skip to main content
10.1145/3627341.3630394acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiccvitConference Proceedingsconference-collections
research-article

Intelligent Decision Making Algorithm Based On Hybrid Cuckoo Algorithm and Deep Learning

Published: 15 December 2023 Publication History

Abstract

The current intelligent decision-making algorithms are not comprehensive enough in data search, resulting in poor quality decision results. For this reason intelligent decision algorithms based on hybrid cuckoo algorithms and deep learning are proposed. Integrated intelligent decision data space. Global search of data in this space based on the hybrid cuckoo algorithm. Establish a non-interference decision data allocation matrix, and use deep learning technology to generate intelligent decision algorithms based on the results of the interference decision data allocation matrix. Experiments show that the highest reward value of the decision results obtained by the algorithm reaches 4.73, which substantially improves the quality of the decision results and has high practical application value.

References

[1]
Li Zhongyang,Deng Zhaohui,Ge Zhiguang,Lv Lishu,Ge Jimin. A hybrid approach of case-based reasoning and process reasoning to typical parts grinding process intelligent decision[J]. International Journal of Production Research,2023,61(2).
[2]
Anda Ungureanu,Andreea Sorina Marcu,Laurentiu Patru Ciprian,Dan Ruican,Rodica Nagy,Ruxandra Stoean,Catalin Stoean,Gabriel Iliescu Dominic. Learning deep architectures for the interpretation of first-trimester fetal echocardiography (LIFE) - a study protocol for developing an automated intelligent decision support system for early fetal echocardiography[J]. BMC Pregnancy and Childbirth,2023,23(1).
[3]
Mourtzis D.,Angelopoulos J.,Panopoulos N. Manufacturing personnel task allocation taking into consideration skills and remote guidance based on augmented reality and intelligent decision making[J]. International Journal of Computer Integrated Manufacturing,2023,36(1).
[4]
Kabildjanov Alexander,Okhunboboyeva Charos,Ismailov Sarvarbek. Intelligent decision support in the optimization of irrigation systems in agriculture[J]. E3S Web of Conferences,2023,365.
[5]
Bartosiak Marcin Lukasz,Modlinski Artur. Fired by an algorithm? Exploration of conformism with biased intelligent decision support systems in the context of workplace discipline[J]. Career Development International,2022,27(6-7).
[6]
Bolnokin V E,Sorokin S A,Mutin D I,Mutina E I,Storozhev S V,Storozhev V I,Yu Zaslavskaya O. Mathematical model of intelligent decision support based on hierarchical logical constructions[J]. Journal of Physics: Conference Series,2022,2373(5).
[7]
Hu Wujin,Li Bo,Chen Likang. An intelligent decision methodology for physical health evaluation of college students based on generalized 2-tuple linguistic neutrosophic geometric HM operators[J]. Journal of Intelligent & Fuzzy Systems,2022,43(6).
[8]
Bai Qiuchan,Li Hao. The application of hybrid cuckoo search-grey wolf optimization algorithm in optimal parameters identification of solid oxide fuel cell[J]. International Journal of Hydrogen Energy,2022,47(9).
[9]
Correction: Deep Learning in the Detection of Rare Fractures – Development of a “Deep Learning Convolutional Network” Model for Detecting Acetabular Fractures[J]. Zeitschrift für Orthopädie und Unfallchirurgie,2023,161(1).
[10]
HASEGAWA Makoto,MATSUO Rui. Skin Visualization Using Smartphone and Deep Learning in the Beauty Industry[J]. IEICE Transactions on Information and Systems,2023,E106.D(1).
[11]
Naka Akira,Komatsu Mamoru. Geometrically Shaped Multi-Dimensional Modulation Formats Designed by Deep Learning[J]. IEICE Communications Express,2023,advpub(0).
[12]
MosqueraZamudio Andrés,Launet Laëtitia,Tabatabaei Zahra,ParraMedina Rafael,Colomer Adrián,Oliver Moll Javier,Monteagudo Carlos,Janssen Emiel,Naranjo Valery. Deep Learning for Skin Melanocytic Tumors in Whole-Slide Images: A Systematic Review[J]. Cancers,2022,15(1).
[13]
Achanccaray Pedro,Gerke Markus,Hoyer Sebastian,Knufinke Ulrich,Krafczyk Christina,Thiele Klaus,Wesche Leonhard. Deep Learning in der Denkmal-Inventarisation[J]. Die Denkmalpflege,2022,80(2).
[14]
Wang Shuhua, Yang Guojie, Mu Xing. S-wave velocity prediction based on deep feedforward neural network [J].Petroleum Geology and Recovery Efficiency, 2022,29 (01): 80-89.
[15]
Zheng Tianshu, Yan Guohui, Ye Chuyang Neural Network for Parpameter Estimation of Intravoxel Incoherent Motion Based on Sparse Coding [J]. Journal of Data Acquisition and Processing, 2022, 37 (04): 747-756
[16]
Jing Peiguang, Ye Xuqing, Liu Yu Micro-Video Popularity Prediction with Bidirectional Deep Encoding Network [J]. Laser & Optoelectronics Progress, 2022,59 (08): 300-308
[17]
Akkurt Burak Han,Wanderer Stefan,Schwyzer Lucia,Berberat Jatta,Henssen Dylan J H A,Sartoretti Thomas,Sartoretti Elisabeth,Musigmann Manfred,Brokinkel Benjamin,Stummer Walter,Heindel Walter,Remonda Luca,Mannil Manoj. Predicting Meningioma Resection Status: Use of Deep Learning.[J]. Academic radiology,2022.
[18]
Z Wei,H Wang,Y Ji.Region Adaptive Dehazing Algorithm Based on Multi-Scale Connection [J].Computer Simulation,2023, 40(2):7.

Index Terms

  1. Intelligent Decision Making Algorithm Based On Hybrid Cuckoo Algorithm and Deep Learning
        Index terms have been assigned to the content through auto-classification.

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image ACM Other conferences
        ICCVIT '23: Proceedings of the 2023 International Conference on Computer, Vision and Intelligent Technology
        August 2023
        378 pages
        ISBN:9798400708701
        DOI:10.1145/3627341
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 15 December 2023

        Permissions

        Request permissions for this article.

        Check for updates

        Qualifiers

        • Research-article
        • Research
        • Refereed limited

        Conference

        ICCVIT 2023

        Acceptance Rates

        ICCVIT '23 Paper Acceptance Rate 54 of 142 submissions, 38%;
        Overall Acceptance Rate 54 of 142 submissions, 38%

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • 0
          Total Citations
        • 13
          Total Downloads
        • Downloads (Last 12 months)10
        • Downloads (Last 6 weeks)0
        Reflects downloads up to 20 Jan 2025

        Other Metrics

        Citations

        View Options

        Login options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        HTML Format

        View this article in HTML Format.

        HTML Format

        Media

        Figures

        Other

        Tables

        Share

        Share

        Share this Publication link

        Share on social media