distributed 3D power grid models spanning multi-regional control centers while maintaining compatibility with existing infrastructure. METHODS: A hierarchical quantum-classical hybrid architecture is proposed that strategically utilizes quantum entanglement channels for time-critical synchronization signals and classical communication channels for bulk data transmission. An adaptive fault-tolerance mechanism dynamically adjusts quantum error correction strategies based on real-time quantum channel quality assessments, with graceful degradation to classical protocols when necessary. RESULTS: Extensive simulations utilizing 128 IEEE 118-bus system models demonstrate sub-10 nanosecond synchronization precision—representing 10,000-fold improvement over NTP—with end-to-end latencies maintained below 10 microseconds. The framework exhibits robust performance with synchronization success rates exceeding 90% under 15% node failure conditions and demonstrates logarithmic O(log M) time complexity compared to O(M²) scaling for classical consensus protocols. CONCLUSION: The quantum communication-assisted framework delivers superior accuracy, scalability, and fault tolerance compared to classical sy…
subject: Research articles
  topic: section
subject: Quantum communication
  topic: keywords
subject: Power grid
  topic: keywords
subject: Digital twin
  topic: keywords
subject: Quantum entanglement
  topic: keywords
subject: Distributed energy resources
  topic: keywords
creator: Youhui Chen
creator: Zhonghua Lv
creator: Ruixue Hu
creator: Xinying Zhao
creator: Dongxue Li
rights: Copyright (c) 2026 Youhui Chen, Zhonghua Lv, Ruixue Hu, Xinying Zhao, Dongxue Li https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-16
publication date: 2026-03-16
doi: 10.4108/eetsis.12031

Quantum Communication Technology for Optimizing 3D Cost Estimation and Secure Data Transmission in Substation Engineering
type: item
description: INTRODUCTION: The highly sensitive 3D cost estimation data in substation projects faces significant security challenges during distributed transmission, as conventional encryption approaches prove inadequate against quantum computing threats for large-scale engineering data protection. OBJECTIVES: This study aims to develop a quantum communication-based secure transmission framework for substation 3D cost estimation data, employing a three-tier quantum architecture to achieve information-theoretic security levels with robust defense against network attacks. METHODS: The proposed solution employs a three-tier quantum architecture encompassing physical, network, and application layers, with optimized quantum channels for engineering data flow. The framework utilizes the BB84 protocol with the decoy state method, implementing quantum key distribution and classical encryption integration to maintain data integrity through a distributed optimization strategy. RESULTS: Simulation validation across 10-50 node networks demonstrates quantum key generation rates of 8-40 kbps and 23% improvement in network throughput compared to star topology over traditional methods. The system achieves 99.99% reduction in key usage compared to pure quantum approaches while maintaining information-theoretic security levels. CONCLUSION: This research establishes fundamental principles and implementation pathways for quantum communication applications in engineering domains, offering significant advancement in secure data transmission for sensitive industrial applications and providing a reliable solution for protecting critical infrastructure engineering data.
subject: Research articles
  topic: section
subject: Quantum communication
  topic: keywords
subject: Secure data transmission
  topic: keywords
subject: Substation engineering
  topic: keywords
subject: 3D cost estimation
  topic: keywords
subject: Distributed systems
  topic: keywords
creator: Junchao Li
creator: Fei You
creator: Dan Chen
rights: Copyright (c) 2026 Junchao Li, Fei You, Dan Chen https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-16
publication date: 2026-03-16
doi: 10.4108/eetsis.12021

A Distributed and Secure Resource Allocation Method for Power Communication Networks Based on Policy Distillation
type: item
description: INTRODUCTION: In the next-generation smart grid communication architecture, how to achieve secure, dynamic, and fine-grained network resource allocation to ensure differentiated QoS for various services has become a key challenge. OBJECTIVES: Therefore, this study proposes a lightweight resource allocation method based on constrained policy distillation to address the challenge of balancing lightweight deployment with strong security assurance in power communication networks. METHODS: By integrating Graph Neural Networks (GNNs) and Bidirectional LSTM (Bi-LSTM), the model extracts three-dimensional features of topology, service, and resources to construct a 128-dimensional joint state representation. Moreover, a multi-objective reward function is designed that employs a double Q-network to mitigate value overestimation and generate a high-fidelity decision trajectory library. Through service-constrained policy distillation, the model innovatively combines KL divergence loss, a squared hard-constraint loss, and a soft-constraint L2 loss to compress the teacher model into a student model, subsequently compiled and deployed at the edge. Finally, a rule engine layer dynamically adjusts priorities for intercepting critical violations and ensures the security of the power system. RESULTS: Experimental results based on real-world power grid datasets demonstrate that our model achieves superior performance in resource efficiency, security, and edge effectiveness, effectively balancing lightweight deployment with strong security assurance in resource allocation for power communication networks. CONCLUSION: It can be seen that this method enables distributed and secure resource allocation in power communication network environments, thus providing reliable QoS guarantees for new-type power systems.
subject: Research articles
  topic: section
subject: Power Communication Networks
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Policy Distillation
  topic: keywords
subject: Distributed and Secure Resource Allocation
  topic: keywords
creator: Yue Zhang
creator: Zongtao Li
creator: Si Chen
creator: Guoqiang Hu
creator: Pengcheng Li
creator: Ruimei Wu
rights: Copyright (c) 2026 Yue Zhang, Zongtao Li, Si Chen, Guoqiang Hu, Pengcheng Li, Ruimei Wu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-16
publication date: 2026-03-16
doi: 10.4108/eetsis.11995

