PurposeHigh-resolution remote sensing images possess a wealth of semantic information. However, these images often contain objects of different sizes and distributions, which make the semantic segmentation task challenging. In this paper, a bidirectional feature fusion network (BFFNet) is designed to address this challenge, which aims at increasing the accurate recognition of surface objects in order to effectively classify special features.Design/methodology/approachThere are two main crucial elements in BFFNet. Firstly, the mean-weighted module (MWM) is used to obtain the key features in the main network. Secondly, the proposed polarization enhanced branch network performs feature extraction simultaneously with the main network to obtain different feature information. The authors then fuse these two features in both directions while applying a cross-entropy loss function to monitor the network training process. Finally, BFFNet is validated on two publicly available datasets, Potsdam and Vaihingen.FindingsIn this paper, a quantitative analysis method is used to illustrate that the proposed network achieves superior performance of 2–6%, respectively, compared to other mainstream segmentation networks from experimental results on two datasets. Complete ablation experiments are also conducted to demonstrate the effectiveness of the elements in the network. In summary, BFFNet has proven to be effective in achieving accurate identification of small objects and in reducing the effect of shadows on the segmentation process.Originality/valueThe originality of the paper is the proposal of a BFFNet based on multi-scale and multi-attention strategies to improve the ability to accurately segment high-resolution and complex remote sensing images, especially for small objects and shadow-obscured objects.
{"title":"BFFNet: a bidirectional feature fusion network for semantic segmentation of remote sensing objects","authors":"Yandong Hou, Zhengbo Wu, Xinghua Ren, Kaiwen Liu, Zhengquan Chen","doi":"10.1108/ijicc-03-2023-0053","DOIUrl":"https://doi.org/10.1108/ijicc-03-2023-0053","url":null,"abstract":"PurposeHigh-resolution remote sensing images possess a wealth of semantic information. However, these images often contain objects of different sizes and distributions, which make the semantic segmentation task challenging. In this paper, a bidirectional feature fusion network (BFFNet) is designed to address this challenge, which aims at increasing the accurate recognition of surface objects in order to effectively classify special features.Design/methodology/approachThere are two main crucial elements in BFFNet. Firstly, the mean-weighted module (MWM) is used to obtain the key features in the main network. Secondly, the proposed polarization enhanced branch network performs feature extraction simultaneously with the main network to obtain different feature information. The authors then fuse these two features in both directions while applying a cross-entropy loss function to monitor the network training process. Finally, BFFNet is validated on two publicly available datasets, Potsdam and Vaihingen.FindingsIn this paper, a quantitative analysis method is used to illustrate that the proposed network achieves superior performance of 2–6%, respectively, compared to other mainstream segmentation networks from experimental results on two datasets. Complete ablation experiments are also conducted to demonstrate the effectiveness of the elements in the network. In summary, BFFNet has proven to be effective in achieving accurate identification of small objects and in reducing the effect of shadows on the segmentation process.Originality/valueThe originality of the paper is the proposal of a BFFNet based on multi-scale and multi-attention strategies to improve the ability to accurately segment high-resolution and complex remote sensing images, especially for small objects and shadow-obscured objects.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46146572","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 : 2023-07-19DOI: 10.1108/ijicc-02-2023-0030
Dilek Sabancı, Serhat Kılıçarslan, Kemal Adem
PurposeBorsa Istanbul 100 Index, known as BIST100, is the main indicator to measure the performance of the 100 highest stocks publicly traded in Borsa Istanbul concerning market and trading volume. BIST 100 index prediction is a popular research domain for its complex data structure caused by stock price, commodity, interest rate and exchange rate effects. The study proposed hybrid models using both Genetic, Particle Swarm Optimization, Harmony Search and Greedy algorithms from metaheuristic algorithms approach for dimension reduction, and MARS for prediction.Design/methodology/approachThis paper aims to model in the simplest way through metaheuristic algorithms hybridized with the MARS model the effects of stock, commodity, interest and exchange rate variables on BIST 100 during the Covid-19 pandemic period (in the process of closing) between January 2020 and June 2021.FindingsThe most suitable hybrid model was chosen as PSO & MARS by calculating the RMSE, MSE, GCV, MAE, MAD, MAPE and R2 measurements of training, test and overall dataset to check every model's efficiency. Empirical results demonstrated that the proposed PSO & MARS hybrid modeling procedure gave results both as good as the MARS model and a simpler and non-complex model structure.Originality/valueUsing metaheuristic algorithms as a supporting tool for variable selection can help to identify important independent variables and contribute to the establishment of more non-complex models.ing, test and overall dataset to check every model's efficiency.
