A Machine Learning Based Investigative Analysis for Predicting the Critical Temperature of Superconductors
DOI:
https://doi.org/10.4108/airo.3622Keywords:
Superconductor, Critical Temperature, Machine Learning, Stacking Ensemble MethodAbstract
INTRODUCTION: Ever since the initial discovery of superconductivity, the fundamental concept and the complex relationship between critical temperature and superconductive materials have been subject to extensive investigation. However, identifying superconductors that exhibit such behavior at normal temperatures remains a significant challenge, and there are still significant gaps in our understanding of this unique phenomenon, particularly regarding the fundamental criteria used to estimate critical temperature.
OBJECTIVES: To address this knowledge gap, a plethora of machine learning techniques have been developed to model critical temperatures, given the inherent difficulty in predicting them using traditional methods.
METHODS: Additionally, the limitations of the standard empirical formula in determining the temperature range require the development of more advanced and viable methods. This article presents an investigative analysis on the performance achieved by different supervised machine learning algorithms when used with three different feature selection techniques.
RESULTS: The stacking model used in this work is found to be the best performer among all the algorithms tested, as reflected by the Root Mean Squared Error (RMSE) of 9.68, R2 score of 0.922, Mean Absolute Error (MAE) score of 5.383, and Mean Absolute Percentage Error (MAPE) score of 4.575.
CONCLUSION: Therefore, it is observed that ML algorithms can contribute significantly in the domain of predictive analysis of modeling critical temperatures in superconductors and can assist in developing a robust computer-aided system to aid the education personals and research scientists to further assess the performance of the ML models.
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References
N. Lazarides and G. Tsironis, “Superconducting metamaterials,” Physics Reports, vol. 752, pp. 1–67, 2018.
P. Richards and M. Tinkham, “Far-infrared energy gap measurements in bulk superconducting in, sn, hg, ta, v, pb, and nb,” Physical Review, vol. 119, no. 2, p. 575, 1960.
A. Costa, Theoretical investigations of charge and spin transport through superconducting tunnel junctions. PhD thesis, 2021.
R. H. Ratul, M. Iqbal, J.-Y. Pan, M. M. Al Deen, M. T. Kawser, and M. M. Billah, “Performance comparison between volte and non-volte voice calls during mobility in commercial deployment: A drive test-based analysis,” in 2023 10th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), pp. 446–452, Sep. 2023.
R. H. Ratul and H.-C. Wang, “Cellular communication network evolution and the reliability of system design from 1g to 6g,” in International Conference on Wireless Intelligent and Distributed Environment for Communication, pp. 165–187, Springer, 2023.
M. Tasnim and R. H. Ratul, “Optoacoustic signal-based underwater node localization technique: Overcoming gps limitations without auv requirements,” in 2023 IEEE Symposium on Wireless Technology & Applications (ISWTA), pp. 7–11, 2023.
M. Mahmud, M. Younis, G. Carter, and F.-S. Choa, “Underwater node localization using optoacoustic signals,” in ICC 2022-IEEE International Conference on Communications, pp. 4444–4449, IEEE, 2022.
M. S. Islam, M. Younis, M. Mahmud, G. Carter, and F.-S. Choa, “A peak detection based ook photoacoustic modulation scheme for air to underwater communication,” Optics Communications, vol. 529, p. 129078, 2023.
D. Wines, K. Choudhary, A. J. Biacchi, K. F. Garrity, and F. Tavazza, “High-throughput dft-based discovery of next generation two-dimensional (2d) superconductors,” Nano letters, vol. 23, no. 3, pp. 969–978, 2023.
F. Faisal and M. M. Nishat, “An investigation for enhancing registration performance with brain atlas by novel image inpainting technique using dice and jaccard score on multiple sclerosis (ms) tissue,” Biomedical and Pharmacology Journal, vol. 12, no. 3, pp. 1249–1262, 2019.
M. M. Nishat, F. Faisal, T. Hasan, M. F. B. Karim, Z. Islam, and M. R. K. Shagor, “An investigative approach to employ support vector classifier as a potential detector of brain cancer from mri dataset,” in 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), pp. 1–4, IEEE, 2021.
