{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T05:56:47Z","timestamp":1773899807159,"version":"3.50.1"},"reference-count":102,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T00:00:00Z","timestamp":1651017600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T00:00:00Z","timestamp":1651017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001409","name":"Department of Science and Technology, Ministry of Science and Technology","doi-asserted-by":"publisher","award":["T-319"],"award-info":[{"award-number":["T-319"]}],"id":[{"id":"10.13039\/501100001409","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1007\/s13042-022-01562-2","type":"journal-article","created":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T02:02:39Z","timestamp":1651024959000},"page":"187-212","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Fractional mega trend diffusion function-based feature extraction for plant disease prediction"],"prefix":"10.1007","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6243-2982","authenticated-orcid":false,"given":"Anshul","family":"Bhatia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anuradha","family":"Chug","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amit Prakash","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dinesh","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,4,27]]},"reference":[{"key":"1562_CR1","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1146\/annurev.phyto.43.113004.133839","volume":"43","author":"RN Strange","year":"2005","unstructured":"Strange RN, Scott PR (2005) Plant disease: a threat to global food security. Annu Rev Phytopathol 43:83\u2013116. https:\/\/doi.org\/10.1146\/annurev.phyto.43.113004.133839","journal-title":"Annu Rev Phytopathol"},{"key":"1562_CR2","unstructured":"Golhani K (2018) Early Detection of orange spotting disease in oil palm using red edge parameters. Dr thesis, Univ Putra Malaysia"},{"key":"1562_CR3","doi-asserted-by":"crossref","unstructured":"Khirade SD, Patil AB (2015) Plant disease detection using image processing. In: 2015 International conference on computing communication control and automation. pp 768\u2013771","DOI":"10.1109\/ICCUBEA.2015.153"},{"key":"1562_CR4","doi-asserted-by":"crossref","unstructured":"Verma S, Bhatia A, Chug A, Singh AP (2020) Recent advancements in multimedia big data computing for IoT applications in precision agriculture: opportunities, issues, and challenges. In: Multimedia Big Data Computing for IoT Applications. Springer, pp 391\u2013416","DOI":"10.1007\/978-981-13-8759-3_15"},{"key":"1562_CR5","doi-asserted-by":"crossref","unstructured":"Bhatia A, Chug A, Singh AP (2020) Hybrid SVM-LR Classifier for Powdery Mildew Disease Prediction in Tomato Plant. In: 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN). pp 218\u2013223","DOI":"10.1109\/SPIN48934.2020.9071202"},{"key":"1562_CR6","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1080\/09720510.2020.1799504","volume":"23","author":"A Bhatia","year":"2020","unstructured":"Bhatia A, Chug A, Prakash SA (2020) Application of extreme learning machine in plant disease prediction for highly imbalanced dataset. J Stat Manag Syst 23:1059\u20131068. https:\/\/doi.org\/10.1080\/09720510.2020.1799504","journal-title":"J Stat Manag Syst"},{"key":"1562_CR7","first-page":"307","volume":"23","author":"S Verma","year":"2020","unstructured":"Verma S, Chug A, Singh AP (2020) Exploring capsule networks for disease classification in plants. J Stat Manag Syst 23:307\u2013315","journal-title":"J Stat Manag Syst"},{"key":"1562_CR8","doi-asserted-by":"crossref","unstructured":"Verma S, Chug A, Singh AP, et al (2019) Deep Learning-Based Mobile Application for Plant Disease Diagnosis: A Proof of Concept With a Case Study on Tomato Plant. In: Applications of Image Processing and Soft Computing Systems in Agriculture. IGI Global, pp 242\u2013271","DOI":"10.4018\/978-1-5225-8027-0.ch010"},{"key":"1562_CR9","first-page":"71","volume":"13","author":"A Bhatia","year":"2020","unstructured":"Bhatia A, Chug A, Singh AP (2020) Plant disease detection for high dimensional imbalanced dataset using an enhanced decision tree approach. Int J Futur Gen Commun Netw 13:71\u201378","journal-title":"Int J Futur Gen Commun