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The purpose of this paper is to propose a distributed deep learning architecture for smart cities in big data systems.

We have proposed an architectural multilayer to describe the distributed deep learning for smart cities in big data systems. The components of our system are Smart city layer, big data layer, and deep learning layer. The Smart city layer responsible for the question of Smart city components, its Internet of things, sensors and effectors, and its integration in the system, big data layer concerns data characteristics 10, and its distribution over the system. The deep learning layer is the model of our system. It is responsible for data analysis.

We apply our proposed architecture in a Smart environment and Smart energy. 10; In a Smart environment, we study the Toluene forecasting in Madrid Smart city. For Smart energy, we study wind energy foresting in Australia. Our proposed architecture can reduce the time of execution and improve the deep learning model, such as Long Term Short Memory10;.

This research needs the application of other deep learning models, such as convolution neuronal network and autoencoder.

Findings of the research will be helpful in Smart city architecture. It can provide a clear view into a Smart city, data storage, and data analysis. The 10; Toluene forecasting in a Smart environment can help the decision-maker to ensure environmental safety. The Smart energy of our proposed model can give a clear prediction of power generation.

The findings of this study are expected to contribute valuable information to decision-makers for a better understanding of the key to Smart city architecture. Its relation with data storage, processing, and data analysis.

Selecting and using the same health monitoring devices for a particular problem is a tedious task. This paper aims to provide a comprehensive review of 40 research papers giving the Smart health monitoring system using Internet of things (IoT) and Deep learning.

Health Monitoring Systems play a significant role in the healthcare sector. The development and testing of health monitoring devices using IoT and deep learning dominate the healthcare sector.

In addition, the detailed conversation and investigation are finished by techniques and development framework. Authors have identified the research gap and presented future research directions in IoT, edge computing and deep learning.

The gathered research articles are examined, and the gaps and issues that the current research papers confront are discussed. In addition, based on various research gaps, this assessment proposes the primary future scope for deep learning and IoT health monitoring model.

This infecological study mainly aimed to know the thematic and conceptual relationship in published papers in deep learning (DL) and smart manufacturing (SM).

The research methodology has specific research objectives based on the type and method of research, data analysis tools. In general, description methods are applied by Web of Science (WoS) analysis tools and Voyant tools as a web-based reading and analysis environment for digital texts. The Yewno tool is applied to draw a knowledge map to show the concept's interaction between DL and SM.

The knowledge map of DL and SM concepts shows that there are currently few concepts interacting with each other, while the rapid growth of technology and the needs of today's society have revealed the need to use more and more DL in SM. The results of this study can provide a coherent and well-mapped road map to the main policymakers of the field of research in DL and SM, through the study of coexistence and interactions of the thematic categories with other thematic areas. In this way, they can design more effective guidelines and strategies to solve the problems of researchers in conducting their studies and direct. The analysis results demonstrated that the information ecosystem of DL and SM studies dynamically developed over time. The continuous conduction flow of scientific studies in this field brought continuous changes into the infoecology of subjects and concepts in this area.

The paper investigated the thematic interaction of the scientific productions in DL and SM and discussed possible implications. We used of the variety tools and techniques to draw our own perspective. Also, we drew arguments from other research work to back up our findings.

The purpose of this study is to propose a bearer service, which generates and maintains a “digital doppelgänger” for every financial contract in the form of a dynamic transaction document that is a standardised “data facility” automatically making important contract data from the transaction counterparties available to relevant authorities mandated by law to request and process such data. This would be achieved by sharing certain elements of the dynamic transaction document on a bearer service, based on a federation of distribution ledgers; such a quasi-simultaneous sharing of risk data becomes possible because the dynamic transaction document maintain a record of state in semi-real time, and this state can be verified by anybody with access to the distribution ledgers, also in semi-real time.

