Pub Date : 2023-01-01DOI: 10.1504/ijbic.2023.10057048
Hang Jia, Ning Ge, Li Zhang, Weiwei Yu, Hui Wang
{"title":"Collaborative manufacturing operation mode and modeling simulation of manufacturing enterprise based on collective intelligence","authors":"Hang Jia, Ning Ge, Li Zhang, Weiwei Yu, Hui Wang","doi":"10.1504/ijbic.2023.10057048","DOIUrl":"https://doi.org/10.1504/ijbic.2023.10057048","url":null,"abstract":"","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"73 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74233884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijbic.2023.10057325
Yang Li, Junhe Wan, Hailin Liu, Wende Ke, Peng Ji, Fangfang Zhang, Jinbo Wu, Lei Kou, Quande Yuan
Offshore wind power is an important part of the new power system, due to the complex and changing situation at ocean, its normal operation and maintenance cannot be done without information such as images, therefore, it is especially important to transmit the correct image in the process of information transmission. In this paper, we propose a new encryption algorithm for offshore wind power based on two-dimensional lagged complex logistic mapping (2D-LCLM) and Zhou Yi Eight Trigrams. Firstly, the initial value of the 2D-LCLM is constructed by the Sha-256 to associate the 2D-LCLM with the plaintext. Secondly, a new encryption rule is proposed from the Zhou Yi Eight Trigrams to obfuscate the pixel values and generate the round key. Then, 2D-LCLM is combined with the Zigzag to form an S-box. Finally, the simulation experiment of the algorithm is accomplished. The experimental results demonstrate that the algorithm can resistant common attacks and has prefect encryption performance.
{"title":"Image encryption for Offshore wind power based on 2D-LCLM and Zhou Yi Eight Trigrams","authors":"Yang Li, Junhe Wan, Hailin Liu, Wende Ke, Peng Ji, Fangfang Zhang, Jinbo Wu, Lei Kou, Quande Yuan","doi":"10.1504/ijbic.2023.10057325","DOIUrl":"https://doi.org/10.1504/ijbic.2023.10057325","url":null,"abstract":"Offshore wind power is an important part of the new power system, due to the complex and changing situation at ocean, its normal operation and maintenance cannot be done without information such as images, therefore, it is especially important to transmit the correct image in the process of information transmission. In this paper, we propose a new encryption algorithm for offshore wind power based on two-dimensional lagged complex logistic mapping (2D-LCLM) and Zhou Yi Eight Trigrams. Firstly, the initial value of the 2D-LCLM is constructed by the Sha-256 to associate the 2D-LCLM with the plaintext. Secondly, a new encryption rule is proposed from the Zhou Yi Eight Trigrams to obfuscate the pixel values and generate the round key. Then, 2D-LCLM is combined with the Zigzag to form an S-box. Finally, the simulation experiment of the algorithm is accomplished. The experimental results demonstrate that the algorithm can resistant common attacks and has prefect encryption performance.","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"529 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135182596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Research on feeding behavior of fish by using spatial and temporal features of depth images","authors":"Donghui Guo, Zhixun Liang, Tianlin Huang, Ping Huang, Lvqing Bi, Jincun Zheng","doi":"10.1504/ijbic.2023.10060063","DOIUrl":"https://doi.org/10.1504/ijbic.2023.10060063","url":null,"abstract":"","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134981063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijbic.2023.10059481
I. Jayagayathri, C. Mythili
{"title":"Inertia Weight updated Mayfly Optimization Algorithm based Thermal breast Cancer Image Segmentation","authors":"I. Jayagayathri, C. Mythili","doi":"10.1504/ijbic.2023.10059481","DOIUrl":"https://doi.org/10.1504/ijbic.2023.10059481","url":null,"abstract":"","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135845057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijbic.2023.133507
Anandbabu Gopatoti, P. Vijayalakshmi
{"title":"Design of optimised lung lobe segmentation and deep learning model for effective COVID-19 prediction","authors":"Anandbabu Gopatoti, P. Vijayalakshmi","doi":"10.1504/ijbic.2023.133507","DOIUrl":"https://doi.org/10.1504/ijbic.2023.133507","url":null,"abstract":"","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135556580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijbic.2023.10058243
V. P, Anandbabu Gopatoti
{"title":"Design of optimized lung lobe segmentation and Deep learning model for effective COVID-19 prediction","authors":"V. P, Anandbabu Gopatoti","doi":"10.1504/ijbic.2023.10058243","DOIUrl":"https://doi.org/10.1504/ijbic.2023.10058243","url":null,"abstract":"","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"2017 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72912979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijbic.2023.130549
Xuhui Zhu, Pingfan Xia, Qizhi He, Zhiwei Ni, Liping Ni
Coke price prediction is critical for smart coking plants to make sensible production plan. The prediction of coke price fluctuations is a time-series problem, and gated recurrent unit (GRU) performs well on dealing with it. Meanwhile, densely connected GRU can improve the information flow of time-series data, but its key parameters are sensitive. Therefore, a novel coke price prediction method, named DGOLSCPP, is proposed using dense GRU (DGRU) and opposition-based learning salp swarm algorithm (OLSSA). Firstly, a model with two layers stacked DGRU is constructed for capturing deeper features. Secondly, OLSSA is proposed by introducing opposition-based learning, following and stochastic walk operation for enhancing searching ability. Finally, OLSSA is employed to adjust the key parameters of DGRU for winning the accurate predictive results. Experimental results on two real-world coke price datasets from a certain smart coking plant suggest DGOLSCPP outperforms other competitive methods.