An Efficient Privacy-Preserving Secure Aggregation Scheme for Federated Learning with Input Verification and Dropout Resistance
type: item
description: Federated learning, as a distributed machine learning paradigm, allows multiple participants to collaboratively train a shared model without sharing their local data. However, the increasing demand for privacy protection during data aggregation within distributed systems underscores the persistent challenge of ensuring both security and efficiency. Many existing Privacy-Preserving Machine Learning (PPML) schemes relying on homomorphic encryption introduce substantial computational overhead during aggregation, rendering them impractical for large-scale PPML applications involving resource-constrained participant devices. Moreover, device dropout events and data poisoning attacks perpetrated by malicious clients adversely affect the integrity of the aggregated results. To address these challenges, this paper proposes an efficient privacy-preserving secure aggregation scheme capable of tolerating participant dropout at arbitrary stages and securing data against both semi-honest and malicious participants. By integrating input verification protocols and applying gradient masking techniques, the scheme enhances its resilience against malicious attacks while ensuring user data privacy. Leveraging the additive homomorphic property of Shamir's secret sharing enables efficient global mask recovery, significantly optimizing the scheme's efficiency. Experimental results demonstrate that the proposed scheme significantly outperforms baseline methods in computational efficiency, communication overhead, and security robustness. By effectively balancing high privacy protection with practical feasibility, this scheme presents a promising solution for secure multi-party aggregation in large-scale distributed systems.
subject: Research articles
  topic: section
subject: Secure Aggregation
  topic: keywords
subject: Privacy Preservation
  topic: keywords
subject: Secret Sharing
  topic: keywords
subject: Zero-Knowledge Proofs
  topic: keywords
creator: Zijun Guo
creator: Yuteng Sun
creator: Xinyue Zhang
creator: Lingling Wu
rights: Copyright (c) 2026 Zijun Guo, Yuteng Sun, Xinyue Zhang, Lingling Wu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-16
publication date: 2026-03-16
doi: 10.4108/eetsis.11991

EBTM: Web Vulnerability Attack Behavior Recognition Method Based on Feature Fusion
type: item
description: INTRODUCTION: With the continuous expansion of network scale, effectively detecting web vulnerability attacks has become a critical challenge for ensuring network security. Existing research, primarily focused on balanced sample recognition, proves inadequate for real-world scenarios due to high false alarm rates in large-scale traffic and poor recognition performance for minority attack samples caused by imbalanced data distribution. OBJECTIVES: This paper aims to address the limitations of current web vulnerability attack detection methods in imbalanced real-world environments. The objective is to propose a novel recognition method that reduces the false positive rate and improves the identification performance for minority attack samples under highly skewed data distributions. METHODS: We propose a feature fusion-based recognition method named EBTM for imbalanced samples. The method integrates expert knowledge to optimize feature selection, focusing on key information and ensuring a more uniform mapping of URL requests. It employs three output features from different advantageous models for feature fusion, thereby generating a richer and more discriminative feature representation for the final recognition task. RESULTS: Experimental results demonstrate that the proposed EBTM method significantly enhances the recognition of web vulnerability attack behaviors. Under a realistic imbalanced condition where attack samples constitute only about 3% of the data, the model achieves a macro-average F1 score of 99.1% and reduces the false positive rate to 0.054%. CONCLUSION: The EBTM method effectively improves the efficiency and accuracy of web vulnerability attack behavior recognition in practical, imbalanced scenarios. By combining expert-guided feature optimization and multi-model feature fusion, it successfully addresses key challenges of high false alarms and poor minority class recognition, offering a robust solution for securing large-scale network environments.
subject: Research articles
  topic: section
subject: Vulnerability Attack Behavior
  topic: keywords
subject: Large-Scale Traffic
  topic: keywords
subject: False Positive Rate
  topic: keywords
subject: Feature Fusion
  topic: keywords
subject: Deep Learning
  topic: keywords
creator: Xiangnan Lin
creator: Bin Lu
rights: Copyright (c) 2026 Xiangnan Lin, Bin Lu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-21
publication date: 2026-01-21
doi: 10.4108/eetsis.11636

NAS-FD: Neural Architecture Search-Based Fraud Detection for Power Audit Data
type: item
description: Power data auditing is the cornerstone of a reliable and efficient modern power system. Various deep learning models have been successfully applied to fraud detection in power audit data. However, most of these methods rely on manual trial-and-error and expert knowledge to design the neural architectures and hyper-parameters. To address this limitation, this paper proposes an innovative automated deep learning approach for fraud detection model design using genetic algorithm (GA)-based neural architecture search (NAS), termed NAS-FD. In NAS-FD, convolutional neural network (CNN) is employed as the primary detection model, leveraging its strong data learning and feature extraction capabilities. First, an effective encoding scheme is developed to represent the nueral architectures and hyper-parameters of CNN, as these parameters significantly influence the detection performance. Then, considering detection performance as the objective function, well-designed GA-based evolutionary operations are implemented to optimize the neural architectures and hyper-parameters of CNN, obtaining the optimized CNN. The detection performance of the proposed NAS-FD method is validated using an electricity theft dataset from the power auditing domain. Experimental results demonstrate that NAS-FD achieves superior detection performance compared with manually designed deep learning models in terms of four performance indices including accuracy, precision, recall, and F1-score.
subject: Research articles
  topic: section
subject: Modern power systems
  topic: keywords
subject: power audit data
  topic: keywords
subject: fraud detection
  topic: keywords
subject: convolutional neural network
  topic: keywords
subject: neural architecture search
  topic: keywords
creator: Yuanzhong Zuo
creator: Jingyi Hu
creator: Rao Kuang
creator: Ping Chen
creator: Zhigao Zheng
creator: Ze Zhou
rights: Copyright (c) 2026 Yuanzhong Zuo, Jingyi Hu, Rao Kuang, Ping Chen, Zhigao Zheng, Ze Zhou https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-14
publication date: 2026-01-14
doi: 10.4108/eetsis.10860

