Pub Date : 2021-01-18DOI: 10.21203/RS.3.RS-139847/V1
Zhisheng Yang, Jinyong Cheng
In recommendation algorithms, data sparsity and cold start problems are always inevitable. In order to solve such problems, researchers apply auxiliary information to recommendation algorithms to mine and obtain more potential information through users' historical records and then improve recommendation performance. This paper proposes a model ST_RippleNet, which combines knowledge graph with deep learning. In this model, users' potential interests are mined in the knowledge graph to stimulate the propagation of users' preferences on the set of knowledge entities. In the propagation of preferences, we adopt a triple-based multi-layer attention mechanism, and the distribution of users' preferences for candidate items formed by users' historical click information is used to predict the final click probability. In ST_RippleNet model, music data set is added to the original movie and book data set, and the improved loss function is applied to the model, which is optimized by RMSProp optimizer. Finally, tanh function is added to predict click probability to improve recommendation performance. Compared with the current mainstream recommendation methods, ST_RippleNet recommendation algorithm has very good performance in AUC and ACC, and has substantial improvement in movie, book and music recommendation.
{"title":"Recommendation Algorithm Based on Knowledge Graph to Propagate User Preference","authors":"Zhisheng Yang, Jinyong Cheng","doi":"10.21203/RS.3.RS-139847/V1","DOIUrl":"https://doi.org/10.21203/RS.3.RS-139847/V1","url":null,"abstract":"\u0000 In recommendation algorithms, data sparsity and cold start problems are always inevitable. In order to solve such problems, researchers apply auxiliary information to recommendation algorithms to mine and obtain more potential information through users' historical records and then improve recommendation performance. This paper proposes a model ST_RippleNet, which combines knowledge graph with deep learning. In this model, users' potential interests are mined in the knowledge graph to stimulate the propagation of users' preferences on the set of knowledge entities. In the propagation of preferences, we adopt a triple-based multi-layer attention mechanism, and the distribution of users' preferences for candidate items formed by users' historical click information is used to predict the final click probability. In ST_RippleNet model, music data set is added to the original movie and book data set, and the improved loss function is applied to the model, which is optimized by RMSProp optimizer. Finally, tanh function is added to predict click probability to improve recommendation performance. Compared with the current mainstream recommendation methods, ST_RippleNet recommendation algorithm has very good performance in AUC and ACC, and has substantial improvement in movie, book and music recommendation.","PeriodicalId":13602,"journal":{"name":"Int. J. Comput. Intell. Syst.","volume":"55 1","pages":"1564-1576"},"PeriodicalIF":0.0,"publicationDate":"2021-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86874008","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 : 2021-01-01DOI: 10.2991/ijcis.d.210112.001
Lei Wang, Lili Rong, Fei Teng, Peide Liu
The quality of teaching can be improved by teaching performance evaluation frommultiple experts, which is a multiple attribute group decision-making (MAGDM) problem. In this paper, a group decision-making method under proportional hesitant fuzzy linguistic environment is proposed to evaluate teaching performance. Firstly, proportional hesitant fuzzy linguistic term set (PHFLTS) is applied to express the decisionmakers’ (DMs) preferences for teaching performance index. Secondly, thePHFLPrCA operator is developed and its properties are discussed. Then based on the PHFLPrCA operator, aMAGDMmethod is formulated. Thirdly, the method is applied in teaching performance evaluation of Chinese-foreign cooperative education project. Finally, this method is proved more scientific, objective and accurate by compared with other two methods.
