How to cite this paper
Al-Ali, R., Mekimah, S., Zighed, R., Shishakly, R., Almaiah, M., Shehab, R., Alkhdour, T & Aldhyani, T. (2025). Evolution and gaps in data mining research: Identifying the bibliometric landscape of data mining in managemen.Decision Science Letters , 14(2), 435-448.
Refrences
Alam, S., Zardari, S., Abbas, M., Huda, N. U., & Khan, M. A. (2023). The Impact of Data Mining on Digital Libraries–A bibli-ometric Study. VFAST Transactions on Software Engineering, 11(4), 124-137.
Altintas, N., & Trick, M. A. (2014). A data mining approach to forecast behavior. Annals of Operations Research, 216(1). https://doi.org/10.1007/s10479-012-1236-9
Amiri, F., Scerri, S., & Khodashahi, M. (2015, September). Lexicon-based sentiment analysis for Persian Text. In Proceedings of the International Conference Recent Advances in Natural Language Processing (pp. 9-16).
AnhKhoa, D. N., & others. (2019). Marketing intelligence from data mining perspective: A literature review. International Journal of Innovation, Management and Technology, 10(5). https://doi.org/10.18178/ijimt.2019.10.5.859
Baek, C., & Doleck, T. (2022). Educational data mining: A bibliometric analysis of an emerging field. IEEE Access, 10. https://doi.org/10.1109/ACCESS.2022.3160457
Bertoni, A., & Larsson, T. (2017). Data mining in product service systems design: Literature review and research questions. Procedia CIRP, 64. https://doi.org/10.1016/j.procir.2017.03.131
Bond, S. D., Carlson, K. A., & Keeney, R. L. (2008). Generating objectives: can decision makers articulate what they want?. Management Science, 54(1), 56-70.
Buchatskaya, V., Buchatsky, P., & Teploukhov, S. (2015). Forecasting methods classification and its applicability. Indian Journal of Science and Technology, 8(30), 1-8.
Chala, N., Voropai, O., & Pichyk, K. (2021). Using data mining to create innovations in education. Електронне наукове фахове видання" Соціально-економічні проблеми і держава", (2 (25)), 21-28.
Cheshmehsohrabi, M., & Mashhadi, A. (2022). Using data mining, text mining, and bibliometric techniques to the research trends and gaps in the field of language and linguistics. Journal of Psycholinguistic Research, 52, 1–24.
Cristescu, M. P., Nerişanu, R. A., & Mara, D. A. (2022). Using Data Mining in the Sentiment Analysis Process on the Finan-cial Market. Journal of Social and Economic Statistics, 11(1-2), 36-58.
Da Silva, J. B. N., Senna, P., Chousa, A., & Coelho, O. (2020). Data mining and operations research techniques in Supply Chain Risk Management: A bibliometric study. Brazilian Journal of Operations & Production Management, 17(3), 1-14.
dos Santos, B. S., Steiner, M. T. A., Fenerich, A. T., & Lima, R. H. P. (2019). Data mining and machine learning techniques applied to public health problems: A bibliometric analysis from 2009 to 2018. Computers & Industrial Engineering, 138, 106120.
Dwivedi, Y. K., Sharma, A., Rana, N. P., Giannakis, M., Goel, P., & Dutot, V. (2023). Evolution of artificial intelligence re-search in Technological Forecasting and Social Change: Research topics, trends, and future directions. Technological Forecasting and Social Change, 192, 122579.
Elhosney, M., & Kumar, U. (2019). Intelligent diagnostic prediction and classification system for chronic kidney disease. Sci-entific Reports, 9(1), 1–14.
Filho, W. L., Yang, P., Eustachio, J. H. P. P., Azul, A. M., Gellers, J. C., Gielczyk, A., ... & Kozlova, V. (2022). Deploying dig-italisation and artificial intelligence in sustainable development research. Environment, Development and Sustainability.
Furkan, M., & Haide, A. S. (2014). An introduction to data mining technique. International Journal of Advancement in Engi-neering Technology, Management & Applied Science, 1(3).
Gora, A. A. (2019). The link between decision making process and performance: A bibliometric analysis. Management and Economics Review, 4(2). https://doi.org/10.24818/mer/2019.12-08
Guermoui, M., Fezzani, A., Mohamed, Z., Rabehi, A., Ferkous, K., Bailek, N., ... & Ghoneim, S. S. (2024). An analysis of case studies for advancing photovoltaic power forecasting through multi-scale fusion techniques. Scientific Reports, 14(1), 6653.
