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Intelligent edge CDN with smart contract-aided local IoT sharing

High-Confidence Computing 2024年第4期4卷 56-68页

作者： Jiamin Fan Daming Liu Guoming Tang Kui Wu Xun Shao Department of Computer Science University of VictoriaVictoria BC V8W 2Y2Canada College of Computer Science and Technology Shanghai University of Electric PowerShanghai 200090China Peng Cheng Laboratory Shenzhen 518000China Department of Electrical and Electronic Information Engineering Toyohashi University of TechnologyToyohashi Aichi 441-8580Japan

A content delivery network(CDN)aims to reduce the content delivery latency to end-users by using distributed cache ***,deploying and maintaining cache servers on a large scale is very *** solve this problem,CDN providers have developed a new content delivery strategy:allowing end-users’s IoT edge devices to share their storage/bandwidth *** new edge CDN platform must address two core questions:(1)how can we incentivize end users to share IoT devices?(2)how can we facilitate a safe and transparent content transaction environment for end users?This paper introduces SmartSharing,a new content delivery network solution to address these *** smartSharing,the over-the-top(OTT)IoT devices belonging to end-users are used as mini-cache *** motivate end users to share the idle devices and storage/bandwidth resources,SmartSharing designs the content delivery schedule and the pricing scheme based on game theory and machine learning algorithms(specifically,a tailored expectation-maximization(EM)algorithm).To facilitate content trading among end users,SmartSharing creates a secure and transparent transaction platform based on smart contracts in *** addition,SmartSharing’s performance evaluation is through trace-driven simulations in the real world and a prototype using content metadata and the achieved pricing *** evaluation results show that CDN providers,end users and content providers can all benefit from our SmartSharing framework.

关键词： CDN over edge IoT Game theory EM algorithm Smart contracts

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Quantum-Powered Autonomous Deep-Sea Explorer Using AI, Microbiofuels, and Advanced Sensors for Oceanic Discovery

Quantum-Powered Autonomous Deep-Sea Explorer Using AI, Micro...

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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Chouhan, Sandeep Sandhu, Ramandeep Yadav, Om Prakash Islam, Sarder M. N. Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India 2 Department of Applied Information Melbourne Australia

ISBN: (纸本)9798331513894

The ocean's depths are among the most enigmatic regions on Earth, with geographical features, temperatures, and marine life remaining widely unexplored. The traditional forms of exploration confront water pressure, cold-temperature conditions, and a lack of energy resources. This research suggests a novel apparatus, that integrates quantum technology, artificial intelligence (AI), and cutting-edge sensors to address these issues and enhance ocean floor mapping, gas detection and element *** energy apparatus of the device is central to ensuring its functionality for a prolonged period. It employs thermoelectric generators exploiting the temperature difference between the cold waters located at depth and warmer surface layers and the microbial fuel cells (MFCs) that convert organic materials in ocean sediments into electricity. This hybrid energy combined cycle, therefore sustainable operation without external power *** sensors such as quantum-enhanced spectroscopy and mass spectrometers will be used for the detection and analysis of gases, trace elements and organic compounds. The balanced engineering systems integrated into the automated equipment will be used with the power distribution control and information processing in the real-time mode performed by the quantum neural networks (QNN). The system's adaptability, and accuracy and that system may function unattended for fairly long periods, this system presents an unprecedented device that should change the course of deep-sea studies as it will provide access to previously unreachable areas. © 2025 IEEE.

关键词： Microbial fuel cells

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Ultra-Dense LEO Satellite-Aircraft Access and Service Management in Civil Aviation

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China Communications 2025年第1期22卷 277-292页

作者： Wang Yilei Ma Ting Liu Xiaoyu Gao Zhuxuan Zhou Haibo Shen Xuemin School of Electronic Science and Engineering Nanjing UniversityNanjing 210046China School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing 210094China The Department of Electrical and Computer Engineering University of WaterlooWaterloo N2L 3G1Canada

