Big Data analytics and Computational Intelligence for Cyber–Physical Systems: Recent trends and state of the art applications

Iqbal, Rahat; Doctor, Faiyaz; More, Brian; Mahmud, Shahid; Yousuf, Usman

doi:10.1016/j.future.2017.10.021

Cited by 103 publications

(38 citation statements)

References 49 publications

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“…Las técnicas de IC forman un conjunto de metodologías y técnicas computacionales que han sido desarrolladas para abordar problemas complejos basados en datos del mundo real para los cuales los modelos matemáticos tradicionales no ofrecen buenos resultados debido a: alta complejidad, incertidumbre y naturaleza estocástica de los procesos. La Lógica Difusa (FL), Algoritmos Evolutivos (EA) y Redes Neuronales Artificiales (ANN) forman tres pilares centrales de la IC que se han desarrollado para manejar esta creciente clase de problemas [43]. El empleo de las técnicas de IC en la industria actual ha permitido desarrollar máquinas capaces de realizar tareas de diseño y fabricación cada vez más avanzadas.…”

Section: Sistemas De Control Inteligenteunclassified

El control de sistemas ciberfísicos industriales: revisión y primera aproximación

Jaén¹,

Romero²,

Haber³

et al. 2020

Actas De Las XXXIX Jornadas De Automática, Badajoz, 5-7 De Septiembre De 2018

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Cyber-Physical systems (CPS) are the new generation of intelligent systems, which gives greater importance to the interaction between the physical processes and the computational components than their predecessors. It has become one of the fundamental pillars of the fourth industrial revolution which has brought with it the emergence of new challenges in the design of their control systems. This paper deals with the basic concepts of CPS and industrial cyber-physical systems (ICPS), presents a review of the main control strategies used for its design.

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Section: Sistemas De Control Inteligenteunclassified

El control de sistemas ciberfísicos industriales: revisión y primera aproximación

Jaén¹,

Romero²,

Haber³

et al. 2020

Actas De Las XXXIX Jornadas De Automática, Badajoz, 5-7 De Septiembre De 2018

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“…However, the second school of thought propagate the use of nature-inspired algorithms to train the ANN to improve its efficiency, robustness and efficiency as proven in the literature. Reference [111] propose hierarchical spatial-temporal state machine comprising of deep belief network, agglomerative hierarchical clustering, auto-encoders, and temporal sequence process for processing big data. We recommend researchers to extensively explore the framework in processing big data using DNN models.…”

Section: Future Research Opportunitiesmentioning

confidence: 99%

Progress on Artificial Neural Networks for Big Data Analytics: A Survey

et al. 2019

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Approximately 2.5 quintillion bytes of data are emitted on a daily basis, and this has brought the world into the era of ''big data.'' Artificial neural networks (ANNs) are known for their effectiveness and efficiency for small datasets, and this era of big data has posed a challenge to the big data analytics using ANN. Recently, much research effort has been devoted to the application of the ANN in big data analytics and is still ongoing, although it is in it is early stages. The purpose of this paper is to summarize recent progress, challenges, and opportunities for future research. This paper presents a concise view of the state of the art, challenges, and future research opportunities regarding the applications of the ANN in big data analytics and reveals that progress has been made in this area. Our review points out the limitations of the previous approaches, the challenges in the ANN approaches in terms of their applications in big data analytics, and several ANN architecture that have not yet been explored in big data analytics and opportunities for future research. We believe that this paper can serve as a yardstick for future progress on the applications of the ANN in big data analytics as well as a starting point for new researchers with an interest in the exploration of the ANN in big data analytics. INDEX TERMS Big data analytics, artificial neural networks, evolutionary neural network, convolutional neural network, dataset. The associate editor coordinating the review of this manuscript and approving it for publication was Shirui Pan. many organizations on an ongoing basis. These datasets are being collected from various sources, including but not limited to the World Wide Web (WWW), social networks and sensor networks [3]. The discovery of knowledge from unstructured data accumulated from the WWW remains a difficult task because the content is suitable for human consumption rather than for machines [4]. Experimental evidence has shown that if big datasets are exploited and managed properly, it can give rise to critical intelligence that can motivate informed decisions and

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“…It is based on the emergence of scientific research methods that can leverage big datasets [11]. Computational intelligence refers to approaches where algorithms intimate human information processes and reasoning [12].…”

Section: Computational Social Sciencementioning

confidence: 99%

Comparative Analysis of Big Data and BI Projects

Miller

2018

Annals of Computer Science and Information Systems

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Decision support systems such as big data, business intelligence (BI), and analytics offer firms capabilities to generate new revenue sources, increase productivity and outputs, and improve competitiveness. However, the field is crowded with terminology that makes it difficult to establish reasonable project scopes and to staff and manage projects. This study clarifies the terminology around the data science, computation social science, big data, business intelligence, and analytics and describes their meaning relative to decision support projects. For BI and big data projects, it identifies the critical success factors, empirically classifies the project scopes, and investigates the similarities and differences between the project types. This comparative analysis provides unique insights into the factors and criteria that influence BI and big data project success. These results should inform project sponsors and project managers of the contingency factors to consider when preparing project charters and plans. Index terms: Big data analytics, data science, business intelligence (BI), project success factors

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Big Data analytics and Computational Intelligence for Cyber–Physical Systems: Recent trends and state of the art applications

Cited by 103 publications

References 49 publications

El control de sistemas ciberfísicos industriales: revisión y primera aproximación

El control de sistemas ciberfísicos industriales: revisión y primera aproximación

Progress on Artificial Neural Networks for Big Data Analytics: A Survey

Comparative Analysis of Big Data and BI Projects

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