Object Detection and Segmentation of Power Equipment in Infrared Images via Improved YOLOv8 and Prompt-Optimized SAM
type: item
description: To achieve automated infrared monitoring of power equipment in substations, this paper proposes an object detection and segmentation method based on improved YOLOv8 and Prompt-Optimized SAM (Segment Anything Model). Firstly, to address the issues of poor resolution and strong background interference in infrared images, the small object feature extraction capability and bounding box regression accuracy of YOLOv8 are improved by introducing a multi-scale feature extraction module, a robust feature downsampling module, and an improved loss function. The Spatial Pyramid Pooling Fast module is improved using large-kernel depthwise separable convolution, enhancing the extraction capability for both global and local features. Secondly, to improve segmentation accuracy, this paper proposes a method that converts detection boxes into prompt points. GrabCut, combined with colour saliency and a superpixel algorithm, is used to segment high-confidence target regions. Zero-shot prompt point segmentation for SAM is achieved by performing clustering on the regions. Experimental validation on an infrared dataset covering seven types of power equipment shows that the improved object detection model achieves an mAP@0.5 of 95.7%, which is 2.3% higher than the original model, with a detection speed of 107.5 FPS. The proposed segmentation method achieves higher accuracy in complex backgrounds than both bounding box-prompted SAM and GrabCut. This study lays a foundation for the precise processing of infrared images of substation power equipment.
subject: Scheduling optimization and load balancing in scalable distributed systems
  topic: section
subject: Infrared Images
  topic: keywords
subject: Power Equipment
  topic: keywords
subject: Object Detection
  topic: keywords
subject: Image Segmentation
  topic: keywords
subject: YOLOv8
  topic: keywords
creator: Bing Xue
creator: Zehui Liu
creator: Zhanhong Wang
creator: Wenyuan Zhou
creator: Baoning Wang
creator: Xukun Yang
rights: Copyright (c) 2026 Bing Xue, Zehui Liu, Zhanhong Wang, Wenyuan Zhou, Baoning Wang, Xukun Yang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-12
publication date: 2026-03-12
doi: 10.4108/eetsis.10727

Risk Early Warning for Police-Related Online Public Opinion Based on Deep Learning
type: item
description: INTRODUCTION: Police-related online public opinion is highly sensitive and can easily have a negative impact on social stability. OBJECTIVES: This paper aims to address the crucial need for early warning systems for the risks associated with police-related online public opinion to ensure social harmony and effectively prevent and resolve major social risks. METHODS: Based on a literature review and deep learning methods, this research constructs an indicator system from four dimensions, analyzing data from representative police-related online public opinion incidents over the past five years. A CNN-BiLSTM sentiment classification model is built for sentiment analysis, and an optimized SSA-CNN-LSTM-Attention model is used for public opinion risk early warning. RESULTS: The experimental results demonstrate that the SSA-CNN-LSTM-Attention model has the minimum error. CONCLUSION: This research provides a theoretical reference for public security organs in responding to and preventing police-related online public opinion.
subject: Scheduling optimization and load balancing in scalable distributed systems
  topic: section
subject: Police-related online public opinion
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Risk early warning
  topic: keywords
subject: Optimization algorithm
  topic: keywords
creator: Juan Wang
creator: Yuxiang Guan
creator: Nan Wang
creator: Jie Pan
creator: Peng Zhang
rights: Copyright (c) 2025 Juan Wang, Yuxiang Guan, Nan Wang, Jie Pan, Peng Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-04
publication date: 2025-12-04
doi: 10.4108/eetsis.10664

Food Derived Biostimulants Technology Revealed and Retrieved by Natural Language Processing
type: item
description: Food-derived biostimulants support sustainable agriculture; however, the scale and heterogeneity of the field hinder their synthesis. We profiled 2005–2025 innovation by mining 2,586 PATENTSCOPE filings and Web of Science articles; texts were analyzed with KH Coder and topic models, with large language models assisting in interpretation. Patent activity surged after 2018, emphasizing plant growth promotion, yield stability, and abiotic stress tolerance (amino acids, seaweed extracts, polyphenols, humic substances, and microbial consortia). In parallel, academic papers have shifted from descriptive trials to mechanism-level work on drought/salinity responses, gene expression, and metabolomics. Together, these signals outline a translation path in which deployable biological inputs converge with mechanistic evidence. Our NLP pipeline distilled heterogeneous texts into actionable indicators, yielding a reproducible map from patent/literature trends to testable hypotheses for formulation, dose, and seed stage delivery.
subject: Research articles
  topic: section
subject: Biostimulants
  topic: keywords
subject: Sustainable agriculture
  topic: keywords
subject: Health information science
  topic: keywords
subject: Text mining
  topic: keywords
subject: Patent analysis
  topic: keywords
creator: Yoshiyuki Kobayashi
creator: Kakeru Ota
creator: Maya Iwano
creator: Itsuki Kageyama
creator: Kota Kodama
creator: Kazuhiko Tsuda
rights: Copyright (c) 2025 Yoshiyuki Kobayashi, Kakeru Ota, Maya Iwano, Itsuki Kageyama, Kota Kodama, Kazuhiko Tsuda https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-25
publication date: 2025-11-25
doi: 10.4108/eetsis.10654

