{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T01:00:54Z","timestamp":1783386054350,"version":"3.54.6"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1007\/s00521-018-3956-5","type":"journal-article","created":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T03:14:58Z","timestamp":1546312498000},"page":"8297-8304","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Prediction of pK(a) values of neutral and alkaline drugs with particle swarm optimization algorithm and artificial neural network"],"prefix":"10.1007","volume":"31","author":[{"given":"Bingsheng","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huaijin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengshan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,1,1]]},"reference":[{"issue":"23","key":"3956_CR1","doi-asserted-by":"publisher","first-page":"9701","DOI":"10.1021\/jm501000a","volume":"57","author":"PS Charifson","year":"2014","unstructured":"Charifson PS, Walters WP (2014) Acidic and basic drugs in medicinal chemistry: a perspective. J Med Chem 57(23):9701\u20139717. \n                    https:\/\/doi.org\/10.1021\/jm501000a","journal-title":"J Med Chem"},{"issue":"4","key":"3956_CR2","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1093\/bioinformatics\/btv607","volume":"32","author":"L Wang","year":"2016","unstructured":"Wang L, Zhang M, Alexov E (2016) DelPhiPKa web server: predicting pK(a) of proteins, RNAs and DNAs. Bioinformatics 32(4):614\u2013615. \n                    https:\/\/doi.org\/10.1093\/bioinformatics\/btv607","journal-title":"Bioinformatics"},{"issue":"12","key":"3956_CR3","doi-asserted-by":"publisher","first-page":"6001","DOI":"10.1021\/acs.jctc.6b00805","volume":"12","author":"AD Bochevarov","year":"2016","unstructured":"Bochevarov AD, Watson MA, Greenwood JR (2016) Multiconformation, density functional theory-based pk(a) prediction in application to large, flexible organic molecules with diverse functional groups. J Chem Theory Comput 12(12):6001\u20136019. \n                    https:\/\/doi.org\/10.1021\/acs.jctc.6b00805","journal-title":"J Chem Theory Comput"},{"issue":"2","key":"3956_CR4","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1002\/prot.25221","volume":"85","author":"YH Peng","year":"2017","unstructured":"Peng YH, Alexov E (2017) Computational investigation of proton transfer, pKa shifts and pH-optimum of protein-DNA and protein-RNA complexes. Proteins Struct Funct Bioinform 85(2):282\u2013295. \n                    https:\/\/doi.org\/10.1002\/prot.25221","journal-title":"Proteins Struct Funct Bioinform"},{"issue":"9","key":"3956_CR5","doi-asserted-by":"publisher","first-page":"170516","DOI":"10.1098\/rsos.170516","volume":"4","author":"H Wang","year":"2017","unstructured":"Wang H, Jiang MY, Li SJ, Hse CY, Jin CD, Sun FL, Li Z (2017) Design of cinnamaldehyde amino acid Schiff base compounds based on the quantitative structure-activity relationship. R Soc Open Sci 4(9):170516. \n                    https:\/\/doi.org\/10.1098\/rsos.170516","journal-title":"R Soc Open Sci"},{"issue":"7","key":"3956_CR6","doi-asserted-by":"publisher","first-page":"170175","DOI":"10.1098\/rsos.170175","volume":"4","author":"R Das","year":"2017","unstructured":"Das R, Wales DJ (2017) Machine learning landscapes and predictions for patient outcomes. R Soc Open Sci 4(7):170175. \n                    https:\/\/doi.org\/10.1098\/rsos.170175","journal-title":"R Soc Open Sci"},{"issue":"3","key":"3956_CR7","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1007\/s11047-015-9509-2","volume":"15","author":"S Balaji","year":"2016","unstructured":"Balaji S, Revathi N (2016) A new approach for solving set covering problem using jumping particle swarm optimization method. Nat Comput 15(3):503\u2013517. \n                    