Netw"},{"key":"1562_CR10","first-page":"24","volume":"9","author":"A Bhatia","year":"2021","unstructured":"Bhatia A, Chug A, Singh AP (2021) Statistical analysis of machine learning techniques for predicting powdery mildew disease in tomato plants. Int J Intell Eng Inform 9:24\u201358","journal-title":"Int J Intell Eng Inform"},{"key":"1562_CR11","doi-asserted-by":"crossref","unstructured":"Bhatia A, Chug A, Singh AP, et al (2022) A Forecasting Technique for Powdery Mildew Disease Prediction in Tomato Plants. In: Proceedings of Second Doctoral Symposium on Computational Intelligence. pp 509\u2013520","DOI":"10.1007\/978-981-16-3346-1_41"},{"key":"1562_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/s42360-021-00430-3","author":"A Bhatia","year":"2021","unstructured":"Bhatia A, Chug A, Singh AP et al (2021) A machine learning-based spray prediction model for tomato powdery mildew disease. Indian Phytopathol. https:\/\/doi.org\/10.1007\/s42360-021-00430-3","journal-title":"Indian Phytopathol"},{"key":"1562_CR13","doi-asserted-by":"publisher","first-page":"651","DOI":"10.3390\/agriculture11070651","volume":"11","author":"S Zhao","year":"2021","unstructured":"Zhao S, Peng Y, Liu J, Wu S (2021) Tomato leaf disease diagnosis based on improved convolution neural network by attention module. Agriculture 11:651","journal-title":"Agriculture"},{"key":"1562_CR14","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1094\/PD-71-0266","volume":"71","author":"WB Jones","year":"1987","unstructured":"Jones WB, Thomson SV et al (1987) Source of inoculum, yield, and quality of tomato as affected by Leveillula taurica. Plant Dis 71:266\u2013268","journal-title":"Plant Dis"},{"key":"1562_CR15","doi-asserted-by":"publisher","unstructured":"Jindo K, Evenhuis A, Kempenaar C, et al Holistic pest management against early blight disease towards sustainable agriculture. Pest Manag Sci. https:\/\/doi.org\/10.1002\/ps.6320","DOI":"10.1002\/ps.6320"},{"key":"1562_CR16","doi-asserted-by":"crossref","unstructured":"Aegerter BJ, Stoddard CS, Miyao EM, et al (2014) Impact of powdery mildew (Leveillula taurica) on yield and fruit quality of processing tomatoes in California. In: XIII International Symposium on Processing Tomato 1081, pp 153\u2013158","DOI":"10.17660\/ActaHortic.2015.1081.17"},{"key":"1562_CR17","doi-asserted-by":"publisher","first-page":"372","DOI":"10.9735\/0975-3710.5.2.372-378","volume":"5","author":"ART Bakeer","year":"2013","unstructured":"Bakeer ART, Abdel-Latef MAE, Afifi MA, Barakat ME (2013) Validation of tomato powdery mildew forecasting model using meteorological data in Egypt. Int J Agric Sci 5:372","journal-title":"Int J Agric Sci"},{"key":"1562_CR18","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1016\/j.cropro.2010.02.019","volume":"29","author":"HF Avenot","year":"2010","unstructured":"Avenot HF, Michailides TJ (2010) Progress in understanding molecular mechanisms and evolution of resistance to succinate dehydrogenase inhibiting (SDHI) fungicides in phytopathogenic fungi. Crop Prot 29:643\u2013651","journal-title":"Crop Prot"},{"key":"1562_CR19","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"key":"1562_CR20","first-page":"62","volume":"24","author":"J Zhang","year":"2019","unstructured":"Zhang J, Chen L (2019) Clustering-based undersampling with random over sampling examples and support vector machine for imbalanced classification of breast cancer diagnosis. Comput Assist Surg 24:62\u201372","journal-title":"Comput Assist Surg"},{"key":"1562_CR21","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1145\/1007730.1007735","volume":"6","author":"GE Batista","year":"2004","unstructured":"Batista GE, Prati RC, Monard MC (2004) A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explor Newsl 6:20\u201329","journal-title":"ACM SIGKDD Explor Newsl"},{"key":"1562_CR22","unstructured":"Branco P, Ribeiro RP, Torgo L (2016) UBL: an R package for utility-based learning. arXiv Prepr: arXiv160408079"},{"key":"1562_CR23","doi-asserted-by":"publisher","first-page":"9977","DOI":"10.1007\/s13369-020-04566-8","volume":"45","author":"S Dalal","year":"2020","unstructured":"Dalal S, Vishwakarma VP (2020) A novel approach of face recognition using optimized adaptive illumination-normalization and KELM. Arab J Sci Eng 45:9977\u20139996. https:\/\/doi.org\/10.1007\/s13369-020-04566-8","journal-title":"Arab J Sci Eng"},{"key":"1562_CR24","doi-asserted-by":"crossref","unstructured":"Dalal S, Vishwakarma VP, Sisaudia V (2018) ECG classification using kernel extreme learning machine. In: 2018 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES). pp 988\u2013992","DOI":"10.1109\/ICPEICES.2018.8897416"},{"key":"1562_CR25","doi-asserted-by":"publisher","unstructured":"Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. 