Harnessing the Power of Business Analytics and Artificial Intelligence: A Roadmap to Data-Driven Success

Authors

  • Seyed Taha Hossein Mortaji * PhD., Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran.
  • Soha Shateri Ph.D. Candidate, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran

DOI:

https://doi.org/10.59615/ijie.3.3.1

Keywords:

Business Analytics, Artificial Intelligence, Data-driven Decision Making, Advanced Analytics Techniques, AI Algorithms

Abstract

This paper explores the intersection of business analytics (BA) and artificial intelligence (AI) and their profound impact on modern enterprises. The integration of advanced analytics techniques and AI algorithms enables organizations to extract valuable insights from vast amounts of data, optimize decision-making processes, and gain a competitive edge in today's data-driven economy. This paper presents an overview of business analytics and AI, their key concepts, methodologies, and applications. Furthermore, it highlights the benefits, challenges, and ethical considerations associated with leveraging these technologies, providing guidance for successful implementation. By harnessing the power of business analytics and AI, organizations can unlock new opportunities for growth, efficiency, and innovation.

Downloads

Download data is not yet available.

References

• Agrawal, R., & Srikant, R. (1994). Fast Algorithms for Mining Association Rules in Large Databases Proceedings of the 20th International Conference on Very Large Data Bases,

• Aliahmadi, A., Nozari, H., & Ghahremani-Nahr, J. (2022a). AIoT-based Sustainable Smart Supply Chain Framework. International Journal of Innovation in Management, Economics and Social Sciences, 2(2), 28-38. https://doi.org/10.52547/ijimes.2.2.28

• Aliahmadi, A., Nozari, H., & Ghahremani-Nahr, J. (2022b). Big Data IoT-based Agile-Lean Logistic in Pharmaceutical Industries. International Journal of Innovation in Management, Economics and Social Sciences, 2(3), 70-81. https://doi.org/10.52547/ijimes.2.3.70

• Alnoukari, M. (2022). From Business Intelligence to Big Data: The Power of Analytics. In I. R. Management Association (Ed.), Research Anthology on Big Data Analytics, Architectures, and Applications (pp. 823-841). IGI Global. https://doi.org/10.4018/978-1-6684-3662-2.ch038

• Barocas, S., & Selbst, A. D. (2016). Big Data's Disparate Impact. California Law Review, 104(3), 671-732. http://www.jstor.org/stable/24758720

• Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82-115. https://doi.org/https://doi.org/10.1016/j.inffus.2019.12.012

• Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. https://books.google.com/books?id=qWPwnQEACAAJ

• Bojarski, M., Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L., Monfort, M., Muller, U., Zhang, J., Zhang, X., Zhao, J., & Zieba, K. (2016). End to End Learning for Self-Driving Cars. https://doi.org/https://doi.org/10.48550/arXiv.1604.07316

• Bostock, M., Ogievetsky, V., & Heer, J. (2011). D³ Data-Driven Documents. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2301-2309. https://doi.org/10.1109/TVCG.2011.185

• Brose, M. S., Flood, M. D., Krishna, D., & Nichols, B. (2014). Handbook of Financial Data and Risk Information I: Principles and Context (M. S. Brose, M. D. Flood, D. Krishna, & B. Nichols, Eds. Vol. 1). Cambridge University Press. https://doi.org/DOI: 10.1017/CBO9780511997723

• Bruun, E. P. G., & Duka, A. (2018). Artificial Intelligence, Jobs and the Future of Work: Racing with the Machines. Basic Income Studies, 13(2). https://doi.org/doi:10.1515/bis-2018-0018

• Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review,, 95(1), 56-66. https://hbr.org/2017/07/the-business-of-artificial-intelligence

• Bughin, J., Chui, M., & Manyika, J. (2018). Notes from the AI frontier: Insights from hundreds of use cases. M. G. Institute.

• Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183-186. https://doi.org/10.1126/science.aal4230

• Campbell, C., Sands, S., Ferraro, C., Tsao, H.-Y., & Mavrommatis, A. (2020). From data to action: How marketers can leverage AI. Business Horizons, 63(2), 227-243. https://doi.org/https://doi.org/10.1016/j.bushor.2019.12.002

• Cavoukian, A. (2018). Privacy by design: The 7 foundational principles. https://iapp.org/media/pdf/knowledge_center/Privacy-by-Design-The-7-Fundamental-Principles.pdf

• Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165-1188. https://doi.org/10.2307/41703503

• Chen, I. Y., Johansson, F. D., & Sontag, D. A. (2018). Why Is My Classifier Discriminatory? Neural Information Processing Systems,

• Chen, P.-T., Lin, C.-L., & Wu, W.-N. (2020). Big data management in healthcare: Adoption challenges and implications. International Journal of Information Management, 53, 102078. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2020.102078

• Chen, X., Kundu, K., Zhu, Y., Berneshawi, A., Ma, H., Fidler, S., & Urtasun, R. (2015). 3D object proposals for accurate object class detection Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1, Montreal, Canada.