https:\/\/doi.org\/10.1007\/s11047-015-9509-2","journal-title":"Nat Comput"},{"issue":"11","key":"3956_CR8","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1007\/s00521-016-2588-x","volume":"29","author":"H Karami","year":"2018","unstructured":"Karami H, Karimi S, Bonakdari H, Shamshirband S (2018) Predicting discharge coefficient of triangular labyrinth weir using extreme learning machine, artificial neural network and genetic programming. Neural Comput Appl 29(11):983\u2013989. \n                    https:\/\/doi.org\/10.1007\/s00521-016-2588-x","journal-title":"Neural Comput Appl"},{"issue":"7","key":"3956_CR9","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1021\/acscombsci.7b00046","volume":"19","author":"S Arefi-Oskoui","year":"2017","unstructured":"Arefi-Oskoui S, Khataee A, Vatanpour V (2017) Modeling and optimization of NLDH\/PVDF ultrafiltration nanocomposite membrane using artificial neural network-genetic algorithm hybrid. ACS Comb Sci 19(7):464\u2013477. \n                    https:\/\/doi.org\/10.1021\/acscombsci.7b00046","journal-title":"ACS Comb Sci"},{"key":"3956_CR10","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.cie.2015.01.003","volume":"86","author":"M Saidi-Mehrabad","year":"2015","unstructured":"Saidi-Mehrabad M, Dehnavi-Arani S, Evazabadian F, Mahmoodian V (2015) An Ant Colony Algorithm (ACA) for solving the new integrated model of job shop scheduling and conflict-free routing of AGVs. Comput Ind Eng 86:2\u201313. \n                    https:\/\/doi.org\/10.1016\/j.cie.2015.01.003","journal-title":"Comput Ind Eng"},{"issue":"4","key":"3956_CR11","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1049\/cje.2015.10.006","volume":"24","author":"DC Tran","year":"2015","unstructured":"Tran DC, Wu ZJ, Wang ZL, Deng CS (2015) A novel hybrid data clustering algorithm based on artificial bee colony algorithm and K-means. Chin J Electron 24(4):694\u2013701. \n                    https:\/\/doi.org\/10.1049\/cje.2015.10.006","journal-title":"Chin J Electron"},{"issue":"1","key":"3956_CR12","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1017\/s0890060417000221","volume":"32","author":"M Mirabi","year":"2018","unstructured":"Mirabi M, Seddighi P (2018) Hybrid ant colony optimization for capacitated multiple-allocation cluster hub location problem. Artif Intell Eng Des Anal Manuf 32(1):44\u201358. \n                    https:\/\/doi.org\/10.1017\/s0890060417000221","journal-title":"Artif Intell Eng Des Anal Manuf"},{"issue":"19","key":"3956_CR13","doi-asserted-by":"publisher","first-page":"9876","DOI":"10.1021\/acs.analchem.5b02349","volume":"87","author":"P Zuvela","year":"2015","unstructured":"Zuvela P, Liu JJ, Macur K, Baczek T (2015) Molecular descriptor subset selection in theoretical peptide quantitative structure-retention relationship model development using nature-inspired optimization algorithms. Anal Chem 87(19):9876\u20139883. \n                    https:\/\/doi.org\/10.1021\/acs.analchem.5b02349","journal-title":"Anal Chem"},{"key":"3956_CR14","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.ces.2016.09.030","volume":"158","author":"LV Pavao","year":"2017","unstructured":"Pavao LV, Borba Costa CB, Ravagnani MASS (2017) Heat exchanger network synthesis without stream splits using parallelized and simplified simulated annealing and particle swarm optimization. Chem Eng Sci 158:96\u2013107. \n                    https:\/\/doi.org\/10.1016\/j.ces.2016.09.030","journal-title":"Chem Eng Sci"},{"issue":"1","key":"3956_CR15","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1039\/c7ra12266g","volume":"8","author":"C Niu","year":"2018","unstructured":"Niu C, Yuan YH, Guo H, Wang X, Yue TL (2018) Recognition of osmotolerant yeast spoilage in kiwi juices by near-infrared spectroscopy coupled with chemometrics and wavelength