Pub Date : 2024-01-03DOI: 10.1108/ijicc-09-2023-0251
A. S. Girsang, Bima Krisna Noveta
PurposeThe purpose of this study is to provide the location of natural disasters that are poured into maps by extracting Twitter data. The Twitter text is extracted by using named entity recognition (NER) with six classes hierarchy location in Indonesia. Moreover, the tweet then is classified into eight classes of natural disasters using the support vector machine (SVM). Overall, the system is able to classify tweet and mapping the position of the content tweet.Design/methodology/approachThis research builds a model to map the geolocation of tweet data using NER. This research uses six classes of NER which is based on region Indonesia. This data is then classified into eight classes of natural disasters using the SVM.FindingsExperiment results demonstrate that the proposed NER with six special classes based on the regional level in Indonesia is able to map the location of the disaster based on data Twitter. The results also show good performance in geocoding such as match rate, match score and match type. Moreover, with SVM, this study can also classify tweet into eight classes of types of natural disasters specifically for the Indonesian region, which originate from the tweets collected.Research limitations/implicationsThis study implements in Indonesia region.Originality/value(a)NER with six classes is used to create a location classification model with StanfordNER and ArcGIS tools. The use of six location classes is based on the Indonesia regional which has the large area. Hence, it has many levels in its regional location, such as province, district/city, sub-district, village, road and place names. (b) SVM is used to classify natural disasters. Classification of types of natural disasters is divided into eight: floods, earthquakes, landslides, tsunamis, hurricanes, forest fires, droughts and volcanic eruptions.
{"title":"Six classes named entity recognition for mapping location of Indonesia natural disasters from twitter data","authors":"A. S. Girsang, Bima Krisna Noveta","doi":"10.1108/ijicc-09-2023-0251","DOIUrl":"https://doi.org/10.1108/ijicc-09-2023-0251","url":null,"abstract":"PurposeThe purpose of this study is to provide the location of natural disasters that are poured into maps by extracting Twitter data. The Twitter text is extracted by using named entity recognition (NER) with six classes hierarchy location in Indonesia. Moreover, the tweet then is classified into eight classes of natural disasters using the support vector machine (SVM). Overall, the system is able to classify tweet and mapping the position of the content tweet.Design/methodology/approachThis research builds a model to map the geolocation of tweet data using NER. This research uses six classes of NER which is based on region Indonesia. This data is then classified into eight classes of natural disasters using the SVM.FindingsExperiment results demonstrate that the proposed NER with six special classes based on the regional level in Indonesia is able to map the location of the disaster based on data Twitter. The results also show good performance in geocoding such as match rate, match score and match type. Moreover, with SVM, this study can also classify tweet into eight classes of types of natural disasters specifically for the Indonesian region, which originate from the tweets collected.Research limitations/implicationsThis study implements in Indonesia region.Originality/value(a)NER with six classes is used to create a location classification model with StanfordNER and ArcGIS tools. The use of six location classes is based on the Indonesia regional which has the large area. Hence, it has many levels in its regional location, such as province, district/city, sub-district, village, road and place names. (b) SVM is used to classify natural disasters. Classification of types of natural disasters is divided into eight: floods, earthquakes, landslides, tsunamis, hurricanes, forest fires, droughts and volcanic eruptions.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":"122 11","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139387854","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}
Pub Date : 2023-12-21DOI: 10.1108/ijicc-07-2023-0174
Majid Rahi, A. Ebrahimnejad, H. Motameni
PurposeTaking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.Design/methodology/approachThe proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.FindingsThe proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.Research limitations/implicationsBy expanding the dimensions of the problem, the model verification space grows exponentially using automata.Originality/valueUnlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.
{"title":"Evaluation of predicted fault tolerance based on C5.0 decision tree algorithm in irrigation system of paddy fields","authors":"Majid Rahi, A. Ebrahimnejad, H. Motameni","doi":"10.1108/ijicc-07-2023-0174","DOIUrl":"https://doi.org/10.1108/ijicc-07-2023-0174","url":null,"abstract":"PurposeTaking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.Design/methodology/approachThe proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.FindingsThe proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.Research limitations/implicationsBy expanding the dimensions of the problem, the model verification space grows exponentially using automata.Originality/valueUnlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":"83 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138952735","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}
Pub Date : 2023-12-19DOI: 10.1108/ijicc-10-2023-0302
Jinchao Huang
PurposeSingle-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.Design/methodology/approachTo address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.FindingsEmpirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.Originality/valueThis paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.
