{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T09:02:50Z","timestamp":1784797370736,"version":"3.55.0"},"reference-count":61,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T00:00:00Z","timestamp":1722988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Future Techno Science Foundation, Indonesia","award":["FTS-GRANT-V-001"],"award-info":[{"award-number":["FTS-GRANT-V-001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>In recent advancements in agricultural technology, quantum mechanics and deep learning integration have shown promising potential to revolutionize rice yield forecasting methods. This research introduces a novel Hybrid Quantum Deep Learning model that leverages the intricate processing capabilities of quantum computing combined with the robust pattern recognition prowess of deep learning algorithms such as Extreme Gradient Boosting (XGBoost) and Bidirectional Long Short-Term Memory (Bi-LSTM). Bi-LSTM networks are used for temporal feature extraction and quantum circuits for quantum feature processing. Quantum circuits leverage quantum superposition and entanglement to enhance data representation by capturing intricate feature interactions. These enriched quantum features are combined with the temporal features extracted by Bi-LSTM and fed into an XGBoost regressor. By synthesizing quantum feature processing and classical machine learning techniques, our model aims to improve prediction accuracy significantly. Based on measurements of mean square error (MSE), the coefficient of determination (R2), and mean average error (MAE), the results are 1.191621 \u00d7 10\u22125, 0.999929482, and 0.001392724, respectively. This value is so close to perfect that it helps make essential decisions in global agricultural planning and management.<\/jats:p>","DOI":"10.3390\/computers13080191","type":"journal-article","created":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T07:01:25Z","timestamp":1723100485000},"page":"191","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Rice Yield Forecasting Using Hybrid Quantum Deep Learning Model"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6615-4457","authenticated-orcid":false,"given":"De Rosal Ignatius Moses","family":"Setiadi","sequence":"first","affiliation":[{"name":"Department Informatic Engineering, Faculty of Computer Science, Dian Nuswantoro University, Semarang 50131, Central Java, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5880-6072","authenticated-orcid":false,"given":"Ajib","family":"Susanto","sequence":"additional","affiliation":[{"name":"Department Informatic Engineering, Faculty of Computer Science, Dian Nuswantoro University, Semarang 50131, Central Java, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristiawan","family":"Nugroho","sequence":"additional","affiliation":[{"name":"Department of Information Technology and Industry, Stikubank University, Semarang 50249, Central Java, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2457-6803","authenticated-orcid":false,"given":"Ahmad Rofiqul","family":"Muslikh","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, University of Merdeka, Malang 65147, East Java, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4150-5163","authenticated-orcid":false,"given":"Arnold Adimabua","family":"Ojugo","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Federal University of Petroleum Resources, Effurun, Warri 330102, Delta State, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong-Seng","family":"Gan","sequence":"additional","affiliation":[{"name":"School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi\u2019an Jiaotong-Liverpool University, Suzhou 215400, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"299","DOI":"10.62411\/jcta.10057","article-title":"Dataset Analysis and Feature Characteristics to Predict Rice Production Based on EXtreme Gradient Boosting","volume":"1","author":"Wijayanti","year":"2024","journal-title":"J. Comput. Theor. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"446","DOI":"10.62411\/jcta.10459","article-title":"Enhanced Vision Transformer and Transfer Learning Approach to Improve Rice Disease Recognition","volume":"1","author":"Rachman","year":"2024","journal-title":"J. Comput. Theor. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.62411\/faith.2024-4","article-title":"Analyzing InceptionV3 and InceptionResNetV2 with Data Augmentation for Rice Leaf Disease Classification","volume":"1","author":"Firnando","year":"2024","journal-title":"J. Futur. Artif. Intell. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bhuyan, B.P., Tomar, R., Singh, T.P., and Cherif, A.R. (2022). Crop Type Prediction: A Statistical and Machine Learning Approach. Sustainability, 15.","DOI":"10.3390\/su15010481"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Satpathi, A., Setiya, P., Das, B., Nain, A.S., Jha, P.K., Singh, S., and Singh, S. (2023). Comparative Analysis of Statistical and Machine Learning Techniques for Rice Yield Forecasting for Chhattisgarh, India. Sustainability, 15.","DOI":"10.3390\/su15032786"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105709","DOI":"10.1016\/j.compag.2020.105709","article-title":"Crop Yield Prediction Using Machine Learning: A Systematic Literature Review","volume":"177","author":"Kassahun","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_7","first-page":"101029","article-title":"Rice Crop Growth Monitoring with Sentinel 1 SAR Data Using Machine Learning Models in Google Earth Engine Cloud","volume":"32","author":"Singha","year":"2023","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1007\/s10681-022-03069-x","article-title":"Genome-Wide Association Study (GWAS) of Germination and Post-Germination Related Seedling Traits in Rice","volume":"218","author":"Panahabadi","year":"2022","journal-title":"Euphytica"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105471","DOI":"10.1016\/j.compag.2020.105471","article-title":"An End-to-End Model for Rice Yield Prediction Using Deep Learning Fusion","volume":"174","author":"Chu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Boppudi, S., and Jayachandran, S. (2024). Biomedical Signal Processing and Control Improved Feature Ranking Fusion Process with Hybrid Model for Crop Yield Prediction. Biomed. Signal Process. Control, 93.","DOI":"10.1016\/j.bspc.2024.106121"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1016\/j.ecolind.2009.06.007","article-title":"Ecological Footprint Simulation and Prediction by ARIMA Model\u2014A Case Study in Henan Province of China","volume":"10","author":"Jia","year":"2010","journal-title":"Ecol. Indic."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1016\/j.ijforecast.2021.11.001","article-title":"Forecasting: Theory and Practice","volume":"38","author":"Petropoulos","year":"2022","journal-title":"Int. J. Forecast."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Alqatawna, A., Abu-Salih, B., Obeid, N., and Almiani, M. (2023). Incorporating Time-Series Forecasting Techniques to Predict Logistics Companies\u2019 Staffing Needs and Order Volume. Computation, 11.","DOI":"10.3390\/computation11070141"},{"key":"ref_14","unstructured":"Singh, P., Niknejad, N., Ru, S., and Bao, Y. (November, January 29). A Deep Learning-Based Smartphone App for Field-Based Blueberry Yield Prediction. Proceedings of the SSSA International Annual Meeting, St. Louis, MO, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"117993","DOI":"10.1016\/j.envres.2023.117993","article-title":"Forecast of Agri-Residues Generation from Rice, Wheat and Oilseed Crops in India Using Machine Learning Techniques: Exploring Strategies for Sustainable Smart Management","volume":"245","author":"Singh","year":"2024","journal-title":"Environ. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"100337","DOI":"10.1016\/j.prime.2023.100337","article-title":"An Efficient Approach for Load Forecasting in Agricultural Sector Using Machine Learning","volume":"6","author":"Sharma","year":"2023","journal-title":"e-Prime\u2014Adv. Electr. Eng. Electron. Energy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"108377","DOI":"10.1016\/j.fcr.2021.108377","article-title":"Machine Learning for Regional Crop Yield Forecasting in Europe","volume":"276","author":"Paudel","year":"2022","journal-title":"Field Crop. Res."