Emotional Artificial Neural Networks and Gaussian Process-Regression-Based Hybrid Machine-Learning Model for Prediction of Security and Privacy Effects on M-Banking Attractiveness
Abstract
:1. Introduction
2. Materials and Methods
2.1. Technology Acceptance Models
2.2. Mobile Banking System Features, and Challenges
2.3. Method
2.3.1. Study Design and Measurement Scales
2.3.2. Data Collection and Sampling Techniques
2.3.3. Data Examination Techniques
2.4. Artificial Intelligence Techniques
2.5. Ensemble Techniques
2.5.1. ANFIS Model
2.5.2. GPR Model
2.5.3. EANN Model
2.5.4. BRT Model
2.6. Data Normalization, Models Validation and Performance Comparisons
2.6.1. Data Normalization
2.6.2. Models’ Validation, and Performance Comparisons
3. Results and Discussion
3.1. Sensitivity Examination Results
3.2. AI Models Results and Discussion
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
1. Gender | ( ) Male | ( ) Female | ||
2. Age group | ( ) 18–25 | ( ) 26–35 | ( ) 36 and above | |
3. Education level | ( ) SSCE | ( ) ND | ( ) Barchelor/HND | 3. Education level |
4. Employment Status | ( ) Academics | ( ) Students | ( ) Banking Staff | ( ) Security expert |
5. Nationality | ( ) Nigeria | ( ) Cyprus |
Items | Sd | D | N | A | Sa | |
---|---|---|---|---|---|---|
H1 | Ease of use (Lewis, 2006) | |||||
EoU1 | “Overall, I am satisfied with how easy it is to use this System” | |||||
EoU2 | “It was simple to use this system” | |||||
EoU3 | “I was able to complete the tasks and scenarios quicklyusing this system” | |||||
EoU4 | “I felt comfortable using this system” | |||||
EoUe5 | “It was easy to learn to use this system”. | |||||
EoU6 | “I believe I could be protected using this System” | |||||
H2 | Privacy (Authors of this study) | |||||
Priv1 | “I fear that while I am paying a bill by mobile phone, I might make mistakes since the correctness of the inputted information is difficult to check from the screen” | |||||
Priv2 | “I fear that while I am using mobile banking services, the Government can intercept my personal information” | |||||
Priv3 | “I fear that while I am using mobile banking services, third parties can access my account or see my account information” | |||||
Priv4 | “I fear that the list of PIN codes may be lost and end up in the wrong hands” | |||||
Priv5 | “Generally, I fear that my personal information can be accessed through m-banking platform” | |||||
H3 | Interface quality (Shama, 2019; Lewis, 2006) | |||||
IntQual1 | “The interface of this system was pleasant”. | |||||
IntQual2 | “I liked using the interface of this system”. | |||||
IntQual3 | “This system has all of the functions and capabilities I expect it to have” | |||||
IntQual4 | “Overall, I am satisfied with this system” | |||||
H4 | System Security (Authors of this study) | |||||
SysSq1 | “The security features of the system are pleasant to me” | |||||
SysSq2 | “Velocity anomalies during login can be handle by the system” | |||||
SysSq3 | “The authentication process provided in the system is more reliable, robust, and can help safeguard customers funds” | |||||
SysSq4 | “The system is capable of handling DDoS attacks” | |||||
SysSq5 | “In general, I am satisfied with the level of security provided in the systems” | |||||
H5 | Service quality (Shama, 2019; Lewis, 2006) | |||||
SQ1 | “The call centre representatives always help me when I need support with m-banking” | |||||
SQ2 | “The call centre representatives always pay personal attention when I experience problems with m-banking” | |||||