M. A.-A.-R. Asif, M. M. Nishat, F. Faisal, R. R. Dip, M. H. Udoy, M. F. Shikder, and R. Ahsan, “Performance evaluation and comparative analysis of different machine learning algorithms in predicting cardiovascular disease.,” Engineering Letters, vol. 29, no. 2, 2021.
M. M. Nishat, F. Faisal, R. R. Dip, S. M. Nasrullah, R. Ahsan, F. Shikder, M. A.-A.-R. Asif, and M. A. Hoque, “A comprehensive analysis on detecting chronic kidney disease by employing machine learning algorithms,” EAI Endorsed Transactions on Pervasive Health and Technology, vol. 7, no. 29, pp. e1–e1, 2021.
M. R. Farazi, F. Faisal, Z. Zaman, and S. Farhan, “Inpainting multiple sclerosis lesions for improving registration performance with brain atlas,” in 2016 International Conference on Medical Engineering, Health Informatics and Technology (MediTec), pp. 1–6, IEEE, 2016.
M. A.-A.-R. Asif, M. M. Nishat, F. Faisal, M. F. Shikder, M. H. Udoy, R. R. Dip, and R. Ahsan, “Computer aided diagnosis of thyroid disease using machine learning algorithms,” in 2020 11th International Conference on Electrical and Computer Engineering (ICECE), pp. 222– 225, IEEE, 2020.
M. M. Nishat and F. Faisal, “An investigation of spectroscopic characterization on biological tissue,” in 2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT), pp. 290–295, IEEE, 2018.
F. Faisal, M. M. Nishat, M. A. Mahbub, M. M. I. Shawon, and M. M.-U.-H. Alvi, “Covid-19 and its impact on school closures: a predictive analysis using machine learning algorithms,” in 2021 International Conference on Science & Contemporary Technologies (ICSCT), pp. 1–6, IEEE, 2021.
A. A. Rahman, F. Faisal, M. M. Nishat, M. I. Siraji, L. I. Khalid, M. R. H. Khan, and M. T. Reza, “Detection of epileptic seizure from eeg signal data by employing machine learning algorithms with hyperparameter optimization,” in 2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART), pp. 1–4, IEEE, 2021.
M. M. Nishat, T. Hasan, S. M. Nasrullah, F. Faisal, M. A.- A.-R. Asif, and M. A. Hoque, “Detection of parkinson’s disease by employing boosting algorithms,” in 2021 Joint 10th International Conference on Informatics, Electronics & Vision (ICIEV) and 2021 5th International Conference on Imaging, Vision & Pattern Recognition (icIVPR), pp. 1–7, IEEE, 2021.
M. M. Nishat, F. Faisal, T. Hasan, S. M. Nasrullah, A. H. Bristy, M. Minhajul Islam Shawon, and M. Ashraful Hoque, “Detection of autism spectrum disorder by discriminant analysis algorithm,” in Proceedings of the International Conference on Big Data, IoT, and Machine Learning: BIM 2021, pp. 473–482, Springer, 2022.
A. A. Rahman, M. I. Siraji, L. I. Khalid, F. Faisal, M. M. Nishat, M. R. Islam, et al., “Detection of mental state from eeg signal data: an investigation with machine learning classifiers,” in 2022 14th International Conference on Knowledge and Smart Technology (KST), pp. 152–156, IEEE, 2022.
M. R. Rahman, S. Tabassum, E. Haque, M. M. Nishat, F. Faisal, and E. Hossain, “Cnn-based deep learning approach for micro-crack detection of solar panels,” in 2021 3rd International Conference on Sustainable Technologies for Industry 4.0 (STI), pp. 1–6, IEEE, 2021.
M. Muntasir Nishat, F. Faisal, I. Jahan Ratul, A. Al- Monsur, A. M. Ar-Rafi, S. M. Nasrullah, M. T. Reza, and M. R. H. Khan, “A comprehensive investigation of the performances of different machine learning classifiers with smote-enn oversampling technique and hyperparameter optimization for imbalanced heart failure dataset,” Scientific Programming, vol. 2022, pp. 1–17, 2022.
M. M. Nishat, F. Faisal, M. A. Mahbub, M. H. Mahbub, S. Islam, and M. A. Hoque, “Performance assessment of different machine learning algorithms in predicting diabetes mellitus,” Biosc. Biotech. Res. Comm, vol. 14, no. 1, pp. 74–82, 2021.