3:95\u201399. https:\/\/doi.org\/10.1023\/A:1022602019183","DOI":"10.1023\/A:1022602019183"},{"key":"1562_CR26","doi-asserted-by":"publisher","first-page":"1850132","DOI":"10.1142\/S0218127418501328","volume":"28","author":"M Kaur","year":"2018","unstructured":"Kaur M, Kumar V (2018) Beta chaotic map based image encryption using genetic algorithm. Int J Bifurc Chaos 28:1850132\u20131850816. https:\/\/doi.org\/10.1142\/S0218127418501328","journal-title":"Int J Bifurc Chaos"},{"key":"1562_CR27","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1080\/13682199.2018.1505327","volume":"66","author":"M Kaur","year":"2018","unstructured":"Kaur M, Kumar V (2018) Parallel non-dominated sorting genetic algorithm-II-based image encryption technique. Imaging Sci J 66:453\u2013462. https:\/\/doi.org\/10.1080\/13682199.2018.1505327","journal-title":"Imaging Sci J"},{"key":"1562_CR28","first-page":"15","volume":"140","author":"M Sahu","year":"2016","unstructured":"Sahu M, Bhurchandi KM (2016) Article: color image segmentation using genetic algorithm. Int J Comput Appl 140:15\u201320","journal-title":"Int J Comput Appl"},{"key":"1562_CR29","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1080\/13682199.2016.1178412","volume":"64","author":"AR Kavitha","year":"2016","unstructured":"Kavitha AR, Chellamuthu C (2016) Brain tumour segmentation from MRI image using genetic algorithm with fuzzy initialisation and seeded modified region growing (GFSMRG) method. Imaging Sci J 64:285\u2013297. https:\/\/doi.org\/10.1080\/13682199.2016.1178412","journal-title":"Imaging Sci J"},{"key":"1562_CR30","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1016\/j.procs.2016.05.192","volume":"85","author":"G Nagarajan","year":"2016","unstructured":"Nagarajan G, Minu RI, Muthukumar B et al (2016) Hybrid genetic algorithm for medical image feature extraction and selection. Procedia Comput Sci 85:455\u2013462","journal-title":"Procedia Comput Sci"},{"key":"1562_CR31","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.patcog.2004.06.004","volume":"38","author":"EY Kim","year":"2005","unstructured":"Kim EY, Jung K (2005) Genetic algorithms for video segmentation. Pattern Recognit 38:59\u201373","journal-title":"Pattern Recognit"},{"key":"1562_CR32","doi-asserted-by":"publisher","first-page":"1252","DOI":"10.1016\/j.patrec.2005.07.023","volume":"27","author":"EY Kim","year":"2006","unstructured":"Kim EY, Park SH (2006) Automatic video segmentation using genetic algorithms. Pattern Recognit Lett 27:1252\u20131265","journal-title":"Pattern Recognit Lett"},{"key":"1562_CR33","doi-asserted-by":"crossref","unstructured":"Peerlinck A, Sheppard J, Pastorino J, Maxwell B (2019) Optimal Design of Experiments for precision agriculture using a genetic algorithm. In: 2019 IEEE Congress on Evolutionary Computation (CEC). pp 1838\u20131845","DOI":"10.1109\/CEC.2019.8790267"},{"key":"1562_CR34","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/S0016-7061(98)00021-4","volume":"85","author":"Y Pachepsky","year":"1998","unstructured":"Pachepsky Y, Acock B (1998) Stochastic imaging of soil parameters to assess variability and uncertainty of crop yield estimates. Geoderma 85:213\u2013229","journal-title":"Geoderma"},{"key":"1562_CR35","doi-asserted-by":"crossref","unstructured":"Wang J, Huang L (2014) Evolving Gomoku solver by genetic algorithm. In: 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA). pp 1064\u20131067","DOI":"10.1109\/WARTIA.2014.6976460"},{"key":"1562_CR36","doi-asserted-by":"crossref","unstructured":"Huo P, Shiu SCK, Wang H, Niu B (2009) Application and comparison of particle swarm optimization and genetic algorithm in strategy defense game. In: 2009 Fifth International Conference on Natural Computation. pp 