Cloud computing based futuristic educational model for virtual learning

References

• Abdillahi HH. (2015). The effects of simulations supported 5e teaching model on academic achievements and attitudes in physics education. Master’s degree thesis. Kırıkkale: University of Kırıkkale.
   Google Scholar
• Albalooshi, F. A. (2013). Graduate Attributes for Higher Education and Their Development in Bahrain. International Education Studies, 6(9), 23-30.
   Google Scholar
• AMD, Advanced Micro Devices. (2005). AMD Pacifica virtualization technology, March 2005. Accessed 29-Dec-2019
   Google Scholar
• Anderson, T. (2004). Teaching in an online learning context. Theory and practice of online learning, 273.
   Google Scholar
• Andrew, G., Arora, R., Bilmes, J., & Livescu, K. (2013, February). Deep canonical correlation analysis. In International conference on machine learning (pp. 1247-1255).
   Google Scholar
• Astya, P. (2017, May). Sentiment analysis: approaches and open issues. In 2017 International Conference on Computing, Communication and Automation (ICCCA) (pp. 154-158). IEEE. DOI: 10.1109/CCAA.2017.8229791
   Google Scholar
• Bali, V., Kumar, A. and Gangwar, S. (2019), “A Novel Approach for Wind Speed Forecasting Using LSTM-ARIMA Deep Learning Models”, Accepted for publication by International Journal of Agricultural and Environmental Information Systems (IJAEIS) Volume 11, Issue 3, Article 2
   Web of Science ®Google Scholar
• Bashir, T., Usman, I., Khan, S., & Rehman, J. U. (2017). Intelligent reorganized discrete cosine transform for reduced reference image quality assessment. Turkish Journal of Electrical Engineering & Computer Sciences, 25(4), 2660-2673.
   Web of Science ®Google Scholar
• Bhatnagar, Vaibhav, et al. “Descriptive analysis of COVID-19 patients in the context of India.” Journal of Interdisciplinary Mathematics (2020): 1-16
   Web of Science ®Google Scholar
• Vijander Singh, Ramesh Chandra Poonia, Sandeep Kumar, Pranav Dass, Pankaj Agarwal, Vaibhav Bhatnagar, Linesh Raja, “Prediction of COVID-19 corona virus pandemic based on time series data using Support Vector Machine”, Journal of Discrete Mathematical Sciences & Cryptography (accepted) (2020)
   Google Scholar
• Bhatnagar, Vaibhav, and Ramesh C. Poonia. “Design of prototype model for irrigation based decision support system.” Journal of Information and Optimization Sciences 39.7 (2018): 1607-1612.
   Web of Science ®Google Scholar
• Chua KH & Karpudewan M. (2017). The role of motivation and perceptions about science laboratory environment on lower secondary learners’ attitude towards science. Asia-Pacific Forum on Science Learning and Teaching, 18(2): 1-16
   Google Scholar
• Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences. Routledge.
   Google Scholar
• Dourado, Antônio O.; Martin, C.A. (2013). “New concept of dynamic flight simulator, Part I”. Aerospace Science and Technology. 30 (1): 79–82. doi:10.1016/j.ast.2013.07.005.
   Web of Science ®Google Scholar
• Estapa A & Nadolny L. (2015). The Effect of an augmented reality enhanced mathematics lesson on student achievement and motivation. Journal of STEM Education, 16(3): 40-48.
   Google Scholar
• Farrokhi, F., & Mahmoudi-Hamidabad, A. (2012). Rethinking Convenience Sampling: Defining Quality Criteria. Theory & Practice in Language Studies, 2(4).
   Google Scholar
• Ghosh, L. (2020). The true impact of SpaceX’s Starlink constellation on astronomy is coming into focus: Available at; https://www.theverge.com/2020/3/24/21190273/spacex-starlink-satellite-internet-constellation-astronomy-coating
   Google Scholar
• Haider, M., Khan, S., Rabbani, M. R. and Thallasinos, Y.E. (2020). An Artificial Intelligence and NLP based Islamic FinTech Model Combining Zakat and Qardh-Al-Hasan for Countering the Adverse Impact of COVID 19 on SMEs and Individuals. International Journal of Economics and Business Administration, 8(2), 348-361.
