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Leveraging Computational Algorithms for Effective Explicit and Tacit Knowledge Capture: A Hybrid Approach Combining Expert Interviews, Machine Learning, and Data Mining Techniques
- 1 Hong Kong Polytechnic University, Hong Kong, China
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Abstract
This research explores a hybrid method for knowledge acquisition that combine expert interviews by human experts with machine learning and data mining to augment the accuracy and richness of explicit and implicit knowledge-creation. With organisations beginning to realize the importance of efficient knowledge management, the boundaries of the traditional methodologies to deal with high dimensional data and advanced knowledge emerge. The work suggests a hybrid approach in which expert knowledge adds context, while computational algorithms add reliability and scalability. Data reveal 15% greater accuracy and 20% greater readability than single methods, which is a testament to the strengths of hybrid techniques. The paper illustrates practical implications for decision-making, employee training, and regulatory compliance, and illustrates how hybrid strategies enable more adaptive and integrated knowledge management practices. This study advances research across academic and industrial boundaries with an effective and scalable knowledge capture model that can be extended across industry verticals.
Keywords
Hybrid Knowledge Capture, Machine Learning, Data Mining, Expert Interviews, Tacit Knowledge
[1]. Al-Huda, Zaid, et al. "A hybrid deep learning pavement crack semantic segmentation." Engineering Applications of Artificial Intelligence 122 (2023): 106142.
[2]. Farbiz, Farzam, et al. "Knowledge-embedded machine learning and its applications in smart manufacturing." Journal of Intelligent Manufacturing 34.7 (2023): 2889-2906.
[3]. Terbuch, Anika, et al. "Detecting anomalous multivariate time-series via hybrid machine learning." IEEE transactions on instrumentation and measurement 72 (2023): 1-11.
[4]. Rajpoot, Vikram, Akhilesh Tiwari, and Anand Singh Jalal. "Automatic early detection of rice leaf diseases using hybrid deep learning and machine learning methods." Multimedia Tools and Applications 82.23 (2023): 36091-36117.
[5]. Abbaszadeh Shahri, Abbas, Shan Chunling, and Stefan Larsson. "A hybrid ensemble-based automated deep learning approach to generate 3D geo-models and uncertainty analysis." Engineering with Computers 40.3 (2024): 1501-1516.
[6]. Sharmin, Selina, et al. "A hybrid dependable deep feature extraction and ensemble-based machine learning approach for breast cancer detection." IEEE Access (2023).
[7]. Li, Jiating, et al. "Improved chlorophyll and water content estimations at leaf level with a hybrid radiative transfer and machine learning model." Computers and Electronics in Agriculture 206 (2023): 107669.
[8]. Kanti, Praveen Kumar, et al. "The effect of pH on stability and thermal performance of graphene oxide and copper oxide hybrid nanofluids for heat transfer applications: application of novel machine learning technique." Journal of Energy Chemistry 82 (2023): 359-374.
[9]. Mohanty, Cheena, et al. "Using deep learning architectures for detection and classification of diabetic retinopathy." Sensors 23.12 (2023): 5726.
[10]. Qazi, Emad Ul Haq, Muhammad Hamza Faheem, and Tanveer Zia. "HDLNIDS: hybrid deep-learning-based network intrusion detection system." Applied Sciences 13.8 (2023): 4921.
[11]. Lilhore, Umesh Kumar, et al. "HIDM: Hybrid intrusion detection model for industry 4.0 Networks using an optimized CNN-LSTM with transfer learning." Sensors 23.18 (2023): 7856.
Cite this article
Li,Z. (2024). Leveraging Computational Algorithms for Effective Explicit and Tacit Knowledge Capture: A Hybrid Approach Combining Expert Interviews, Machine Learning, and Data Mining Techniques. Applied and Computational Engineering,114,40-45.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
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