F. Faisal, Image Inpainting to Improve the Registration Performance of Multiple Sclerosis (MS) Patient Brain with Brain Atlas. PhD thesis, Department of Electrical and Electronic Engineering, Islamic University of . . . , 2016.
M. M. Nishat, F. Faisal, R. R. Dip, M. F. Shikder, R. Ahsan, M. A.-A.-R. Asif, and M. H. Udoy, “Performance investigation of different boosting algorithms in predicting chronic kidney disease,” in 2020 2nd International Conference on Sustainable Technologies for Industry 4.0 (STI), pp. 1–5, IEEE, 2020.
M. Islam, M. Tabassum, M. M. Nishat, F. Faisal, and M. S. Hasan, “Real-time clinical gait analysis and foot anomalies detection using pressure sensors and convolutional neural network,” in 2022 7th International Conference on Business and Industrial Research (ICBIR), pp. 717–722, IEEE, 2022.
I. J. Ratul, A. Al-Monsur, B. Tabassum, A. M. Ar-Rafi, M. M. Nishat, and F. Faisal, “Early risk prediction of cervical cancer: A machine learning approach,” in 2022 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), pp. 1–4, IEEE, 2022.
A. Al-Monsur, M. R. Kabir, A. M. Ar-Rafi, M. M. Nishat, and F. Faisal, “Covid-ensemblenet: an ensemble based approach for detecting covid-19 by utilising chest x-ray images,” in 2022 IEEE World AI IoT Congress (AIIoT), pp. 351–356, IEEE, 2022.
I. A. Jahan, I. A. Jamil, M. S. H. Fahim, A. S. Huq, F. Faisal, and M. M. Nishat, “Accident detection and road condition monitoring using blackbox module,” in 2022 Thirteenth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 247–252, IEEE, 2022.
A. A. Rahman, M. I. Siraji, L. I. Khalid, F. Faisal, M. M. Nishat, A. Ahmed, and M. A. Al Mamun, “Perceived stress analysis of undergraduate students during covid-19: a machine learning approach,” in 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON), pp. 1129–1134, IEEE, 2022.
F. Faisal, M. M. Nishat, and M. A. M. Oninda, “Spectroscopic characterization of biological tissue using quantitative acoustics technique,” in 2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT), pp. 38–43, IEEE, 2018.
Z. K. Eisham, M. M. Haque, M. S. Rahman, M. M. Nishat, F. Faisal, and M. R. Islam, “Chimp optimization algorithm in multilevel image thresholding and image clustering,” Evolving Systems, vol. 14, no. 4, pp. 605–648, 2023.
F. Faisal, M. A. Salam, M. B. Habib, M. S. Islam, and M. M. Nishat, “Depth estimation from video using computer vision and machine learning with hyperparameter optimization,” in 2022 4th International Conference on Smart Sensors and Application (ICSSA), pp. 39–44, IEEE, 2022.
M. M. Nishat, M. A. M. Oninda, F. Faisal, and M. A. Hoque, “Modeling, simulation and performance analysis of sepic converter using hysteresis current control and pi control method,” in 2018 International Conference on Innovations in Science, Engineering and Technology (ICISET), pp. 7–12, IEEE, 2018.
M. M. Nishat, F. Faisal, M. Rahman, and M. A. Hoque, “Modeling and design of a fuzzy logic based pid controller for dc motor speed control in different loading condition for enhanced performance,” in 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), pp. 1–6, IEEE, 2019.
A. Al Mehadi, M. M. Nishat, F. Faisal, A. R. H. Bhuiyan, M. Hussain, and M. A. Hoque, “Design, simulation and feasibility analysis of bifacial solar pv system in marine drive road, cox’s bazar,” in 2021 International Conference on Science & Contemporary Technologies (ICSCT), pp. 1–6, IEEE, 2021.
M. M. Nishat, F. Faisal, A. J. Evan, M. M. Rahaman, M. S. Sifat, H. F. Rabbi, et al., “Development of genetic algorithm (ga) based optimized pid controller for stability analysis of dc-dc buck converter,” Journal of Power and Energy Engineering, vol. 8, no. 09, p. 8, 2020.
