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A Fault Diagnosis Method for Smart Meters via Two-layer Stacking Ensemble Optimization and Data Augmentation

Journal of Modern Power Systems and Clean Energy 2024年第4期12卷 1272-1284页

作者： Leijiao Ge Tianshuo Du Zhengyang Xu Luyang Hou Jun Yan Yuanliang Li School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China Concordia Institute for Information Systems Engineering Concordia UniversityMontrealQC H3G 1M8Canada

The accurate identification of smart meter(SM)fault types is crucial for enhancing the efficiency of operationand maintenance(O&M)and the reliability of power ***,the intelligent classification of SM fault typesfaces significant challenges owing to the complexity of featuresand the imbalance between fault *** address these issues,this study presents a fault diagnosis method for SM incorporatingthree distinct *** first module employs acombination of standardization,data imputation,and featureextraction to enhance the data quality,thereby facilitating improvedtraining and learning by the *** enhance theclassification performance,the data imputation method considersfeature correlation measurement and sequential imputation,and the feature extractor utilizes the discriminative enhancedsparse *** tackle the interclass imbalance of datawith discrete and continuous features,the second module introducesan assisted classifier generative adversarial network,which includes a discrete feature generation ***,anovel Stacking ensemble classifier for SM fault diagnosis is *** contrast to previous studies,we construct a two-layerheuristic optimization framework to address the synchronousdynamic optimization problem of the combinations and hyperparametersof the Stacking ensemble classifier,enabling betterhandling of complex classification tasks using SM *** proposedfault diagnosis method for SM via two-layer stacking ensembleoptimization and data augmentation is trained and validatedusing SM fault data collected from 2010 to 2018 in Zhejiang Province,*** results demonstrate the effectivenessof the proposed method in improving the accuracyof SM fault diagnosis,particularly for minority classes.

关键词： Data augmentation fault diagnosis feature extraction smart meter Stacking ensemble optimization

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Unified Approach Integrating Machine Learning Algorithms for Detection of e-Mail Phishing Attacks 1

Unified Approach Integrating Machine Learning Algorithms for...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Mathur, Abeer Dubey, Abhinav Sudhakar Mahara, Pranav Singh Gandhi, Usha Devi School of Computer Science & Engineering Vellore Institute of Technology Department of Software Systems Vellore India School of Computer Science Engineering & Information Systems Vellore Institute of Technology Department of Information Technology Vellore India

ISBN: (纸本)9798350353648

With the ever-rising risk of phishing attacks, which capitalize on vulnerable human behavior in the contemporary digital space, requires new cybersecurity methods. This literary work contributes to the solution by novel incorporation of three techniques: Support Vector Machine (SVM), Natural Language Processing (NLP) and Random Forest (RF). Utilizing NLP for deep analysis of text in phishing e-mails, SVM for strong categorization and Random Forest's decision making by group helps in improving detection. This combination of language and pattern analysis along with group learning builds a fortified system. This approach differs from prior work on a variety of factors such as reliability, security, integrity, scalability, interpretability, transparency, and robustness. Thorough evaluation on a reliable dataset aimed to demonstrate the method's effectiveness, which resulted in 98.7 % of accuracy in detection. This majorly aims at showcasing the potential of ensemble techniques to combat against cyber-attacks like phishing for laying a strong foundation for a secure digital environment. © 2024 IEEE.

关键词： Phishing

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Enhancing sustainable 5G powered agriculture 4.0 : Summary of low power connectivity, internet of UAV things, AI solutions and research trends

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Multimedia Tools and Applications 2025年第17期84卷 17389-17433页

作者： Majumdar, Parijata Mitra, Sanjoy Bhattacharya, Diptendu Bhushan, Bharat Department of Computer Science and Engineering Techno College of Engineering Agartala Maheshkhola Madhuban799004 India Department of Computer Science and Engineering Tripura Institute of Technology Agartala Narsingarh Agartala799009 India Department of Computer Science and Engineering National Institute of Technology Agartala Jirania Agartala799046 India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida201310 India

