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28th International Conference on Knowledge Based and Intelligent information and Engineering systems, KES 2024

作者： Stanczyk, Urszula Baron, Grzegorz Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

The paper presents research dedicated to observations of relations between attribute properties and discretisation. In the investigations described, the gradually increasing sets of features were discretised by selected approaches, and several variants of data were constructed. The continuous, partially discrete, and completely translated datasets were explored by the chosen classifiers and their performance studied in the context of a number of discretised attributes, discretisation procedures, and the way of processing of features and datasets. The stylometric problem of authorship attribution was the machine learning task under study. The experimental results enable to observe closer the specificity of style-markers employed as characteristic features, and indicate conditions for efficient recognition of authorship. They can be extended to other application domains with similar characteristics. © 2024 The Authors.

关键词： Data assimilation

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Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

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computers, Materials & Continua 2023年第1期74卷 1905-1921页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and *** more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the ***,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole *** proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering *** is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor *** hybridmethod is primarily designed to combat active black-hole *** the LEACH clustering phase,however,can also detect passive black-hole *** effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested *** diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution ***,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

关键词： Black-hole attack mobile ad-hoc network optimization dipper throated optimization

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Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

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computers, Materials & Continua 2023年第2期74卷 2677-2693页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Manal Abdullah Alohali Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in IoT applications,have led to major security *** are on the rise as a result of the usage of these technologies since present security measures are *** artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent *** technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features *** the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent *** this paper,we proposed a hybrid optimization algorithm for feature selection in *** proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as *** proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better *** the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its *** addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed *** results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.

关键词： Feature selection grey wolf optimization dipper throated optimization intrusion detection internet-of-things(IoT)

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Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Management

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IEEE Transactions on Cognitive Communications and Networking 2023年第5期9卷 1282-1295页

作者： Zhang, Yunchuan Simeone, Osvaldo Jose, Sharu Theresa Maggi, Lorenzo Valcarce, Alvaro King's College London Department of Engineering LondonWC2R 2LS United Kingdom University of Birmingham Department of Computer Science BirminghamB15 2TT United Kingdom Nokia Bell Labs France Department of Radio Systems and Ai Nozay91620 France

Optimal resource allocation in modern communication networks calls for the optimization of objective functions that are only accessible via costly separate evaluations for each candidate solution. The conventional approach carries out the optimization of resource-allocation parameters for each system configuration, characterized, e.g., by topology and traffic statistics, using global search methods such as Bayesian optimization (BO). These methods tend to require a large number of iterations, and hence a large number of key performance indicator (KPI) evaluations. In this paper, we propose the use of meta-learning to transfer knowledge from data collected from related, but distinct, configurations in order to speed up optimization on new network configurations. Specifically, we combine meta-learning with BO, as well as with multi-armed bandit (MAB) optimization, with the latter having the potential advantage of operating directly on a discrete search space. Furthermore, we introduce novel contextual meta-BO and meta-MAB algorithms, in which transfer of knowledge across configurations occurs at the level of a mapping from graph-based contextual information to resource-allocation parameters. Experiments for the problem of open loop power control (OLPC) parameter optimization for the uplink of multi-cell multi-antenna systems provide insights into the potential benefits of meta-learning and contextual optimization. © 2015 IEEE.

关键词： Network topology

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Automated Heartbeat Classification for Arrhythmia Patients Using a Deep Convolutional Neural Network 8

Automated Heartbeat Classification for Arrhythmia Patients U...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Kerdoudi, Saoueb Guezouli, Larbi Hattab, Abdessalam Dilekh, Tahar University of Batna 2 LaSTIC Laboratory Computer Science Department Batna Algeria Higher National School of Renewable Energies Environment and Sustainable Development LEREESI Laboratory of Renewable Energy Energy Efficiency and Smart Systems Batna Algeria University of Batna 2 LAMIE Laboratory Computer Science Department Batna Algeria

ISBN: (纸本)9798350309249

The electrocardiogram (ECG) is widely used for diagnosing heart diseases, including arrhythmia, due to its noninvasive nature and simplicity. Accurate detection and classification of arrhythmic types are crucial in preventing heart disease and reducing mortality. To enhance ECG heartbeat classification, we propose an automated system based on a fine-tuned deep convolutional neural network consisting of 13 residual blocks with 28 layers in total. This model was trained using a dataset of 103247 single-lead ECG recordings. Our deep convolutional neural network achieved remarkable results by validating the independent test dataset from the MIT-BIH Arrhythmia Database. It demonstrated an Accuracy of 99%, a Macro avg Precision of 96%, a Macro avg Recall of 93%, a Weighted avg of 99%, and an impressive Macro avg F1-score of 94%. Notably, our approach has demonstrated superior performance to the original architecture, with a 3% increase in F1-score. These findings showcase the potential of our improved deep learning approach to effectively classify a wide range of arrhythmias from single-lead ECGs, yielding high diagnostic performance comparable to that of cardiologists. © 2024 IEEE.