387\u2013392","DOI":"10.1109\/ICNC.2009.552"},{"key":"1562_CR37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-016-0028-x","volume":"7","author":"H Li","year":"2017","unstructured":"Li H, Yuan D, Ma X et al (2017) Genetic algorithm for the optimization of features and neural networks in ECG signals classification. Sci Rep 7:1\u201312","journal-title":"Sci Rep"},{"key":"1562_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-94363-6","volume":"11","author":"S Dalal","year":"2021","unstructured":"Dalal S, Vishwakarma VP (2021) Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier. Sci Rep 11:1\u201325","journal-title":"Sci Rep"},{"key":"1562_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1097\/MD.0000000000006879","volume":"96","author":"T Wen","year":"2017","unstructured":"Wen T, Zhang Z (2017) Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification. Medicine (Baltimore) 96:1\u201311. https:\/\/doi.org\/10.1097\/MD.0000000000006879","journal-title":"Medicine (Baltimore)"},{"key":"1562_CR40","doi-asserted-by":"crossref","unstructured":"Choubey DK, Paul S, Kumar S, Kumar S (2017) Classification of Pima indian diabetes dataset using naive bayes with genetic algorithm as an attribute selection. In: Communication and Computing Systems: Proceedings of the International Conference on Communication and Computing System (ICCCS 2016). pp 451\u2013455","DOI":"10.1201\/9781315364094-82"},{"key":"1562_CR41","first-page":"756","volume":"2","author":"D Lavanya","year":"2011","unstructured":"Lavanya D, Rani DKU (2011) Analysis of feature selection with classification: breast cancer datasets. Indian J Comput Sci Eng 2:756\u2013763","journal-title":"Indian J Comput Sci Eng"},{"key":"1562_CR42","doi-asserted-by":"crossref","unstructured":"Aldayel MS (2012) K-Nearest Neighbor classification for glass identification problem. In: 2012 International Conference on Computer Systems and Industrial Informatics. pp 1\u20135","DOI":"10.1109\/ICCSII.2012.6454522"},{"key":"1562_CR43","unstructured":"Dua D, Graff C (2017) {UCI} Machine Learning Repository. Absenteeism Work dataset was donated by Andrea Martiniano, Ricardo Pinto Ferreira, Renato Jose Sassi"},{"key":"1562_CR44","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1094\/PHYTO.2003.93.6.720","volume":"93","author":"K Steddom","year":"2003","unstructured":"Steddom K, Heidel G, Jones D, Rush CM (2003) Remote detection of rhizomania in sugar beets. Phytopathology 93:720\u2013726","journal-title":"Phytopathology"},{"key":"1562_CR45","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1186\/1471-2105-7-485","volume":"7","author":"R Kaundal","year":"2006","unstructured":"Kaundal R, Kapoor AS, Raghava GPS (2006) Machine learning techniques in disease forecasting: a case study on rice blast prediction. BMC Bioinform 7:485","journal-title":"BMC Bioinform"},{"key":"1562_CR46","doi-asserted-by":"crossref","unstructured":"Yao Q, Guan Z, Zhou Y, et al (2009) Application of support vector machine for detecting rice diseases using shape and color texture features. In: 2009 international conference on engineering computation. pp 79\u201383","DOI":"10.1109\/ICEC.2009.73"},{"key":"1562_CR47","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.compag.2010.06.009","volume":"74","author":"T Rumpf","year":"2010","unstructured":"Rumpf T, Mahlein A-K, Steiner U et al (2010) Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Comput Electron Agric 74:91\u201399","journal-title":"Comput Electron Agric"},{"key":"1562_CR48","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.compag.2011.09.011","volume":"79","author":"C R\u00f6mer","year":"2011","unstructured":"R\u00f6mer C, B\u00fcrling K, Hunsche M et al (2011) Robust fitting of fluorescence spectra for pre-symptomatic wheat leaf rust detection with support vector machines. Comput Electron Agric 79:180\u2013188","journal-title":"Comput Electron Agric"},{"key":"1562_CR49","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/s11119-011-9217-6","volume":"12","author":"SD Bauer","year":"2011","unstructured":"Bauer SD, Kor\u010d F, F\u00f6rstner W (2011) The potential of automatic methods of classification to identify leaf diseases from multispectral images. Precis Agric 12:361\u2013377","journal-title":"Precis