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Article
Publication date: 12 February 2024

Boyi Li, Miao Tian, Xiaohan Liu, Jun Li, Yun Su and Jiaming Ni

The purpose of this study is to predict the thermal protective performance (TPP) of flame-retardant fabric more economically using machine learning and analyze the factors…

Abstract

Purpose

The purpose of this study is to predict the thermal protective performance (TPP) of flame-retardant fabric more economically using machine learning and analyze the factors affecting the TPP using model visualization.

Design/methodology/approach

A total of 13 machine learning models were trained by collecting 414 datasets of typical flame-retardant fabric from current literature. The optimal performance model was used for feature importance ranking and correlation variable analysis through model visualization.

Findings

Five models with better performance were screened, all of which showed R2 greater than 0.96 and root mean squared error less than 3.0. Heat map results revealed that the TPP of fabrics differed significantly under different types of thermal exposure. The effect of fabric weight was more apparent in the flame or low thermal radiation environment. The increase in fabric weight, fabric thickness, air gap width and relative humidity of the air gap improved the TPP of the fabric.

Practical implications

The findings suggested that the visual analysis method of machine learning can intuitively understand the change trend and range of second-degree burn time under the influence of multiple variables. The established models can be used to predict the TPP of fabrics, providing a reference for researchers to carry out relevant research.

Originality/value

The findings of this study contribute directional insights for optimizing the structure of thermal protective clothing, and introduce innovative perspectives and methodologies for advancing heat transfer modeling in thermal protective clothing.

Details

International Journal of Clothing Science and Technology, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 26 December 2023

Sohaib Mustafa, Sehrish Rana and Muhammad Mateen Naveed

This study explores the adoption of Industry 4.0 in developing countries' export industries, focusing on factors influencing this adoption, the moderating role of market pressure…

168

Abstract

Purpose

This study explores the adoption of Industry 4.0 in developing countries' export industries, focusing on factors influencing this adoption, the moderating role of market pressure and prioritizing key factors for sustainable growth.

Design/methodology/approach

Based on the “TOE theory” this study has proposed a research framework to identify the factors influencing the adoption and sustainable implementation of Industry 4.0 in the export industry. This study has collected valid datasets from 387 export-oriented industries and applied SEM-ANN dual-stage hybrid model to capture linear and nonlinear interaction between variables.

Findings

Results revealed that Technical Capabilities, System Flexibility, Software Infrastructure, Human Resource Competency and Market pressure significantly influence the Adoption of Industry 4.0. Higher market pressure as a moderator also improves the Industry 4.0 adoption process. Results also pointed out that system flexibility is a gray area in Industry 4.0 adoption, which can be enhanced in the export industry to maintain a sustainable adoption and implementation of Industry 4.0.

Originality/value

Minute information is available on the factors influencing the adoption of Industry 4.0 in export-oriented industries. This study has empirically explored the role of influential factors in Industry 4.0 and ranked them based on their normalized importance.

Details

Journal of Manufacturing Technology Management, vol. 35 no. 2
Type: Research Article
ISSN: 1741-038X

Keywords

Open Access
Article
Publication date: 14 March 2024

Inma Rodríguez-Ardura, Antoni Meseguer-Artola, Doaa Herzallah and Qian Fu

There is an ongoing challenge to map the efficacy of e-retailing strategies in building both value co-creation opportunities for online customers and customer value for companies…

Abstract

Purpose

There is an ongoing challenge to map the efficacy of e-retailing strategies in building both value co-creation opportunities for online customers and customer value for companies. Based on the service-dominant (S-D) logic, an integrative model is provided that connects the impact of convenience and personalisation strategies (CPSs) on an e-retailer's performance – by offering co-creation opportunities and customer engagement.

Design/methodology/approach

The survey instrument is validated and the model is tested with data from active online customers using a novel methodology that blends artificial neural network (ANN) analysis with partial least squares (PLS) in both the measurement model and the path analysis.