   Google Scholar
• Harald, K., Salomaa, V., Jousilahti, P., Koskinen, S., & Vartiainen, E. (2007). Non-participation and mortality in different socioeconomic groups: the FINRISK population surveys in 1972–92. Journal of Epidemiology & Community Health, 61(5), 449-454.
   PubMed Web of Science ®Google Scholar
• Hassan, M. K., Rabbani, M. R. and Ali, M, (2020). Challenges for the Islamic Finance and banking in post COVID era and the role of Fintech. Journal of Economic Cooperation and Development. 41(2).
   Google Scholar
• Hsu, YS. Lin, YH. & Yang, B. (2017). Impact of augmented reality lessons on learners’ STEM interest. Research and Practice in Technology Enhanced Learning, 12(2): 1-14. doi: 10.1186/s41039-016-0039-z
   PubMedGoogle Scholar
• Hu, X., Le, H., Bourgeois, A. G., & Pan, Y. (2018). Collaborative Learning in Cloud-based Virtual Computer Labs. 2018 IEEE Frontiers in Education Conference (FIE). doi:10.1109/fie.2018.8659018
   Google Scholar
• Iksal, Sébastien. (2018). “A Process of Design and Production of Virtual Reality Learning Environments.” In The Challenges of the Digital Transformation in Education: Proceedings of the 21st International Conference on Interactive Collaborative Learning (ICL2018)-, vol. 1, p. 353. Springer, 2018.
   Google Scholar
• Khan, S. and Rabbani, M. R., (2020), Artificial Intelligence and NLP based Chatbot as Islamic Banking and Finance Expert, 2020 International Conference on Computational Linguistics and Natural Language Processing (CLNLP 2020), Seoul, South Korea on July 20-22, 2020.
   Google Scholar
• Khan, S. and Rabbani, M.R., (2020b), Artificial Intelligence and NLP based Chabot as Islamic Banking and Finance Expert, International Journal of Information Retrieval Research (IJIRR), Special Issue On: Optimization and Convergence of Machine Learning Algorithms for Leveraging IoT, Block chain and Artificial Intelligence, 2020
   Google Scholar
• Khan, S. N., & Usman, I. (2019). Amodel for english to urdu and hindi machine translation system using translation rules and artificial neural network. Int. Arab J. Inf. Technol., 16(1), 125-131.
   Web of Science ®Google Scholar
• Khan, S., & Kannapiran, T. (2019). Indexing Issues in Spatial Big Data Management. Available at SSRN 3387792.
   Google Scholar
• Khan, S., & Mishra, R. B. (2011). Translation rules and ANN based model for English to Urdu machine translation. INFOCOMP, 10(3), 36-47.
   Google Scholar
• Khan, S., Mir, U., Shreem, S. S., & Alamri, S. (2018). Translation Divergence Patterns Handling in English to Urdu Machine Translation. International Journal on Artificial Intelligence Tools, 27(05), 1850017.
   Google Scholar
• Lee, Y., Kozar, K. A., & Larsen, K. R. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for information systems, 12(1), 50.
   Google Scholar
• Lytras, M. D., Zhuhadar, L., Zhang, J. X., & Kurilovas, E. (2014). Advances of Scientific Research on Technology Enhanced Learning in Social Networks and Mobile Contexts: Towards High Effective Educational Platforms for Next Generation Education. J. UCS, 20(10), 1402-1406.
   Web of Science ®Google Scholar
• Mishra, R. B. (2012). A neural network based approach for English to Hindi machine translation. International Journal of Computer Applications 53, no. 18 (2012): 50-56.
   Google Scholar
• Nicolai, S. (Ed.). (2009). Opportunities for change: Education innovation and reform during and after conflict. UNESCO. International Institute for educational planning (IIEP).
   Google Scholar
• Oubahssi, L., Mahdi, O., Piau-Toffolon, C., & Iksal, S. (2018, September). A process of design and production of Virtual Reality Learning Environments. In International Conference on Interactive Collaborative Learning (pp. 353-364). Springer, Cham.
   Google Scholar
• Paul, P. K., & Lata Dangwal, K. (2014). Cloud Based Educational Systems and Its Challenges and Opportunities and Issues. Turkish Online Journal of Distance Education, 15(1), 89-98.
   Google Scholar
• Penn, Mafor, and Umesh Ramnarain. “A comparative analysis of virtual and traditional laboratory chemistry learning.” Perspectives in Education 37, no. 2 (2019): 80-97.
