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Article
Publication date: 28 December 2021

Faris Elghaish, Sandra T. Matarneh and Mohammad Alhusban

The digital construction transformation requires using emerging digital technology such as deep learning to automate implementing tasks. Therefore, this paper aims to…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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.

Originality/value

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.

Details

Construction Innovation , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1471-4175

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Article
Publication date: 27 October 2021

Lei Mee Thien, Mi-Chelle Leong and Fei Ping Por

This study aims to examine the relationship between undergraduates' course experience and their deep learning approach and to identify areas of improvement to facilitate…

Abstract

Purpose

This study aims to examine the relationship between undergraduates' course experience and their deep learning approach and to identify areas of improvement to facilitate students' deep learning in the private higher education context.

Design/methodology/approach

Data were collected from 844 Malaysian undergraduate students who studied in six private higher education institutions (HEIs) in Penang and Selangor. This study used partial least squares structural equation modelling (PLS-SEM) for data analysis.

Findings

The findings revealed that good teaching and appropriate assessment have no significant relationship with deep learning. Generic skills, clear goals and standards, appropriate workload and emphasis on independence are positively related to deep learning. Generic skills and emphasis on independence are two domains that deserve attention to enhance deep learning among undergraduates.

Practical implications

Lecturers need to focus on to the cultivation of generic skills to facilitate students' deep learning. Student autonomy and student-centred teaching approaches should be empowered and prioritised in teaching and learning.

Originality/value

The current study has its originality in providing empirical findings to inform the significant relationship between dimensions of course experience and deep learning in Malaysian private HEIs. Besides, it also identifies the areas of improvement concerning teaching and learning at the private HEIs using importance-performance matrix analysis (IPMA) in a non-Western context.

Details

Journal of Applied Research in Higher Education, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-7003

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Article
Publication date: 9 November 2021

Faris Elghaish, Sandra T. Matarneh, Saeed Talebi, Soliman Abu-Samra, Ghazal Salimi and Christopher Rausch

The massive number of pavements and buildings coupled with the limited inspection resources, both monetary and human, to detect distresses and recommend maintenance…

Abstract

Purpose

The massive number of pavements and buildings coupled with the limited inspection resources, both monetary and human, to detect distresses and recommend maintenance actions lead to rapid deterioration, decreased service life, lower level of service and increased community disruption. Therefore, this paper aims at providing a state-of-the-art review of the literature with respect to deep learning techniques for detecting distress in both pavements and buildings; research advancements per asset/structure type; and future recommendations in deep learning applications for distress detection.

Design/methodology/approach

A critical analysis was conducted on 181 papers of deep learning-based cracks detection. A structured analysis was adopted so that major articles were analyzed according to their focus of study, used methods, findings and limitations.

Findings

The utilization of deep learning to detect pavement cracks is advanced compared to assess and evaluate the structural health of buildings. There is a need for studies that compare different convolutional neural network models to foster the development of an integrated solution that considers the data collection method. Further research is required to examine the setup, implementation and running costs, frequency of capturing data and deep learning tool. In conclusion, the future of applying deep learning algorithms in lieu of manual inspection for detecting distresses has shown promising results.

Practical implications

The availability of previous research and the required improvements in the proposed computational tools and models (e.g. artificial intelligence, deep learning, etc.) are triggering researchers and practitioners to enhance the distresses’ inspection process and make better use of their limited resources.

Originality/value

A critical and structured analysis of deep learning-based crack detection for pavement and buildings is conducted for the first time to enable novice researchers to highlight the knowledge gap in each article, as well as building a knowledge base from the findings of other research to support developing future workable solutions.

Details

Construction Innovation , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1471-4175

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Article
Publication date: 24 September 2021

Guanzheng Wang, Yinbo Xu, Zhihong Liu, Xin Xu, Xiangke Wang and Jiarun Yan

This paper aims to realize a fully distributed multi-UAV collision detection and avoidance based on deep reinforcement learning (DRL). To deal with the problem of low…

Abstract

Purpose

This paper aims to realize a fully distributed multi-UAV collision detection and avoidance based on deep reinforcement learning (DRL). To deal with the problem of low sample efficiency in DRL and speed up the training. To improve the applicability and reliability of the DRL-based approach in multi-UAV control problems.

Design/methodology/approach

In this paper, a fully distributed collision detection and avoidance approach for multi-UAV based on DRL is proposed. A method that integrates human experience into policy training via a human experience-based adviser is proposed. The authors propose a hybrid control method which combines the learning-based policy with traditional model-based control. Extensive experiments including simulations, real flights and comparative experiments are conducted to evaluate the performance of the approach.