SQ3 | “The call centre representatives have adequate knowledge to answer my queries related to m-banking” | |||||
H6 | Cultural values (Shama, 2019) | |||||
CV1 | “People who are important to me think that I should use Mobile banking apps” | |||||
CV2 | “People who influence my behaviour think that I should use Mobile banking apps” | |||||
CV3 | “People whose opinions that I value prefer that I use Mobile Banking” | |||||
CV4 | “Generally, my culture encourages the use of m-banking services” |
Appendix B
Demographic Variable | Frequencies | Percentage |
---|---|---|
Nationality | ||
Nigeria | 427 | 57.3 |
Cyprus | 318 | 42.7 |
Gender | ||
Male | 411 | 55.2 |
Female | 334 | 44.8 |
Age group | ||
18–25 | 241 | 32.3 |
26–35 | 381 | 51.2 |
36 and above | 123 | 16.5 |
Educational qualification | ||
SSCE | 73 | 9.8 |
ND | 110 | 14.8 |
Bachelor/HND | 397 | 53.3 |
Masters or higher | 165 | 22.1 |
Employment status | ||
Academics | 173 | 23.2 |
Students | 368 | 49.4 |
Other civil servants | 89 | 11.9 |
Business | 107 | 14.4 |
Security experts | 8 | 1.1 |
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Input Parameters | DC Values | Rank |
---|---|---|
Security | 0.9991 | 1 |
Privacy | 0.9899 | 2 |
Service excellence | 0.8693 | 3 |
Interface quality | 0.8211 | 4 |
Ease of use | 0.4762 | 5 |
Culture | 0.4386 | 6 |
Training | Testing | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
MODELS | NSE | RMSE | MAE | MAPE | rRMSE | NSE | RMSE | MAE | MAPE | rRMSE |
ANFIS | 0.9951 | 0.0931 | 0.0617 | 23.0142 | 8.5317 | 0.9962 | 0.0364 | 0.0168 | 8.4587 | 5.2631 |
GPR | 0.9999 | 0.0001 | 0.0009 | 0.0428 | 0.0201 | 0.9999 | 0.0001 | 0.0002 | 0.0123 | 0.0147 |
EANN | 0.9999 | 0.0012 | 0.0082 | 0.0720 | 0.0964 | 0.9998 | 0.0009 | 0.0037 | 0.0623 | 0.0792 |
BRT | 0.9773 | 0.0612 | 0.0197 | 13.9462 | 6.6381 | 0.9573 | 0.0536 | 0.0636 | 11.3127 | 8.0736 |
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Cavus, N.; Mohammed, Y.B.; Gital, A.Y.; Bulama, M.; Tukur, A.M.; Mohammed, D.; Isah, M.L.; Hassan, A. Emotional Artificial Neural Networks and Gaussian Process-Regression-Based Hybrid Machine-Learning Model for Prediction of Security and Privacy Effects on M-Banking Attractiveness. Sustainability 2022, 14, 5826. https://doi.org/10.3390/su14105826
Cavus N, Mohammed YB, Gital AY, Bulama M, Tukur AM, Mohammed D, Isah ML, Hassan A. Emotional Artificial Neural Networks and Gaussian Process-Regression-Based Hybrid Machine-Learning Model for Prediction of Security and Privacy Effects on M-Banking Attractiveness. Sustainability. 2022; 14(10):5826. https://doi.org/10.3390/su14105826
Chicago/Turabian StyleCavus, Nadire, Yakubu Bala Mohammed, Abdulsalam Ya’u Gital, Mohammed Bulama, Adamu Muhammad Tukur, Danlami Mohammed, Muhammad Lamir Isah, and Abba Hassan. 2022. "Emotional Artificial Neural Networks and Gaussian Process-Regression-Based Hybrid Machine-Learning Model for Prediction of Security and Privacy Effects on M-Banking Attractiveness" Sustainability 14, no. 10: 5826. https://doi.org/10.3390/su14105826
APA StyleCavus, N., Mohammed, Y. B., Gital, A. Y., Bulama, M., Tukur, A. M., Mohammed, D., Isah, M. L., & Hassan, A. (2022). Emotional Artificial Neural Networks and Gaussian Process-Regression-Based Hybrid Machine-Learning Model for Prediction of Security and Privacy Effects on M-Banking Attractiveness. Sustainability, 14(10), 5826. https://doi.org/10.3390/su14105826