{"title":"Manifold embedded global and local discriminative features selection for single-shot multi-categories clothing recognition and retrieval","authors":"Jinchao Huang","doi":"10.1108/ijicc-10-2023-0302","DOIUrl":"https://doi.org/10.1108/ijicc-10-2023-0302","url":null,"abstract":"PurposeSingle-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.Design/methodology/approachTo address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.FindingsEmpirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.Originality/valueThis paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":" 14","pages":""},"PeriodicalIF":4.3,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138960632","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}
Pub Date : 2023-11-20DOI: 10.1108/ijicc-07-2023-0189
Keqing Li, Xiaojia Wang, Changyong Liang, Wenxing Lu
PurposeThe elderly service industry is emerging in China. The Chinese government introduced a series of policies to guide elderly service enterprises to improve their service quality. This study explores novel differentiated subsidy strategies that not only promote the improvement of service quality in elderly service enterprises but also alleviate the financial burden on the government.Design/methodology/approachEvolutionary game and Hotelling models are employed to investigate this issue. First, a Hotelling model that considers consumer word-of-mouth preferences is established. Subsequently, an evolutionary game model between local governments and enterprises is constructed, and the evolutionary stable strategies of both parties are analyzed. Finally, simulation experiments are conducted.FindingsThe findings indicate that local government decisions have a significant influence on the behavior of elderly service enterprises. Increasing the proportion of local governments opting for subsidy strategies helps incentivize elderly service enterprises to improve their service quality. Furthermore, providing differentiated subsidies based on the preferences of the customer base of elderly service enterprises can encourage service quality improvement while reducing government expenditure. The findings offer valuable insights into the design of government subsidy policies.Originality/valueCompared with previous research, this study examines the role of consumer preferences in a differentiated subsidy policy. This enriches the authors’ understanding of the field by incorporating neglected aspects of consumer preferences in the context of the emerging elderly service industry.
{"title":"Exploring the differentiated elderly service subsidies considering consumer word-of-mouth preferences","authors":"Keqing Li, Xiaojia Wang, Changyong Liang, Wenxing Lu","doi":"10.1108/ijicc-07-2023-0189","DOIUrl":"https://doi.org/10.1108/ijicc-07-2023-0189","url":null,"abstract":"PurposeThe elderly service industry is emerging in China. The Chinese government introduced a series of policies to guide elderly service enterprises to improve their service quality. This study explores novel differentiated subsidy strategies that not only promote the improvement of service quality in elderly service enterprises but also alleviate the financial burden on the government.Design/methodology/approachEvolutionary game and Hotelling models are employed to investigate this issue. First, a Hotelling model that considers consumer word-of-mouth preferences is established. Subsequently, an evolutionary game model between local governments and enterprises is constructed, and the evolutionary stable strategies of both parties are analyzed. Finally, simulation experiments are conducted.FindingsThe findings indicate that local government decisions have a significant influence on the behavior of elderly service enterprises. Increasing the proportion of local governments opting for subsidy strategies helps incentivize elderly service enterprises to improve their service quality. Furthermore, providing differentiated subsidies based on the preferences of the customer base of elderly service enterprises can encourage service quality improvement while reducing government expenditure. The findings offer valuable insights into the design of government subsidy policies.Originality/valueCompared with previous research, this study examines the role of consumer preferences in a differentiated subsidy policy. This enriches the authors’ understanding of the field by incorporating neglected aspects of consumer preferences in the context of the emerging elderly service industry.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":"10 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139257504","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}
Pub Date : 2023-11-13DOI: 10.34133/icomputing.0063
Yaobo Liang
{"title":"TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs","authors":"Yaobo Liang","doi":"10.34133/icomputing.0063","DOIUrl":"https://doi.org/10.34133/icomputing.0063","url":null,"abstract":"","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":"66 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136282647","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}
Pub Date : 2023-11-03DOI: 10.1108/ijicc-07-2023-0167
Vimala Balakrishnan, Aainaa Nadia Mohammed Hashim, Voon Chung Lee, Voon Hee Lee, Ying Qiu Lee
Purpose This study aims to develop a machine learning model to detect structure fire fatalities using a dataset comprising 11,341 cases from 2011 to 2019. Design/methodology/approach Exploratory data analysis (EDA) was conducted prior to modelling, in which ten machine learning models were experimented with. Findings The main fatal structure fire risk factors were fires originating from bedrooms, living areas and the cooking/dining areas. The highest fatality rate (20.69%) was reported for fires ignited due to bedding (23.43%), despite a low fire incident rate (3.50%). Using 21 structure fire features, Random Forest (RF) yielded the best detection performance with 86% accuracy, followed by Decision Tree (DT) with bagging (accuracy = 84.7%). Research limitations/practical implications Limitations of the study are pertaining to data quality and grouping of categories in the data pre-processing stage, which could affect the performance of the models. Originality/value The study is the first of its kind to manipulate risk factors to detect fatal structure classification, particularly focussing on structure fire fatalities. Most of the previous studies examined the importance of fire risk factors and their relationship to the fire risk level.
