Determination of Dimensionality of the Psychosocial Risk Assessment of Internal, Individual, Double Presence and External Factors in Work Environments

Viloria, Amelec; Bucci, Nunziatina; Luna, Marisabel; Lis-Gutiérrez, Jenny-Paola; Parody, Alexander; Solano, Darwin; López, Luz Adriana Borrero

doi:10.1007/978-3-319-93803-5_29

Cited by 22 publications

(1 citation statement)

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“…Factor analysis technique enhances establishment of the best portion of permissions variables interrelationship among factors. Using Kaiser-Meyer-Olkin's approach [80], we extracted three factors by MSA extraction procedure. The factors in the structure matrix of the selected variables using the equamax rotation method (see Table 2) explain the correlation among the permission variables.…”

Section: Exploratory Factor Analysismentioning

confidence: 99%

Modeling Correlation between Android Permissions Based on Threat and Protection Level Using Exploratory Factor Plane Analysis

Ashawa

Morris

2021

JCP

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The evolution of mobile technology has increased correspondingly with the number of attacks on mobile devices. Malware attack on mobile devices is one of the top security challenges the mobile community faces daily. While malware classification and detection tools are being developed to fight malware infection, hackers keep deploying different infection strategies, including permissions usage. Among mobile platforms, Android is the most targeted by malware because of its open OS and popularity. Permissions is one of the major security techniques used by Android and other mobile platforms to control device resources and enhance access control. In this study, we used the t-Distribution stochastic neighbor embedding (t-SNE) and Self-Organizing Map techniques to produce a visualization method using exploratory factor plane analysis to visualize permissions correlation in Android applications. Two categories of datasets were used for this study: the benign and malicious datasets. Dataset was obtained from Contagio, VirusShare, VirusTotal, and Androzoo repositories. A total of 12,267 malicious and 10,837 benign applications with different categories were used. We demonstrate that our method can identify the correlation between permissions and classify Android applications based on their protection and threat level. Our results show that every permission has a threat level. This signifies those permissions with the same protection level have the same threat level.

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Section: Exploratory Factor Analysismentioning

confidence: 99%

Modeling Correlation between Android Permissions Based on Threat and Protection Level Using Exploratory Factor Plane Analysis

Ashawa

Morris

2021

JCP

View full text Add to dashboard Cite

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Use of Artificial Neural Networks in Determining Domestic Violence Predictors

Silva

Aleman

Acuña

et al. 2019

Lecture Notes in Computer Science

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This paper aims to determine the predictors of violence against women by their partners, according to the National Survey of Demography and Health-ENDS-Colombia, 2017 using artificial neural networks. The results indicate that the best forecasting model found is the artificial neural network, perceptron model, multilayer associative memory with a hidden layer of 20 units, through functions of sigmoidal activation and sum of square of the error as error function. The ten main explanatory variables are: respect for human rights of the partner, respect for wishes, love expressed by the partner, a history of domestic violence, engaging in joint decision making, decision of contraceptive use, number of connections (partners) of the respondent, decision-making at the financial level, correction of children behavior, and decisions regarding women's health at home.

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Integration of Statistical Techniques to Evaluate the Fatigue of Operators on the Productivity of a Company

Parody

Viloria

Hernandez

et al. 2019

Advances in Intelligent Systems and Computing

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The present study seeks to determine the influence of fatigue according to the Yoshitake's physical, mental, and general classification on the percentage of performance in workers of an assembly line of energy accumulators in a plant located on the north coast of Colombia. The study shows that the type of fatigue affects the performance of workers and this effect varies depending on: the work shift (morning, afternoon, or evening), the position (operator, assistant, or packager), and the experience or the age of the worker. All relationships were established from multiple linear statistical models, applying a 95% of confidence in the models for the three studied shifts, presenting values of R squared above 76% showing that the models are highly explanatory and reliable

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Determination of Dimensionality of the Psychosocial Risk Assessment of Internal, Individual, Double Presence and External Factors in Work Environments

Cited by 22 publications

References 0 publications

Modeling Correlation between Android Permissions Based on Threat and Protection Level Using Exploratory Factor Plane Analysis

Modeling Correlation between Android Permissions Based on Threat and Protection Level Using Exploratory Factor Plane Analysis

Use of Artificial Neural Networks in Determining Domestic Violence Predictors

Integration of Statistical Techniques to Evaluate the Fatigue of Operators on the Productivity of a Company

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