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Item type:Publication, 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Cyber security of smart metering infrastructure using median absolute deviation methodology(Hindawi Limited, 2022-01-01)To combat cyber threats in the smart grid, an intrusion detection system can be integrated into the advanced metering infrastructure. Anomaly-based intrusion detection can detect even the tiniest changes in the parameter under investigation, whereas signature-based intrusion detection only recognises known attacks. The growing usage of smart grids necessitates the classification, identification, and implementation of countermeasures to threats. At the absolute least, smart grids must be protected against cyberattacks; thus, the highest level of information security must be offered. As a result of digitisation and the usage of more smart applications, the research looked at a variety of attack types, smart grid assaults, and major cyber threats on the voltage regulation. Machine learning techniques that analyse data in real time and formulate patterns to recognise an attack and scan through huge data for anomalies can be implemented into the advanced metering infrastructure (AMI) for intrusion detection for anomaly-based intrusion detection. The comparative test study done for the research found that the proposed method, median absolute deviation for anomaly identification in smart metering datasets, produced the most accurate and precise differentiations with the highest accuracy and precision. The median absolute deviation (MAD) algorithm model is trained using test data, and raw predictions are made, before true data are used to derive final test result parameters, precision, recall, and F1 scores. The methodology of the entire study is discussed in this paper, as well as how the MAD algorithm is best suited for anomaly-based intrusion detection, as well as result comparisons of other machine learning algorithms. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Characterization of the stress level of university students using data mining algorithms(Frontiers Media, 2025-11-21)There is concern about the levels of stress faced by college students and their effects on mental health and academic performance. This study aimed to characterize academic stress levels in college students, using data mining algorithms to classify and predict risk patterns. Data were collected from 287 students using the SISCO Academic Stress Inventory, and classification algorithms and association rules were applied using WEKA software. The results revealed that 75.3% of the students experienced high stress levels, primarily linked to psychological reactions and academic demands. It also compared the predictive performance of 13 algorithms, where J48, LMT, and SimpleLogistic achieved classification accuracies above 89%, surpassing results previously reported in similar educational contexts. Association rule mining further showed that being single and childless was strongly correlated with elevated stress levels, highlighting demographic risk profiles often overlooked in earlier research. By integrating predictive modeling with demographic and behavioral factors, this study extended prior literature by showing how data mining can simultaneously classify and explain academic stress, offering actionable insights for universities to design targeted, evidence-based interventions.2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Bayesian Calibration of a 2d Hydraulic Model Using a Convolutional Neural Network Emulator(RELX Group (Netherlands), 2025-01-01)6 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Analysis of the Severity of Road Accidents Using Combined Data Mining Techniques(Multidisciplinary Digital Publishing Institute (MDPI), 2026-06-01)Road traffic accidents represent a critical road safety issue, the severity of which depends on the complex interplay of multiple factors. This issue directly impacts Target 3.6 of Sustainable Development Goal (SDG) 3, which aims to halve global deaths and injuries by 2030, and SDG 11, which focuses on safe and sustainable transport systems. The study of these factors and their interrelationships is important in the scientific literature. The objective of this study is to analyze the factors that determine the severity of road traffic accidents, identifying the most important ones and their correlations. A dataset containing variables such as infrastructure, location, time, and vehicle type, among others, was used to predict severity, applying Association Rules to identify latent correlations and the Classification and Regression Tree for hierarchical risk classification. The results reveal that the type of collision is the primary predictor of severity; the highest severity is associated with heavy traffic and head-on or side-impact collisions, involving critical scenarios, in the early morning hours and in rural areas, linked to trucks. The combined use of both tools provides a scientific basis for designing interventions on highly vulnerable road segments, contributing to the fulfillment of the 2030 Agenda for safe mobility.