Open Access
| Issue |
BIO Web Conf.
Volume 146, 2024
2nd Biology Trunojoyo Madura International Conference (BTMIC 2024)
|
|
|---|---|---|
| Article Number | 01036 | |
| Number of page(s) | 6 | |
| Section | Dense Matter | |
| DOI | https://doi.org/10.1051/bioconf/202414601036 | |
| Published online | 27 November 2024 | |
- H. Siebert, The world economy, Routledge, (2018) [CrossRef] [PubMed] [Google Scholar]
- R. Badi'ah, D. Wiratama, M. F. Yusuf, D. A. Sari, and D. Ulya, Dynamics of Rice Imports in Indonesia: Analysis of Development, Causative Factors, Impacts and Solutions [Google Scholar]
- M. Radetzki and L. Warell. A handbook of primary commodities in the global Economy. Cambridge University Press, (2020) [Google Scholar]
- L. Kramer. (2023, September 27) How Importing and Exporting Impacts the Economy. [Economy] [Google Scholar]
- A. I. Suyanto, Searching, Reasoning, Planning, dan Learning (Revisi Kedua), Bandung: Informatika Bandung, (2014) [Google Scholar]
- X. Zhao, M. Han, L. Ding, and W. Kang, Usefulness of economic and energy data at different frequencies for carbon price forecasting in the EU ETS, Applied Energy, 216, 132–141, (2018) [CrossRef] [Google Scholar]
- J. Zhu, P. Wu, H. Chen, J. Liu, and L. Zhou, Carbon price forecasting with variational mode decomposition and optimal combined model, Physica A: Statistical Mechanics and Its Applications, 519, 140–158, (2019) [CrossRef] [Google Scholar]
- W. Sun and C. Zhang, Analysis and forecasting of the carbon price using multi—resolution singular value decomposition and extreme learning machine optimized by adaptive whale optimization algorithm," Applied energy, 231, 1354–1371, (2018) [CrossRef] [Google Scholar]
- H. Liu, X. Mi, and Y. Li, An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm, Renewable energy, 123, 694–705, 2018. [CrossRef] [Google Scholar]
- S. Akkoyun, N. Yildiz, and H. Kaya, Neural Network Estimation for Attenuation Coefficients for Gamma-Ray Angular Distribution, Physics of Particles and Nuclei Letters, 16, 4, 397–401, (2019) [CrossRef] [Google Scholar]
- M. Aritonang and D. J. C. Sihombing, An application of b ackpropagation neural network for sales forecasting rice miling unit, in 2019 International Conference of Computer Science and Information Technology (ICoSNIKOM), IEEE, 1–4, (2019) [Google Scholar]
- T. Zaw, K. M. M. Tun, and A. N. Oo, Price forecasting by back propagation neural network model, in 2019 International Conference on Advanced Information Technologies (ICAIT), IEEE, 84–89 (2019) [CrossRef] [Google Scholar]
- B. S. Chauhan, K. Jabran, and G. Mahajan, Rice production worldwide. Springer, (2017) [Google Scholar]
- C. Panpakdee and B. Limnirankul, Indicators for assessing social-ecological resilience: A case study of organic rice production in northern Thailand, Kasetsart Journal of Social Sciences, 39, 3, 414–421, (2018) [CrossRef] [Google Scholar]
- J. Xu and Y. Ding, Research on early warning of food security using a system dynamics model: evidence fTom Jiangsu province in China, Journal of food science, 80, 1, R1–R9, (2015) [PubMed] [Google Scholar]
- H. Pathak, A. K. Nayak, M. Jena, O. Singh, P. Samal, and S. Sharma, Rice research for enhancing productivity, profitability and climate resilience, ed: Not Available, (2018) [Google Scholar]
- G. G. Marten and N. Atalan-Helicke, Introduction to the symposium on American food resilience, Journal of Environmental Studies and Sciences, 5, 3, 308–320, (2015) [CrossRef] [Google Scholar]
- M. R. Haque, M. M. Islam, H. Iqbal, M. S. Reza, and M. K. Hasan, Performance evaluation of random forests and artificial neural networks for the classification of liver disorder, in 2018 international conference on computer, communication, chemical, material and electronic engineering (IC4ME2), IEEE, 1–5, (2018) [Google Scholar]
- M. Shaban, Deep convolutional neural network for Parkinson’s disease based handwriting screening, in 2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops), IEEE, 1–4, (2020) [Google Scholar]
- W. Yan et al., Discriminating schizophrenia from normal controls using resting state functional network connectivity: A deep neural network and layer-wise relevance propagation method, in 2017 IEEE 27th international workshop on machine learning for signal processing (MLSP), IEEE, 1–6, (2017) [Google Scholar]
- N. I. Nwulu, An artificial neural network model for predicting building heating and cooling loads, in 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), IEEE, pp. 1–5, (2017) [Google Scholar]
- P. Prasetyawan, I. Ahmad, R. I. Borman, Y. A. Pahlevi, and D. E. Kurniawan, Classification of the Period Undergraduate Study Using Backpropagation Neural Network, in 2018 International Conference on Applied Engineering (ICAE), IEEE, 1–5, (2018) [Google Scholar]
- S. W. Sidehabi, A. Suyuti, I. S. Areni, and I. Nurtanio, Classification on passion fruit’s ripeness using K-means clustering and artificial neural network, in 2018 International Conference on Information and Communications Technology (ICOIACT), IEEE, pp. 304–309, (2018) [CrossRef] [Google Scholar]
- D. Maulana and J. Jondri, Deteksi Gangguan Jantung Premature Ventricular Contractions Menggunakan Sinyal Elektrokardiogram Dengan Algoritma Backpropagation Dan Algoritma Firefly, eProceedings of Engineering, 6, 2, (2019) [Google Scholar]
- Y. Tao, Research on the impact of trade uncertainty on national grain supply and risk cost control, Acta Agriculturae Scandinavica, Section B—Soil & Plant Science, 72, 1, 92–104, (2022) [Google Scholar]
- P. D. Purnamasari, A. A. P. Ratna, and B. Kusumoputro, EEG based patient emotion monitoring using relative wavelet energy feature and Back Propagation Neural Network, in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2820–2823, (2015) [Google Scholar]
- N. Alsharif, K. Aldubaikhy, and X. S. Shen, Link duration estimation using neural networks based mobility prediction in vehicular networks," in 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), IEEE, 1–4, (2016) [Google Scholar]
- L. Li, Y. Chen, T. Xu, R. Liu, K. Shi, and C. Huang, Super-resolution mapping of wetland inundation from remote sensing imagery based on integration of backpropagation neural network and genetic algorithm, Remote Sensing of Environment, 164, 142–154, 2015. [CrossRef] [Google Scholar]
- N. Mancosu, R. L. Snyder, G. Kyriakakis, and D. Spano, Water scarcity and future challenges for food production, Water, 7, 3, 975–992, (2015) [CrossRef] [Google Scholar]
- T. K. Rudel et al., LivestockPlus: Forages, sustainable intensification, and food security in the tropics, Ambio, 44, 7, pp. 685–693, (2015) [CrossRef] [PubMed] [Google Scholar]
- N. Bandumula, Rice production in Asia: Key to global food security, Proceedings of the National Academy of Sciences, India Section B: Biological Sciences, 88, 4, 1323–1328, (2018) [CrossRef] [Google Scholar]
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