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A New Cybersecurity Approach Enhanced by xAI-Derived Rules to Improve Network Intrusion Detection and SIEM

computers, Materials & Continua 2025年第5期83卷 1607-1621页

作者： Federica Uccello Marek Pawlicki Salvatore D'Antonio RafałKozik MichałChoras Centro Direzionale Department of EngineeringUniversity of Naples‘Parthenope’Isola C4Napoli80133Italy Department of Computer and Information Science Software and SystemsLinköping UniversityLinköping58183Sweden ITTI Sp.z o.o. Pozna´n61-612Poland Faculty of Telecommunications Computer Science and Electrical EngineeringBydgoszcz University of Science and TechnologyBydgoszcz85-796Poland

The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security Information and Event Management *** machine learning–based intrusion detection systems can capture complex network behaviours,their“black-box”nature often limits trust and actionable insight for security *** study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules,thereby enhancing the detection of Distributed Denial of Service(DDoS)*** proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained *** methodology was validated on two benchmark datasets,CICIDS2017 and *** rules were evaluated against conventional Security Information and Event Management systems rules with metrics such as precision,recall,accuracy,balanced accuracy,and Matthews Correlation *** results demonstrate that xAI-derived rules consistently outperform traditional static ***,the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.

关键词： Cybersecurity explainable artificial intelligence intrusion detection system rule-based SIEM distributed denial of service

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Revisiting the thermal ageing on the metallised polypropylene film capacitor:from device to dielectric film

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High Voltage 2023年第2期8卷 305-314页

作者： Yushuang He Feipeng Wang Guoqiang Du Lei Pan Kaizheng Wang Reimund Gerhard Ronald Plath Pawel Rozga Pavel Trnka State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing UniversityChongqingChina Faculty of Electrical Engineering Kunming University of Science and TechnologyKunmingChina Soft Transducer Materials and Systems Universität PotsdamPotsdamGermany Faculty IV Electrical Engineering and Computer Science Technische Universität BerlinBerlinGermany Institute of Electrical Power Engineering Lodz University of TechnologyLodzPoland Faculty of Electrical Engineering University of West BohemiaPilsenCzech Republic

Metallised polypropylene film capacitors(MPPFCs)are widely used in power electronics and are generally degraded by elevated *** work aims to determine the relationships between the structural changes of MPPFC and the microstructural variations of the PP film during the thermal ageing of MPPFC at 100℃ for 38 *** capacitance of MPPFC has a slight decrease during thermal ***,the breakdown voltage of the MPPFC decreases by 39.4%by the *** partial discharge(PD)number of MPPFC increases linearly with ageing *** tear-down analysis of the MPPFC reveals that the molecular structure of the PP film has not been altered but has led to molecular chain scission and the generation of some polar fragments/***,the relative permittivity of the PP films rises as the ageing time ***,thermal ageing causes the conversion of aluminum to alumina in the metallised electrode,which is hydrophilic for polar groups and leads to an adhesion effect between the metallised electrodes and the PP *** angle measurements prove that the surface hydrophilicity of the PP sample increased after thermal ***,the PD/breakdown voltage in the MPPFC increases/decreases due to the uneven adhesion of the metallised PP film.

关键词： film thermal ageing

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Automatic Detection of Some Tajweed Rules 20

Automatic Detection of Some Tajweed Rules

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20th International Learning and Technology Conference, L and T 2023

作者： Omran, Dahlia Fawzi, Sahar Kandil, Ahmed Cairo University Systems and Biomedical Engineering Faculty of Engineering Giza Egypt Nile University Information Technology & Computer Science Giza Egypt

ISBN: (纸本)9798350300307

correct understanding of the Holy Quran is an essential duty for all Muslims. Tajweed rules guide the reciter to perform Holy Quran reading exactly as it was uttered by Prophet Muhammad peace be upon him. This work focused on the recognition of one Quranic recitation rule. Qalqalah rule is applied to five letters of the Arabic Alphabet (Baa/Daal/Jeem/Qaaf/Taa) having sukun vowelization. The proposed system used the Mel Frequency Cepstral Coefficients (MFCC) as the feature extraction technique, and the Convolutional Neural Networks (CNN) model was used for recognition. The available dataset consists of 3322 audio samples from different surahs of the Quran for four professional readers (Sheihk) AlHussary, AlMinshawy, Abdel Baset, and Ayman Swayed. The best results were gained using Ayman Swayed audio samples with a validation accuracy of 90.8%. © 2023 IEEE.