Guo, L., Cheng, J., & Zhang, Z. (2022). Mapping the knowledge domain of financial decision making: A scientometric and bibliometric study. Frontiers in Psychology, 13, 1006412.
Hearst, M., & Hirsh, H. (2017). AI's greatest trends and controversies. IEEE Intelligent Systems, 15(1).
Hu, Y., Yu, Z., Cheng, X., Luo, Y., & Wen, C. (2020). A bibliometric analysis and visualization of medical data mining re-search. Medicine, 99(22), e20338.
Hussein, D. J., Rashad, M. N., Mirza, K. I., & Hussein, D. L. (2022). Machine learning approach to sentiment analysis in data mining. Passer Journal of Basic and Applied Sciences, 4(1), 71-77.
Islam, S., Ab Ghani, N., & Ahmed, M. (2020). A review on recent advances in Deep learning for Sentiment Analysis: Perfor-mances, Challenges and Limitations. Compusoft, 9(7), 3775-3783.
Islam, S., Ab Ghani, N., & Ahmed, M. (2020). A review on recent advances in Deep learning for Sentiment Analysis: Perfor-mances, Challenges and Limitations. Compusoft, 9(7), 3775-3783.
Karno, M. R. (2015). Facilitating resource allocation decision through bibliomining: the case of UTM's library (Doctoral dis-sertation, Universiti Teknologi Mara).
Kharde, V. A., & Sonawane, S. (2016). Sentiment analysis of Twitter data: A survey of techniques. International Journal of Computer Applications, 139(11), 5–15.
Kolling, M. L., Furstenau, L. B., Sott, M. K., Rabaioli, B., Ulmi, P. H., Bragazzi, N. L., & Tedesco, L. P. C. (2021). Data min-ing in healthcare: Applying strategic intelligence techniques to depict 25 years of research development. International journal of environmental research and public health, 18(6), 3099.
Kusiak, A. (2018). Smart manufacturing. International journal of production Research, 56(1-2), 508-517.
Leal Filho, W., Yang, P., Eustachio, J. H. P. P., Azul, A. M., Gellers, J. C., Gielczyk, A., ... & Kozlova, V. (2023). Deploying digitalisation and artificial intelligence in sustainable development research. Environment, development and sustainabil-ity, 25(6), 4957-4988.
Li, W., Huang, Z., Jia, X., & Cai, X. (2016). Neighborhood based decision-theoretic rough set models. International Journal of Approximate Reasoning, 69, 1-17.
Lundberg, L. (2023). Bibliometric mining of research directions and trends for big data. Journal of Big Data, 10(1), 112.
Luo, T., Chen, S., Xu, G., Zhou, J., Luo, T., Chen, S., & Zhou, J. (2013). Sentiment analysis. Trust-based collective view pre-diction, 53-68.
Maksood, F. Z., & Achuthan, G. (2016). Analysis of data mining techniques and its applications. International Journal of Computer Applications, 140(3).
Mannila, H. (2000). Theoretical frameworks for data mining. ACM SIGKDD Explorations Newsletter, 1(2). https://doi.org/10.1145/846183.846191
MARcHISottI, G. G., Domingos, M. D. L., & Almeida, R. L. D. (2018). Decision-making at the first management level: The interference of the organizational culture. RAM. Revista de Administração Mackenzie, 19(3), eRAMR180106.
Mariani, M. M., Hashemi, N., & Wirtz, J. (2023). Artificial intelligence empowered conversational agents: A systematic liter-ature review and research agenda. Journal of Business Research, 161, 113838.
Maryoosh, A. A., & Hussein, E. A. (2022). A review: Data mining techniques and its applications. International Journal of Computer Science and Mobile Applications, 10(3). https://doi.org/10.47760/ijcsma.2022.v10i03.001
Matta, C. E. D., Bianchesi, N. M. P., Oliveira, M. S. D., Balestrassi, P. P., & Leal, F. (2021). A comparative study of forecast-ing methods using real-life econometric series data. Production, 31, e20210043.
Millemann, J. A., De Waal, G. A., & Maritz, A. (2022). Connecting the dots: A bibliometric analysis on the consumer innova-tion− decision process. International Journal of Innovation Management, 26(04), 2250031.
Moorjani, G., & Sadath, L. (2019). Sentiment analysis: A tool for data mining in big data analytics. International Journal of Innovative Technology and Exploring Engineering, 8(9), 2125–2131.