Emerging technologies of Agriculture 4.0 such as the Internet of Things (IoT), Cloud Computing, Artificial Intelligence (AI), and 5G network services are being rapidly deployed to address smart farming implementation-related concerns and to fulfill the increasing food demand of the expanding demographics. Adoption of 5G offers numerous benefits including Ultra-Reliable Low Latency Communication (URLLC) required to reduce communication delays in time-sensitive applications, non-public networks to allocate part of the frequency spectrum on demand, and network slicing to fulfill the diverse requirements requested by a specific application etc. for realizing the full potential of Agriculture 4.0 operations. In this paper, a comprehensive review of 5G radio enhancements, enabling technologies, and multi-tier heterogeneous 5G network architecture are presented. The main focus is to concisely cover AI integration with Unmanned Aerial Vehicles (UAVs) leveraging 5G technologies and Low Power Wide Area Networks (LPWANs) for supporting intelligent, long-range, cost-effective and energy-saving data transmission across the network and seamless Agriculture 4.0 operations. Cloud computing architectures that permit centralized management of Agriculture 4.0 operations through sharing of computations, configurable networks, and servers are also studied in detail to reveal the need for edge computation as increasing number of IoT devices allows only to perform complex computations on-site. Extensive survey reveals the recent advances of 5G-enabled Agriculture 4.0 operations, future research opportunities of 5G networks including Energy Harvesting techniques for the deployment of massive to critical IoT applications, and the socio-demographic impacts of 5G networks in Agriculture 4.0 to support ubiquitous connectivity of exponentially increasing IoT devices. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Energy efficiency

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Face Anti-Spoofing with Unknown Attacks:A Comprehensive Feature Extraction and Representation Perspective

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Journal of computer science & Technology 2024年第4期39卷 827-840页

作者： Li-Min Li Bin-Wu Wang Xu Wang Peng-Kun Wang Yu-Dong Zhang Yang Wang School of Software Engineering University of Science and Technology of ChinaHefei 230000China School of Data Science University of Science and Technology of ChinaHefei 23000China Suzhou Institute for Advanced Research University of Science and Technology of ChinaSuzhou 215000China

Face anti-spoofing aims at detecting whether the input is a real photo of a user(living)or a fake(spoofing)*** new types of attacks keep emerging,the detection of unknown attacks,known as Zero-Shot Face Anti-Spoofing(ZSFA),has become increasingly important in both academia and *** ZSFA methods mainly focus on extracting discriminative features between spoofing and living ***,the nature of the spoofing faces is to trick anti-spoofing systems by mimicking the livings,therefore the deceptive features between the known attacks and the livings,which have been ignored by existing ZSFA methods,are essential to comprehensively represent the ***,existing ZSFA models are incapable of learning the complete representations of living faces and thus fall short of effectively detecting newly emerged *** tackle this problem,we propose an innovative method that effectively captures both the deceptive and discriminative features distinguishing between genuine and spoofing *** method consists of two main components:a two-against-all training strategy and a semantic *** two-against-all training strategy is employed to separate deceptive and discriminative *** address the subsequent invalidation issue of categorical functions and the dominance disequilibrium issue among different dimensions of features after importing deceptive features,we introduce a modified semantic *** autoencoder is designed to map all extracted features to a semantic space,thereby achieving a balance in the dominance of each feature *** combine our method with the feature extraction model ResNet50,and experimental results show that the trained ResNet50 model simultaneously achieves a feasible detection of unknown attacks and comparably accurate detection of known *** results confirm the superiority and effectiveness of our proposed method in identifying the living with the interference of both known

关键词： face anti-spoofing spoof detection zero-shot learning convolutional neural network deep learning

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Network Traffic Anomaly Detection Based on DGBi-SA Model

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engineering Letters 2025年第3期33卷 612-619页

作者： Wang, Xin Dai, Hong Huang, Zheng Han, Yue University of Science and Technology Liaoning Liaoning CO Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning CO Anshan114051 China

