Pub Date : 2023-01-01DOI: 10.1504/ijcse.2023.10059393
Xiaoli Zhang, Jing Zheng, Guocai Zuo
{"title":"Research on tracking of moving objects based on depth feature detection","authors":"Xiaoli Zhang, Jing Zheng, Guocai Zuo","doi":"10.1504/ijcse.2023.10059393","DOIUrl":"https://doi.org/10.1504/ijcse.2023.10059393","url":null,"abstract":"","PeriodicalId":47380,"journal":{"name":"International Journal of Computational Science and Engineering","volume":"28 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":"135749897","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 : 2023-01-01DOI: 10.1504/ijcse.2023.10059723
Vishakha A. Metre, S.D. Sawarkar
{"title":"Convolutional neural network optimization for discovering plant leaf diseases with particle swarm optimizer","authors":"Vishakha A. Metre, S.D. Sawarkar","doi":"10.1504/ijcse.2023.10059723","DOIUrl":"https://doi.org/10.1504/ijcse.2023.10059723","url":null,"abstract":"","PeriodicalId":47380,"journal":{"name":"International Journal of Computational Science and Engineering","volume":"16 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":"136209890","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 : 2023-01-01DOI: 10.1504/ijcse.2023.10058662
Yang Yu, Heting Li, Dongsheng Jing
{"title":"Integrated power information operation and maintenance system based on D3QN algorithm with experience replay","authors":"Yang Yu, Heting Li, Dongsheng Jing","doi":"10.1504/ijcse.2023.10058662","DOIUrl":"https://doi.org/10.1504/ijcse.2023.10058662","url":null,"abstract":"","PeriodicalId":47380,"journal":{"name":"International Journal of Computational Science and Engineering","volume":"59 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74217862","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}
{"title":"Research on econometric safety model for export structure of manufacturing industry","authors":"Dongfang Hua, Hongbin Wang, Haoze Feng, Guan Ben, Jingyuan Tan, Dongjie Zhu","doi":"10.1504/ijcse.2023.10058661","DOIUrl":"https://doi.org/10.1504/ijcse.2023.10058661","url":null,"abstract":"","PeriodicalId":47380,"journal":{"name":"International Journal of Computational Science and Engineering","volume":"64 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85846069","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 : 2023-01-01DOI: 10.1504/ijcse.2023.10059058
Jyoti Chauhan, Taj Alam
{"title":"Adjustable rotation gate-based quantum evolutionary algorithm for energy optimisation in cloud computing systems","authors":"Jyoti Chauhan, Taj Alam","doi":"10.1504/ijcse.2023.10059058","DOIUrl":"https://doi.org/10.1504/ijcse.2023.10059058","url":null,"abstract":"","PeriodicalId":47380,"journal":{"name":"International Journal of Computational Science and Engineering","volume":"62 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":"135400376","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 : 2023-01-01DOI: 10.1504/ijcse.2023.10060448
Deepak Jayaprakash Doggalli, B. S. Sunil Kumar
{"title":"A novel DenseNet-based architecture for liver and liver tumour segmentation","authors":"Deepak Jayaprakash Doggalli, B. S. Sunil Kumar","doi":"10.1504/ijcse.2023.10060448","DOIUrl":"https://doi.org/10.1504/ijcse.2023.10060448","url":null,"abstract":"","PeriodicalId":47380,"journal":{"name":"International Journal of Computational Science and Engineering","volume":"29 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":"135705512","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 : 2023-01-01DOI: 10.1504/ijcse.2023.10060450
Jinbiao Wu, Muhammad Salihu Isa, Ibrahim Yusuf, U.A. Ali, Tijjani W. Ali, Abubakar Sadiq Abdulkadir
{"title":"Performance assessment of multi-unit web and database servers distributed system","authors":"Jinbiao Wu, Muhammad Salihu Isa, Ibrahim Yusuf, U.A. Ali, Tijjani W. Ali, Abubakar Sadiq Abdulkadir","doi":"10.1504/ijcse.2023.10060450","DOIUrl":"https://doi.org/10.1504/ijcse.2023.10060450","url":null,"abstract":"","PeriodicalId":47380,"journal":{"name":"International Journal of Computational Science and Engineering","volume":"20 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":"135706381","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 : 2023-01-01DOI: 10.1504/ijcse.2023.10059381
Luca D'Acierno, Ilaria Tufano, Marilisa Botte
{"title":"A methodology for introducing an energy-efficient component within the rail infrastructure access charges in Italy","authors":"Luca D'Acierno, Ilaria Tufano, Marilisa Botte","doi":"10.1504/ijcse.2023.10059381","DOIUrl":"https://doi.org/10.1504/ijcse.2023.10059381","url":null,"abstract":"","PeriodicalId":47380,"journal":{"name":"International Journal of Computational Science and Engineering","volume":"103 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":"135749893","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 : 2023-01-01DOI: 10.1504/ijcse.2023.133672
Armando Cartenì, Ilaria Henke, Assunta Errico, Maria Ida Di Bartolomeo
The big data and cloud computing are an extraordinary opportunity to implement multipurpose smart applications for the management and the control of transport systems. The aim of the paper was to propose a big data and cloud computing model architecture for a multi-class origin-destination demand estimation based on the application of a bi-level transport algorithm using traffic counts on congested network, also for proposing sustainable policies at urban scale. The proposed methodology has been applied to a real case study in terms of travel demand estimation within the city of Naples (Italy), also aiming to verify the effectiveness of a sustainable policy in terms of reducing traffic congestion of about 20% through en-route travel information. The obtained results, although preliminary, suggest the usefulness of the proposed methodology in terms of ability in real-time/pre-fixed time periods traffic demand estimation.
