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2024 International Conference on decision Aid Sciences and Applications, DASA 2024

作者： Ali, Mariam Kassem, Sally S. Elatriby, Sherif A. Nile University Industrial Engineering Program Smart Engineering Systems Research Center Giza12613 Egypt Cairo Univesityt Faculty of Computers and Artificial Intelligence Operations Research and Decision Support Department Giza12613 Egypt University Helwan Faculty of Engineering Helwan Cairo11792 Egypt

ISBN: (纸本)9798350369106

Productivity and safety of the workers have been issues in most manufacturing facilities in Egypt and all over the world, as the main aim of any manufacturing facility is to increase profit and limit costs as much as possible. Through analysis and simulation, this paper aims to create a safe workplace for the worker;risk assessment and Failure Mode and Effect Analysis (FMEA) and the basics of the international standards ISO 31000 and ISO 45001 have been conducted to evaluate different hazards and potential risks. Moreover, lean tools such as 6S and Visual Management are proposed solutions to enhance workplace safety and decrease the possibility or severity of potential risks. Therefore, a new workplace design, an example of a high-level hazard in industry in Egypt's local market, has been established for the steel manufacturing facility under consideration using Sketch Up. The results of the proposed solutions, such as new frameworks, worker safety training, equipment replacements, and applying lean tools, have shown a practically acceptable percentage of safety improvements and a decreasing risk priority number (RPN). The percentage decrease for (RPN) ranges between 31.43% to 90.47% for different potential risks. © 2024 IEEE.

关键词： Agile manufacturing systems

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Fullexpression using transfer learning in the classification of human emotions 11th

Fullexpression using transfer learning in the classification...

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11th International Symposium on Ambient Intelligence, ISAmI 2020

作者： Rocha, Ricardo Praça, Isabel Porto Portugal GECAD – Knowledge Engineering and Decision Support Research Centre Porto Portugal

ISBN: (纸本)9783030583552

During human evolution emotion expression became an important social tool that contributed to the complexification of societies. Human-computer interaction is commonly present in our daily life, and the industry is struggling for solutions that can analyze human emotions, to improve workers safety and security, as well as processes optimization. In this work we present a software built using the transfer-learning technique on a deep learning model, and conclude about how it can classify human emotions through facial expression analysis. A Convolutional Neuronal Network model was trained and used in a web application. Several tools were created to facilitate the software development process, including the training and validation processes. Data was collected by combining several facial expression emotion databases. Software evaluation revealed an accuracy in identifying the correct emotions close to 80%. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Software design

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Enhancing Safety Measures in a Steel Manufacturing Facility;Factory X Case Study

Enhancing Safety Measures in a Steel Manufacturing Facility;...

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decision Aid Sciences and Application (DASA), International Conference on

作者： Mariam Ali Sally S. Kassem Sherif A. Elatriby Industrial Engineering Program Smart Engineering Systems Research Center Nile University Giza Egypt Operations Research and Decision Support Department Faculty of Computers and Artificial Intelligence Cairo Univesityt Giza Egypt Faculty of Engineering Helwan University Helwan Cairo Egypt Industrial Engineering Program Smart Engineering Systems Research Center Nile University Giza Egypt

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Productivity and safety of the workers have been issues in most manufacturing facilities in Egypt and all over the world, as the main aim of any manufacturing facility is to increase profit and limit costs as much as possible. Through analysis and simulation, this paper aims to create a safe workplace for the worker; risk assessment and Failure Mode and Effect Analysis (FMEA) and the basics of the international standards ISO 31000 and ISO 45001 have been conducted to evaluate different hazards and potential risks. Moreover, lean tools such as 6S and Visual Management are proposed solutions to enhance workplace safety and decrease the possibility or severity of potential risks. Therefore, a new workplace design, an example of a high-level hazard in industry in Egypt's local market, has been established for the steel manufacturing facility under consideration using Sketch Up. The results of the proposed solutions, such as new frameworks, worker safety training, equipment replacements, and applying lean tools, have shown a practically acceptable percentage of safety improvements and a decreasing risk priority number (RPN). The percentage decrease for (RPN) ranges between 31.43% to 90.47% for different potential risks.