In this paper, the authors propose a novel, regular technology (RegTech) cum automated legal text approach for financial transaction as well as financial risk reporting that is based on cutting-edge distributed computing and decentralised data management technologies such as distributed ledger (Swanson, 2015), distributed storage (Arner et al., 2016; Chandra et al., 2013; Caron et al., 2014), algorithmic financial contract standards (Brammertz and Mendelowitz, 2014; Breymann and Mendelowitz, 2015; Braswell, 2016), automated legal text (Hazard and Haapio, 2017) and document engineering methods and techniques (Glushko and McGrath, 2005). This approach is equally inspired by the concept of the “bearer service” and its capacity to span over existing and future technological systems and substrates (Kavassalis et al., 2000; Clark, 1988).

The result is a transformation of supervisors’ capacity to monitor risk in the financial system based on data which preserve informational content of financial instruments at the most granular level, in combination with a mathematically robust time stamping approach using blockchain technology.

The RegTech approach has the potential to contain operational risk linked to inadequate handling of risk data and to rein in compliance cost of supervisory reporting.

The present RegTech approach to financial risk monitoring and supervisory reporting is the first integration of algorithmic financial data standards with blockchain functionality.

This study aims to investigate the relationship between building smart factories in manufacturing small- and medium-sized enterprises (SMEs) and firm performance and the moderating effect according to product complexity and company size.

Data were collected from 206 companies selected in the list of SMEs, which had built smart factories, provided by the Smart Manufacturing Innovation Center in Korea. The collected data were analyzed using structural equation modeling (SEM) technique.

First, production automation and big data utilization are associated positively with productivity, but not significantly with export performance. Second, supply chain integration is associated positively with both productivity and export performance. Third, product complexity moderates negatively the relationship of productivity with each of production automation, big data utilization and supply chain integration while moderating positively the relationship between supply chain integration and export performance. Finally, company size does not moderate significantly the relationship between productivity or export performance with any of production automation, big data utilization and supply chain integration.

This study contributes theoretically to literature by demonstrating the usefulness of building smart factories and suggesting how SMEs build a smart factory to enhance productivity and export performance from a business perspective. Moreover, this study contributes practically by proposing that SMEs should put priority on supply chain integration over production automation and big data utilization and execute different strategies of building smart factories depending on product complexity.

Price reliability for complex and highly complicated infrastructure projects is problematic. Traditional project delivery approaches generally fail in achieving targeted end cost reliability. However, integrated project delivery (and particularly Alliancing), develop a far more reliable and robust project delivery plan and outturn time-cost targets. This paper aims to explore why this may be the case.

This case study investigated the project design, planning, cost/time estimation approach and how risk/uncertainty was dealt with. Five senior project delivery experts from an organisation that delivers multi-billion-dollar infrastructure projects in Australia were interviewed. These five experts collectively had 100+ cross-disciplinary experience years delivering complex infrastructure projects.

Alliancing adopts a radically different approach to project design, time/cost planning and risk assessment and management to traditional project delivery approaches. Key findings explain how the project alliance agreement designs-in processes that maximises team integration and collaboration. Analysis concludes that design thinking is used to craft and shape collaborative behaviours and project governance. Additionally, including project owner and facilities operator representatives in the project team adds valuable insights, expertise and knowledge contributing to planning reliability.

This study is exploratory and focussed on complex infrastructure projects so findings cannot be generalised.

We unpack Alliancing processes that develop the target outturn cost plan, comprising a holistic and realistic plan to design a project to meet expected project outcomes. This case study may serve as an exemplar for complex project delivery.

This paper illustrates how Alliancing more effectively delivers best value than traditional procurement approaches through its TOC-TAE processes.

The paper contributes to the scant existing academic literature analysing these processes. Its novel contribution is explaining how Alliancing treats unexpected events that in traditional delivery forms trigger expensive and time-energy-wasting disputation. This case study may serve as an exemplar for complex project delivery.

This paper aims to propose a machine learning approach-based power theft detection using Garra Rufa Fish (GRF) optimization. Here, the analyzing of power theft is an important part to reduce the financial loss and protect the electricity from fraudulent users.

In this section, a new method is implemented to reduce the power theft in transmission lines and utility grids. The detection of power theft using smart meter with reliable manner can be achieved by the help of GRF algorithm.

