Outlier percentage estimation for shape‐ and parameter‐independent outlier detection

Goh, Michael Joon Seng; Chiew, Yeong Shiong; Foo, Ji Jinn

doi:10.1049/iet-ipr.2020.0334

Cited by 3 publications

(2 citation statements)

References 28 publications

(30 reference statements)

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“…However, this method requires some manual adjustment of the parameters or setting them according to the rule of thumb. Besides, Goh, Chiew & Foo (2020) introduced a novel method based on combined distances, capable of high accuracy outlier detection. But this did not work for outliers in high-density regions.…”

Section: Literature Reviewmentioning

confidence: 99%

OES-Fed: a federated learning framework in vehicular network based on noise data filtering

Lei

Wang

Caiyu

et al. 2022

PeerJ Computer Science

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The Internet of Vehicles (IoV) is an interactive network providing intelligent traffic management, intelligent dynamic information service, and intelligent vehicle control to running vehicles. One of the main problems in the IoV is the reluctance of vehicles to share local data resulting in the cloud server not being able to acquire a sufficient amount of data to build accurate machine learning (ML) models. In addition, communication efficiency and ML model accuracy in the IoV are affected by noise data caused by violent shaking and obscuration of in-vehicle cameras. Therefore we propose a new Outlier Detection and Exponential Smoothing federated learning (OES-Fed) framework to overcome these problems. More specifically, we filter the noise data of the local ML model in the IoV from the current perspective and historical perspective. The noise data filtering is implemented by combining data outlier, K-means, Kalman filter and exponential smoothing algorithms. The experimental results of the three datasets show that the OES-Fed framework proposed in this article achieved higher accuracy, lower loss, and better area under the curve (AUC). The OES-Fed framework we propose can better filter noise data, providing an important domain reference for starting field of federated learning in the IoV.

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Section: Literature Reviewmentioning

confidence: 99%

OES-Fed: a federated learning framework in vehicular network based on noise data filtering

Lei

Wang

Caiyu

et al. 2022

PeerJ Computer Science

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“…It realized several cryptographic modules via efficient and interchangeable protocols to support the above cryptographic operations and composed them in the overall protocol to enable outlier detection over encrypted data sets. In (Goh et al, 2020), a novel combinatory-distance-based method capable of high accuracy outlier detection named as the sorted distance divergence point was introduced. Moreover, the introduced distance function and outlier percentage allowed clear labelling of inliers and outliers cloud points.…”

Section: Related Workmentioning

confidence: 99%

AI for Health-Related Data Modeling

Cheng¹

2022

International Journal of Information System Modeling and Design

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Data modeling of health-related data from Data Center (DC) has positive effects for health monitoring, disease prevention, and healthcare research. However, health-related data has the characteristics of huge, high-dimensional, and non-normalized, which are not beneficial to direct analysis, so data needs to be preprocessed before data modeling. This paper focuses on the features of health-related data, and outlier detection during data preprocessing is studied. Meanwhile, we propose an improved algorithm for health-related data based outlier detection. The experimental results reveal that the proposed outlier detection algorithm has a smaller running time, and more outliers are detected compared to three baselines. In addition, local importance based random forest feature selection algorithm is proposed to measure the importance of each feature. The experimental results indicate that the proposed algorithm can select optimal feature subset to apply health-related data.

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Fractal-induced 2D flexible net undulation

Goh

Chiew

Foo

2021

Sci Rep

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A net immersed in fractal-induced turbulence exhibit a transient time-varying deformation. The anisotropic, inhomogeneous square fractal grid (SFG) generated flow interacts with the flexible net to manifest as visible cross-sectional undulations. We hypothesize that the net’s response may provide a surrogate in expressing local turbulent strength. This is analysed as root-mean-squared velocity fluctuations in the net, displaying intensity patterns dependent on the grid conformation and grid-net separation. The net’s fluctuation strength is found to increase closer to the turbulator with higher thickness ratio while presenting stronger fluctuations compared to regular-square-grid (RSG) of equivalent blockage-ratio, σ. Our findings demonstrate a novel application where 3D-reconstruction of submerged nets is used to experimentally contrast the turbulence generated by RSG and multilength scale SFGs across the channel cross-section. The net’s response shows the unique turbulence developed from SFGs can induce 9 × higher average excitation to a net when compared against RSG of similar σ.

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Outlier percentage estimation for shape‐ and parameter‐independent outlier detection

Cited by 3 publications

References 28 publications

OES-Fed: a federated learning framework in vehicular network based on noise data filtering

OES-Fed: a federated learning framework in vehicular network based on noise data filtering

AI for Health-Related Data Modeling

Fractal-induced 2D flexible net undulation

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