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A review of multi-objective evolutionary based fuzzy classifiers

Recent Advances in computer science and Communications 2020年第1期13卷 77-85页

作者： Dwivedi, Praveen Kumar Tripathi, Surya Prakash Software Technology Parks of India LucknowUttar Pradesh India Department of Computer Science and Engineering Institute of Engineering and Technology LucknowUttar Pradesh India

Background: Fuzzy systems are employed in several fields like data processing, regres-sion, pattern recognition, classification and management as a result of their characteristic of handling uncertainty and explaining the feature of the advanced system while not involving a particular math-ematical model. Fuzzy rule-based systems (FRBS) or fuzzy rule-based classifiers (mainly designed for classification purpose) are primarily the fuzzy systems that consist of a group of fuzzy logical rules and these FRBS are unit annexes of ancient rule-based systems, containing the "If-then" rules. During the design of any fuzzy systems, there are two main objectives, interpretability and accuracy, which are conflicting with each another, i.e., improvement in any of those two options causes the decrement in another. This condition is termed as Interpretability –Accuracy Trade-off. To handle this condition, Multi-Objective Evolutionary Algorithms (MOEA) are often applied within the design of fuzzy systems. This paper reviews the approaches to the problem of developing fuzzy systems victimization evolutionary process Multi-Objective Optimization (EMO) algorithms consider-ing ‘Interpretability-Accuracy Trade-off, current research trends and improvement in the design of fuzzy classifier using MOEA in the future scope of authors. Methods: The state-of-the-art review has been conducted for various fuzzy classifier designs, and their optimization is reviewed in terms of multi-objective. Result: This article reviews the different Multi-Objective Optimization (EMO) algorithms in the context of Interpretability-Accuracy tradeoff during fuzzy classification. Conclusion: The evolutionary multi-objective algorithms are being deployed in the development of fuzzy systems. Improvement in the design using these algorithms include issues like higher spatiali-ty, exponentially inhabited solution, I-A tradeoff, interpretability quantification, and describing the ability of the system of the fuzzy doma

关键词： Multiobjective optimization

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Credit Card Fraud Prevention Planning using Fuzzy Cognitive Maps and Simulation 8

Credit Card Fraud Prevention Planning using Fuzzy Cognitive ...

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8th IEEE International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2020

作者： Dar, Hafsa Abbasi, Almas Naveed, Asim International Islamic University Department of Computer Science and Software Engineering Islamabad Pakistan University of Engineering and Technology Department of Computer Science and Engineering Lahore Pakistan

ISBN: (数字)9781728170169

ISBN: (纸本)9781728170169

For any financial organization, development and management of fraud prevention plan is a complex yet crucial process. Most of the organizations opt for fraud detection and prevention plans to sustain their marketplace. With the advancement of technology and diversification in user's demand for high security and privacy, it is important to improve fraud prevention planning for financial organizations and banks. Different processes like fraud detection, fraud risk assessment, fraud perception, monitoring and control are involved in fraud prevention planning. It is difficult for Chief Investigating Officer (CIO) to grasp the dependencies among all processes. Similarly, the effect of inefficiencies may raise severe concerns to the planning model and organization as well. This paper considers the formulation of fraud prevention plan for credit cards through implementation of Fuzzy Cognitive Maps (FCM). Furthermore, our aim is to represent the complexity of relationships that exists in different processes of fraud prevention model. The model has been validated through mathematical simulation using log sigmoid function and then risk prediction has been done on single what-if scenario. The contribution of this paper is to analyse and predict risks related to fraud prevention planning in credit cards and help in better decision making. © 2020 IEEE.

关键词： Fuzzy Cognitive Maps

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Reconfigurable Intelligent Surface Assisted Semantic Communication Systems

arXiv

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arXiv 2023年

作者： Shi, Jiajia Chan, Tse-Tin Pan, Haoyuan Lok, Tat-Ming Department of Information Engineering The Chinese University of Hong Kong Hong Kong Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Semantic communication, which focuses on conveying the meaning of information rather than exact bit reconstruction, has gained considerable attention in recent years. Meanwhile, reconfigurable intelligent surface (RIS) is a promising technology that can achieve high spectral and energy efficiency by dynamically reflecting incident signals through programmable passive components. In this paper, we put forth a semantic communication scheme aided by RIS. Using text transmission as an example, experimental results demonstrate that the RIS-assisted semantic communication system outperforms the point-to-point semantic communication system in terms of BLEU scores in Rayleigh fading channels, especially at low signal-to-noise ratio (SNR) regimes. In addition, the RIS-assisted semantic communication system exhibits superior robustness against channel estimation errors compared to its point-to-point counterpart. RIS can improve performance as it provides extra line-of-sight (LoS) paths and enhances signal propagation conditions compared to point-to-point systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Fading channels

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A co-optimization pso for fuzzy rule-based classifier design problem based on enlarged hedge algebras

Periodica polytechnica Electrical engineering and computer s...

