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Item type:Publication, Artificial intelligence model based on Grey clustering for integral analysis of industrial hygiene risk(Science and Information Organization, 2021-01-01)The article proposes a model with an artificial intelligence approach that integrates risks through the Grey Clustering method applying the "Triangulation of center-point based on Whitening functions -CTWF", for this, the data established is standard data (minimum standards that the four workshops of a company in the industrial sector must meet) and sampled data (real data obtained in the field) to test the grey classes. In this study, the different types of risks (lighting, noise and hand-arm vibration) were globally evaluated and analyzed in the four workshops of a heavy machinery maintenance services company in the industrial sector (welding shop, hydraulic shop, machine shop 1 and machine shop 2), located in Lima, Peru. According to the results obtained from the level of hygienic quality in each workshop, the welding workshop is at a very poor-quality level, while the others are at a good and very good level; regarding the four workshops, it was determined that the noise level is not recommended as they do not meet the minimum required standards. Therefore, control measures were proposed in the four workshops where the level of irrigation is bad and very bad. This study will benefit companies in the industrial sector that need to analyze the level of hygienic quality in their work areas with a global approach in order to apply control measures with prevention, protection of health and physical integrity of workers. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Grey clustering method for water quality assessment to determine the impact of mining company, Peru(Science and Information Organization, 2021-01-01)Mining operations have a significant impact on environment, where the quality of water is an important affected issue that need to be controlled. In that way, the Grey Clustering Method based on center-point triangular whitenization weight (CTWF), is an artificial intelligence criterion that evaluates water samples according to selected parameters, in order to realize an effective water quality assessment. In the present study, the analysis is made on the Crisnejas River Basin, by using fifteen monitoring points based on an investigation realized by the National Water Authority (ANA) in 2019, based on the Peruvian law (ECA) about water quality standards. The results reveal that almost all of the monitoring points on the Crisnejas River Basin were classified as “irrigation of vegetables unrestricted”, but only one point was classified as “animal drink”, which is ubicated in an urbanized area. This implies that mining discharges are being well treated by the company, but another deal is the contamination generated in towns. Further, the present study might be helpful to audit processes made by the state or companies, to justify the quality of surface waters using a more accurate methodology. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Implementation of machine learning in health management to improve the process of medical appointmentsin Perú(IJETAE Publication House, 2022-02-01)The Peruvian health system has presented various deficiencies, one in particular is the difficulty and time for a user to schedule a medical appointment, in various health centers nationwide it is a problem for many citizens to easily access the health service. This research proposes developing a mobile app that allows automating this process by streamlining the procedures that lead to good health management to optimize both financial and human resources for better performance, quality and user experience that is insured at the service of ESSALUD. The results show that digital transformation and modernization with the support of technology significantly improve health management and therefore the medical appointment process, as well as other aspects that reduce the time of care and guarantee a reduction in the administrative work of the personnel of health allowing them to spend more time in patient care. Keywords—artificial intelligence; health; health management; machine learning; medical appointment - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Artificial Intelligence Model Based on Grey Clustering to Access Quality of Industrial Hygiene: A Case Study in Peru(MDPI, 2023-03-01)Industrial hygiene is a preventive technique that tries to avoid professional illnesses and damage to health caused by several possible toxic agents. The purpose of this study is to simultaneously analyze different risk factors (body vibration, lighting, heat stress and noise), to obtain an overall risk assessment of these factors and to classify them on a scale of levels of Unacceptable, Not recommended or Acceptable. In this work, an artificial intelligence model based on the grey clustering method was applied to evaluate the quality of industrial hygiene. The grey clustering method was selected, as it enables the integration of objective factors related to hazards present in the workplace with subjective employee evaluations. A case study, in the three warehouses of a beer industry in Peru, was developed. The results obtained showed that the warehouses have an acceptable level of quality. These results could help industries to make decisions about conducting evaluations of the different occupational agents and determine whether the quality of hygiene represents a risk, as well as give certain recommendations with respect to the factors presented.