{"title":"Fatal structure fire classification from building fire data using machine learning","authors":"Vimala Balakrishnan, Aainaa Nadia Mohammed Hashim, Voon Chung Lee, Voon Hee Lee, Ying Qiu Lee","doi":"10.1108/ijicc-07-2023-0167","DOIUrl":"https://doi.org/10.1108/ijicc-07-2023-0167","url":null,"abstract":"Purpose This study aims to develop a machine learning model to detect structure fire fatalities using a dataset comprising 11,341 cases from 2011 to 2019. Design/methodology/approach Exploratory data analysis (EDA) was conducted prior to modelling, in which ten machine learning models were experimented with. Findings The main fatal structure fire risk factors were fires originating from bedrooms, living areas and the cooking/dining areas. The highest fatality rate (20.69%) was reported for fires ignited due to bedding (23.43%), despite a low fire incident rate (3.50%). Using 21 structure fire features, Random Forest (RF) yielded the best detection performance with 86% accuracy, followed by Decision Tree (DT) with bagging (accuracy = 84.7%). Research limitations/practical implications Limitations of the study are pertaining to data quality and grouping of categories in the data pre-processing stage, which could affect the performance of the models. Originality/value The study is the first of its kind to manipulate risk factors to detect fatal structure classification, particularly focussing on structure fire fatalities. Most of the previous studies examined the importance of fire risk factors and their relationship to the fire risk level.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135776671","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}
Pub Date : 2023-11-03DOI: 10.34133/icomputing.0061
Shi-Ju Ran, Gan Su
It is a critical challenge to simultaneously gain high interpretability and efficiency with the current schemes of deep machine learning (ML). Tensor network (TN), which is a well-established mathematical tool originating from quantum mechanics, has shown its unique advantages on developing efficient ``white-box'' ML schemes. Here, we give a brief review on the inspiring progresses made in TN-based ML. On one hand, interpretability of TN ML is accommodated with the solid theoretical foundation based on quantum information and many-body physics. On the other hand, high efficiency can be rendered from the powerful TN representations and the advanced computational techniques developed in quantum many-body physics. With the fast development on quantum computers, TN is expected to conceive novel schemes runnable on quantum hardware, heading towards the ``quantum artificial intelligence'' in the forthcoming future.
