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NCFS:new chaotic fuzzy system as a general function approximator

Journal of Control and Decision 2023年第4期10卷 514-528页

作者： Hamid Abbasi Mahdi Yaghoobi Arash Sharifi Mohammad Teshnehlab Department of Computer Engineering Science and Research BranchIslamic Azad UniversityTehranIran Department of Electrical and Computer Engineering Mashhad BranchIslamic Azad UniversityMashhadIran Department of Electrical and Computer Engineering K.N.T University of TechnologyTehranIran

Conventional fuzzy systems(type-1 and type 2)are universal *** goal of this paper is to design and implement a new chaotic fuzzy system(NCFS)based on the Lee oscil-lator for function approximation and chaotic *** incorporates fuzzy reasoning of the fuzzy systems,self-adaptation of the neural networks,and chaotic signal generation in a unique *** features enable the structure to handle uncertainties by generating new information or by chaotic search among prior *** fusion of chaotic structure into the neurons of the membership layer of a conventional fuzzy system makes the NCFS more capable of confronting nonlinear *** on the GFA and Stone-Weierstrass theorems,we show that the proposed model has the function approximation *** NCFS perfor-mance is investigated by applying it to the problem of chaotic *** results are demonstrated to ilustrate the concept of function approximation.

关键词： Chaotic fuzzy system function approximation chaotic neural network oscillatory neuron

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Early Parkinson's Disease Detection and Classification using Machine Learning and Neutrosophic Sets

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Neutrosophic Sets and Systems 2025年 82卷 873-886页

作者： Oudah, Hasan H. Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed Nagm, Ahmad M. Salama, Ahmed S. College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Engineering and Electronics Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

Optimizing therapy and rehabilitation for Parkinson's disease (PD) requires early identification and precise evaluation of the illness's course. However, there is disagreement about the best way to use gait analysis to categorize the severity of motor symptoms and identify early-stage Parkinson's disease. The precision of machine learning (ML) models in identifying early and intermediate stages of Parkinson's disease was assessed in this study. Six ML models are used in this study for the prediction of PD. Different metrics are used to evaluate ML models such as accuracy, precision, recall, f1-score, and AUC score. Then we propose a multi-criteria decision-making methodology (MCDM) to evaluate the ML models and select the best one. Two MCDM methods are used such as CRITIC method to compute the criteria weights and the TOPSIS method to rank the alternatives. These methods are used under the bipolar neutrosophic sets (BNSs) to deal with uncertainty information. The results show the support vector machine is the best ML model for the prediction of PD. © 2025, University of New Mexico. All rights reserved.

关键词： Neurodegenerative diseases

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Cloud computing security: a taxonomy, threat detection and mitigation techniques

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International Journal of computers and Applications 2024年第5期46卷 348-361页

作者： Rani, Pinki Singh, Sukhdip Singh, Karanbir Computer Science and Engineering Department Deenbandhu Chhotu Ram University of Science and Technology Sonipat India Computer Engineering Department Seth Jai Parkash Polytechnic Yamuna Nagar India

Cloud Computing is a rapidly growing emerging technology in the IT environment. Internet-based computing provides services like sharing resources e.g. network, storage, applications and software through the Internet. Cloud customers can access these services according to their need and pay only for those services which they utilize. Almost every industrial sector like banking, retail, education, healthcare, manufacturing and businesses is adopting cloud techniques within their enterprise either privately or in sharing with other enterprises. However, many security issues must be resolved because they are a barrier in adopting this fast-growing technology. This paper reviews various threats on cloud computing like distributed denial of service attack, spoofing attacks (e.g. ARP Spoofing, IP Spoofing, DNS Spoofing), man-in-the middle attack, data loss, data breach, reused IP address attack, malicious insiders, insecure interfaces & APIs etc. These security issues prevent users from adopting cloud services. This study presents various detection and prevention methods to address cloud security concerns. The effectiveness of each strategy is then analyzed based on its attributes, benefits and drawbacks. Deep learning and machine learning algorithms produce more precise findings for both known and unidentified attack patterns. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： MapReduce

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Multi-feature Fusion Deep Network for Skin Disease Diagnosis

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Multimedia Tools and Applications 2025年第1期84卷 419-444页

作者： Gairola, Ajay Krishan Kumar, Vidit Sahoo, Ashok Kumar Diwakar, Manoj Singh, Prabhishek Garg, Deepak Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era deemed to be university Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India School of Computer Science and Artificial Intelligence SR University Warangal Telangana Warangal506371 India