Murray, P. W., Agard, B., & Barajas, M. A. (2018). Forecast of individual customer’s demand from a large and noisy da-taset. Computers & industrial engineering, 118, 33-43.
Mydyti, H. (2021). Data mining approach improving decision-making competency along the business digital transformation journey: A case study – Home appliances after-sales service. SEEU Review, 16(1). https://doi.org/10.2478/seeur-2021-0008.
Nagendhra, R. Y., & Jen, C. C. (2024). Bibliometric insights into data mining in education research: A decade in review. Con-temporary Educational Technology, 16(2). https://doi.org/10.30935/cedtech/14333
Omuya, E. O., Okeyo, G. O., & Kimwele, M. W. (2021). Feature selection for classification using principal component analy-sis and information gain. Expert Systems with Applications, 174, 114765.
Pacheco-Velázquez, E. A., Vázquez-Parra, J. C., Cruz-Sandoval, M., Salinas-Navarro, D. E., & Carlos-Arroyo, M. (2023). Business decision-making and complex thinking: A bibliometric study. Administrative Sciences, 13(3), 80.
PANȚA, N., & POPESCU, N.-E. (2023). Charting the course of AI in business sustainability: A bibliometric analysis. Studies in Business and Economics, 18(3), 214–229.
Pejic-Bach, M., Dumičić, K., Žmuk, B., & Ćurlin, T. (2020). Data mining approach to internal fraud in a project-based organi-zation. International Journal of Information Systems and Project Management, 8(2), 81-101.
Poria, S., Cambria, E., & Gelbukh, A. (2016). Aspect extraction for opinion mining with a deep convolutional neural net-work. Knowledge-Based Systems, 108, 42-49.
Pynadath, M. F., Rofin, T. M., & Thomas, S. (2023). Evolution of customer relationship management to data mining-based customer relationship management: a scientometric analysis. Quality & quantity, 57(4), 3241-3272.
Ramageri, B. M. (2010). Data mining techniques and applications. Indian Journal of Computer Science and Engineering, 1(4).
Ramezani, F., Strasbourg, M., Parvez, S., Saxena, R., Jariwala, D., Borys, N. J., & Whitaker, B. M. (2024). Predicting quantum emitter fluctuations with time-series forecasting models. Scientific Reports, 14(1), 6920.
Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: tasks, approaches and applica-tions. Knowledge-based systems, 89, 14-46.
Rosamilha, N., Silva, L., & Penha, R. (2023). Competence of project management professionals according to type of project: a systematic literature review. International Journal of Information Systems and Project Management, 11(4), 54-100.
Saad, H., Nagarur, N., & Shamsan, A. (2021). Analysis of Data Mining Process for Improvement of Production Quality in In-dustrial Sector. arXiv preprint arXiv:2108.07615.
Salehan, M., & Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30-40.
Santoso, B., Hikmawan, T., & Imaniyati, N. (2022). Management information systems: bibliometric analysis and its effect on decision making. Indonesian Journal of Science and Technology, 7(3), 583-602.
Taylor, S. J., & Letham, B. (2018). Forecasting at scale. The American Statistician, PeerJ Preprints. https://doi.org/10.7287/peerj.preprints.3190v2
Umarani, V., Julian, A., & Deepa, J. (2021). Sentiment analysis using various machine learning and deep learning Tech-niques. Journal of the Nigerian Society of Physical Sciences, 385-394.
Upadhyay, A. (2018). A review on bibliometric analysis of data mining. International Journal of Engineering Research in Computer Science and Engineering (IJERCSE), 5(4).
Varajão, J., Trigo, A., Pereira, J. L., & Moura, I. (2021). Information systems project management success. International Journal of Information Systems and Project Management, 9(4), 62-74.
Wiśniewsk, J. W. (2021). Forecasting in small business management. Risks, 9(69). https://doi.org/10.3390/risks9040069
Xiuyi, T., & Yuxia, G. (2018). Research on application of machine learning in data mining. In IOP Conference Series: Materi-als Science and Engineering, 392(6). https://doi.org/10.1088/1757-899X/392/6/062202
Zahlan, A., Ranjan, R. P., & Hayes, D. (2023). Artificial intelligence innovation in healthcare: Literature review, exploratory analysis, and future research. Technology in society, 102321.
Zhan, M. (2016). Exploring the feasibility of applying data mining for library reference service improvement: A case study of Turku Main Library. Finland: ÅboAkademi University.
Zhao, L., Chen, L., Liu, Q., Zhang, M., & Copland, H. (2019). Artificial intelligence-based platform for online teaching man-agement systems. Journal of Intelligent & Fuzzy Systems, 37(1), 45-51.
