Event detection on temporal-dynamic graphs aims at detecting significant events based on deviations from the normal behavior of the graphs. With the widespread use of social media, many real-world events manifest as social media interactions, making them suitable for modeling as temporal-dynamic graphs. This paper presents a workflow for event detection on temporal-dynamic graphs using graph representation learning. Our workflow leverages generated embeddings of a temporal-dynamic graph to reframe the problem as an unsupervised time-series anomaly detection task. We evaluated our workflow on four distinct real-world social media datasets and compared our results with the related work. The results show that the performance depends on how anomalies deviate from normal. These include changes in both size and topology. Our results are similar to the related work for the graphs where the deviation from a normal state of the temporal-dynamic graph is apparent, e.g., Reddit. On the other hand, we achieved a 3-fold improvement in precision for the graphs where deviations exist on size and topology, e.g., Twitter. Also, our results are 20% to 5-fold better even if we introduced some delay factor. That is, we beat our competition while detecting events that occurred some time ago. As a result, our study proves that graph embeddings as time-series data can be used for event detection tasks.
{"title":"Transforming temporal-dynamic graphs into time-series data for solving event detection problems","authors":"KUTAY TAŞCI, FUAT AKAL","doi":"10.55730/1300-0632.4023","DOIUrl":"https://doi.org/10.55730/1300-0632.4023","url":null,"abstract":"Event detection on temporal-dynamic graphs aims at detecting significant events based on deviations from the normal behavior of the graphs. With the widespread use of social media, many real-world events manifest as social media interactions, making them suitable for modeling as temporal-dynamic graphs. This paper presents a workflow for event detection on temporal-dynamic graphs using graph representation learning. Our workflow leverages generated embeddings of a temporal-dynamic graph to reframe the problem as an unsupervised time-series anomaly detection task. We evaluated our workflow on four distinct real-world social media datasets and compared our results with the related work. The results show that the performance depends on how anomalies deviate from normal. These include changes in both size and topology. Our results are similar to the related work for the graphs where the deviation from a normal state of the temporal-dynamic graph is apparent, e.g., Reddit. On the other hand, we achieved a 3-fold improvement in precision for the graphs where deviations exist on size and topology, e.g., Twitter. Also, our results are 20% to 5-fold better even if we introduced some delay factor. That is, we beat our competition while detecting events that occurred some time ago. As a result, our study proves that graph embeddings as time-series data can be used for event detection tasks.","PeriodicalId":49410,"journal":{"name":"Turkish Journal of Electrical Engineering and Computer Sciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135246530","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}
Intent detection and slot filling are two crucial subtasks of a text-based goal-oriented dialogue system. In a goal-oriented dialogue system, users interact with the system to complete a goal (or to fulfill their intent) and provide the necessary information (slot values) to achieve that goal. Therefore, a user?s text input includes information about the user?s intent and contains required slot values. Recently, joint models that simultaneously detect the intent and extract the slots are proposed to benefit from the interaction between the two tasks. The proposed methods are usually tested using benchmark data sets in English such as ATIS and SNIPS. Intent detection and slot filling problems are much less studied for the Turkish language mainly due to the lack of publicly available Turkish data sets. In this paper, we translate ATIS in English to Turkish and report intent detection and slot filling accuracies of several different joint models for the translated data set. We publicly share the Turkish ATIS data set to accelerate the research on the tasks. In our experiments, the best performance is obtained with the state-of-the-art bidirectional encoder representations from a transformers (BERT) based model. The BERT model is trained using a combination of intent detection and slot filling losses to jointly optimize a single model for both tasks. We achieved 96.54% intent detection accuracy and 91.56% slot filling F1 for the Turkish language. These accuracies significantly improve (7% absolute in slot filling F1) previously reported results for the same tasks in Turkish. On the other hand, we observe that the accuracy in Turkish is still slightly lower compared to the accuracy in English counterparts. This observation indicates that there is still room for improvement in the results for Turkish.
