PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

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• Ali, M. K. M., Sulaiman, J., Yasir, S. M., Ruslan, M. H., Fudholi, A., Muthuvalu, M. S., & Ramu, V. (2017). Cubic spline as a powerful tools for processing experimental drying rate data of seaweed using solar drier. Article in Malaysian Journal of Mathematical Sciences, 11(S), 159-172.

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• Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, Article e623. https://doi.org/10.7717/peerj-cs.623

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• Echave, J., Otero, P., Garcia-Oliveira, P., Munekata, P. E. S., Pateiro, M., Lorenzo, J. M., Simal-Gandara, J., & Prieto, M. A. (2022). Seaweed-derived proteins and peptides: Promising marine bioactives. Antioxidants, 11(1), 1-26. https://doi.org/10.3390/antiox11010176

• Freund, R. M., Grigas, P., & Mazumder, R. (2017). A new perspective on boosting in linear regression via subgradient optimization and relatives. Annals of Statistics, 45(6), 2328-2364. https://doi.org/10.1214/16-AOS1505

• Friedman, J. H. (2001). Greedy Function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189-1232.

• Georganos, S., Grippa, T., Niang Gadiaga, A., Linard, C., Lennert, M., Vanhuysse, S., Mboga, N., Wolff, E., & Kalogirou, S. (2021). Geographical random forests: A spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling. Geocarto International, 36(2), 121-136. https://doi.org/10.1080/10106049.2019.1595177

• Gouda, S. G., Hussein, Z., Luo, S., & Yuan, Q. (2019). Model selection for accurate daily global solar radiation prediction in China. Journal of Cleaner Production, 221, 132-144. https://doi.org/10.1016/j.jclepro.2019.02.211

• Gunn, H. J., Rezvan, P. H., Fernández, M. I., & Comulada, W. S. (2022). How to apply variable selection machine learning algorithms with multiply imputed data: A missing discussion. Psychological Methods, 28(2), 452-471. https://doi.org/10.1037/met0000478

• Ibidoja, O. J., Ajare, E. O., & Jolayemi, E. T. (2016). Reliability measures of academic performance. International Journal of Science for Global Sustainability, 2(4), 59-64.

• Javaid, A., Ismail, M. T., & Ali, M. K. M. (2020). Comparison of sparse and robust regression techniques in efficient model selection for moisture ratio removal of seaweed using solar drier. Pertanika Journal of Science and Technology, 28(2), 609-625.

• Javaid, A., Muthuvalu, M. S., Sulaiman, J., Ismail, M. T., & Ali, M. K. M. (2019). Forecast the moisture ratio removal during seaweed drying process using solar drier. AIP Conference Proceedings, 2184, Article 050016. https://doi.org/10.1063/1.5136404

• Jierula, A., Wang, S., Oh, T. M., & Wang, P. (2021). Study on accuracy metrics for evaluating the predictions of damage locations in deep piles using artificial neural networks with acoustic emission data. Applied Sciences, 11(5), 1-21. https://doi.org/10.3390/app11052314

• Kabari, L. G., Onwuka, U., & Onwuka, U. C. (2019). Comparison of bagging and voting ensemble machine learning algorithm as a classifier. International Journal of Computer Science and Software Engineering, 9(3), 19-23.

• Kaneko, H. (2021). Examining variable selection methods for the predictive performance of regression models and the proportion of selected variables and selected random variables. Heliyon, 7(6), 1-12. https://doi.org/10.1016/j.heliyon.2021.e07356

• Kim, S., & Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32(3), 669-679. https://doi.org/10.1016/J.IJFORECAST.2015.12.003

• Leys, C., Delacre, M., Mora, Y. L., Lakens, D., & Ley, C. (2019). How to classify, detect, and manage univariate and multivariate outliers, with emphasis on pre-registration. International Review of Social Psychology, 32(1), 1-10. https://doi.org/10.5334/irsp.289

• Lim, H. Y., Fam, P. S., Javaid, A., & Ali, M. K. M. (2020). Ridge regression as efficient model selection and forecasting of fish drying using v-groove hybrid solar drier. Pertanika Journal of Science and Technology, 28(4), 1179-1202. https://doi.org/10.47836/pjst.28.4.04