selection. RSC Adv 8(1):222\u2013229. \n                    https:\/\/doi.org\/10.1039\/c7ra12266g","journal-title":"RSC Adv"},{"issue":"78","key":"3956_CR16","doi-asserted-by":"publisher","first-page":"49817","DOI":"10.1039\/C7RA09531G","volume":"7","author":"L Mengshan","year":"2017","unstructured":"Mengshan L, Liang L, Xingyuan H, Hesheng L, Bingsheng C, Lixin G, Yan W (2017) Prediction of supercritical carbon dioxide solubility in polymers based on hybrid artificial intelligence method integrated with the diffusion theory. RSC Adv 7(78):49817\u201349827. \n                    https:\/\/doi.org\/10.1039\/C7RA09531G","journal-title":"RSC Adv"},{"key":"3956_CR17","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1016\/j.asoc.2017.07.050","volume":"60","author":"MS Kiran","year":"2017","unstructured":"Kiran MS (2017) Particle swarm optimization with a new update mechanism. Appl Soft Comput 60:670\u2013678. \n                    https:\/\/doi.org\/10.1016\/j.asoc.2017.07.050","journal-title":"Appl Soft Comput"},{"key":"3956_CR18","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.knosys.2017.03.032","volume":"130","author":"F Jiang","year":"2017","unstructured":"Jiang F, Xia HY, Tran QA, Ha QM, Tran NQ, Hu JK (2017) A new binary hybrid particle swarm optimization with wavelet mutation. Knowl Based Syst 130:90\u2013101. \n                    https:\/\/doi.org\/10.1016\/j.knosys.2017.03.032","journal-title":"Knowl Based Syst"},{"issue":"16","key":"3956_CR19","doi-asserted-by":"publisher","first-page":"953","DOI":"10.1002\/jcc.25168","volume":"39","author":"P Zuvela","year":"2018","unstructured":"Zuvela P, David J, Wong MW (2018) Interpretation of ANN-based QSAR models for prediction of antioxidant activity of flavonoids. J Comput Chem 39(16):953\u2013963. \n                    https:\/\/doi.org\/10.1002\/jcc.25168","journal-title":"J Comput Chem"},{"issue":"1","key":"3956_CR20","doi-asserted-by":"publisher","first-page":"3991","DOI":"10.1038\/s41598-018-22332-7","volume":"8","author":"MS Li","year":"2018","unstructured":"Li MS, Zhang HJ, Chen BS, Wu Y, Guan LX (2018) Prediction of pKa values for neutral and basic drugs based on hybrid artificial intelligence methods. Sci Rep 8(1):3991. \n                    https:\/\/doi.org\/10.1038\/s41598-018-22332-7","journal-title":"Sci Rep"},{"issue":"9","key":"3956_CR21","doi-asserted-by":"publisher","first-page":"2794","DOI":"10.1109\/tcyb.2017.2710133","volume":"47","author":"QL Zhu","year":"2017","unstructured":"Zhu QL, Lin QZ, Chen WN, Wong KC, Coello CAC, Li JQ, Chen JY, Zhang J (2017) An external archive-guided multiobjective particle swarm optimization algorithm. IEEE Trans Cybern 47(9):2794\u20132808. \n                    https:\/\/doi.org\/10.1109\/tcyb.2017.2710133","journal-title":"IEEE Trans Cybern"},{"key":"3956_CR22","doi-asserted-by":"publisher","first-page":"S1245","DOI":"10.1007\/s00521-016-2433-2","volume":"28","author":"DX Yang","year":"2017","unstructured":"Yang DX, Liu ZJ, Yi P (2017) Computational efficiency of accelerated particle swarm optimization combined with different chaotic maps for global optimization. Neural Comput Appl 28:S1245\u2013S1264. \n                    https:\/\/doi.org\/10.1007\/s00521-016-2433-2","journal-title":"Neural Comput Appl"},{"key":"3956_CR23","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1016\/j.asoc.2017.04.035","volume":"57","author":"J Yan","year":"2017","unstructured":"Yan J, He WX, Jiang XL, Zhang ZL (2017) A novel phase performance evaluation method for particle swarm optimization algorithms using velocity-based state estimation. Appl Soft Comput 57:517\u2013525. \n                    https:\/\/doi.org\/10.1016\/j.asoc.2017.04.035","journal-title":"Appl Soft Comput"},{"issue":"8","key":"3956_CR24","doi-asserted-by":"publisher","first-page":"2099","DOI":"10.1007\/s00521-016-2184-0","volume":"28","author":"S