Zhisheng, C. (2023). Artificial intelligence virtual trainer: Innovative didactics aimed at personalized training needs. Journal of the Knowledge Economy. https://doi.org/10.1007/s13132-022-00985-0
Altintas, N., & Trick, M. A. (2014). A data mining approach to forecast behavior. Annals of Operations Research, 216(1). https://doi.org/10.1007/s10479-012-1236-9
Amiri, F., Scerri, S., & Khodashahi, M. (2015, September). Lexicon-based sentiment analysis for Persian Text. In Proceedings of the International Conference Recent Advances in Natural Language Processing (pp. 9-16).
AnhKhoa, D. N., & others. (2019). Marketing intelligence from data mining perspective: A literature review. International Journal of Innovation, Management and Technology, 10(5). https://doi.org/10.18178/ijimt.2019.10.5.859
Baek, C., & Doleck, T. (2022). Educational data mining: A bibliometric analysis of an emerging field. IEEE Access, 10. https://doi.org/10.1109/ACCESS.2022.3160457
Bertoni, A., & Larsson, T. (2017). Data mining in product service systems design: Literature review and research questions. Procedia CIRP, 64. https://doi.org/10.1016/j.procir.2017.03.131
Bond, S. D., Carlson, K. A., & Keeney, R. L. (2008). Generating objectives: can decision makers articulate what they want?. Management Science, 54(1), 56-70.
Buchatskaya, V., Buchatsky, P., & Teploukhov, S. (2015). Forecasting methods classification and its applicability. Indian Journal of Science and Technology, 8(30), 1-8.
Chala, N., Voropai, O., & Pichyk, K. (2021). Using data mining to create innovations in education. Електронне наукове фахове видання" Соціально-економічні проблеми і держава", (2 (25)), 21-28.
Cheshmehsohrabi, M., & Mashhadi, A. (2022). Using data mining, text mining, and bibliometric techniques to the research trends and gaps in the field of language and linguistics. Journal of Psycholinguistic Research, 52, 1–24.
Cristescu, M. P., Nerişanu, R. A., & Mara, D. A. (2022). Using Data Mining in the Sentiment Analysis Process on the Finan-cial Market. Journal of Social and Economic Statistics, 11(1-2), 36-58.
Da Silva, J. B. N., Senna, P., Chousa, A., & Coelho, O. (2020). Data mining and operations research techniques in Supply Chain Risk Management: A bibliometric study. Brazilian Journal of Operations & Production Management, 17(3), 1-14.
dos Santos, B. S., Steiner, M. T. A., Fenerich, A. T., & Lima, R. H. P. (2019). Data mining and machine learning techniques applied to public health problems: A bibliometric analysis from 2009 to 2018. Computers & Industrial Engineering, 138, 106120.
Dwivedi, Y. K., Sharma, A., Rana, N. P., Giannakis, M., Goel, P., & Dutot, V. (2023). Evolution of artificial intelligence re-search in Technological Forecasting and Social Change: Research topics, trends, and future directions. Technological Forecasting and Social Change, 192, 122579.
Elhosney, M., & Kumar, U. (2019). Intelligent diagnostic prediction and classification system for chronic kidney disease. Sci-entific Reports, 9(1), 1–14.
Filho, W. L., Yang, P., Eustachio, J. H. P. P., Azul, A. M., Gellers, J. C., Gielczyk, A., ... & Kozlova, V. (2022). Deploying dig-italisation and artificial intelligence in sustainable development research. Environment, Development and Sustainability.
Furkan, M., & Haide, A. S. (2014). An introduction to data mining technique. International Journal of Advancement in Engi-neering Technology, Management & Applied Science, 1(3).
Gora, A. A. (2019). The link between decision making process and performance: A bibliometric analysis. Management and Economics Review, 4(2). https://doi.org/10.24818/mer/2019.12-08
Guermoui, M., Fezzani, A., Mohamed, Z., Rabehi, A., Ferkous, K., Bailek, N., ... & Ghoneim, S. S. (2024). An analysis of case studies for advancing photovoltaic power forecasting through multi-scale fusion techniques. Scientific Reports, 14(1), 6653.
Guo, L., Cheng, J., & Zhang, Z. (2022). Mapping the knowledge domain of financial decision making: A scientometric and bibliometric study. Frontiers in Psychology, 13, 1006412.
Hearst, M., & Hirsh, H. (2017). AI's greatest trends and controversies. IEEE Intelligent Systems, 15(1).
