Carlos Montenegro

Bio: Carlos Montenegro is an academic researcher from District University of Bogotá. The author has contributed to research in topics: Computer science & Fuzzy logic. The author has an hindex of 8, co-authored 16 publications receiving 277 citations.

Metamodel for integration of Internet of Things, Social Networks, the Cloud and Industry 4.0

Abstract: Internet of Things (IoT) is changing the world. Software for manufacturing industry is perceived as the new industrial revolution. It is creating new opportunities for both the economies and the society. Deployment of Internet of Things for development of Industry 4.0 changes processes and manufacturing systems while it also changes players in a wide variety of types and shapes. In that sort of systems, information is related to manufacturing status, trends in energy consumption by machinery, movement of materials, customer orders, supply data and all data related to smart devices deployed in the processes. This paper analyzes Internet of Things (IoT), its use into manufacturing industry, its foundation principles, available elements and technologies for the man-things-software communication already developed in this area. And it proves how important its deployment is. Describes a proposal of architecture of the Internet of things applied to the industry, a metamodel of integration (Internet of Things, Social Networks, Cloud and Industry 4.0) for generation of applications for the Industry 4.0, and the manufacturing monitoring prototype implemented with the Raspberry Pi microcomputer, a cloud storage server and a mobile device for controlling an online production process.

Supporting academic decision making at higher educational institutions using machine learning-based algorithms

Abstract: Decisions made by deans and university managers greatly impact the entire academic community as well as society as a whole. In this paper, we present survey results on which academic decisions they concern and the variables involved in them. Using machine learning algorithms, we predicted graduation rates in a real case study to support decision making. Real data from five undergraduate engineering programs at District University Francisco Jose de Caldas in Colombia illustrate our results. The comparison between support vector machine and artificial neural network is held using the confusion matrix and the receiver operating characteristic curve. The algorithm methods and architecture are presented.

Usage of Machine Learning for Strategic Decision Making at Higher Educational Institutions

Abstract: Decisions made at the strategic level of Higher Educational Institutions (HEIs) affect policies, strategies, and actions that the institutions make as a whole. Decision’s structures at HEIs are depicted in this paper and their effectiveness in supporting the institutions’ governance. The disengagement of the stakeholders and the lack of using efficient computational algorithms lead to 1) the decision process takes longer; 2) the “whole picture” is not involved along with all data necessary; and 3) small academic impact is produced by the decision, among others. Machine learning is an emerging field of artificial intelligence that using various algorithms analyzes information and provides a richer understanding of the data contained in a specific context. Based on the author’s previous works, we focus on supporting decision-making at a strategic level, being deans’ concerns the preeminent mission to bolster. In this paper, three supervised classification algorithms are deployed to predict graduation rates from real data about undergraduate engineering students in South America. The analysis of receiver operating characteristic (ROC) curve and accuracy are executed as measures of effectiveness to compare and evaluate decision tree, logistic regression, and random forest, where this last one demonstrates the best outcomes.

Fuzzy Logic Models for Non-Programmed Decision-Making in Personnel Selection Processes Based on Gamification

Abstract: Non-programmed decision-making is an activity that requires a number of methods to try to capture the rational behaviour of an aspirant in situations of uncertainty. Thus, there is a varied list of attributes, methods, and mechanisms that are intended to describe the way in which aspirants can be profiled. However, this modelling proves to be complex if it is approached in scenarios based on game mechanics from gamification. For this reason, the following article aims to contribute to the processes of selection of personnel delimited only to the making of non-programmed decisions, through the implementation of game mechanics. In order to model this selection, the purpose of the following study is to carry out the formulation of inference rules based on fuzzy logic in order to capture the tacit transfer of certain types of information in personnel selection processes and to determine aspects that allow the shaping of aspirants. Finally, the results and conclusions obtained are presented.

Some q‐rung orthopair fuzzy Heronian mean operators in multiple attribute decision making

Abstract: The generalized Heronian mean and geometric Heronian mean operators provide two aggregation operators that consider the interdependent phenomena among the aggregated arguments. In this paper, the generalized Heronian mean operator and geometric Heronian mean operator under the q‐rung orthopair fuzzy sets is studied. First, the q‐rung orthopair fuzzy generalized Heronian mean (q‐ROFGHM) operator, q‐rung orthopair fuzzy geometric Heronian mean (q‐ROFGHM) operator, q‐rung orthopair fuzzy generalized weighted Heronian mean (q‐ROFGWHM) operator, and q‐rung orthopair fuzzy weighted geometric Heronian mean (q‐ROFWGHM) operator are proposed, and some of their desirable properties are investigated in detail. Furthermore, we extend these operators to q‐rung orthopair 2‐tuple linguistic sets (q‐RO2TLSs). Then, an approach to multiple attribute decision making based on q‐ROFGWHM (q‐ROFWGHM) operator is proposed. Finally, a practical example for enterprise resource planning system selection is given to verify the developed approach and to demonstrate its practicality and effectiveness.

The applications of Industry 4.0 technologies in manufacturing context: a systematic literature review

Abstract: Industry 4.0 (I4.0) encompasses a plethora of digital technologies effecting on manufacturing enterprises. Most research on this topic examines the effects in the smart factory domain, focusing on ...

Some q-rung orthopair fuzzy maclaurin symmetric mean operators and their applications to potential evaluation of emerging technology commercialization

Abstract: The Maclaurin symmetric mean (MSM) operator is a classical mean type aggregation operator used in modern information fusion theory, which is suitable to aggregate numerical values. The prominent characteristic of the MSM operator is that it can capture the interrelationship among the multi‐input arguments. In this paper, we extend the MSM operator and dual MSM operator to q‐rung orthopair fuzzy sets to propose the q‐rung orthopair fuzzy MSM operator, q‐rung orthopair fuzzy dual MSM operator, q‐rung orthopair fuzzy weighted MSM operator, and q‐rung orthopair fuzzy weighted dual MSM operator. Then, some desirable properties and special cases of these operators are discussed in detail. Finally, a numerical example is provided to illustrate the feasibility of the proposed methods and deliver the sensitivity analysis and comparative analysis.

A Survey on Information and Communication Technologies for Industry 4.0: State-of-the-Art, Taxonomies, Perspectives, and Challenges

Abstract: A new industrial revolution is undergoing, based on a number of technological paradigms. The will to foster and guide this phenomenon has been summarized in the expression “Industry 4.0” (I4.0). Initiatives under this term share the vision that many key technologies underlying Cyber-Physical Systems and Big Data Analytics are converging to a new distributed, highly automated, and highly dynamic production network , and that this process needs regulatory and cultural advancements to effectively and timely develop. In this work, we focus on the technological aspect only, highlighting the unprecedented complexity of I4.0 emerging from the scientific literature. While previous works have focused on one or up to four related enablers, we consider ten technological enablers, including besides the most cited Big Data, Internet of Things, and Cloud Computing, also others more rarely considered as Fog and Mobile Computing, Artificial Intelligence, Human-Computer Interaction, Robotics, down to the often overlooked, very recent, or taken for granted Open-Source Software, Blockchain, and the Internet. For each we explore the main characteristics in relation to I4.0 and its interdependencies with other enablers. Finally we provide a detailed analysis of challenges in leveraging each of the enablers in I4.0, evidencing possible roadblocks to be overcome and pointing at possible future directions of research. Our goal is to provide a reference for the experts in some of the technological fields involved, for a reconnaissance of integration and hybridization possibilities with other fields in the endeavor of I4.0, as well as for the laymen, for a high-level grasp of the variety (and often deep history) of the scientific research backing I4.0.