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Enhancing Cybersecurity Competency in the Kingdom of Saudi Arabia:A Fuzzy Decision-Making Approach

computers, Materials & Continua 2024年第5期79卷 3211-3237页

作者： Wajdi Alhakami Department of information technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

The Kingdom of Saudi Arabia(KSA)has achieved significant milestones in *** has maintained solid regulatorymechanisms to prevent,trace,and punish offenders to protect the interests of both individual users and organizations from the online threats of data poaching and *** widespread usage of information technology(IT)and IT Enable Services(ITES)reinforces *** constantly evolving cyber threats are a topic that is generating a lot of *** this league,the present article enlists a broad perspective on how cybercrime is developing in KSA at present and also takes a look at some of the most significant attacks that have taken place in the *** existing legislative framework and measures in the KSA are geared toward deterring criminal activity *** competency models have been devised to address the necessary cybercrime competencies in this *** research specialists in this domain can benefit more by developing a master competency level for achieving optimum *** address this research query,the present assessment uses the Fuzzy Decision-Making Trial and Evaluation Laboratory(Fuzzy-DMTAEL),Fuzzy Analytic Hierarchy Process(***),and Fuzzy TOPSIS methodology to achieve segment-wise competency development in cyber security *** similarities and differences between the three methods are also *** cybersecurity analysis determined that the National Cyber Security Centre got the highest *** study concludes by perusing the challenges that still need to be examined and resolved in effectuating more credible and efficacious online security mechanisms to offer amoreempowered ITES-driven economy for ***,cybersecurity specialists and policymakers need to collate their efforts to protect the country’s digital assets in the era of overt and covert cyber warfare.

关键词： Cyber security fuzzy DMTAEL security policy cyber crime MCDM

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An Adaptive Hate Speech Detection Approach Using Neutrosophic Neural Networks for Social Media Forensics

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computers, Materials & Continua 2024年第4期79卷 243-262页

作者： Yasmine M.Ibrahim Reem Essameldin Saad M.Darwish Department of information technology Institute of Graduate Studies and ResearchAlexandria UniversityAlexandria21526Egypt Faculty of computers and information technology Egyptian E-Learning University(EELU)Giza12611Egypt Faculty of computers and Data Science Alexandria UniversityAlexandria21554Egypt

Detecting hate speech automatically in social media forensics has emerged as a highly challenging task due tothe complex nature of language used in such platforms. Currently, several methods exist for classifying hatespeech, but they still suffer from ambiguity when differentiating between hateful and offensive content and theyalso lack accuracy. The work suggested in this paper uses a combination of the Whale Optimization Algorithm(WOA) and Particle Swarm Optimization (PSO) to adjust the weights of two Multi-Layer Perceptron (MLPs)for neutrosophic sets classification. During the training process of the MLP, the WOA is employed to exploreand determine the optimal set of weights. The PSO algorithm adjusts the weights to optimize the performanceof the MLP as fine-tuning. Additionally, in this approach, two separate MLP models are employed. One MLPis dedicated to predicting degrees of truth membership, while the other MLP focuses on predicting degrees offalse membership. The difference between these memberships quantifies uncertainty, indicating the degree ofindeterminacy in predictions. The experimental results indicate the superior performance of our model comparedto previous work when evaluated on the Davidson dataset.

关键词： Hate speech detection whale optimization neutrosophic sets social media forensics

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Shear Let Transform Residual Learning Approach for Single-Image Super-Resolution

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computers, Materials & Continua 2024年第5期79卷 3193-3209页

作者： Israa Ismail Ghada Eltaweel Mohamed Meselhy Eltoukhy Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia51422Egypt Department of information technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia

Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution ***-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance ***-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater *** study introduces a single-image super-resolution approach for remote sensing images,utilizing deep shearlet residual learning in the shearlet transform domain,and incorporating the Enhanced Deep Super-Resolution network(EDSR).Unlike conventional approaches that estimate residuals between high and low-resolution images,the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high-and low-resolution *** shearlet transform is chosen for its excellent sparse approximation ***,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high *** shearlet coefficients are fed into the EDSR *** high-resolution image is subsequently reconstructed using the inverse shearlet *** incorporation of the EDSR network enhances training stability,leading to improved generated *** experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery,effectively restoring both global topology and local edge detail information,thereby enhancing image *** to other networks,our proposed approach outperforms the state-of-the-art in terms of image quality,achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.

