Ingeniería Solidaria

16

Authors: Madapuri Rudra Kumar; Vinit Kumar Gunjan

Introduction:Increase in computing power and the deeper usage of the robust computing systems in the financial system is propelling the business growth, improving the operational efficiency of the financial institutions, and increasing the effectiveness of the transaction processing solutions used by the organizations. Problem:Despite that the financial institutions are relying on the credit scoring patterns for analyzing the credit worthiness of the clients, still there are many factors that are imminent for improvement in the credit score evaluation patterns. Objective:Machine learning is offering immense potential in Fintech space and determining a personal credit score. Organizations by applying deep learning and machine learning techniques can tap individuals who are not being serviced by traditional financial institutions. Methodology:One of the major insights into the system is that the traditional models of banking intelligence solutions are predominantly the programmed models that can align with the information and banking systems that are used by the banks. But in the case of the machine-learning models that rely on algorithmic systems require more integral computation which is intrinsic. Results:The test analysis of the proposed machine learning model indicates effective and enhanced analysis process compared to the non-machine learning solutions. The model in terms of using various classifiers indicate potential ways in which the solution can be significant. Conclusion: If the systems can be developed to align with more pragmatic terms for analysis, it can help in improving the process conditions of customer profile analysis, wherein the process models have to be developed for comprehensive analysis and the ones that can make a sustainable solution for the credit system management. Originality:The proposed solution is effective and the one conceptualized to improve the credit scoring system patterns. Limitations: The model is tested in isolation and not in comparison to any of the existing credit scoring patterns.

20

Authors: Hong Nhung Luu Thi; Trinh Nguyen Nhat; Linh Nguyen Thi Dieu;

Introduction: This paper is the result of the research “Development of bra pressure measuring system using air pressure sensor” developed in the Ha Noi University of Science and Technology in 2019. Proplem: The pressure of the bra on the human body affects the comfort of the wearer and the wearer's health. Therefore, bra pressure is one of the most important parameters taken into account when designing and manufacturing bras. Objective: To determine the pressure value of a bra, it is necessary to use a suitable pressure measuring device. Methodology: The device operates on the principle of resitive gas pressure sensor, with 8 measuring channels. The system is controlled by the computer via programming software in the Visual C language, measuring the pressure of the shirt on the position on the curved surface of the human body. Pressure values ​​and timed charts are displayed on the computer. The measuring device was tested with GE Druck DPI 610 pressure generator. Results: Test results showed that the device has a sensitivity of 0.01 kPa, measuring range from 0.1to 10 kPa and an accuracy of 0.01 kPa. Conclusion: This measurement system is important for objectively assessing the pressure of the bra on the wearer. Originality: This paper presents the results of research and design of the PB228 system when measuring the pressure of the bra on the wearer's body in static, dynamic and static-dynamic combined states. Limitations: The accuracy of the device only applies in standard conditions

21

Authors: Baudilio Acevedo Buitrago; Deisy Mireya Ortíz Bernal; Estefanía Ramírez Trujillo

Introduction:This article shows the evaluation of environmental noise around the medical centers located in the UPZ Americas and Kennedy Central - Bogotá, and the proposal of some actions related to the planning of the territory as a strategy for mitigation. Problem:The Kennedy town has presented high levels of environmental noise, which is a worrying aspect due to its correlation with the effects on people's health. Objective:This research was conducted for the purpose of compiling updated information on environmental noise in the Kennedy locality and evaluating regulatory compliance and identifing the phenomena to establish actions that lead to its mitigation. Methodology:The measurement process was guided under the guidelines of ISO 1996:1 and the parameters established in Res. 627/2006 of the Ministry of Environment. Results:The ambient noise levels exceed the maximum permissible limits (55 dB) on the different days and hours of measurement. It was also determined that this phenomenon is mainly related to high vehicular flow as well as formal and informal commercial activities around medical centers. Conclusion:There is no regulatory compliance in relation to environmental noise and the territorial plan established in the UPZ. The suggested actions are: use of parks to plant native trees that act as biological barriers, non-use of surrounding streets as secondary traffic roads and reorganization of trade activities. Originality:Updated environmental noise data was obtained around medical centers in Kennedy and problems of inappropriate growth and development were identified. Limitations:Lack of information on environmental noise measurements in other sectors of the area.

24

Authors: Jhon Jairo Álvarez Londoño; Julio Ariel Hurtado Alegría

Introduction: This publication is the product of research: “SPEM 2.0 Process Model Metrics in the Reliability of its Visual Analysis” throughout 2019, which supports the work of a master’s degree in Systems Engineering at the University of Cauca. Objective: Rebase a process model metrics set in order to increase AVISPA reliability to support the visual analysis of SPEM 2.0 software process models. Methodology: In order to improve AVISPA, a systematic literature review had been performed to find software process model metrics that are potentially apt to be represented in AVISPA. Next, a set of assessments were performed in order to enhance visual analysis tool. Finally, an ANOVA statistical assessment was realized in order to find a variance differential between AVISPA versions by comparing their F1-Score process model elements classification values. Results: AVISPA significantly improved its general classification algorithm. Most of errors were found in SPEM 2.0 variability resolution feature and collections with duplicated elements. Multiple misclassifications still persists. Conclusion: General AVISPA process model elements classification is improved. However, some process model samples remain scattered according to ANOVA results. Originality: AVISPA is a recent solution for SPEM 2.0 software process model assessment. It's recent emergence carried to a lack of articles about software process model metrics and few works about AVISPA improvements. These are the main contributions of this paper. Limitations: The project has been widely expensive in terms of execution time, traceability with all software process model elements, and mainly to find experts in software process that can meet the research requirements

30

Authors: Sivadi Balakrishna; Moorthy Thirumaran; Vijender Kumar Solanki

Introduction: The article is the product of the research “Due to the increase in popularity of Internet of Things (IoT), a huge amount of sensor data is being generated from various smart city applications”, developed at Pondicherry University in the year 2019. Problem:To acquire and analyze the huge amount of sensor-generated data effectively is a significant problem when processing the data. Objective: To propose a novel framework for IoT sensor data analysis using machine learning based improved Gaussian Mixture Model (GMM) by acquired real-time data. Methodology:In this paper, the clustering based GMM models are used to find the density patterns on a daily or weekly basis for user requirements. The ThingSpeak cloud platform used for performing analysis and visualizations. Results:An analysis has been performed on the proposed mechanism implemented on real-time traffic data with Accuracy, Precision, Recall, and F-Score as measures. Conclusions:The results indicate that the proposed mechanism is efficient when compared with the state-of-the-art schemes. Originality:Applying GMM and ThingSpeak Cloud platform to perform analysis on IoT real-time data is the first approach to find traffic density patterns on busy roads. Restrictions:There is a need to develop the application for mobile users to find the optimal traffic routes based on density patterns. The authors could not concentrate on the security aspect for finding density patterns.