Agric"},{"key":"1562_CR50","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.compag.2011.03.004","volume":"77","author":"S Sankaran","year":"2011","unstructured":"Sankaran S, Mishra A, Maja JM, Ehsani R (2011) Visible-near infrared spectroscopy for detection of Huanglongbing in citrus orchards. Comput Electron Agric 77:127\u2013134","journal-title":"Comput Electron Agric"},{"key":"1562_CR51","doi-asserted-by":"crossref","unstructured":"zhong Liu L, Zhang W, bao Shu S, Jin X (2013) Image Recognition of Wheat Disease Based on RBF Support Vector Machine. In: 2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013)","DOI":"10.2991\/icacsei.2013.77"},{"key":"1562_CR52","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.17950\/ijer\/v3s8\/803","volume":"3","author":"SP Patil","year":"2014","unstructured":"Patil SP, Zambre RS (2014) Classification of cotton leaf spot disease using support vector machine. Int J Eng Res 3:1511\u20131514","journal-title":"Int J Eng Res"},{"key":"1562_CR53","doi-asserted-by":"publisher","first-page":"1802","DOI":"10.1016\/j.procs.2015.02.137","volume":"46","author":"JD Pujari","year":"2015","unstructured":"Pujari JD, Yakkundimath R, Byadgi AS (2015) Image processing based detection of fungal diseases in plants. Procedia Comput Sci 46:1802\u20131808","journal-title":"Procedia Comput Sci"},{"key":"1562_CR54","doi-asserted-by":"crossref","unstructured":"Padol PB, Yadav AA (2016) SVM classifier based grape leaf disease detection. In: 2016 Conference on advances in signal processing (CASP). pp 175\u2013179","DOI":"10.1109\/CASP.2016.7746160"},{"key":"1562_CR55","first-page":"622","volume":"14","author":"H Sabrol","year":"2016","unstructured":"Sabrol H, Kumar S (2016) Intensity based feature extraction for tomato plant disease recognition by classification using decision tree. Int J Comput Sci Inf Secur 14:622","journal-title":"Int J Comput Sci Inf Secur"},{"key":"1562_CR56","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1016\/j.compag.2016.01.008","volume":"121","author":"C-L Chung","year":"2016","unstructured":"Chung C-L, Huang K-J, Chen S-Y et al (2016) Detecting Bakanae disease in rice seedlings by machine vision. Comput Electron Agric 121:404\u2013411","journal-title":"Comput Electron Agric"},{"key":"1562_CR57","first-page":"6","volume":"3","author":"D Pujari","year":"2016","unstructured":"Pujari D, Yakkundimath R, Byadgi AS (2016) SVM and ANN based classification of plant diseases using feature reduction technique. Int J Interact Multimed Artif Intell 3:6\u201314","journal-title":"Int J Interact Multimed Artif Intell"},{"key":"1562_CR58","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1186\/s13007-017-0173-7","volume":"13","author":"HS Naik","year":"2017","unstructured":"Naik HS, Zhang J, Lofquist A et al (2017) A real-time phenotyping framework using machine learning for plant stress severity rating in soybean. Plant Methods 13:23","journal-title":"Plant Methods"},{"key":"1562_CR59","doi-asserted-by":"publisher","first-page":"2022","DOI":"10.3390\/s17092022","volume":"17","author":"A Fuentes","year":"2017","unstructured":"Fuentes A, Yoon S, Kim S, Park D (2017) A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17:2022. https:\/\/doi.org\/10.3390\/s17092022","journal-title":"Sensors"},{"key":"1562_CR60","doi-asserted-by":"crossref","unstructured":"Verma S, Chug A, Singh AP (2018) Prediction Models for Identification and Diagnosis of Tomato Plant Diseases. In: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). pp 1557\u20131563","DOI":"10.1109\/ICACCI.2018.8554842"},{"key":"1562_CR61","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1080\/09720529.2020.1721890","volume":"23","author":"S Verma","year":"2020","unstructured":"Verma S, Chug A, Singh AP (2020) Application of convolutional neural networks for evaluation of disease severity in tomato plant. J Discret Math Sci Cryptogr 23:273\u2013282","journal-title":"J Discret Math Sci Cryptogr"},{"key":"1562_CR62","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1179\/174313108X319397","volume":"56","author":"P Sanyal","year":"2008","unstructured":"Sanyal P, Patel SC (2008) Pattern recognition method to detect two diseases in rice plants. Imaging Sci J 56:319\u2013325","journal-title":"Imaging Sci