关键词： Electrocardiograms

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Physics-Informed Neural Networks for Solving Blood Flows 26

Physics-Informed Neural Networks for Solving Blood Flows

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26th International Conference on Advanced Communications Technology, ICACT 2024

作者： Chang, Yao-Chung Lin, Yu-Shan Hu, Jeu-Jiun National Taitung University Department of Computer Science and Information Engineering Taitung Taiwan University Rd. Department of Information Science and Management Systems 369 Sec. 2 University Rd. Taitung Taiwan

ISBN: (纸本)9791188428120

The Physics-informed Neural Networks Deep Learning (PINN) framework has been introduced with the primary objective of advancing the field of blood flow simulations. PINN Deep Learning involves data-driven training for flow prediction and can incorporate the understanding of physical laws described by partial differential equations (PDEs). This paper employs the PINN Method for simulating blood flows. Multiple test cases will be computed and compared with other numerical and experimental results to validate the approach. The results demonstrate that the PINN method functions as expected, and validation against experimental and other researchers' results ensures the generation of meaningful output data and the prudent selection of parameters. © 2024 Global IT Research Institute - GIRI.

关键词： Blood

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Weighting Attributes Based on the Greedy Algorithm Properties 28th

Weighting Attributes Based on the Greedy Algorithm Propertie...

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28th International Conference on Knowledge Based and Intelligent information and Engineering systems, KES 2024

作者： Zielosko, Beata Stanczyk, Urszula Jablonski, Kamil University of Silesia in Katowice Institute of Computer Science Bedzinska 39 Sosnowiec41-200 Poland Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

Estimation of importance for considered features is an important issue for any knowledge exploration process and it can be executed by a variety of approaches. In the research reported in this study, the primary aim was the development of a methodology for creating attribute rankings. Based on the properties of the greedy algorithm for inducing decision rules, a new application of this algorithm has been proposed. Instead of constructing a single ordering of features, attributes were weighted multiple times. The input datasets were discretised with several algorithms representing supervised and unsupervised discretisation approaches. Each resulting discrete data variant was exploited to construct a ranking of attributes. The effectiveness of the obtained rankings was confirmed through a rule filtering process governed by weighted attributes. The methodology was applied to the stylometric task of authorship attribution. The experimental outcomes demonstrate the value of the proposed research method, as it generally led to improved predictions while taking into account a noticeably decreased sets of attributes and decision rules. © 2024 The Authors.

关键词： Consensus algorithm

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Towards econometric estimation of the cost of cyber conflict 28th

Towards econometric estimation of the cost of cyber conflict

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28th International Conference on Knowledge Based and Intelligent information and Engineering systems, KES 2024

作者： Larsson, Emil Sigholm, Johan Department of Computer Science Karlstad University Universitetsgatan 2 Karlstad651 88 Sweden Department of Systems Science for Defence and Security Swedish Defence University Drottning Kristinas väg 37 Stockholm114 28 Sweden

In the modern era, understanding the costs and implications of conflict extends beyond traditional kinetic boundaries into the cyber realm. While economic analysis has historically been concerned primarily with the state's ability to sustain and engage in physical warfare, recent research has begun to quantify the additional societal and collateral costs. To shed light on the total costs of the use of cyber weapons and capabilities, we argue that a comprehensive analysis must be done using econometric tools. This paper describes two important tools in this toolkit, counterfactual analysis and bottom-up accounting, in the context of cyber conflict. It discusses how significant collateral costs manifest in the inadvertent aftermath of vulnerability stockpiling for use in cyber weapons, coupled with the losses and thefts of these resources. Such incidents represent not only a direct financial burden but also erode the trust and goodwill of nations who do not disclose the discovered vulnerabilities. Finally, multiple data sources and bottom-up accounting techniques are used to conduct a case study estimating the aggregate societal cost of cyber conflict in the Ukrainian war between late 2013 and 2020. The aggregate cost of the 76 recorded cyberattacks is estimated to be approximately $160M. Finally, counterfactual analysis is concluded to face significant data availability challenges preventing high quality synthesis of the two methods described. © 2024 The Authors.

关键词： Cyber attacks

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Should supervised discretisation always be trusted unreservedly? On combining characteristics of supervised and unsupervised discretisation algorithms in two-step processing 27

Should supervised discretisation always be trusted unreserve...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Stanczyk, Urszula Baron, Grzegorz Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

The paper presents a description of the research methodology dedicated to a two-step discretisation process applied to the input numeric data, with combining the characteristics of selected supervised and unsupervised algorithms, which leads to extended processing of some attributes in train and test sets. The methodology was illustrated with the investigations carried out in the domain of stylometric analysis of texts, for two datasets prepared for the task of binary authorship attribution. The several variants of transformed input data obtained were subjected to exploration using two selected machine learning methods capable of inducing knowledge from both continuous and categorical forms, namely the PART and J48 classifiers. The results from the experiments indicate that, as can be expected, supervised transformations of data work well enough, however, they do not always return the best outcome. The two-step processing of some attributes shows sufficient promise to warrant a closer study, as opposed to always unconditionally relying only on supervised algorithms as outperforming all other approaches. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Authorship attribution Data representation Pattern recognition Stylometry Supervised discretisation Unsupervised discretisation

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Direct and Inverse Optimization Problems in a Gas Deposit Model 15

Direct and Inverse Optimization Problems in a Gas Deposit Mo...

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15th International Conference Management of Large-Scale System Development, MLSD 2022

作者： Skiba, Alexander Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Design Methods for Developing Systems Department of Mathematical Methods of Regional Programming Moscow Russia

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

A continuous dynamic model for the development of a gas field is studied. Two mathematical problems are posed, solved and analyzed. In the direct problem, we are looking for the maximum accumulated profit on a set of temporary functions of a certain type. In the inverse optimization problem, we define the entire set of planning horizons. We subject the assigned tasks to a comprehensive analysis. © 2022 IEEE.

关键词： Profitability

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