Heterogeneous Distributed Computing-Based AI Video Generation: Real-Time Load Balancing and Intelligent Scheduling in New Media Art
type: item
description: INTRODUCTION: The rapid proliferation of Generative AI (AIGC) in new media art has intensified the need for real-time, distributed video generation with stable performance and low latency. Conventional centralized rendering and static scheduling frameworks often encounter load imbalance and communication bottlenecks in heterogeneous environments, resulting in degraded visual coherence and responsiveness. To address these challenges, this study develops a unified and adaptive distributed framework, termed H-RLSCO (Heterogeneity-aware Reinforcement Learning and Scheduling Co-Optimization), designed to enhance both computational efficiency and artistic consistency in large-scale AI video generation. The framework integrates three complementary modules: a Heterogeneity Perception Module (HPM) for node profiling and adaptive task partitioning, a Reinforcement Learning Scheduling Controller (RLSC) for dynamic task migration, and a Generation-Scheduling Co-Optimization (GSCO) mechanism that incorporates content-complexity feedback into scheduling decisions to maintain multimodal synchronization. Experiments on the ArtScene-4K and StageSyn-Real datasets demonstrate that H-RLSCO reduces average latency by 14.4% and decreases Fréchet Video Distance by approximately 12.5% compared with the RL-Scheduler baseline, while limiting performance fluctuation to within 3% under multi-noise conditions (p < 0.01). These gains remain consistent across varying bandwidths and node capabilities on a five-node heterogeneous cluster, confirming robust real-time behavior and balanced utilization. Nevertheless, the scalability of H-RLSCO remains constrained when applied to large-scale node clusters, suggesting future work should explore multi-agent reinforcement learning and lightweight diffusion-Transformer architectures to enhance efficiency and expand applicability.
subject: Scheduling optimization and load balancing in scalable distributed systems
  topic: section
subject: Heterogeneous distributed computing
  topic: keywords
subject: AI video generation
  topic: keywords
subject: reinforcement learning scheduling
  topic: keywords
subject: multimodal co-optimizaiton
  topic: keywords
subject: real-time new media art
  topic: keywords
creator: Qian Fu
rights: Copyright (c) 2025 Qian Fu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-02
publication date: 2025-12-02
doi: 10.4108/eetsis.10614

Method for Analysing Information on Psychosomatic Issues Based on Service Recipient Dissatisfaction
type: item
description: Recently, the proliferation of social networking services has facilitated free sharing of opinions and complaints by consumers. For companies, such reviews are a valuable source of information for product and service evaluation, and for identifying areas for improvement. In this investigation, the cosmetics industry was used as a case study for this phenomenon. Content related to ‘skin’ was extracted from consumer review information and related physical and mental problems were analysed. Specifically, a polarity dictionary specialized for cosmetics was built, and related reviews were extracted by identifying expressions of dissatisfaction related to the skin, such as ‘itchiness’, ‘redness’, and ‘dryness’. By classifying and analysing problem types, this analysis revealed the trends in consumer dissatisfaction and related issues, discovering insights useful for improving services and products. Therefore, the present study identified the types and causes of problems in providing practical knowledge that contributes to enhanced consumer satisfaction, reduced product risk, and effective marketing strategies.
subject: Research articles
  topic: section
subject: Social Networking Service
  topic: keywords
subject: Cosmetics
  topic: keywords
subject: Consumer Dissatisfaction
  topic: keywords
subject: text mining
  topic: keywords
creator: Maya Iwano
creator: Yoshiyuki Kobayashi
creator: Kakeru Ota
creator: Kazuhiko Tsuda
rights: Copyright (c) 2025 Maya Iwano, Yoshiyuki Kobayashi, Kakeru Ota, Kazuhiko Tsuda https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-25
publication date: 2025-11-25
doi: 10.4108/eetsis.10574

Research on the Application of Large Language Model in Data Integration
type: item
description: INTRODUCTION: Large Language Models (LLMs), a major breakthrough in artificial intelligence, have been widely applied across various domains in recent years. Their powerful capabilities in language comprehension and generation enable effective handling of diverse natural language processing tasks, such as text generation, question answering, machine translation, and information retrieval. This paper investigates the application of LLM technology in data integration, a core aspect of data governance. In contrast to end-to-end black-box approaches, we reframe data integration as a problem of discovering interpretable mapping rules through symbolic regression. OBJECTIVES: We begin by defining the fundamental problem of data integration. We then propose a general-purpose large model framework for data governance, built on a deep symbolic regression foundation. The framework comprises a symbolic expression generator and a metadata-enhanced executor, aiming to achieve both high accuracy and interpretability. METHODS: The model is trained using a combination of recurrent neural networks and reinforcement learning techniques, for expression generation and the execution of the discovered rules is structured based on a Transformer encoder architecture enhanced with a dedicated metadata embedding layer. To enhance performance, we incorporate metadata fine-tuning, where the generated symbolic expressions serve as key metadata to guide the integration process. RESULTS: Finally, the proposed model is evaluated on two representative data integration tasks, with experimental results demonstrating its effectiveness. CONCLUSION: The results validate its practical quality and highlight the advantage of the symbolic regression paradigm in enhancing interpretability.
subject: Scheduling optimization and load balancing in scalable distributed systems
  topic: section
subject: large language models (LLMs)
  topic: keywords
subject: metadata fine-tuning
  topic: keywords
subject: Transformer
  topic: keywords
subject: data governance
  topic: keywords
subject: reinforcement learning
  topic: keywords
subject: symbolic regression
  topic: keywords
creator: Zhanfang Chen
creator: Yuan Ren
creator: Xiaoming Jiang
creator: Ruipeng Qi
rights: Copyright (c) 2026 Zhanfang Chen, Yuan Ren, Xiaoming Jiang, Ruipeng Qi https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-26
publication date: 2026-01-26
doi: 10.4108/eetsis.10245