Findings

A fully distributed multi-UAV collision detection and avoidance method based on DRL is realized. The reward curve shows that the training process when integrating human experience is significantly accelerated and the mean episode reward is higher than the pure DRL method. The experimental results show that the DRL method with human experience integration has a significant improvement than the pure DRL method for multi-UAV collision detection and avoidance. Moreover, the safer flight brought by the hybrid control method has also been validated.

Originality/value

The fully distributed architecture is suitable for large-scale unmanned aerial vehicle (UAV) swarms and real applications. The DRL method with human experience integration has significantly accelerated the training compared to the pure DRL method. The proposed hybrid control strategy makes up for the shortcomings of two-dimensional light detection and ranging and other puzzles in applications.

Details

Industrial Robot: the international journal of robotics research and application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0143-991X

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Article
Publication date: 18 October 2021

Saurabh Kumar

Decision-making in human beings is affected by emotions and sentiments. The affective computing takes this into account, intending to tailor decision support to the…

Abstract

Purpose

Decision-making in human beings is affected by emotions and sentiments. The affective computing takes this into account, intending to tailor decision support to the emotional states of people. However, the representation and classification of emotions is a very challenging task. The study used customized methods of deep learning models to aid in the accurate classification of emotions and sentiments.

Design/methodology/approach

The present study presents affective computing model using both text and image data. The text-based affective computing was conducted on four standard datasets using three deep learning customized models, namely LSTM, GRU and CNN. The study used four variants of deep learning including the LSTM model, LSTM model with GloVe embeddings, Bi-directional LSTM model and LSTM model with attention layer.

Findings

The result suggests that the proposed method outperforms the earlier methods. For image-based affective computing, the data was extracted from Instagram, and Facial emotion recognition was carried out using three deep learning models, namely CNN, transfer learning with VGG-19 model and transfer learning with ResNet-18 model. The results suggest that the proposed methods for both text and image can be used for affective computing and aid in decision-making.

Originality/value

The study used deep learning for affective computing. Earlier studies have used machine learning algorithms for affective computing. However, the present study uses deep learning for affective computing.

Details

Journal of Enterprise Information Management, vol. 34 no. 5
Type: Research Article
ISSN: 1741-0398

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Article
Publication date: 19 February 2021

Zulkifli Halim, Shuhaida Mohamed Shuhidan and Zuraidah Mohd Sanusi

In the previous study of financial distress prediction, deep learning techniques performed better than traditional techniques over time-series data. This study…

Abstract

Purpose

In the previous study of financial distress prediction, deep learning techniques performed better than traditional techniques over time-series data. This study investigates the performance of deep learning models: recurrent neural network, long short-term memory and gated recurrent unit for the financial distress prediction among the Malaysian public listed corporation over the time-series data. This study also compares the performance of logistic regression, support vector machine, neural network, decision tree and the deep learning models on single-year data.

Design/methodology/approach

The data used are the financial data of public listed companies that been classified as PN17 status (distress) and non-PN17 (not distress) in Malaysia. This study was conducted using machine learning library of Python programming language.

Findings

The findings indicate that all deep learning models used for this study achieved 90% accuracy and above with long short-term memory (LSTM) and gated recurrent unit (GRU) getting 93% accuracy. In addition, deep learning models consistently have good performance compared to the other models over single-year data. The results show LSTM and GRU getting 90% and recurrent neural network (RNN) 88% accuracy. The results also show that LSTM and GRU get better precision and recall compared to RNN. The findings of this study show that the deep learning approach will lead to better performance in financial distress prediction studies. To be added, time-series data should be highlighted in any financial distress prediction studies since it has a big impact on credit risk assessment.

Research limitations/implications

The first limitation of this study is the hyperparameter tuning only applied for deep learning models. Secondly, the time-series data are only used for deep learning models since the other models optimally fit on single-year data.

Practical implications

This study proposes recommendations that deep learning is a new approach that will lead to better performance in financial distress prediction studies. Besides that, time-series data should be highlighted in any financial distress prediction studies since the data have a big impact on the assessment of credit risk.

Originality/value

To the best of authors' knowledge, this article is the first study that uses the gated recurrent unit in financial distress prediction studies based on time-series data for Malaysian public listed companies. The findings of this study can help financial institutions/investors to find a better and accurate approach for credit risk assessment.

Details

Business Process Management Journal, vol. 27 no. 4
Type: Research Article
ISSN: 1463-7154

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Article
Publication date: 3 April 2017

Yasser F. Hassan

This paper aims to utilize machine learning and soft computing to propose a new method of rough sets using deep learning architecture for many real-world applications.

Abstract

Purpose

This paper aims to utilize machine learning and soft computing to propose a new method of rough sets using deep learning architecture for many real-world applications.

