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45th Jubilee International Convention on Information, Communication and Electronic Technology, MIPRO 2022

作者： Horvat, M. Stojanovic, A. Kovacevic, Z. University of Zagreb Faculty of Electrical Engineering and Computing Department of Applied Computing Zagreb Croatia Zagreb University of Applied Sciences Department of Computer Science and Information Technology Zagreb Croatia

ISBN: (纸本)9789532331035

Emotions are an omnipresent and important factor in the interaction and communication between people. Since emotions are an indispensable part of human life, it would accelerate the progress of artificial intelligence and other fields of science that require data about emotions if they could be adequately described by computer systems. Today there are many different theories of affect, but few of them are used in affective computing. Other areas of computing also benefit from structured and expressive data models of the affective domain, such as human-computer interaction and brain-computer interfaces. Typical tasks include automated recognition and analysis of emotional states, mental fatigue, individual motivation, vigilance and stress resilience. In this paper four often used models of emotion and cognitive behavior are listed and their properties explained: discrete, dimensional, appraisal and action tendency models. For each model, algorithms are provided for similarity measures that can be used to determine the relatedness between different stimulation and estimation artefacts in their respective emotion spaces. The goal of this article is to help professionals find the optimal emotion model for their research and quickly become familiar with data modelling of affective states. © 2022 Croatian Society MIPRO.

关键词： Brain computer interface

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computer Vision Approaches for Automated Bee Counting Application

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IFAC-PapersOnLine 2024年第9期58卷 43-48页

作者： Simon Bilik Ilona Janakova Adam Ligocki Dominik Ficek Karel Horak Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic and Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic

Many application from the bee colony health state monitoring could be efficiently solved using a computer vision techniques. One of such challenges is an efficient way for counting the number of incoming and outcoming bees, which could be used to further analyse many trends, such as the bee colony health state, blooming periods, or for investigating the effects of agricultural spraying. In this paper, we compare three methods for the automated bee counting over two own datasets. The best performing method is based on the ResNet-50 convolutional neural network classifier, which achieved accuracy of 87% over the BUT1 dataset and the accuracy of 93% over the BUT2 dataset.

关键词： Signal Processing Biomedical systems bee counting bee traffic monitoring computer vision deep classification object detection

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When Zero-Trust Meets Federated Learning

When Zero-Trust Meets Federated Learning

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Xinran Wang, Dan Zhu, Yifei Chen, Weilong Chang, Zheng Han, Zhu The Hong Kong Polytechnic University Department of Computing Hong Kong Shanghai Jiao Tong University UM-SJTU Joint Institute Shanghai China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China University of Jyväskylä Faculty of Information Technology Jyväskylä Finland University of Houston Department of Electrical and Computer Engineering HoustonTX United States Kyung Hee University Department of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350351255

Nowadays, Federated Learning (FL) has emerged as a promising and critical machine learning scheme to protect data privacy and reduce communication overhead. As the scale and connectivity expand in the FL system, enhancing the model's robustness against security threats from malicious clients grows ever more critical. An effective defensive solution involves selecting benign clients appropriately, thereby mitigating the vulnerability of the FL system to malicious attacks. However, clients exhibit varying behaviors over time, which complicates the task of accurately modeling their future trustworthiness. Moreover, blindly trusting clients with high trust values poses risks, given the potential for severe losses from betrayal. To tackle these problems, we propose a zero-trust policy in FL aimed at establishing continuous trust in each client while maintaining skepticism towards potential betrayal attacks. Specifically, we develop a Dirichlet-based trust evaluation technique to enable a comprehensive selection of trustworthy participants. This technique leverages the posterior distribution to estimate clients' trust values from their evolving behavior records over time. Then, we anticipate potential betrayal from a selected client and formulate a min-max optimization problem to minimize the worst-case betrayal loss, thereby boosting the system's betrayalaware robustness. Next, we convert this problem into a convex optimization problem and utilize the interior point method for resolution. We conduct extensive simulations to validate the efficacy of our proposed zero-trust policy in accurately assessing trust and enhancing the model's robustness to betrayal. © 2024 IEEE.

