Prediction of white spot disease susceptibility in shrimps using decision trees based machine learning models

Ahmad MW, Reynolds J, Rezgui Y (2018) Predictive modelling for solar thermal energy systems: a comparison of support vector regression, random forest, extra trees and regression trees. J Clean Prod 203:810–821. https://doi.org/10.1016/j.jclepro.2018.08.207 Ajayram KA, Jegadeeshwaran R, Sakthivel G, Sivakumar R, Patange AD (2021) Condition monitoring of carbide and non-carbide coated tool insert using decision tree and random tree – A statistical learning. Mater Today Proceed. https://doi.org/10.1016/j.matpr.2021.02.065 Caipang CMA, Verjan N, Ooi EL, Kondo H, Hirono I, Aoki T, Kiyono H, Yuki Y (2008) Enhanced survival of shrimp, Penaeus (Marsupenaeus) japonicus from white spot syndrome disease after oral administration of recombinant VP28 expressed in Brevibacillus brevis. Fish Shellfish Immunol 25(3):315–320. https://doi.org/10.1016/j.fsi.2008.04.012 Chaijarasphong T, Thammachai T, Itsathitphaisarn O, Sritunyalucksana K, Suebsing R (2019) Potential application of CRISPR-Cas12a fluorescence assay coupled with rapid nucleic acid amplification for detection of white spot syndrome virus in shrimp. Aquaculture 512:734340. https://doi.org/10.1016/j.aquaculture.2019.734340 Chen W, Zhao X, Tsangaratos P, Shahabi H, Ilia I, Xue W, Wang X, Ahmad BB (2020) Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping. J Hydrol 583:124602. https://doi.org/10.1016/j.jhydrol.2020.124602 Chien C-F, Chen L-F (2008) Data mining to improve personnel selection and enhance human capital: a case study in high-technology industry. Exp Syst Appl 34(1):280–290 Depina I, Oguz EA, Thakur V (2020) Novel Bayesian framework for calibration of spatially distributed physical-based landslide prediction models. Comput Geotech 125:103660. https://doi.org/10.1016/j.compgeo.2020.103660 Díaz JD, Hansen E, Cabrera G (2020) A random walk through the trees: Forecasting copper prices using decision learning methods. Resour Policy 69:101859. https://doi.org/10.1016/j.resourpol.2020.101859 Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu Z, Chen C-W, Han Z, Pham BT (2020) Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed. Japan Landslides 17(3):641–658. https://doi.org/10.1007/s10346-019-01286-5 Edeh MO, Dalal S, Obagbuwa IC, Prasad BS, Ninoria SZ, Wajid MA, Adesina AO (2022) Bootstrapping random forest and CHAID for prediction of white spot disease among shrimp farmers. Sci Rep 12(1):20876 Falconer L, Telfer TC (2016) Ross LGJJoem investigation of a novel approach for aquaculture site selection. J Environ Manag 181:791–804 Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 63(1):3–42. https://doi.org/10.1007/s10994-006-6226-1 Giap DH, Yi Y, Yakupitiyage AJO, Management C (2005) GIS for land evaluation for shrimp farming in Haiphong of Vietnam. Ocean & Coastal Manag 48(1):51–63 Han JE, Kim J-E, Jo H, Eun J-S, Lee C, Kim JH, Lee K-J, Kim J-W (2019) Increased susceptibility of white spot syndrome virus-exposed Penaeus vannamei to Vibrio parahaemolyticus causing acute hepatopancreatic necrosis disease. Aquaculture 512:734333. https://doi.org/10.1016/j.aquaculture.2019.734333 Hasan NA, Haque MM (2020) Dataset of white spot disease affected shrimp farmers disaggregated by the variables of farm site, environment, disease history, operational practices, and saline zones. Data Brief 31:105936. https://doi.org/10.1016/j.dib.2020.105936 Hong H, Liu J, Bui DT, Pradhan B, Acharya TD, Pham BT, Zhu AX, Chen W, Ahmad BB (2018) Landslide susceptibility mapping using J48 decision Tree with AdaBoost, bagging and rotation forest ensembles in the Guangchang area (China). CATENA 163:399–413. https://doi.org/10.1016/j.catena.2018.01.005 Hou N, Zhang X, Zhang W, Xu J, Feng C, Yang S, Jia K, Yao Y, Cheng J, Jiang B (2020) A new long-term downward surface solar radiation dataset over