Shirazian","year":"2017","unstructured":"Shirazian S, Alibabaei M (2017) Using neural networks coupled with particle swarm optimization technique for mathematical modeling of air gap membrane distillation (AGMD) systems for desalination process. Neural Comput Appl 28(8):2099\u20132104. \n                    https:\/\/doi.org\/10.1007\/s00521-016-2184-0","journal-title":"Neural Comput Appl"},{"issue":"3","key":"3956_CR25","doi-asserted-by":"publisher","first-page":"1805","DOI":"10.1021\/acs.analchem.7b03795","volume":"90","author":"Y Date","year":"2018","unstructured":"Date Y, Kikuchi J (2018) Application of a deep neural network to metabolomics studies and its performance in determining important variables. Anal Chem 90(3):1805\u20131810. \n                    https:\/\/doi.org\/10.1021\/acs.analchem.7b03795","journal-title":"Anal Chem"},{"key":"3956_CR26","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.ins.2017.01.001","volume":"388","author":"J Chen","year":"2017","unstructured":"Chen J, Hu Q, Xue X, Ha M, Ma L (2017) Support function machine for set-based classification with application to water quality evaluation. Inf Sci 388:48\u201361. \n                    https:\/\/doi.org\/10.1016\/j.ins.2017.01.001","journal-title":"Inf Sci"},{"key":"3956_CR27","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-017-0226-y","author":"A Koutsoukas","year":"2017","unstructured":"Koutsoukas A, Monaghan KJ, Li XL, Huan J (2017) Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data. J Cheminform. \n                    https:\/\/doi.org\/10.1186\/s13321-017-0226-y","journal-title":"J Cheminform"},{"issue":"9","key":"3956_CR28","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1002\/jcc.24715","volume":"38","author":"L Li","year":"2017","unstructured":"Li L, Chakravorty A, Alexov E (2017) DelPhiForce, a tool for electrostatic force calculations: applications to macromolecular binding. J Comput Chem 38(9):584\u2013593. \n                    https:\/\/doi.org\/10.1002\/jcc.24715","journal-title":"J Comput Chem"},{"issue":"19","key":"3956_CR29","doi-asserted-by":"publisher","first-page":"11016","DOI":"10.1039\/c7nj02497e","volume":"41","author":"C Dardonville","year":"2017","unstructured":"Dardonville C, Caine BA, Navarro de la Fuente M, Martin Herranz G, Corrales Mariblanca B, Popelier PLA (2017) Substituent effects on the basicity (pK(a)) of aryl guanidines and 2-(arylimino) imidazolidines: correlations of pH-metric and UV-metric values with predictions from gas-phase ab initio bond lengths. New J Chem 41(19):11016\u201311028. \n                    https:\/\/doi.org\/10.1039\/c7nj02497e","journal-title":"New J Chem"},{"issue":"2","key":"3956_CR30","doi-asserted-by":"publisher","first-page":"86","DOI":"10.2166\/aqua.2017.035","volume":"66","author":"N Heidarzadeh","year":"2017","unstructured":"Heidarzadeh N (2017) A practical low-cost model for prediction of the groundwater quality using artificial neural networks. J Water Supply Res Technol AQUA 66(2):86\u201395","journal-title":"J Water Supply Res Technol AQUA"},{"issue":"2","key":"3956_CR31","doi-asserted-by":"publisher","first-page":"04017054","DOI":"10.1061\/(ASCE)ME.1943-5479.0000583","volume":"34","author":"S Han","year":"2018","unstructured":"Han S, Ko Y, Kim J, Hong T (2018) Housing market trend forecasts through statistical comparisons based on big data analytic methods. J Manag Eng 34(2):04017054","journal-title":"J Manag Eng"},{"issue":"2","key":"3956_CR32","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1007\/s10822-017-0094-6","volume":"32","author":"MAV Hasanloei","year":"2018","unstructured":"Hasanloei MAV, Sheikhpour R, Sarram MA, Sheikhpour E, Sharifi H (2018) A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities. J