ケッペン マリオ (ケッペン マリオ)

KOEPPEN Mario

写真a

職名

教授

研究室住所

福岡県飯塚市川津680-4

研究分野・キーワード

Pattern Recognition, Image Processing, Soft Computing, Computational Intelligence, Security Technologies, (Multi-Objective) Optimization, Algorithm Theory

メールアドレス

メールアドレス

研究室電話

081 948 29 7945

研究室FAX

081 948 29 7946

ホームページ

http://mkoeppen.science-city.org/

Scopus 論文情報  
総論文数: 0  総Citation: 0  h-index: 8

Citation Countは当該年に発表した論文の被引用数

出身大学院 【 表示 / 非表示

  • 2005年03月  ベルリン工科大学  機械工学及び転送システム  コンピューター・サイエンス  博士課程・博士後期課程  修了  ドイツ連邦共和国

  • 1990年03月  ベルリン・フンボルト大学  物理研究科  物理  修士課程・博士前期課程  修了  ドイツ連邦共和国

取得学位 【 表示 / 非表示

  • ベルリン工科大学 -  博士  2005年07月

学内職務経歴 【 表示 / 非表示

  • 2019年04月
    -
    継続中

    九州工業大学   大学院情報工学研究院   情報・通信工学研究系   教授  

  • 2013年04月
    -
    2019年03月

    九州工業大学   大学院情報工学研究院   情報創成工学研究系   教授  

  • 2008年06月
    -
    2013年03月

    九州工業大学   ネットワークデザイン研究センター   教授  

 

論文 【 表示 / 非表示

  • Behavior selection metaheuristic search algorithm for the pollination optimization: A simulation case of cocoa flowers

    Syafruddin W.A., Paweroi R.M., Köppen M.

    Algorithms    14 ( 8 )   2021年08月  [査読有り]

     概要を見る

    Since nature is an excellent source of inspiration for optimization methods, many optimization algorithms have been proposed, are inspired by nature, and are modified to solve various optimization problems. This paper uses metaheuristics in a new field inspired by nature; more precisely, we use pollination optimization in cocoa plants. The cocoa plant was chosen as the object since its flower type differs from other kinds of flowers, for example, by using cross-pollination. This complex relationship between plants and pollinators also renders pollination a real-world problem for chocolate production. Therefore, this study first identified the underlying optimization problem as a deferred fitness problem, where the quality of a potential solution cannot be immediately determined. Then, the study investigates how metaheuristic algorithms derived from three well-known techniques perform when applied to the flower pollination problem. The three techniques examined here are Swarm Intelligence Algorithms, Individual Random Search, and Multi-Agent Systems search. We then compare the behavior of these various search methods based on the results of pollination simulations. The criteria are the number of pollinated flowers for the trees and the amount and fairness of nectar pickup for the pollinator. Our results show that Multi-Agent System performs notably better than other methods. The result of this study are insights into the co-evolution of behaviors for the collaborative pollination task. We also foresee that this investigation can also help farmers increase chocolate production by developing methods to attract and promote pollinators.

    DOI Scopus

  • An extended hesitant fuzzy set using SWARA-MULTIMOORA approach to adapt online education for the control of the pandemic spread of COVID-19 in higher education institutions

    Saraji M.K., Mardani A., Köppen M., Mishra A.R., Rani P.

    Artificial Intelligence Review      2021年01月  [査読有り]

     概要を見る

    The world has been challenged since late 2019 by COVID-19. Higher education institutions have faced various challenges in adapting online education to control the pandemic spread of COVID-19. The present study aims to conduct a survey study through the interview and scrutinizing the literature to find the key challenges. Subsequently, an integrated MCDM framework, including Stepwise Weight Assessment Ratio Analysis (SWARA) and Multiple Objective Optimization based on Ratio Analysis plus Full Multiplicative Form (MULTIMOORA), is developed. The SWARA procedure is applied to the analysis and assesses the challenges to adapt the online education during the COVID-19 outbreak, and the MULTIMOORA approach is utilized to rank the higher education institutions on hesitant fuzzy sets. Further, an illustrative case study is considered to express the proposed idea's feasibility and efficacy in real-world decision-making. Finally, the obtained result is compared with other existing approaches, confirming the proposed framework's strength and steadiness. The identified challenges were systemic, pedagogical, and psychological challenges, while the analysis results found that the pedagogical challenges, including the lack of experience and student engagement, were the main essential challenges to adapting online education in higher education institutions during the COVID-19 outbreak.