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Periodica polytechnica Electrical engineering and computer science 2021年第4期65卷 290-301页

作者： Nguyen, Du Duc Pham, Phong Dinh Software Engineering Department Faculty of Information Technology University of Transport and Communications 3 Cau Giay street Hanoi100000 Viet Nam Computer Science Department Faculty of Information Technology University of Transport and Communications 3 Cau Giay street Hanoi100000 Viet Nam

Fuzzy Rule-Based Classifier (FRBC) design problem has been widely studied due to many practical applications. Hedge Algebras based Classifier Design Methods (HACDMs) are the outstanding and effective approaches because these approaches based on a mathematical formal formalism allowing the fuzzy sets based computational semantics generated from their inherent qualitative semantics of linguistic terms. HACDMs include two phase optimization process. The first phase is to optimize the semantic parameter values by applying an optimization algorithm. Then, in the second phase, the optimal fuzzy rule based system for FRBC is extracted based on the optimal semantic parameter values provided by the first phase. The performance of FRBC design methods depends on the quality of the applied optimization algorithms. This paper presents our proposed co-optimization Particle Swarm Optimization (PSO) algorithm for designing FRBC with trapezoidal fuzzy sets based computational semantics generated by Enlarged Hedge Algebras (EHAs). The results of experiments executed over 23 real world datasets have shown that Enlarged Hedge Algebras based classifier with our proposed co-optimization PSO algorithm outperforms the existing classifiers which are designed based on Enlarged Hedge Algebras methodology with two phase optimization process and the existing fuzzy set theory based classifiers. © 2021 Budapest University of Technology and Economics. All rights reserved.

关键词： Semantics

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A novel channel model and optimal power control schemes for mobile mmWave two-tier networks

TechRxiv

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TechRxiv 2022年

作者： Ghosh, Joydev Haci, Huseyin Kumar, Neeraj Al-Utaibi, Khaled A. Sait, Sadiq M. So-In, Chakchai School of Computer Science and Robotics Tomsk Polytechnic University Tomsk Russia Department of Electrical and Electronics Engineering Near East University Mersin 10 Turkey Department of Computer Science & Engineering Thapar Institute of Engineering & Technology Punjab India Computer Science and Software Engineering Department University of Ha’il Saudi Arabia Department of Computer Engineering King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of Computer Science College of Computing Khon Kaen University Thailand

We present a unified system model and framework for the analytical performance study of two heterogeneous and physically-distinct, but coexisting, networks that work harmoniously at the same time, space, and frequency domains. The two-tier network model considered in this paper is an overlaying of femtocells on a macrocell. Overlaying femtocells improves the performance by offloading traffic from macrocells and providing spatial diversity. The mmWave channel model employed considers the number of clusters and rays within each cluster to vary due to the end-user mobility. This is a new and different model compared to the widely used channel models for mmWave two-tier networks. Optimal power control is formulated as a sum-rate maximization problem for downlink and uplink transmissions at two-tier networks and a power allocation scheme is proposed by following Shannon-Hartley theorem. A comprehensive and interesting performance investigation is provided, where it is shown that the upper bound on the number of admitted secondary users has a linear relationship with the outage probability threshold, logarithmic relationship with SINR and exponential relationship with channel gain factors. Simulation results show that the proposed scheme with sub-channel iterative Lagrange multipliers search algorithm is very effective at managing the cross-tier interference and can outperform a competitive scheme from literature that is based on cognitive radio technology. The computational complexity analysis of proposed algorithms are also given, since the complexity of second algorithm can be a performance-complexity trade-off issue for systems with limited computation power and time requirements. © 2022, CC BY.

关键词： Femtocell

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A Robust Anonymous Authentication Scheme for Securing IoT-Enabled Healthcare Services

A Robust Anonymous Authentication Scheme for Securing IoT-En...