A Method for Extracting Dissatisfaction Entities in the Pharmaceutical Sector Using Large Language Model
type: item
description: In today’s VUCA environment, pharmaceutical companies face mounting pressure to enhance both innovation and patient satisfaction amid shrinking domestic markets and intensifying global competition. Despite the growing emphasis on Patient Centricity—the integration of patients' voices into the drug development process—Japan still lags in the systematic incorporation of patient feedback. This study proposes a novel, patient-centered approach that leverages large language models (LLMs) and prompt engineering to extract dissatisfaction entities from patient-generated content, automatically generate a Dissatisfaction Dictionary, and visualize the interrelationships between these entities. Using the “Medical & Welfare_Pharmaceuticals” category from the Dissatisfaction Survey Dataset provided by the National Institute of Informatics, we analysed 300 patient comments with a Conversation Chain built on the GPT-4o API and LangChain. As a result, dissatisfaction entities were extracted and vectorized using TF-IDF and cosine similarity to form thematic clusters. This analysis revealed interconnected concerns related to pricing, efficacy, usability, and medical service processes. For example, discrepancies in pricing and perceived ineffectiveness of generics frequently co-occurred with complaints about pharmacist communication. Our findings offer pharmaceutical firms a systematic, scalable framework to reflect patient dissatisfaction in drug development, thereby enhancing patient engagement, satisfaction, and strategic alignment with Patient Centricity principles.
subject: Research articles
  topic: section
subject: Patient Centricity
  topic: keywords
subject: Pharmaceutical Industry
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Prompt Engineering
  topic: keywords
subject: Text Mining
  topic: keywords
creator: Kakeru Ota
creator: Takumi Uchida
creator: Maya Iwano
creator: Tsuda Kazuhiko
rights: Copyright (c) 2025 Kakeru Ota, Takumi Uchida, Maya Iwano, Tsuda Kazuhiko https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-26
publication date: 2025-11-26
doi: 10.4108/eetsis.10511

KADP-SQL: Knowledge-Augmented Generation and Dual-Path Validation in Text-to-SQL
type: item
description: The widespread application of Large Language Models (LLMs) has significantly advanced the development of the Text-to-SQL task, which aims to translate natural language questions posed by users into executable structured query statements. This allows non-expert users to efficiently access databases. However, existing models still struggle to generate correct SQL statements in the absence of sufficient knowledge, particularly under complex query scenarios, where they are prone to syntactic errors and semantic deviations. Moreover, most current approaches rely solely on execution feedback for SQL correction via a single path, making it difficult to detect semantic errors. To address these challenges, we propose KADP-SQL, which divides the Text-to-SQL task into two main modules: Knowledge-Augmented Generation and Dual-Path Validation. This framework structurally represents manually annotated evidence and incorporates web-based external knowledge to enhance SQL generation. Additionally, we introduce a dual-path SQL validation method to detect and correct errors in generated SQL queries. Through extensive experiments conducted on multiple closed-source and open-source LLMs, our proposed KADP-SQL achieves an execution accuracy of 70.53% on the BIRD development set and 88.22% on the Spider test set. These results demonstrate the effectiveness and adaptability of the proposed method.
subject: Research articles
  topic: section
subject: LLM
  topic: keywords
subject: Text-to-SQL
  topic: keywords
subject: Knowledge-Augmented Generation
  topic: keywords
subject: Dual-Path Validation
  topic: keywords
creator: Jun Peng
creator: Jinguo You
creator: Xiang Li
creator: Jiaman Ding
creator: Lianyin Jia
rights: Copyright (c) 2026 Jun Peng, Jinguo You, Xiang Li, Jiaman Ding, Lianyin Jia https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-02
publication date: 2026-02-02
doi: 10.4108/eetsis.10499

An Artifact-Centric Process Mining Approach for Learning Style Analytics
type: item
description: As an integrated discipline encompassing data mining, machine learning, process modeling and analytics, process mining is increasingly being applied in the field of education and has emerged as a prominent research topic. Traditional business process modeling approaches, which are primarily based on control flow rather than data flow, exhibit a limited capacity to capture a holistic view of critical business data within complex business procedures. This study focuses on the impact of data-driven process modeling techniques on the performance of analytical models and proposes an artifact-centric process mining approach for learning style analysis. Based on the artifact life-cycle model, we extracted sequences of data attribute operations that encapsulate learning style features. The similarity among different data attribute operation sequences was quantified. The proposed method was evaluated using the OULAD, a benchmark dataset in the learning analytics domain. Experimental results demonstrate that the method effectively enhances the performance of learning style prediction models, with SVM and GBoost algorithms outperforming other modeling approaches.
subject: Scheduling optimization and load balancing in scalable distributed systems
  topic: section
subject: Artifact
  topic: keywords
subject: Process Mining
  topic: keywords
subject: learning style analysis
  topic: keywords
subject: Data Attribute Operation sequence
  topic: keywords
subject: OULAD
  topic: keywords
creator: Jiehua Lu
creator: Jun Li
rights: Copyright (c) 2025 Jiehua Lu, Jun Li https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-26
publication date: 2025-11-26
doi: 10.4108/eetsis.10390

