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Hidden Markov Model-Based Cyberattack prediction in Power Systems

IEEE TRANSACTIONS ON SMART GRID 2025年第2期16卷 1694-1705页

作者： Zhang, Bo Liu, Xuan Zheng, Haofeng Song, Yufei Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China

The deep coupling between the information domain and the physical domain in power systems has increased the risk of cyberattacks on power systems. Determining an attacker's intention immediately following an attack is crucial for security personnel in choosing corresponding defending strategies. In order to accurately predict the attacker's intent, we propose a dynamic prediction method that takes into account the evolving nature of cyberattack intent in power systems. Initially, we use an attribute selection and clustering algorithm to reduce the amount of alarm data. Then, by leveraging the game characteristics of the attack-defense process, we introduce a dynamic hidden Markov model prediction model that is suitable for attack scenarios in a real power system. Finally, we establish a fully physical cyberattack simulation platform and test the proposed prediction model using an alarm dataset generated from a real 126-node system. The experimental results validate the effectiveness of our method in cyberattack prediction for power systems and demonstrate its superiority compared with other prediction methods.

关键词： Hidden Markov models Predictive models Cyberattack Power system dynamics Data models prediction algorithms Power systems Analytical models Accuracy Power system stability Cyberattack prediction dynamic hidden Markov model alarm data power systems

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A Cloud-Based Optimized Ensemble Model for Risk prediction of Diabetic Progression-An Azure Machine Learning Perspective

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IEEE ACCESS 2025年 13卷 11560-11575页

作者： Daliya, V. K. Ramesh, T. K. Amrita Vishwa Vidyapeetham Dept Elect & Commun Engn Amrita Sch Engn Bengaluru 560035 India

The application of Machine Learning for predictive analysis in healthcare, particularly for diseases like diabetes, has proven highly beneficial. This study introduces an optimized Light Gradient-Boosting Machine (Light GBM) and K-Nearest Neighbour (KNN) based ensemble algorithm for predicting diabetic progression of Type 2 Diabetes, classifying it as high or low risk, using patient health parameters and serum measurements. Our model uses LightGBM, a rapid and efficient gradient boosting framework, coupled with KNN, which uses proximity to classify data points. The proposed model uses various optimization techniques, such as 10 fold cross validation, grid search method etc. to get the best results out of the ensemble model. As the model combines optimized version of LightGBM and KNN through a voting classifier which uses soft voting technique to find the final class, it utilizes the predictive capabilities of both the methods in an effective manner. The experiment is performed and implemented in Microsoft's Azure cloud, using Azure Machine Learning service, that leverages the advantages of cloud computing with respect to scalability, security and its potential integration possibilities into IoT-based smart healthcare *** aspect highlights its versatility and impact with respect to remote monitoring of patients as well. The ensemble achieves an 83.2% Area Under the Curve (AUC) of Receiver Operating Characteristics (ROC) score, indicating good classification efficiency. It produced 75% accuracy as well. The proposed model is compared with other classification and ensemble models, showcasing its superiority against other *** ensemble is also tested with some meta heuristic optimization methods, which produced comparable scores. The method's effectiveness is validated against another risk prediction dataset, proving its reliability. The model's accurate predictions can aid individuals in understanding disease progression risks and guide medical professio

关键词： Diabetes Diseases Predictive models Glucose prediction algorithms Optimization Machine learning Classification algorithms Nearest neighbor methods Accuracy Diabetic prediction ensemble learning KNN LightGBM voting classifier azure cloud azure machine learning

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Dual-Channel Degradation Monitoring Based on Graph Neural Network for Aero-Engine Remaining Useful Life prediction

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Cao, Li'ang Li, Yuanfu Chen, Jinwei Wu, Wenjie Zhang, Huisheng Shanghai Jiao Tong Univ Key Lab Power Machinery & Engn Educ Minist Shanghai 200240 Peoples R China AVIC Civil Aircraft Airborne Syst Engn Ctr Co Ltd Shanghai 200241 Peoples R China Shanghai Jiao Tong Univ Coll Smart Energy Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Aeronaut & Astronaut Shanghai 200240 Peoples R China

For aircraft engine predictive maintenance (PdM) programs, accurate remaining useful life (RUL) predictions can significantly reduce unscheduled maintenance downtime and ensure engine safety. To this end, we introduce a novel dual-channel degradation monitoring (DCDM) algorithm designed to minimize RUL prediction errors. Unlike traditional RUL prediction algorithms based on graph neural networks (GNNs), the proposed DCDM model extracts fault features from both node embeddings and changes in graph structures. This dual-channel approach allows for the fusion of fault information, improving the model's ability to extract features across different fault patterns for RUL prediction. During the graph structure learning (GSL) process, domain-specific knowledge and a dynamic graph learning algorithm are integrated to generate graphs, enhancing the interpretability of graph representation. In addition, by introducing node sparse encoding as a model input, the DCDM model's capability to discern critical features is significantly improved. The predictive performance of the DCDM model and the effectiveness of its individual components are validated using the C-MAPSS dataset. The results demonstrate the superior accuracy of the proposed method compared to existing approaches.