With the deployment of ultra-dense low earth orbit(LEO)satellite constellations,LEO satellite access network(LEO-SAN)is envisioned to achieve global Internet ***,the civil aviation communications have increased dramatically,especially for providing airborne Internet ***,due to dynamic service demands and onboard LEO resources over time and space,it poses huge challenges in satellite-aircraft access and service management in ultra-dense LEO satellite networks(UDLSN).In this paper,we propose a deep reinforcement learning-based approach for ultra-dense LEO satellite-aircraft access and service ***,we develop an airborne Internet architecture based on UDLSN and design a management mechanism including medium earth orbit satellites to guarantee lightweight ***,considering latency-sensitive and latency-tolerant services,we formulate the problem of satellite-aircraft access and service management for civil aviation to ensure service ***,we propose a proximal policy optimization-based access and service management algorithm to solve the formulated *** results demonstrate the convergence and effectiveness of the proposed algorithm with satisfying the service continuity when applying to the UDLSN.

关键词： civil aviation deep reinforcement learning satellite-aircraft access service management ultra-dense LEO satellite network

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Cuckoo Search-Optimized Deep CNN for Enhanced Cyber Security in IoT Networks

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computers, Materials & Continua 2024年第12期81卷 4109-4124页

作者： Brij B.Gupta Akshat Gaurav Varsha Arya Razaz Waheeb Attar Shavi Bansal Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413TaiwanChina Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057MaharashtraIndia Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Business Administration Asia UniversityTaichung413TaiwanChina Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Research and Innovation Insights2TechinfoJaipur302001India Department of Computer Science Faculty of ScienceNorthern Border UniversityArar91431Saudi Arabia Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University(HKMU)Hong Kong518031China

Phishing attacks seriously threaten information privacy and security within the Internet of Things(IoT)*** phishing attack detection solutions have been developed for IoT;however,many of these are either not optimally efficient or lack the lightweight characteristics needed for practical *** paper proposes and optimizes a lightweight deep-learning model for phishing attack *** model employs a two-fold optimization approach:first,it utilizes the analysis of the variance(ANOVA)F-test to select the optimal features for phishing detection,and second,it applies the Cuckoo Search algorithm to tune the hyperparameters(learning rate and dropout rate)of the deep learning ***,our model is trained in only five epochs,making it more lightweight than other deep learning(DL)and machine learning(ML)*** proposed model achieved a phishing detection accuracy of 91%,with a precision of 92%for the’normal’class and 91%for the‘attack’***,the model’s recall and F1-score are 91%for both *** also compared our approach with traditional DL/ML models and past literature,demonstrating that our model is more *** study enhances the security of sensitive information and IoT devices by offering a novel and effective approach to phishing detection.

关键词： Deep learning phishing Cuckoo Search cable news network(CNN) IoT ANOVA F-test

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End-to-end dynamic residual focal transformer network for multimodal medical image fusion

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Neural Computing and Applications 2024年第19期36卷 11579-11601页

作者： Zhang, Weihao Yu, Lei Wang, Huiqi Pedrycz, Witold College of Computer and Information Science Chongqing Normal University Chongqing401331 China College of Mathematics and Statistics Chongqing University Chongqing401331 China Department of Electrical and Computer Engineering University of Alberta EdmontonT6G 2V4 Canada

Multimodal medical image fusion aims to improve the clinical practicability of medical images by integrating complementary information from multiple medical images. However, in traditional fusion methods, the fusion rules based on prior knowledge or logic usually cannot match the feature representation perfectly, which results in partial information loss. Furthermore, most deep learning-based fusion methods depend on convolutional operations, which only focus on local features and have limited retention of context information. To address the above issues, we propose an end-to-end dynamic residual focal transformer network for multimodal medical image fusion, termed DRFT. The DRFT framework is an end-to-end network with no need to manually design fusion rules. Firstly, the context-gated convolution is introduced to construct the context dynamic extraction module (CDEM) to extract the key semantic information more accurately from multimodal medical images. Then, a new residual transformer fusion module (RTFM) is designed by incorporating the focal transformer into the residual mechanism, which can not only extract the deep semantic features, but also adaptively learn the optimal fusion scheme. Finally, the nest architecture is employed to extract multiscale features. In addition, a new objective function consisting of global detail loss and fusion enhancement loss is designed to enrich the modal information in the fused image. Notably, the proposed network does not require the two-stage training strategy as opposed to the traditional encoder–decoder fusion structure. Extensive experimental results on mainstream datasets show that, compared with the state-of-the-art methods, the proposed DRFT delivers better performance in both qualitative and quantitative evaluation. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolution

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Modeling Local Demand for Mobile Spectrum: An Interpretable Machine Learning Approach

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 4063-4082页

作者： Parekh, Janaki Yackoboski, Elizabeth Ghasemi, Amir Yanikomeroglu, Halim Communications Research Centre Canada Department of Data Science OttawaONK2H 8S2 Canada Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

With the expansion of 5G networks and the ongoing development of future 6G networks, the demand for mobile spectrum is expected to continue to grow, particularly at a local level. In response, spectrum regulators globally are exhibiting growing interest in enhancing their understanding of current mobile spectrum demand. The goal is twofold: to maximize the socioeconomic benefits of this finite resource and to ensure that spectrum policy and licensing decisions continue to drive innovation within the wireless industry. Despite its importance, research in modeling mobile spectrum demand has been notably scarce, particularly at the granularity required in the spectrum regulatory domain. To address this gap, this paper presents a data-driven approach to estimate localized mobile spectrum demand within the context of spectrum regulation. A novel demand proxy is first introduced, derived from a large and diverse dataset of crowdsourced commercial mobile measurements. Subsequently, spectrum demand modeling is formulated as a regression task and a variety of classical machine learning models are explored, leveraging publicly available geospatial data as input features. The top-performing model successfully achieves an R2 of 0.76 and a Root Mean Square Error of 51.02 on the hold-out test set. Finally, a machine learning interpretability technique is applied to demonstrate how these models can be used for regulatory decision-making, particularly in scenarios requiring transparency and accountability. © 2020 IEEE.

关键词： 5G mobile communication systems

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Enhancing Skin Cancer Diagnosis with Deep Learning:A Hybrid CNN-RNN Approach

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computers, Materials & Continua 2024年第4期79卷 1497-1519页

作者： Syeda Shamaila Zareen Guangmin Sun Mahwish Kundi Syed Furqan Qadri Salman Qadri Faculty of Information Technology Beijing University of TechnologyBeijing100124China Computer Science International Engineering Collage Maynooth UniversityKildareW23 F2H6Irland Research Center for Data Hub and Security Zhejiang LabHangzhou311121China Computer Science Department MNS University of AgricultureMultan59220Pakistan

Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.

关键词： Skin cancer classification deep learning Convolutional Neural Network(CNN) RNN ResNet-50

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A Temporal Type-2 Fuzzy System for Time-Dependent Explainable Artificial Intelligence

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第3期4卷 573-586页

作者： Kiani, Mehrin Andreu-Perez, Javier Hagras, Hani School of Computer and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Simbad2 Department of Computer Science University of Jaén Jaén23071 Spain

Explainable artificial intelligence (XAI) focuses on transparent AI models and decisions, which are easy to understand, analyze, and augment by a nontechnical audience. Fuzzy logic systems (FLS)-based XAI provides an explainable framework while also modeling uncertainties in real-world environments. However, most real-life processes are not characterized by high uncertainty alone;they are also inherently time dependent, i.e., the processes are time variant. In this work, we present a novel temporal type-2 FLS-based approach for time-dependent XAI (TXAI) systems, which can account for the likelihood of a sample occurrence in the time domain by its the frequency. In the proposed temporal type-2 fuzzy sets (TT2FSs), a 4-D time-dependent membership function integrates the universe of discourse, its membership, and its frequency of occurrence across time. The TXAI system manifested better classification prowess in cross-validation tests, with a mean recall of 95.40% than a standard XAI system (based on nontemporal general type-2 fuzzy sets) that had a mean recall of 87.04%. TXAI also performed significantly better than most nonexplainable AI systems, with between 3.95% and 19.04% improvement gain in mean recall. In addition, TXAI can also outline the most likely time-dependent trajectories using the frequency and time dimensions embedded in the TXAI model;viz. given a rule at a determined time interval, what will be the next most likely rule at a subsequent time interval. In this regard, the proposed TXAI system can have profound implications for delineating the evolution of real-life time-dependent processes, such as behavioral or biological processes. © 2020 IEEE.