关键词： Federated learning

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Optimized Phishing Detection with Recurrent Neural Network and Whale Optimizer Algorithm

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computers, Materials & Continua 2024年第9期80卷 4895-4916页

作者： Brij Bhooshan Gupta Akshat Gaurav Razaz Waheeb Attar Varsha Arya Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPuneMaharashtra411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India Ronin Institute MontclairNJ 07043USA Management Department College of Business AdministrationPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Business Administration Asia UniversityTaichung413Taiwan Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University(HKMU)HomantinHong KongChina

Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection *** machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishingUniformResource Locator(URLs).Addressing these challenge,we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network(RNN)with the hyperparameter optimization prowess of theWhale Optimization Algorithm(WOA).Ourmodel capitalizes on an extensive Kaggle dataset,featuring over 11,000 URLs,each delineated by 30 *** WOA’s hyperparameter optimization enhances the RNN’s performance,evidenced by a meticulous validation *** results,encapsulated in precision,recall,and F1-score metrics,surpass baseline models,achieving an overall accuracy of 92%.This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection.

关键词： Phishing detection Recurrent Neural Network(RNN) Whale Optimization Algorithm(WOA) cybersecurity machine learning optimization

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Modified ChannelNet for Estimating SISO-OFDM Channels

Modified ChannelNet for Estimating SISO-OFDM Channels

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International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Sunita Khichar Sanika Wijayasekara Wiroonsak Santipach Lunchakorn Wuttisittikulkij Sushank Chaudhary Imran A. Khoso Ejaz Ul Haq Pisit Vanichchanunt Isara Josiah Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Department of Computer System Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka Department of Electrical Engineering Fculty of Engineering Kasetsart University Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

We propose to modify ChannelNet, which is a convolutional neural network (CNN) used to estimate fast-fading channels in orthogonal frequency division multiplexing (OFDM). By replacing linear interpolation with zero padding and removing the denoising CNN, we significantly reduce the computational complexity of the estimation with minimal performance degradation. The numerical results confirm that the proposed modifications can achieve a comparable mean square error (MSE) performance close to the linear minimum MSE estimation.

关键词： Degradation Interpolation OFDM Simulation Channel estimation Estimation Telecommunications

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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Energy Aware Metaheuristic Optimization with Location Aided Routing Protocol for MANET

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computers, Materials & Continua 2022年第4期71卷 1567-1580页

作者： E.Ahila Devi K.C.Ramya K.Sathesh Kumar Sultan Ahmad Seifedine Kadry Hyung Ju Park Byeong-Gwon Kang Department of Instrumentation and Control Engineering St.Joseph’s College of EngineeringChennai600119India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea

A mobile ad hoc network(MANET)involves a group of wireless mobile nodes which create an impermanent network with no central authority and *** nodes in the MANET are highly mobile and it results in adequate network topology,link loss,and increase the re-initialization of the route discovery *** planning in MANET is a multi-hop communication process due to the restricted transmission range of the *** aided routing(LAR)is one of the effective routing protocols in MANET which suffers from the issue of high energy *** few research works have focused on resolving energy consumption problem in LAR,energy efficiency still remains a major design *** this aspect,this study introduces an energy aware metaheuristic optimization with LAR(EAMO-LAR)protocol for *** EAMO-LAR protocol makes use of manta ray foraging optimization algorithm(MRFO)to help the searching process for the individual solution to be passed to the LAR *** fitness value of the created solutions is determined next to pass the solutions to the objective *** MRFO algorithm is incorporated into the LAR protocol in the EAMO-LAR protocol to reduce the desired energy *** ensure the improved routing efficiency of the proposed EAMO-LAR protocol,a series of simulations take *** resultant experimental values pointed out the supreme outcome of the EAMO-LAR protocol over the recently compared *** resultant values demonstrated that the EAMO-LAR protocol has accomplished effectual results over the other existing techniques.