{"title":"An application on forecasting for stock market prices: hybrid of some metaheuristic algorithms with multivariate adaptive regression splines","authors":"Dilek Sabancı, Serhat Kılıçarslan, Kemal Adem","doi":"10.1108/ijicc-02-2023-0030","DOIUrl":"https://doi.org/10.1108/ijicc-02-2023-0030","url":null,"abstract":"PurposeBorsa Istanbul 100 Index, known as BIST100, is the main indicator to measure the performance of the 100 highest stocks publicly traded in Borsa Istanbul concerning market and trading volume. BIST 100 index prediction is a popular research domain for its complex data structure caused by stock price, commodity, interest rate and exchange rate effects. The study proposed hybrid models using both Genetic, Particle Swarm Optimization, Harmony Search and Greedy algorithms from metaheuristic algorithms approach for dimension reduction, and MARS for prediction.Design/methodology/approachThis paper aims to model in the simplest way through metaheuristic algorithms hybridized with the MARS model the effects of stock, commodity, interest and exchange rate variables on BIST 100 during the Covid-19 pandemic period (in the process of closing) between January 2020 and June 2021.FindingsThe most suitable hybrid model was chosen as PSO & MARS by calculating the RMSE, MSE, GCV, MAE, MAD, MAPE and R2 measurements of training, test and overall dataset to check every model's efficiency. Empirical results demonstrated that the proposed PSO & MARS hybrid modeling procedure gave results both as good as the MARS model and a simpler and non-complex model structure.Originality/valueUsing metaheuristic algorithms as a supporting tool for variable selection can help to identify important independent variables and contribute to the establishment of more non-complex models.ing, test and overall dataset to check every model's efficiency.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44643157","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 : 2023-07-17DOI: 10.1108/ijicc-03-2023-0039
Youping Lin
PurposeThe interval multi-objective optimization problems (IMOPs) are universal and vital uncertain optimization problems. In this study, an interval multi-objective grey wolf optimization algorithm (GWO) based on fuzzy system is proposed to solve IMOPs effectively.Design/methodology/approachFirst, the classical genetic operators are embedded into the interval multi-objective GWO as local search strategies, which effectively balanced the global search ability and local development ability. Second, by constructing a fuzzy system, an effective local search activation mechanism is proposed to save computing resources as much as possible while ensuring the performance of the algorithm. The fuzzy system takes hypervolume, imprecision and number of iterations as inputs and outputs the activation index, local population size and maximum number of iterations. Then, the fuzzy inference rules are defined. It uses the activation index to determine whether to activate the local search process and sets the population size and the maximum number of iterations in the process.FindingsThe experimental results show that the proposed algorithm achieves optimal hypervolume results on 9 of the 10 benchmark test problems. The imprecision achieved on 8 test problems is significantly better than other algorithms. This means that the proposed algorithm has better performance than the commonly used interval multi-objective evolutionary algorithms. Moreover, through experiments show that the local search activation mechanism based on fuzzy system proposed in this study can effectively ensure that the local search is activated reasonably in the whole algorithm process, and reasonably allocate computing resources by adaptively setting the population size and maximum number of iterations in the local search process.Originality/valueThis study proposes an Interval multi-objective GWO, which could effectively balance the global search ability and local development ability. Then an effective local search activation mechanism is developed by using fuzzy inference system. It closely combines global optimization with local search, which improves the performance of the algorithm and saves computing resources.
{"title":"Interval multi-objective grey wolf optimization algorithm based on fuzzy system","authors":"Youping Lin","doi":"10.1108/ijicc-03-2023-0039","DOIUrl":"https://doi.org/10.1108/ijicc-03-2023-0039","url":null,"abstract":"PurposeThe interval multi-objective optimization problems (IMOPs) are universal and vital uncertain optimization problems. In this study, an interval multi-objective grey wolf optimization algorithm (GWO) based on fuzzy system is proposed to solve IMOPs effectively.Design/methodology/approachFirst, the classical genetic operators are embedded into the interval multi-objective GWO as local search strategies, which effectively balanced the global search ability and local development ability. Second, by constructing a fuzzy system, an effective local search activation mechanism is proposed to save computing resources as much as possible while ensuring the performance of the algorithm. The fuzzy system takes hypervolume, imprecision and number of iterations as inputs and outputs the activation index, local population size and maximum number of iterations. Then, the fuzzy inference rules are defined. It uses the activation index to determine whether to activate the local search process and sets the population size and the maximum number of iterations in the process.FindingsThe experimental results show that the proposed algorithm achieves optimal hypervolume results on 9 of the 10 benchmark test problems. The imprecision achieved on 8 test problems is significantly better than other algorithms. This means that the proposed algorithm has better performance than the commonly used interval multi-objective evolutionary algorithms. Moreover, through experiments show that the local search activation mechanism based on fuzzy system proposed in this study can effectively ensure that the local search is activated reasonably in the whole algorithm process, and reasonably allocate computing resources by adaptively setting the population size and maximum number of iterations in the local search process.Originality/valueThis study proposes an Interval multi-objective GWO, which could effectively balance the global search ability and local development ability. Then an effective local search activation mechanism is developed by using fuzzy inference system. It closely combines global optimization with local search, which improves the performance of the algorithm and saves computing resources.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43564839","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 : 2023-07-10DOI: 10.34133/icomputing.0046
