Using a distributed deep learning algorithm for analyzing big data in smart cities

Naoui, Mohammed Anouar; Lejdel, Brahim; Ayad, Mouloud; Amamra, Abdelfattah; Kazar, Okba

doi:10.1108/sasbe-04-2019-0040

Cited by 12 publications

(13 citation statements)

References 26 publications

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“…Deep learning and OR are closely interrelated, as both use advanced analytics to solve complex problems through statistics, computer science, and mathematics (Kraus et al, 2020 ). Deep learning uses mathematical rules that could elaborate an event (Naoui et al, 2021 ). It can impact a firm’s operational activities (Curtis & Scheinberg, 2017 ; Garousi Mokhtarzadeh et al, 2020 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…This is the core idea behind RBV theory. It highlights that, in adopting a new technology, a strategist will select the strategy that exploits the firm’s internal capabilities rather than external opportunities (Hassan, 2017 ; Naoui et al, 2021 ). This theory posits that a firm’s managers should identify the firm’s key resources and evaluate which follow the VRIN criteria, and then protect as well as use such resources for adopting the targeted technology.…”

Section: Theoretical Background and Development Of Conceptual Modelmentioning

confidence: 99%

“…Deep learning technology helps the predictive maintenance process. Deep learning is used as a time-setting data management technique to ascertain the conditions and performances of the in-service equipment of the manufacturing unit (Naoui et al, 2021 ; Shahin et al, 2018 ; You, 2017 ). The technology collects useful data for giving timely support to the in-service equipment.…”

Section: Theoretical Background and Development Of Conceptual Modelmentioning

confidence: 99%

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Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

et al. 2022

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Data science can create value by extracting structured and unstructured data using an appropriate algorithm. Data science operations have undergone drastic changes because of accelerated deep learning progress. Deep learning is an advanced process of machine learning algorithm. Its simple process of presenting data to the system is sharply different from other machine learning processes. Deep learning uses advanced analytics to solve complex problems for accurate business decisions. Deep leaning is considered a promising area for creating additional value in firms’ productivity and sustainability as they develop their smart manufacturing activities. Deep learning capability can help a manufacturing firm’s predictive maintenance, quality control, and anomaly detection. The impact of deep learning technology capability on manufacturing firms is an underexplored area in the literature. With this background, the purpose of this study is to examine the impact of deep learning technology capability on manufacturing firms with moderating roles of deep learning related technology turbulence and top management support of the manufacturing firms. With the help of literature review and theories, a conceptual model has been prepared, which is then validated with the PLS-SEM technique analyzing 473 responses from employees of manufacturing firms. The study shows the significance of deep learning technology capability on smart manufacturing systems. Also, the study highlights the moderating impacts of top management team (TMT) support as well as the moderating impacts of deep learning related technology turbulence on smart manufacturing systems.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Theoretical Background and Development Of Conceptual Modelmentioning

confidence: 99%

Section: Theoretical Background and Development Of Conceptual Modelmentioning

confidence: 99%

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Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

et al. 2022

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“…More recently, the development of strategies that require the participation of the construction industry such as smart cities whose objective is "to increase the quality of life in multiple dimensions, such as health, transport, energy efficiency, etc. " Naoui et. al., (2020) andSaya et.…”

Section: Big Data In Constructionmentioning

confidence: 99%

Awareness of Big Data concept in the Dominican Republic construction industry: an empirical study

Renukappa

Suresh

Reyes-Veras

2021

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Purpose The construction industry, being one of the main activities in the ever-demanding need for technology developments, sometimes falls short of other industries in terms of implementation. The adoption of Big Data (BD) in industries such as health and retail has had positive impacts in aspects such as decision-making processes and forecasting trends that allow planning some future business movements. Hence, the question of whether these results can be imitated in the construction industry. Therefore, this paper aims to address the level of awareness identified as a first step towards implementation of the BD concept within the construction industry in the Dominican Republic (DR). Design/methodology/approach As little to no information exist on the subject; the selected approach to perform this research was qualitative methodology; 21 semi-structured interviews were studied using situational awareness. Four levels of awareness were developed based on the Endsley’s Situation Awareness model. Findings The results showed that nearly 95% of the interviewees had either no knowledge or very basic awareness of the BD requirements or intermediate awareness, but only 5% had applied BD concepts in the construction industry. Originality/value This study shows the gaps that exist in the understanding and implementation of BD concepts in the DR construction industry. This paper establishes the need to develop continuous professional development programmes for construction professionals and a need to update curriculum in construction-related education.

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“…Solutions such as problemsolving throughout the different stages of a project in the case of BIM (Nik-Bakht et. al., 2021), and the efficient allocation of resources and life independence as a result of Smart Cities implementations (Naoui et. al., 2021), are only some of the benefits that these technologies bring to the industry have made their adoption considered a necessity nowadays.…”

Section: Introductionmentioning

confidence: 99%

Challenges faced by the adoption of big data in the Dominican Republic construction industry: an empirical study

Reyes-Veras¹,

Renukappa²,

Suresh³

2021

ITcon

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The adoption of Big Data (BD) in the construction industry has been identified as a possible solution to the demand of the current needs of projects, but the integration of this technology has proven to be a challenge specially in industries such as construction that are not technological driven. The understanding of the key elements for the BD adoption has become the focus of many industries that seek to exploit the benefits offered by this technology. Therefore, this paper aims to explore the challenges faced by the adoption of BD in the Dominican Republic (DR) construction industry. To identify these challenges qualitative research was undertaken due to the paucity of scientific data. Twenty-one individuals representing 19 companies who have great impact in the DR construction sector were interviewed. From the analysis six main challenges were identified. They are: lack BD awareness, high cost of investment, resistance to change, lack of government support and regulation, lack of technological expertise, and security concerns of BD. The challenges identified in this study, will serve to help companies better plan their technology adoption process, mainly considering aspects such as the need to tackle the lack of awareness by disseminating and promoting the concept of BD which will not only generate a better understanding of technology by making sure that present and future professionals understand the technology and its benefits. This study provides insight in the challenges to overcome for a successful adoption of BD technology, which would help companies to prepare for a future adoption.

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Using a distributed deep learning algorithm for analyzing big data in smart cities

Cited by 12 publications

References 26 publications

Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

Awareness of Big Data concept in the Dominican Republic construction industry: an empirical study

Challenges faced by the adoption of big data in the Dominican Republic construction industry: an empirical study

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