China from 1958 to 2015. Sensors (basel) 20(21):6167. https://doi.org/10.3390/s20216167 John V, Liu Z, Guo C, Mita S, Kidono K Real-Time Lane Estimation Using Deep Features and Extra Trees Regression. In: Bräunl T, McCane B, Rivera M, Yu X (eds) Image and Video Technology, Cham, 2016// 2016. Springer International Publishing, pp 721–733 Kamiński B, Prałat P (2019) Sub-trees of a random tree. Discret Appl Math 268:119–129. https://doi.org/10.1016/j.dam.2019.05.003 Kathyayani SA, Poornima M, Sukumaran S, Nagavel A, Muralidhar M (2019) Effect of ammonia stress on immune variables of Pacific white shrimp Penaeus vannamei under varying levels of pH and susceptibility to white spot syndrome virus. Ecotoxicol Environ Saf 184:109626. https://doi.org/10.1016/j.ecoenv.2019.109626 Keivani O, Sinha K (2021) Random projection-based auxiliary information can improve tree-based nearest neighbor search. Inf Sci 546:526–542. https://doi.org/10.1016/j.ins.2020.08.054 Khiem NM, Takahashi Y, Oanh DTH, Hai TN, Yasuma H, Kimura NJFS (2020) The use of machine learning to predict acute hepatopancreatic necrosis disease (AHPND) in shrimp farmed on the east coast of the Mekong Delta of Vietnam. Fisheries Sci 86:673–683 Li C, Gao X-X, Huang J, Liang Y (2016) Studies of the viral binding proteins of shrimp BP53, a receptor of white spot syndrome virus. J Invertebr Pathol 134:48–53. https://doi.org/10.1016/j.jip.2016.01.006 Lu X, Kong J, Luan S, Dai P, Meng X, Cao B, Luo K (2016) Transcriptome analysis of the hepatopancreas in the Pacific white shrimp (Litopenaeus vannamei) under acute ammonia stress. PLoS ONE 11(10):e0164396 Madhusudana CK, Kumar H, Narendranath S (2018) Fault diagnosis of face milling tool using decision tree and sound signal. Mater Today: Proceed 5:12035–12044. https://doi.org/10.1016/j.matpr.2018.02.178 Meng X-H, Jang IK, Seo H-C, Cho Y-R (2010) A TaqMan real-time PCR assay for survey of white spot syndrome virus (WSSV) infections in Litopenaeus vannamei postlarvae and shrimp of farms in different grow-out seasons. Aquaculture 310(1):32–37. https://doi.org/10.1016/j.aquaculture.2010.10.010 Millard RS, Ellis RP, Bateman KS, Bickley LK, Tyler CR, van Aerle R, Santos EM (2020) How do abiotic environmental conditions influence shrimp susceptibility to disease? A critical analysis focussed on white spot disease. J Invertebrate Pathol. https://doi.org/10.1016/j.jip.2020.107369 Myung SJ, Kim YS, Seo DW, Shim KN, Kim HJ, Won SY, Yang SH, Lee SK, Kim MH, Min YI (1998) The new strategy in the diagnosis of pancreatic cancer and cholangiocarcinoma with CA19-9: New cutoff value from ROC (receiver operating characteristic curve. Gastroenterology 114:A650–A651. https://doi.org/10.1016/S0016-5085(98)82662-0 Nguyen QH, Ly H-B, Ho LS, Al-Ansari N, Le HV, Tran VQ, Prakash I, Pham BT (2021) Influence of data splitting on performance of machine learning models in prediction of shear strength of soil. Mathematical Problems in Engineering 2021 Nunan LM, Lightner DV (1997) Development of a non-radioactive gene probe by PCR for detection of white spot syndrome virus (WSSV). J Virol Methods 63(1):193–201. https://doi.org/10.1016/S0166-0934(96)02128-3 Okoro EE, Obomanu T, Sanni SE, Olatunji DI, Igbinedion P (2021) Application of artificial intelligent in predicting the dynamics of bottom hole pressure for under-balanced drilling: Extra tree compared with feed forward neural network model. Petroleum. https://doi.org/10.1016/j.petlm.2021.03.001 Pham BT, Tien Bui D, Pourghasemi HR, Indra P, Dholakia MJT, Climatology A (2017) Landslide susceptibility assesssment in the Uttarakhand area India using GIS a comparison study of prediction capability of naïve bayes multilayer perceptron neural networks and functional trees methods. Theor Appl Climatol 128:255–273 Pham BT, Tien Bui D, Prakash I (2017b) Landslide susceptibility assessment using bagging ensemble based alternating