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Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

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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Hamiltonian learning using machine-learning models trained with continuous measurements

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Physical Review Applied 2024年第4期22卷 044080页

作者： Kris Tucker Amit Kiran Rege Conor Smith Claire Monteleoni Tameem Albash Department of Applied Mathematics Department of Computer Science Center for Quantum Information and Control Department of Electrical and Computer Engineering Paris France Center for Computing Research

We build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

关键词： Entanglement detection Nonlocality Quantum parameter estimation Machine learning Weak measurements

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Maintenance of High Fuzzy Utility Itemsets Using the Pre-Large-Itemset Concept and Tree Structure 23

Maintenance of High Fuzzy Utility Itemsets Using the Pre-Lar...

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10th Multidisciplinary International Social Networks Conference, MISNC 2023

作者： Hong, Tzung-Pei Hung, Wei-Teng Huang, Wei-Ming Department of Computer Science and Information Engineering National University of Kaohsiung Taiwan Department of Computer Science and Engineering National Sun Yat-Sen University Library and Information Center National University of Kaohsiung Taiwan Department of Electrical and Control China Steel Inc. China

ISBN: (纸本)9798400708176

Utility mining has recently attracted much attention in real-world applications because it fits actual situations. Fuzzy utility mining approaches can discover important high-utility patterns in linguistic terms. Data, however, usually come intermittently, and users may want to know currently updated mining results instead of only the ones at the previous stage. Thus, maintaining correct knowledge is very important to this requirement. In the past, we used the fast-updated (FUP) approach and tree structures to manage the maintenance problem of fuzzy utility mining for incremental databases. In this work, we adopt the pre-large-itemset concept to speed up the tree-based maintenance of high fuzzy utility itemsets. The proposed method uses the header table and the pre-large threshold to reduce database scans for efficiency improvement. From the experimental results, the proposed approach performs better than the FUP-tree-based and batching method. © 2023 ACM.

关键词： Maintenance

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Learning Probabilistic Coordinate Fields for Robust Correspondences

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第10期45卷 12004-12021页

作者： Zhao, Weiyue Lu, Hao Ye, Xinyi Cao, Zhiguo Li, Xin Huazhong University of Science and Technology The Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Wuhan430074 China West Virginia University The Lane Department of Computer Science and Electrical Engineering MorgantownWV26506-6109 United States

We introduce Probabilistic Coordinate Fields (PCFs), a novel geometric-invariant coordinate representation for image correspondence problems. In contrast to standard Cartesian coordinates, PCFs encode coordinates in correspondence-specific barycentric coordinate systems (BCS) with affine invariance. To know when and where to trust the encoded coordinates, we implement PCFs in a probabilistic network termed PCF-Net, which parameterizes the distribution of coordinate fields as Gaussian mixture models. By jointly optimizing coordinate fields and their confidence conditioned on dense flows, PCF-Net can work with various feature descriptors when quantifying the reliability of PCFs by confidence maps. An interesting observation of this work is that the learned confidence map converges to geometrically coherent and semantically consistent regions, which facilitates robust coordinate representation. By delivering the confident coordinates to keypoint/feature descriptors, we show that PCF-Net can be used as a plug-in to existing correspondence-dependent approaches. Extensive experiments on both indoor and outdoor datasets suggest that accurate geometric invariant coordinates help to achieve the state of the art in several correspondence problems, such as sparse feature matching, dense image registration, camera pose estimation, and consistency filtering. Further, the interpretable confidence map predicted by PCF-Net can also be leveraged to other novel applications from texture transfer to multi-homography classification. © 1979-2012 IEEE.

