Semisupervised learning is a type of machine learning technique that constructs a classifier by learning from a small collection of labeled samples and a large collection of unlabeled ones. Although some progress has been made in this research area, the existing semisupervised methods provide a nominal classification task. However, semisupervised learning for ordinal classification is yet to be explored. To bridge the gap, this study combines two concepts “semisupervised learning” and “ordinal classification” for the categorical class labels for the first time and introduces a new concept of “semisupervised ordinal classification”. This paper proposes a new algorithm for semisupervised learning that takes into account the relationships between the class labels, especially class orderings such as low, medium, and high. We also performed an extensive empirical study that involves 10 benchmark ordinal datasets with different quantities of labeled samples varying from 15% to 50% with an increment of 5%, aiming to evaluate the performance of our method by combining different base learners. The experimental results were also validated with a nonparametric statistical test. The experiments show that the proposed method improves the classification accuracy of the model compared to the existing semisupervised method on ordinal data.
{"title":"A new approach: semisupervised ordinal classification","authors":"Ferda Ünal, Derya Birant, Özlem Şeker","doi":"10.3906/elk-2008-148","DOIUrl":"https://doi.org/10.3906/elk-2008-148","url":null,"abstract":"Semisupervised learning is a type of machine learning technique that constructs a classifier by learning from a small collection of labeled samples and a large collection of unlabeled ones. Although some progress has been made in this research area, the existing semisupervised methods provide a nominal classification task. However, semisupervised learning for ordinal classification is yet to be explored. To bridge the gap, this study combines two concepts “semisupervised learning” and “ordinal classification” for the categorical class labels for the first time and introduces a new concept of “semisupervised ordinal classification”. This paper proposes a new algorithm for semisupervised learning that takes into account the relationships between the class labels, especially class orderings such as low, medium, and high. We also performed an extensive empirical study that involves 10 benchmark ordinal datasets with different quantities of labeled samples varying from 15% to 50% with an increment of 5%, aiming to evaluate the performance of our method by combining different base learners. The experimental results were also validated with a nonparametric statistical test. The experiments show that the proposed method improves the classification accuracy of the model compared to the existing semisupervised method on ordinal data.","PeriodicalId":49410,"journal":{"name":"Turkish Journal of Electrical Engineering and Computer Sciences","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75914566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Ramli, M. Usama, H. Mokhlis, W. Wong, Muhamad Hatta Hussain, M. Muhammad, Nurulafiqah Nadzirah Binti Mansor
An exponential increase in diverse load demand in the last decade has influenced the integration of more power plants into the power system. This increases the fault current due to the bidirectional flow of current, resulting in unwanted tripping of the relays if not properly coordinated. Therefore, it is imperative to ensure the installation of relays in the grid being able to sense the fault current from any direction (i.e. upstream or downstream). This can be accomplished by introducing an optimal directional overcurrent relay (DOCR) coordination scheme into the system. This paper presents an in-depth review of the applications of various optimization techniques for optimal coordination of directional overcurrent relays (DOCRs) in integrated power networks. The review highlights the advantages and limitations of techniques implemented to mitigate the DOCR coordination issues. Furthermore, potential research directions for optimal DOCR coordination are also discussed in this paper.
{"title":"Optimal directional overcurrent relay coordination based on computational intelligence technique: a review","authors":"S. Ramli, M. Usama, H. Mokhlis, W. Wong, Muhamad Hatta Hussain, M. Muhammad, Nurulafiqah Nadzirah Binti Mansor","doi":"10.3906/ELK-2012-98","DOIUrl":"https://doi.org/10.3906/ELK-2012-98","url":null,"abstract":"An exponential increase in diverse load demand in the last decade has influenced the integration of more power plants into the power system. This increases the fault current due to the bidirectional flow of current, resulting in unwanted tripping of the relays if not properly coordinated. Therefore, it is imperative to ensure the installation of relays in the grid being able to sense the fault current from any direction (i.e. upstream or downstream). This can be accomplished by introducing an optimal directional overcurrent relay (DOCR) coordination scheme into the system. This paper presents an in-depth review of the applications of various optimization techniques for optimal coordination of directional overcurrent relays (DOCRs) in integrated power networks. The review highlights the advantages and limitations of techniques implemented to mitigate the DOCR coordination issues. Furthermore, potential research directions for optimal DOCR coordination are also discussed in this paper.","PeriodicalId":49410,"journal":{"name":"Turkish Journal of Electrical Engineering and Computer Sciences","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70199820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Adaptive Output Tracking of Distributed Parameter Systems","authors":"","doi":"10.3906/elk-2104-157","DOIUrl":"https://doi.org/10.3906/elk-2104-157","url":null,"abstract":"","PeriodicalId":49410,"journal":{"name":"Turkish Journal of Electrical Engineering and Computer Sciences","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74861142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A new similarity-based multi-criteria recommendation algorithm based on