The skin acts as an important barrier between the body and the external environment, playing a vital role as an organ. The application of deep learning in the medical field to solve various health problems has generated increasing interest. The objective of this research is to create a new model using a deep network with multi-feature fusion that enables accurate identification of various skin diseases. This paper proposes a fully fused network (FFN), which includes an improved single block (ISB) and an improved fusion block (IFB) to achieve optimal performance. To implement this, a convolutional neural network-based model for multi-class recognition of skin images has been developed, in which ISB is used to segment diseases in skin images. The IFB module is designed to enhance the effectiveness of the fused network. The model is built and evaluated using our proprietary dataset (Skin_disease_v1) along with publicly available datasets, namely ISIC2016, ISIC2017, and HAM10000. The highest accuracy achieved in experiments was 86% for ISB, 90% for IFB, and 92% for FFN using HAM10000 (for ResNet101V2), HAM10000 (for Resnet50 + ResNet101V2), and HAM10000 (for Resnet50 + ResNet101V2). This shows an accuracy improvement of 9.2%, 13.2%, and 15.2% compared to the state-of-the-art ISB, IFB, and FFN approaches, respectively. Our network outperforms existing networks in terms of performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Photobiomodulation Therapy;Survey and Principal Study Leading to Design Rules for Implants

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IEEE Transactions on Biomedical engineering 2025年第7期72卷 2237-2248页

作者： Kaffash, Zohreh Saha, Reepa Mirbozorgi, S. Ali Faezipour, Miad Mirbozorgi, S. Abdollah University of Alabama at Birmingham Electrical and Computer Engineering Department BirminghamAL United States Purdue University School of Engineering Technology Electrical and Computer Engineering Technology West LafayetteIN United States Birjand University Mechanical Engineering Department Birjand Iran

Photobiomodulation (PBM) therapy using red- and near-infrared (NIR) light has shown beneficial regenerative effects on cell functionalities and consequently on health applications. Light parameter values, particularly power density, significantly affect the treatment outcomes. The limited use of light in transcutaneous applications is due to the power attenuation challenge, which restricts the transmission of light energy to deeper tissues. However, the potential of light therapy to restore cellular function presents a promising strategy for regenerating of cells in internal human organs. We have 1) studied the mechanism and impact of red light at the cellular level, 2) reviewed the literature and classified the research done using light therapy, indicating the positive and negative effects associated with the light power density and exposure time levels, and 3) proposed a design rule for designing PBM implants targeting interior organs. Therefore, this work leads to 1) designing safe and efficient implantable devices by extracting the necessary light parameters, and 2) expanding the applications of light therapy. The range of the red-light power density used in different studies starts from 10 mW-cm2 to as big as 5000 mW-cm2 in a few cases, and from 10 s to 3000 s for different exposure times. According to the light parameters, we have tested different types of off-the-shelf LEDs to experimentally find the correlation between the generated light power densities at different distances from light sources and the LEDs' input electrical power. We have found that for an average light power density of 100 mW-cm2 over an area of 1 cm2, the input power of 18 mW must be delivered to the LED, which is achievable safely via wireless inductive links for delivering sufficient power deep in the body with under 1.6 W-kg of specific absorption rate (SAR). A design rule and approach are provided in this work as a starting point for designing PBM implants. © 1964-2012 IEEE.

关键词： Laser beams

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White shark optimizer via support vector machine for video-based gender classification system

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Multimedia Tools and Applications 2025年 1-17页

作者： Oyediran, Mayowa Oyedepo Ajagbe, Sunday Adeola Ojo, Olufemi Samuel Alshahrani, Reem Awodoye, Olufemi O. Adigun, Matthew O. Department of Computer Engineering Ajayi Crowther University Oyo Nigeria Department of Computer Science University of Zululand Kwadlangezwa3886 South Africa Department of Computer Engineering Abiola Ajimobi Technical University Ibadan200255 Nigeria Department of Computer Science College of Computers and IT Taif University P.O.Box 11099 Taif21944 Saudi Arabia Department of Computer Engineering Ladoke Akintola University of Technology Ogbomoso Nigeria

Gender identification from videos is a challenging task with significant real-world applications, such as video content analysis and social behavior research. In this study, we propose a novel approach, the White Shark Optimizer-Support Vector Machine (WSO-SVM), tailored specifically for gender identification from video data. The WSO-SVM integrates the White Shark Optimizer, a bio-inspired optimization algorithm mimicking the hunting behavior of white sharks, with the Support Vector Machine, a powerful machine learning technique for classification. By combining these two methods, we aim to exploit the advantages of both algorithms and enhance gender identification accuracy. To evaluate the performance of the WSO-SVM in gender identification, the work conducted extensive experiments using a diverse dataset of video clips containing individuals of various genders and backgrounds. The work compared the results with conventional SVM-based gender identification and state-of-the-art methods. The findings demonstrate that the WSO-SVM achieves superior accuracy in gender identification compared to traditional SVM-based approaches. The WSO-SVM's ability to efficiently explore the solution space and select optimal SVM parameters contributes to its improved performance. Moreover, the WSO-SVM exhibits robustness in handling variations in lighting conditions, poses, and facial expressions, making it well-suited for real-world video-based gender identification tasks. The outcomes derived from the SVM approach demonstrate that WSO-SVM produced an average FPR of 7.14%, Sensitivity of 93.06%, Specificity of 92.86%, Precision of 91.0%, and overall accuracy of 93.00% in 45.83 s with a recognition time of 45.83 s. © The Author(s) 2025.