decision trees, logistic regression and J48 decision trees methods: a comparative study. Geotech Geol Eng 35:2597–2611 Ramachandran L, Mohan V, Senthilkumar S, Ganesh J Early detection and identification of white spot syndrome in shrimp using an improved deep convolutional neural network. Journal of Intelligent & Fuzzy Systems (Preprint):1–12 Rustam F, Mehmood A, Ullah S, Ahmad M, Muhammad Khan D, Choi GS, On BW (2020) Predicting pulsar stars using a random tree boosting voting classifier (RTB-VC). Astronomy and Comput 32:100404. https://doi.org/10.1016/j.ascom.2020.100404 Schleder DD, Blank M, Peruch LGB, Poli MA, Gonçalves P, Rosa KV, Fracalossi DM, Vieira FdN, Andreatta ER, Hayashi L (2020) Impact of combinations of brown seaweeds on shrimp gut microbiota and response to thermal shock and white spot disease. Aquaculture 519:734779. https://doi.org/10.1016/j.aquaculture.2019.734779 Seyyedattar M, Ghiasi MM, Zendehboudi S, Butt S (2020) Determination of bubble point pressure and oil formation volume factor: extra trees compared with LSSVM-CSA hybrid and ANFIS models. Fuel 269:116834. https://doi.org/10.1016/j.fuel.2019.116834 Sridhar Raj S, Nandhini M (2018) Ensemble human movement sequence prediction model with Apriori based probability tree classifier (APTC) and bagged J48 on machine learning. J King Saud University – Comput Inform Sci. https://doi.org/10.1016/j.jksuci.2018.04.002 Sun Y-L, Zhang X-Y, Sun T, He N, Li J-Y, Zhuang Y, Zeng Q, Yu J, Fang Q, Wang R (2013) The anti-inflammatory potential of neuropeptide FF in vitro and in vivo. Peptides 47:124–132. https://doi.org/10.1016/j.peptides.2013.07.003 Tangirala S (2020) Evaluating the impact of GINI index and information gain on classification using decision tree classifier algorithm. Int J Adv Comput Sci Appl 11(2):612–619 Tien Bui D, Shirzadi A, Shahabi H, Geertsema M, Omidvar E, Clague JJ, Thai Pham B, Dou J, Talebpour Asl D, Bin Ahmad B, Lee S (2019) New ensemble models for shallow landslide susceptibility modeling in a semi-arid watershed. Forests 10(9):743 Tripepi G, Jager KJ, Dekker FW, Zoccali C (2009) Diagnostic methods 2: receiver operating characteristic (ROC) curves. Kidney Int 76(3):252–256. https://doi.org/10.1038/ki.2009.171 Tuyen N, Verreth J, Vlak J, De Jong M (2014) Horizontal transmission dynamics of White spot syndrome virus by cohabitation trials in juvenile Penaeus monodon and P vannamei. Preventive Veterinary Med 117(1):286–294 Van Phong T, Ly H-B, Trinh PT, Prakash I, Btjvjoes P (2020) Landslide susceptibility mapping using Forest by Penalizing Attributes (FPA) algorithm based machine learning approach. Vietnam J Earth Sci 42(3):237–246 Verbruggen B, Bickley LK, Van Aerle R, Bateman KS, Stentiford GD, Santos EM, Tyler CR (2016) Molecular mechanisms of white spot syndrome virus infection and perspectives on treatments. Viruses 8(1):23 Wang Q, White BL, Redman RM, Lightner DV (1999) Per os challenge of Litopenaeus vannamei postlarvae and Farfantepenaeus duorarum juveniles with six geographic isolates of white spot syndrome virus. Aquaculture 170(3):179–194. https://doi.org/10.1016/S0044-8486(98)00425-6 Zacarias S, Fegan D, Wangsoontorn S, Yamuen N, Limakom T, Carboni S, Davie A, Metselaar M, Little DC, Shinn AP (2021) Increased robustness of postlarvae and juveniles from non-ablated Pacific whiteleg shrimp, Penaeus vannamei, broodstock post-challenged with pathogenic isolates of Vibrio parahaemolyticus (VpAHPND) and white spot disease (WSD). Aquaculture 532:736033. https://doi.org/10.1016/j.aquaculture.2020.736033 Zhang L, Lin Z, Wang J, He B (2020) Rapidly-exploring Random Trees multi-robot map exploration under optimization framework. Robot Auton Syst 131:103565. https://doi.org/10.1016/j.robot.2020.103565 Zito M, Cooper C (2006) Dominating sets of random recursive trees. Electron Notes in Dis Math 27:107–108. https://doi.org/10.1016/j.endm.2006.08.078