Findings

The findings robustly support the model and yield evidence of the contribution of CPSs in effective value propositions, the interface between the S-D logic and customer engagement, and the direct effect of customer engagement on tangible forms of value for companies.

Originality/value

This study is the first scholarly effort to provide a comprehensive understanding of how and why CPSs can maximise customer value for the e-retailer, while simultaneously testing the customer value/engagement interface with a new blended ANN-PLS method.

Details

Journal of Research in Interactive Marketing, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2040-7122

Keywords

Article
Publication date: 19 February 2024

Eiman Almheiri, Mostafa Al-Emran, Mohammed A. Al-Sharafi and Ibrahim Arpaci

The proliferation of smartwatches in the digital age has radically transformed health and fitness management, offering users a multitude of functionalities that extend beyond mere…

Abstract

Purpose

The proliferation of smartwatches in the digital age has radically transformed health and fitness management, offering users a multitude of functionalities that extend beyond mere physical activity tracking. While these modern wearables have empowered users with real-time data and personalized health insights, their environmental implications remain relatively unexplored despite a growing emphasis on sustainability. To bridge this gap, this study extends the UTAUT2 model with smartwatch features (mobility and availability) and perceived security to understand the drivers of smartwatch usage and its consequent impact on environmental sustainability.

Design/methodology/approach

The proposed theoretical model is evaluated based on data collected from 303 smartwatch users using a hybrid structural equation modeling–artificial neural network (SEM-ANN) approach.

Findings

The PLS-SEM results supported smartwatch features’ effect on performance and effort expectancy. The results also supported the role of performance expectancy, social influence, price value, habit and perceived security in smartwatch usage. The use of smartwatches was found to influence environmental sustainability significantly. However, the results did not support the association between effort expectancy, facilitating conditions and hedonic motivation with smartwatch use. The ANN results further complement these outcomes by showing that habit with a normalized importance of 100% is the most significant factor influencing smartwatch use.

Originality/value

Theoretically, this research broadens the UTAUT2 by introducing smartwatch features as external variables and environmental sustainability as a new outcome of technology use. On a practical level, the study offers insights for various stakeholders interested in smartwatch use and their environmental implications.

Details

Asia-Pacific Journal of Business Administration, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1757-4323

Keywords

Article
Publication date: 1 January 2024

Shrutika Sharma, Vishal Gupta, Deepa Mudgal and Vishal Srivastava

Three-dimensional (3D) printing is highly dependent on printing process parameters for achieving high mechanical strength. It is a time-consuming and expensive operation to…

Abstract

Purpose

Three-dimensional (3D) printing is highly dependent on printing process parameters for achieving high mechanical strength. It is a time-consuming and expensive operation to experiment with different printing settings. The current study aims to propose a regression-based machine learning model to predict the mechanical behavior of ulna bone plates.

Design/methodology/approach

The bone plates were formed using fused deposition modeling (FDM) technique, with printing attributes being varied. The machine learning models such as linear regression, AdaBoost regression, gradient boosting regression (GBR), random forest, decision trees and k-nearest neighbors were trained for predicting tensile strength and flexural strength. Model performance was assessed using root mean square error (RMSE), coefficient of determination (R2) and mean absolute error (MAE).

Findings

Traditional experimentation with various settings is both time-consuming and expensive, emphasizing the need for alternative approaches. Among the models tested, GBR model demonstrated the best performance in predicting both tensile and flexural strength and achieved the lowest RMSE, highest R2 and lowest MAE, which are 1.4778 ± 0.4336 MPa, 0.9213 ± 0.0589 and 1.2555 ± 0.3799 MPa, respectively, and 3.0337 ± 0.3725 MPa, 0.9269 ± 0.0293 and 2.3815 ± 0.2915 MPa, respectively. The findings open up opportunities for doctors and surgeons to use GBR as a reliable tool for fabricating patient-specific bone plates, without the need for extensive trial experiments.

Research limitations/implications

The current study is limited to the usage of a few models. Other machine learning-based models can be used for prediction-based study.