关键词： Support vector machines

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An Efficient Federated Learning Framework for IoT Intrusion Detection 100

An Efficient Federated Learning Framework for IoT Intrusion ...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Chen, Yushen Fang, Fang Wang, Boyu Zhang, Lan Western University Department of Computer Science London Canada Western University Department of Electrical and Computer Engineering London Canada Clemson University Department of Electrical and Computer Engineering Clemson United States

ISBN: (纸本)9798331517786

The exponential growth of the Internet of Things (IoT) ecosystems has raised significant cybersecurity concerns. Deep learning (DL)-based methods have shown promising performance in detecting potential cyber threats in IoT networks. However, as these methods often involve data centralization, they can pose serious data privacy issues for IoT users and increase the communication burden of local networks. Federated learning (FL), as a distributed learning paradigm, enables privacy-preserving training of IoT intrusion detection models by requiring only model updates from IoT devices. However, the resource-constrained nature of IoT devices can significantly decrease FL training efficiencies, such as increased training latency and delayed convergence speed. Moreover, the data heterogeneous issues of IoT devices can also impact the accuracy and robustness of the trained model. To address these challenges, we propose an efficient FL framework, FedKD-Prox, based on federated proximal (FedProx) and knowledge distillation (KD). To improve the prediction accuracy within a limited time budget, the proposed framework aims to efficiently exploit the computation capability of the IoT trainers, reduce the communication overhead of FL, and alleviate the impact of heterogeneous data issues. The simulation results show that FedKD-Prox achieves higher accuracy and improves the robustness of the trained intrusion detection model. © 2024 IEEE.

关键词： Federated learning

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Finding broadcast 2-centers of a tree under the postal model

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Discrete Applied Mathematics 2024年 356卷 182-190页

作者： Tsou, Cheng-Hsiao Lin, Ching-Chi Hsu, Chan-Hung Department of Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Department of Computer Science and Engineering National Taiwan Ocean University Keelung20224 Taiwan

In this paper, we delve into the investigation of locating broadcast 2-centers of a tree T under the postal model. The problem asks to deploy two broadcast centers so that the maximum communication time from the centers to their corresponding vertices is minimized. The broadcast 2-center problem explored in this study is essentially an extension of the classical 2-center problem. To efficiently address this challenge, we employ the binary search method, which enables us to present a highly effective O(nlogn)-time algorithm for solving this problem. © 2024 Elsevier B.V.

关键词： Trees (mathematics)

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Energy-efficient resource allocation in blockchain-based Cybertwin-driven 6G

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 103-114页

作者： Salim, Mikail Mohammed Pan, Yi Park, Jong Hyuk Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea Republic of Department of Computer Science Georgia State University Atlanta United States

Inefficient task scheduling schemes compromise network performance and increase latency for delay intolerant tasks. Cybertwin based 6G services support data logging of operational queries for appropriate resource allocation in Multi-Access Edge and Cloud Computing. In this paper, a Cybertwin assisted task scheduling scheme is proposed for energy-efficient job allocation to Edge-Cloud based on delay tolerance. Cybertwins using the Logger Function and the Digital Asset Function exchange Digital Assets with cloud operators using Smart Contracts to ensure maximum computational resources. The novelty of this study resides in using Communication Assistant Cybertwins to build profiles for all network entities and assign tasks based on their delay tolerance and the available resources of each Edge Datacenter and Cloud service provider. Compared with the Full Offloading scheme, experimental results demonstrate the higher energy efficiency of the task scheduling scheme. Offloading tasks to multiple data centers and smart contract-based selection of cloud operators result in reduced computation time. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Smart contract

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TDE based model-free control for rigid robotic manipulators under nonlinear friction

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Scientia Iranica 2024年第2期31卷 137-148页

作者： Ahmed, S. Ghous, I. Mumtaz, F. Department of Mechatronics SZABIST Karachi Pakistan Department of Electrical and Computer Engineering COMSATS University Lahore Pakistan

This paper establishes a model-free finite-time tracking control of nonlinear robotic manipulator systems. The proposed controller incorporates both Time Delay Estimation (TDE) and an enhanced Terminal Sliding Mode Control (TSMC). The improved TSMC scheme is devised using Fractional-Order TSMC (FOTSMC) and Proportional- Integral-Derivative (PID) control to obtain robust tracking and high control performance. The TDE is designed to estimate the unknown nonlinear dynamics of robotic manipulators, including the Stribeck friction and the external disturbances. Due to Stribeck friction, the effect of TDE error may fail to obtain the desired error performance;thus, another TDE loop is devised to compensate for TDE error generated by non-smooth frictions. The Lyapunov criterion is used to investigate the finite-time stability to analyze the behavior of the designed approach. Finally, computer simulations of the proposed method on PUMA 560 robotic manipulators are performed in contrast with FOTSMC and Adaptive Fractional-Order Nonsingular Terminal Sliding Mode Control (AFONTSMC). © 2024 Sharif University of Technology.

关键词： Friction

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