Comput Aided Mol Des 32(2):375\u2013384. \n                    https:\/\/doi.org\/10.1007\/s10822-017-0094-6","journal-title":"J Comput Aided Mol Des"},{"issue":"3","key":"3956_CR33","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1089\/cmb.2017.0135","volume":"25","author":"L Wang","year":"2018","unstructured":"Wang L, You ZH, Chen X, Xia SX, Liu F, Yan X, Zhou Y, Song KJ (2018) A computational-based method for predicting drug-target interactions by using stacked autoencoder deep neural network. J Comput Biol 25(3):361\u2013373. \n                    https:\/\/doi.org\/10.1089\/cmb.2017.0135","journal-title":"J Comput Biol"},{"issue":"2","key":"3956_CR34","doi-asserted-by":"publisher","first-page":"105","DOI":"10.3233\/ica-170540","volume":"24","author":"Y Zeinali","year":"2017","unstructured":"Zeinali Y, Story BA (2017) Competitive probabilistic neural network. Integr Comput Aided Eng 24(2):105\u2013118. \n                    https:\/\/doi.org\/10.3233\/ica-170540","journal-title":"Integr Comput Aided Eng"},{"issue":"12","key":"3956_CR35","doi-asserted-by":"publisher","first-page":"1495","DOI":"10.1007\/s00521-016-2666-0","volume":"29","author":"KTT Bui","year":"2018","unstructured":"Bui KTT, Bui DT, Zou JG, Doan CV, Revhaug I (2018) A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam. Neural Comput Appl 29(12):1495\u20131506. \n                    https:\/\/doi.org\/10.1007\/s00521-016-2666-0","journal-title":"Neural Comput Appl"},{"issue":"12","key":"3956_CR36","doi-asserted-by":"publisher","first-page":"8419","DOI":"10.1039\/c7sc03542j","volume":"8","author":"F Hase","year":"2017","unstructured":"Hase F, Kreisbeck C, Aspuru-Guzik A (2017) Machine learning for quantum dynamics: deep learning of excitation energy transfer properties. Chem Sci 8(12):8419\u20138426. \n                    https:\/\/doi.org\/10.1039\/c7sc03542j","journal-title":"Chem Sci"},{"issue":"16","key":"3956_CR37","doi-asserted-by":"publisher","first-page":"1291","DOI":"10.1002\/jcc.24764","volume":"38","author":"GB Goh","year":"2017","unstructured":"Goh GB, Hodas NO, Vishnu A (2017) Deep learning for computational chemistry. J Comput Chem 38(16):1291\u20131307. \n                    https:\/\/doi.org\/10.1002\/jcc.24764","journal-title":"J Comput Chem"},{"issue":"5","key":"3956_CR38","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1016\/S0097-8485(00)00064-4","volume":"24","author":"J Polanski","year":"2000","unstructured":"Polanski J, Walczak B (2000) The comparative molecular surface analysis (COMSA): a novel tool for molecular design. Comput Chem 24(5):615\u2013625. \n                    https:\/\/doi.org\/10.1016\/S0097-8485(00)00064-4","journal-title":"Comput Chem"},{"issue":"9","key":"3956_CR39","doi-asserted-by":"publisher","first-page":"1454","DOI":"10.1007\/s11095-005-6246-8","volume":"22","author":"F Luan","year":"2005","unstructured":"Luan F, Ma WP, Zhang HX, Zhang XY, Liu MC, Hu ZD, Fan BT (2005) Prediction of pK(a) for neutral and basic drugs based on radial basis function neural networks and the heuristic method. Pharm Res 22(9):1454\u20131460. \n                    https:\/\/doi.org\/10.1007\/s11095-005-6246-8","journal-title":"Pharm Res"},{"issue":"3","key":"3956_CR40","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1111\/cote.12027","volume":"129","author":"F Luan","year":"2013","unstructured":"Luan F, Xu X, Liu HT, Cordeiro M (2013) Review of quantitative structure-activity\/property relationship studies of dyes: recent advances and perspectives. Color Technol 129(3):173\u2013186. \n                    https:\/\/doi.org\/10.1111\/cote.12027","journal-title":"Color Technol"},{"issue":"11","key":"3956_CR41","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1007\/s00521-016-2619-7","volume":"29","author":"A