autoencoders","authors":"","doi":"10.3906/elk-2107-145","DOIUrl":"https://doi.org/10.3906/elk-2107-145","url":null,"abstract":"","PeriodicalId":49410,"journal":{"name":"Turkish Journal of Electrical Engineering and Computer Sciences","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89408435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"DEFECT CLASSIFICATION OF RAILWAY FASTENERS USING IMAGE PREPROCESSING AND A LIGHTWEIGHT CONVOLUTIONAL NEURAL NETWORK","authors":"","doi":"10.3906/elk-2106-42","DOIUrl":"https://doi.org/10.3906/elk-2106-42","url":null,"abstract":"","PeriodicalId":49410,"journal":{"name":"Turkish Journal of Electrical Engineering and Computer Sciences","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74016431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"New fail operational powernet methods and topologies for automated driving with electric vehicle","authors":"","doi":"10.3906/elk-2005-14","DOIUrl":"https://doi.org/10.3906/elk-2005-14","url":null,"abstract":"","PeriodicalId":49410,"journal":{"name":"Turkish Journal of Electrical Engineering and Computer Sciences","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86645057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sentiment analysis received a lot of attention recently due to its potential use in business intelligence. 4 Understanding variable length sentences to extract the sentimental context is the main challenge of this concept. Our 5 proposed models are moderations of a deep neural model named comprehensive attention recurrent model [5]. A new 6 layer of attention mechanism and replacement of LSTM with gated-CNN have been introduced to make learning of CA 7 model [5] faster and efficient. IMDB movie review sentiment-labelled dataset has been used in our experiments. Our 8 paper solely focuses on the comparison of performances among proposed and inspired models. Experimental results 9 imply that accuracy and precision of our proposed models are better compared to the state-of-the-art CA model. 10
{"title":"Sentiment Classification using Attention based Gated-CNN with Deep Recurrent Neural Model","authors":"S. Rahman, Ashmita Riya, S. Haque","doi":"10.3906/elk-1909-58","DOIUrl":"https://doi.org/10.3906/elk-1909-58","url":null,"abstract":"Sentiment analysis received a lot of attention recently due to its potential use in business intelligence. 4 Understanding variable length sentences to extract the sentimental context is the main challenge of this concept. Our 5 proposed models are moderations of a deep neural model named comprehensive attention recurrent model [5]. A new 6 layer of attention mechanism and replacement of LSTM with gated-CNN have been introduced to make learning of CA 7 model [5] faster and efficient. IMDB movie review sentiment-labelled dataset has been used in our experiments. Our 8 paper solely focuses on the comparison of performances among proposed and inspired models. Experimental results 9 imply that accuracy and precision of our proposed models are better compared to the state-of-the-art CA model. 10","PeriodicalId":49410,"journal":{"name":"Turkish Journal of Electrical Engineering and Computer Sciences","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80627515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Robust Position/Force Control of Nonholonomic Mobile Manipulator for Constrained Motion on Surface in Task Space","authors":"","doi":"10.3906/elk-2106-134","DOIUrl":"https://doi.org/10.3906/elk-2106-134","url":null,"abstract":"","PeriodicalId":49410,"journal":{"name":"Turkish Journal of Electrical Engineering and Computer Sciences","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72507129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Version: 21.03.2022 Abstract: In this article, control oriented uncertainty modeling of an uncommon quadrotor in hover is discussed. This quadrotor consists of two counter-rotating big rotors on longitudinal axis and two counter-rotating small tilt rotors on lateral axis. Firstly, approximate linear model of this vehicle around hover is obtained by using Newton–Euler formulation. Secondly, specific uncertainty is assigned to each parameter. Resulting uncertain model is converted into a linear fractional transformation framework for robustness analysis. Next, the most critical uncertain parameters in terms of robust stability in a proposed quadrotor model are investigated using µ sensitivities. Finally, skewed- µ analysis determines maximum possible uncertainty bounds for model parameters that are difficult to
{"title":"Determining allowable parametric uncertainty in an uncommon quadrotor model for closed loop stability","authors":"M. Baskin, Mehmet Kemal Lebleb, Lu","doi":"10.3906/elk-2105-142","DOIUrl":"https://doi.org/10.3906/elk-2105-142","url":null,"abstract":"Version: 21.03.2022 Abstract: In this article, control oriented uncertainty modeling of an uncommon quadrotor in hover is discussed. This quadrotor consists of two counter-rotating big rotors on longitudinal axis and two counter-rotating small tilt rotors on lateral axis. Firstly, approximate linear model of this vehicle around hover is obtained by using Newton–Euler formulation. Secondly, specific uncertainty is assigned to each parameter. Resulting uncertain model is converted into a linear fractional transformation framework for robustness analysis. Next, the most critical uncertain parameters in terms of robust stability in a proposed quadrotor model are investigated using µ sensitivities. Finally, skewed- µ analysis determines maximum possible uncertainty bounds for model parameters that are difficult to","PeriodicalId":49410,"journal":{"name":"Turkish Journal of Electrical Engineering and Computer Sciences","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83463816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Analysis and design of symmetric lattice based wideband 2-bit digital phase shifter","authors":"","doi":"10.3906/elk-1905-99","DOIUrl":"https://doi.org/10.3906/elk-1905-99","url":null,"abstract":"","PeriodicalId":49410,"journal":{"name":"Turkish Journal of Electrical Engineering and Computer Sciences","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90061558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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