关键词： Feature extraction Sensors Heuristic algorithms prediction algorithms Degradation Data mining Aerodynamics Transformers Predictive models Engines Graph neural network (GNN) knowledge remaining useful life (RUL) prediction sparse representation Transformer

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Fault prediction of Double Toothed Roller Crusher Failure in Open Pit Mines Based on Digital Twinning Technique of ASL-CatBoost Algorithm

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IEEE ACCESS 2025年 13卷 70179-70194页

作者： Gao, Pubo Zhao, Sihai China Univ Min & Technol Sch Mech & Elect Engn Beijing 100083 Peoples R China

The bearings of double toothed roller crushers in open pit mines are prone to failure during prolonged operation, resulting in reduced productivity and shortened equipment life. Therefore, a bearing fault prediction model based on asymmetric loss function gradient boosting algorithm and digital twin has been proposed for effective fault prediction of double toothed roller crusher bearings. First, the fault signal is extracted by Wigner-Ville time-frequency distribution and empirical mode decomposition. Then, the asymmetric loss function gradient boosting algorithm is used to model and predict the signal characteristics. Finally, a digital twin model is constructed to visualize bearing fault information by combining sensor data and historical fault data. The experimental results showed that the fault prediction model achieved a precision rate of 98%, a recall rate of 99%, and an F1 value of 98.5% in predicting bearing rolling element failures. Among them, the running time of fault prediction was 1 minute and 49s. The experimental results demonstrate that the proposed fault prediction model can learn complex feature representations from large amounts of data, with good fault prediction performance and efficiency, and has certain practical application value. The research results contribute to improving the accuracy and intelligence level of fault prediction methods, thereby ensuring the stable operation of open-pit mine double toothed roller crushers.

关键词： Fault diagnosis Predictive models prediction algorithms Feature extraction Accuracy Time-frequency analysis Maintenance Digital twins Boosting Mathematical models Digital twin double toothed roller crusher fault prediction ASL algorithm CatBoost algorithm

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Transformer-Based Intelligent prediction Model for Multimodal Multi-Objective Optimization

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IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 2025年第1期20卷 34-49页

作者： Dang, Qianlong Zhang, Guanghui Wang, Ling Yu, Yang Yang, Shuai He, Xiaoyu Northwest A&F Univ Yangling 712100 Peoples R China Hebei Agr Univ Baoding 071001 Peoples R China Tsinghua Univ Beijing 100084 Peoples R China Jiangsu Univ Technol Changzhou 213001 Peoples R China Anhui Agr Univ Hefei 230036 Peoples R China Sun Yat Sen Univ Guangzhou 510006 Peoples R China

Traditional multimodal multi-objective evolutionary algorithms (MMOEAs) usually adopt the reproduction strategy based on meta-heuristic and fail to make full use of changes of multi-generation population distribution, which can help the population to further evolve and improve the exploitation ability of algorithms. To address this issue, this paper proposes a deep learning-based evolutionary algorithm (DLEA), which converts historical population information into time series and uses deep neural networks to predict the population distribution. Specifically, a transformer-based prediction model is constructed to reproduce promising offspring by capturing changes of population distribution in adjacent generations. Moreover, many MMOEAs focus only on global Pareto optimal solution sets (PSs) and ignore local PSs. Although local PSs are inferior to global PSs in terms of objective values, the cost of obtaining global PSs in some practical applications is huge. Local PSs with similar objectives to global PSs are acceptable alternatives for decision makers. Therefore, a difference-based attention mechanism is designed for archive update, which saves solutions on the global PSs and local PSs by calculating the attention value. Experimental studies indicate that DLEA outperforms other six competitive MMOEAs.

