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    A novel prediction model for educational planning of human resources with data mining approach: a national tax administration case study
    (Springer Science+Business Media, 2021-08-13)
    Human resources training is considered an effective solution in empowering human resources. Organizations try to have effective educational planning for this precious resource by identifying shortcomings through a need assessment. This study provides a model based on organizational data analysis to achieve a unique and appropriate training planning for each staff. Therefore, job performance, organizational promotion and lay-off have become the basis for staff training planning. For this purpose, the tax assessor’s information was investigated. Then, the CRISP-DM methodology was selected, and the project was implemented. Furthermore, a decision tree model was selected to extract unknown rules and patterns in the educational decision-making staff; the neural network model was selected as the predictive model to predict the target variables. The results revealed the decision tree for predicting job performance variables and organizational promotion status, and the neural network model was more effective in predicting service lay-off variables.
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    Big data analytics for critical information classification in online social networks using classifier chains
    (Springer, 2022-01-01)
    Industrial and academic organizations are using online social network (OSN) for different purposes, such as social and economic aspects. Now, OSN is a new mean of obtaining information from people about their preferences, and interests. Due to the large volume of user-generated content, researchers use various techniques, such as sentiment analysis or data mining to evaluate this information automatically. However, the sentiment analysis of OSN content is performed by different methods, but there are some problems to obtain highly reliable results, mainly because of the lack of user profile information, such as gender and age. In this work, a novel dataset is built, which contains the writing characteristics of 160,000 users of the Twitter OSN. Before creating classification models with Machine Learning (ML) techniques, feature transformation and feature selection methods are applied to determine the most relevant set of characteristics. To create the models, the Classifier Chain (CC) transformation technique and different machine learning algorithms are applied to the training set. Simulation results show that the Random Forest, XGBoost and Decision Tree algorithms obtain the best performance results. In the testing phase, these algorithms reached Hamming Loss values of 0.033, 0.033, and 0.034, respectively, and all of them reached the same F1 micro-average value equal to 0.976. Therefore, our proposal based on a multidimensional learning technique using CC transformation overcomes other similar proposals.
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    Boosted decision tree reweighting of simulated neutrino interactions for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow> <mml:mi mathvariant="script">O</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mn>1</mml:mn> <mml:mo stretchy="false">)</mml:mo> <mml:mtext> </mml:mtext> <mml:mtext> </mml:mtext> <mml:mi>GeV</mml:mi> </mml:mrow> </mml:math> neutrino cross-section measurements
    (American Physical Society, 2026-06-12)
    This paper illustrates a generic method for multidimensional reweighting of <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:mi mathvariant="script">O</a:mi> <a:mo stretchy="false">(</a:mo> <a:mn>1</a:mn> <a:mo stretchy="false">)</a:mo> <a:mtext> </a:mtext> <a:mtext> </a:mtext> <a:mi>GeV</a:mi> </a:math> neutrino interaction Monte Carlo samples. The reweighting is based on a boosted decision tree algorithm trained on high-dimensional space in detector final-state observables. This enables one generator's events to be reweighted so that its reconstructed particle content and kinematics distributions, as well as detector efficiency, match those of a target model. The approach establishes an efficient way to reuse legacy Monte Carlo data, avoiding regeneration. As an example, we test its use in a measurement of transverse kinematic imbalance of the <f:math xmlns:f="http://www.w3.org/1998/Math/MathML" display="inline"> <f:msup> <f:mi>μ</f:mi> <f:mo>−</f:mo> </f:msup> </f:math> and proton in charged-current quasielastic like <h:math xmlns:h="http://www.w3.org/1998/Math/MathML" display="inline"> <h:msub> <h:mi>ν</h:mi> <h:mi>μ</h:mi> </h:msub> </h:math> events from the MINERvA experiment.
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