    DOI Scopus

  • Design and implementation of lora based iot scheme for Indonesian rural area

    Prakosa S.W., Faisal M., Adhitya Y., Leu J.S., Köppen M., Avian C.

    Electronics (Switzerland)    10 ( 1 ) 1 - 12   2021年01月  [査読有り]

     概要を見る

    The development of the Internet of Things (IoT) in electronics, computer, robotics, and internet technology is inevitable and has rapidly accelerated more than before as the IoT paradigm is a promising solution in terms of solving real world problems, especially for digitizing and monitoring in real time. Various IoT schemes have successfully been applied to some areas such as smart health and smart agriculture. Since the agriculture areas are getting narrow, the development of IoT in agriculture should be prioritized to enhance crop production. This paper proposes the IoT scheme for long range communication based on Long Range (LoRa) modules applied to smart agriculture. The scheme utilizes the low power modules and long-distance communication for monitoring temperature, humidity, soil moisture, and pH soil. Our IoT design has successfully been applied to agriculture areas which have unstable network connections. The design is analyzed to obtain the maximum coverage using different spreading factors and bandwidths. We show that as the spreading factor increases to 12, the maximum coverage can be transmitted to 1000 m. However, the large coverage also comes with the disadvantages of the increased delays.

    DOI Scopus

  • An Empirical Investigation on Evolutionary Algorithm Evolving Developmental Timings

    Kei Ohnishi, Kouta Hamano and Mario Köppen

    International Journal of Electronics  ( MDPI )  9 ( 11 ) 1 - 27   2020年11月  [査読有り]

     概要を見る

    Recently, evolutionary algorithms that can efficiently solve decomposable binary optimization problems have been developed. They are so-called model-based evolutionary algorithms, which build a model for generating solution candidates by applying a machine learning technique to a population. Their central procedure is linkage detection that reveals a problem structure, that is, how the entire problem consists of sub-problems. However, the model-based evolutionary algorithms have been shown to be ineffective for problems that do not have relevant structures or those whose structures are hard to identify. Therefore, evolutionary algorithms that can solve both types of problems quickly, reliably, and accurately are required. The objective of the paper is to investigate whether the evolutionary algorithm evolving developmental timings (EDT) that we previously proposed can be the desired one. The EDT makes some variables values more quickly converge than the remains for any problems, and then, decides values of the remains to obtain a higher fitness value under the fixation of the variables values. In addition, factors to decide which variable values converge more quickly, that is, developmental timings are evolution targets. Simulation results reveal that the EDT has worse performance than the linkage tree genetic algorithm (LTGA), which is one of the state-of-the-art model-based evolutionary algorithms, for decomposable problems and also that the difference in the performance between them becomes smaller for problems with overlaps among linkages and also that the EDT has better performance than the LTGA for problems whose structures are hard to identify. Those results suggest that an appropriate search strategy is different between decomposable problems and those hard to decompose.

    DOI

  • Feature Extraction for Cocoa Bean Digital Image Classification Prediction for Smart Farming Application

    Yudhi Adhitya, Setya Widyawan Prakosa, Mario Köppen and Jenq-Shiou Leu

    International Journal of Agronomy,   ( MDPI )  10(11) ( 1642 ) 1 - 16   2020年10月  [査読有り]

     概要を見る

    The implementation of Industry 4.0 emphasizes the capability and competitiveness in agriculture application, which is the essential framework of a country’s economy that procures raw materials and resources. Human workers currently employ the traditional assessment method and classification of cocoa beans, which requires a significant amount of time. Advanced agricultural development and procedural operations differ significantly from those of several decades earlier, principally because of technological developments, including sensors, devices, appliances, and information technology. Artificial intelligence, as one of the foremost techniques that revitalized the implementation of Industry 4.0, has extraordinary potential and prospective applications. This study demonstrated a methodology for textural feature analysis on digital images of cocoa beans. The co-occurrence matrix features of the gray level co-occurrence matrix (GLCM) were compared with the convolutional neural network (CNN) method for the feature extraction method. In addition, we applied several classifiers for conclusive assessment and classification to obtain an accuracy performance analysis. Our results showed that using the GLCM texture feature extraction can contribute more reliable results than using CNN feature extraction from the final classification. Our method was implemented through on-site preprocessing within a low-performance computational device. It also helped to foster the use of modern Internet of Things (IoT) technologies among farmers and to increase the security of the food supply chain as a whole.