关键词： Super-resolution shearlet transform shearlet coefficients enhanced deep super-resolution network

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Study on Coded Permutation Entropy of Finite Length Gaussian White Noise Time Series

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Chinese Journal of Electronics 2024年第1期33卷 185-194页

作者： Huihui SUN Xiaofeng ZHANG school of Physics and information technology Shaanxi Normal University

As an extension of permutation entropy(PE), coded permutation entropy(CPE) improves the performance of PE by making a secondary division for ordinal patterns defined in PE. In this study, we provide an exploration of the statistical properties of CPE using a finite length Gaussian white noise time series theoretically. By means of the Taylor series expansion, the approximate expressions of the expected value and variance of CPE are deduced and the Cramér-Rao low bound(CRLB) is obtained to evaluate the performance of the CPE estimator. The results indicate that CPE is a biased estimator, but the bias only depends on relevant parameters of CPE and it can be easily corrected for an arbitrary time series. The variance of CPE is related to the encoding patterns distribution,and the value converges to the CRLB of the CPE estimator when the time series length is large enough. For a finitelength Gaussian white noise time series model, the predicted values can match well with the actual values, which further validates the statistic theory of CPE. Using the theoretical expressions of CPE, it is possible to better understand the behavior of CPE for most of the time series.

关键词： Time series analysis White noise Predictive models Taylor series Entropy Encoding Time measurement

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Privacy-preserved learning from non-i.i.d data in fog-assisted IoT:A federated learning approach

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Digital Communications and Networks 2024年第2期10卷 404-415页

作者： Mohamed Abdel-Basset Hossam Hawash Nour Moustafa Imran Razzak Mohamed Abd Elfattah Faculty of computers and Informatics Zagazig UniversityZagazigSharqiyah44519Egypt school of Engineering and information technology University of New South Wales@ADFACanberraACT2600Australia Deakin University Geelong Waurn Ponds CampusAustralia Computer Science Department Misr Higher Institute for Commerce and ComputersMansoura35511Egypt

With the prevalence of the Internet of Things(IoT)systems,smart cities comprise complex networks,including sensors,actuators,appliances,and cyber *** complexity and heterogeneity of smart cities have become vulnerable to sophisticated cyber-attacks,especially privacy-related attacks such as inference and data poisoning *** Learning(FL)has been regarded as a hopeful method to enable distributed learning with privacypreserved intelligence in IoT *** though the significance of developing privacy-preserving FL has drawn as a great research interest,the current research only concentrates on FL with independent identically distributed(i.i.d)data and few studies have addressed the non-i.i.d *** is known to be vulnerable to Generative Adversarial Network(GAN)attacks,where an adversary can presume to act as a contributor participating in the training process to acquire the private data of other *** paper proposes an innovative Privacy Protection-based Federated Deep Learning(PP-FDL)framework,which accomplishes data protection against privacy-related GAN attacks,along with high classification rates from non-i.i.d ***-FDL is designed to enable fog nodes to cooperate to train the FDL model in a way that ensures contributors have no access to the data of each other,where class probabilities are protected utilizing a private identifier generated for each *** PP-FDL framework is evaluated for image classification using simple convolutional networks which are trained using MNIST and CIFAR-10 *** empirical results have revealed that PF-DFL can achieve data protection and the framework outperforms the other three state-of-the-art models with 3%–8%as accuracy improvements.

关键词： Privacy preservation Federated learning Deep learning Fog computing Smart cities

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A Constraint Adaptive Multi-Tasking Differential Evolution Algorithm:Designed for Dispatch of Integrated Energy System in Coal Mine

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Tsinghua Science and technology 2024年第2期29卷 368-385页

作者： Canyun Dai Xiaoyan Sun Hejuan Hu Yong Zhang Dunwei Gong school of information and Control Engineering China University of Mining and TechnologyXuzhou 221116China school of information Science and technology Qingdao University of Science and TechnologyQingdao 266061China

The dispatch of integrated energy systems in coal mines(IES-CM)with mine-associated supplies is vital for efficient energy utilization and carbon emissions ***,IES-CM dispatch is highly challenging due to its feature as multi-objective and strong *** constrained multi-objective evolutionary algorithms often fall into locally feasible domains with poorly distributed Pareto front,which greatly deteriorates dispatch *** tackle this problem,we transform the traditional dispatch model of IES-CM into two tasks:the main task with all constraints and the helper task with constraint *** we propose a constraint adaptive multi-tasking differential evolution algorithm(CA-MTDE)to optimize these two tasks *** helper task with constraint adaptive is developed to obtain infeasible solutions near the feasible *** purpose of this infeasible solution is to transfer guiding knowledge to help the main task move away from local ***,a dynamic dual-learning strategy using DE/current-to-rand/1 and DE/current-to-best/1 is developed to maintain task diversity and ***,we comprehensively evaluate the performance of CA-MTDE by applying it to a coal mine in Shanxi Province,considering two IES-CM *** demonstrate the feasibility of CA-MTDE and its ability to generate a Pareto front with exceptional convergence,diversity,and distribution.