关键词： Image registration

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Research on Distributed Cooperative control Strategy of Microgrid Hybrid Energy Storage Based on Adaptive Event Triggering

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computer Modeling in engineering & sciences 2022年第8期132卷 585-604页

作者： Wenqian Zhang Jingwen Chen Saleem Riaz Naiwen Zheng Li Li School of Electrical and Control Engineering Shaanxi University of Science and TechnologyXi’an710021China School of Mathematics and Computer Science Yan’an UniversityXi’an716000China School of Automation Northwestern Polytechnical UniversityXi’an170072China

Distributed collaborative control strategies for microgrids often use periodic time to trigger communication,which is likely to enhance the burden of communication and increase the frequency of controller updates,leading to greater waste of communication *** response to this problem,a distributed cooperative control strategy triggered by an adaptive event is *** introducing an adaptive event triggering mechanism in the distributed controller,the triggering parameters are dynamically adjusted so that the distributed controller can communicate only at a certain time,the communication pressure is reduced,and the DC bus voltage deviation is effectively reduced,at the same time,the accuracy of power distribution is *** MATLAB/Simulink modeling and simulation results prove the correctness and effectiveness of the proposed control strategy.

关键词： DC microgrid hybrid energy storage adaptive event trigger distributed collaborative control

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Al-Biruni Based Optimization of Rainfall Forecasting in Ethiopia

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computer Systems science & engineering 2023年第6期45卷 2885-2899页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Fadwa Alrowais Mostafa Abotaleb Abdelhameed Ibrahim Doaa Sami Khafaga Department of Communications and Elecronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science F aculty of Computer and Infomation SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Progamming South Ual State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Rainfall plays a significant role in managing the water level in the *** unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the *** individuals,especially those in the agricultural sector,rely on rain *** rainfall is challenging because of the changing nature of the *** area of Jimma in southwest Oromia,Ethiopia is the subject of this research,which aims to develop a rainfall forecasting *** estimate Jimma's daily rainfall,we propose a novel approach based on optimizing the parameters of long short-term memory(LSTM)using Al-Biruni earth radius(BER)optimization algorithm for boosting the fore-casting accuracy.N ash-Sutcliffe model eficiency(NSE),mean square error(MSE),root MSE(RMSE),mean absolute error(MAE),and R2 were all used in the conducted experiments to assess the proposed approach,with final scores of(0.61),(430.81),(19.12),and(11.09),***,we compared the proposed model to current machine-learning regression models;such as non-optimized LSTM,bidirectional LSTM(BiLSTM),gated recurrent unit(GRU),and convolutional LSTM(ConvLSTM).It was found that the proposed approach achieved the lowest RMSE of(19.12).In addition,the experimental results show that the proposed model has R-with a value outperforming the other models,which confirms the superiority of the proposed *** the other hand,a statistical analysis is performed to measure the significance and stability of the proposed approach and the recorded results proved the expected perfomance.

关键词： Rainfall prediction long short-term memory Al-Biruni earth radius algorithm meta-heuristic optimization

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Reinforcement Learning-Based Output Stabilization control for Nonlinear Systems With Generalized Disturbances 25

Reinforcement Learning-Based Output Stabilization Control fo...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Tang, Zezhi Rossiter, J Anthony Dong, Yi Panoutsos, George University Of Sheffield Department Of Automatic Control And Systems Engineering SheffieldS13JD United Kingdom University Of Southampton Department Of Electronics And Computer Science Southampton United Kingdom

ISBN: (纸本)9798350340266

This paper proposes a new disturbance observer (DO)-based reinforcement learning (RL) control approach for nonlinear systems with unmatched (generalized) disturbances. While a nonlinear disturbance observer (NDO) is utilized to measure the plant uncertainties, disturbances can exist in the plant via distinct channels from those of the control signals;so-called mismatched disturbances are theoretically difficult to attenuate within the channel of the system's states. A generalized disturbance observer-based compensator is implemented to address the uncertainty cancellation problem by removing the influence of uncertainties from the output channels. Con-currently, a composite actor-critic RL scheme is utilized for approximating the optimal control policy as well as the ideal value function pertaining to the compensated system by solving a Hamilton-Jacobi-Bellman (HJB) equation for both online and offline iterations simultaneously. Stability analysis verifies the convergence of the proposed framework. Simulation results are included to illustrate the effectiveness of the proposed scheme. © 2024 IEEE.