J"},{"key":"1562_CR63","first-page":"41","volume":"2","author":"DG Kim","year":"2009","unstructured":"Kim DG, Burks TF, Qin J, Bulanon DM (2009) Classification of grapefruit peel diseases using color texture feature analysis. Int J Agric Biol Eng 2:41\u201350","journal-title":"Int J Agric Biol Eng"},{"key":"1562_CR64","first-page":"151","volume-title":"Computer and computing technologies in agriculture","author":"G Li","year":"2012","unstructured":"Li G, Ma Z, Wang H (2012) Image recognition of grape downy mildew and grape powdery mildew based on support vector machine. In: Li D, Chen Y (eds) Computer and computing technologies in agriculture. Springer, Berlin Heidelberg, pp 151\u2013162"},{"key":"1562_CR65","first-page":"211","volume":"15","author":"S Arivazhagan","year":"2013","unstructured":"Arivazhagan S, Shebiah RN, Ananthi S, Varthini SV (2013) Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric Eng Int CIGR J 15:211\u2013217","journal-title":"Agric Eng Int CIGR J"},{"key":"1562_CR66","doi-asserted-by":"crossref","unstructured":"Ramakrishnan M et al. (2015) Groundnut leaf disease detection and classification by using back probagation algorithm. In: 2015 International Conference on Communications and Signal Processing (ICCSP). pp 964\u2013968","DOI":"10.1109\/ICCSP.2015.7322641"},{"key":"1562_CR67","doi-asserted-by":"crossref","unstructured":"Aravind KR, Raja P, Mukesh K V, et al (2018) Disease classification in maize crop using bag of features and multiclass support vector machine. In: 2018 2nd International Conference on Inventive Systems and Control (ICISC). pp 1191\u20131196","DOI":"10.1109\/ICISC.2018.8398993"},{"key":"1562_CR68","first-page":"100415","volume":"28","author":"J Chen","year":"2020","unstructured":"Chen J, Yin H, Zhang D (2020) A self-adaptive classification method for plant disease detection using GMDH-Logistic model. Sustain Comput Inform Syst 28:100415","journal-title":"Sustain Comput Inform Syst"},{"key":"1562_CR69","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/8387680","author":"R Bharti","year":"2021","unstructured":"Bharti R, Khamparia A, Shabaz M et al (2021) Prediction of heart disease using a combination of machine learning and deep learning. Comput Intell Neurosci. https:\/\/doi.org\/10.1155\/2021\/8387680","journal-title":"Comput Intell Neurosci"},{"key":"1562_CR70","unstructured":"Pima Indians Diabetes Database. https:\/\/www.kaggle.com\/uciml\/pima-indians-diabetes-database"},{"key":"1562_CR71","unstructured":"Breast Cancer Wisconsin (Original) Dataset. https:\/\/archive.ics.uci.edu\/ml\/datasets\/Breast+Cancer+Wisconsin+%28Original%29"},{"key":"1562_CR72","doi-asserted-by":"crossref","unstructured":"Buuren S van, Groothuis-Oudshoorn K (2010) mice: Multivariate imputation by chained equations in R. J Stat Softw 1\u201368","DOI":"10.18637\/jss.v045.i03"},{"key":"1562_CR73","unstructured":"Glass Identification Dataset. https:\/\/archive.ics.uci.edu\/ml\/datasets\/glass+identification"},{"key":"1562_CR74","doi-asserted-by":"publisher","first-page":"966","DOI":"10.1016\/j.cor.2005.05.019","volume":"34","author":"D-C Li","year":"2007","unstructured":"Li D-C, Wu C-S, Tsai T-I, Lina Y-S (2007) Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge. Comput Oper Res 34:966\u2013982","journal-title":"Comput Oper Res"},{"key":"1562_CR75","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.artmed.2011.02.001","volume":"52","author":"D-C Li","year":"2011","unstructured":"Li D-C, Liu C-W, Hu SC (2011) A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets. Artif Intell Med 52:45\u201352. https:\/\/doi.org\/10.1016\/j.artmed.2011.02.001","journal-title":"Artif Intell Med"},{"key":"1562_CR76","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"G-B Huang","year":"2006","unstructured":"Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489\u2013501","journal-title":"Neurocomputing"},{"key":"1562_CR77","first-page":"985","volume":"2","author":"G-B Huang","year":"2004","unstructured":"Huang G-B, Zhu Q-Y, Siew C-K et al (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. Neural Netw 2:985\u2013990","journal-title":"Neural Netw"},{"key":"1562_CR78","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/j.neucom.2017.08.040","volume":"275","author":"W Cao","year":"2018","unstructured":"Cao