Marjani","year":"2018","unstructured":"Marjani A, Shirazian S, Asadollahzadeh M (2018) Topology optimization of neural networks based on a coupled genetic algorithm and particle swarm optimization techniques (c-GA-PSO-NN). Neural Comput Appl 29(11):1073\u20131076. \n                    https:\/\/doi.org\/10.1007\/s00521-016-2619-7","journal-title":"Neural Comput Appl"},{"key":"3956_CR42","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.ins.2012.10.012","volume":"223","author":"H Wang","year":"2013","unstructured":"Wang H, Sun H, Li CH, Rahnamayan S, Pan JS (2013) Diversity enhanced particle swarm optimization with neighborhood search. Inf Sci 223:119\u2013135. \n                    https:\/\/doi.org\/10.1016\/j.ins.2012.10.012","journal-title":"Inf Sci"},{"issue":"2","key":"3956_CR43","doi-asserted-by":"publisher","first-page":"1222","DOI":"10.1016\/j.asoc.2012.10.016","volume":"13","author":"A Martinez-Vargas","year":"2013","unstructured":"Martinez-Vargas A, Andrade AG (2013) Comparing particle swarm optimization variants for a cognitive radio network. Appl Soft Comput 13(2):1222\u20131234. \n                    https:\/\/doi.org\/10.1016\/j.asoc.2012.10.016","journal-title":"Appl Soft Comput"},{"issue":"1","key":"3956_CR44","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1080\/18756891.2013.756227","volume":"6","author":"Y Xiao","year":"2013","unstructured":"Xiao Y, Xiao J, Lu FB, Wang SY (2013) Ensemble ANNs-PSO-GA approach for day-ahead stock e-exchange prices forecasting. Int J Comput Intell Syst 6(1):96\u2013114. \n                    https:\/\/doi.org\/10.1080\/18756891.2013.756227","journal-title":"Int J Comput Intell Syst"},{"issue":"2","key":"3956_CR45","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1016\/j.eswa.2012.01.166","volume":"40","author":"HS Wang","year":"2013","unstructured":"Wang HS, Wang YN, Wang YC (2013) Cost estimation of plastic injection molding parts through integration of PSO and BP neural network. Expert Syst Appl 40(2):418\u2013428. \n                    https:\/\/doi.org\/10.1016\/j.eswa.2012.01.166","journal-title":"Expert Syst Appl"},{"issue":"3","key":"3956_CR46","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1016\/j.ejor.2012.10.020","volume":"225","author":"G Sermpinis","year":"2013","unstructured":"Sermpinis G, Theofilatos K, Karathanasopoulos A, Georgopoulos EF, Dunis C (2013) Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization. Eur J Oper Res 225(3):528\u2013540. \n                    https:\/\/doi.org\/10.1016\/j.ejor.2012.10.020","journal-title":"Eur J Oper Res"},{"issue":"56","key":"3956_CR47","doi-asserted-by":"publisher","first-page":"45520","DOI":"10.1039\/c5ra07129a","volume":"5","author":"M Li","year":"2015","unstructured":"Li M, Huang X, Liu H, Liu B, Wu Y, Wang L (2015) Solubility prediction of supercritical carbon dioxide in 10 polymers using radial basis function artificial neural network based on chaotic self-adaptive particle swarm optimization and K-harmonic means. RSC Adv 5(56):45520\u201345527. \n                    https:\/\/doi.org\/10.1039\/c5ra07129a","journal-title":"RSC Adv"},{"issue":"56","key":"3956_CR48","doi-asserted-by":"publisher","first-page":"35274","DOI":"10.1039\/c7ra04200k","volume":"7","author":"M Li","year":"2017","unstructured":"Li M, Wu W, Chen B, Wu Y, Huang X (2017) Solubility prediction of gases in polymers based on an artificial neural network: a review. RSC Adv 7(56):35274\u201335282. \n                    https:\/\/doi.org\/10.1039\/c7ra04200k","journal-title":"RSC Adv"},{"issue":"2","key":"3956_CR49","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1007\/s00521-016-2455-9","volume":"29","author":"L Zhang","year":"2018","unstructured":"Zhang L, Wang FL, Sun T, Xu B (2018) A constrained optimization method based on BP neural network. Neural Comput Appl 