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On the Use of Interpretable Machine Learning for the Management of Data Quality
[article]
2020
arXiv
pre-print
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In this paper, we focus on the specific problem and propose the use of interpretable machine learning to deliver the features that are important to be based for any data processing activity. ...
We focus on multiple methodologies for having interpretability in our learning models and adopt an ensemble scheme for the final decision. ...
ACKNOWLEDGMENT This research received funding from the European's Union Horizon 2020 research and innovation programme under the grant agreement No. 745829. ...
arXiv:2007.14677v1
fatcat:wjb6pze23vaxpn3pwbenkgldje
Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review
2023
Water
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In the past 20 years, significant progress has been made in groundwater management using hybrid machine learning (ML) models as artificial intelligence (AI). ...
This review article aims to understand the current state-of-the-art hybrid ML models used for groundwater management and the achievements made in this domain. ...
Acknowledgments: Mojtaba Zaresefat would like to express his sincere appreciation for the Ministry of Science, Research, and Technology's (MSRT) financial support of the Islamic Republic of Iran government's ...
doi:10.3390/w15091750
fatcat:ybxbs4rqubcitd2mqlrumjyjz4
Human Resource Information System with Machine Learning Integration
2022
Qubahan Academic Journal
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The human resource information system with machine learning integration was developed to aid in the management of employees' records, profiling, turnover, data analytics, and the generation of electronic ...
It was developed with the feature of predicting employee turnover using a supervised machine learning method. ...
Machine Learning Integration based on the characteristics defined in the ISO 25010 Software Quality Model. ...
doi:10.48161/qaj.v2n2a120
fatcat:2o7hhocuinfndmbdjrpknifajy
Deriving a Quantitative Relationship Between Resolution and Human Classification Error
[article]
2019
arXiv
pre-print
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The quantitative heuristic we derived could prove useful for predicting machine model performance, predicting data storage requirements, and saving valuable resources in the deployment of machine learning ...
For machine learning perception problems, human-level classification performance is used as an estimate of top algorithm performance. ...
Private or public entities might start with an idea for using deep learning; obtain data they believe suits the purpose; collect, manage, and label the data; and train models on the data. ...
arXiv:1908.09183v1
fatcat:cruof5poq5f55izuymrlpygfkm
A review of previous researches about machine learning theory in production management: Focusing on reinforcement learning
2024
International Research Journal of Modernization in Engineering Technology and Science
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In this study, we review existing studies focusing on reinforcement learning theory among machine learning theories in the field of production management, especially scheduling, and suggest limitations ...
High-tech IT product production lines are comprised of a complex of various processes and technologies, so problems that may arise at each stage can have a significant impact on the final product. ...
However, the following limitations exist in existing studies. First, there is data dependency. Many machine learning and reinforcement learning models rely on large amounts of high-quality data. ...
doi:10.56726/irjmets50593
fatcat:gfujwwxlyzerfnmgm3gygzk4fq
Who will dropout from university? Academic risk prediction based on interpretable machine learning
[article]
2021
arXiv
pre-print
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It predicts academic risk based on the LightGBM model and the interpretable machine learning method of Shapley value. ...
Second, the introduction of Shapley value calculation makes machine learning that lacks a clear reasoning process visualized, and provides intuitive decision support for education managers. ...
Acknowledgments Thank my students and ex-colleagues for providing the raw data analyzed in this study, including but not limited to: [34] Lundberg S , Lee S I . ...
arXiv:2112.01079v1
fatcat:5v5nfj7fdvctharoprbontz65m
Integration Of Machine Learning Techniques in Banana Production: A Literature Review
2024
Zenodo
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By expanding on earlier research to uncover critical techniques for enhancing banana quality assessment, it highlights the revolutionary influence of machine learning algorithms on this process. ...
The significance of classifying bananas in determining crop quality and market value is emphasized in this article. ...
Furthermore, the quality of annotated data exerts a substantial influence on the accuracy of machine learning models that aim to predict subjective impressions [56. ...
doi:10.5281/zenodo.10667894
fatcat:d7cgztdp2zc4fp7vr23qfnrufu
EFFICACY AND EFFICIENCY OF EDUCATIONAL DATA MINING THROUGH MULTISTRATEGY MACHINE LEARNING
2018
International Journal of Advanced Research in Computer Science
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Reference [9] study the quality of predictive models offered by machine learning algorithms for student retention management. ...