• Cohen, M. C. (2018). Big Data and Service Operations. Production and Operations Management, 27(9), 1709-1723. https://doi.org/https://doi.org/10.1111/poms.12832

• Corbett-Davies, S., & Goel, S. (2018). The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning. ArXiv, abs/1808.00023.

• Davenport, T. H., & Harris, J. G. (2007). Competing on analytics. Harvard Business Review, 85(1), 98-107. https://hbr.org/2006/01/competing-on-analytics

• Davenport, T. H., & Patil, D. J. (2012). Data Scientist: The Sexiest Job of the 21st Century Harvard Business Review, 90(10), 70-76. https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century

• Delen, D., & Ram, S. (2018). Research challenges and opportunities in business analytics. Journal of Business Analytics, 1(1), 2-12. https://doi.org/10.1080/2573234X.2018.1507324

• Demajo, L., Vella, V., & Dingli, A. (2020). Explainable AI for Interpretable Credit Scoring. https://doi.org/10.5121/csit.2020.101516

• Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. North American Chapter of the Association for Computational Linguistics,

• Dubey, R., Gunasekaran, A., Childe, S. J., Roubaud, D., Fosso Wamba, S., Giannakis, M., & Foropon, C. (2019). Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain. International Journal of Production Economics, 210, 120-136. https://doi.org/https://doi.org/10.1016/j.ijpe.2019.01.023

• European-Union. (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). . L. Official Journal of the European Union. http://data.europa.eu/eli/reg/2016/679/oj

• Fan, W., & Geerts, F. (2012). Foundations of Data Quality Management. https://doi.org/10.1007/978-3-031-01892-3

• Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd. https://books.google.com/books?id=3C7QAAAAMAAJ

• Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2021). An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. In L. Floridi (Ed.), Ethics, Governance, and Policies in Artificial Intelligence (pp. 19-39). Springer International Publishing. https://doi.org/10.1007/978-3-030-81907-1_3

• Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234-246. https://doi.org/https://doi.org/10.1016/j.ijpe.2014.12.031

• Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280. https://doi.org/https://doi.org/10.1016/j.techfore.2016.08.019

• Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2014.10.007

• Gauss, C. F., & Gottingensis, S. R. S. (1821). Theoria combinationis observationum: erroribus minimis obnoxiae. https://books.google.com/books?id=4BrCswEACAAJ

• Ghahremani nahr, J., Nozari, H., & Sadeghi, M. E. (2021a). Artificial intelligence and Machine Learning for Real-world problems (A survey). International Journal of Innovation in Engineering, 1(3), 38-47. https://doi.org/10.59615/ijie.1.3.38

• Ghahremani Nahr, J., Nozari, H., & Sadeghi, M. E. (2021b). Green supply chain based on artificial intelligence of things (AIoT). International Journal of Innovation in Management, Economics and Social Sciences, 1(2), 56-63. https://doi.org/10.52547/ijimes.1.2.56

• Goodman, B., & Flaxman, S. (2017). European Union Regulations on Algorithmic Decision Making and a “Right to Explanation”. AI Magazine, 38(3), 50-57. https://doi.org/https://doi.org/10.1609/aimag.v38i3.2741

• hajiaghajani, A. (2023). Concepts and applications of data mining and analysis of social networks. Journal of Data Analytics, 2(1), 1-8. https://doi.org/10.59615/jda.2.1.1

• Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. Springer New York. https://books.google.com/books?id=tVIjmNS3Ob8C

• He, K., Zhang, X., Ren, S., & Sun, J. (2016, 27-30 June 2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),

• Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735

• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning: with Applications in R. Springer New York. https://books.google.com/books?id=qcI_AAAAQBAJ

• Jędrzejka, D. (2019). Robotic process automation and its impact on accounting. Zeszyty Teoretyczne Rachunkowości, 2019, 137-166. https://doi.org/10.5604/01.3001.0013.6061

• John, A., & Latha, T. (2023). Stock market prediction based on deep hybrid RNN model and sentiment analysis. Automatika, 64(4), 981-995. https://doi.org/10.1080/00051144.2023.2217602

• Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard: Measures that drive performance. Harvard Business Review, 70(1), 71-79. https://hbr.org/1992/01/the-balanced-scorecard-measures-that-drive-performance-2

• Kavanagh, M. J., Thite, M., & Johnson, R. D. (2011). Human Resource Information Systems: Basics, Applications, and Future Directions: Basics, Applications, and Future Directions. SAGE Publications. https://books.google.com/books?id=yDe-rMyZSbkC

• Kian, R. (2021). Provide a model for an e-commerce system with the impact of artificial intelligence. International Journal of Innovation in Management, Economics and Social Sciences, 1(3), 88-94. https://doi.org/10.52547/ijimes.1.3.88

• Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, 1746–1751. https://doi.org/10.3115/v1/D14-1181

• Kober, J., Bagnell, J. A., & Peters, J. (2013). Reinforcement learning in robotics: A survey. The International Journal of Robotics Research, 32(11), 1238-1274. https://doi.org/10.1177/0278364913495721

• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In F. Pereira, C. J. Burges, L. Bottou, & K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems (Vol. 25).

• Larson, D., & Chang, V. (2016). A review and future direction of agile, business intelligence, analytics and data science. International Journal of Information Management, 36(5), 700-710. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2016.04.013

• Lavalle, S., Lesser, E., Shockley, R., Hopkins, M., & Kruschwitz, N. (2011). Big Data, Analytics and the Path From Insights to Value. MIT Sloan Management Review, 52, 21-32. https://sloanreview.mit.edu/article/big-data-analytics-and-the-path-from-insights-to-value/

• LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539

• Lee, I., & Mangalaraj, G. (2022). Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions. Big Data and Cognitive Computing, 6(1), 17. https://www.mdpi.com/2504-2289/6/1/17

• Levine, S., Finn, C., Darrell, T., & Abbeel, P. (2015). End-to-End Training of Deep Visuomotor Policies. Journal of Machine Learning Research, 17(39), 1-40. http://jmlr.org/papers/v17/15-522.html

• Li, L., Chi, T., Hao, T., & Yu, T. (2018). Customer demand analysis of the electronic commerce supply chain using Big Data. Annals of Operations Research, 268(1), 113-128. https://doi.org/10.1007/s10479-016-2342-x

• Liebowitz, J. (2013). Business Analytics: An Introduction (1st ed.). CRC Press. https://doi.org/https://doi.org/10.1201/b16246

• Lipton, Z. C. (2018). The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue, 16(3), 31–57. https://doi.org/10.1145/3236386.3241340

• MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In L. M. L. Cam & J. Neyman (Eds.), Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. University of California Press. https://books.google.com/books?id=IC4Ku_7dBFUC

• Maleki, R., & Sabet, E. (2022). Business Intelligence Analysis in Small and Medium Enterprises. International journal of Innovation in Marketing Elements, 2(1), 1-11. https://doi.org/10.59615/ijime.2.1.1

• McCarthy, J., Minsky, M., Shannon, C. E., Rochester, N., & Dartmouth, C. (1955). A proposal for the Dartmouth Summer Research Project on Artificial Intelligence.

• Merchán, D., Arora, J., Pachon, J., Konduri, K., Winkenbach, M., Parks, S., & Noszek, J. (2022). 2021 Amazon Last Mile Routing Research Challenge: Data Set. Transportation Science. https://doi.org/10.1287/trsc.2022.1173

• Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016). Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records. Scientific Reports, 6(1), 26094. https://doi.org/10.1038/srep26094

• Mirowski, P., Pascanu, R., Viola, F., Soyer, H., Ballard, A., Banino, A., Denil, M., Goroshin, R., Sifre, L., Kavukcuoglu, K., Kumaran, D., & Hadsell, R. (2016). Learning to Navigate in Complex Environments. https://doi.org/https://doi.org/10.48550/arXiv.1611.03673

• Mitchell, T. M. (1997). Machine Learning. McGraw-Hill Education. https://books.google.com/books?id=xOGAngEACAAJ

• Mohseni Kiasari, M., & Fartash, K. (2023). Prioritizing policy tools to support development of IoT technologies in Iran. International Journal of Innovation in Engineering, 3(2), 53-67. https://ijie.ir/index.php/ijie/article/view/121

• Mortaji, S. T. H., Noori, S., & Bagherpour, M. (2021a). Adaptive Project Monitoring and Control with Variable Reviewing Intervals. International Journal of Innovation in Engineering, 1(4), 34-46. https://doi.org/10.59615/ijie.1.4.34