{"title":"Joint intent detection and slot filling for Turkish natural language understanding","authors":"OSMAN BÜYÜK","doi":"10.55730/1300-0632.4021","DOIUrl":"https://doi.org/10.55730/1300-0632.4021","url":null,"abstract":"Intent detection and slot filling are two crucial subtasks of a text-based goal-oriented dialogue system. In a goal-oriented dialogue system, users interact with the system to complete a goal (or to fulfill their intent) and provide the necessary information (slot values) to achieve that goal. Therefore, a user?s text input includes information about the user?s intent and contains required slot values. Recently, joint models that simultaneously detect the intent and extract the slots are proposed to benefit from the interaction between the two tasks. The proposed methods are usually tested using benchmark data sets in English such as ATIS and SNIPS. Intent detection and slot filling problems are much less studied for the Turkish language mainly due to the lack of publicly available Turkish data sets. In this paper, we translate ATIS in English to Turkish and report intent detection and slot filling accuracies of several different joint models for the translated data set. We publicly share the Turkish ATIS data set to accelerate the research on the tasks. In our experiments, the best performance is obtained with the state-of-the-art bidirectional encoder representations from a transformers (BERT) based model. The BERT model is trained using a combination of intent detection and slot filling losses to jointly optimize a single model for both tasks. We achieved 96.54% intent detection accuracy and 91.56% slot filling F1 for the Turkish language. These accuracies significantly improve (7% absolute in slot filling F1) previously reported results for the same tasks in Turkish. On the other hand, we observe that the accuracy in Turkish is still slightly lower compared to the accuracy in English counterparts. This observation indicates that there is still room for improvement in the results for Turkish.","PeriodicalId":49410,"journal":{"name":"Turkish Journal of Electrical Engineering and Computer Sciences","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135246525","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}
Cognitive load detection is eminent during the mental assignment of neural activity because it indicates how the brain reacts to stimuli. The level of cognitive load experienced during mental arithmetic tasks can be determined using an electroencephalogram (EEG). The EEG data were collected from publicly available datasets, namely, mental arithmetic task (MAT) and simultaneous task workload (STEW). The first phase comprises decomposing the electroencephalogram (EEG) signal into intrinsic mode functions (IMFs) using circulant singular spectrum analysis (Ci-SSA). In the second phase, entropy-based features were evaluated using IMFs. After that, the extracted features were fed to nature-inspired feature selection algorithms: genetic algorithm (GA), binary particle swarm optimization (BPSO), particle swarm optimization (PSO), binary bat algorithm (BBA), and binary dragonfly algorithm (BDA) for optimal selection of features by using machine learning (ML) techniques: K-nearest neighbor (KNN), support vector machine (SVM) to analyse the classification accuracy (Ac), sensitivity (Se), specificity (Sp), precision (Pr), and F-score with 10-fold cross-validation in the third phase. The highest classification Ac, Se, Sp, Pr, and F-score of the MAT dataset were 97.30%, 0.98, 0.97, and 97.40% from multileads, and 96.20%, 0.96, 0.94, and 96.70% from a single lead (F4) of EEG, respectively. However, we achieved 97.98%, 0.98, 0.98, 0.97, and 98.1% values from multi-leads and 96.67%, 0.96, 0.97, 0.95, and 96.90% from a single-lead STEW dataset. When compared to previous state-of-the-art methods, the proposed method (Ci-SSA+BDA+KNN) has proven to be more successful.
{"title":"Cognitive load detection using Ci-SSA for EEG signal decomposition and nature-inspired feature selection","authors":"JAMMISETTY YEDUKONDALU, LAKHAN DEV SHARMA","doi":"10.55730/1300-0632.4017","DOIUrl":"https://doi.org/10.55730/1300-0632.4017","url":null,"abstract":"Cognitive load detection is eminent during the mental assignment of neural activity because it indicates how the brain reacts to stimuli. The level of cognitive load experienced during mental arithmetic tasks can be determined using an electroencephalogram (EEG). The EEG data were collected from publicly available datasets, namely, mental arithmetic task (MAT) and simultaneous task workload (STEW). The first phase comprises decomposing the electroencephalogram (EEG) signal into intrinsic mode functions (IMFs) using circulant singular spectrum analysis (Ci-SSA). In the second phase, entropy-based features were evaluated using IMFs. After that, the extracted features