关键词： dispatch integrated energy system coal mine evolutionary multi-tasking constraint differential evolution

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Modifying Lyapunov exponent of chaotic map by self-cascading

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Science China(Technological Sciences) 2024年第7期67卷 2203-2214页

作者： YI ChenLong LI ChunBiao LI YongXin XIA Ming HUA ZhongYun school of Electronic and information Engineering Nanjing University of Information Science&TechnologyNanjing 210044China school of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing 210044China school of Computer Science and technology Harbin Institute of Technology Shenzhen Graduate SchoolShenzhen 518055China

The self-cascade(SC) method is an effective technique for chaos enhancement and complexity increasing in chaos ***, the controllable self-cascade(CSC) method allows for more accurate control of Lyapunov exponents of the discrete map. In this work, the SC and CSC systems of the original map are derived, which enhance the chaotic performance while preserving the fundamental dynamical characteristics of the original map. Higher Lyapunov exponent of chaotic sequences corresponding to higher frequency are obtained in SC and CSC systems. Meanwhile, the Lyapunov exponent could be linearly controlled with greater flexibility in the CSC system. The verification of the numerical simulation and theoretical analysis is carried out based on the platform of CH32.

关键词： self-cascade controllable self-cascade chaos enhancement chaos control

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A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics--A Supply Chain Backlog Elimination Framework

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computers, Materials & Continua 2024年第6期79卷 4081-4105页

作者： Yasser Hachaichi Ayman E.Khedr Amira M.Idrees College of Computing and information technology at Khulais Department of Information SystemsUniversity of JeddahJeddah21959Saudi Arabia Faculty of computers and information technology Future University in EgyptCairo11835Egypt

The diversity of data sources resulted in seeking effective manipulation and *** challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of *** of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search *** research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO *** the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain *** proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant *** confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve ***,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different *** proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.

关键词： Optimization particle swarm optimization algorithm feature selection logistics supply chain management backlogs

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Energy consumption optimization for edge computing-supported cellular networks based on optimal transport theory

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Science China(information Sciences) 2024年第2期67卷 201-218页

作者： Xiangyu LV Xiaohu GE Yi ZHONG Qiang LI Yong XIAO school of Electronic information and Communications Huazhong University of Science and Technology

With advancements in mobile communication technologies, there have been multiple user device(UD) connections to cellular networks. Because of changes in the spatial distribution of UDs and application requirements, the traditional offloading mechanism based on the nearest distance will result in heavy loads for some base stations(BSs). Because there is a high-order relationship between the energy consumption of the BS processing tasks and the number of computing tasks, the traditional offloading mechanism will result in high energy consumption, and UD’s offloading decision must be dynamically adjusted. The offloading decision of UDs is optimized based on detailed information about various parameters associated with a network from the standpoint of distribution. This information includes details regarding the spatial distribution of UDs,application requirements, and the offloading period of computing tasks. Based on optimal transport theory,an energy consumption optimization algorithm is suggested to lower the total amount of energy consumed by the offloading process of UDs’ computing tasks by reasonably planning the offloading BSs of the UDs in the networks. The simulation results show that the proposed offloading mechanism based on energy consumption optimization reduces the total energy consumption of the offloading process by 28.09% when compared to the traditional offloading mechanism, and the traffic managed by each BS is balanced.

关键词： optimal transport theory edge computing energy consumption reduction dynamic voltage frequency scaling

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Federated Meta Reinforcement Learning for Personalized Tasks

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Tsinghua Science and technology 2024年第3期29卷 911-926页

作者： Wentao Liu Xiaolong Xu Jintao Wu Jielin Jiang school of Computer Science Nanjing University of Information Science and TechnologyNanjing 210044China school of Software Nanjing University of Information Science and TechnologyNanjing 210044China

As an emerging privacy-preservation machine learning framework,Federated Learning(FL)facilitates different clients to train a shared model collaboratively through exchanging and aggregating model parameters while raw data are kept local and *** this learning framework is applied to Deep Reinforcement Learning(DRL),the resultant Federated Reinforcement Learning(FRL)can circumvent the heavy data sampling required in conventional DRL and benefit from diversified training data,besides privacy preservation offered by *** FRL implementations presuppose that clients have compatible tasks which a single global model can *** practice,however,clients usually have incompatible(different but still similar)personalized tasks,which we called task *** may severely hinder the implementation of FRL for practical *** this paper,we propose a Federated Meta Reinforcement Learning(FMRL)framework by integrating Model-Agnostic Meta-Learning(MAML)and ***,we innovatively utilize Proximal Policy Optimization(PPO)to fulfil multi-step local training with a single round of ***,considering the sensitivity of learning rate selection in FRL,we reconstruct the aggregation optimizer with the Federated version of Adam(Fed-Adam)on the server *** experiments demonstrate that,in different environments,FMRL outperforms other FL methods with high training efficiency brought by Fed-Adam.

关键词： federated learning reinforcement learning meta-learning personalization

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