System Design and Development for Industry-Education Integration in Art Universities Based on Generative Adversarial Networks and Attention Mechanism
type: item
description: This study focuses on the algorithm design and system development of Generative Adversarial Networks and attention mechanisms in the context of industry-education integration at art universities. To address issues such as training instability, missing details, and insufficient personalization in the intelligent generation of art resources, a hybrid algorithm architecture integrating multi-head self-attention mechanisms and Wasserstein distance optimization is proposed. The generator incorporates multi-scale feature extraction and local-global joint attention mechanisms, significantly enhancing the style consistency and detail expression in image generation. The discriminator combines gradient penalty strategies to enhance the model's training stability. The study trains and evaluates using the COCO and ArtBench datasets, achieving excellent results in terms of generation quality, computational efficiency, and diversity. The highest image quality score is 94.8, and the diversity score is 92.1. The experimental results demonstrate the effectiveness of the designed algorithm in meeting the customized and high-quality resource generation needs of art universities, providing reliable technical support and application foundation for intelligent content generation in industry-education integration.
subject: Scheduling optimization and load balancing in scalable distributed systems
  topic: section
subject: generative adversarial network
  topic: keywords
subject: attention mechanism
  topic: keywords
subject: multi-head self-attention
  topic: keywords
subject: WGAN
  topic: keywords
subject: art resource generation
  topic: keywords
creator: Xingye Leng
creator: Zhao Yifeng
rights: Copyright (c) 2025 Xingye Leng, Zhao Yifeng https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-18
publication date: 2025-12-18
doi: 10.4108/eetsis.10217

Mitigating Latency in Chord-Based Routing under IPv6
type: item
description: As the transition to IPv6 enables a massive expansion of the peer-to-peer (P2P) network landscape, traditional Chord-based routing protocols face significant performance bottlenecks due to the lack of awareness of the underlying physical network topology. In this paper, we propose eChord, a topology-aware routing system designed to mitigate latency in large-scale IPv6 environments by exploiting network locality. The key of eChord is a bifurcated routing architecture that maintains dual routing states: a localFinger table for intradomain routing within autonomous systems (AS) and a globalFinger table for inter-domain connectivity. By optimizing the identifier space and introducing a locality factor ρ to prioritize local lookups, eChord can effectively reduce the reliance on high-latency backbone links.We then perform amulti-dimensional efficiency analysis of the eChord system, in terms of expected hop number, end-to-end latency, and state maintenance overhead. Numerical simulations are finally provided to demonstrate that the proposed eChord system can significantly outperform standard Chord in various network scales. In particular, in a network of 106 nodes distributed across 500 ASs with a locality factor of ρ = 0.8, the proposed eChord system reduces the average routing latency by approximately 75% compared to the traditional, locality-agnostic Chord protocol.
subject: AIGC - Empowered Covert Communications for Scalable Information Systems
  topic: section
subject: IPv6
  topic: keywords
subject: latency
  topic: keywords
subject: system design
  topic: keywords
subject: performance evaluation
  topic: keywords
creator: Xubin Lin
creator: Feifei Hu
creator: Liu Wu
rights: Copyright (c) 2026 Xubin Lin, Feifei Hu, Liu Wu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-27
publication date: 2026-01-27
doi: 10.4108/eetsis.9992

A Review of Reference Architectures for the Next-Generation Internet of Things Systems
type: item
description: The rapid evolution of the Internet of Things (IoT) demands robust architectural models capable of integrating emerging technologies and managing growing system complexity. This paper presents a structured review of IoT Reference Architectures (RAs), analysing their conceptual foundations, structural organization, and technological readiness in the context of Next-Generation IoT (NGIoT) technologies. The study examines RAs based on their support for disruptive technologies, including Edge/Fog/Cloud Computing, 5G, Artificial Intelligence, Digital Twins, Augmented Reality, Blockchain, and the Tactile Internet. Special emphasis is placed on the ASSIST-IoT RA, evaluated as a modular, cloud-native blueprint aligned with modern software design principles. Reflecting decentralization, scalability, interoperability, and resilience, ASSIST-IoT emerges as a production-ready framework for building intelligent and adaptive IoT systems. The findings synthesize current RAs trends and limitations, offering a forward-looking perspective that informs the development of NGIoT systems driven by data-driven and human-centric innovation.
subject: Review article
  topic: section
subject: Internet of Things
  topic: keywords
subject: Reference Architectures
  topic: keywords
subject: Next-Generation IoT Systems
  topic: keywords
subject: System Engineering
  topic: keywords
subject: Industry 5.0
  topic: keywords
creator: Sorin-Daniel Gheorghe
rights: Copyright (c) 2026 Sorin-Daniel Gheorghe https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-03
publication date: 2026-02-03
doi: 10.4108/eetsis.9877

An Integrated Cybersecurity Defense Framework for Attack Intelligence Analysis, Counteraction, and Traceability in Complex Network Architectures
type: item
description: INTRODUCTION: In response to the increasingly severe and complex cybersecurity threats posed by advanced persistent threats (APTs) and other sophisticated attacks, this paper proposes an integrated security defense framework for attack intelligence analysis, counteraction, and traceability in complex network architectures. Consolidating threat intelligence from multiple sources that provide different types of intelligence is a major obstacle for the cyber security defense community, particularly when serious challenges arise from complex advanced persistent threats (APTs) involving multiple entities. Inconsistent data formats and methods of structuring data further complicate analyzing the intelligence and creating improved strategies for defense from an all-source intelligence model. OBJECTIVES: The proposed approach leverages both internal and external threat intelligence sources, standardizes and integrates them into a heterogeneous threat intelligence knowledge graph, and transforms it into a homogeneous representation to facilitate analysis. Here introduces a framework that allows integration of a heterogeneous threat intelligence knowledge graph, Bayesian game theoretic modeling, and a Graph Attention Network (GAT). It is a method that converts multi-source intelligence into a single, homogeneous graph that allows for more informative analysis and a greater adaptive decision-making capacity. METHODS: To model the strategic interaction between attackers and defenders under incomplete information and resource constraints, we construct a Network Attack-Defense Game Model (NADGM) based on Bayesian game theory and derive the equilibrium strategies using linear programming and Harsanyi transformation. RESULTS: Furthermore, a graph attention network (GAT) is applied to perform node classification on the threat intelligence reports, exploiting the semantic relations between entities to enhance the accuracy of organization-level attribution. The framework is validated …
subject: Research articles
  topic: section
subject: Threat Intelligence
  topic: keywords
subject: Advanced Persistent Threat
  topic: keywords
subject: Bayesian Game Theory
  topic: keywords
subject: Graph Attention Network
  topic: keywords
subject: Ransomware
  topic: keywords
subject: Cybersecurity Defense
  topic: keywords
creator: Xiangyu Le
creator: Hushuang Zeng
rights: Copyright (c) 2025 Xiangyu Le, Hushuang Zeng https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-03
publication date: 2025-12-03
doi: 10.4108/eetsis.9770