关键词： Convolutional neural networks

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Probabilistic Co-Optimization of Hybrid Battery/Super-Capacitor systems Integrated into Microgrids

Probabilistic Co-Optimization of Hybrid Battery/Super-Capaci...

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2023 IEEE PES Generation, Transmission and Distribution International Conference and Exposition, GTD 2023

作者： Mohseni, Soheil Brent, Alan C School of Engineering and Computer Science Sustainable Energy Systems Victoria University of Wellington Wellington Faculty of Engineering Wellington6140 New Zealand

ISBN: (纸本)9781728170251

While long-Term strategic investment modelling frameworks tailored to energy storage system-supported grid-connected microgrids are widely studied in the literature, the co-optimization of the integration of hybrid battery/super-capacitor systems is not well explored, particularly in terms of the impact analysis of the parametric uncertainties involved. In response, this paper introduces a novel stochastic energy storage sizing modelling framework for hybrid battery/super-capacitor systems. The proposed framework concurrently optimizes the operational scheduling and investment planning of the system, whilst employing the 'low-pass energy filter' concept to determine the optimal share of each energy storage medium in addressing the supply-demand mismatches within the system. Collectively, the quantification of various sources of uncertainty and the intelligent, optimization-based scheduling of storage devices allow for better nearing the global optimality. To show the effectiveness of the model, a case study is performed for a hypothetical microgrid system to be installed in a residential subdivision in Aotearoa-New Zealand. Importantly, the simulation results indicate a significant \sim\mathbf{36}\% underestimation of the total discounted cost of the system by business-As-usual deterministic frameworks compared to the worst-case probabilistic scenario obtained using the proposed probabilistic optimization method. © 2023 IEEE.

关键词： Optimization

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Spatial Attention Integrated EfficientNet Architecture for Breast Cancer Classification with Explainable AI

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computers, Materials & Continua 2024年第9期80卷 5029-5045页

作者： Sannasi Chakravarthy Bharanidharan Nagarajan Surbhi Bhatia Khan Vinoth Kumar Venkatesan Mahesh Thyluru Ramakrishna Ahlam AlMusharraf Khursheed Aurungzeb Department of Electronics and Communication Engineering Bannari Amman Institute of TechnologySathyamangalam638402India School of Computer Science Engineering and Information Systems(SCORE) Vellore Institute of TechnologyVellore632014India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India Department of Management College of Business AdministrationPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpura140401India

Breast cancer is a type of cancer responsible for higher mortality rates among *** cruelty of breast cancer always requires a promising approach for its earlier *** light of this,the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast *** addition,the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input ***,the work proposed an EfficientNet-B0 having a Spatial Attention Layer with XGBoost(ESA-XGBNet)for binary classification of *** this,the work is trained,tested,and validated using original and augmented mammogram images of three public datasets namely CBIS-DDSM,INbreast,and MIAS *** accuracy of 97.585%(CBISDDSM),98.255%(INbreast),and 98.91%(MIAS)is obtained using the proposed ESA-XGBNet architecture as compared with the existing ***,the decision-making of the proposed ESA-XGBNet architecture is visualized and validated using the Attention Guided GradCAM-based Explainable AI technique.

关键词： EfficientNet mammograms breast cancer Explainable AI deep-learning transfer learning

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Stochastic Planning of Solar PV/Wind Turbine/Micro- Hydro/Biomass Microgrids Integrating Fuel Cell Electric Vehicles 11

Stochastic Planning of Solar PV/Wind Turbine/Micro- Hydro/Bi...