{"title":"Teaching Performance Evaluation Based on the Proportional Hesitant Fuzzy Linguistic Prioritized Choquet Aggregation Operator","authors":"Lei Wang, Lili Rong, Fei Teng, Peide Liu","doi":"10.2991/ijcis.d.210112.001","DOIUrl":"https://doi.org/10.2991/ijcis.d.210112.001","url":null,"abstract":"The quality of teaching can be improved by teaching performance evaluation frommultiple experts, which is a multiple attribute group decision-making (MAGDM) problem. In this paper, a group decision-making method under proportional hesitant fuzzy linguistic environment is proposed to evaluate teaching performance. Firstly, proportional hesitant fuzzy linguistic term set (PHFLTS) is applied to express the decisionmakers’ (DMs) preferences for teaching performance index. Secondly, thePHFLPrCA operator is developed and its properties are discussed. Then based on the PHFLPrCA operator, aMAGDMmethod is formulated. Thirdly, the method is applied in teaching performance evaluation of Chinese-foreign cooperative education project. Finally, this method is proved more scientific, objective and accurate by compared with other two methods.","PeriodicalId":13602,"journal":{"name":"Int. J. Comput. Intell. Syst.","volume":"17 1","pages":"635-650"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75721480","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 : 2021-01-01DOI: 10.2991/ijcis.d.201215.005
W. K. Mashwani, Syed Ali Raza Shah, S. Belhaouari, A. Hamdi
In the last two decades, evolutionary computing has become the mainstream to attract the attention of the experts in both academia and industrial applications due to the advent of the fast computerwithmulti-coreGHzprocessors have had a capacity of processing over 100 billion instructions per second. Today’s different evolutionary algorithms are found in the existing literature of evolutionary computing that is mainly belong to swarm intelligence and nature-inspired algorithms. In general, it is quite realistic that not always each developed evolutionary algorithms can perform all kinds of optimization and search problems. Recently, ensemble-based techniques are considered to be a good alternative for dealing with various benchmark functions and real-world problems. In this paper, an ameliorated ensemble strategy-based evolutionary algorithm is developed for solving largescale global optimization problems. The suggested algorithm employs the particle swam optimization, teaching learning-based optimization, differential evolution, and bat algorithm with a self-adaptive procedure to evolve their randomly generated set of solutions. The performance of the proposed ensemble strategy-based evolutionary algorithm evaluated over thirty benchmark functions that are recently designed for the special session of the 2017 IEEE congress of evolutionary computation (CEC’17). The experimental results provided by the suggested algorithm over most CEC’17 benchmark functions are much promising in terms of proximity and diversity.
{"title":"Ameliorated Ensemble Strategy-Based Evolutionary Algorithm with Dynamic Resources Allocations","authors":"W. K. Mashwani, Syed Ali Raza Shah, S. Belhaouari, A. Hamdi","doi":"10.2991/ijcis.d.201215.005","DOIUrl":"https://doi.org/10.2991/ijcis.d.201215.005","url":null,"abstract":"In the last two decades, evolutionary computing has become the mainstream to attract the attention of the experts in both academia and industrial applications due to the advent of the fast computerwithmulti-coreGHzprocessors have had a capacity of processing over 100 billion instructions per second. Today’s different evolutionary algorithms are found in the existing literature of evolutionary computing that is mainly belong to swarm intelligence and nature-inspired algorithms. In general, it is quite realistic that not always each developed evolutionary algorithms can perform all kinds of optimization and search problems. Recently, ensemble-based techniques are considered to be a good alternative for dealing with various benchmark functions and real-world problems. In this paper, an ameliorated ensemble strategy-based evolutionary algorithm is developed for solving largescale global optimization problems. The suggested algorithm employs the particle swam optimization, teaching learning-based optimization, differential evolution, and bat algorithm with a self-adaptive procedure to evolve their randomly generated set of solutions. The performance of the proposed ensemble strategy-based evolutionary algorithm evaluated over thirty benchmark functions that are recently designed for the special session of the 2017 IEEE congress of evolutionary computation (CEC’17). The experimental results provided by the suggested algorithm over most CEC’17 benchmark functions are much promising in terms of proximity and diversity.","PeriodicalId":13602,"journal":{"name":"Int. J. Comput. Intell. Syst.","volume":"11 1","pages":"412-437"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74799576","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 : 2021-01-01DOI: 10.2991/ijcis.d.210203.001
Tomasz Zurek, Michail Mokkas
The issue of decision-making of autonomous agents constitutes the current work topic for many researchers. In this paper we propose to extend the existing model of value-based teleological reasoning by a new, numerical manner of representation of the level of value promotion. The authors of the paper present and discuss proofs of compatibility of both previous and current models, a formalmechanism of conversion of the parameters of the autonomous device into the levels of promotion of values, the mechanism of integration with machine learning approaches, and a comprehensive argumentation-based reasoning mechanism allowing for making decisions.