Ensuring Security in Multi-Tag Backscatter Communication Systems
type: item
description: This paper studies the security performance of a multi-tag backscatter communication system, where a reader exchanges its information with multiple distributed tags in the presence of an eavesdropper. The system security performance is assessed using the secrecy outage probability (SOP), which quantifies the likelihood that the system secrecy capacity drops below a specified threshold. The paper provides a mathematical analysis on the communication links, considering Rayleigh fading, and investigates the effect of system monostatic and bistatic RFID configurations on the overall security. An optimal tag selection scheme is proposed to maximize the secrecy capacity by choosing the tag with the best link between the reader and the eavesdropper. Moreover, an analytical expression is derived for the system SOP, which incorporates the joint probability distributions of channel gains and the asymptotic SOP is provided as the signal-to-noise ratio increases. Simulation results are presented to verify the theoretical analysis, where the simulation results closely match the analytical predictions, confirming the accuracy of the proposed SOP analysis. Moreover, the asymptotic analysis offers an upper bound on the outage probability, consistent with theoretical analysis.
subject: AIGC - Empowered Covert Communications for Scalable Information Systems
  topic: section
subject: Power data network
  topic: keywords
subject: multi-tag
  topic: keywords
subject: secure transmission
  topic: keywords
subject: backscatter communication
  topic: keywords
creator: Zilin You
creator: Zhihong Liang
creator: Siliang Suo
creator: Lifeng Mai
creator: Qiaoling Lin
rights: Copyright (c) 2026 Zilin You, Zhihong Liang, Siliang Suo, Lifeng Mai, Qiaoling Lin https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-12
publication date: 2026-01-12
doi: 10.4108/eetsis.9691

Deep Learning Based Power Load Prediction in Smart Grid Networks
type: item
description: This paper presents a novel deep learning scheme for power load prediction in smart grid networks, combining temporal modeling with adaptive feature integration to tackle the complex dynamics of electricity consumption. The proposed scheme features a hybrid architecture that merges recurrent neural networks with attention mechanisms, enabling simultaneous capture of long-term load patterns and dynamic weighting of external influences like weather conditions and temporal features. Moreover, the model incorporates specialized preprocessing to decompose load data into periodic and volatile components while employing robust normalization techniques to handle non-stationary behavior. Then, a dual-objective loss function is used to enhance both prediction accuracy and resilience to outliers, supported by adaptive optimization with regularization. Simulation results are provided to demonstrate the proposed scheme’s superior performance, achieving 96.1% prediction accuracy with 5 hidden layers. The attention mechanism proves particularly effective, reducing weather-related prediction errors by 22% while maintaining faster convergence rates than conventional methods. This comprehensive solution offers grid operators a reliable tool for demand-side management, renewable integration, and operational planning in modern power systems.
subject: AIGC - Empowered Covert Communications for Scalable Information Systems
  topic: section
subject: Deep learning
  topic: keywords
subject: power load prediction
  topic: keywords
subject: smart grid networks
  topic: keywords
subject: performance evaluation
  topic: keywords
creator: Yifan Tian
creator: Xiaoyi Chen
creator: Yuanyuan Dai
creator: Jiajia Shao
creator: Jiankai Ma
creator: Xiaoyu Yan
creator: Zan Huang
creator: Qi Wang
creator: Lijun Wu
creator: Zhijing Zhang
creator: Shiyun Huang
rights: Copyright (c) 2025 Yifan Tian, Xiaoyi Chen, Yuanyuan Dai, Jiajia Shao, Jiankai Ma, Xiaoyu Yan, Zan Huang, Qi Wang, Lijun Wu, Zhijing Zhang, Shiyun Huang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-10-24
publication date: 2025-10-24
doi: 10.4108/eetsis.9591

A Cholesky decomposition and fusion clustering based spectrum sensing method
type: item
description: Spectrum sensing is a key technology to detect unused frequency bands, and is widely applied in spectrum sharing and dynamic channel allocation. However, it is a challenge to provide high sensing accuracy under low signal-to-noise-ratio (SNR) environments. To address this issue, this paper proposes a novel method based on feature extraction and fusion clustering. First, the sampling matrix of the received signal is decomposed into two orthogonal components I and Q, and Cholesky decomposition is performed on the covariance matrices of I and Q components to extract their two-dimensional feature vectors. Then, the fusion clustering algorithm is proposed, where the GMM clustering algorithm is performed to classify the feature vectors, and the initial parameters of GMM, such as centroids, weights and covariance matrices, are generated by K-means clustering. Simulation results show that the proposed method accelerates the convergence speed of GMM and improves the classification accuracy. It effectively enhances the performance of spectrum sensing compared to other mainstream methods.
subject: Research articles
  topic: section
subject: cooperative spectrum sensing
  topic: keywords
subject: Cholesky
  topic: keywords
subject: K-means
  topic: keywords
subject: Gaussian mixture model
  topic: keywords
creator: Jian Li
creator: Yue Li
creator: Xinpei Zhao
creator: Xiaoxu Chen
rights: Copyright (c) 2025 Jian Li, Yue Li, Xinpei Zhao, Xiaoxu Chen https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-06-24
publication date: 2025-06-24
doi: 10.4108/eetsis.9552