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Elbasi, E., Zaki, C., Topcu, A.E., Abdelbaki, W., Zreikat, A.I., Cina, E., Shdefat, A., and Saker, L. (2023). Crop Prediction Model Using Machine Learning Algorithms. Appl. Sci., 13.","DOI":"10.20944\/preprints202305.1519.v1"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"100049","DOI":"10.1016\/j.atech.2022.100049","article-title":"Crops Yield Prediction Based on Machine Learning Models: Case of West African Countries","volume":"2","author":"Cedric","year":"2022","journal-title":"Smart Agric. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2024168","DOI":"10.31893\/multiscience.2024168","article-title":"New Approach for Forecasting Rice and Corn Production in the Philippines through Machine Learning Models","volume":"6","author":"Anter","year":"2024","journal-title":"Multidiscip. Sci. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3354","DOI":"10.1109\/JBHI.2022.3151091","article-title":"XGBoost Regression of the Most Significant Photoplethysmogram Features for Assessing Vascular Aging","volume":"26","author":"Shin","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wen, H.-T., Wu, H.-Y., and Liao, K.-C. (2022). Using XGBoost Regression to Analyze the Importance of Input Features Applied to an Artificial Intelligence Model for the Biomass Gasification System. Inventions, 7.","DOI":"10.3390\/inventions7040126"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1016\/j.asej.2020.11.011","article-title":"Extreme Gradient Boosting (Xgboost) Model to Predict the Groundwater Levels in Selangor Malaysia","volume":"12","author":"Chow","year":"2021","journal-title":"Ain Shams Eng. J."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shahani, N.M., Zheng, X., Liu, C., Hassan, F.U., and Li, P. (2021). Developing an XGBoost Regression Model for Predicting Young\u2019s Modulus of Intact Sedimentary Rocks for the Stability of Surface and Subsurface Structures. Front. Earth Sci., 9.","DOI":"10.3389\/feart.2021.761990"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"23","DOI":"10.62411\/faith.2024-11","article-title":"Integrating SMOTE-Tomek and Fusion Learning with XGBoost Meta-Learner for Robust Diabetes Recognition","volume":"1","author":"Setiadi","year":"2024","journal-title":"J. Futur. Artif. Intell. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"107868","DOI":"10.1016\/j.compag.2023.107868","article-title":"Deep Spatial and Temporal Graph Convolutional Network for Rice Planthopper Population Dynamic Forecasting","volume":"210","author":"Zhang","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e15245","DOI":"10.1016\/j.heliyon.2023.e15245","article-title":"An Ensemble Deep Learning Approach for Predicting Cocoa Yield","volume":"9","author":"Olofintuyi","year":"2023","journal-title":"Heliyon"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"151","DOI":"10.33633\/jcta.v1i2.9469","article-title":"Dynamic and Static Handwriting Assessment in Parkinson\u2019s Disease: A Synergistic Approach with C-Bi-GRU and VGG19","volume":"1","author":"Ali","year":"2023","journal-title":"J. Comput. Theor. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.matpr.2022.02.109","article-title":"Forecasting Crop Yield with Deep Learning Based Ensemble Model","volume":"58","author":"Divakar","year":"2022","journal-title":"Mater. Today Proc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"108665","DOI":"10.1016\/j.agwat.2023.108665","article-title":"Estimating Reference Crop Evapotranspiration Using Improved Convolutional Bidirectional Long Short-Term Memory Network by Multi-Head Attention Mechanism in the Four Climatic Zones of China","volume":"292","author":"Dong","year":"2024","journal-title":"Agric. Water Manag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"100412","DOI":"10.1016\/j.rinam.2023.100412","article-title":"Estimation of Walnut Crop Evapotranspiration under Different Micro-Irrigation Techniques in Arid Zones Based on Deep Learning Sequence Models","volume":"20","author":"Huang","year":"2023","journal-title":"Results Appl. Math."