{"title":"Value-Based Reasoning in Autonomous Agents","authors":"Tomasz Zurek, Michail Mokkas","doi":"10.2991/ijcis.d.210203.001","DOIUrl":"https://doi.org/10.2991/ijcis.d.210203.001","url":null,"abstract":"The issue of decision-making of autonomous agents constitutes the current work topic for many researchers. In this paper we propose to extend the existing model of value-based teleological reasoning by a new, numerical manner of representation of the level of value promotion. The authors of the paper present and discuss proofs of compatibility of both previous and current models, a formalmechanism of conversion of the parameters of the autonomous device into the levels of promotion of values, the mechanism of integration with machine learning approaches, and a comprehensive argumentation-based reasoning mechanism allowing for making decisions.","PeriodicalId":13602,"journal":{"name":"Int. J. Comput. Intell. Syst.","volume":"14 1","pages":"896-921"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84973208","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 : 2021-01-01DOI: 10.2991/ijcis.d.210203.003
Juanyong Wu, A. Khalil, Nasruddin Hassan, F. Smarandache, A. Azzam, Hui Yang
School of Mathematics and Statistics, Guizhou University, Guiyang, 550025, China School of Mathematics and Statistics, Guizhou University of Finance and Economics, Guiyang, 550025, China Department of Mathematics, Faculty of Science, Al-Azhar University, Assiut, 71524, Egypt School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia Department of Mathematics, University of New Mexico, Gallup, NM, 87301, USA Department of Mathematics, Faculty of Science and Humanities, Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia Department of Mathematics, Faculty of Science, New Valley University, Elkharga, 72511, Egypt
{"title":"Similarity Measures and Multi-person TOPSIS Method Using m-polar Single-Valued Neutrosophic Sets","authors":"Juanyong Wu, A. Khalil, Nasruddin Hassan, F. Smarandache, A. Azzam, Hui Yang","doi":"10.2991/ijcis.d.210203.003","DOIUrl":"https://doi.org/10.2991/ijcis.d.210203.003","url":null,"abstract":"School of Mathematics and Statistics, Guizhou University, Guiyang, 550025, China School of Mathematics and Statistics, Guizhou University of Finance and Economics, Guiyang, 550025, China Department of Mathematics, Faculty of Science, Al-Azhar University, Assiut, 71524, Egypt School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia Department of Mathematics, University of New Mexico, Gallup, NM, 87301, USA Department of Mathematics, Faculty of Science and Humanities, Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia Department of Mathematics, Faculty of Science, New Valley University, Elkharga, 72511, Egypt","PeriodicalId":13602,"journal":{"name":"Int. J. Comput. Intell. Syst.","volume":"133 1","pages":"869-885"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73624543","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}
During the outbreak of novel coronavirus pneumonia, the number of confirmed cases and deaths in Hubei province of China increased sharply, and the situation in Hubei was more severe than that in non-Hubei, so we do a research on psychological health status evaluation of the public in Hubei and non-Hubei areas. In this paper, we adopt textual analysis and contextual analysis using Simplified Chinese Microblog Word Count (SCMBWC), Five-Factors Model (FFM), Semantic Role Labeling (SRL) to interpret and analyze the public perception and psychological personality based on media news. Through the analysis, it was found that there were great differences in public perception to novel coronavirus pneumonia. In Hubei areas, the public perception was mainly reflected in the overall prevention and the treatment of patients, while in non-Hubei areas, the perception was mainly in the orderly promotion of enterprises to return to work. Through contextual analysis, the novel coronavirus pneumonia had a great psychological impact on the public in different regions. The media covered a large number of social process words and cognitive process words, public showed a personality that was inclined to be “open” and “neurotic” in different areas. Furthermore, we find out some reasons like all kinds of rumors, wildlife trade, all kinds of illegal and criminal acts disturbing social order cause this psychology personality through emotional entity mining based on semantic role labeling. This is conducive to the government’s better policies and management in line with local conditions.
{"title":"Psychological Health Status Evaluation of the Public in Different Areas Under the Outbreak of Novel Coronavirus Pneumonia","authors":"Xiaolan Wu, Chengzhi Zhang, Ningning Song, Weiwei Zhang, Yaya Bian","doi":"10.2991/ijcis.d.210225.001","DOIUrl":"https://doi.org/10.2991/ijcis.d.210225.001","url":null,"abstract":"During the outbreak of novel coronavirus pneumonia, the number of confirmed cases and deaths in Hubei province of China increased sharply, and the situation in Hubei was more severe than that in non-Hubei, so we do a research on psychological health status evaluation of the public in Hubei and non-Hubei areas. In this paper, we adopt textual analysis and contextual analysis using Simplified Chinese Microblog Word Count (SCMBWC), Five-Factors Model (FFM), Semantic Role Labeling (SRL) to interpret and analyze the public perception and psychological personality based on media news. Through the analysis, it was found that there were great differences in public perception to