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IEEE Globecom Workshops

作者： Ziqing Yu Zheng Chang Jian Li Yuan Li Yulu Zhang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Information Technology University of Jyväskylä Jyväskylä Finland School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Department of IoT Technology Research China Mobile Research Institute Beijing China

Utilizing the flexibility advantages of the unmanned aerial vehicle (UAV), the emerging vehicular edge computing (VEC) is able to expand service and computing capability to the vehicular network. In this paper, we consider a crowdsourcing problem and propose a joint blockchain-based framework that enables end users to request services from UAV-assisted VEC system in emergency scenarios. Our framework leverages the global data relevant to the task by uploading it to the public blockchain for storage and tracing, where UAV and vehicles play important roles. Meanwhile, data specific to private networks is stored in the consortium blockchain to relieve data pressure on the public blockchain and improve the efficiency of the lookup process of public blockchain. During the task processing phase, we create a two-layer contract for the crowdsourcing network to incentivize the UAV network and vehicles to participate in task processing phase. We validate the feasibility of our two-layer contract through extensive simulations, which demonstrate the potential of our proposed framework to improve the efficiency of crowdsourcing in an UAV-assisted VEC.

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Enhanced prairie dog optimization with Levy flight and dynamic opposition-based learning for global optimization and engineering design problems

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Neural Computing and Applications 2024年第19期36卷 11137-11170页

作者： Biswas, Saptadeep Shaikh, Azharuddin Ezugwu, Absalom El-Shamir Greeff, Japie Mirjalili, Seyedali Bera, Uttam Kumar Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Natural and Agricultural Sciences School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taoyuan32003 Taiwan MEU Research Unit Middle East University Amman11831 Jordan

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study. © The Author(s) 2024.

关键词： Global optimization

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Multi-Tier Kernel for Disease Prediction using Texture Analysis with MR Images

Multi-Tier Kernel for Disease Prediction using Texture Analy...

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2022 International Conference on Edge Computing and Applications, ICECAA 2022

作者： Mohan, M. Anuradhapatil Mohana, S. Subhashini, P. Kushwaha, Sumit Pandian, S. Manikandaprabu Department of Computer Science and Engineering SRM University Delhi-NCR Haryana Sonepat131029 India Department of Electronics and Communication Engineering Faculty of Engineering and Technology Exclusive for Women Sharnbasva University Karnataka585105 India Department of Computer Science and Engineering Saranathan College of Engineering Tamil Nadu Panjappur620012 India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Tamil Nadu Chennai600062 India Department of Computer Applications University Institute of Computing Chandigarh University Punjab Gharuan140413 India Department of Mechanical Engineering Sona College of Technology Tamil Nadu Salem636005 India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

Denoising magnetic resonance images are traditionally done individually, introducing undesired artefacts like blurring. To solve this issue, this paper offers a unique adaptive interpolation architecture that simultaneously allows for image data augmentation, noise removal, and detail augmentation. The multi-tier kernel (MTK) algorithm adjusts the extrapolation weights based on mathematical features in magnetic resonance (MR) data. The MTK weight matrix is then adaptively sharpened, and a Rician bias corrective is used to reduce Rician noise and improve small details. After processing, the noise eliminates the bias produced by the asymmetric sources. The adaptive MTK, in this way, extends the zero ordering estimating methodology to higher regression power facilitating edge maintenance and detail restoration. Investigation outcomes using genuine and MR images (with/without noise) evidenced that the algorithm delivered good restoration outcomes than conventional deep-learning-based approaches but with fewer requirements and calculation burden. © 2022 IEEE.

关键词： Matrix algebra

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Study the effect of the Stimulated Raman Scattering (SRS) in optical fiber-based on OptiSystem

Study the effect of the Stimulated Raman Scattering (SRS) in...

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2021 International Conference on Intelligent technology, System and Service for Internet of Everything, ITSS-IoE 2021

作者： Noori, Mustafa Sabah Sahbudin, Ratna Kalos Zakiah Abood, Mohammed Salah Hamdi, Mustafa Maad Thajeel, Isam Kareem Universiti Putra Malaysia Faculty Of Engineering Department Of Computer And Communication Systems Engineering Selangor Malaysia Beijing Institute Of Technology School Of Information And Electronics Engineering Beijing China Al-Maarif University College Department Of Computer Engineering Techniques Al-Anbar Iraq

ISBN: (纸本)9781665433051

The phenomenon Stimulated Raman Scattering SRS in fiber optical denote as the lights shifting to diverse frequencies due to light's contact with the material of the optical fibers, therefore it is imperative to focus on the attributes of the affected lights in this phenomenon and its impact on the transmission, the comprehensive tool software OptiSystem simulator has been used to create the layout for the propagation in five hundred meters long fiber optical signal generated from six continuous waves laser components with diverse frequencies, the outcomes illustrated the same values in numerical values of gain and after and before propagation with exception appearance of a new pump like parameterized while calculations with parameterized signals are typically faster. © 2021 IEEE.

