{"title":"Enhancing Compressive Strength of Sulfate-Rich Concrete Using Electromagnetic Fields","authors":"Alaa K. Abdullah, Samer Almashhadi","doi":"10.18280/mmep.110313","DOIUrl":"https://doi.org/10.18280/mmep.110313","url":null,"abstract":"","PeriodicalId":37338,"journal":{"name":"Mathematical Modelling of Engineering Problems","volume":"137 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140369455","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}
{"title":"Numerical Study on the Impact Response of Steel Beams with Large Web Openings: Investigating Key Parameters","authors":"Maryam Jebur Al-Sultan, A. Al-Rifaie","doi":"10.18280/mmep.110305","DOIUrl":"https://doi.org/10.18280/mmep.110305","url":null,"abstract":"","PeriodicalId":37338,"journal":{"name":"Mathematical Modelling of Engineering Problems","volume":"88 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140371153","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}
{"title":"Unreliable Multi Server Retrial Queueing System with Reneging and Diverse Outgoing Services","authors":"Saravanan Vadivel, Poongothai Venugopal, Godhandaraman Pakkirisamy","doi":"10.18280/mmep.110301","DOIUrl":"https://doi.org/10.18280/mmep.110301","url":null,"abstract":"","PeriodicalId":37338,"journal":{"name":"Mathematical Modelling of Engineering Problems","volume":"7 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140372539","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}
{"title":"Schizophrenia Patient Classification with Long Short-Term Memory Analysis of Electroencephalography Signals","authors":"Fikri Badru Salam, D. Utari","doi":"10.18280/mmep.110325","DOIUrl":"https://doi.org/10.18280/mmep.110325","url":null,"abstract":"","PeriodicalId":37338,"journal":{"name":"Mathematical Modelling of Engineering Problems","volume":"11 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140373490","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}
Samuel Vega-Zuñiga, J. Rueda-Bayona, Adalberto Ospino-Castro
{"title":"DOE-ANOVA Analysis to Estimate the Effect of Ambient Temperature, Pressure and Humidity on Surface Wind Speed","authors":"Samuel Vega-Zuñiga, J. Rueda-Bayona, Adalberto Ospino-Castro","doi":"10.18280/mmep.110309","DOIUrl":"https://doi.org/10.18280/mmep.110309","url":null,"abstract":"","PeriodicalId":37338,"journal":{"name":"Mathematical Modelling of Engineering Problems","volume":"58 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140368969","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}
In the realm of energy conservation, managing power consumption within buildings emerges as a pivotal challenge. This study introduces sophisticated models that optimize energy usage by intelligently managing power distribution in various zones of a building. To achieve this, four machine learning classifiers, Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) algorithm, and Naive Bayes (NB), were employed. These classifiers were integrated with feature reduction techniques, namely Boruta and Principal Component Analysis (PCA), to diminish model complexity. The study delineates three distinct power management strategies: Full, Selected, and Shutdown. The effectiveness of these models was evaluated using a dataset obtained from a building's energy consumption measurements. A comparative analysis revealed that the integration of the RF classifier with the Boruta feature reduction method significantly excelled, achieving a classification accuracy of 98%. Additionally, this combination demonstrated an execution time of merely 0.4549 seconds. The findings of this research not only underscore the efficacy of combining specific machine learning classifiers with feature reduction techniques but also highlight the potential of such integrations in optimizing energy consumption in building environments. This approach paves the way for more energy-efficient and sustainable building management practices.
{"title":"Optimizing Energy Consumption in Buildings: Intelligent Power Management Through Machine Learning","authors":"M. M. Talib, M. Croock","doi":"10.18280/mmep.110321","DOIUrl":"https://doi.org/10.18280/mmep.110321","url":null,"abstract":"In the realm of energy conservation, managing power consumption within buildings emerges as a pivotal challenge. This study introduces sophisticated models that optimize energy usage by intelligently managing power distribution in various zones of a building. To achieve this, four machine learning classifiers, Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) algorithm, and Naive Bayes (NB), were employed. These classifiers were integrated with feature reduction techniques, namely Boruta and Principal Component Analysis (PCA), to diminish model complexity. The study delineates three distinct power management strategies: Full, Selected, and Shutdown. The effectiveness of these models was evaluated using a dataset obtained from a building's energy consumption measurements. A comparative analysis revealed that the integration of the RF classifier with the Boruta feature reduction method significantly excelled, achieving a classification accuracy of 98%. Additionally, this combination demonstrated an execution time of merely 0.4549 seconds. The findings of this research not only underscore the efficacy of combining specific machine learning classifiers with feature reduction techniques but also highlight the potential of such integrations in optimizing energy consumption in building environments. This approach paves the way for more energy-efficient and sustainable building management practices.","PeriodicalId":37338,"journal":{"name":"Mathematical Modelling of Engineering Problems","volume":"110 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140370404","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}
Ol'ga Kunickaya, Michael Zyryanov, Sergey Medvedev, A. Mokhirev, Anastasia Spiridonova, Pavel Perfiliev, Aleksei Teppoev
{"title":"Efficient Technologies for Harvesting and Reutilizing Logging Residues in Russia: A Sustainable Forestry Approach","authors":"Ol'ga Kunickaya, Michael Zyryanov, Sergey Medvedev, A. Mokhirev, Anastasia Spiridonova, Pavel Perfiliev, Aleksei Teppoev","doi":"10.18280/mmep.110319","DOIUrl":"https://doi.org/10.18280/mmep.110319","url":null,"abstract":"","PeriodicalId":37338,"journal":{"name":"Mathematical Modelling of Engineering Problems","volume":"109 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140370506","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}
M. S. Mauludin, S. D. Prasetyo, N. F. Alfaiz, Zainal Arifin
{"title":"Techno-Economic Modeling of Hybrid PV-Hydroelectric Generator Systems in Semarang","authors":"M. S. Mauludin, S. D. Prasetyo, N. F. Alfaiz, Zainal Arifin","doi":"10.18280/mmep.110323","DOIUrl":"https://doi.org/10.18280/mmep.110323","url":null,"abstract":"","PeriodicalId":37338,"journal":{"name":"Mathematical Modelling of Engineering Problems","volume":"14 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140371828","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}
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