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Cyber Security in Networking Conference (CSNet)

作者： Sana Ullah Shanshan Tu Akhtar Badshah Muhammad Waqas Department of Enterprise and Technology Atlantic Technological University Galway Ireland College of Computer Science Beijing University of Technology Beijing China Department of Software Engineering Faculty of Engineering University of Malakand Dir Lower Pakistan Faculty of Engineering and Science School of Computing and Mathematical Sciences University of Greenwich U.K. School of Engineering Edith Cowan University Perth WA Australia

ISBN: (数字)9798331534103

ISBN: (纸本)9798331534110

Smart wearables and body implanted IoT devices continuously track and transmit health metrics wirelessly to a central controller, such as a personal server, enabling real-time monitoring, proactive treatment and timely assistance. However, the security of IoT-enabled healthcare systems remains a concern, particularly regarding the protection of sensitive patient data. To address the aforementioned issue and enhance security in IoT-enabled healthcare services, we propose a robust anonymous authentication scheme in this work. By leveraging lightweight cryptographic primitives including hash functions and XOR operations coupled with physical unclonable functions (PUFs) and fuzzy extractors, the proposed scheme introduces a novel authentication and key agreement mechanism for secure communication in IoT-enabled healthcare systems. The integration of PUF technology ensure the physical security of resource constrained implantable medical devices (IMDs) against various attacks including device physical capture and impersonation attacks that compromise the IoT-enabled healthcare operations. A thorough security analysis demonstrated the robustness and resilience of the proposed scheme against various active and pas-sive security attacks, thus ensure the integrity and confidentiality of sensitive healthcare data. Finally, comparative analysis of the proposed scheme with other state-of-the-art highlighted that our scheme outperformed other approaches in terms of providing security and additional features, positioning it as a comprehensive solution for authentication in IoT-enabled healthcare services.

关键词： Hash functions Medical devices Authentication Medical services Physical unclonable function Security Internet of Things Servers Wearable devices Resilience

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Developing mapping and allotment in volunteer cloud systems using reliability profile algorithms in a virtual machine 2

Developing mapping and allotment in volunteer cloud systems ...

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2nd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2021

作者： Jeyalaksshmi, S. Nidhya, M.S. Suseendran, G. Pal, Souvik Akila, D. Information Technology Vels Institute of Science Technology Advanced Studies Chennai India Periyar Maniammai Institute of Science Technology Dept of Software Engineering Vallam Thanjavur India Global Institute of Management and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9781728194912

While the placement of the Virtual computer was It remains an open challenge for Volunteer Cloud Computing, which reveals many divergent gaps in conventional cloud computing contexts, and has been thoroughly studied. Features, including sporadic usability of nodes and Infrastructure that's inefficient. In this article, we are modeling the Virtual In Volunteer Cloud Computing, computer positioning dilemma As a multi-dimensional, constrained 0-1 knapsack issue and Built algorithms to satisfy the basic aims and shortcomings of Volunteer Cloud Computing. The proof of a dedicated Cloud Computing Volunteer, The competitive success outcomes of these test beds are illustrated With algorithm. © 2021 IEEE.

关键词： Cloud computing

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Enhancing Pakistan Rice Plant Disease Detection: A Highly Effective Pre-Trained CNN Model

Enhancing Pakistan Rice Plant Disease Detection: A Highly Ef...

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IEEE International Conference on Multi Topic

作者： Muhammad Asad Arshed Zeeshan Akram Haroon Zahid Bajwa Muhammad Huzaifa Azeem Muhammad Ahmad Dawood Faisal Riaz Department of Software Engineering University of Management & Technology (UMT) Lahore Pakistan Department of Computer Science University of Central Punjab (UCP) Lahore Pakistan Department of INFS University of Management & Technology (UMT) Lahore Pakistan

Rice, a crucial global food source, faces numerous challenges impacting its quality and quantity. Pakistan is a significant rice producer and needs more agricultural technological advancements. Conventional disease detection methods for rice plants are time-consuming and ineffective. However, recent progress in agricultural technology, particularly machine learning & deep learning, holds great promise for improved disease identification. In this study, different datasets were considered for preparing a dataset that is specifically related to Pakistan’s environmental rice diseases. This study uses a prepared dataset to train the proposed fine-tuned ResNet101V2 model and for the robustness of the proposed model comparison with ResNet101, ResNet50V2, and DenseNet121 also performed in this study. Among the models evaluated, ResNet101V2 exhibited superior performance with a test accuracy of 81%, demonstrating high accuracy in identifying six distinct classes, including five rice leaf diseases (Leaf Blight, Leaf Smut, Hispa, Leaf Blast, and Brown Spot) and one healthy category specific to Pakistan environments. This study represents a significant advancement toward developing more efficient disease detection and management systems in rice farming, ultimately leading to reduced financial losses and improved agricultural practices.