were fed to nature-inspired feature selection algorithms: genetic algorithm (GA), binary particle swarm optimization (BPSO), particle swarm optimization (PSO), binary bat algorithm (BBA), and binary dragonfly algorithm (BDA) for optimal selection of features by using machine learning (ML) techniques: K-nearest neighbor (KNN), support vector machine (SVM) to analyse the classification accuracy (Ac), sensitivity (Se), specificity (Sp), precision (Pr), and F-score with 10-fold cross-validation in the third phase. The highest classification Ac, Se, Sp, Pr, and F-score of the MAT dataset were 97.30%, 0.98, 0.97, and 97.40% from multileads, and 96.20%, 0.96, 0.94, and 96.70% from a single lead (F4) of EEG, respectively. However, we achieved 97.98%, 0.98, 0.98, 0.97, and 98.1% values from multi-leads and 96.67%, 0.96, 0.97, 0.95, and 96.90% from a single-lead STEW dataset. When compared to previous state-of-the-art methods, the proposed method (Ci-SSA+BDA+KNN) has proven to be more successful.","PeriodicalId":49410,"journal":{"name":"Turkish Journal of Electrical Engineering and Computer Sciences","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135246665","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}
This study presents the simulation, experimentation, and design considerations of a Poly(vinylidene fluoride co-trifluoroethylene)/ Polyethylene Terephthalate (PVDF-TrFe / PET), laser-cut, flexible piezoelectric energy harvester. It is possible to obtain energy from the environment around autonomous sensor systems, which can then be used to power various equipment. This article investigates the actuation means of ambient vibration, which is a good candidate for using piezoelectric energy harvester (PEH) devices. The output voltage characteristics were analyzed in a wind test apparatus. Finite element modeling (FEM) was done for von Mises stress and modal analysis. Resonance frequency sweeps, quality factors, and damping ratios of the circular plate were given numerically. For a PVDF-TrFe piezoelectric layer thickness of 18 µm and 1.5 mm radius, a damping ratio of 0.117 and a quality factor of 4.284 was calculated. Vmax was calculated as 984 mV from the wind setup experiments and compared with the FEM outputs.
{"title":"Design and Modeling of a PVDF-TrFe Flexible Wind Energy Harvester","authors":"BERKAY KULLUKÇU, LEVENT BEKER","doi":"10.55730/1300-0632.3986","DOIUrl":"https://doi.org/10.55730/1300-0632.3986","url":null,"abstract":"This study presents the simulation, experimentation, and design considerations of a Poly(vinylidene fluoride co-trifluoroethylene)/ Polyethylene Terephthalate (PVDF-TrFe / PET), laser-cut, flexible piezoelectric energy harvester. It is possible to obtain energy from the environment around autonomous sensor systems, which can then be used to power various equipment. This article investigates the actuation means of ambient vibration, which is a good candidate for using piezoelectric energy harvester (PEH) devices. The output voltage characteristics were analyzed in a wind test apparatus. Finite element modeling (FEM) was done for von Mises stress and modal analysis. Resonance frequency sweeps, quality factors, and damping ratios of the circular plate were given numerically. For a PVDF-TrFe piezoelectric layer thickness of 18 µm and 1.5 mm radius, a damping ratio of 0.117 and a quality factor of 4.284 was calculated. Vmax was calculated as 984 mV from the wind setup experiments and compared with the FEM outputs.","PeriodicalId":49410,"journal":{"name":"Turkish Journal of Electrical Engineering and Computer Sciences","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135185329","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}
Millimeter-wave (mmWave) antennas are indispensable components in the fifth-generation (5G) wireless communication systems. With the inherent advantages of integration capability, substrate integrated waveguide (SIW) antenna is an excellent choice for applications in the mmWave frequency bands. However, reflection losses occur at dielectric-filled thin apertures of SIW antennas. These reflections can be overcome by impedance matching between the aperture and the free space. In this study, we introduce an mmWave SIW horn antenna having impedance matching transitions (IMTs) across the horn's aperture width. The designed antenna, operating in the 24-28 GHz band, is simulated with a full-wave analysis tool. The simulation results of the modified SIW horn have been confirmed by the experimental results and shown to be satisfactory. The IMTs result in an enhancement of the front-to-back ratio (FTBR). The modified SIW horn antenna with a novel printed transition achieves sidelobe levels (SLLs) of better than ?9 dB at 27 GHz, with an enhanced FTBR above 15 dB. In the 24?28 GHz band, the antenna has a reflection coefficient variation of better than ?10 dB.