    DOI

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著書 【 表示 / 非表示

  • Soft Computing in Data Science: Conference proceedings SCDS 2019, Iizuka, Japan, August 28–29, 2019

    Michael W. Berry, Bee Wah Yap, Azlinah Mohamed and Mario Köppen ( 編者 )

    Springer, Singapore  2019年08月 ISBN: 978-981-15-0398-6

     概要を見る

    This book constitutes the refereed proceedings of the 5th International Conference on Soft Computing in Data Science, SCDS 2019, held in Iizuka, Japan, in August 2019.
    The 30 revised full papers presented were carefully reviewed and selected from 75 submissions. The papers are organized in topical sections on ​information and customer analytics; visual data science; machine and deep learning; big data analytics; computational and artificial intelligence; social network and media analytics.

  • MEDES '18: Proceedings of the 10th International Conference on Management of Digital EcoSystems,Tokyo, Japan, September, 2018

    Richard Chbeir and Hiroshi Ishikawa and Kazutoshi Sumiya and Kenji Hatano and Mario Köppen ( 編者 )

    Association for Computing Machinery (ACM), New York, NY, United States  2018年09月 ISBN: 9781450356220

     概要を見る

    On behalf of the Organizing Committee, we welcome you to the 10th International Conference on Management of Emergent Digital EcoSystems (MEDES'18) hosted by the Tokyo Metropolitan University, Minami-Osawa campus, Tokyo/Japan. The 1st MEDES conference was organized in Lyon-France. Since then, it has always been technically supported by the ACM Special Group On Applied Computing (ACM SIGAPP and ACM SIGAPP.fr). After 10 years of continuous presence, this conference has become the premier forum for the dissemination of leading edge research in Digital Ecosystems. The MEDES conference series continues this tradition of bringing students, researchers and practitioners together to exchange and share their contributions related to various areas of the expanding universe of digital ecosystems. Previous events took place in Thailand (2017), France (2016), Brazil (2015), Saudi Arabia (2014), Luxembourg (2013), Ethiopia (2012), USA (2011), Thailand (2010), and France (2009).

  • Hybrid intelligence for social networks

    Banati H., Bhattacharyya S., Mani A., Köppen M. ( 単著 )

    Hybrid Intelligence for Social Networks  2017年11月 ISBN: 9783319651392

     概要を見る

    © Springer International Publishing AG 2017. All rights reserved. This book explains aspects of social networks, varying from development and application of new artificial intelligence and computational intelligence techniques for social networks to understanding the impact of social networks. Chapters 1 and 2 deal with the basic strategies towards social networks such as mining text from such networks and applying social network metrics using a hybrid approach; Chaps. 3 to 8 focus on the prime research areas in social networks: community detection, influence maximization and opinion mining. Chapter 9 to 13 concentrate on studying the impact and use of social networks in society, primarily in education, commerce, and crowd sourcing. The contributions provide a multidimensional approach, and the book will serve graduate students and researchers as a reference in computer science, electronics engineering, communications, and information technology.

    Scopus

  • Computational Intelligence in Wireless Sensor Networks

    Ajith Abraham, Rafael Falcon, Mario Koeppen ( 編者 )

    Springer, Cham  2017年01月

     概要を見る

    This book emphasizes the increasingly important role that Computational Intelligence (CI) methods are playing in solving a myriad of entangled Wireless Sensor Networks (WSN) related problems. The book serves as a guide for surveying several state-of-the-art WSN scenarios in which CI approaches have been employed. The reader finds in this book how CI has contributed to solve a wide range of challenging problems, ranging from balancing the cost and accuracy of heterogeneous sensor deployments to recovering from real-time sensor failures to detecting attacks launched by malicious sensor nodes and enacting CI-based security schemes. Network managers, industry experts, academicians and practitioners alike (mostly in computer engineering, computer science or applied mathematics) benefit from the spectrum of successful applications reported in this book. Senior undergraduate or graduate students may discover in this book some problems well suited for their own research endeavors.

