International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 14, No. 1, 8 Feb. 2022
Cover page and Table of Contents: PDF (size: 354KB)
Artificial Intelligence in Collaborative Information System
Full Text (PDF, 354KB), PP.44-55
Views: 0 Downloads: 0
Author(s)
Monika Arora
1,*
Indira Bhardwaj
2
Index Terms
AI, Deep Learning, Blockchain, Information System, Security
Abstract
All organizations have a collaborative information system, which is a shared system between employees and teams in the organisation. All such information systems in organizations need to be flawlessly secure. Securing information systems through the latest technologies like Artificial Intelligence, Deep Learning and Blockchain is one of the latest trends in information sciences. This paper tries to explore them in detail through data on user’s login time and time spent on the websites along with user actions. The objective is to develop a model that will be used for authentication of the user. This will allow early detection of frauds so that preventive and remedial actions like blocking access to the user can be initiated well in advance. The dataset used to develop this model is the user log data and technique of logistic regression is used to create the regression model for authentication of the user. Logistic regression-based classification is used on the attributes taken to record and analyze entries recorded on the system leading to identification of a cluster based on normal and suspicious users. The accuracy of logistic regression has been analyzed and implemented to secure the collaborative system. This study will help the researcher to implement the AI in the system. It also discusses its future prospects and the disruptive changes in implementation of Information Systems. Finally, the research considers combining blockchain (BC) and deep learning (DL) with Artificial Intelligence (AI) and discusses the revolutionary changes that would result by rapidly advancing the AI field.
Cite This Paper
Monika Arora, Indira Bhardwaj, "Artificial Intelligence in Collaborative Information System", International Journal of Modern Education and Computer Science(IJMECS), Vol.14, No.1, pp. 44-55, 2022.DOI: 10.5815/ijmecs.2022.01.04
Reference
[1] Arora M., Chopra A.B., Dixit V.S. (2020) An Approach to Secure Collaborative Recommender System Using Artificial Intelligence, Deep Learning, and Blockchain. In: Choudhury S., Mishra R., Mishra R., Kumar A. (eds) Intelligent Communication, Control and Devices. Advances in Intelligent Systems and Computing, vol 989. Springer, Singapore. https://doi.org/10.1007/978-981-13-8618-3_51
[2] Waheed, N., He, X., Ikram, M., Usman, M., Hashmi, S. S., & Usman, M. (2020). Security and privacy in IoT using machine learning and blockchain: Threats and countermeasures. ACM Computing Surveys (CSUR), 53(6), 1–37.
[3] Salah, K., Rehman, M. H. U., Nizamuddin, N., & Al-Fuqaha, A. (2019). Blockchain for AI: Review and open research challenges. IEEE Access, 7, 10127–10149.
[4] Singh, S., Sharma, P. K., Yoon, B., Shojafar, M., Cho, G. H., & Ra, I.-H. (2020). Convergence of blockchain and artificial intelligence in IoT network for the sustainable smart city. Sustainable Cities and Society, 63, 102364.
[5] Ekramifard, A., Amintoosi, H., Seno, A. H., Dehghantanha, A., & Parizi, R. M. (2020). A systematic literature review of integration of blockchain and artificial intelligence. In Blockchain Cybersecurity, Trust and Privacy (pp. 147–160). Springer.
[6] Lu, Y. (2019). Artificial intelligence: A survey on evolution, models, applications and future trends. Journal of Management Analytics, 6(1), 1–29.
[7] Wang, X., Han, Y., Leung, V. C., Niyato, D., Yan, X., & Chen, X. (2020). Convergence of edge computing and Deep Learning: A comprehensive survey. IEEE Communications Surveys & Tutorials, 22(2), 869–904.
[8] Singh, S. K., Rathore, S., & Park, J. H. (2020). Blockiotintelligence: A blockchain-enabled intelligent IoT architecture with artificial intelligence. Future Generation Computer Systems, 110, 721–743
[9] Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69, S36-S40.
[10] Mustapha, A. M., Arogundade, O. T., Misra, S., Damasevicius, R., & Maskeliunas, R. (2020). A systematic literature review on compliance requirements management of business processes. International Journal of System Assurance Engineering and Management, 11(3), 561-576.
[11] Arogundade, O. T., Abayomi-Alli, A., & Misra, S. (2020). An Ontology-Based Security Risk Management Model for Information Systems. Arabian Journal for Science and Engineering, 1-16.
[12] Kamta Nath Mishra, "A Proficient Mechanism for Cloud Security Supervision in Distributive Computing Environment", International Journal of Computer Network and Information Security, Vol.12, No.6, pp.57-77, 2020.
[13] Arora M and Arora A.(2018) , “ Digital Information Tracking Framework using Blockchain, Journal of Supply Chain management Systems(UGC Approved), Volume 7 Issue 2, pp 1-7, ISSN : 2277-1387
[14] Lotter, W., Kreiman, G., Cox, D (2016).”Deep predictive coding networks for video prediction and unsupervised learning”. arXiv preprint arXiv:1605.08104
[15] Wong, B. K., Bodnovich, T. A., & Selvi, Y. (1997). Neural network applications in business: A review and analysis of the literature (1988–1995). Decision Support Systems, 19(4), 301-320.