Hu, Y., Yu, Z., Cheng, X., Luo, Y., & Wen, C. (2020). A bibliometric analysis and visualization of medical data mining re-search. Medicine, 99(22), e20338.
Hussein, D. J., Rashad, M. N., Mirza, K. I., & Hussein, D. L. (2022). Machine learning approach to sentiment analysis in data mining. Passer Journal of Basic and Applied Sciences, 4(1), 71-77.
Islam, S., Ab Ghani, N., & Ahmed, M. (2020). A review on recent advances in Deep learning for Sentiment Analysis: Perfor-mances, Challenges and Limitations. Compusoft, 9(7), 3775-3783.
Islam, S., Ab Ghani, N., & Ahmed, M. (2020). A review on recent advances in Deep learning for Sentiment Analysis: Perfor-mances, Challenges and Limitations. Compusoft, 9(7), 3775-3783.
Karno, M. R. (2015). Facilitating resource allocation decision through bibliomining: the case of UTM's library (Doctoral dis-sertation, Universiti Teknologi Mara).
Kharde, V. A., & Sonawane, S. (2016). Sentiment analysis of Twitter data: A survey of techniques. International Journal of Computer Applications, 139(11), 5–15.
Kolling, M. L., Furstenau, L. B., Sott, M. K., Rabaioli, B., Ulmi, P. H., Bragazzi, N. L., & Tedesco, L. P. C. (2021). Data min-ing in healthcare: Applying strategic intelligence techniques to depict 25 years of research development. International journal of environmental research and public health, 18(6), 3099.
Kusiak, A. (2018). Smart manufacturing. International journal of production Research, 56(1-2), 508-517.
Leal Filho, W., Yang, P., Eustachio, J. H. P. P., Azul, A. M., Gellers, J. C., Gielczyk, A., ... & Kozlova, V. (2023). Deploying digitalisation and artificial intelligence in sustainable development research. Environment, development and sustainabil-ity, 25(6), 4957-4988.
Li, W., Huang, Z., Jia, X., & Cai, X. (2016). Neighborhood based decision-theoretic rough set models. International Journal of Approximate Reasoning, 69, 1-17.
Lundberg, L. (2023). Bibliometric mining of research directions and trends for big data. Journal of Big Data, 10(1), 112.
Luo, T., Chen, S., Xu, G., Zhou, J., Luo, T., Chen, S., & Zhou, J. (2013). Sentiment analysis. Trust-based collective view pre-diction, 53-68.
Maksood, F. Z., & Achuthan, G. (2016). Analysis of data mining techniques and its applications. International Journal of Computer Applications, 140(3).
Mannila, H. (2000). Theoretical frameworks for data mining. ACM SIGKDD Explorations Newsletter, 1(2). https://doi.org/10.1145/846183.846191
MARcHISottI, G. G., Domingos, M. D. L., & Almeida, R. L. D. (2018). Decision-making at the first management level: The interference of the organizational culture. RAM. Revista de Administração Mackenzie, 19(3), eRAMR180106.
Mariani, M. M., Hashemi, N., & Wirtz, J. (2023). Artificial intelligence empowered conversational agents: A systematic liter-ature review and research agenda. Journal of Business Research, 161, 113838.
Maryoosh, A. A., & Hussein, E. A. (2022). A review: Data mining techniques and its applications. International Journal of Computer Science and Mobile Applications, 10(3). https://doi.org/10.47760/ijcsma.2022.v10i03.001
Matta, C. E. D., Bianchesi, N. M. P., Oliveira, M. S. D., Balestrassi, P. P., & Leal, F. (2021). A comparative study of forecast-ing methods using real-life econometric series data. Production, 31, e20210043.
Millemann, J. A., De Waal, G. A., & Maritz, A. (2022). Connecting the dots: A bibliometric analysis on the consumer innova-tion− decision process. International Journal of Innovation Management, 26(04), 2250031.
Moorjani, G., & Sadath, L. (2019). Sentiment analysis: A tool for data mining in big data analytics. International Journal of Innovative Technology and Exploring Engineering, 8(9), 2125–2131.
Murray, P. W., Agard, B., & Barajas, M. A. (2018). Forecast of individual customer’s demand from a large and noisy da-taset. Computers & industrial engineering, 118, 33-43.
Mydyti, H. (2021). Data mining approach improving decision-making competency along the business digital transformation journey: A case study – Home appliances after-sales service. SEEU Review, 16(1). https://doi.org/10.2478/seeur-2021-0008.