关键词： Fuzzy logic

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Predictive Analytics in Maternal Health: A Machine Learning Approach for Classification of Preeclampsia

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International Journal of Computational Intelligence Systems 2025年第1期18卷 1-31页

作者： Amin, Pakiza Gulzar Ahmad, Saima Khan, Hikmat Ullah Munir, Ehsan Ullah Ramzan, Naeem Department of Computer Science COMSATS University Islamabad Wah Campus Punjab Wah Cantt47040 Pakistan Department of Computer Science University of Sargodha Punjab Sargodha40100 Pakistan School of Computing Engineering and Physical Sciences University of the West of Scotland High Street PaisleyPA1 2BE United Kingdom

The condition known as preeclampsia is a hypertensive disorder that occurs during pregnancy and has serious implications for both the mother and the fetus. Proper management of the condition depends on early detection of preeclampsia to make a correct prognosis. In this study, we classify pre-eclampsia using three datasets: two of which are the public datasets acquired from Mendeley and Kaggle, respectively, while the third is a real-world clinical dataset obtained from a local hospital. Recursive feature elimination, principal component analysis, correlation-based feature selection, and particle swarm optimization were used to select significant features from the predictor variables of the public datasets. To improve the classification performance, several models were created, with an emphasis on ensemble learning methods. Specifically, we propose three models: the alternative classification models include the Soft Decision Fusion Model, which applies soft-voting;the Stacking-Based Classifier, which is an ensemble stacking;and the Hybrid Soft Stacking Model. These models were assessed in detail concerning their quantitative indicators for the AUC-ROC criterion. The performance of our proposed models in the public datasets was an AUC-ROC of more than 95% and in the clinical dataset an even higher 96%. These ensemble methods accurately show that they have effective results in improving the precision and reliability of pre-eclampsia forecasts. With the help of real and public clinical data, the present work presents an effective and ecological approach that can help healthcare professionals make appropriate and timely decisions about the management of pre-eclampsia. In particular, the results of the Hybrid Soft Stacking Model look quite convincing in terms of predictive value, so the model could be considered a useful tool in the clinical context. © The Author(s) 2025.

关键词： Obstetrics

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An Integrated Fusion Framework for Ensemble Learning Leveraging Gradient-Boosting and Fuzzy Rule-Based Models

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5771-5785页

作者： Li, Jinbo Liu, Peng Chen, Long Pedrycz, Witold Ding, Weiping Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China China Unicom Research Institute Beijing100048 China Henan University International Business School Zhengzhou47500 China University of Macau Department of Computer and Information Science 999078 China University of Alberta Department of Electrical and Computer Engineering EdmontonAB T6R 2V4 Canada City University of Macau Faculty of Data Science 999078 China

The integration of different learning paradigms has long been a focus of machine learning research, aimed at overcoming the inherent limitations of individual methods. Fuzzy rule-based models excel in interpretability and have seen widespread application across diverse fields. However, they face challenges such as complex design specifications and scalability issues with large datasets. The fusion of different techniques and strategies, particularly gradient boosting, with fuzzy rule-based models offers a robust solution to these challenges. This article proposes an integrated fusion framework that merges the strengths of both paradigms to enhance model performance and interpretability. At each iteration, a fuzzy rule-based model is constructed and controlled by a dynamic factor to optimize its contribution to the overall ensemble. This control factor serves multiple purposes: it prevents model dominance, encourages diversity, acts as a regularization parameter, and provides a mechanism for dynamic tuning based on model performance, thus mitigating the risk of overfitting. Additionally, the framework incorporates a sample-based correction mechanism that allows for adaptive adjustments based on feedback from a validation set. Experimental results substantiate the efficacy of the presented gradient-boosting framework for fuzzy rule-based models, demonstrating performance enhancement, especially in terms of mitigating overfitting and complexity typically associated with many rules. By leveraging an optimal factor to govern the contribution of each model, the framework improves performance, maintains interpretability, and simplifies the maintenance and update of the models. © 2024 IEEE.

关键词： Optimization

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