• Liu, C., Tang, F., & Bak, C. L. (2018). An accurate online dynamic security assessment scheme based on random forest. Energies, 11(7), Article 1914. https://doi.org/10.3390/en11071914

• Meyer, H., Reudenbach, C., Wöllauer, S., & Nauss, T. (2019). Importance of spatial predictor variable selection in machine learning applications - Moving from data reproduction to spatial prediction. Ecological Modelling, 411, Article 108815. https://doi.org/10.1016/j.ecolmodel.2019.108815

• Namana, M. S. K., Rathnala, P., Sura, S. R., Patnaik, P., Rao, G. N., & Naidu, P. V. (2022). Internet of things for smart agriculture - State of the art and challenges. Ecological Engineering and Environmental Technology, 23(6), 147-160. https://doi.org/10.12912/27197050/152916

• Nuroğlu, E., Öz, E., Bakırdere, S., Bursalıoğlu, E. O., Kavanoz, H. B., & İçelli, O. (2019). Evaluation of magnetic field assisted sun drying of food samples on drying time and mycotoxin production. Innovative Food Science and Emerging Technologies, 52, 237-243. https://doi.org/10.1016/j.ifset.2019.01.004

• Pradhan, B., Bhuyan, P. P., Patra, S., Nayak, R., Behera, P. K., Behera, C., Behera, A. K., Ki, J. S., & Jena, M. (2022). Beneficial effects of seaweeds and seaweed-derived bioactive compounds: Current evidence and future prospective. Biocatalysis and Agricultural Biotechnology, 39, Article 102242. https://doi.org/10.1016/j.bcab.2021.102242

• Prosekov, A. Y., & Ivanova, S. A. (2018). Food security: The challenge of the present. Geoforum, 91, 73-77. https://doi.org/10.1016/j.geoforum.2018.02.030

• Rahimi, P., Islam, M. S., Duarte, P. M., Tazerji, S. S., Sobur, M. A., el Zowalaty, M. E., Ashour, H. M., & Rahman, M. T. (2022). Impact of the COVID-19 pandemic on food production and animal health. Trends in Food Science and Technology, 121, 105-113. https://doi.org/10.1016/j.tifs.2021.12.003

• Rahman, S., Irfan, M., Raza, M., Ghori, K. M., Yaqoob, S., & Awais, M. (2020). Performance analysis of boosting classifiers in recognizing activities of daily living. International Journal of Environmental Research and Public Health, 17(3), Article 1082. https://doi.org/10.3390/ijerph17031082

• Rajarathinam, A., & Vinoth, B. (2014). Outlier detection in simple linear regression models and robust regression-A case study on wheat production data. International Journal of Scientific Research, 3(2), 531-536.

• Rashidi, H. H., Tran, N. K., Betts, E. V., Howell, L. P., & Green, R. (2019). Artificial intelligence and machine learning in pathology: The present landscape of supervised methods. Academic Pathology, 6, 1-17. https://doi.org/10.1177/2374289519873088

• Safronova, O. V., Polyakova, E. D., Evdokimova, O. V., Demina, E. N., Lazareva, T. N., & Petrova, O. A. (2022). Development of sustainable systems of food production using spirulina platensis dairy technology as a functional filler. IOP Conference Series: Earth and Environmental Science, 981(2), Article 022074. https://doi.org/10.1088/1755-1315/981/2/022074

• Solyali, D. (2020). A comparative analysis of machine learning approaches for short-/long-term electricity load forecasting in Cyprus. Sustainability, 12(9), Article 3612. https://doi.org/10.3390/SU12093612

• Ssemwanga, M., Makule, E., & Kayondo, S. I. (2020). Performance analysis of an improved solar dryer integrated with multiple metallic solar concentrators for drying fruits. Solar Energy, 204, 419-428. https://doi.org/10.1016/j.solener.2020.04.065

• Sumari, A. D. W., Charlinawati, D. S., & Ariyanto, Y. (2021). A simple approach using statistical-based machine learning to predict the weapon system operational readiness. Proceedings of the International Conference on Data Science and Official Statistics, 2021(1), 343-351. https://doi.org/10.34123/icdsos.v2021i1.58

• Yang, W., Yuan, T., & Wang, L. (2020). Micro-blog sentiment classification method based on the personality and bagging algorithm. Future Internet, 12(4), Article 75. https://doi.org/10.3390/fi12040075