{"title":"H-plane SIW horn antenna with enhanced front-to-back ratio for 5G applications","authors":"ÖZLEM AKGÜN, NURHAN TÜRKER TOKAN","doi":"10.55730/1300-0632.3982","DOIUrl":"https://doi.org/10.55730/1300-0632.3982","url":null,"abstract":"Millimeter-wave (mmWave) antennas are indispensable components in the fifth-generation (5G) wireless communication systems. With the inherent advantages of integration capability, substrate integrated waveguide (SIW) antenna is an excellent choice for applications in the mmWave frequency bands. However, reflection losses occur at dielectric-filled thin apertures of SIW antennas. These reflections can be overcome by impedance matching between the aperture and the free space. In this study, we introduce an mmWave SIW horn antenna having impedance matching transitions (IMTs) across the horn's aperture width. The designed antenna, operating in the 24-28 GHz band, is simulated with a full-wave analysis tool. The simulation results of the modified SIW horn have been confirmed by the experimental results and shown to be satisfactory. The IMTs result in an enhancement of the front-to-back ratio (FTBR). The modified SIW horn antenna with a novel printed transition achieves sidelobe levels (SLLs) of better than ?9 dB at 27 GHz, with an enhanced FTBR above 15 dB. In the 24?28 GHz band, the antenna has a reflection coefficient variation of better than ?10 dB.","PeriodicalId":49410,"journal":{"name":"Turkish Journal of Electrical Engineering and Computer Sciences","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135185328","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}
: The agglutinative nature of the Turkish language has a complex morphological structure, and there are generally more than one parse for a given word. Before further processing, morphological disambiguation is required to determine the correct morphological analysis of a word. Morphological disambiguation is one of the first and crucial steps in natural language processing since its success determines later analyses. In our proposed morphological disambiguation method, we used a transformer-based sequence-to-sequence neural network architecture. Transformers are commonly used in various NLP tasks, and they produce state-of-the-art results in machine translation. However, to the best of our knowledge, transformer-based encoder-decoders have not been studied in morphological disambiguation. In this study, in addition to character level tokenization, three input subword representations are evaluated, which are unigram, bytepair, and wordpiece tokenization methods. We have achieved the best accuracy with character input representation which is 96.25%. Although the proposed model is developed for Turkish language, it is not language-dependent, so it can be applied to a larger set of languages.
{"title":"Transmorph: a transformer based morphological disambiguator for Turkish","authors":"Hilal Özer, E. E. Korkmaz","doi":"10.55730/1300-0632.3912","DOIUrl":"https://doi.org/10.55730/1300-0632.3912","url":null,"abstract":": The agglutinative nature of the Turkish language has a complex morphological structure, and there are generally more than one parse for a given word. Before further processing, morphological disambiguation is required to determine the correct morphological analysis of a word. Morphological disambiguation is one of the first and crucial steps in natural language processing since its success determines later analyses. In our proposed morphological disambiguation method, we used a transformer-based sequence-to-sequence neural network architecture. Transformers are commonly used in various NLP tasks, and they produce state-of-the-art results in machine translation. However, to the best of our knowledge, transformer-based encoder-decoders have not been studied in morphological disambiguation. In this study, in addition to character level tokenization, three input subword representations are evaluated, which are unigram, bytepair, and wordpiece tokenization methods. We have achieved the best accuracy with character input representation which is 96.25%. Although the proposed model is developed for Turkish language, it is not language-dependent, so it can be applied to a larger set of languages.","PeriodicalId":49410,"journal":{"name":"Turkish Journal of Electrical Engineering and Computer Sciences","volume":"30 1","pages":"1897-1913"},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70798514","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":"Permissioned Blockchain based Remote Electronic Examination","authors":"Öznur Kalkar, I. Sertkaya","doi":"10.3906/elk-2105-204","DOIUrl":"https://doi.org/10.3906/elk-2105-204","url":null,"abstract":"","PeriodicalId":49410,"journal":{"name":"Turkish Journal of Electrical Engineering and Computer Sciences","volume":"30 1","pages":"361-375"},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70199833","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}
: Multiinput converters allow to create hybrid energy systems in electric vehicles with a reduced part count. In addition, interleaved structures help to build efficient converters with several possible benefits, such as low current ripple and high power density. This paper proposes utilizing a multiphase multiinput converter (MPMIC), which concentrates the aforementioned advantages and presents a comprehensive comparison with its single-phase version, called single-phase multiinput converter (SPMIC). After analysing their steady-state characteristics, SPMIC and MPMIC are designed considering same conditions. Then, two laboratory prototypes rated at 2.5kW output power are implemented to validate the analysis. Finally, these prototypes are compared in terms of voltage-gain, input current ripple, efficiency, complexity, cost, and power density. The results show that MPMIC surpasses SPMIC in efficiency and in input current ripple at the expense of increments in the complexity and cost. Besides, MPMIC results in comparatively high voltage gain in low power region thanks to the discontinuous current mode operation. On the other hand, it is explored that SPMIC can reach higher power density in the event of effective cooling.