Network traffic anomaly detection plays a crucial role in today's network security and performance management. In response to the challenges in current network traffic data processing, such as insufficient structuring and dynamics, the paper proposes a network traffic anomaly detection approach based on DGBi-SA model, which integrates Dynamic Graph Convolutional Networks (DyGCN), Bi-directional Long Short-Term Memory (Bi-LSTM), and the Self-Attention mechanism. To solve the problem of insufficient structuring, the model employs DyGCN to capture network traffic's dynamic graph structural characteristics proficiently. Simultaneously, combining Bi-LSTM and the Self-Attention mechanism enhances the model's ability to capture temporal and spatial dependencies. The main objective of the research in the paper is to improve the accuracy and detection efficiency of network traffic prediction, thereby achieving effective detection and timely response to abnormal behaviors in complex network environments. Through a sequence of experiments conducted on the CICIDS-2017 dataset, the paper verifies that the DGBi-SA model can effectively solve the above problems. The experimental results indicate that the model outperforms existing methods in key performance indicators such as AUC, precision, recall, and F1 score, thereby proving that the DGBi-SA model has important application value and scientific significance in network traffic anomaly detection. © 2025, International Association of Engineers. All rights reserved.

关键词： Long short-term memory

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Stroke Disease Prediction Based on Multi-Model Ensemble Learning 5

Stroke Disease Prediction Based on Multi-Model Ensemble Lear...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Cui, Xiaojian School of Computer Science and Engineering School of Software Guangxi Normal University Guilin China

ISBN: (纸本)9798350385557

Stroke is a kind of acute cerebrovascular disease, which is caused by the sudden rupture of blood vessels in the brain or the blockage of blood vessels that can not flow into the brain and cause brain tissue damage. Stroke has the characteristics of acute onset and rapid progress. Once stroke, it can lead to extremely serious consequences, and many people suffer from it. But because of the lack of effective treatment for stroke, prevention is the best response. So we use multi-model ensemble learning method to train a model. It can analyze biological signals and lifestyle habits to predict the probability of a person having a stroke. People can adjust their living habits in time according to the probability of stroke. In order to reduce the probability of stroke, protect their own life and health. According to our experiments, the accuracy of the trained model is high, which can help people to prevent stroke disease. © 2024 IEEE.

关键词： Learning systems

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Transforming the electrical grid: the role of AI in advancing smart, sustainable, and secure energy systems

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Energy Informatics 2025年第1期8卷 1-43页

作者： Rajaperumal, T.A. Columbus, C. Christopher School of Electrical Engineering Vellore Institute of Technology Tamil Nadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

The evolution of the electrical grid from its early centralized structure to today’s advanced "smart grid" reflects significant technological progress. Early grids, designed for simple power delivery from large plants to consumers, faced challenges in efficiency, reliability, and scalability. Over time, the grid has transformed into a decentralized network driven by innovative technologies, particularly artificial intelligence (AI). AI has become instrumental in enhancing efficiency, security, and resilience by enabling real-time data analysis, predictive maintenance, demand-response optimization, and automated fault detection, thereby improving overall operational efficiency. This paper examines the evolution of the electrical grid, tracing its transition from early limitations to the methodologies adopted in present smart grids for addressing those challenges. Current smart grids leverage AI to optimize energy management, predict faults, and seamlessly integrate electric vehicles (EVs), reducing transmission losses and improving performance. However, these advancements are not without limitations. Present grids remain vulnerable to cyberattacks, necessitating the adoption of more robust methodologies and advanced technologies for future grids. Looking forward, emerging technologies such as Digital Twin (DT) models, the Internet of Energy (IoE), and decentralized grid management are set to redefine grid architectures. These advanced technologies enable real-time simulations, adaptive control, and enhanced human–machine collaboration, supporting dynamic energy distribution and proactive risk management. Integrating AI with advanced energy storage, renewable resources, and adaptive access control mechanisms will ensure future grids are resilient, sustainable, and responsive to growing energy demands. This study emphasizes AI’s transformative role in addressing the challenges of the early grid, enhancing the capabilities of the present smart grid, and shaping a secure

关键词： Risk management

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Fed-BRMC: Byzantine-Robust Federated Learning via Dual-Model Contrastive Detection

