Res. Comput. Sci.最新文献

Implementation of an Intelligent Model based on Big Data and Decision Making using Fuzzy Logic Type-2 for the Car Assembly Industry in an Industrial Estate in Northern Mexico 基于大数据和模糊2型决策的智能模型在墨西哥北部某工业园区汽车装配产业中的实现

Res. Comput. Sci. Pub Date : 2021-07-07 DOI: 10.1201/9781003191148-20

José Luis Peinado Portillo, Carlos A. Ochoa-Ortíz, S. Paiva, Darwin Young

{"title":"Implementation of an Intelligent Model based on Big Data and Decision Making using Fuzzy Logic Type-2 for the Car Assembly Industry in an Industrial Estate in Northern Mexico","authors":"José Luis Peinado Portillo, Carlos A. Ochoa-Ortíz, S. Paiva, Darwin Young","doi":"10.1201/9781003191148-20","DOIUrl":"https://doi.org/10.1201/9781003191148-20","url":null,"abstract":". In our days, we are living the epitome of Industry 4.0, where each component is intelligent and suitable for Smart Manufacturing users, which is why the specific use of Big Data is proposed to determine the continuous improvement of the competitiveness of a car assembling industry. The Boston Consulting Group [1] has identified nine pillars of I4.0, which are: (i) Big Data and Analytics, (ii) Autonomous Robots, (iii) Simulation, (iv) Vertical and Horizontal Integration of Systems, (v) Industrial Internet of Things (IoT for its acronym in English), (vi) Cybersecurity, (vii) Cloud or Cloud, (viii) Additive Manufacturing including 3D printing, and (ix) Augmented Reality. These pillars can all be implemented in factories or take some depending on the case you want to improve. In Industry 4.0, the Industrial IoT is a fundamental component and its penetration in the market is growing. Car manufacturers such as General Motors or Ford expect that by 2020 there will be 50 billion (trillion in English) of connected devices and Ericsson Inc. estimates 18 billion. These estimated quantities of connected devices will be due to the increase in technological development, development in telecommunications and adoption of digital devices, and this will invariably lead to the increase in the generation of data and digital transactions, which leads to the mandatory increase in regulations, for security, privacy and informed consent in the integration of these diverse entities that will be connected and interacting among themselves and with the users. Finally, the use of Fuzzy Logic type 2 is proposed to adapt the correct decision making and achieve the reduction of uncertainty in the car assembly industry in the Northeast of Mexico. fuzzy logic type 2 for decision makings.","PeriodicalId":220522,"journal":{"name":"Res. Comput. Sci.","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121715184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Random Forest and Deep Learning Performance on the Malaria DREAM Sub Challenge One 随机森林和深度学习在Malaria DREAM Sub上的性能挑战一

Res. Comput. Sci. Pub Date : 2020-09-07 DOI: 10.5281/ZENODO.4018883

Didier Barradas-Bautista

引用次数: 1

Towards a Learning Ecosystem for Linemen Training 前锋训练的学习生态系统

Res. Comput. Sci. Pub Date : 2019-12-31 DOI: 10.13053/rcs-148-5-14

G. Bonfil

引用次数: 1

DNA Sequence Recognition using Image Representation 基于图像表示的DNA序列识别

Res. Comput. Sci. Pub Date : 2019-12-31 DOI: 10.13053/rcs-148-3-9

C. LuisA.Santamaría, H. SarahíZuñiga, I. H. P. Torres, M. J. S. García, Mario Rossainz López

{"title":"DNA Sequence Recognition using Image Representation","authors":"C. LuisA.Santamaría, H. SarahíZuñiga, I. H. P. Torres, M. J. S. García, Mario Rossainz López","doi":"10.13053/rcs-148-3-9","DOIUrl":"https://doi.org/10.13053/rcs-148-3-9","url":null,"abstract":"In recent years, the field of machine learning has progressed enormously in addressing difficult classification problems. The problem raised in this article is to recognize DNA sequences, recognize the boundaries between exons and introns using a graphic representation of DNA sequences and recent methods of deep learning. The objective of this work is to classify DNA sequences using a convolutional neuronal network (CNN). The set of DNA sequences used for the recognition were 1847 sequences from a database with 4 types of hepatitis C virus (type 1, 2, 3 and 6) taken from the repository available on the ViPR page. The other set of sequences used to recognize limits between exons and introns were sequences from the Molecular database (Splice-junction Gene Sequences) Data Set that has 3190 sequences, available on the ICU page, with three classes of sequences: limit exon-intron, limit intron-exon and none. For the processing of the DNA sequences, a representation method was designed where each nitrogenous base is represented in gray scale to form an image. The generated images were used to train the convolutional neuronal network. The results obtained from the CNN trained with the Hepatitis C virus database suggest that the CNNs are suitable for the classification of the images generated from the DNA sequences. This result led us to perform the experiments for the recognition of exons and introns with the UCI database for the recognition of limits between exons and introns. The results obtained were a training precision of 82%, a validation accuracy of 75% and an evaluation accuracy of 80.8%. It is concluded that it is possible to classify the images of DNA sequences of the databases used.","PeriodicalId":220522,"journal":{"name":"Res. Comput. Sci.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125054259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Caracterización óptica, química y nuclear del ónix mexicano (CaCO3), correspondiente a la zona del semidesierto Zacatecano 萨卡特卡诺半沙漠地区墨西哥缟玛瑙(CaCO3)的光学、化学和核特征