29(2):413\u2013421. \n                    https:\/\/doi.org\/10.1007\/s00521-016-2455-9","journal-title":"Neural Comput Appl"},{"issue":"1\u20132","key":"3956_CR50","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.aca.2011.03.006","volume":"692","author":"RM Balabin","year":"2011","unstructured":"Balabin RM, Smirnov SV (2011) Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data. Anal Chim Acta 692(1\u20132):63\u201372. \n                    https:\/\/doi.org\/10.1016\/j.aca.2011.03.006","journal-title":"Anal Chim Acta"},{"issue":"3","key":"3956_CR51","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1007\/s00521-016-2508-0","volume":"29","author":"WB Hu","year":"2018","unstructured":"Hu WB, Wang H, Qiu ZY, Nie C, Yan LP (2018) A quantum particle swarm optimization driven urban traffic light scheduling model. Neural Comput Appl 29(3):901\u2013911. \n                    https:\/\/doi.org\/10.1007\/s00521-016-2508-0","journal-title":"Neural Comput Appl"},{"key":"3956_CR52","doi-asserted-by":"publisher","DOI":"10.3390\/app8010145","author":"N Kalaiarasi","year":"2018","unstructured":"Kalaiarasi N, Dash SS, Padmanaban S, Paramasivam S, Morati PK (2018) Maximum power point tracking implementation by dspace controller integrated through z-source inverter using particle swarm optimization technique for photovoltaic applications. Appl Sci Basel. \n                    https:\/\/doi.org\/10.3390\/app8010145","journal-title":"Appl Sci Basel"},{"key":"3956_CR53","doi-asserted-by":"publisher","first-page":"S539","DOI":"10.1007\/s00521-016-2367-8","volume":"28","author":"GS Das","year":"2017","unstructured":"Das GS (2017) Forecasting the energy demand of Turkey with a NN based on an improved Particle Swarm Optimization. Neural Comput Appl 28:S539\u2013S549. \n                    https:\/\/doi.org\/10.1007\/s00521-016-2367-8","journal-title":"Neural Comput Appl"},{"key":"3956_CR54","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1016\/j.asoc.2017.07.023","volume":"60","author":"F Javidrad","year":"2017","unstructured":"Javidrad F, Nazari M (2017) A new hybrid particle swarm and simulated annealing stochastic optimization method. Appl Soft Comput 60:634\u2013654. \n                    https:\/\/doi.org\/10.1016\/j.asoc.2017.07.023","journal-title":"Appl Soft Comput"},{"issue":"1","key":"3956_CR55","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1002\/jcc.20309","volume":"27","author":"F Eckert","year":"2006","unstructured":"Eckert F, Klamt A (2006) Accurate prediction of basicity in aqueous solution with COSMO-RS. J Comput Chem 27(1):11\u201319. \n                    https:\/\/doi.org\/10.1002\/jcc.20309","journal-title":"J Comput Chem"},{"key":"3956_CR56","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.2335","author":"JC Kromann","year":"2016","unstructured":"Kromann JC, Larsen F, Moustafa H, Jensen JH (2016) Prediction of pKa values using the PM6 semiempirical method. Peerj. \n                    https:\/\/doi.org\/10.7717\/peerj.2335","journal-title":"Peerj"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-3956-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-018-3956-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-3956-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T05:56:07Z","timestamp":1577858167000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-018-3956-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,1]]},"references-count":56,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["3956"],"URL":"https:\/\/doi.org\/10.1007\/s00521-018-3956-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,1]]},"assertion":[{"value":"28 August 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 December 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 January 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}