{"title":"Comparative study of a bidirectional multi-phase multiinput converter for electric vehicles","authors":"F. Akar, Murat Kale, Sebahattin Yalçin, Gözde Tas","doi":"10.55730/1300-0632.3898","DOIUrl":"https://doi.org/10.55730/1300-0632.3898","url":null,"abstract":": Multiinput converters allow to create hybrid energy systems in electric vehicles with a reduced part count. In addition, interleaved structures help to build efficient converters with several possible benefits, such as low current ripple and high power density. This paper proposes utilizing a multiphase multiinput converter (MPMIC), which concentrates the aforementioned advantages and presents a comprehensive comparison with its single-phase version, called single-phase multiinput converter (SPMIC). After analysing their steady-state characteristics, SPMIC and MPMIC are designed considering same conditions. Then, two laboratory prototypes rated at 2.5kW output power are implemented to validate the analysis. Finally, these prototypes are compared in terms of voltage-gain, input current ripple, efficiency, complexity, cost, and power density. The results show that MPMIC surpasses SPMIC in efficiency and in input current ripple at the expense of increments in the complexity and cost. Besides, MPMIC results in comparatively high voltage gain in low power region thanks to the discontinuous current mode operation. On the other hand, it is explored that SPMIC can reach higher power density in the event of effective cooling.","PeriodicalId":49410,"journal":{"name":"Turkish Journal of Electrical Engineering and Computer Sciences","volume":"30 1","pages":"1677-1694"},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70798465","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}
The COVID-19 pandemic has had a widespread impact on health and economy across the globe. It is leading to a huge number of deaths per day. Few researchers have been attracted to analyzing the mortality rate of COVID-19 from various perspectives. During the research, it has become evident that these fatalities are not only caused by COVID-19, but they are also affected by some other factors. The authors of this paper aim to encompass three important types of factors viz. risk factors, clinical factors, and miscellaneous factors that influence the mortality of COVID-19. This manuscript presents a rule-based model under the Mamdani-based fuzzy expert system (FES) to analyze the mortality rate of the highly contagious COVID-19. The proposed model creates three FESs and thereafter generates the final FES which aggregates these three FESs. The FES for risk value considers 5 aggregate factors viz. immunity, temperature, ventilation, population density, and pollution. The second FES is to model the clinical facilities based on ICU count, quarantine centers, and tests performed. The third FES is created to model the miscellaneous factors. Finally, the concluding FES combines three base FESs to evaluate the mortality value. The results obtained by the suggested model are promising and hence advocate the efficacy of the proposed model. [ABSTRACT FROM AUTHOR] Copyright of Turkish Journal of Electrical Engineering & Computer Sciences is the property of Scientific and Technical Research Council of Turkey and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
{"title":"A fuzzy expert system for predicting the mortality of COVID'19","authors":"M. Mangla, N. Sharma, Poonam Mittal","doi":"10.3906/elk-2008-27","DOIUrl":"https://doi.org/10.3906/elk-2008-27","url":null,"abstract":"The COVID-19 pandemic has had a widespread impact on health and economy across the globe. It is leading to a huge number of deaths per day. Few researchers have been attracted to analyzing the mortality rate of COVID-19 from various perspectives. During the research, it has become evident that these fatalities are not only caused by COVID-19, but they are also affected by some other factors. The authors of this paper aim to encompass three important types of factors viz. risk factors, clinical factors, and miscellaneous factors that influence the mortality of COVID-19. This manuscript presents a rule-based model under the Mamdani-based fuzzy expert system (FES) to analyze the mortality rate of the highly contagious COVID-19. The proposed model creates three FESs and thereafter generates the final FES which aggregates these three FESs. The FES for risk value considers 5 aggregate factors viz. immunity, temperature, ventilation, population density, and pollution. The second FES is to model the clinical facilities based on ICU count, quarantine centers, and tests performed. The third FES is created to model the miscellaneous factors. Finally, the concluding FES combines three base FESs to evaluate the mortality value. The results obtained by the suggested model are promising and hence advocate the efficacy of the proposed model. [ABSTRACT FROM AUTHOR] Copyright of Turkish Journal of Electrical Engineering & Computer Sciences is the property of Scientific and Technical Research Council of Turkey and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)","PeriodicalId":49410,"journal":{"name":"Turkish Journal of Electrical Engineering and Computer Sciences","volume":"42 1","pages":"1628-1642"},"PeriodicalIF":1.1,"publicationDate":"2021-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70199783","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}
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":"251 1","pages":"1797-1820"},"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}
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