Machine Learning Technologies for Big Data Analytics

Gandomi, Amir H.; Chen, Fang; Abualigah, Laith

doi:10.3390/electronics11030421

Cited by 39 publications

(23 citation statements)

References 14 publications

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“…However, to learn and grow, computers need data to analyze and train. Therefore, big data are consideblack the essence of AI in general and ML in particular, and it is the technique that unlocks the full potential of big data 54 …”

Section: Ml‐based Solutions For Iotmentioning

confidence: 99%

A systematic literature review of machine learning applications in IoT

Gherbi

Senouci

Harbi

et al. 2023

Int J Communication

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The Internet of Things (IoT) is a network of interconnected smart objects having capabilities that collectively form an ecosystem and enable the delivery of smart services to users. The IoT is providing several benefits into people's lives through the environment. The various applications that are run in the IoT environment offer facilities and services. The most crucial services provided by IoT applications are quick decision for efficient management. Recently, machine learning (ML) techniques have been successfully used to maximize the potential of IoT systems. This paper presents a systematic review of the literature on the integration of ML methods in the IoT. The challenges of IoT systems are split into two categories: fundamental operation and performance. We also look at how ML is assisting in the resolution of fundamental system operation challenges such as security, big data, clustering, routing, and data aggregation.

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Section: Ml‐based Solutions For Iotmentioning

confidence: 99%

A systematic literature review of machine learning applications in IoT

Gherbi

Senouci

Harbi

et al. 2023

Int J Communication

View full text Add to dashboard Cite

show abstract

“…With the development of machine learning, methods of analytic data modelling are gradually emerging (L 'Heureux et al, 2017;Munawar et al, 2021;Gandomi et al, 2022). In order to better manage the stormwater pipe network and avoid urban flooding, Hao Wang and Lixiang Son (Wang and Lixiang, 2020) employed the SVM machine learning technology to estimate the water level of the system.…”

Section: Related Workmentioning

confidence: 99%

Drainage network flow anomaly classification based on XGBoost

2023

Global NEST: The International Journal

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<p>Identifying and classifying anomalies in on-line monitoring systems of drainage systems is important to reduce urban water pollution. In the context of big data, the mini-batch K-means combined with the XGBoost drainage network abnormal flow identification and classification model is proposed to precisely identify and classify abnormalities that occur in real-time updates of online drainage network data while resolving problems with subjectivity and the lack of uniform standards for classification. First, using Mini Batch K-means, the unclassified drainage network data were sorted into four categories: normal drainage, sneaky drainage, rainwater and sewage mixing, and inflow and infiltration. Next, XGBoost performs data modeling to create a model for classification and identification of drainage flow anomalies. To increase the accuracy of the model, the features were ultimately chosen based on the ranking of the importance of the features, and the model parameters were established using grid search and cross-validation. The results showed that the XGBoost Drainage Network Anomaly Classification and Identification Model can accurately identify four drainage network situations with high classification accuracy and good performance. It was also validated through the application of data from the online system monitoring points in Changsha, China, in 2020.</p>

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“…Because the Machine Learning (ML) technique is capable of dealing with complex nonlinear structural systems under extreme action, along with the availability of large data sets, the use of ML in structural engineering has become increasingly popular in recent years. The applications of ML mainly include: (1) structural design and analysis; (2) structural health monitoring and damage detection; (3) structural fire resistance; (4) structural member resistance to various actions; (5) concrete mechanical properties and mix design [4][5][6][7][8]. Professional technicians can familiarize themselves with the field as quickly as possible by providing database and machine learning code.…”

Section: Introductionmentioning

confidence: 99%

Structural Damage Identification System Suitable for Old Arch Bridge in Rural Regions: Random Forest Approach

Xiong¹,

Liang²,

She³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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A huge number of old arch bridges located in rural regions are at the peak of maintenance. The health monitoring technology of the long-span bridge is hardly applicable to the small-span bridge, owing to the absence of technical resources and sufficient funds in rural regions. There is an urgent need for an economical, fast, and accurate damage identification solution. The authors proposed a damage identification system of an old arch bridge implemented with a machine learning algorithm, which took the vehicle-induced response as the excitation. A damage index was defined based on wavelet packet theory, and a machine learning sample database collecting the denoised response was constructed. Through comparing three machine learning algorithms: Back-Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (R.F.), the R.F. damage identification model were found to have a better recognition ability. Finally, the Particle Swarm Optimization (PSO) algorithm was used to optimize the number of subtrees and split features of the R.F. model. The PSO optimized R.F. model was capable of the identification of different damage levels of old arch bridges with sensitive damage index. The proposed framework is practical and promising for the old bridge's structural damage identification in rural regions.

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Machine Learning Technologies for Big Data Analytics

Abstract: Big data analytics is one high focus of data science and there is no doubt that big data is now quickly growing in all science and engineering fields [...]

Cited by 39 publications

References 14 publications

A systematic literature review of machine learning applications in IoT

A systematic literature review of machine learning applications in IoT

Drainage network flow anomaly classification based on XGBoost

Structural Damage Identification System Suitable for Old Arch Bridge in Rural Regions: Random Forest Approach

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