A Low-Latency Edge Computation Offloading Scheme for Trust Evaluation in Finance-Level Artificial Intelligence of Things

Zhu, Xiaogang; Ma, Feicheng; Ding, Feng; Guo, Zhiwei; Yang, Junchao; Yu, Keping

doi:10.1109/jiot.2023.3297834

Cited by 47 publications

(10 citation statements)

References 48 publications

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“…This section compares the proposed method with several existing ones, including PCT [43], MST-FaDe [37], Fuzzy SVM [29], and Artificial Intelligence-Based Student Assessment and Recommendation (AISAR) [38]. It evaluates its effectiveness using performance metrics such as accuracy, precision, recall, F1-score, true positive rate, false positive rate, true negative rate, and false negative rate.…”

Section: Comparative Analysismentioning

confidence: 99%

Visual Data and Pattern Analysis for Smart Education: A Robust Drl-Based Early Warning System for Student Performance Prediction

Bagunaid,

Chilamkurti,

Shahraki

et al. 2024

Preprint

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Artificial Intelligence (AI) and Deep Reinforcement Learning (DRL) have revolutionised e-learning by creating personalised, adaptive, and secure environments. However, challenges such as privacy, bias, and data limitations persist. E-FedCloud aims to address these issues by providing more agile, personalised, and secure e-learning experiences. This study introduces E-FedCloud, an AI-assisted adaptive e-learning system that automates personalised recommendations and tracking, thereby enhancing student performance. It employs federated learning-based authentication to ensure secure and private access for both course instructors and students. Intelligent Software Agents (ISAs) evaluate weekly student engagement using the Shannon Entropy method, classifying students into either engaged or not-engaged clusters. E-FedCloud utilises weekly engagement status, demographic information, and an innovative DRL-based early warning system, specifically ID2QN, to predict the performance of not-engaged students. Based on these predictions, the system categorises students into three groups: risk of dropping out, risk of scoring lower in the final exam, and risk of failing the end exam. It employs a multi-disciplinary ontology graph and an attention-based capsule network for automated, personalised recommendations. The system also integrates performance tracking to enhance student engagement. Data is securely stored on a blockchain using the LWEA encryption method.

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Section: Comparative Analysismentioning

confidence: 99%

Visual Data and Pattern Analysis for Smart Education: A Robust Drl-Based Early Warning System for Student Performance Prediction

Bagunaid,

Chilamkurti,

Shahraki

et al. 2024

Preprint

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show abstract

“…The common noise disturbances or artifacts in low-dose computed tomography (CT) or undersampling magnetic resonance imaging (MRI) hinder the accurate estimation of data distribution gradients. This affects the overall performance of SGM when using these data for training [22]. LM (Levenberg-Marquardt) optimization algorithm is a commonly used nonlinear least square method, which combines the respective advantages of steepest descent method and Gaussian Newton method [32].…”

Section: B 3d Image Vrmentioning

confidence: 99%

A Kruppa Equation-Based Virtual Reconstruction Method for 3D Images Under Visual Communication Scenes

Yang,

Yang

2023

IEEE Access

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Visual communication (VC) manages to demonstrate optimal perception effect through reasonable element planning. It has been a more prevalent tendency to introduce 3D image reconstruction into VC activities. Currently, there still lacks reliable solution for this purpose under VC scenes. To deal with this issue, we propose a Kruppa equation-based virtual reconstruction method for 3D images under VC scenes. Firstly, the feature descriptor procedure in scale-invariant feature transform (SIFT) algorithm is introduced into image matching. Then, Harris corner extraction (HCE) algorithm is utilized for selection of feature points. And according to HCE algorithm, we develop a specific direction feature description method. In the proposal, main camera points are calibrated separately, which not only makes the calibration accuracy of the main points higher, but also reduces the unknown parameters of Kruppa equation. Finally, we conduct some experiments to verify efficiency of the proposal. The obtained results show that calibration accuracy of the proposal is 12% higher than other comparison methods, and better virtual reconstruction results can be achieved.

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“…The internet of things (IoT) has a broad development prospect in the field of distribution grid energy dispatch. By connecting distribution grid operators with energy users and electric equipment (Tariq et al, 2020;Liao et al, 2023a;Fizza et al, 2023;Liu et al, 2023;Safdar Malik et al, 2023;Zhu et al, 2024), IoT provides dynamic data acquisition and realtime state perception of key electric equipment. Then, these collected data can be uploaded to the edge server or cloud server for deep state analysis and intelligent energy dispatch Yao et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Delay and cost-balanced communication resource management for IoT-empowered distribution grid energy dispatch

Shi

2024

Front. Energy Res.

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The combination of internet of things (IoT), 5G, and power line communication (PLC) provides real-time and low-cost data transmission services to meet the quality of service (QoS) requirements for distribution grid energy dispatch. However, the IoT-empowered communication resource management system faces challenges in optimizing delay and traffic cost for distribution grid energy dispatch. There is a contradiction between the long-term performance guarantee and short-term optimization objectives, compounded by competition for communication resources. In this paper, we construct the minimization problem of the weighted sum of data transmission delay and traffic cost. Utilizing Lyapunov optimization, we aim to decouple the long-term constraint of average queuing delay with the short-term optimization objective. Then, a delay and cost-balanced communication resource management algorithm based on two-layer iterative matching is proposed. It optimizes the communication mode selection by I-to-2 bidding matching in a large timescale and subchannel allocation by many-to-many deferred acceptance matching in a small timescale. The simulation data present that the proposed algorithm excels in reducing data transmission delay, minimizing traffic cost, and decreasing queuing delay.

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A Low-Latency Edge Computation Offloading Scheme for Trust Evaluation in Finance-Level Artificial Intelligence of Things

Cited by 47 publications

References 48 publications

Visual Data and Pattern Analysis for Smart Education: A Robust Drl-Based Early Warning System for Student Performance Prediction

Visual Data and Pattern Analysis for Smart Education: A Robust Drl-Based Early Warning System for Student Performance Prediction

A Kruppa Equation-Based Virtual Reconstruction Method for 3D Images Under Visual Communication Scenes

Delay and cost-balanced communication resource management for IoT-empowered distribution grid energy dispatch

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