scholarly journals Impact of Fog Computing on Indian Smart-Cities: An Empirical Study

2021 ◽  
Author(s):  
Pragati Priyadarshinee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Internet Of Things ◽  
Structural Equation Modelling ◽  
Smart City ◽  
Structural Equation ◽  
Smart Cities ◽  
Fog Computing ◽  
Equation Modelling ◽  
Artificial Neural

Abstract The article introduces a two-stage Structural equation modelling- Artificial Neural Network (SEM-ANN) model for the Smart city creation through Fog Computing (FC) and Internet of Things (IOT) by identifying the critical success factors in Indian context. The research article introduces a new factor Fog Computing (FC). Internet of Things (IoT) is again sub-divided into three more factors as Internet of People (IoP), Internet of Services (IoS) and Internet of Energy (IoE) as the independent variables. 13 Smart cities and 379 respondents are involved for this study. The data analysis is done through Structural equation modelling (SEM) and artificial neural network (ANN) which measures both the linear and non-linear relationships respectively. From Structural Equation Modeling (SEM) output, it is identified that Internet of Things (IOT), Internet of People (IOP) and Internet of Services (IOS) have some significant positive effect on Fog Computing (FC). Internet of Energy (IOE) has the negative effect on Fog Computing (FC) which is the only exception in the study for future research direction in this area. The SEM accepted variables are considered as the input for the next layer of ANN analysis that identified IOT has the major effect on Fog Computing (FC). A comparison is also done on SEM and neural network results. The outcome of the study will help more number of Smart City (SC) creations and will fulfil the target of 100 Smart city creation by Government of India taking forward towards a sustainable development.

scholarly journals The Underlying Drivers of Underprivileged Households’ Intention and Behavior towards Community Forestry Management: A Study Using Structural Equation Modelling and Artificial Neural Network Approach

2020 ◽  
Vol 12 (18) ◽  
pp. 7330
Author(s):  
Abdullah Al Mamun ◽  
Syed Ali Fazal ◽  
Muhammad Mehedi Masud ◽  
Ganeshsree Selvachandran ◽  
Noor Raihani Zainol ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Structural Equation Modelling ◽  
Structural Equation ◽  
Community Forestry ◽  
Significant Positive Effect ◽  
Equation Modelling ◽  
Forestry Management ◽  
Artificial Neural ◽  
Positive Effect

In acknowledging the significant role of forestry on the environmental, social, and economic sustainability of local communities, this study focused on examining how different factors affect the intentional behavior towards community forestry among the poor households in Malaysia. Employing theory of planned behavior (TPB) in an expanded model, this study collected data from 420 underprivileged households from 10 states in Malaysia using a survey questionnaire. Final analysis is performed using two methods, one being the well-established, conventional way of partial least square–structural equation modelling (PLS-SEM); the other being a frontier technology of computing using artificial neural network (ANN), which is generated through a deep learning algorithm to achieve the maximum possible accuracy for each of the five scenarios aforementioned. The study found that perceived benefits (PB) and eco-literacy (EL) have a significant positive effect on the attitude towards environment (ATE) while normative belief (NB) and motivation (MO) have a significant positive effect on subjective norms (SUN). Perceived control (PC) has a significant positive effect on perceived behavioral control (PBC). ATE, SUN, and PBC have a significant positive effect on the intention towards community forestry (ITCF), whereas the ITCF has a significant positive effect on community forestry adoption behavior (CFAB). When formulating and enforcing carbon reduction and poverty elevating programs through community forestry, the Malaysian government should consider the perceptions of poor families and the prerogative from their special reference groups to enhance the perceived ability of the vulnerable groups for positive and effective pro-environmental behavior that can lead to sustainable forestry management.

scholarly journals Hybrid artificial neural network and structural equation modelling techniques: a survey

2021 ◽  
Author(s):  
A. S. Albahri ◽  
Alhamzah Alnoor ◽  
A. A. Zaidan ◽  
O. S. Albahri ◽  
Hamsa Hameed ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Structural Equation Modelling ◽  
Structural Equation ◽  
Healthcare Sector ◽  
Autistic Children ◽  
Equation Modelling ◽  
Wide Range ◽  
Artificial Neural ◽  
Future Work