Fed-BRMC: Byzantine-Robust Federated Learning via Dual-Model...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Gan, Xiaoyun Lu, Peng Gan, Shanyu Xian, Youqian Peng, Kaichen Liu, Peng Li, Dongcheng School of Computer Science and Engineering School of Software Guangxi Normal University Guilin China

ISBN: (纸本)9781665410205

Federated Learning (FL) is a distributed privacy-protecting machine learning paradigm that enables collaborative training among multiple parties without the need to share raw data. This mode of training renders FL particularly susceptible to attacks. A pivotal challenge within FL is the defense against Byzantine attacks, which can alter the model aggregation process and ultimately lead to the failure of Federated Learning convergence. Existing Byzantine defense mechanisms often result in diminished model accuracy in scenarios characterized by non-independent and identically distributed (non-IID) data. To alleviate this issue, we propose a novel defense approach named Fed-BRMC. This method combines a self-developed federated Dual-Model Contrastive Detection technique with Similarity Detection methods, leveraging historical update information from users to deeply mine the characteristics of model parameters, effectively distinguishing between Byzantine and honest users. Extensive experimentation has shown that Fed-BRMC significantly improves user identification accuracy in non-IID data scenarios compared to existing methods and notably enhances the global model accuracy of Byzantine-robust Federated Learning. © 2024 IEEE.

关键词： Adversarial machine learning

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Improving Machine Translation Formality with Large Language Models

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computers, Materials & Continua 2025年第2期82卷 2061-2075页

作者： Murun Yang Fuxue Li School of Computer Science and Engineering Northeastern UniversityShenyang110819China College of Electrical Engineering Yingkou Institute of TechnologyYingkou115014China

Preserving formal style in neural machine translation (NMT) is essential, yet often overlooked as an optimization objective of the training processes. This oversight can lead to translations that, though accurate, lack formality. In this paper, we propose how to improve NMT formality with large language models (LLMs), which combines the style transfer and evaluation capabilities of an LLM and the high-quality translation generation ability of NMT models to improve NMT formality. The proposed method (namely INMTF) encompasses two approaches. The first involves a revision approach using an LLM to revise the NMT-generated translation, ensuring a formal translation style. The second approach employs an LLM as a reward model for scoring translation formality, and then uses reinforcement learning algorithms to fine-tune the NMT model to maximize the reward score, thereby enhancing the formality of the generated translations. Considering the substantial parameter size of LLMs, we also explore methods to reduce the computational cost of INMTF. Experimental results demonstrate that INMTF significantly outperforms baselines in terms of translation formality and translation quality, with an improvement of +9.19 style accuracy points in the German-to-English task and +2.16 COMET score in the Russian-to-English task. Furthermore, our work demonstrates the potential of integrating LLMs within NMT frameworks to bridge the gap between NMT outputs and the formality required in various real-world translation scenarios.

关键词： Neural machine translation formality large language model text style transfer style evaluation reinforcement learning

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CBBFT - A Byzantine Fault Tolerance Mechanism for Education Credit Banks 7

CBBFT - A Byzantine Fault Tolerance Mechanism for Education ...

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7th International Conference on computer Information science and Application Technology, CISAT 2024

作者： Li, Xin Zhou, Lianghaojie Chen, Ningtian Jiang, Jianyong Liu, Peng GuangXi Normal University School of Computer Science and Engineering School of Software Guilin China

ISBN: (纸本)9798350375107

In the education sector, an increasing amount of research is beginning to explore the application of blockchain technology to credit banks. This paper proposes a consortium blockchain consensus mechanism tailored for credit bank applications, aiming to address issues such as single point of failure, large message volumes, and slowest node limitations inherent in existing BFT-like consensus protocols. This includes algorithms for secret random selection of block proposer nodes based on reputation scores and block selection, as well as introducing a reputation model. Subsequently, a detailed analysis of the security of this consensus protocol is conducted. Finally, the paper evaluates the performance of the proposed consensus protocol through experimentation. © 2024 IEEE.

关键词： Blockchain

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