novel coronavirus pneumonia. In Hubei areas, the public perception was mainly reflected in the overall prevention and the treatment of patients, while in non-Hubei areas, the perception was mainly in the orderly promotion of enterprises to return to work. Through contextual analysis, the novel coronavirus pneumonia had a great psychological impact on the public in different regions. The media covered a large number of social process words and cognitive process words, public showed a personality that was inclined to be “open” and “neurotic” in different areas. Furthermore, we find out some reasons like all kinds of rumors, wildlife trade, all kinds of illegal and criminal acts disturbing social order cause this psychology personality through emotional entity mining based on semantic role labeling. This is conducive to the government’s better policies and management in line with local conditions.","PeriodicalId":13602,"journal":{"name":"Int. J. Comput. Intell. Syst.","volume":"5 1","pages":"978-990"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82166384","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 : 2021-01-01DOI: 10.2991/ijcis.d.210420.002
G. Chen, Ping-Shun Chen, Jr-Fong Dang, Sung-Lien Kang, Li-Jen Cheng
Department of Logistics Management, National Kaohsiung University of Science and Technology, Yanchao District, Kaohsiung City, 824, Taiwan, Republic of China Department of Industrial and Systems Engineering, Chung Yuan Christian University, Chung Li District, Taoyuan City, 320314, Taiwan, Republic of China Department of Industrial Engineering and Systems Management, Feng Chia University, Xitun District, Taichung City, 40724, Taiwan, Republic of China Division of Library and Information Affairs, Chihlee University of Technology, , Banqiao District, New Taipei City, 220305, Taiwan, Republic of China
{"title":"Applying Meta-Heuristics Algorithm to Solve Assembly Line Balancing Problem with Labor Skill Level in Garment Industry","authors":"G. Chen, Ping-Shun Chen, Jr-Fong Dang, Sung-Lien Kang, Li-Jen Cheng","doi":"10.2991/ijcis.d.210420.002","DOIUrl":"https://doi.org/10.2991/ijcis.d.210420.002","url":null,"abstract":"Department of Logistics Management, National Kaohsiung University of Science and Technology, Yanchao District, Kaohsiung City, 824, Taiwan, Republic of China Department of Industrial and Systems Engineering, Chung Yuan Christian University, Chung Li District, Taoyuan City, 320314, Taiwan, Republic of China Department of Industrial Engineering and Systems Management, Feng Chia University, Xitun District, Taichung City, 40724, Taiwan, Republic of China Division of Library and Information Affairs, Chihlee University of Technology, , Banqiao District, New Taipei City, 220305, Taiwan, Republic of China","PeriodicalId":13602,"journal":{"name":"Int. J. Comput. Intell. Syst.","volume":"125 1","pages":"1438-1450"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82890845","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 : 2021-01-01DOI: 10.2991/ijcis.d.210216.001
Jing Feng, Xiaobin Xu, Pan Liu, F. Ma, Chengrong Ma, Z. Tao
School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China Nanjing Smart Waterway Corp. Ltd, Nanjing, 210000, China College of Civil Engineering, Shaoxing University, Shaoxing, 312000, China State Key Laboratory for Geomechanics and Deep Underground Engineering, Beijing, 100083, China School of Mechanics and Civil Engineering, China University of Mining and Technology, Beijing, 100083, China
{"title":"Slope Sliding Force Prediction via Belief Rule-Based Inferential Methodology","authors":"Jing Feng, Xiaobin Xu, Pan Liu, F. Ma, Chengrong Ma, Z. Tao","doi":"10.2991/ijcis.d.210216.001","DOIUrl":"https://doi.org/10.2991/ijcis.d.210216.001","url":null,"abstract":"School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China Nanjing Smart Waterway Corp. Ltd, Nanjing, 210000, China College of Civil Engineering, Shaoxing University, Shaoxing, 312000, China State Key Laboratory for Geomechanics and Deep Underground Engineering, Beijing, 100083, China School of Mechanics and Civil Engineering, China University of Mining and Technology, Beijing, 100083, China","PeriodicalId":13602,"journal":{"name":"Int. J. Comput. Intell. Syst.","volume":"43 5 1","pages":"965-977"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91531286","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 : 2021-01-01DOI: 10.2991/ijcis.d.210301.004
Wijdan Jaber AL-kubaisy, Mohammed Yousif, Belal Al-Khateeb, M. Mahmood, Dac-Nhuong Le
The presented study suggests a new nature–inspired metaheuristic optimization algorithm referred to as Red Colobuses Monkey (RCM) that can be used for optimization problems; this algorithm mimics the behavior related to red monkeys in nature. In preparation for proving the suggested algorithm’s advantages, a set of standard unconstrained and constrained test functions is employed, sixty–four of identified test functions utilized in optimization were applied as benchmarks for checking the RCM performance. The solutions have also been upgrading their positions based on the optimal solution, which was reached thus far. Also, RCM can replace the worst red monkey by the best child found so far to give an extra enhancement to the solutions. Also, comparative performance checks with Biogeography–Based Optimizer (BBO), Artificial–Bee–Colony (ABC), Particle Swarm Optimization (PSO), and Gravitational Search Algorithm (GSA) were done. The acquired results showed that RCM is competitive in comparison to the chosen metaheuristic algorithms.