关键词： Attention mechanisms Time series analysis Metaheuristics Evolutionary computation Artificial neural networks Predictive models Transformers prediction algorithms Path planning Planning

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Landslide Displacement prediction Model Based on Optimal Decomposition and Deep Attention Mechanism

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IEEE ACCESS 2025年 13卷 51573-51588页

作者： Ren, Shuai Ghazali, Kamarul Hawari Ji, Yuanfa Khan, Samra Urooj Univ Malaysia Pahang Al Sultan Abdullah Fac Elect & Elect Engn Technol Pekan 26600 Pahang Malaysia Guilin Univ Elect Technol Informat & Commun Sch Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Precis Nav Technol & Applicat Guilin 541004 Peoples R China

Landslide displacement forecasting is crucial for disaster prevention and risk management, as it enables timely warnings and effective mitigation strategies. However, the highly nonlinear and complex nature of landslide displacement poses significant challenges for accurate prediction. To address this, this study proposes an advanced forecasting framework integrating the Chebyshev Levy Flight-Sparrow Search Algorithm (CLF-SSA) with Variational Mode Decomposition (VMD) to enhance decomposition accuracy and optimize parameter selection. The trend component is modeled using the Autoregressive Integrated Moving Average (ARIMA) with a grid search strategy, while the periodic component is predicted using a Bidirectional Long Short-Term Memory network with an Attention mechanism (BiLSTM-Attention), which dynamically adjusts the contribution of influencing factors. Grey Relational Analysis (GRA) is further employed to identify key external driving factors, enhancing prediction accuracy. Experimental results demonstrate that the proposed model significantly improves predictive performance, reducing the Root Mean Square Error (RMSE) by 60% compared to the traditional XGBoost model and by 33% compared to the Empirical Mode Decomposition-BiLSTM (EMD-BiLSTM) model. Moreover, the Mean Absolute Scaled Error (MASE) analysis confirms the robustness of the model in capturing both short-term fluctuations and long-term trends. Given its superior predictive accuracy and practical applicability, this approach provides valuable technical support for landslide monitoring and early warning systems.

关键词： Terrain factors Predictive models Accuracy Optimization Market research prediction algorithms Robustness Disasters Attention mechanisms Computational efficiency Landslide displacement prediction intelligent optimization algorithm variational mode decomposition bidirectional long short term memory network attention mechanism

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Early prediction of Cardiac Arrhythmia using Novel Bio-inspired algorithms 8

Early Prediction of Cardiac Arrhythmia using Novel Bio-inspi...

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8th International Conference on Smart Structures and Systems, ICSSS 2022

作者： Digumarthi, Jyothirmai Gayathri, V.M. Pitchai, R. School of Computing Srm Institute of Science and Technology Tamilnadu Kattankulathur India Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India B v Raju Institute of Technology Department of Computer Science and Engineering Narsapur India

ISBN: (纸本)9781665497619

Cardiovascular disease is one of the world's most dangerous problems since it cannot be seen with the naked eye and strikes suddenly when its levels are reached. Cardiovascular disease (CVD) has reached the top of the list of non-infectious and non-transmissible disease deaths worldwide. Arrhythmias are a set of cardiovascular disorders described by irregular heartbeats that can be slow, rapid, or both. Cardiac arrhythmias are responsible for a large number of cardiac arrests and sudden deaths. They can happen on their own or in combination with other heart conditions. Some significant arrhythmias can happen out of nowhere, resulting in sudden death, stroke, heart failure, and coronary artery disease. Many of the medical procedures used to treat cardiac arrhythmias are traditional. Especially when contrasted to costly procedures that may be required if detection and therapy are delayed and the patient has advanced illness stages. To deliver an early, effective, prediction of CVDs, health systems need to be further strengthened. A computer-assisted automated prediction and classification of heart diseases using Bio-Inspired Optimization algorithms is suggested in this research work. Extraction, classification, and diagnostic sections would be part of the proposed method. This work helps in diagnosis of cardiac arrhythmias. This study also helps effectively and accurately to diagnose the heart condition, which could help the doctor to treat and manage the disease effectively. © 2022 IEEE.

关键词： Heart Hypertension Heart beat Medical treatment prediction algorithms Classification algorithms Intelligent structures

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Clustering-Based Short-Term Wind Speed Interval prediction With Multi-Objective Ensemble Learning

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第1期9卷 304-317页

作者： Zhu, Qingling Xu, Yaojian Lin, Qiuzhen Ming, Zhong Tan, Kay Chen Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

As a renewable and green energy source, wind energy has attracted great attention from academia and industry in recent decades. However, it is challenging to integrate wind energy into smart grids due to the instability and randomness of wind speed. To solve this problem, this paper proposes a clustering-based short-term wind speed interval prediction with multi-objective ensemble learning, which can provide an accurate and reliable wind speed interval prediction to support energy dispatch planning. First, a clustering-based uncertainties estimation method segments the initial wind sequence into several groups and determines the estimated width for each group. Second, a variational mode decomposition is employed to acquire the sub-sequence matrix of wind speed, and then a Hurst exponent-based model selection method is used to choose and train an optimal model for each sub-sequence based on its long-term correlation. Finally, an improved multi-objective optimizer is utilized to determine the optimal superposition weights of the prediction results for each model. The proposed approach is evaluated using eight cases from two wind farms, which are published by the National Renewable Energy Laboratory. Experimental results indicate that the proposed approach outperforms several state-of-the-art studies, demonstrating a higher prediction interval coverage probability and a narrower prediction interval width.