关键词： MANET routing lar protocol energy efficiency mrfo algorithm metaheuristics

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Metaheuristic Lightweight Cryptography for Security Enhancement in Internet of Things

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computers, Materials & Continua 2022年第8期72卷 3009-3023页

作者： Mahmoud Ragab Ehab Bahaudien Ashary Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia

The advancements made in Internet of Things(IoT)is projected to alter the functioning of healthcare industry in addition to increased penetration of different ***,data security and private are challenging tasks to accomplish in IoT and necessary measures to be taken to ensure secure *** this background,the current paper proposes a novel lightweight cryptography method for enhance the security in *** proposed encryption algorithm is a blend of Cross Correlation Coefficient(CCC)and Black Widow Optimization(BWO)*** the presented encryption technique,CCC operation is utilized to optimize the encryption process of cryptography *** projected encryption algorithm works in line with encryption and decryption *** key selection is performed with the help of Artificial Intelligence(AI)tool named BWO *** the combination of AI technique and CCC operation,optimal security operation is improved in *** different sets of images collected from databases,the projected technique was validated in MATLAB on the basis of few performance metrics such as encryption time,decryption time,Peak Signal to Noise Ratio(PSNR),CC,Error,encryption time and decryption *** results were compared with existing methods such as Elliptical Curve cryptography(ECC)and Rivest-Shamir-Adleman(RSA)and the supremacy of the projected method is established.

关键词： Security images encryption time correlation coefficient black widow optimization artificial intelligence decryption time

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Image cyberbullying detection and recognition using transfer deep machine learning

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International Journal of Cognitive computing in engineering 2024年第1期5卷 14-26页

作者： Almomani, Ammar Nahar, Khalid Alauthman, Mohammad Al-Betar, Mohammed Azmi Yaseen, Qussai Gupta, Brij B. School of Computing Skyline University College University City of Sharjah P.O. Box 1797 Sharjah United Arab Emirates I.T. department Al-Huson University College AlBalqa Applied University Irbid Jordan Computer Science Department Faculty of I.T. Yarmouk University Irbid Jordan Department of Information Security Faculty of Information Technology University of Petra Amman Jordan Ajman University Ajman United Arab Emirates Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Symbiosis International University Pune India Department of Electrical and Computer Engineering Lebanese American University Beirut1102 Lebanon Dehradun India

Cyberbullying detection on social media platforms is increasingly important, necessitating robust computational methods. Current approaches, while promising, have not fully leveraged the combined strengths of deep learning and traditional machine learning for enhanced performance. Moreover, online content complexity requires models that can capture nuanced contexts beyond text, which many current methods lack. This research proposes a novel hybrid approach using deep learning models as feature extractors and machine learning classifiers to improve cyberbullying detection. Extracting features using pre-trained deep learning models like InceptionV3, ResNet50, and VGG16, then feeding them into classifiers like Logistic Regression and Support Vector Machines, enhances understanding of the complex contexts where cyberbullying occurs. Experiments on an image dataset showed that combining deep learning and machine learning achieved higher accuracy than using either approach alone. This novel framework bridges the gap in existing literature and contributes to broader efforts to combat cyberbullying through more nuanced, context-aware detection methods. The hybrid technique demonstrates the potential of blending deep learning's representation learning strengths with machine learning's sample efficiency and interpretability. © 2023

关键词： Learning systems

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BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving engineering Design Problems

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computer Modeling in engineering & Sciences 2024年第10期141卷 219-265页

作者： Farouq Zitouni Saad Harous Abdulaziz S.Almazyad Ali Wagdy Mohamed Guojiang Xiong Fatima Zohra Khechiba Khadidja Â Kherchouche Department of Computer Science and Information Technology Kasdi Merbah UniversityP.O.Box 30000OuarglaAlgeria Department of Computer Science College of Computing and InformaticsUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd

关键词： Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning

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