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11th International Conference on Smart Grid and Clean Energy Technologies, ICSGCE 2023

作者： Mohseni, Soheil Brent, Alan C School of Engineering and Computer Science Victoria University of Wellington Sustainable Energy Systems Wellington Faculty of Engineering Wellington6140 New Zealand

ISBN: (纸本)9798350305364

This paper presents a novel approach for stochastic planning of multi-dimensional microgrids (MGs) integrating solar photovoltaic panels, wind turbines, a micro-hydro power plant, a biomass power plant, battery storage, a super-capacitor bank, hydrogen storage, and fuel cell electric vehicles (FCEVs). More specifically, the paper introduces a stochastic planning framework that incorporates uncertainties in renewable energy generation, energy demand, and wholesale electricity price dynamics. An advanced metaheuristic optimization algorithm is applied to optimize the MG planning, considering the stochastic nature of renewable energy sources and dynamic component interactions. The proposed approach aims to improve the reliability and robustness of MGs under uncertain conditions. The performance of the proposed approach is evaluated through simulation studies and compared with standard deterministic methods to demonstrate its effectiveness in addressing uncertainties whilst optimizing multi-dimensional MGs. Importantly, the results indicate percentage changes of -22%, +4%, and +23% in the whole-life system cost in the best-case, most likely case, and worst-case stochastic scenarios compared to the deterministic methods. © 2023 IEEE.

关键词： Hydroelectric power plants

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Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

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computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

Software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： Software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

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An Architecture Supporting Intelligent Mobile Healthcare Using Human-computer Interaction HCI Principles

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computer systems science & engineering 2022年第2期40卷 557-569页

作者： Mesfer Alrizq Shauban Ali Solangi Abdullah Alghamdi Muhammad Ali Nizamani Muhammad Ali Memon Mohammed Hamdi College of Computer Science and Information Systems Najran UniversityNajranSaudi Arabia Faculty of Engineering&Technology University of SindhJamshoro78060Pakistan

Recent advancements in the Internet of Things IoT and cloud computing have paved the way for mobile Healthcare(mHealthcare)services.A patient within the hospital is monitored by several ***,upon leaving the hospital,the patient can be remotely monitored whether directly using body wearable sensors or using a smartphone equipped with sensors to monitor different user-health *** raises potential challenges for intelligent monitoring of patient's *** this paper,an improved architecture for smart mHealthcare is proposed that is supported by HCI design *** HCI also provides the support for the User-Centric Design(UCD)for smart mHealthcare ***,the HCI along with IoT's(Internet of Things)5-layered architecture has the potential of improving User Experience(UX)in mHealthcare design and help saving *** intelligent mHealthcare system is supported by the IoT sensing and communication layers and health care providers are supported by the application layer for the medical,behavioral,and health-related *** care providers and users are further supported by an intelligent layer performing critical situation assessment and performing a multi-modal communication using an intelligent *** HCI design focuses on the ease-of-use,including user experience and safety,alarms,and error-resistant displays of the end-user,and improves user's experience and user satisfaction.

关键词： Human computer interaction mhealthcare user-centric design sensor network nternet-of-things

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical Data 3

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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3rd International Conference on Communication, Control, and Intelligent systems, CCIS 2024

作者： Prasanna, V. Suresh Kumar, N. Divyaprabha, P. Sundaram, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Ganesh College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798331528201

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN-GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors' information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python. © 2024 IEEE.

关键词： Recurrent neural networks

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SPD Miscoordination in PV systems Due to Lightning Strikes 16

SPD Miscoordination in PV Systems Due to Lightning Strikes

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16th International Conference on Applied and Theoretical Electricity, ICATE 2024

作者： Baluja, Osmel Perez Leon Avila, Bernardo Yaser Garcia Vazquez, Carlos A. Cotfas, Daniel Tudor Cotfas, Petru Adrian Breffe, Orlys E. Torres Transilvania University of Braşov Faculty of Electrical Engineering and Computer Science Department of Electronics and Computers Brasov Romania Technological University of Havana Department of Electric Power Systems Faculty of Electrical Engineering Havana Cuba

ISBN: (纸本)9798350388107

This study deals with the coordination of surge protection devices (SPD) in photovoltaic systems (PV). A PV farm model was developed in MATLAB Simulink with all the main components, solar panels, converter, grounding system, power transformer, cables in the DC and AC branches with their capacitances, and surge protection device throughout the installation. It was found that when lightning strikes the grounding system or in the vicinity of the PV installation, the coordination among SPD types 1, 2, and 3 is lost. Miscoordination can cause damage to several elements of the PV system, mainly the inverters. Finally, solutions were proposed to avoid SPD miscoordination during lightning strikes. © 2024 IEEE.

关键词： DC transformers

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