关键词： Stimulated Raman scattering

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Expression of Concern for: Systematic Analysis of DDoS Attacks in Blockchain

Expression of Concern for: Systematic Analysis of DDoS Attac...

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International Conference on Advanced Communication technology (ICACT)

作者： Mohamad Arsalan Sheikh Gul Zameen Khan Farookh Khadeer Hussain School of Computer Science Faculty of Engineering and Information and Technology University of Technology Sydney Broadway Ultimo NSW NLT Digital Solutions Australia

ISBN: (数字)9791188428083

ISBN: (纸本)9781665410557

With the expansion of the internet, the identification and recuperation of digital attacks have become a primary concern for digital businesses. Damage brought about by network attacks has caused far and wide concern. Distributed Denial of Service (DDoS) attack is a dangerous digital attack. It’s an attack that annihilates the network and can cause multiple computers to be assaulted simultaneously, fizzling to perform service appropriately. A blockchain-based DDoS recognition model is proposed for the systematic evaluation of DDoS attacks to understand the vulnerabilities of the Blockchain better. The advantage of Blockchain is that Blacklisted IP addresses are effectively stored. The use of such a framework gives the benefit of additional security components over existing DDoS moderation frameworks. This paper has assessed the Tab Transformer, the XGBoost, and the Random Forest algorithm to discover the better classifying algorithm. Tree-Based classifier procedure utilized for feature selection to boost the computational time. Out of the three algorithms, the Tab Transformer gives an accuracy of about 97% real-time investigation of the $attacks$.

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Tuning a Mamdani Fuzzy Controller with an Imperialist Competitive Algorithm 7th

Tuning a Mamdani Fuzzy Controller with an Imperialist Compet...

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7th International Conference on Modelling and Development of Intelligent Systems, MDIS 2020

作者： Ciurea, Stelian Faculty of Engineering Department of Computer and Electrical Engineering Lucian Blaga University of Sibiu Sibiu Romania Research Center in Informatics and Information Technology Lucian Blaga University of Sibiu Sibiu Romania

ISBN: (纸本)9783030685263

We have implemented a fuzzy controller with a view to regulating a single-input and single-output second-order linear system. The fuzzy controller was a Mamdami proportional-derivative controller. To determine the parameters of the fuzzy controller we have used an imperialist competitive algorithm. This type of algorithm has a long running time so we implemented also a parallel version of the algorithm that we run on HPC Zamolxes located at the engineering faculty of "Lucian Blaga" University from Sibiu. Because we did not have on this computer a version of MATLAB allowing to write parallel algorithms, we implemented the entire application in the C language using the MPI library. © 2021, Springer Nature Switzerland AG.

关键词： Controllers

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Swallowing Assessment using High-Resolution Cervical Auscultations and Transformer-based Neural Networks

Swallowing Assessment using High-Resolution Cervical Auscult...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ayman Anwar Yassin Khalifa Erin Lucatorto James L. Coyle Ervin Sejdic Department of Electrical and Computer Engineering Faculty of Applied Science & Engineering University of Toronto Toronto Canada Center for Research Computing and Information Technology Analytics University of Pittsburgh Pittsburgh PA USA Systems and Biomedical Engineering Department Faculty of Engineering Cairo University Giza Egypt Department of Communication Science and Disorders School of Health and Rehabilitation Sciences University of Pittsburgh Pittsburgh PA USA Department of Electrical and Computer Engineering Faculty of Applied Science & Engineering University of Toronto and North York General Hospital Toronto Canada

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Swallowing assessment is a crucial task to reveal swallowing abnormalities. There are multiple modalities to analyze swallowing kinematics, such as videofluoroscopic swallow studies (VFSS), which is the gold standard method, and high-resolution cervical auscultation (HRCA), which is a noninvasive technique that uses a triaxial accelerometer attached to the patient’s neck. Deep learning models play an essential role in data driven analysis of swallowing landmarks using VFSS and/or HRCA as input data. Most of these models utilize convolutional and recurrent neural networks. Here, we investigate the ability of transformers to analyze swallowing kinematics; specifically upper esophageal sphincter opening and laryngeal vestibule closure using HRCA signals. We tested the model using an independent test dataset to assess the generalizability of the proposed network. The proposed network achieved an average detection accuracy higher than 90% and 85% for both segmentation tasks, which outperform the hybrid neural networks from the literature, and the model obtained high-performance measures for the independent dataset, showing the transformers’ ability to generalize on unseen data.