Originality/value

This study uses machine learning to predict the mechanical properties of FDM-based distal ulna bone plate, replacing traditional design of experiments methods with machine learning to streamline the production of orthopedic implants. It helps medical professionals, such as physicians and surgeons, make informed decisions when fabricating customized bone plates for their patients while reducing the need for time-consuming experimentation, thereby addressing a common limitation of 3D printing medical implants.

Details

Rapid Prototyping Journal, vol. 30 no. 3
Type: Research Article
ISSN: 1355-2546

Keywords

Article
Publication date: 25 January 2024

Jain Vinith P.R., Navin Sam K., Vidya T., Joseph Godfrey A. and Venkadesan Arunachalam

This paper aims to Solar photovoltaic (PV) power can significantly impact the power system because of its intermittent nature. Hence, an accurate solar PV power forecasting model…

Abstract

Purpose

This paper aims to Solar photovoltaic (PV) power can significantly impact the power system because of its intermittent nature. Hence, an accurate solar PV power forecasting model is required for appropriate power system planning.

Design/methodology/approach

In this paper, a long short-term memory (LSTM)-based double deep Q-learning (DDQL) neural network (NN) is proposed for forecasting solar PV power indirectly over the long-term horizon. The past solar irradiance, temperature and wind speed are used for forecasting the solar PV power for a place using the proposed forecasting model.

Findings

The LSTM-based DDQL NN reduces over- and underestimation and avoids gradient vanishing. Thus, the proposed model improves the forecasting accuracy of solar PV power using deep learning techniques (DLTs). In addition, the proposed model requires less training time and forecasts solar PV power with improved stability.

Originality/value

The proposed model is trained and validated for several places with different climatic patterns and seasons. The proposed model is also tested for a place with a temperate climatic pattern by constructing an experimental solar PV system. The training, validation and testing results have confirmed the practicality of the proposed solar PV power forecasting model using LSTM-based DDQL NN.

Details

World Journal of Engineering, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1708-5284

Keywords

Article
Publication date: 5 February 2024

Nikita Dhankar, Srikanta Routroy and Satyendra Kumar Sharma

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India…

Abstract

Purpose

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Design/methodology/approach

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

Findings

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R2 values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

Research limitations/implications

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

Originality/value

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.

Details

Journal of Modelling in Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1746-5664

Keywords

Article
Publication date: 4 January 2023

Shilpa Sonawani and Kailas Patil

Indoor air quality monitoring is extremely important in urban, industrial areas. Considering the devastating effect of declining quality of air in major part of the countries like…

Abstract

Purpose

Indoor air quality monitoring is extremely important in urban, industrial areas. Considering the devastating effect of declining quality of air in major part of the countries like India and China, it is highly recommended to monitor the quality of air which can help people with respiratory diseases, children and elderly people to take necessary precautions and stay safe at their homes. The purpose of this study is to detect air quality and perform predictions which could be part of smart home automation with the use of newer technology.

Design/methodology/approach

This study proposes an Internet-of-Things (IoT)-based air quality measurement, warning and prediction system for ambient assisted living. The proposed ambient assisted living system consists of low-cost air quality sensors and ESP32 controller with new generation embedded system architecture. It can detect Indoor Air Quality parameters like CO, PM2.5, NO2, O3, NH3, temperature, pressure, humidity, etc. The low cost sensor data are calibrated using machine learning techniques for performance improvement. The system has a novel prediction model, multiheaded convolutional neural networks-gated recurrent unit which can detect next hour pollution concentration. The model uses a transfer learning (TL) approach for prediction when the system is new and less data available for prediction. Any neighboring site data can be used to transfer knowledge for early predictions for the new system. It can have a mobile-based application which can send warning notifications to users if the Indoor Air Quality parameters exceed the specified threshold values. This is all required to take necessary measures against bad air quality.

Findings

The IoT-based system has implemented the TL framework, and the results of this study showed that the system works efficiently with performance improvement of 55.42% in RMSE scores for prediction at new target system with insufficient data.