W, Wang X, Ming Z, Gao J (2018) A review on neural networks with random weights. Neurocomputing 275:278\u2013287","journal-title":"Neurocomputing"},{"key":"1562_CR79","doi-asserted-by":"crossref","unstructured":"Cao W, Hu L, Gao J, et al (2020) A study on the relationship between the rank of input data and the performance of random weight neural network. Neural Comput Appl 1\u201312","DOI":"10.1007\/s00521-020-04719-8"},{"key":"1562_CR80","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.neunet.2021.03.016","volume":"140","author":"W Cao","year":"2021","unstructured":"Cao W, Xie Z, Li J et al (2021) Bidirectional stochastic configuration network for regression problems. Neural Netw 140:237\u2013246","journal-title":"Neural Netw"},{"key":"1562_CR81","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1016\/j.procs.2020.03.322","volume":"167","author":"S Dalal","year":"2020","unstructured":"Dalal S, Vishwakarma VP (2020) GA based KELM optimization for ECG classification. Procedia Comput Sci 167:580\u2013588","journal-title":"Procedia Comput Sci"},{"key":"1562_CR82","doi-asserted-by":"crossref","unstructured":"Vishwakarma VP, Dalal S (2018) A Novel Approach for Compensation of Light Variation Effects with KELM Classification for Efficient Face Recognition. In: International Conference on VLSI, Communication and Signal Processing (VCAS 2018)","DOI":"10.1007\/978-981-32-9775-3_89"},{"key":"1562_CR83","doi-asserted-by":"crossref","unstructured":"Dalal S, Vishwakarma VP (2020) PHT and KELM Based Face Recognition. In: Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough. Springer, pp 157\u2013167","DOI":"10.1007\/978-3-030-38445-6_12"},{"key":"1562_CR84","doi-asserted-by":"publisher","DOI":"10.4108\/eai.13-7-2018.163575","author":"VP Vishwakarma","year":"2020","unstructured":"Vishwakarma VP, Dalal S (2020) Neuro-Fuzzy Hybridization using Modified S Membership Function and Kernel Extreme Learning Machine for Robust Face Recognition under Varying Illuminations. EAI Endorsed Trans Scalable Inf Syst Online First. https:\/\/doi.org\/10.4108\/eai.13-7-2018.163575","journal-title":"EAI Endorsed Trans Scalable Inf Syst Online First"},{"key":"1562_CR85","doi-asserted-by":"publisher","first-page":"3460","DOI":"10.1016\/j.neucom.2007.10.008","volume":"71","author":"G-B Huang","year":"2008","unstructured":"Huang G-B, Chen L (2008) Enhanced random search based incremental extreme learning machine. Neurocomputing 71:3460\u20133468","journal-title":"Neurocomputing"},{"key":"1562_CR86","doi-asserted-by":"publisher","first-page":"4600","DOI":"10.1016\/j.eswa.2010.09.133","volume":"38","author":"P Luukka","year":"2011","unstructured":"Luukka P (2011) Feature selection using fuzzy entropy measures with similarity classifier. Expert Syst Appl 38:4600\u20134607","journal-title":"Expert Syst Appl"},{"key":"1562_CR87","doi-asserted-by":"publisher","first-page":"135","DOI":"10.14257\/ijbsbt.2015.7.5.13","volume":"7","author":"DK Choubey","year":"2015","unstructured":"Choubey DK, Paul S (2015) GA_J48graft DT: a hybrid intelligent system for diabetes disease diagnosis. Int J Bio-Sci Bio-Technol 7:135\u2013150","journal-title":"Int J Bio-Sci Bio-Technol"},{"key":"1562_CR88","first-page":"49","volume":"8","author":"DK Choubey","year":"2016","unstructured":"Choubey DK, Paul S (2016) GA_MLP NN: a hybrid intelligent system for diabetes disease diagnosis. Int J Intell Syst Appl 8:49","journal-title":"Int J Intell Syst Appl"},{"key":"1562_CR89","first-page":"1","volume":"19","author":"D Bani-Hani","year":"2019","unstructured":"Bani-Hani D, Patel P, Alshaikh T (2019) An optimized recursive general regression neural network oracle for the prediction and diagnosis of diabetes. Glob J Comput Sci Technol 19:1\u201312","journal-title":"Glob J Comput Sci Technol"},{"key":"1562_CR90","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2020.12.012","author":"PD Sheth","year":"2020","unstructured":"Sheth PD, Patil ST, Dhore ML (2020) Evolutionary computing for clinical dataset classification using a novel feature selection algorithm. J King Saud Univ Comput Inf Sci. https:\/\/doi.org\/10.1016\/j.jksuci.2020.12.012","journal-title":"J King Saud Univ Comput Inf Sci"},{"key":"1562_CR91","first-page":"1","volume":"29","author":"KS