Design/methodology/approach

The objective of this work is to propose a model for deep rough set theory that uses more than decision table and approximating these tables to a classification system, i.e. the paper propose a novel framework of deep learning based on multi-decision tables.

Findings

The paper tries to coordinate the local properties of individual decision table to provide an appropriate global decision from the system.

Research limitations/implications

The rough set learning assumes the existence of a single decision table, whereas real-world decision problem implies several decisions with several different decision tables. The new proposed model can handle multi-decision tables.

Practical implications

The proposed classification model is implemented on social networks with preferred features which are freely distribute as social entities with accuracy around 91 per cent.

Social implications

The deep learning using rough sets theory simulate the way of brain thinking and can solve the problem of existence of different information about same problem in different decision systems

Originality/value

This paper utilizes machine learning and soft computing to propose a new method of rough sets using deep learning architecture for many real-world applications.

Details

Kybernetes, vol. 46 no. 4
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 16 August 2021

Faris Elghaish, Saeed Talebi, Essam Abdellatef, Sandra T. Matarneh, M. Reza Hosseini, Song Wu, Mohammad Mayouf, Aso Hajirasouli and The-Quan Nguyen

This paper aims to Test the capabilities/accuracies of four deep learning pre trained convolutional neural network (CNN) models to detect and classify types of highway…

Abstract

Purpose

This paper aims to Test the capabilities/accuracies of four deep learning pre trained convolutional neural network (CNN) models to detect and classify types of highway cracks, as well as developing a new CNN model to maximize the accuracy at different learning rates.

Design/methodology/approach

A sample of 4,663 images of highway cracks were collected and classified into three categories of cracks, namely, “vertical cracks,” “horizontal and vertical cracks” and “diagonal cracks,” subsequently, using “Matlab” to classify the sample to training (70%) and testing (30%) to apply the four deep learning CNN models and compute their accuracies. After that, developing a new deep learning CNN model to maximize the accuracy of detecting and classifying highway cracks and testing the accuracy using three optimization algorithms at different learning rates.

Findings

The accuracies result of the four deep learning pre-trained models are above the averages between top-1 and top-5 and the accuracy of classifying and detecting the samples exceeded the top-5 accuracy for the pre-trained AlexNet model around 3% and by 0.2% for the GoogleNet model. The accurate model here is the GoogleNet model as the accuracy is 89.08% and it is higher than AlexNet by 1.26%. While the computed accuracy for the new created deep learning CNN model exceeded all pre-trained models by achieving 97.62% at a learning rate of 0.001 using Adam’s optimization algorithm.

Practical implications

The created deep learning CNN model will enable users (e.g. highway agencies) to scan a long highway and detect types of cracks accurately in a very short time compared to traditional approaches.

Originality/value

A new deep learning CNN-based highway cracks detection was developed based on testing four pre-trained CNN models and analyze the capabilities of each model to maximize the accuracy of the proposed CNN.

Details

Journal of Engineering, Design and Technology , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1726-0531

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Article
Publication date: 15 September 2021

Nguyen N.Q. Thu, Nguyen T.M. Trang and Nguyen Dinh Tho

This study, based on self-determination theory (SDT), investigates the effect of business students' future time perspective (FTP), directly and indirectly (mediated by deep

Abstract

Purpose

This study, based on self-determination theory (SDT), investigates the effect of business students' future time perspective (FTP), directly and indirectly (mediated by deep learning approaches), on quality of university life.

Design/methodology/approach

A sample of 547 business students in Ho Chi Minh City, Vietnam, was surveyed via a two-wave process to collect data to validate the measures and to test the hypotheses using structural equation modeling (SEM).

Findings

The results produced by SEM demonstrated that FTP had no direct effect on quality of university life and that deep learning approaches fully mediated the impact of FTP on quality of university life.

Practical implications

The study findings provide business educators with a better understanding of the role that FTP can play for business students. Increased awareness of this issue may help nurture the FTP of business students, which in turn directs them to pursue deep learning approaches to achieve a higher level of quality of university life.

Originality/value

This study is among the first to empirically investigate the overarching role that FTP plays in both deep learning approaches and quality of university life.

Details

Education + Training, vol. 63 no. 9
Type: Research Article
ISSN: 0040-0912

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Article
Publication date: 21 April 2020

Mohammed Anouar Naoui, Brahim Lejdel, Mouloud Ayad, Abdelfattah Amamra and Okba kazar

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

Abstract

Purpose

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

Design/methodology/approach

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.

Findings

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;.

Research limitations/implications

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

Practical implications

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.

Originality/value

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.

Details

Smart and Sustainable Built Environment, vol. 10 no. 1
Type: Research Article
ISSN: 2046-6099

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