关键词： Training Productivity Visualization ISO Standards Employment Materials handling Production facilities Hazards Loss measurement Steel

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Battery scheduling models in smart buildings: a systematic literature review

Battery scheduling models in smart buildings: a systematic l...

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Power Electronics and Control (SEPOC), Seminar on

作者： Júlia Possebon Spellmeier Carmen Brum Rosa Luis Gomes Zita Vale Rogerio Moreira Alves Depart. of Production and Systems Engineering Universidade Federal de Santa Maria (UFSM) Brasil Knowledge Engineering and Decision-Support Research Center (GECAD) Instituto Superior de Engenharia do Porto (ISEP) Porto Portugal Department of Electrical Engineering Universidade Federal de Goiás Goiás Brasil

Economic development brings the current scenario closer to the depletion of natural resources. Directly related to this it’s the energy consumption and the need to seek sustainable solutions to prevent the harmful consequences of this depletion. Solar energy harnessed through photovoltaic technology is a solution capable of reducing the environmental impact, as it is a resource that is less harmful to the environment. The energy demand is seen as rising worldwide, and strategies must be developed to improve energy efficiency and maintain the balance of the electrical network, considering the comfort of consumers and the minimization of polluting emissions. In this sense, the concept of Home Energy Management Systems (HEMS) aims to reduce electricity bills, maintain the efficiency of electronic equipment, and Demand Response, which seeks to change the use of electricity by consumers. Thus, the present study aims to search the literature for different HEMS models, specifically battery scheduling, for the future application of a model for a case study in the laboratory GECAD - research Group on Intelligent engineering and Computing for Advanced Innovation and Development.

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Fuzzy Algorithms for Fractional PID Control Systems 18th

Fuzzy Algorithms for Fractional PID Control Systems

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18th International Conference on Hybrid Intelligent Systems, HIS 2018

作者： Barbosa, Ramiro S. Jesus, Isabel S. GECAD - Knowledge Engineering and Decision Support Research Center Department of Electrical Engineering ISEP/IPP - School of Engineering Polytechnic Institute of Porto Porto Portugal

ISBN: (纸本)9783030143466

This article investigates the use of fuzzy algorithms for fractional PID control systems. The tuning of the fuzzy controllers is based on the prior knowledge of integer or fractional-order control strategy. The suggested fuzzy controllers are fine tuned using a genetic algorithm (GA). The effectiveness and robustness of the proposed methodology is illustrated through its application on the control of a fractional-order plant. The simulation results show the better performance of nonlinear fuzzy algorithms of fractional-order. © 2020, Springer Nature Switzerland AG.

关键词： Three term control systems

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A Machine Learning Analysis to Identify Biomarkers on Holter Data of White Matter Lesions in Fabry Disease Patients

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SSRN 2025年

作者： Araújo, Bárbara Moura, Ana Rita Veloso, Bruno Azevedo, Olga Gago, Miguel F. Erlhagen, Wolfram Bicho, Estela Ferreira, Flora Algoritmi Research Center School of Engineering University of Minho Portugal Department of Industrial Electronics School of Engineering University of Minho Portugal Laboratory of Artificial Intelligence and Decision Support INESC TEC Porto Portugal School of Economics University of Porto Porto Portugal Cardiology and Neurology Services Hospital da Senhora da Oliveira Guimarães Portugal University of Minho Braga Portugal Research Centre of Mathematics School of Sciences University of Minho Portugal