关键词： Nonlinear systems

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Output Tracking control of a Nonlinear System Based on Takagi-Sugeno Fuzzy Model: Generalized Partial Eigenstructure Assignment Approach

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IEEE Access 2024年 12卷 18520-18535页

作者： Soltanian, Farzad Shasadeghi, Mokhtar Mobayen, Saleh Skruch, Pawel University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2M9 Canada Shiraz University of Technology Electrical Engineering Faculty Shiraz71557-13876 Iran National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan AGH University of Science and Technology Department of Automatic Control and Robotics Kraków30-059 Poland

This paper introduces an innovative optimal control approach to achieve output tracking while incorporating H2-performance specifications in a specific class of nonlinear dynamics modeled by the Takagi-Sugeno fuzzy model (TSFM). The primary innovation lies in extending partial eigenstructure assignment to TSFM-based nonlinear systems within the framework of sliding mode control (SMC). We propose a two-step methodology for designing optimal sliding surface gains. Initially, optimal state feedback gains are computed for each rule consequence containing a linear subsystem, adhering to predetermined eigenvalues and satisfying H2-performance criteria. Subsequently, using a convex combination, the overall state feedback gain is calculated and utilized to design sliding matrix gains. The sliding matrix gains are then determined by strategically combining previously calculated state-feedback gains in a convex optimization problem. We reframe the output tracking strategy as a stabilization problem using a virtual control input and reformulate the optimization task concerning tracking state errors. This process yields state feedback gains, sliding gains, and the formulation of the virtual control input. The effectiveness of our approach is verified through comprehensive simulations, emphasizing its capability in addressing output tracking challenges within nonlinear systems. © 2013 IEEE.

关键词： Transient analysis

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A Novel Facial Manipulation Detection Method Based on Contrastive Learning 5

A Novel Facial Manipulation Detection Method Based on Contra...

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5th IEEE International Conference on Electronics Technology, ICET 2022

作者： Ma, Zhiyuan Xu, Pengxiang Mei, Xue Shen, Jie Nanjing Tech University College of Electrical Engineering and Control Science Nanjing China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9781665485081

Nowadays, numerous synthesized face-swapping videos generated by face forgery algorithms have become an emerging problem, which promotes facial manipulation detection to be a significant topic. With the development of face forgery algorithms, some fake face images or videos generated by those strong forgery algorithms are very realistic, which have brought much difficulty to facial manipulation detection. In this paper, we present a novel facial manipulation detection method based on contrastive learning. We analyze theure features of manipulated facial images and propose to compare and learn the features of the whole face and the center face in order to get more general features. We calculate the similarity and distribution distance between the whole face and the center face. The experiments implemented on FaceForensics++ dataset demonstrate that the proposed method achieves outstanding results and can learn the general features. © 2022 IEEE.

关键词： Deep learning

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Fractional-order binary bat algorithm for feature selection on high-dimensional microarray data

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7453-7467页

作者： Esfandiari, Ahmad Farivar, Faezeh Khaloozadeh, Hamid Department of Computer and Mechatronics Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Systems and Control Faculty of Electrical Engineering K. N. Toosi University of Technology Seyed-Khandan Shariati Ave. Tehran16317-14191 Iran

High-dimensional microarray data suffer from the confounding effects of irrelevant, redundant and noisy genes on the scalability and efficiency of classification algorithms. In order for an effective dimensionality reduction and the selection of informative genes, this paper introduces a novel approach using fractional calculus concepts. This study proposes a modified version of binary the bat algorithm named fractional-order binary bat algorithm (FBBA) able to control the convergence process using more historical memory of bat behaviors. The gene selection technique contains a two-stage hybrid filter/wrapper method which employs a new correlation-based feature clustering (CFC) algorithm in the filter stage and the FBBA in the wrapper stage. The CFC-FBBA is evaluated on ten microarray gene expression datasets by employing the support vector machine classifier with a k-fold Monte Carlo cross validation data partitioning model. Furthermore, the results show that the CFC-FBBA, while minimizing the size of the gene subset, achieves the highest classification accuracy in most cases compared to several state-of-art hybrid techniques. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Classification (of information)

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