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sasani, F., Moghareh Dehkordi, M., Ebrahimi, Z., Dustmohammadloo, H., Bouzari, P., Ebrahimi, P., Lencs\u00e9s, E., and Fekete-Farkas, M. (2024). Forecasting of Bitcoin Illiquidity Using High-Dimensional and Textual Features. Computers, 13.","DOI":"10.3390\/computers13010020"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ma, Y., Sun, D., Meng, Q., Ding, Z., and Li, C. (2017, January 9\u201310). Learning Multiscale Deep Features and SVM Regressors for Adaptive RGB-T Saliency Detection. Proceedings of the 2017 10th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China.","DOI":"10.1109\/ISCID.2017.92"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Chetty, G., and Tran, D. (2019, January 6\u20139). Deep Learning with XGBoost for Real Estate Appraisal. Proceedings of the 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China.","DOI":"10.1109\/SSCI44817.2019.9002790"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Rezaei, M.J., Woodward, J.R., Ram\u00edrez, J., and Munroe, P. (2021). A Novel Two-Stage Heart Arrhythmia Ensemble Classifier. Computers, 10.","DOI":"10.3390\/computers10050060"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"127346","DOI":"10.1016\/j.neucom.2024.127346","article-title":"A Super Resolution Method Based on Generative Adversarial Networks with Quantum Feature Enhancement: Application to Aerial Agricultural Images","volume":"577","author":"Pu","year":"2024","journal-title":"Neurocomputing"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"7438","DOI":"10.1016\/j.jfranklin.2023.06.003","article-title":"Efficient Quantum Feature Extraction for CNN-Based Learning","volume":"360","author":"Dou","year":"2023","journal-title":"J. Franklin Inst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1016\/j.icte.2023.12.005","article-title":"Short-Term Photovoltaic Power Forecasting Based on Hybrid Quantum Gated Recurrent Unit","volume":"10","author":"Jeong","year":"2023","journal-title":"ICT Express"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.neunet.2015.07.013","article-title":"Complex Rotation Quantum Dynamic Neural Networks (CRQDNN) Using Complex Quantum Neuron (CQN): Applications to Time Series Prediction","volume":"71","author":"Cui","year":"2015","journal-title":"Neural Netw."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"116583","DOI":"10.1016\/j.eswa.2022.116583","article-title":"QuantumLeap: Hybrid Quantum Neural Network for Financial Predictions","volume":"195","author":"Paquet","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"117555","DOI":"10.1016\/j.enconman.2023.117555","article-title":"Forecasting Solar Irradiance with Hybrid Classical\u2013Quantum Models: A Comprehensive Evaluation of Deep Learning and Quantum-Enhanced Techniques","volume":"294","author":"Sushmit","year":"2023","journal-title":"Energy Convers. Manag."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"122645","DOI":"10.1016\/j.eswa.2023.122645","article-title":"Hybrid Deep Learning and Quantum-Inspired Neural Network for Day-Ahead Spatiotemporal Wind Speed Forecasting","volume":"241","author":"Hong","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Jahin, M.A., Shovon, M.S.H., Islam, M.S., Shin, J., Mridha, M.F., and Okuyama, Y. (2023). QAmplifyNet: Pushing the Boundaries of Supply Chain Backorder Prediction Using Interpretable Hybrid Quantum-Classical Neural Network. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-45406-7"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"025019","DOI":"10.1088\/2632-2153\/abd486","article-title":"A Hybrid Quantum Regression Model for the Prediction of Molecular Atomization Energies","volume":"2","author":"Reddy","year":"2021","journal-title":"Mach. Learn. Sci. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1016\/j.ijepes.2013.10.020","article-title":"Application of a Hybrid Quantized Elman Neural Network in Short-Term Load Forecasting","volume":"55","author":"Li","year":"2014","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TQE.2022.3213474","article-title":"Mixed Quantum\u2013Classical Method for Fraud Detection With Quantum Feature Selection","volume":"3","author":"Grossi","year":"2022","journal-title":"IEEE Trans. Quantum Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"7057","DOI":"10.1109\/JSTARS.2021.3095377","article-title":"A Quantum Annealer for Subset Feature Selection and the Classification of Hyperspectral Images","volume":"14","author":"Otgonbaatar","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Yang, C.-H.H., Qi, J., Chen, S.Y.-C., Chen, P.-Y., Siniscalchi, S.M., Ma, X., and Lee, C.-H. (2021, January 6\u201311). Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition. Proceedings of the ICASSP 2021\u20142021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada.","DOI":"10.1109\/ICASSP39728.2021.9413453"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Shivwanshi, R.R., and Nirala, N. (2024). Quantum-Enhanced Hybrid Feature Engineering in Thoracic CT Image Analysis for State-of-the-Art Nodule Classification: An Advanced Lung Cancer Assessment. Biomed. Phys. Eng. Express, 10.","DOI":"10.1088\/2057-1976\/ad4360"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.33633\/jcta.v1i1.8877","article-title":"Plant Diseases Classification Based Leaves Image Using Convolutional Neural Network","volume":"1","author":"Imanulloh","year":"2023","journal-title":"J. Comput. Theor. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s40537-020-00305-w","article-title":"Survey on Categorical Data for Neural Networks","volume":"7","author":"Hancock","year":"2020","journal-title":"J. Big Data"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/nature23474","article-title":"Quantum Machine Learning","volume":"549","author":"Biamonte","year":"2017","journal-title":"Nature"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1038\/s42254-021-00348-9","article-title":"Variational Quantum Algorithms","volume":"3","author":"Cerezo","year":"2021","journal-title":"Nat. Rev. Phys."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"043001","DOI":"10.1088\/2058-9565\/ab4eb5","article-title":"Parameterized Quantum Circuits as Machine Learning Models","volume":"4","author":"Benedetti","year":"2019","journal-title":"Quantum Sci. Technol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1109\/TETC.2020.3000734","article-title":"A Quantum Mechanics-Based Framework for EEG Signal Feature Extraction and Classification","volume":"10","author":"Li","year":"2022","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Liu, Y., Li, W.-J., Zhang, X., Lewenstein, M., Su, G., and Ran, S.-J. (2021). Entanglement-Based Feature Extraction by Tensor Network Machine Learning. Front. Appl. Math. Stat., 7.","DOI":"10.3389\/fams.2021.716044"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"51","DOI":"10.62411\/faith.2024-12","article-title":"Analyzing Quantum Feature Engineering and Balancing Strategies Effect on Liver Disease Classification","volume":"1","author":"Safriandono","year":"2024","journal-title":"J. Futur. Artif. Intell. Technol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"39","DOI":"10.62411\/faith.2024-2","article-title":"Analyzing Preprocessing Impact on Machine Learning Classifiers for Cryotherapy and Immunotherapy Dataset","volume":"1","author":"Setiadi","year":"2024","journal-title":"J. Futur. Artif. Intell. Technol."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Tokuyama, Y., Miki, R., Fukushima, Y., Tarutani, Y., and Yokohira, T. (2020, January 28\u201330). Performance Evaluation of Feature Encoding Methods in Network Traffic Prediction Using Recurrent Neural Networks. Proceedings of the 2020 8th International Conference on Information and Education Technology, Okayama, Japan.","DOI":"10.1145\/3395245.3396441"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Reza Rezvan, M., Ghanbari Sorkhi, A., Pirgazi, J., and Mehdi Pourhashem Kallehbasti, M. (2024). AdvanceSplice: Integrating N-Gram One-Hot Encoding and Ensemble Modeling for Enhanced Accuracy. Biomed. Signal Process. Control, 92.","DOI":"10.1016\/j.bspc.2024.106017"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"17184","DOI":"10.1109\/ACCESS.2019.2895604","article-title":"Deep Learning for Daily Peak Load Forecasting-A Novel Gated Recurrent Neural Network Combining Dynamic Time Warping","volume":"7","author":"Yu","year":"2019","journal-title":"IEEE Access"}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/13\/8\/191\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:31:36Z","timestamp":1760110296000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/13\/8\/191"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,7]]},"references-count":61,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["computers13080191"],"URL":"https:\/\/doi.org\/10.3390\/computers13080191","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,7]]}}}