Fabry disease (FD) is a rare genetic disorder characterized by significant cardiac complications and the potential presence of brain white matter lesions (WMLs). Early diagnosis is critical for optimizing treatment and improving patient outcomes, as the average delay between symptom onset and diagnosis remains significantly high. Despite significant progress in recent years, WMLs in FD remains poorly underdiagnosed, making the identification of biomarkers for early diagnosis critically important. This study employed machine learning techniques to identify a signature of electrocardiographic features associated with brain WMLs in middle-aged FD patients. A comprehensive feature selection process utilizing Variance Inflation Factor (VIF) analysis reduced the data to nine relevant features, including heart rate variability metrics and QT interval measurements. Several machine learning classifiers, including Logistic Regression, support Vector Machines, Random Forest, and k-Nearest Neighbors, were implemented through rigorous performance metrics such as accuracy, sensitivity, specificity, and area under the curve (AUC). The Random Forest algorithm demonstrated the highest classification accuracy (0.8125) when utilizing all features, while a two-feature combination of SDANN 5 and QTc Min achieved an accuracy 0.75 with other models. Additionally, SHAP (SHapley Additive exPlanations) analysis highlighted the importance of SDANN 5 classification outcomes. Our results indicate that our machine learning models were effective in identifying a electrocardiographic signature correlated to brain WMLs in FD, further supporting the interconnection between cardiovascular and neurovascular dysfunction in FD. This work underscores the potential of computational approaches in leveraging electrocardiographic data for improved diagnostic capabilities in complex genetic disorders, paving the way for more timely interventions and better patient management. © 2025, The Authors. All rights r

关键词： Heart

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The Relative Importance of Model Structure and Input Features for Water Supply Forecasting in Snow-Dominated River Basins of the Us Southwest

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SSRN 2024年

作者： Pernat, Madeline R. Kasprzyk, Joseph Zagona, Edith Walker, Sydney D. Livneh, Ben Department of Civil Environmental and Architectural Engineering University of Colorado Boulder 1111 Engineering Drive BoulderCO80309 United States Center for Advanced Decision Support for Water and Environmental Systems University of Colorado Boulder 421 UCB BoulderCO80309 United States Cooperative Institute for Research in Environmental Science University of Colorado Boulder 216 UCB BoulderCO80309 United States

In snow-dominated basins, water managers are tasked with producing seasonal forecasts of warm-season water supply. Machine learning is a promising approach for seasonal forecasting, but there exist many model types (e.g., linear regression versus tree-based models). Prior studies have sought the best model type for such forecasts, but model types exhibit different performance depending on feature set (i.e., predictor variables) and study basin. Moreover, comparatively less research has focused on the influence of different features on forecast performance. This study of multiple snow-dominated basins in the US compares the predictive performance of five machine learning model types by using a semi-exhaustive feature selection framework for optimizing model performance. This study also provides insight into which features, both currently and in the future, are important for producing accurate and stable forecasts. For each basin, the multiple linear regression (MLR) model built with its optimized feature set significantly outperformed the MLR model whose only feature was snow water equivalent (SWE). Furthermore, all model types performed similarly when built on their respective optimal feature set, further demonstrating the relative importance of feature selection over the decision of which model type to employ. © 2024, The Authors. All rights reserved.

关键词： Snow

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Optimum Location of Field Hospitals for COVID-19: A Nonlinear Binary Metaheuristic Algorithm

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Computers, Materials & Continua 2021年第7期68卷 1183-1202页

作者： Said Ali Hassan Khalid Alnowibet Prachi Agrawal Ali Wagdy Mohamed Department of Operations Research and Decision Support Faculty of Computers and Artificial IntelligenceCairo UniversityGiza12613Egypt College of Science Department of Statistics and Operations ResearchKing Saud UniversityRiyadhSaudi Arabia Department of Mathematics and Scientific Computing National Institute of Technology HamirpurHimachal Pradesh177005India Department of Operations Research Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt

Determining the optimum location of facilities is critical in many fields,particularly in *** study proposes the application of a suitable location model for field hospitals during the novel coronavirus 2019(COVID-19)*** used model is the most appropriate among the three most common location models utilized to solve healthcare problems(the set covering model,the maximal covering model,and the P-median model).The proposed nonlinear binary constrained model is a slight modification of the maximal covering model with a set of nonlinear *** model is used to determine the optimum location of field hospitals for COVID-19 risk *** designed mathematical model and the solution method are used to deploy field hospitals in eight governorates in Upper *** this case study,a discrete binary gaining–sharing knowledge-based optimization(DBGSK)algorithm is *** DBGSK algorithm is based on how humans acquire and share knowledge throughout their *** DBGSK algorithm mainly depends on two junior and senior binary *** two stages enable DBGSK to explore and exploit the search space efficiently and effectively,and thus it can solve problems in binary space.

