Merve Yildiz, Muhammet Berigel, F. Kalyoncu, Özlem Özgenç Keleş
{"title":"Usability Evaluation of the Online Skill Assessment Tool","authors":"Merve Yildiz, Muhammet Berigel, F. Kalyoncu, Özlem Özgenç Keleş","doi":"10.26650/acin.1077400","DOIUrl":"https://doi.org/10.26650/acin.1077400","url":null,"abstract":"","PeriodicalId":309427,"journal":{"name":"Acta Infologica","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131207171","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}
Y. Dzhelil, T. Mihalev, B. Ivanov, D. Dobrudzhaliev
{"title":"Mathematical Modeling and Optimization of Supply Chain for Bioethanol","authors":"Y. Dzhelil, T. Mihalev, B. Ivanov, D. Dobrudzhaliev","doi":"10.26650/acin.817655","DOIUrl":"https://doi.org/10.26650/acin.817655","url":null,"abstract":"","PeriodicalId":309427,"journal":{"name":"Acta Infologica","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126950123","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 this study, the emerging, novel marine predators algorithm is proposed to adjust the proportional–integral– derivative controller of the automatic voltage regulator system. With the proposed algorithm, this study aimed to minimize the maximum percent excess of the terminal voltage, settling time, rise time, and steady-state error and improve the transient response of the automatic voltage regulator system with an optimal proportional–integral– derivative controller. The integral of squared error, integral of weighted squared error, squared integral of time, and Zwe-Lee Gaing objective functions were used to set the controller parameters. The performance of the proportional–integral–derivative controller based on the marine predators algorithm was compared with those of the proportional–integral–derivative controllers adapted by different metaheuristic algorithms using various objective functions suggested in the literature. These analyses were conducted using analysis methods such as transient response, root locus, and robustness. The simulation results show better performance in terms of the settling time, over-peak, and stability of the proportional–integral–derivative-controlled automatic voltage regulator system tuned with the marine predators algorithm.
{"title":"Otomatik Gerilim Regülatör Sistemi için Deniz Yırtıcıları Algoritmasının Performans Analizi","authors":"Zeynep Garip, MuratErhan Çimen, A. Boz","doi":"10.26650/acin.1026494","DOIUrl":"https://doi.org/10.26650/acin.1026494","url":null,"abstract":"In this study, the emerging, novel marine predators algorithm is proposed to adjust the proportional–integral– derivative controller of the automatic voltage regulator system. With the proposed algorithm, this study aimed to minimize the maximum percent excess of the terminal voltage, settling time, rise time, and steady-state error and improve the transient response of the automatic voltage regulator system with an optimal proportional–integral– derivative controller. The integral of squared error, integral of weighted squared error, squared integral of time, and Zwe-Lee Gaing objective functions were used to set the controller parameters. The performance of the proportional–integral–derivative controller based on the marine predators algorithm was compared with those of the proportional–integral–derivative controllers adapted by different metaheuristic algorithms using various objective functions suggested in the literature. These analyses were conducted using analysis methods such as transient response, root locus, and robustness. The simulation results show better performance in terms of the settling time, over-peak, and stability of the proportional–integral–derivative-controlled automatic voltage regulator system tuned with the marine predators algorithm.","PeriodicalId":309427,"journal":{"name":"Acta Infologica","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128643175","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":"An Empirical Study on Strategic Alignment of Enterprise Systems","authors":"Nazim Taskin, Jacques C. Verville, M. Yu","doi":"10.26650/acin.1079619","DOIUrl":"https://doi.org/10.26650/acin.1079619","url":null,"abstract":"","PeriodicalId":309427,"journal":{"name":"Acta Infologica","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131536463","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}
Proteins play a crucial function in the biological processes of living organisms. Knowing the function of the protein offers significant insight into future biological and medical research. Since a protein’s shape determines its function, it is important to understand the protein’s 3D structure. Although experimental methods such as X-ray crystallography and nuclear magnetic resonance (NMR) have been used to examine the shape of proteins, so far the results have been insufficient. As a result, predicting the 3D structure of proteins is crucial. Determining the 3D structure of a protein from its primary structure is challenging. Therefore, predicting the protein secondary structure becomes important for studying its structure and function. Many emerging methods, including machine learning, as well as deep learning, have been used to predict the secondary structure of proteins and comprise a crucial part of Structural Bioinformatics. The goal of this study is to compare the results generated by predictive models that were created using the four most frequently utilized deep learning methods: convolutional neural networks (CNN), recurrent neural networks (RNN), long short term memory networks (LSTM), and gated recurrent units (GRU). The CB513 dataset was used to train and test these models, and performance evaluation metrics viz. accuracy, f1 score, recall, and precision were applied. The CNN, RNN, LSTM, and GRU models had an accuracy of 82.54%, 82.06%, 81.1%, and 81.48%, respectively.
{"title":"Derin Öğrenme (CNN, RNN, LSTM, GRU) Kullanarak Protein İkincil Yapı Tahmini","authors":"Ezgi Çakmak, İ. Selvi̇","doi":"10.26650/acin.1008075","DOIUrl":"https://doi.org/10.26650/acin.1008075","url":null,"abstract":"Proteins play a crucial function in the biological processes of living organisms. Knowing the function of the protein offers significant insight into future biological and medical research. Since a protein’s shape determines its function, it is important to understand the protein’s 3D structure. Although experimental methods such as X-ray crystallography and nuclear magnetic resonance (NMR) have been used to examine the shape of proteins, so far the results have been insufficient. As a result, predicting the 3D structure of proteins is crucial. Determining the 3D structure of a protein from its primary structure is challenging. Therefore, predicting the protein secondary structure becomes important for studying its structure and function. Many emerging methods, including machine learning, as well as deep learning, have been used to predict the secondary structure of proteins and comprise a crucial part of Structural Bioinformatics. The goal of this study is to compare the results generated by predictive models that were created using the four most frequently utilized deep learning methods: convolutional neural networks (CNN), recurrent neural networks (RNN), long short term memory networks (LSTM), and gated recurrent units (GRU). The CB513 dataset was used to train and test these models, and performance evaluation metrics viz. accuracy, f1 score, recall, and precision were applied. The CNN, RNN, LSTM, and GRU models had an accuracy of 82.54%, 82.06%, 81.1%, and 81.48%, respectively.","PeriodicalId":309427,"journal":{"name":"Acta Infologica","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115070232","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：