Nagendhra, R. Y., & Jen, C. C. (2024). Bibliometric insights into data mining in education research: A decade in review. Con-temporary Educational Technology, 16(2). https://doi.org/10.30935/cedtech/14333
Omuya, E. O., Okeyo, G. O., & Kimwele, M. W. (2021). Feature selection for classification using principal component analy-sis and information gain. Expert Systems with Applications, 174, 114765.
Pacheco-Velázquez, E. A., Vázquez-Parra, J. C., Cruz-Sandoval, M., Salinas-Navarro, D. E., & Carlos-Arroyo, M. (2023). Business decision-making and complex thinking: A bibliometric study. Administrative Sciences, 13(3), 80.
PANȚA, N., & POPESCU, N.-E. (2023). Charting the course of AI in business sustainability: A bibliometric analysis. Studies in Business and Economics, 18(3), 214–229.
Pejic-Bach, M., Dumičić, K., Žmuk, B., & Ćurlin, T. (2020). Data mining approach to internal fraud in a project-based organi-zation. International Journal of Information Systems and Project Management, 8(2), 81-101.
Poria, S., Cambria, E., & Gelbukh, A. (2016). Aspect extraction for opinion mining with a deep convolutional neural net-work. Knowledge-Based Systems, 108, 42-49.
Pynadath, M. F., Rofin, T. M., & Thomas, S. (2023). Evolution of customer relationship management to data mining-based customer relationship management: a scientometric analysis. Quality & quantity, 57(4), 3241-3272.
Ramageri, B. M. (2010). Data mining techniques and applications. Indian Journal of Computer Science and Engineering, 1(4).
Ramezani, F., Strasbourg, M., Parvez, S., Saxena, R., Jariwala, D., Borys, N. J., & Whitaker, B. M. (2024). Predicting quantum emitter fluctuations with time-series forecasting models. Scientific Reports, 14(1), 6920.
Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: tasks, approaches and applica-tions. Knowledge-based systems, 89, 14-46.
Rosamilha, N., Silva, L., & Penha, R. (2023). Competence of project management professionals according to type of project: a systematic literature review. International Journal of Information Systems and Project Management, 11(4), 54-100.
Saad, H., Nagarur, N., & Shamsan, A. (2021). Analysis of Data Mining Process for Improvement of Production Quality in In-dustrial Sector. arXiv preprint arXiv:2108.07615.
Salehan, M., & Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30-40.
Santoso, B., Hikmawan, T., & Imaniyati, N. (2022). Management information systems: bibliometric analysis and its effect on decision making. Indonesian Journal of Science and Technology, 7(3), 583-602.
Taylor, S. J., & Letham, B. (2018). Forecasting at scale. The American Statistician, PeerJ Preprints. https://doi.org/10.7287/peerj.preprints.3190v2
Umarani, V., Julian, A., & Deepa, J. (2021). Sentiment analysis using various machine learning and deep learning Tech-niques. Journal of the Nigerian Society of Physical Sciences, 385-394.
Upadhyay, A. (2018). A review on bibliometric analysis of data mining. International Journal of Engineering Research in Computer Science and Engineering (IJERCSE), 5(4).
Varajão, J., Trigo, A., Pereira, J. L., & Moura, I. (2021). Information systems project management success. International Journal of Information Systems and Project Management, 9(4), 62-74.
Wiśniewsk, J. W. (2021). Forecasting in small business management. Risks, 9(69). https://doi.org/10.3390/risks9040069
Xiuyi, T., & Yuxia, G. (2018). Research on application of machine learning in data mining. In IOP Conference Series: Materi-als Science and Engineering, 392(6). https://doi.org/10.1088/1757-899X/392/6/062202
Zahlan, A., Ranjan, R. P., & Hayes, D. (2023). Artificial intelligence innovation in healthcare: Literature review, exploratory analysis, and future research. Technology in society, 102321.
Zhan, M. (2016). Exploring the feasibility of applying data mining for library reference service improvement: A case study of Turku Main Library. Finland: ÅboAkademi University.
Zhao, L., Chen, L., Liu, Q., Zhang, M., & Copland, H. (2019). Artificial intelligence-based platform for online teaching man-agement systems. Journal of Intelligent & Fuzzy Systems, 37(1), 45-51.
Zhisheng, C. (2023). Artificial intelligence virtual trainer: Innovative didactics aimed at personalized training needs. Journal of the Knowledge Economy. https://doi.org/10.1007/s13132-022-00985-0