AbstractTopical treatments with structural equation modelling (SEM) and an artificial neural network (ANN), including a wide range of concepts, benefits, challenges and anxieties, have emerged in various fields and are becoming increasingly important. Although SEM can determine relationships amongst unobserved constructs (i.e. independent, mediator, moderator, control and dependent variables), it is insufficient for providing non-compensatory relationships amongst constructs. In contrast with previous studies, a newly proposed methodology that involves a dual-stage analysis of SEM and ANN was performed to provide linear and non-compensatory relationships amongst constructs. Consequently, numerous distinct types of studies in diverse sectors have conducted hybrid SEM–ANN analysis. Accordingly, the current work supplements the academic literature with a systematic review that includes all major SEM–ANN techniques used in 11 industries published in the past 6 years. This study presents a state-of-the-art SEM–ANN classification taxonomy based on industries and compares the effort in various domains to that classification. To achieve this objective, we examined the Web of Science, ScienceDirect, Scopus and IEEE Xplore® databases to retrieve 239 articles from 2016 to 2021. The obtained articles were filtered on the basis of inclusion criteria, and 60 studies were selected and classified under 11 categories. This multi-field systematic study uncovered new research possibilities, motivations, challenges, limitations and recommendations that must be addressed for the synergistic integration of multidisciplinary studies. It contributed two points of potential future work resulting from the developed taxonomy. First, the importance of the determinants of play, musical and art therapy adoption amongst autistic children within the healthcare sector is the most important consideration for future investigations. In this context, the second potential future work can use SEM–ANN to determine the barriers to adopting sensing-enhanced therapy amongst autistic children to satisfy the recommendations provided by the healthcare sector. The analysis indicates that the manufacturing and technology sectors have conducted the most number of investigations, whereas the construction and small- and medium-sized enterprise sectors have conducted the least. This study will provide a helpful reference to academics and practitioners by providing guidance and insightful knowledge for future studies.

scholarly journals The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247582
Author(s):  
Ghazanfar Ali Abbasi ◽  
Lee Yin Tiew ◽  
Jinquan Tang ◽  
Yen-Nee Goh ◽  
Ramayah Thurasamy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Technology Adoption ◽  
Structural Equation Modelling ◽  
Structural Equation ◽  
Single Stage ◽  
Equation Modelling ◽  
Dual Stage ◽  
Stage Analysis

In recent years, the growth of cryptocurrency has undergone an enormous increase in cryptocurrency markets all around the world. Sadly, only insignificant heed has been paid to the unveiling of determinants of cryptocurrency adoption globally, particularly in emerging markets like Malaysia. The purpose of the study is to examine whether the application of deep learning-based dual-stage Partial Least Square-Structural Equation Modelling (PLS-SEM) & Artificial Neural Network (ANN) analysis enable better in-depth research results as compared to single-step PLS-SEM approach and to excavate factors which can predict behavioural intention to adopt cryptocurrency. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model were extended with the inclusion of trust and personnel innovativeness. The model was further validated by introducing a new path model compared to the original UTAUT2 model and the moderating role of personal innovativeness between performance expectancy and price value, with a sample of 314 respondents. Contrary to previous technology adoption studies that used PLS-SEM & ANN as single-stage analysis, this study further enhanced the analysis by applying a deep learning-based dual-stage PLS-SEM and ANN method. The application of deep learning-based dual-stage PLS-SEM & ANN analysis is a novel methodological approach, detecting both linear and non-linear associations among constructs. At the same time, it is regarded as a superior statistical approach as compared to traditional hybrid shallow SEM & ANN single-stage analysis. Also, sensitivity analysis provides normalised importance using multi-layer perceptron with the feed-forward-back-propagation algorithm. Furthermore, the deep learning-based dual-stage PLS-SEM & ANN revealed that trust proved to be the strongest predictor in driving user intention. The introduction of this new methodology and the theoretical contribution opens the vistas of the extant body of knowledge in technology-adoption related literature. This study also provides theoretical, practical and methodological contributions.

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