Faculty Profiles - KOEPPEN Mario

Papers - KOEPPEN Mario

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A metaverse assessment model for sustainable transportation using ordinal priority approach and Aczel-Alsina norms Reviewed International journal

Pamucar D., Deveci M., Gokasar I., Tavana M., Köppen M.

Technological Forecasting and Social Change 182 2022.09

Prioritizing transport planning strategies for freight companies towards zero carbon emission using ordinal priority approach Reviewed International journal

Pamucar D., Deveci M., Gokasar I., Martínez L., Köppen M.

Computers and Industrial Engineering 169 2022.07

Implementing a Hand Gesture Recognition System Based on Range-Doppler Map Reviewed International journal

Jhaung Y.C., Lin Y.M., Zha C., Leu J.S., Köppen M.

Sensors 22 ( 11 ) 2022.06

Supporting farming smart documentation system by modular blockchain solutions Reviewed International journal

Arsyad, A. A., Widayat, I. W., & Köppen, M.

Decision Making: Applications in Management and Engineering 5 ( 1 ) 1 - 26 2022.03

SUPPORTING FARMING SMART DOCUMENTATION SYSTEM by MODULAR BLOCKCHAIN SOLUTIONS Reviewed International journal

Arsyad A.A., Widayat I.W., Köppen M.

Decision Making: Applications in Management and Engineering 5 ( 1 ) 1 - 26 2022.01

Personal Mobility in Metaverse With Autonomous Vehicles Using Q-Rung Orthopair Fuzzy Sets Based OPA-RAFSI Model Reviewed International journal

Deveci M., Pamucar D., Gokasar I., Koppen M., Gupta B.B.

IEEE Transactions on Intelligent Transportation Systems 2022.01

Calibration Cost Reduction of Indoor Localization Using Bluetooth Low Energy Beacon Reviewed International journal

As M., Shimizu H., Benaissa B., Yoshida K., Koppen M.

Journal of Advanced Computational Intelligence and Intelligent Informatics 26 ( 1 ) 97 - 106 2022.01

Blockchain Simulation Environment on Multi-image Encryption for Smart Farming Application Reviewed International journal

Widi Widayat I., Köppen M.

Lecture Notes in Networks and Systems 312 316 - 326 2022.01

A Genetic Algorithm for Parallel Unmanned Aerial Vehicle Scheduling: A Cost Minimization Approach Reviewed International journal

Mantau A.J., Widayat I.W., Köppen M.

Lecture Notes in Networks and Systems 312 125 - 135 2022.01

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

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

Algorithms 14 ( 8 ) 2021.08

A Genetic Algorithm for Parallel Unmanned Aerial Vehicle Scheduling: A Cost Minimization Approach Reviewed

Mantau, A.J., Widayat, I.W., Köppen, M.

Advances in Intelligent Networking and Collaborative Systems. INCoS 2021. Lecture Notes in Networks and Systems 312 2021.08

Blockchain Simulation Environment on Multi-image Encryption for Smart Farming Application Reviewed International journal

Widi Widayat, I., Köppen, M.

Advances in Intelligent Networking and Collaborative Systems. INCoS 2021. Lecture Notes in Networks and Systems 312 2021.08

Behavior Selection Metaheuristic Search Algorithm for the Pollination Optimization: A Simulation Case of Cocoa Flowers Reviewed International journal

Syafruddin, Willa A., Rio M. Paweroi, and Mario Köppen

Algorithms 14 ( 8 ) 230 2021.07

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

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

Electronics (Switzerland) 10 ( 1 ) 1 - 12 2021.01

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 Reviewed

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

Artificial Intelligence Review 2021.01

An Empirical Investigation on Evolutionary Algorithm Evolving Developmental Timings Reviewed

Kei Ohnishi, Kouta Hamano and Mario Köppen

International Journal of Electronics ( MDPI ) 9 ( 11 ) 1 - 27 2020.11

Language：English Publishing type：Research paper (scientific journal)

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： 10.3390/electronics9111866

Feature Extraction for Cocoa Bean Digital Image Classification Prediction for Smart Farming Application Reviewed International journal

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

International Journal of Agronomy, ( MDPI ) 10(11) ( 1642 ) 1 - 16 2020.10

Language：English Publishing type：Research paper (scientific journal)

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： 10.3390/agronomy10111642

Smart Watering System Based on Framework of Low-Bandwidth Distributed Applications (LBDA) in Cloud Computing Reviewed International journal

Nurdiansyah Sirimorok, Mansur As, Kaori Yoshida and Mario Köppen

In Proceedings of the 12th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2020). ( Springer, Cham ) 1263 447 - 459 2020.08

Language：English Publishing type：Research paper (international conference proceedings)

Victoria, Canada 2020.08.31 - 2020.09.02

The estimation of soil moisture content is required for agriculture, mainly to build the irrigation scheduling model. In this study, we present a smart watering system to deal with various factors derived from the stochastic information in the agricultural operational, i.e., air temperature, air humidity, soil moisture, soil temperature, and light intensity. The methodology consists of exploiting the Internet of Things (IoT) using Low-Bandwidth Distributed Applications (LBDA) in cloud computing to integrate the real data sets collected by the several sensor technologies. We conducted experiments for the watering system used two types of soil and different plant. Here, the Long Short Term Memory Networks (LSTMs) approach in deep learning techniques used to build smart decisions concerning watering requirements and deal with heterogeneous information coming from agricultural environments. Results show that our models can effectively improve the prediction accuracy for the watering system over various soil and plants.

DOI： 10.1007/978-3-030-57796-4_43

Other Link： https://link.springer.com/chapter/10.1007%2F978-3-030-57796-4_43

Identification and Classification of Sashimi Food Using Multispectral Technology Reviewed International journal

Ismail Parewai, Mansur As, Tsunenori Mine and Mario Köppen

Proceedings of the 2020 2nd Asia Pacific Information Technology Conference ( Association for Computing Machinery (ACM) New York, USA ) 73 - 77 2020.01

Language：English Publishing type：Research paper (international conference proceedings)

Indonesia 2020.01.17 - 2020.01.19

Food quality inspection is an essential factor in our daily lives. Food inspection is analyzing heterogeneous food data from different sources for perception, recognition, judgment, and monitoring. This study aims to provide an accurate system in image processing techniques for the inspection and classification of sashimi food damage based on detecting external data. The external texture was identified based on the visible and invisible system that was acquired using multispectral technology. We proposed the Grey Level Co-occurrence Matrix (GLCM) model for analysis of the texture features of images and the classification process was performed using Artificial Neural Network (ANN) method. This study showed that multispectral technology is a useful system for the assessment of sashimi food and the experimental also indicates that the invisible channels have the potential in the classification model, since the hidden texture features that are not clearly visible to the human eye

DOI： 10.1145/3379310.3379317

Identification and Classification of Sashimi Food Using Multispectral Technology Reviewed International journal

Ismail Parewai, Mansur As, Tsunenori Mine, and Mario Koeppen

Proceedings of the 2020 2nd Asia Pacific Information Technology Conference (APIT 2020) ( Association for Computing Machinery ) 66 - 72 2020.01

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Papers - KOEPPEN Mario