Abstract
People’s cognition of objectively existing things is developing in the direction of digitization, and more and more objectively existing things are constantly being presented in the form of data. In order to realize the analysis of constantly changing large-scale data and the automatic extraction of valuable information, this paper analyzes the application of artificial intelligence auxiliary equipment based on machine learning in the cognitive learning process, and proposes a collaborative filtering method based on fusion of global and local parameters. Moreover, this paper studies a cognitive computing model based on context-aware data stream to realize effective analysis of context-aware data and obtain effective cognitive results. In addition, this paper proposes a task scheduling algorithm based on improved queue matching to solve the task scheduling problem in a distributed computing environment. Finally, this paper constructs an improved algorithmic cognitive system architecture, and verifies the performance of the system through experimental research. The research results show that the system constructed in this paper is highly reliable.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Chowdhury A, Kautz E, Yener B et al (2016) Image driven machine learning methods for microstructure recognition. Comput Mater Sci 123(8):176–187
Mahindru A, Sangal AL (2021) MLDroid—framework for Android malware detection using machine learning techniques. Neural Comput Appl 33:5183–5240
Voyant C, Notton G, Kalogirou S et al (2017) Machine learning methods for solar radiation forecasting: a review. Renew Energy 105(2):569–582
Folberth C, Baklanov A, Balkovič J et al (2019) Spatio-temporal downscaling of gridded crop model yield estimates based on machine learning. Agric For Meteorol 264(4):1–15
Sieg J, Flachsenberg F, Rarey M (2019) In need of bias control: evaluating chemical data for machine learning in structure-based virtual screening. J Chem Inf Model 59(3):947–961
Thabtah F, Peebles D (2020) A new machine learning model based on induction of rules for autism detection. Health Inform J 26(1):264–286
Narudin FA, Feizollah A, Anuar NB et al (2016) Evaluation of machine learning classifiers for mobile malware detection. Soft Comput 20(1):343–357
Yao Q, Yang H, Zhu R et al (2018) Core, mode, and spectrum assignment based on machine learning in space division multiplexing elastic optical networks. IEEE Access 6(6):15898–15907
Nasar Z, Jaffry SW, Malik MK (2019) Textual keyword extraction and summarization: state-of-the-art. Inf Process Manag 56(6):102088
Chen M, Hao Y, Hwang K et al (2017) Disease prediction by machine learning over big data from healthcare communities. IEEE Access 5(1):8869–8879
Itu L, Rapaka S, Passerini T et al (2016) A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. J Appl Physiol 121(1):42–52
Jayasinghe U, Lee GM, Um TW et al (2018) Machine learning based trust computational model for IoT services. IEEE Trans Sustain Comput 4(1):39–52
Nourani V, Baghanam AH, Adamowski J et al (2014) Applications of hybrid wavelet–artificial intelligence models in hydrology: a review. J Hydrol 514(9):358–377
Bui XN, Nguyen H, Choi Y et al (2020) prediction of slope failure in open-pit mines using a novel hybrid artificial intelligence model based on decision tree and evolution algorithm. Sci Rep 10(1):1–17
Yaseen ZM, El-Shafie A, Jaafar O et al (2015) Artificial intelligence based models for stream-flow forecasting: 2000–2015. J Hydrol 530(7):829–844
Laird JE, Lebiere C, Rosenbloom PS (2017) A standard model of the mind: toward a common computational framework across artificial intelligence, cognitive science, neuroscience, and robotics. AI Mag 38(4):13–26
Chapi K, Singh VP, Shirzadi A et al (2017) A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environ Model Softw 95(1):229–245
Hashemi MR, Spaulding ML, Shaw A et al (2016) An efficient artificial intelligence model for prediction of tropical storm surge. Nat Hazards 82(1):471–491
Sustrova T (2016) A suitable artificial intelligence model for inventory level optimization. Trends Econ Manag 10(25):48–55
Pham BT, Nguyen MD, Van Dao D et al (2019) Development of artificial intelligence models for the prediction of compression coefficient of soil: an application of Monte Carlo sensitivity analysis. Sci Total Environ 679(8):172–184
Enshaei A, Robson CN, Edmondson RJ (2015) Artificial intelligence systems as prognostic and predictive tools in ovarian cancer. Ann Surg Oncol 22(12):3970–3975
Chou JS, Bui DK (2014) Modeling heating and cooling loads by artificial intelligence for energy-efficient building design. Energy Build 82(6):437–446
Ghahramani Z (2015) Probabilistic machine learning and artificial intelligence. Nature 521(7553):452–459
Giacoumidis E, Matin A, Wei J et al (2018) Blind nonlinearity equalization by machine-learning-based clustering for single-and multichannel coherent optical OFDM. J Lightw Technol 36(3):721–727
Tsoi KKF, Chan NB, Yiu KKL et al (2020) Machine learning clustering for blood pressure variability applied to Systolic Blood Pressure Intervention Trial (SPRINT) and the Hong Kong Community Cohort. Hypertension 76(2):569–576
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors have no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wu, F., Liu, X. & Wang, Y. Effect of artificial intelligence auxiliary equipment in the process of cognitive learning. Neural Comput & Applic 34, 12453–12466 (2022). https://doi.org/10.1007/s00521-021-06470-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-021-06470-0