Res. Comput. Sci. Pub Date : 2019-12-31 DOI: 10.13053/rcs-148-1-4

Claudia Angélica Márquez-Mata, H. R. Vega-Carrillo, María De Jesús Mata Chávez, José de Jesús Araiza-Ibarra

引用次数: 1

Hill Algorithm Decryption using Parallel Calculations by Brute Force 使用暴力并行计算的Hill算法解密

Res. Comput. Sci. Pub Date : 2019-12-31 DOI: 10.13053/rcs-148-3-7

B. Sánchez-Rinza, Juan Carlos García Lezama

引用次数: 0

Evaluation of Five Classifiers for Depression Episodes Detection 五种分类器对抑郁症发作检测的评价

Res. Comput. Sci. Pub Date : 2019-12-31 DOI: 10.13053/rcs-148-10-11

Susana L. Pacheco-González, L. A. Zanella-Calzada, C. Galván-Tejada, Nubia M. Chávez-Lamas, J. F. Rivera-Gómez, J. Galván-Tejada

{"title":"Evaluation of Five Classifiers for Depression Episodes Detection","authors":"Susana L. Pacheco-González, L. A. Zanella-Calzada, C. Galván-Tejada, Nubia M. Chávez-Lamas, J. F. Rivera-Gómez, J. Galván-Tejada","doi":"10.13053/rcs-148-10-11","DOIUrl":"https://doi.org/10.13053/rcs-148-10-11","url":null,"abstract":". Depression is a mental disorder manifested through a set of psychological and physical symptoms, such as the presence of sad-ness, apathy, hopelessness and irritability, among others. According to the World Health Organization (WHO), depression is aﬀecting more than 300 million people worldwide, presenting a prevalence between 3 and 21%. One of the main problems of this high prevalence is the incorrect classiﬁcation of patients, since many cases are false positive and false negative diagnoses. In this work it is proposed the study of the behavior of ﬁve diﬀerent classiﬁcation techniques, random forest (RF), conditional inference trees (cTree), K-nearest neighbor (K-NN), support vector machine (SVM) and Na¨ıve Bayes, to identify depressive states through the motor activity of patients contained in the Depresjon dataset. The activity of this dataset is acquired through the smart watch “Actigraph”, based on actigraphy. The evaluation of these classiﬁcation techniques is ﬁnally performed in terms of sensitivity, speciﬁcity, the receiver operating characteristic (ROC) curve and area under the curve (AUC), to know their performance to automatically detect depressive patients. The results shown values of sensitivity, speciﬁcity and AUC, statistically signiﬁcant, specially for the RF method, which presents sensitivity = 0.8148, speciﬁcity = 0.8158 and AUC = 0.8314. Therefore, it is concluded that these classiﬁers are able to distinguish patients with depression from controls, based on their motor activity, allowing the development of a non-invasive diagnosis tool to support specialists in the correct diagnosis of depression.","PeriodicalId":220522,"journal":{"name":"Res. Comput. Sci.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129081152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Obstacle Detection and Trajectory Estimation in Vehicular Displacements based on Computational Vision 基于计算视觉的车辆位移障碍物检测与轨迹估计

Res. Comput. Sci. Pub Date : 2019-12-31 DOI: 10.13053/rcs-148-9-5

Lauro Reyes Cocoletzi, Iván Olmos, J. A. Olvera-López

引用次数: 1

Modelado de un sistema multi-agente para el monitoreo de residuos peligrosos en la industria manufacturera 为制造业危险废物监测的多智能体系统建模

Res. Comput. Sci. Pub Date : 2019-12-31 DOI: 10.13053/rcs-148-8-36

C. Barrera, J. Soto, Adrian Vázquez Osorio, Elvira Rolón Aguilar, Julio C. Rolón

引用次数: 0

Sistema de clasificación SVM de señales electromiográficas extraídas en un sistema embebido 在嵌入式系统中提取的肌电信号的SVM分类系统

Res. Comput. Sci. Pub Date : 2019-12-31 DOI: 10.13053/rcs-148-2-11

Luis Daniel Reyes Crusaley, J. R. Cárdenas-Valdez, G. Vázquez, Manuel Ortega, A. Calvillo-Téllez

引用次数: 1