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QoE Maximization for Multiple-UAV-Assisted Multi-Access Edge Computing: An Online Joint Optimization Approach

arXiv

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arXiv 2024年

作者： He, Long Sun, Geng Sun, Zemin Wu, Qingqing Kang, Jiawen Niyato, Dusit Han, Zhu Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China The School of Automation Guangdong University of Technology Guangzhou510006 China The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446701 Korea Republic of The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

In disaster scenarios, conventional terrestrial multi-access edge computing (MEC) paradigms, which rely on fixed infrastructure, may become unavailable due to infrastructure damage. With high-probability line-of-sight (LoS) communication, flexible mobility, and low cost, unmanned aerial vehicle (UAV)-assisted MEC is emerging as a new promising paradigm to provide edge computing services for ground user devices (UDs) in disaster-stricken areas. However, the limited battery capacity, computing resources, and spectrum resources also pose serious challenges for UAV-assisted MEC, which can potentially shorten the service time of UAVs and degrade the quality of experience (QoE) of UDs without an effective control approach. To this end, in this work, we first present a hierarchical architecture of multiple-UAV-assisted MEC networks that enables the coordinated provision of edge computing services by multiple UAVs. Then, we formulate a joint task offloading, resource allocation, and UAV trajectory planning optimization problem (JTRTOP) to maximize the QoE of UDs while considering the energy consumption constraints of UAVs. Since the problem is proven to be a future-dependent and NP-hard problem, we propose a novel online joint task offloading, resource allocation, and UAV trajectory planning approach (OJTRTA) to solve the problem. Specifically, the JTRTOP is first transformed into a per-slot real-time optimization problem (PROP) using the Lyapunov optimization framework. Then, a two-stage optimization method based on game theory and convex optimization is proposed to solve the PROP. Simulation results provide empirical evidence supporting the superior system performance of the proposed OJTRTA in comparison to alternative approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

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Conflict-Free Genetic Algorithm with Nash Equilibrium Seeking for Game-Based Battery Swapping Station Recommendation

Conflict-Free Genetic Algorithm with Nash Equilibrium Seekin...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Chang-Long Sun Xin-Xin Xu Chun-Hua Chen Jun Hong Zhenan He Dengxiu Yu Sam Kwong Zhi-Hui Zhan School of Computer Science and Engineering South China University of Technology Guangzhou China School of Computer Science and Technology Ocean University of China Qingdao China School of Software Engineering South China University of Technology Guangzhou China College of Computer Science Sichuan University Chengdu China School of Artificial Intelligence Northwestern Polytechnical University Xi'an China Department of Computing and Decision Science Lingnan University Hong Kong SAR College of Artificial Intelligence Nankai University Tianjin China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The rapid growth of electric vehicles (EVs) has led to significant challenges in providing efficient and sustainable charging solutions. This paper addresses the battery swapping station (BSS) recommendation problem by proposing a novel conflict-free genetic algorithm (CFGA) integrated with a Nash equilibrium seeking (NES) approach to identify optimal Nash equilibrium (ONE) solutions to such a non-cooperative optimization problem. The CFGA employs specialized crossover and mutation operators to generate offspring that satisfy the constraints of the problem, ensuring that each EV decides a unique battery swap strategy without conflict. Firstly, an order crossover operator is proposed to preserve the order of genes in the chromosomes. Secondly, a replacement and exchange mutation operator is proposed to enhance mutation diversity. The resulting optimal solution is then used as the initial strategy for the NES, which iteratively converges to the ONE. The proposed CFGA with NES algorithm is evaluated under both small-scale and large-scale cases, demonstrating its effectiveness in achieving a balance between costs for EVs and utilization for BSSs. The study's findings have practical implications for the smart grid and EV integration, offering a robust method for optimizing EV infrastructure and operations.

关键词： Costs Scalability Ecosystems Nash equilibrium Electric vehicles Batteries Smart grids Optimization Cybernetics Genetic algorithms

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