{"title":"Coke price prediction approach based on dense GRU and opposition-based learning salp swarm algorithm","authors":"Xuhui Zhu, Pingfan Xia, Qizhi He, Zhiwei Ni, Liping Ni","doi":"10.1504/ijbic.2023.130549","DOIUrl":"https://doi.org/10.1504/ijbic.2023.130549","url":null,"abstract":"Coke price prediction is critical for smart coking plants to make sensible production plan. The prediction of coke price fluctuations is a time-series problem, and gated recurrent unit (GRU) performs well on dealing with it. Meanwhile, densely connected GRU can improve the information flow of time-series data, but its key parameters are sensitive. Therefore, a novel coke price prediction method, named DGOLSCPP, is proposed using dense GRU (DGRU) and opposition-based learning salp swarm algorithm (OLSSA). Firstly, a model with two layers stacked DGRU is constructed for capturing deeper features. Secondly, OLSSA is proposed by introducing opposition-based learning, following and stochastic walk operation for enhancing searching ability. Finally, OLSSA is employed to adjust the key parameters of DGRU for winning the accurate predictive results. Experimental results on two real-world coke price datasets from a certain smart coking plant suggest DGOLSCPP outperforms other competitive methods.","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"231 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135637164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijbic.2023.130040
Tingting Dong, Li Zhou, Lei Chen, Yanxing Song, Hengliang Tang, Huilin Qin
The advances in cloud computing promote the problem in processing speed. Computing resources in cloud play a vital role in solving user demands, which can be regarded as workflows. Efficient workflow scheduling is a challenge in reducing the task execution time and cost. In recent years, deep reinforcement learning algorithm has been used to solve various combinatorial optimisation problems. However, the trained models often have volatility and can not be applied in real situation. In addition, evolutionary algorithm with a complete framework is a popular method to tackle the scheduling problem. But, it has a poor convergence speed. In this paper, we propose a hybrid algorithm to address the workflow scheduling problem, which combines deep reinforcement algorithm and evolutionary algorithm. The solutions generated by deep reinforcement learning are the initial population in the evolutionary algorithm. Results show that the proposed algorithm is effective.
{"title":"A hybrid algorithm for workflow scheduling in cloud environment","authors":"Tingting Dong, Li Zhou, Lei Chen, Yanxing Song, Hengliang Tang, Huilin Qin","doi":"10.1504/ijbic.2023.130040","DOIUrl":"https://doi.org/10.1504/ijbic.2023.130040","url":null,"abstract":"The advances in cloud computing promote the problem in processing speed. Computing resources in cloud play a vital role in solving user demands, which can be regarded as workflows. Efficient workflow scheduling is a challenge in reducing the task execution time and cost. In recent years, deep reinforcement learning algorithm has been used to solve various combinatorial optimisation problems. However, the trained models often have volatility and can not be applied in real situation. In addition, evolutionary algorithm with a complete framework is a popular method to tackle the scheduling problem. But, it has a poor convergence speed. In this paper, we propose a hybrid algorithm to address the workflow scheduling problem, which combines deep reinforcement algorithm and evolutionary algorithm. The solutions generated by deep reinforcement learning are the initial population in the evolutionary algorithm. Results show that the proposed algorithm is effective.","PeriodicalId":49059,"journal":{"name":"International Journal of Bio-Inspired Computation","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136008733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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