The loss of power due to non-technical loss is small by using this proposed algorithm. It provides some benefits like increased predicting capacity, less complexity, high speed and high reliable output. The result is analyzed using MATLAB/Simulink platform. The result is compared with an existing method. According to the comparison result, the proposed method provides the good performance than existing method.

The proposed method gives good results of comparison than those of the other techniques and has an ability to overcome the associated problems.

Unlocking and transferring skills and capabilities in individuals to the teams they work within, and across, is the key to positive organisational development and improved patient care. Using the “deep smarts” model, the purpose of this paper is to examine these issues.

The “deep smarts” model is described, reviewed and proposed as a way of transferring knowledge and capabilities within healthcare organisations.

Effective healthcare delivery is achieved through, and continues to require, integrative care involving numerous, dispersed service providers. In the space of overlapping organisational boundaries, there is a need for “deep smarts” people who act as “boundary spanners”. These are critical integrative, networking roles employing clinical, organisational and people skills across multiple settings.

Studies evaluating the barriers and enablers to the application of the deep smarts model and 13 knowledge development strategies proposed are required. Such future research will empirically and contemporary ground our understanding of organisational development in modern complex healthcare settings.

An organisation with “deep smarts” people – in managerial, auxiliary and clinical positions – has a greater capacity for integration and achieving improved patient-centred care.

In total, 13 developmental strategies, to transfer individual capabilities into organisational capability, are proposed. These strategies are applicable to different contexts and challenges faced by individuals and teams in complex healthcare organisations.

With the popularity of the internet and the increasing numbers of netizens, tremendous information flows are generated daily by the intelligently interconnected individuals. The diffusion processes of different information are not independent, and they interact with and influence each other. Modeling and analyzing the interaction between correlated information play an important role in the understanding of the characteristics of information dissemination and better control of the information flows. This paper aims to model the correlated information diffusion process over the crowd intelligence networks.

This study extends the classic epidemic susceptible–infectious–recovered (SIR) model and proposes the SIR mixture model to describe the diffusion process of two correlated pieces of information. The whole crowd is divided into different groups with respect to their forwarding state of the correlated information, and the transition rate between different groups shows the property of each piece of information and the influences between them.

The stable state of the SIR mixture model is analyzed through the linearization of the model, and the stable condition can be obtained. Real data are used to validate the SIR mixture model, and the detailed diffusion process of correlated information can be inferred by the analysis of the parameters learned through fitting the real data into the SIR mixture model.

The proposed SIR mixture model can be used to model the diffusion of correlated information and analyze the propagation process.

The digital construction transformation requires using emerging digital technology such as deep learning to automate implementing tasks. Therefore, this paper aims to evaluate the current state of using deep learning in the construction management tasks to enable researchers to determine the capabilities of current solutions, as well as finding research gaps to carry out more research to bridge revealed knowledge and practice gaps.

The scientometric analysis is conducted for 181 articles to assess the density of publications in different topics of deep learning-based construction management applications. After that, a thematic and gap analysis are conducted to analyze contributions and limitations of key published articles in each area of application.

The scientometric analysis indicates that there are four main applications of deep learning in construction management, namely, automating progress monitoring, automating safety warning for workers, managing construction equipment, integrating Internet of things with deep learning to automatically collect data from the site. The thematic and gap analysis refers to many successful cases of using deep learning in automating site management tasks; however, more validations are recommended to test developed solutions, as well as additional research is required to consider practitioners and workers perspectives to implement existing applications in their daily tasks.

This paper enables researchers to directly find the research gaps in the existing solutions and develop more workable applications to bridge revealed gaps. Accordingly, this will be reflected on speeding the digital construction transformation, which is a strategy over the world.

To the best of the authors’ knowledge, this paper is the first of its kind to adopt a structured technique to assess deep learning-based construction site management applications to enable researcher/practitioners to either adopting these applications in their projects or conducting further research to extend existing solutions and bridging revealed knowledge gaps.