关键词： Predictive models Wind speed Logic gates Training Time series analysis Reliability prediction algorithms Clustering hurst exponent model selection multi-objective optimization wind speed prediction

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A Novel Ensemble Classifier Selection Method for Software Defect prediction

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IEEE ACCESS 2025年 13卷 25578-25597页

作者： Dong, Xin Wang, Jie Liang, Yan Chengdu Technol Univ Comp Engn Coll Chengdu 611730 Peoples R China Sci & Technol Elect Informat Control Lab Chengdu 610036 Peoples R China

The presence of software defects significantly impacts the quality of software systems and increases development and maintenance costs. To improve system quality and reduce costs, it is necessary to predict software defects in the early stages of the software development lifecycle. This paper proposes Double Fault Disagreement (DFD), a novel diversity metric and method for selecting competent base classifiers for ensemble learning-based software defect prediction. To consider the diversity features of the base learners, several base learners with strong diversity are chosen to build ensemble learning. This method makes full use of the diversity characteristics of base learners, leverages their classification ability, optimizes the selection method for ensemble learning, and enhances the predictive performance of the ensemble model. The experimental results demonstrate that the DFD ensemble learning-based software defect prediction model outperforms the ten other models, including five common machine learning (ML) classification algorithms (logistic regression (LR), na & iuml;ve Bayes (NB), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM)), two deep learning (DL) algorithms (multi-layer perceptron (MLP) and convolutional neural network (CNN)), and three ensemble learning algorithms (random forest (RF), extreme gradient boosting (XGB), and stacking). The DFD model achieves superior performance on eight public NASA and PROMISE datasets (six of which are imbalanced) across five performance indicators, including area under the curve (AUC), geometric mean (G-Mean), F1 score, Matthews correlation coefficient (MCC), and Balance. Furthermore, the DFD method is not only highly performant but also requires a small number of base learners to converge rapidly, thereby reducing the amount of data and computation time required.

关键词： Ensemble learning Predictive models Measurement Computational modeling Classification algorithms Software Volume measurement Weight measurement Stacking prediction algorithms Diversity measures double fault disagreement ensemble classifier selection imbalanced data classification software defect prediction

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Residential Building Energy Usage prediction Using Bayesian-Based Optimized XGBoost Algorithm

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IEEE ACCESS 2025年 13卷 36036-36049页

作者： Baqer, Nabaa Riyadh Rashidi-Khazaee, Parviz Urmia Univ Dept Comp Engn Orumiyeh *** Iran Urmia Univ Technol Dept Informat Technol & Comp Engn Orumiyeh *** Iran

With the growing energy demand in residential buildings, selecting energy-efficient building designs is crucial for sustainable development. Therefore, engineers and designers try to find a reliable tool to help them analyze and predict the energy consumption of buildings in the early design stages before construction. The state-of-the-art eXtreme Gradient Boosting (XGB) algorithm was successfully used to estimate the Heating Load (HL) and Cooling Load (CL) energy usage based on building design characteristics. To enhance the performance of XGB and improve prediction reliability, we propose a novel Bayesian optimization approach using the Tree-Structured Parzen Estimator (TPE), leading to the development of the TPE-XGB model for HL and CL estimation. Also, a new fair evaluation method was proposed to have a fair evaluation. The evaluation results based on the proposed fair evaluation method showed that the proposed model has outperformed other state-of-the-art models and improved the prediction accuracy of HL and CL by 3.4% (from 0.175 to 0.167) and 10.4% (from 0.307 to 0.275), respectively. As a result, the new proposed model provides the opportunity to be used as a strong and reliable tool in the early stages of building construction and to evaluate different designs/plans regarding energy consumption efficiency to select the best design with high reliability.

关键词： Predictive models Buildings prediction algorithms Energy consumption Tuning Radio frequency Metaheuristics Data models Bayes methods Heuristic algorithms Energy efficiency energy usage prediction tree-structured Parzen estimator hyper-parameter tuning heating load cooling load

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