On the Performance of RIS-Enhanced RSMA Networks with On-Off Control
type: item
description: As a pivotal enabling technology for the 6G wireless communication system, Reconfigurable intelligent surface (RIS) is capable of dynamically adjusting its electromagnetic elements, thereby significantly enhancing wireless network coverage, spectral efficiency, and energy sustainability. In this context, this paper investigates the integration of RIS into the rate-splitting multiple access (RSMA) framework, where the base station employs the RIS to transmit superimposed signals to multiple users simultaneously. To reduce computational complexity, a On-Off control scheme is adopted, considering both imperfect successive interference cancellation (ipSIC) and perfect successive interference cancellation (pSIC) scenarios. closed-form expressions for the exact outage probability of the $k$-th user under Rayleigh fading channels are derived. Furthermore, to more comprehensively evaluate system performance, asymptotic outage probability expressions for ipSIC/pSIC in the high signal-to-noise ratio (SNR) regime are also derived. The results demonstrate that, under identical system configurations, the RSMA-based communication scheme outperforms the non-orthogonal multiple access (NOMA) scheme, exhibiting superior spectral resource utilization and interference management capabilities. In the pSIC scenario, the user diversity order is contingent upon the number of RIS reflecting elements and the channel ordering approach. Increasing the number of RIS reflecting elements can effectively compensate for the performance degradation caused by residual interference in the ipSIC scenario, thereby further enhancing system performance
subject: Research articles
  topic: section
subject: Reconfigurable Intelligent Surface
  topic: keywords
subject: Rate-Splitting Multiple Access
  topic: keywords
subject: mperfect SIC
  topic: keywords
subject: On-Off Control
  topic: keywords
creator: Haiyan Huang
creator: Dongjie Jiang
creator: Nina Zhang
creator: Linlin Liang
creator: Hongyan Zhang
rights: Copyright (c) 2025 Haiyan Huang, Dongjie Jiang, Nina Zhang, Linlin Liang, Hongyan Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-06-11
publication date: 2025-06-11
doi: 10.4108/eetsis.9518

Direction-of-Arrival Estimation for Deterministic Networks
type: item
description: Direction-of-arrival (DOA) estimation is a key physical-layer technique for guaranteeing the stringent latency and reliability requirements of emerging deterministic networks. This paper investigates a generalized dual-subarray linear array model in which the inter-subarray displacement vectors can be arbitrarily specified, and devises two subspace-based DOA estimation schemes tailored to this setting. The first scheme is a spectral-search (SS) estimator that exploits the structured relationship between the signal subspaces of the two subarrays via a parametrized phase-rotation matrix, where the DOAs are obtained by searching over angles that induce a rank deficiency in a residual matrix constructed from the estimated signal subspace, yielding a high-resolution spectral function analogous to, but more flexible than the conventional schemes. The second scheme is a search-free (SF) estimator that, under mild geometric assumptions on the linear array, reformulates the same criterion as a polynomial in a complex exponential variable and recovers the DOAs from the roots closest to the unit circle, thereby eliminating grid search and significantly reducing computational complexity. Simulation results are provided for deterministic-network scenarios to show that, for a 2 × 5-element dual-subarray and moderate SNRs, the proposed SF-based estimator achieves an RMSE below 1 while conventional MUSIC exhibits an RMSE around 10, demonstrating roughly an order-of-magnitude accuracy gain and confirming the superior performance and scalability of the proposed SS and SF schemes.
subject: AIGC - Empowered Covert Communications for Scalable Information Systems
  topic: section
subject: Deterministic networks
  topic: keywords
subject: DOA estimation
  topic: keywords
subject: performance evaluation
  topic: keywords
creator: Feifei Hu
creator: Yu Huang
creator: Xubin Lin
creator: Liu Wu
rights: Copyright (c) 2025 Feifei Hu, Yu Huang, Xubin Lin, Liu Wu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-08
publication date: 2025-12-08
doi: 10.4108/eetsis.9487

Enhancing Stakeholder Analysis with AI: A Comparative Study of Productivity and Quality in the Educational Context
type: item
description: This paper examines the application of generative artificial intelligence in stakeholder management while studying the business aspects of software development and project management in two different universities. It explores a novel intersection of AI with software development and project management practices, offering valuable insights for both academia and industry. By investigating how students use AI alongside traditional methods under supervision, this study evaluates the effectiveness, quality of results, and creativity of students’ project assignments in identifying stakeholders and defining communication strategies. The findings suggest that AI can enhance work completion speed and contribute to greater project success due to a more complete identification of stakeholders and formulation of innovative stakeholder engagement strategies. There is a consensus, within this context, that while AI can be invaluable for project stakeholder management, human judgment remains essential.
subject: Research articles
  topic: section
subject: Stakeholder Analysis
  topic: keywords
subject: Information System Development
  topic: keywords
subject: System Analysis and Design Project Management
  topic: keywords
subject: Generative Artificial Intelligence
  topic: keywords
subject: AI
  topic: keywords
creator: Vijay Kanabar
creator: Kalinka Kaloyanova
rights: Copyright (c) 2025 Vijay Kanabar, Kalinka Kaloyanova https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-05-23
publication date: 2025-05-23
doi: 10.4108/eetsis.9376