{"title":"The Red Colobuses Monkey: A New Nature-Inspired Metaheuristic Optimization Algorithm","authors":"Wijdan Jaber AL-kubaisy, Mohammed Yousif, Belal Al-Khateeb, M. Mahmood, Dac-Nhuong Le","doi":"10.2991/ijcis.d.210301.004","DOIUrl":"https://doi.org/10.2991/ijcis.d.210301.004","url":null,"abstract":"The presented study suggests a new nature–inspired metaheuristic optimization algorithm referred to as Red Colobuses Monkey (RCM) that can be used for optimization problems; this algorithm mimics the behavior related to red monkeys in nature. In preparation for proving the suggested algorithm’s advantages, a set of standard unconstrained and constrained test functions is employed, sixty–four of identified test functions utilized in optimization were applied as benchmarks for checking the RCM performance. The solutions have also been upgrading their positions based on the optimal solution, which was reached thus far. Also, RCM can replace the worst red monkey by the best child found so far to give an extra enhancement to the solutions. Also, comparative performance checks with Biogeography–Based Optimizer (BBO), Artificial–Bee–Colony (ABC), Particle Swarm Optimization (PSO), and Gravitational Search Algorithm (GSA) were done. The acquired results showed that RCM is competitive in comparison to the chosen metaheuristic algorithms.","PeriodicalId":13602,"journal":{"name":"Int. J. Comput. Intell. Syst.","volume":"5 1","pages":"1108-1118"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90054120","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 : 2021-01-01DOI: 10.2991/ijcis.d.210602.001
Junqi Luo, Liucun Zhu, Hongbing Zhu, W. Chien, Jiahai Liang
The daily 10.7-cm Solar Radio Flux (F10.7) data is a time series with highly volatile. The accurate prediction of F10.7 has a great significance in the fields of aerospace and meteorology. At present, the prediction of F10.7 is mainly carried out by linear models, nonlinearmodels, or a combination of the two. The combinationmodel is a promising strategy, which attempts to benefit from the strength of both. This paper proposes an Empirical Mode Decomposition (EMD) -Long Short-Term Memory Neural Network (LSTMNN) hybrid method, which is assembled by a particular frame, namely EMD–LSTM. The original dataset of F10.7 is firstly processed by EMD and decomposed into a series of components with different frequency characteristics. Then the output values of EMD are respectively fed to a developed LSTM model to acquire the predicted values of each component. The final forecasting values are obtained after a procedure of information reconstruction. The evaluation is undertaken by some statistical evaluation indexes in the cases of 1-27 days ahead and different years. Experimental results show that the proposed method gives superior accuracy as compared with benchmarkmodels, including other isolated algorithms and hybrid methods.
{"title":"A New Approach for the 10.7-cm Solar Radio Flux Forecasting: Based on Empirical Mode Decomposition and LSTM","authors":"Junqi Luo, Liucun Zhu, Hongbing Zhu, W. Chien, Jiahai Liang","doi":"10.2991/ijcis.d.210602.001","DOIUrl":"https://doi.org/10.2991/ijcis.d.210602.001","url":null,"abstract":"The daily 10.7-cm Solar Radio Flux (F10.7) data is a time series with highly volatile. The accurate prediction of F10.7 has a great significance in the fields of aerospace and meteorology. At present, the prediction of F10.7 is mainly carried out by linear models, nonlinearmodels, or a combination of the two. The combinationmodel is a promising strategy, which attempts to benefit from the strength of both. This paper proposes an Empirical Mode Decomposition (EMD) -Long Short-Term Memory Neural Network (LSTMNN) hybrid method, which is assembled by a particular frame, namely EMD–LSTM. The original dataset of F10.7 is firstly processed by EMD and decomposed into a series of components with different frequency characteristics. Then the output values of EMD are respectively fed to a developed LSTM model to acquire the predicted values of each component. The final forecasting values are obtained after a procedure of information reconstruction. The evaluation is undertaken by some statistical evaluation indexes in the cases of 1-27 days ahead and different years. Experimental results show that the proposed method gives superior accuracy as compared with benchmarkmodels, including other isolated algorithms and hybrid methods.","PeriodicalId":13602,"journal":{"name":"Int. J. Comput. Intell. Syst.","volume":"146 1","pages":"1742-1752"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80540982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：