Kumar","year":"2021","unstructured":"Kumar KS (2021) Evolutionary computation technique combined with ensemble model for classification of diabetes. Afr J Diabetes Med 29:1\u201314","journal-title":"Afr J Diabetes Med"},{"key":"1562_CR92","doi-asserted-by":"publisher","first-page":"9014","DOI":"10.1016\/j.eswa.2011.01.120","volume":"38","author":"H-L Chen","year":"2011","unstructured":"Chen H-L, Yang B, Liu J, Liu D-Y (2011) A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Syst Appl 38:9014\u20139022","journal-title":"Expert Syst Appl"},{"key":"1562_CR93","first-page":"17","volume":"2","author":"D Lavanya","year":"2012","unstructured":"Lavanya D, Rani KU (2012) Ensemble decision tree classifier for breast cancer data. Int J Inf Technol Converg Serv 2:17","journal-title":"Int J Inf Technol Converg Serv"},{"key":"1562_CR94","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1007\/s00521-015-2103-9","volume":"28","author":"E Ali\u010dkovi\u0107","year":"2017","unstructured":"Ali\u010dkovi\u0107 E, Subasi A (2017) Breast cancer diagnosis using GA feature selection and Rotation Forest. Neural Comput Appl 28:753\u2013763","journal-title":"Neural Comput Appl"},{"key":"1562_CR95","unstructured":"Prince MSM, Hasan A, Shah FM (2019) An Efficient Ensemble Method for Cancer Detection. In: 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT). pp 1\u20136"},{"key":"1562_CR96","doi-asserted-by":"crossref","unstructured":"Yu J, Li H, Liu D (2020) Modified immune evolutionary algorithm for medical data clustering and feature extraction under cloud computing environment. J Healthc Eng 2020:","DOI":"10.1155\/2020\/1051394"},{"key":"1562_CR97","doi-asserted-by":"publisher","first-page":"695","DOI":"10.3390\/make3030035","volume":"3","author":"A Pickens","year":"2021","unstructured":"Pickens A, Sengupta S (2021) Benchmarking Studies Aimed at Clustering and Classification Tasks Using K-Means, Fuzzy C-Means and Evolutionary Neural Networks. Mach Learn Knowl Extr 3:695\u2013719","journal-title":"Mach Learn Knowl Extr"},{"key":"1562_CR98","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.procs.2015.12.117","volume":"72","author":"R Panthong","year":"2015","unstructured":"Panthong R, Srivihok A (2015) Wrapper feature subset selection for dimension reduction based on ensemble learning algorithm. Procedia Comput Sci 72:162\u2013169","journal-title":"Procedia Comput Sci"},{"key":"1562_CR99","doi-asserted-by":"publisher","first-page":"179","DOI":"10.3390\/sym9090179","volume":"9","author":"Y Akbulut","year":"2017","unstructured":"Akbulut Y, Sengur A, Guo Y, Smarandache F (2017) Ns-k-nn: Neutrosophic set-based k-nearest neighbors classifier. Symmetry (Basel) 9:179","journal-title":"Symmetry (Basel)"},{"key":"1562_CR100","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1016\/j.asoc.2018.10.036","volume":"74","author":"H Rao","year":"2019","unstructured":"Rao H, Shi X, Rodrigue AK et al (2019) Feature selection based on artificial bee colony and gradient boosting decision tree. Appl Soft Comput 74:634\u2013642","journal-title":"Appl Soft Comput"},{"key":"1562_CR101","doi-asserted-by":"crossref","unstructured":"Syaliman K, Labellapansa A, Yulianti A (2020) Improving the Accuracy of Features Weighted k-Nearest Neighbor using Distance Weight. In: Journal of Physics: Conference Series. pp 1\u20136","DOI":"10.5220\/0009390903260330"},{"key":"1562_CR102","doi-asserted-by":"crossref","unstructured":"Kaur A, Kumar Y (2021) Water Wave Optimization Based Data Clustering Model. In: Journal of Physics: Conference Series. p 12054","DOI":"10.1088\/1742-6596\/1950\/1\/012054"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01562-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-022-01562-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01562-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T04:09:11Z","timestamp":1674101351000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-022-01562-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,27]]},"references-count":102,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["1562"],"URL":"https:\/\/doi.org\/10.1007\/s13042-022-01562-2","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,27]]},"assertion":[{"value":"31 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}