  • Soft Computing in Industrial Applications: Proceedings of the 17th Online World Conference on Soft Computing in Industrial Applications

    Snášel, Václav, Pavel Krömer, Mario Köppen, and Gerald Schaefer ( 共編者 )

    Springer  2014年07月 ISBN: 978-3-319-00929-2

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口頭発表・ポスター発表等 【 表示 / 非表示

  • Network Fairnessの評価に関する研究

    第12回日本知能情報ファジィ学会九州支部学術講演会  2010年12月  -  2010年12月   

  • 自己組織化マップを用いたアンケート結果の可視化と分析

    第9回日本知能情報ファジィ学会九州支部学術講演会予稿集  (日本 熊本)  2007年12月  -  2007年12月   

  • The Gestalt of Web-services

    Tutorial at 2007 International Conference on Next Generation Web Services Practices  ( Korea Seoul)  2007年10月  -  2007年10月   

  • 視覚認知に基づくWebインタフェースデザインに関する考察

    情報処理学会研究報告. HCI, ヒューマンコンピュータインタラクション研究会報告  (日本 )  2007年10月  -  2007年10月   

科研費獲得実績 【 表示 / 非表示

  • フェアネス理論に基づく高度中継ルータによるネットワーク資源制御

    挑戦的萌芽研究

    研究期間:  2012年04月  -  2013年03月

    研究課題番号:  24650030

 

担当授業科目 【 表示 / 非表示

  • 2020年度  コンピュテーショナルセキュリティ

  • 2019年度  Computer Literacy

  • 2019年度  コンピュテーショナルセキュリティ

  • 2018年度  Computer Literacy

  • 2018年度  コンピュテーショナルセキュリティ

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その他教育活動 【 表示 / 非表示

  • External Examiner, Master in Computer Science Course, Stellenbosch University, South Africa

    2020年11月
     
     

     概要を見る

    Scalability of the Multi-Guide Particle Swarm Optimization Algorithm

  • Ph.D. Proposal reviewer, Edith Cowan University, Perth, Australia

    2020年08月
    -
    2020年10月

     概要を見る

    Machine Learning Approaches for Anatomical Feature Measurement in Fetal Ultrasound Images

 

学会・委員会等活動 【 表示 / 非表示

  • 2021年04月
     
     

    European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP 2021), as part of the EvoStar 2021   Program Committee Member

  • 2019年08月
     
     

    The 5th International Conference on Soft Computing in Data Science (SCDS2019), Iizuka, Fukuoka, Japan, August 28-29, 2019.   Conference Chair

  • 2019年01月
     
     

    2019 International Conference on Platform Technology and Service (PlatCon-19), Jeju, Korea, January 28-30, 2019   General Chairs

 

国際会議の開催 【 表示 / 非表示

  • A Special Session on Evolutionary Computer Vision and Image Processing (ECVIP) at the 2021 IEEE Congress on Evolutionary Computation (CEC 2021)

    Poland  2021年06月01日  -  2021年07月01日  IEEE Congress on Evolutionary Computation

  • The 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 2021 (VISAPP2021 is part of VISIGRAPP)

    Vianna, Austria  2021年02月08日  -  2021年02月10日  Vienna University of Technology/Technische Universität (TU) Wien

  • The 10th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2021)

    Vienna, Austria  2021年02月04日  -  2021年02月04日  Vienna University of Technology/Technische Universität (TU) Wien

  • The 16th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2020)

    Xi'an, China  2020年12月12日  -  2020年12月21日  Shaanxi University of Science and Technology and Lanzhou Jiaotong University

  • 2020 IEEE Symposium on Computational Intelligence for Multimedia Signal and Vision Processing (IEEE CIMSIVP) 2020.

    Canberra, Australia  2020年12月01日  -  2020年12月04日  IEEE SSCI

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