Hiroaki Shinkawa, N. Chauvet, André Röhm, Takatomo Mihana, R. Horisaki, G. Bachelier, M. Naruse
{"title":"Bandit approach to conflict-free parallel Q-learning in view of photonic implementation","authors":"Hiroaki Shinkawa, N. Chauvet, André Röhm, Takatomo Mihana, R. Horisaki, G. Bachelier, M. Naruse","doi":"10.34133/icomputing.0046","DOIUrl":"https://doi.org/10.34133/icomputing.0046","url":null,"abstract":"","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84454688","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 : 2023-07-04DOI: 10.34133/icomputing.0044
Aryo Jamshidpey, M. Dorigo, Mary Katherine Heinrich
{"title":"Reducing Uncertainty in Collective Perception using Self-organized Hierarchy","authors":"Aryo Jamshidpey, M. Dorigo, Mary Katherine Heinrich","doi":"10.34133/icomputing.0044","DOIUrl":"https://doi.org/10.34133/icomputing.0044","url":null,"abstract":"","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75191640","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 : 2023-06-29DOI: 10.1108/ijicc-02-2023-0033
Jialiang Xie, Wen Wang, Yanling Chen, Feng Li, Xiaohui Liu
PurposeThe purpose of this paper is to develop a novel interval Multi-Objective Optimization by a Ratio Analysis plus the Full Multiplicative Form(MULTIMOORA) with combination weights to evaluate the employment quality of college graduates, where the criteria are expressed by interval numbers and the weights of criteria are completely unknown.Design/methodology/approachFirstly, considering the subjective uncertainty of the weights of the criteria, the interval best worst method (I-BWM) was present to determine the subjective weights of the criteria. Secondly, by the improved interval number distance measure, an improved interval deviation maximization method (I-MDM) was introduced to detemine the objective weights. In the following, based on the I-BWM and the improved I-MDM, a combination weighting method that takes into account the subjective and objective weights is proposed. Finally, a multi-criteria decision-making method based on the interval MULTIMOORA with combination weights is present to evaluate the employment quality of college graduates, and then a comparative analysis with some of the existing distance measures of interval numberswas conducted to illustrate the flexibility.FindingsAccording to the data of the Report on Employment Quality of Chinese College Graduats released by Mycos Research Institute in 2016–2020 and 2021–2022, the proposed method was used to evaluate the employment quality of college graduates during the period before and after the COVID-19 epidemic. The results verify that the method is more reasonable because the subjective and objective weights of the criteria can be fully considered. Finally, the feasibility and practicability of the proposed method are further verified by varying parameters.Originality/valuePresent an evaluation method on the employment quality of college graduates based on the Interval MULTIMOORA with combination weights considering the subjective and objective weights. And the proposed method is proved that it can provide a more reasonable evaluation results. At the same time, it is verified that the feasibility and the practicability of the proposed method are affected by varying parameters in the paper.
{"title":"Evaluation of employment quality of college graduates based on interval MULTIMOORA with combination weights","authors":"Jialiang Xie, Wen Wang, Yanling Chen, Feng Li, Xiaohui Liu","doi":"10.1108/ijicc-02-2023-0033","DOIUrl":"https://doi.org/10.1108/ijicc-02-2023-0033","url":null,"abstract":"PurposeThe purpose of this paper is to develop a novel interval Multi-Objective Optimization by a Ratio Analysis plus the Full Multiplicative Form(MULTIMOORA) with combination weights to evaluate the employment quality of college graduates, where the criteria are expressed by interval numbers and the weights of criteria are completely unknown.Design/methodology/approachFirstly, considering the subjective uncertainty of the weights of the criteria, the interval best worst method (I-BWM) was present to determine the subjective weights of the criteria. Secondly, by the improved interval number distance measure, an improved interval deviation maximization method (I-MDM) was introduced to detemine the objective weights. In the following, based on the I-BWM and the improved I-MDM, a combination weighting method that takes into account the subjective and objective weights is proposed. Finally, a multi-criteria decision-making method based on the interval MULTIMOORA with combination weights is present to evaluate the employment quality of college graduates, and then a comparative analysis with some of the existing distance measures of interval numberswas conducted to illustrate the flexibility.FindingsAccording to the data of the Report on Employment Quality of Chinese College Graduats released by Mycos Research Institute in 2016–2020 and 2021–2022, the proposed method was used to evaluate the employment quality of college graduates during the period before and after the COVID-19 epidemic. The results verify that the method is more reasonable because the subjective and objective weights of the criteria can be fully considered. Finally, the feasibility and practicability of the proposed method are further verified by varying parameters.Originality/valuePresent an evaluation method on the employment quality of college graduates based on the Interval MULTIMOORA with combination weights considering the subjective and objective weights. And the proposed method is proved that it can provide a more reasonable evaluation results. At the same time, it is verified that the feasibility and the practicability of the proposed method are affected by varying parameters in the paper.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45090532","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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