Originality/value

This study demonstrates the implementation of an IoT system which uses low-cost sensors and deep learning model for predicting pollution concentration. The system is tackling the issues of the low-cost sensors for better performance. The novel approach of pretrained models and TL work very well at the new system having data insufficiency issues. This study contributes significantly with the usage of low-cost sensors, open-source advanced technology and performance improvement in prediction ability at new systems. Experimental results and findings are disclosed in this study. This will help install multiple new cost-effective monitoring stations in smart city for pollution forecasting.

Details

International Journal of Pervasive Computing and Communications, vol. 20 no. 1
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 24 October 2022

Douglas Aghimien, Clinton Ohis Aigbavboa, Daniel W.M. Chan and Emmanuel Imuetinyan Aghimien

This paper presents the findings from the assessment of the determinants of cloud computing (CC) deployment by construction organisations. Using the…

Abstract

Purpose

This paper presents the findings from the assessment of the determinants of cloud computing (CC) deployment by construction organisations. Using the technology-organisation-environment (TOE) framework, the study strives to improve construction organisations' project delivery and digital transformation by adopting beneficial technologies like CC.

Design/methodology/approach

This study adopted a post-positivism philosophical stance using a deductive approach with a questionnaire administered to construction organisations in South Africa. The data gathered were analysed using descriptive and inferential statistics. Also, the fusion of structural equation modelling (SEM) and machine learning (ML) regression models helped to gain a robust understanding of the key determinants of using CC.

Findings

The study found that the use of CC by construction organisations in South Africa is still slow. SEM indicated that this slow usage is influenced by six technology and environmental factors, namely (1) cost-effectiveness, (2) availability, (3) compatibility, (4) client demand, (5) competitors' pressure and (6) trust in cloud service providers. ML models developed affirmed that these variables have high predictive power. However, sensitivity analysis revealed that the availability of CC and CC's ancillary technologies and the pressure from competitors are the most important predictors of CC usage in construction organisations.

Originality/value

The paper offers a theoretical backdrop for future works on CC in construction, particularly in developing countries where such a study has not been explored.

Details

Engineering, Construction and Architectural Management, vol. 31 no. 3
Type: Research Article
ISSN: 0969-9988

Keywords

Article
Publication date: 3 July 2023

Sachin Kashyap, Sanjeev Gupta and Tarun Chugh

The present work has proposed and employed an innovative hybrid method based on the combination of factor analysis and an artificial neural network (ANN) model to forecast…

Abstract

Purpose

The present work has proposed and employed an innovative hybrid method based on the combination of factor analysis and an artificial neural network (ANN) model to forecast customer satisfaction from the identified dimensions of service quality in India, a developing country.

Design/methodology/approach

The qualitative study is conducted with Internet banking users to understand e-banking clients' perceptions. The data is collected with the help of a questionnaire from randomly selected 208 customers in India. Firstly, factor analysis was performed to determine the influential factors of customer satisfaction, and four factors i.e. efficiency, reliability, security and privacy, and issue and problem handling were extracted accordingly. The neural network model is then applied to the factor scores to validate the key elements. Lastly, the comparative analysis of the actual ANN and the regression predicted result is done.

Findings

The success ability of the linear regression model is challenged when approximated to nonlinear problems such as customer satisfaction. It is concluded that the ANN model is a better fit than the linear regression model, and it can recognise the complex connections between the exogenous and endogenous variables. The results also show that reliability, security and privacy are the most influencing factors; however, problem handling and efficiency have the slightest effect on bank client satisfaction.

Research limitations/implications

This research is conducted in India, and the sample is chosen from the urban area. The limitation of the purposeful sampling technique and the cross-sectional nature of the data may hamper the generalisation of the results.

Originality/value

The conclusions from the study will be helpful for policymakers, bankers and academicians. To our knowledge, few studies used ANN modelling to predict customer satisfaction in the service sector

Details

International Journal of Quality & Reliability Management, vol. 41 no. 1
Type: Research Article
ISSN: 0265-671X

Keywords

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