   Google Scholar
• Price, J. H., & Murnan, J. (2004). Research limitations and the necessity of reporting them. American Journal of Health Education, 35(2), 66.
   Google Scholar
• Rabbani, M. R. (2020). The competitive structure and strategic positioning of commercial banks in Saudi Arabia. International Journal on Emerging Technologies 11(3), 43-46.
   Google Scholar
• Rabbani, M.R., Khan, S., Thalassinos, I.E. (2020). FinTech, Blockchain and Islamic Finance: An Extensive Literature Review. International Journal of Economics and Business Administration, 8(2), 65-86.
   Google Scholar
• Razali, N. M., & Wah, Y. B. (2011). Power comparisons of shapiro-wilk, kolmogorov-smirnov, lilliefors and anderson-darling tests. Journal of statistical modeling and analytics, 2(1), 21-33.
   Google Scholar
• Schwalbe, K. (2015). Information technology project management. Cengage Learning.
   Google Scholar
• Selviandro, N., & Hasibuan, Z. A. (2013, March). Cloud-based e-learning: A proposed model and benefits by using e-learning based on cloud computing for educational institution. In Information and Communication Technology-EurAsia Conference (pp. 192-201). Springer, Berlin, Heidelberg.
   Google Scholar
• Shahnawaz, & Mishra, R. B. (2013). Rule-based approach for handling of case markers in English to Urdu/Hindi translation. International Journal of Knowledge Engineering and Soft Data Paradigms, 4(2), 138-165. (2013a)
   Google Scholar
• Shahnawaz, & Mishra, R. B. (2013). Statistical machine translation system for English to Urdu. International Journal of Advanced Intelligence Paradigms, 5(3), 182-203. DOI: 10.1504/IJAIP.2013.056421 (2013b)
   Google Scholar
• Shahnawaz, & Mishra, R. B. (2015). An English to Urdu translation model based on CBR, ANN and translation rules. International Journal of Advanced Intelligence Paradigms, 7(1), 1-23.
   Google Scholar
• Shahnawaz, M. R. (2011). ANN and rule based model for English to Urdu-Hindi machine translation system. In Proceedings of National Conference on Artificial Intelligence and agents: Theory& Application (AIAIATA 2011) (pp. 115-121).
   Google Scholar
• Shiveley, R. (2005) Enhanced virtualization on Intel architecturebased servers. Technology@Intel Magazine, pp. 1–9, Apr. 2005. http://www.intel.com/technology/magazine/computing/intel-virtualization-0405.pdf
   Google Scholar
• Starlink, (2020). https://www.starlink.com/, Accessed 12-Apr-20.
   Google Scholar
• Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273-1296.
   Web of Science ®Google Scholar
• Tondeur, J., Van Braak, J., Sang, G., Voogt, J., Fisser, P., & Ottenbreit-Leftwich, A. (2012). Preparing pre-service educators to integrate technology in education: A synthesis of qualitative evidence. Computers & Education, 59(1), 134-144.
   Web of Science ®Google Scholar
• Villasenor Alva, J. A., & Estrada, E. G. (2009). A generalization of Shapiro–Wilk’s test for multivariate normality. Communications in Statistics—Theory and Methods, 38(11), 1870-1883.
   Web of Science ®Google Scholar
• WHO (2020). Situation report No. 180. Available at; https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200718-covid-19-sitrep-180.pdf?sfvrsn=39b31718_2. Accessed on 18/07/2020.
   Google Scholar
• Wiley, D. A., & Edwards, E. K. (2002). Online self-organizing social systems: The decentralized future of online learning. Quarterly review of distance education, 3(1), 33-46.
   Google Scholar
• Wilson, R. C., Nassar, M. R., & Gold, J. I. (2010). Bayesian online learning of the hazard rate in change-point problems. Neural computation, 22(9), 2452-2476.
   PubMed Web of Science ®Google Scholar
• Yap, B. W., & Sim, C. H. (2011). Comparisons of various types of normality tests. Journal of Statistical Computation and Simulation, 81(12), 2141-2155.
   Web of Science ®Google Scholar
• Zhao, H., Chen, P. L., Khan, S., & Khalafe, O. I. (2020). Research on the optimization of the management process on internet of things (Iot) for electronic market. The Electronic Library.
   Google Scholar