Real-Time Outlier Detection and Bayesian Classification using Incremental Computations for Efficient and Scalable Stream Analytics for IoT for Manufacturing

Parto, Mahmoud; Saldaña, Christopher

doi:10.1016/j.promfg.2020.05.136

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…Another classifier quite popular for OD problems is naïve Bayes because of its ease of use and simplicity [70,71]. Parto et al [72] evaluated classical Bayesian techniques with slight modification for OD in streaming IoT platforms in the manufacturing industry. Further, similar to [66], Lam et al [73] addressed the OD issue in traffic data using a fusion of naïve Bayes and Gaussian mixture-model techniques.…”

Section: Outlier Detectionmentioning

confidence: 99%

Machine Learning-Enabled Internet of Things (IoT): Data, Applications, and Industry Perspective

Bzai

Alam²,

Dhafer

et al. 2022

Electronics

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Machine learning (ML) allows the Internet of Things (IoT) to gain hidden insights from the treasure trove of sensed data and be truly ubiquitous without explicitly looking for knowledge and data patterns. Without ML, IoT cannot withstand the future requirements of businesses, governments, and individual users. The primary goal of IoT is to perceive what is happening in our surroundings and allow automation of decision-making through intelligent methods, which will mimic the decisions made by humans. In this paper, we classify and discuss the literature on ML-enabled IoT from three perspectives: data, application, and industry. We elaborate with dozens of cutting-edge methods and applications through a review of around 300 published sources on how ML and IoT work together to play a crucial role in making our environments smarter. We also discuss emerging IoT trends, including the Internet of Behavior (IoB), pandemic management, connected autonomous vehicles, edge and fog computing, and lightweight deep learning. Further, we classify challenges to IoT in four classes: technological, individual, business, and society. This paper will help exploit IoT opportunities and challenges to make our societies more prosperous and sustainable.

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Section: Outlier Detectionmentioning

confidence: 99%

Machine Learning-Enabled Internet of Things (IoT): Data, Applications, and Industry Perspective

Bzai

Alam²,

Dhafer

et al. 2022

Electronics

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show abstract

“…A study was presented to explain, examine, and assess the precision and idleness of the novel variety of these strategies. It is concluded that, by adjusting the conventional strategies and characterizing progressive arrangements, strategies such as Real-Time Dynamic Statistical Process Control Chart (RTDSPCC) and Incremental Gaussian Naïve Bayes (IGNB) can be shaped, which is exceedingly advantageous for IoT [23].…”

Section: Smart Iot Systems For Enhancing Energy Consumptionmentioning

confidence: 99%

Internet of Things Based Intelligent Techniques in Workable Computing: An Overview

Jiayi

Nazir

2021

Scientific Programming

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With the constant developments in Internet communication and the rise of the Internet of Things (IoT), technologies incorporating intelligent manufacturing have given birth to the growing industry and production lines. The network of IoT is generally interconnected with different devices through the Internet. The interactions of the IoT devices form smooth and functional communication require the connectivity of billions of objects. The devices of IoT can preserve, capture, share, and analyze data with nodes connected to the world. Various issues of the IoT such as monitoring the data, stealing the data, privacy of the data, tracking of the data, and many other aspects of the data are becoming challenges for the modern-day industry. The role of computational intelligence in proper analysis, managing, and many different perspectives of the IoT is prominent. Such computational intelligence can solve real-time problems with low cost and time. The IoT has provided solutions for poor scalability, system integration, and difficulties in coordinated operation across the emerging systems. The influence of the proposed study is to offer a wide-ranging overview of the current literature related to the Internet of Things based on intelligent techniques in workable computing. The study has considered the search process in the most popular libraries and presented an analysis of the research work done so far. The analysis and results of the study support the progress in the field, which will help researchers come up with new solutions.

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A Novel Three-Layer IoT Architecture for Shared, Private, Scalable, and Real-time Machine Learning from Ubiquitous Cyber-Physical Systems

Parto

Saldaña

2020

Procedia Manufacturing

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Real-Time Outlier Detection and Bayesian Classification using Incremental Computations for Efficient and Scalable Stream Analytics for IoT for Manufacturing

Cited by 7 publications

References 11 publications

Machine Learning-Enabled Internet of Things (IoT): Data, Applications, and Industry Perspective

Machine Learning-Enabled Internet of Things (IoT): Data, Applications, and Industry Perspective

Internet of Things Based Intelligent Techniques in Workable Computing: An Overview

A Novel Three-Layer IoT Architecture for Shared, Private, Scalable, and Real-time Machine Learning from Ubiquitous Cyber-Physical Systems

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