关键词： Accuracy Sensitivity Recurrent neural networks Convolution Kinematics Transformers Feature extraction Solids Data models Signal resolution

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Retraction Note: A Systematic Survey on Implementation of Fuzzy Regression Models for Real Life Applications

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Archives of Computational Methods in engineering 2025年 1-2页

作者： Khan, Mufala Kumar, Rakesh Aledaily, Arwa N. Kariri, Elham Viriyasitavat, Wattana Yadav, Kusum Dhiman, Gaurav Kaur, Amandeep Sharma, Ashutosh Vimal, S. Department of Mathematics Lovely Professional University Phagwara India College of Computer Science and Engineering University of Ha’il Hail Saudi Arabia College of Computer Science and Engineering Prince Sattam Bin Abdulaziz University Al‑Kharj Saudi Arabia Business Information Technology Division Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand Department of Computer Science Government Bikram College of Commerce Patiala India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Gharuan India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajaplayam India

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Resource Allocation Optimization in 5G Networks Using Variational Quantum Regressor

Resource Allocation Optimization in 5G Networks Using Variat...

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Quantum Communications, Networking, and Computing (QCNC), International Conference on

作者： Param Pathak Vidhi Oad Aditya Prajapati Nouhaila Innan Dept. of Computer Engineering Sardar Vallabhbhai Patel Institute of Technology India Dept. of Electronics and Communication Vishwakarma Government Engineering College India Dept. of Information and Communication Technology Dhirubhai Ambani Institute of Information and Communication Technology India Quantum Physics and Magnetism Team LPMC Faculty of Sciences Ben M'sick Hassan II University of Casablanca Morocco

ISBN: (数字)9798350366778

ISBN: (纸本)9798350366785

Quantum Machine Learning (QML) is a powerful tool for addressing complex telecommunications challenges. As 5G technology continues to evolve, our research provides a roadmap for integrating QML into the resource allocation framework, potentially improving the efficiency and performance of 5G networks. Through the application of the Variational Quantum Regressor (VQR), our study demonstrates the potential for quantum models to enhance predictive accuracy in resource allocation scenarios. Our VQR accurately aligns predicted quantum states with their intended targets, thereby reducing discrepancies. Such precision in regression analysis, particularly in the context of 5G networks, represents a significant breakthrough. A key highlight of our algorithm is its ability to substantially reduce the mean squared error to an impressively low value of 0.0081, indicating near-perfect prediction accuracy. This innovative approach is set against the backdrop of the evolving landscape of communication networks and computational systems, where the challenge of resource allocation is increasingly pronounced due to the rising demand for bandwidth-intensive and computation-heavy services.

关键词： Accuracy Quantum computing Systematics 5G mobile communication Heuristic algorithms Telecommunications Resource management

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Explainable AI for Android Malware Detection: Towards Understanding Why the Models Perform So Well?

Explainable AI for Android Malware Detection: Towards Unders...

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International Symposium on Software Reliability engineering (ISSRE)

作者： Yue Liu Chakkrit Tantithamthavorn Li Li Yepang Liu Faculty of Information Technology Monash University Melbourne Australia Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (数字)9781665451321

ISBN: (纸本)9781665451338

Machine learning (ML)-based Android malware detection has been one of the most popular research topics in the mobile security community. An increasing number of research studies have demonstrated that machine learning is an effective and promising approach for malware detection, and some works have even claimed that their proposed models could achieve 99% detection accuracy, leaving little room for further improvement. However, numerous prior studies have suggested that unrealistic experimental designs bring substantial biases, resulting in over-optimistic performance in malware detection. Unlike previous research that examined the detection performance of ML classifiers to locate the causes, this study employs Explainable AI (XAI) approaches to explore what ML-based models learned during the training process, inspecting and interpreting why ML-based malware classifiers perform so well under unrealistic experimental settings. We discover that temporal sample inconsistency in the training dataset brings over-optimistic classification performance (up to 99%F1 score and accuracy). Importantly, our results indicate that ML models classify malware based on temporal differences between malware and benign, rather than the actual malicious behaviors. Our evaluation also confirms the fact that unrealistic experimental designs lead to not only unrealistic detection performance but also poor reliability, posing a significant obstacle to real-world applications. These findings suggest that XAI approaches should be used to help practitioners/researchers better understand how do AI/ML models (i.e., malware detection) work-not just focusing on accuracy improvement.

关键词： Training Measurement Training data Focusing Machine learning Reliability engineering Malware

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