A. J. Moshayedi, A. Kolahdooz, A. S. Roy, S. A. L. Rostami, and X. Xie, “Design and promotion of cost-effective iot-based heart rate monitoring,” in International Conference on Cloud Computing, Internet of Things, and Computer Applications (CICA 2022), vol. 12303, pp. 405–410, SPIE, 2022.
A. J. Moshayedi, A. S. Roy, L. Liao, and S. Li, “Raspberry pi scada zonal based system for agricultural plant monitoring,” in 2019 6th International Conference on Information Science and Control Engineering (ICISCE), pp. 427–433, IEEE, 2019.
A. J. Moshayedi, A. S. Roy, S. K. Sambo, Y. Zhong, and L. Liao, “Review on: The service robot mathematical model,” EAI Endorsed Transactions on AI and Robotics, vol. 1, no. 1, 2022.
X. Gui, Design, Synthesis and Characterization of New Superconductors. Louisiana State University and Agricultural & Mechanical College, 2020.
B. Lilia, R. Hennig, P. Hirschfeld, G. Profeta, A. Sanna, E. Zurek,W. E. Pickett, M. Amsler, R. Dias, M. I. Eremets, et al., “The 2021 room-temperature superconductivity roadmap,” Journal of Physics: Condensed Matter, vol. 34, no. 18, p. 183002, 2022.
M. Yazdani-Asrami, W. Song, A. Morandi, G. De Carne, J. Murta-Pina, A. Pronto, R. Oliveira, F. Grilli, E. Pardo, M. Parizh, et al., “Roadmap on artificial intelligence and big data techniques for superconductivity,” Superconductor Science and Technology, vol. 36, no. 4, p. 043501, 2023.
F. Faisal, M. M. Nishat, and M. R. Mia, “An investigation on dc motor braking system by implementing electromagnetic relay and timer,” in 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1–6, IEEE, 2019.
M. M. Nishat, M. R. K. Shagor, H. Akter, S. A. Mim, and F. Faisal, “An optimal design of pid controller for dcdc zeta converter using particle swarm optimization,” in 2020 23rd International Conference on Computer and Information Technology (ICCIT), pp. 1–6, IEEE, 2020.
M. D. Rahman, F. Faisal, M. M. Nishat, and M. R. K. Shagor, “Design and analysis of passive lc 3 component boost converter,” in 2021 IEEE Madras Section Conference (MASCON), pp. 1–6, IEEE, 2021.
M. R. K. Shagor, M. M. Nishat, F. Faisal, M. H. Mithun, M. A. Khan, et al., “Firefly algorithm based optimized pid controller for stability analysis of dc-dc sepic converter,” in 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 0957–0963, IEEE, 2021.
A. Al Mehadi, M. A. Chowdhury, M. M. Nishat, F. Faisal, and M. M. Islam, “Design, simulation and analysis of monofacial solar pv panel based energy system for university residence: a case study,” in IOP Conference Series: Materials Science and Engineering, vol. 1045, p. 012011, IOP Publishing, 2021.
F. Faisal, M. Rahman, and F. B. Hashem, Speed control of dc motor using Self-tuned fuzzy PID controller. PhD thesis, Department of Electrical and Electronic Engineering, Islamic University of . . . , 2015.
S. Hassan, A. A. Hassan, I. Marshad, M. A. Al Hosain, M. Amin, F. Faisal, and M. M. Nishat, “Comparative analysis of machine learning algorithms in detection of brain tumor,” in 2022 3rd International Conference on Big Data Analytics and Practices (IBDAP), pp. 31–36, IEEE, 2022.
I. J. Ratul, U. H. Wani, M. M. Nishat, A. Al- Monsur, A. M. Ar-Rafi, F. Faisal, M. R. Kabir, et al., “Survival prediction of children undergoing hematopoietic stem cell transplantation using different machine learning classifiers by performing chi-square test and hyperparameter optimization: a retrospective analysis,” Computational and Mathematical Methods in Medicine, vol. 2022, 2022.
T. Hasan, M. F. Bin Karim, M. K. Mahadi, M. M. Nishat, F. Faisal, et al., “Employment of ensemble machine learning methods for human activity recognition,” Journal of Healthcare Engineering, vol. 2022, 2022.