{"title":"A big data and cloud computing model architecture for a multi-class travel demand estimation through traffic measures: a real case application in Italy","authors":"Armando Cartenì, Ilaria Henke, Assunta Errico, Maria Ida Di Bartolomeo","doi":"10.1504/ijcse.2023.133672","DOIUrl":"https://doi.org/10.1504/ijcse.2023.133672","url":null,"abstract":"The big data and cloud computing are an extraordinary opportunity to implement multipurpose smart applications for the management and the control of transport systems. The aim of the paper was to propose a big data and cloud computing model architecture for a multi-class origin-destination demand estimation based on the application of a bi-level transport algorithm using traffic counts on congested network, also for proposing sustainable policies at urban scale. The proposed methodology has been applied to a real case study in terms of travel demand estimation within the city of Naples (Italy), also aiming to verify the effectiveness of a sustainable policy in terms of reducing traffic congestion of about 20% through en-route travel information. The obtained results, although preliminary, suggest the usefulness of the proposed methodology in terms of ability in real-time/pre-fixed time periods traffic demand estimation.","PeriodicalId":47380,"journal":{"name":"International Journal of Computational Science and Engineering","volume":"158 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":"135844463","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 : 2023-01-01DOI: 10.1504/ijcse.2023.133692
Longxin Zhang, Peng Zhou, Miao Wang, Chengkang Weng, Xiaojun Deng
The fault detection of freight train image has some problems, such as low detection accuracy and slow detection speed. Aiming at the problem of slow detection speed in the process of train image fault detection, a lightweight object detection model fast channel attention network (FCAODNet) is proposed in this study. FCAODNet consists of four modules, including feature extraction network (FEN), lightweight multi-scale feature fusion (LMFF), prediction across scales (PAS), and decoding modules. FEN extracts image features, LMFF fuses features, PAS predicts the location of the target object, and the decoding module obtains the final prediction result. FCAODNet's FEN adopts CSPDarknet53tiny. The designed LMFF is embedded with two FCA modules to improve the detection accuracy. Experiments on train datasets and public datasets show that FCAODNet outperforms other state-of-the-art models in detection speed and has good detection accuracy and robustness.
{"title":"FCAODNet: a fast freight train image detection model based on embedded FCA","authors":"Longxin Zhang, Peng Zhou, Miao Wang, Chengkang Weng, Xiaojun Deng","doi":"10.1504/ijcse.2023.133692","DOIUrl":"https://doi.org/10.1504/ijcse.2023.133692","url":null,"abstract":"The fault detection of freight train image has some problems, such as low detection accuracy and slow detection speed. Aiming at the problem of slow detection speed in the process of train image fault detection, a lightweight object detection model fast channel attention network (FCAODNet) is proposed in this study. FCAODNet consists of four modules, including feature extraction network (FEN), lightweight multi-scale feature fusion (LMFF), prediction across scales (PAS), and decoding modules. FEN extracts image features, LMFF fuses features, PAS predicts the location of the target object, and the decoding module obtains the final prediction result. FCAODNet's FEN adopts CSPDarknet53tiny. The designed LMFF is embedded with two FCA modules to improve the detection accuracy. Experiments on train datasets and public datasets show that FCAODNet outperforms other state-of-the-art models in detection speed and has good detection accuracy and robustness.","PeriodicalId":47380,"journal":{"name":"International Journal of Computational Science and Engineering","volume":"286 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":"135844749","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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