{"title":"Tensor Networks for Interpretable and Efficient Quantum-Inspired Machine Learning","authors":"Shi-Ju Ran, Gan Su","doi":"10.34133/icomputing.0061","DOIUrl":"https://doi.org/10.34133/icomputing.0061","url":null,"abstract":"It is a critical challenge to simultaneously gain high interpretability and efficiency with the current schemes of deep machine learning (ML). Tensor network (TN), which is a well-established mathematical tool originating from quantum mechanics, has shown its unique advantages on developing efficient ``white-box'' ML schemes. Here, we give a brief review on the inspiring progresses made in TN-based ML. On one hand, interpretability of TN ML is accommodated with the solid theoretical foundation based on quantum information and many-body physics. On the other hand, high efficiency can be rendered from the powerful TN representations and the advanced computational techniques developed in quantum many-body physics. With the fast development on quantum computers, TN is expected to conceive novel schemes runnable on quantum hardware, heading towards the ``quantum artificial intelligence'' in the forthcoming future.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":"43 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135868088","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}
Pub Date : 2023-11-03DOI: 10.34133/icomputing.0057
xijun yuan, ZiQiao chen
Aerodynamics plays an important role in aviation industry and aircraft design. Detecting and minimizing the phenomenon of flow separation from scattered pressure data on airfoil is critical for ensuring stable and efficient aviation. However, since it is challenging to understand the mechanics of flow field separation, the aerodynamic parameters are emphasized for the identification and control of flow separation. It has been investigated extensively using traditional algorithms and machine learning methods such as the support vector machine (SVM) models. Recently, a growing interest in quantum computing and its applications among wide research communities sheds light upon the use of quantum techniques to solve aerodynamic problems. In this paper, we apply qSVM, a quantum SVM algorithm based on the quantum annealing model, to identify whether there is flow separation, with their performance in comparison to the widely-used classical SVM. We show that our approach outperforms the classical SVM with an 11.1% increase of the accuracy, from 0.818 to 0.909, for this binary classification task. We further develop multi-class qSVMs based on one-against-all algorithm. We apply it to classify multiple types of the attack angles to the wings, where the advantage over the classical multi-class counterpart is maintained with an accuracy increased from 0.67 to 0.79, by 17.9%. Our work demonstrates a useful quantum technique for classifying flow separation scenarios, and may promote rich investigations for quantum computing applications in fluid dynamics.
{"title":"Quantum Support Vector Machines for Aerodynamic Classification","authors":"xijun yuan, ZiQiao chen","doi":"10.34133/icomputing.0057","DOIUrl":"https://doi.org/10.34133/icomputing.0057","url":null,"abstract":"Aerodynamics plays an important role in aviation industry and aircraft design. Detecting and minimizing the phenomenon of flow separation from scattered pressure data on airfoil is critical for ensuring stable and efficient aviation. However, since it is challenging to understand the mechanics of flow field separation, the aerodynamic parameters are emphasized for the identification and control of flow separation. It has been investigated extensively using traditional algorithms and machine learning methods such as the support vector machine (SVM) models. Recently, a growing interest in quantum computing and its applications among wide research communities sheds light upon the use of quantum techniques to solve aerodynamic problems. In this paper, we apply qSVM, a quantum SVM algorithm based on the quantum annealing model, to identify whether there is flow separation, with their performance in comparison to the widely-used classical SVM. We show that our approach outperforms the classical SVM with an 11.1% increase of the accuracy, from 0.818 to 0.909, for this binary classification task. We further develop multi-class qSVMs based on one-against-all algorithm. We apply it to classify multiple types of the attack angles to the wings, where the advantage over the classical multi-class counterpart is maintained with an accuracy increased from 0.67 to 0.79, by 17.9%. Our work demonstrates a useful quantum technique for classifying flow separation scenarios, and may promote rich investigations for quantum computing applications in fluid dynamics.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":"43 18","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135821340","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}
Pub Date : 2023-11-03DOI: 10.34133/icomputing.0048
Christian Leonardo Camacho Villalón, Thomas Stützle, Marco Dorigo
{"title":"Designing New Metaheuristics: Manual versus Automatic Approaches","authors":"Christian Leonardo Camacho Villalón, Thomas Stützle, Marco Dorigo","doi":"10.34133/icomputing.0048","DOIUrl":"https://doi.org/10.34133/icomputing.0048","url":null,"abstract":"","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":"43 9-10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135868090","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}
Pub Date : 2023-11-03DOI: 10.34133/icomputing.0058
Tianjiao Wan, Kele Xu, Ting Yu, Xu Wang, Dawei Feng, Bo Ding, Huaiming Wang
{"title":"A Survey of Deep Active Learning for Foundation Models","authors":"Tianjiao Wan, Kele Xu, Ting Yu, Xu Wang, Dawei Feng, Bo Ding, Huaiming Wang","doi":"10.34133/icomputing.0058","DOIUrl":"https://doi.org/10.34133/icomputing.0058","url":null,"abstract":"","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":"43 22","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135821336","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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