关键词： Facility location nonlinear binary model field hospitals for COVID-19 gaining-sharing knowledge-based metaheuristic algorithm

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A Stochastic Flight Problem Simulation to Minimize Cost of Refuelling

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Computers, Materials & Continua 2021年第10期69卷 849-871页

作者： Said Ali Hassan Khalid Alnowibet Miral H.Khodeir Prachi Agrawal Adel F.Alrasheedi Ali Wagdy Mohamed Department of Operations Research and Decision Support Faculty of Computers and Artificial IntelligenceCairo UniversityGiza12613Egypt Department of Statistics and Operations Research College of ScienceKing Saud UniversityRiyadh11451Kingdom of Saudi Arabia Department of Mathematics and Scientific Computing National Institute of Technology HamirpurHimachal Pradesh177005India Department of Operations Research Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt

Commercial airline companies are continuously seeking to implement strategies for minimizing costs of fuel for their flight routes as acquiring jet fuel represents a significant part of operating and managing expenses for airline activities.A nonlinear mixed binary mathematical programming model for the airline fuel task is presented to minimize the total cost of refueling in an entire flight route *** model is enhanced to include possible discounts in fuel prices,which are performed by adding dummy variables and some restrictive constraints,or by fitting a suitable distribution function that relates prices to purchased *** obtained fuel plan explains exactly the amounts of fuel in gallons to be purchased from each airport considering tankering strategy while minimizing the pertinent cost of the whole flight *** relation between the amount of extra burnt fuel taken through tinkering strategy and the total flight time is also considered.A case study is introduced for a certain flight rotation in domestic US air transport *** mathematical model including stepped discounted fuel prices is *** problem has a stochastic nature as the total flight time is a random variable,the stochastic nature of the problem is realistic and more appropriate than the deterministic *** stochastic style of the problem is simulated by introducing a suitable probability distribution for the flight time duration and generating enough number of runs to mimic the probabilistic real *** similar real application problems are modelled as nonlinear mixed binary ones that are difficult to handle by exact ***,metaheuristic approaches are widely used in treating such different optimization *** this paper,a gaining sharing knowledge-based procedure is used to handle the mathematical *** algorithm basically based on the process of gaining and sharing knowledge throughout the human *** generated simulation runs of

关键词： Stochastic flight problem cost of refuelling ferrying strategy tankering discounted prices simulation procedure nonlinear mixed binary model metaheuristic algorithm gaining-sharing knowledge-based algorithm

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Workers benchmarking using multi-directional efficiency analysis in a manufacturing production system

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Procedia Computer Science 2022年 200卷 1451-1460页

作者： Eugénio M. Rocha Ângela F. Brochado Ana Moura Department of Mathematics University of Aveiro 3810-193 Portugal Center for Research and Development in Mathematics and Applications (CIDMA) Aveiro 3810-193 Portugal Department of Economics Management Industrial Engineering and Tourism University of Aveiro 3810-193 Portugal Systems for Decision Support Research Group (GOVCOPP) Aveiro 3810-193 Portugal

The human factor plays a relevant role in all manual or partially automatic production systems, specially, the ones showing reliable and balanced dynamics. In the literature, parametric or survey-based models are quite common for performance evaluation of production workers. In this work, multi-directional efficiency analysis is used instead, for root cause analysis of product reworks and bottlenecks occurrence, according to four worker-related parameters: experience time, wage, delay time and response time. The approach allows to identify individual inefficiencies per tuple worker/working shift and to cluster them according to similar inefficiency parameters. In addition, this work opens a path to new applications of multi-directional efficiency analysis to problems in the manufacturing industry.

关键词： multi-directional efficiency analysis bottleneck workers performance clustering manufacturing production system

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