[16] Gleichauf, R. E., Teal, D. M., & Wiley, K. L. (2002). U.S. Patent No. 6,499,107. Washington, DC: U.S. Patent and Trademark Office.
[17] Lu, H., Li, Y., Chen, M., Kim, H., & Serikawa, S. (2018). Brain intelligence: go beyond artificial intelligence. Mobile Networks and Applications, 23(2), 368-375.
[18] Park,H.,D., Kim., K., H., Choi, Y.,II., and Kim, K.,J., ( 2012 ). “A literature review and classification of systems research”, Expert Systems with Applications ( 39: 1) pp. 10059-10072
[19] Ahn L., Blum M., Hopper N.J., Langford J. (2003) CAPTCHA: Using Hard AI Problems for Security. In: Biham E. (eds) Advances in Cryptology — EUROCRYPT 2003. EUROCRYPT 2003. Lecture Notes in Computer Science, vol 2656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39200-9_18
[20] McLeod, A. I., & Xu, C. (2010). bestglm: Best subset GLM. URL http://CRAN. R-project. org/package= bestglm accessed on 15 December 2020.
[21] Bozic, J., & Wotawa, F. (2017). Planning the attack! or how to use ai in security testing?. In IWAISe: First International Workshop on Artificial Intelligence in Security (Vol. 50) http://iwaise.it.nuigalway.ie/wp-content/uploads/2017/02/IWAISe-17_paper_10-jb.pdf accessed on 20 December 2020.
[22] Hilbe, J. M. (2009). Logistic regression models. CRC press.
[23] Hilbe, J. M. (2011). Logistic Regression. International encyclopedia of statistical science, 1, 15-32. https://encyclopediaofmath.org/images/6/69/Logistic_regression.pdf accessed on 20 December 2020
[24] Sellers, Kimberly F., and Galit Shmueli. "A flexible regression model for count data." The Annals of Applied Statistics (2010): 943-961.
[25] Sfetsos, A., & Coonick, A. H. (2000). Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques. Solar Energy, 68(2), 169-178.
[26] Gelman, A., Jakulin, A., Pittau, M. G., & Su, Y. S. (2008). A weakly informative default prior distribution for logistic and other regression models. The annals of applied statistics, 2(4), 1360-1383
[27] Ciampi, F. (2015). Corporate governance characteristics and default prediction modeling for small enterprises. An empirical analysis of Italian firms. Journal of Business Research, 68(5), 1012-1025.
[28] Ciampi, F., & Gordini, N. (2008, January). Using economic-financial ratios for small enterprise default prediction modeling: An empirical analysis. In 2008 Oxford Business & Economics Conference Proceedings, Association for Business and Economics Research (ABER) (pp. 1-21) accessed on 20 December 2020.
[29] Fumo, N., & Biswas, M. R. (2015). Regression analysis for prediction of residential energy consumption. Renewable and Sustainable Energy Reviews, 47, 332–343
[30] Kumar, R., Nandy, S., Agarwal, R., & Kushwaha, S. P. S. (2014). Forest cover dynamics analysis and prediction modeling using logistic regression model. Ecological Indicators, 45, 444–455.
[31] Lehmann, A., Overton, J. M., & Leathwick, J. R. (2002). GRASP: Generalized regression analysis and spatial prediction. Ecological Modelling, 157(2–3), 189–207
[32] Song, S. Y., Lee, Y. K., & Kim, I.-J. (2016). Sugar and acid content of Citrus prediction modeling using FT-IR fingerprinting in combination with multivariate statistical analysis. Food Chemistry, 190, 1027–1032
[33] Steyerberg, E. W., Harrell Jr, F. E., Borsboom, G. J., Eijkemans, M. J. C., Vergouwe, Y., & Habbema, J. D. F. (2001). Internal validation of predictive models: Efficiency of some procedures for logistic regression analysis. Journal of Clinical Epidemiology, 54(8), 774–781
[34] Bahrammirzaee, A. (2010). A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems. Neural Computing and Applications, 19(8), 1165-1195.
[35] Nakamoto, S., & Bitcoin, A. (2008). A peer-to-peer electronic cash system. Bitcoin.–URL: https://bitcoin. org/bitcoin.pdf accessed on 8 January 2021
[36] Gill, S. S., Tuli, S., Xu, M., Singh, I., Singh, K. V., Lindsay, D. ... & Garraghan, P. (2019). Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges. Internet of Things, 8, 100118.
[37] N. M. Tahir, Adam N. Ausat, Usman I. Bature, Kamal A. Abubakar, Ibrahim Gambo, "Off-line Handwritten Signature Verification System: Artificial Neural Network Approach", International Journal of Intelligent Systems and Applications, Vol.13, No.1, pp.45-57, 2021.
[38] Anozie Onyezewe, Armand F. Kana, Fatimah B. Abdullahi, Aminu O. Abdulsalami, "An Enhanced Adaptive k-Nearest Neighbor Classifier Using Simulated Annealing", International Journal of Intelligent Systems and Applications, Vol.13, No.1, pp.34-44, 2021.
[39] Hossein Mohammadinejad, Fateme Mohammadhoseini, "Privacy Protection in Smart Cities by a Personal Data Management Protocol in Blockchain", International Journal of Computer Network and Information Security, Vol.12, No.3, pp.44-52, 2020.