M. R. Kabir, M. M.Muhaimin, M. A. Mahir, M. M. Nishat, F. Faisal, and N. N. I. Moubarak, “Procuring mfccs from crema-d dataset for sentiment analysis using deep learning models with hyperparameter tuning,” in 2021 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things (RAAICON), pp. 50–55, IEEE, 2021.
M. K. Mahadi, S. R. Abir, A.-M. Moon, M. Adnan, M. A. N. I. Khan, M. M. Nishat, F. Faisal, and M. T. Reza, “Machine learning assisted decision support system for prediction of prostrate cancer,” in 2023 20th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), pp. 1–5, IEEE, 2023.
A. A. Rahman, M. D. R. Kabir, R. Ratul, F. A. Shams, M. M. Nishat, and F. Faisal, “An efficient analysis of eeg signals to perform emotion analysis,” in 4th International Conference on Artificial Intelligence, Robotics and Control (AIRC), 2023. In Press.
F. Faisal, M. M. Nishat, K. R. Raihan, A. Shafiullah, and S. Ali, “A machine learning approach for analyzing and predicting suicidal thoughts and behaviors,” in 2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 43–48, IEEE, 2023.
I. J. Ratul, M. M. Nishat, F. Faisal, S. Sultana, A. Ahmed, and M. A. Al Mamun, “Analyzing perceived psychological and social stress of university students: A machine learning approach,” Heliyon, 2023.
M. N. S. Shahi, S. S. Muhaimin, M. M. Nishat, F. Faisal, and M. S. B. Motahar, “Automated vehicle access and exit control: A smart parking management system using finite state machine in vhdl,” in 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C), pp. 170–177, IEEE, 2022.
F. Faisal, M. M. Nishat, N. Tazreen, M. F. B. Karim, A. H. Bristy, and S. Haque, “Ex-vivo design of ph meter to analyze nutrients feature assessment for economic domestic purpose,” in 2022 37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), pp. 637–640, IEEE, 2022.
N. Sakib, Z. B. Saif, M. T. Hasan, M. Adnan, F. Faisal, M. M. Nishat, and et al., “Eeg-driven age prediction: Advancements in machine learning models,” in Proceedings of the ICE3IS, IEEE 2023. Accepted, in press.
A. Newaz, M. S. Mohosheu, and M. A. Al Noman, “Predicting complications of myocardial infarction within several hours of hospitalization using data mining techniques,” Informatics in Medicine Unlocked, vol. 42, p. 101361, 2023.
H.-C. Wang and J.-J. Zhuang, “Effective fatigue driving detection by machine learning,” in International Conference on Wireless Intelligent and Distributed Environment for Communication, pp. 59–75, Springer, 2023.
F. A. Shams, M. S. Mohosheu, M. A. al Noman, S. R. Abir, Al-Amin, M. M. Nishat, and F. Faisal, “Roc based performance evaluation of machine learning classifiers for multiclass imbalanced intrusion detection dataset,” in 8th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE 2023), in press.
V. Stanev, C. Oses, A. G. Kusne, E. Rodriguez, J. Paglione, S. Curtarolo, and I. Takeuchi, “Machine learning modeling of superconducting critical temperature,” npj Computational Materials, vol. 4, no. 1, p. 29, 2018.
K. Hamidieh, “A data-driven statistical model for predicting the critical temperature of a superconductor,” Computational Materials Science, vol. 154, pp. 346–354, 2018.
S. Li, Y. Dan, X. Li, T. Hu, R. Dong, Z. Cao, and J. Hu, “Critical temperature prediction of superconductors based on atomic vectors and deep learning,” Symmetry, vol. 12, no. 2, p. 262, 2020.
P. J. García-Nieto, E. García-Gonzalo, and J. P. Paredes- Sánchez, “Prediction of the critical temperature of a superconductor by using the woa/mars, ridge, lasso and elastic-net machine learning techniques,” Neural Computing and Applications, vol. 33, pp. 17131–17145, 2021.
R. V. Babu, G. Ayyappan, and A. Kumaravel, “Comparison of linear regression and simple linear regression for critical temperature of semiconductor,” Indian J. Comput. Sci. Eng., vol. 10, pp. 177–183, 2020.