Reference [15] used decision tree classification technique on students' assessment outcome to identify students at risk of poor performance to support the quality management of teaching learning process ...
doi:10.26483/ijarcs.v9i1.5268
fatcat:xjywhpnjt5hlvp5fq6zwjxnpe4
RESEARCHING MACHINE LEARNING ALGORITHMS AND BIG DATA ANALYSIS TO PREDICT DEMAND AND CUSTOMER BEHAVIOR
2023
Zenodo
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This article explores the application of machine learning algorithms and big data analytics in predicting demand and customer behavior. ...
With the increasing availability of vast amounts of data and advancements in machine learning techniques, organizations can leverage these tools to gain insights into customer preferences, anticipate demand ...
The performance of the algorithms heavily relies on the quality and representativeness of the data used for training and testing. ...
doi:10.5281/zenodo.8363422
fatcat:qnmhqzckivggjoxfr3voiyxv5y
The use of machine learning "black boxes" explanation systems to improve the quality of school education
2020
Cogent Engineering
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It is proposed to apply interpretable machine learning models for making decisions on improving the quality of education in secondary schools. ...
Thirdly, we use machine learning model interpreters to develop recommendations. ...
The obtained results demonstrate the possibility of using machine learning models in combination with this interpreter as the basis of the MCDSS education quality management system in secondary schools ...
doi:10.1080/23311916.2020.1769349
fatcat:3gr6uehhanamzf65uo6gpxpd3q
Prediction and Interpretation of Water Quality Recovery after a Disturbance in a Water Treatment System Using Artificial Intelligence
2022
Water
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XGBoost, one of the most popular ensemble machine learning models, was used as the main framework of the model. ...
In this study, an ensemble machine learning model was developed to predict the recovery rate of water quality in a water treatment plant after a disturbance. ...
Conflicts of Interest: The authors declare no conflict of interest. Water 2022, 14, 2423 ...
doi:10.3390/w14152423
fatcat:ufu2yxdjlbasxlpxk3rqa52aja
A Survey on Cleaning Dirty Data Using Machine Learning Paradigm for Big Data Analytics
2018
Indonesian Journal of Electrical Engineering and Computer Science
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Also challenges faced in cleaning big data due to nature of data are discussed. Machine learning algorithms can be used to analyze data and make predictions and finally clean data automatically. ...
<span>Recently Big Data has become one of the important new factors in the business field. This needs to have strategies to manage large volumes of structured, unstructured and semi-structured data. ...
SAS
Data Management
Data Quality Desktop
sas.com
The learning curve is manageable. ...
doi:10.11591/ijeecs.v10.i3.pp1234-1243
fatcat:4a6hqthlavcy5disedzsww67ha
Machine learning for sustainable investing: Current applications and overcoming obstacles in ESG analysis
2023
Applied and Computational Engineering
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The intersection of Environmental, Social, and Governance (ESG) issues and Machine Learning (ML) has garnered significant attention in recent years as companies and investors increasingly recognize the ...
The overall process of applying ML models in ESG analysis involves data collection, preprocessing, model training and evaluation, and model interpretation. ...
In addition, there is a risk of bias and ethical issues in using ML techniques for ESG analysis. The effectiveness of ML models relies entirely on the quality of the data they are trained on [6] . ...
doi:10.54254/2755-2721/29/20230894
fatcat:jbg7jkr3ufeclde3wy36ixj53m
Information-Based Medicine in Glioma Patients: A Clinical Perspective
2018
Computational and Mathematical Methods in Medicine
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Machine learning, however, promises to provide the analytical support for personalizing treatment decisions, and deep learning allows clinicians to unlock insight from the vast amount of unstructured data ...
Future challenges include the assembly of well-curated cross-institutional datasets, improvement of the interpretability of machine learning models, and balancing novel evidence-based decision-making with ...
Conflicts of Interest The authors declare that there are no conflicts of interest regarding the publication of this paper. ...
doi:10.1155/2018/8572058
pmid:30008798
pmcid:PMC6020490
fatcat:sd3pwgtnazcaneh22fhmh627ki
Data Infrastructure for Machine Learning
2019
International Journal for Research in Applied Science and Engineering Technology
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Data quality is critical for effective machine learning, and this makes data a first-class citizen in the context of machine learning, on par with algorithms, software, and infrastructure. ...
As a result, machine-learning platforms need to support data analysis and validation in a principled manner, throughout the lifecycle of the machine learning process. ...
The generated data is then used to train and evaluate a machine learning model for a small number of steps. ...
doi:10.22214/ijraset.2019.4133
fatcat:b5iojbgus5ai3lbqsevsinvquu
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