• Mortaji, S. T. H., Noori, S., & Bagherpour, M. (2021b). Directed earned value management based on ordered fuzzy numbers. Journal of Intelligent & Fuzzy Systems, 40, 10183-10196. https://doi.org/10.3233/JIFS-201248

• Mortaji, S. T. H., Noori, S., Noorossana, R., & Bagherpour, M. (2018). An ex ante control chart for project monitoring using earned duration management observations. Journal of Industrial Engineering International, 14(4), 793-806. https://doi.org/10.1007/s40092-017-0251-5

• Mortaji, S. T. H., Noorossana, R., & Bagherpour, M. (2015). Project Completion Time and Cost Prediction Using Change Point Analysis. Journal of Management in Engineering, 31(5), 04014086. https://doi.org/doi:10.1061/(ASCE)ME.1943-5479.0000329

• Moslemi, S. (2021). Examining the Dimensions of Big Data Privacy (Block Chain Solution for Privacy Protection). International Journal of Innovation in Management, Economics and Social Sciences, 1(4), 10-16. https://doi.org/10.52547/ijimes.1.4.10

• Mousakhani, M., Saghafi, F., Hasanzadeh, M., & Sadeghi, M. E. (2020). Proposing Dynamic Model of Functional Interactions of IoT Technological Innovation System by Using System Dynamics and Fuzzy DEMATEL [Research]. Journal of Operational Research and Its Applications, 17(4), 1-21. http://jamlu.liau.ac.ir/article-1-1869-en.html

• nazari, E., Edalati Khodabandeh, M., Dadashi, A., Aldaghi, T., Rasoulian, M., & Tabesh, H. (2022). Studying Students' Knowledge of the Benefits, Challenges, and Applications of Big Data Analytics in Healthcare. International Journal of Innovation in Engineering, 2(1), 40-57. https://doi.org/10.59615/ijie.2.1.40

• Nilsson, N. J. (1998). Artificial Intelligence: A New Synthesis. Morgan Kaufmann Publishers. https://books.google.com/books?id=GYOFSd6fETgC

• Nozari, H., Ghahremani-Nahr, J., Fallah, M., & Szmelter-jarosz, A. (2022). Assessment of Cyber Risks in an IoT-based Supply Chain using a Fuzzy Decision-Making Method. International Journal of Innovation in Management, Economics and Social Sciences, 2(1), 52-64. https://doi.org/10.52547/ijimes.2.1.52

• Nozari, H., Sadeghi, M. E., & Najafi, S. E. (2022). Quantitative Analysis of Implementation Challenges of IoT-Based Digital Supply Chain (Supply Chain 0/4). arXiv preprint arXiv:2206.12277.

• Obaid, H. S. (2022). Review the challenges of using big data in the supply chain. Journal of Data Analytics, 1(1), 16-24. https://doi.org/10.59615/jda.1.1.16

• Popat, R. R., & Chaudhary, J. (2018, 11-12 May 2018). A Survey on Credit Card Fraud Detection Using Machine Learning. 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI),

• Power, D. (2002). Decision Support Systems: Concepts and Resources for Managers. Bloomsbury Academic. https://books.google.com/books?id=9NA6QMcte3cC

• Prat, N. (2019). Augmented Analytics. Business & Information Systems Engineering, 61(3), 375-380. https://doi.org/10.1007/s12599-019-00589-0

• Pumsirirat, A., & Yan, L. (2018). Credit Card Fraud Detection using Deep Learning based on Auto-Encoder and Restricted Boltzmann Machine. International Journal of Advanced Computer Science and Applications, 9(1). https://doi.org/http://dx.doi.org/10.14569/IJACSA.2018.090103

• Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Elsevier Science. https://doi.org/https://doi.org/10.1016/C2009-0-27846-9

• Rahmaty, M. (2023). Machine learning with big data to solve real-world problems. Journal of Data Analytics, 2(1), 9-16. https://doi.org/10.59615/jda.2.1.9

• Rajaraman, A., & Ullman, J. D. (2011). Mining of Massive Datasets. Cambridge University Press. https://doi.org/DOI: 10.1017/CBO9781139058452

• Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C. P., Patel, B. N., Yeom, K. W., Shpanskaya, K., Blankenberg, F. G., Seekins, J., Amrhein, T. J., Mong, D. A., Halabi, S. S., Zucker, E. J., . . . Lungren, M. P. (2018). Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLOS Medicine, 15(11), e1002686. https://doi.org/10.1371/journal.pmed.1002686

• Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2017). Reshaping business with artificial intelligence: Closing the gap between ambition and action. MIT Sloan Management Review, 59(1).

• Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: towards real-time object detection with region proposal networks Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1, Montreal, Canada.

• Roman, R., Lopez, J., & Mambo, M. (2018). Mobile edge computing, Fog et al.: A survey and analysis of security threats and challenges. Future Generation Computer Systems, 78, 680-698. https://doi.org/https://doi.org/10.1016/j.future.2016.11.009

• Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach. CreateSpace Independent Publishing Platform. https://books.google.com/books?id=PQI7vgAACAAJ

• Sabet, E. (2021). Reviewing the Challenges of Big Data Use in Smart Industries. International Journal of Innovation in Management, Economics and Social Sciences, 1(4), 66-72. https://doi.org/10.52547/ijimes.1.4.66

• Sadeghi, M. E. (2022). Big data based on IoT in the agriculture industry: developments, opportunities, and challenges ahead. Journal of Data Analytics, 1(1), 25-32. https://doi.org/10.59615/jda.1.1.25

• Sadeghi, M. E., & Jafari, H. (2021). Investigating the dimensions, components and key indicators of supply chain management based on digital technologies. International Journal of Innovation in Management, Economics and Social Sciences, 1(3), 82-87. https://doi.org/10.52547/ijimes.1.3.82

• Samadi-Parviznejad, P. (2022). The role of big data in digital transformation. Journal of Data Analytics, 1(1), 42-47. https://doi.org/10.59615/jda.1.1.42

• Schroff, F., Kalenichenko, D., & Philbin, J. (2015, 7-12 June 2015). FaceNet: A unified embedding for face recognition and clustering. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),

• Shahvaroughi Farahani, M., & Esfahani, A. (2022). Opportunities and Challenges of Applying Artificial Intelligence in the Financial Sectors and Startups during the Coronavirus Outbreak. International Journal of Innovation in Management, Economics and Social Sciences, 2(4), 33-55. https://doi.org/10.52547/ijimes.2.4.33

• Shahvaroughi Farahani, M., Esfahani, A., Nejad Falatouri Moghaddam, M., & Ramezani, A. (2022). The Impact of Fintech and Artificial Intelligence on COVID 19 and Sustainable Development Goals. International Journal of Innovation in Management, Economics and Social Sciences, 2(3), 14-31. https://doi.org/10.52547/ijimes.2.3.14

• Sharda, R., Turban, E., Delen, D., Aronson, J. E., Liang, T. P., & King, D. (2014). Business Intelligence and Analytics: Systems for Decision Support. Pearson. https://books.google.com/books?id=FLYDnwEACAAJ

• Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

• Souza, R. M., Nascimento, E. G. S., Miranda, U. A., Silva, W. J. D., & Lepikson, H. A. (2021). Deep learning for diagnosis and classification of faults in industrial rotating machinery. Computers & Industrial Engineering, 153, 107060. https://doi.org/https://doi.org/10.1016/j.cie.2020.107060

• Student. (1908). The Probable Error of a Mean. Biometrika, 6(1), 1-25. https://doi.org/10.2307/2331554

• Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks

• Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning, second edition: An Introduction. MIT Press. https://books.google.com/books?id=sWV0DwAAQBAJ

• Tufte, E. R. (1983). The Visual Display of Quantitative Information. Graphics Press. https://books.google.com/books?id=SqVpAAAAMAAJ

• Vaswani, A., Shazeer, N. M., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is All you Need. Neural Information Processing Systems,

• Vergara, J. R., & Estévez, P. A. (2014). A review of feature selection methods based on mutual information. Neural Computing and Applications, 24(1), 175-186. https://doi.org/10.1007/s00521-013-1368-0

• Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2015). From Multi-Channel Retailing to Omni-Channel Retailing: Introduction to the Special Issue on Multi-Channel Retailing. Journal of Retailing, 91(2), 174-181. https://doi.org/https://doi.org/10.1016/j.jretai.2015.02.005

• World-Economic-Forum. (2020). The future of jobs report 2020. W. E. Forum. http://www3.weforum.org/docs/WEF_Future_of_Jobs_2020.pdf

• Zarsky, T. Z. (2019). Privacy and Manipulation in the Digital Age. Theoretical Inquiries in Law, 20(1), 157-188. https://doi.org/doi:10.1515/til-2019-0006

Downloads

Published

2023-09-28

How to Cite

Mortaji, S. T. H., & Shateri, S. (2023). Harnessing the Power of Business Analytics and Artificial Intelligence: A Roadmap to Data-Driven Success. International Journal of Innovation in Engineering, 3(3), 1–27. https://doi.org/10.59615/ijie.3.3.1

Issue

Section

Original Research