P. Moscato, M. N. Haque, K. Huang, J. Sloan, and J. Corrales de Oliveira, “Learning to extrapolate using continued fractions: Predicting the critical temperature of superconductor materials,” Algorithms, vol. 16, no. 8, p. 382, 2023.
F. A. Shams, R. H. Ratul, A. I. Naf, S. S. H. Samir, M. M. Nishat, F. Faisal, and M. A. Hoque, “Investigation on machine learning based approaches for estimating the critical temperature of superconductors,” arXiv preprint arXiv:2308.01932, 2023.
National Institute of Materials Science, “Materials information station, supercon.” http://supercon.nims.go.jp/index_en.html, 2022. Accessed: July 16, 2023.
A. Frank, “Uci machine learning repository,” http://archive. ics. uci. edu/ml, 2010.
M. Muntasir Nishat, F. Faisal, I. Jahan Ratul, A. Al- Monsur, A. M. Ar-Rafi, S. M. Nasrullah, M. T. Reza, and M. R. H. Khan, “A comprehensive investigation of the performances of different machine learning classifiers with smote-enn oversampling technique and hyperparameter optimization for imbalanced heart failure dataset,” Scientific Programming, vol. 2022, pp. 1–17, 2022.
R. Zebari, A. Abdulazeez, D. Zeebaree, D. Zebari, and J. Saeed, “A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction,” Journal of Applied Science and Technology Trends, vol. 1, no. 2, pp. 56–70, 2020.
G. Chandrashekar and F. Sahin, “A survey on feature selection methods,” Computers & Electrical Engineering, vol. 40, no. 1, pp. 16–28, 2014.
E. Elgeldawi, A. Sayed, A. R. Galal, and A. M. Zaki, “Hyperparameter tuning for machine learning algorithms used for arabic sentiment analysis,” in Informatics, vol. 8, p. 79, MDPI, 2021.
C. Yan, J. Zhang, X. Kang, Z. Gong, J. Wang, and G. Zhang, “Comparison and evaluation of the combinations of feature selection and classifier on microarray data,” in 2021 IEEE 6th International Conference on Big Data Analytics (ICBDA), pp. 133–137, IEEE, 2021.
M. Schleich, D. Olteanu, and R. Ciucanu, “Learning linear regression models over factorized joins,” in Proceedings of the 2016 International Conference on Management of Data, pp. 3–18, 2016.
G. Van Dijck and M. M. Van Hulle, “Speeding up the wrapper feature subset selection in regression by mutual information relevance and redundancy analysis,” in International Conference on Artificial Neural Networks, pp. 31–40, Springer, 2006.
C. Shu and D. H. Burn, “Artificial neural network ensembles and their application in pooled flood frequency analysis,” Water Resources Research, vol. 40, no. 9, 2004.
R. Xia, Y. Gao, Y. Zhu, G. Dexi, and C. Wu, “A fast and efficient method combined data-driven for detecting electricity theft to secure the smart grid with stacking structure,” Available at SSRN 4019865.
O. Sagi and L. Rokach, “Ensemble learning: A survey,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, no. 4, p. e1249, 2018.
O. Sagi and L. Rokach, “Ensemble learning: A survey,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, no. 4, p. e1249, 2018.
B. Choudhury, “Evaluation of an empirical equation for annual evaporation using field observations and results from a biophysical model,” Journal of Hydrology, vol. 216, no. 1-2, pp. 99–110, 1999.
J. Tayman and D. A. Swanson, “On the validity of mape as a measure of population forecast accuracy,” Population Research and Policy Review, vol. 18, pp. 299–322, 1999.
J. Lupón, H. K. Gaggin, M. De Antonio, M. Domingo, A. Galán, E. Zamora, J. Vila, J. Peñafiel, A. Urrutia, E. Ferrer, et al., “Biomarker-assist score for reverse remodeling prediction in heart failure: The st2-r2 score,” International journal of cardiology, vol. 184, pp. 337–343, 2015.
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Copyright (c) 2023 Fatin Abrar Shams, Rashed Hasan Ratul, Ahanf Islam Naf, Syed Shaek Hossain Samir, Samiur Rashid Abir, Fahim Faisal, Mirza Muntasir Nishat, Md Ashraful Hoque
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