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Prognostic relevance of blood pressure variability. Hypertension. 2006;47:137–8.\nIlies C, Bauer M, Berg P, Rosenberg J, Hedderich J, Bein B, Hinz J, Hanss R. Investigation of the agreement of a continuous non-invasive arterial pressure device in comparison with invasive radial artery measurement. Br J Anaesth. 2012;108:202–10.\nTegtmeyer K, Brady G, Lai S, Hodo R, Braner D (2006) Placement of an Arterial Line.\nVerdecchia P, Schillaci G, Borgioni C, Ciucci A, Zampi I, Gattobigio R, Sacchi N, Porcellati C. White coat hypertension and white coat effect similarities and differences. Am J Hypertens. 1995;8:790–8.\nAr SČ, G, Mlakar N, Luštrek M. Blood pressure estimation from photoplethysmogram using a spectro-temporal deep neural network. Sensors (Switzerland). 2019. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs19153420.\nBillings SA. Nonlinear system identification: NARMAX methods in the time, frequency, and spatio-temporal domains. John Wiley & Sons, 2013\nJain P, Garibaldi JM, Hirst JD. Supervised machine learning algorithms for protein structure classification. Comput Biol Chem. 2009;33:216–23.\nBakhtiarizadeh MR, Moradi-Shahrbabak M, Ebrahimi M, Ebrahimie E. Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology. J Theor Biol. 2014;356:213–22.\nPollastri G, Przybylski D, Rost B, Baldi P. Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins Struct Funct Genet. 2002;47:228–35.\nAmato F, López A, Peña-Méndez EM, Vaňhara P, Hampl A, Havel J. Artificial neural networks in medical diagnosis. J Appl Biomed. 2013;11:47–58.\nIeracitano C, Mammone N, Hussain A, Morabito FC. A novel explainable machine learning approach for EEG-based brain-computer interface systems. Neural Comput Appl. 2021. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00521-020-05624-w.\nIeracitano C, Mammone N, Hussain A, Morabito FC. A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia. Neural Netw. 2020;123:176–90.\nReese MG. Application of a time-delay neural network to promoter annotation in the Drosophila melanogaster genome. Comput Chem. 2001;26:51–6.\nHosanee M, Chan G, Welykholowa K, et al. Cuffless single-site photoplethysmography for blood pressure monitoring. J Clin Med. 2020;9:723.\nRandazzo V, Ferretti J, Pasero E. ECG WATCH: A real time wireless wearable ECG. Med Meas Appl MeMeA 2019 - Symp Proc. https:\u002F\u002Fdoi.org\u002F10.1109\u002FMeMeA.2019.8802210\nRandazzo V, Ferretti J, Pasero E. A wearable smart device to monitor multiple vital parameters—VITAL ECG. Electronics. 2020;9:300. https:\u002F\u002Fdoi.org\u002F10.3390\u002Felectronics9020300\nRandazzo V, Pasero E, Navaretti S. VITAL-ECG: a portable wearable hospital. In: 2018 IEEE Sensors Appl. Symp. SAS 2018 - Proc. Institute of Electrical and Electronics Engineers Inc., pp 1–6\nPaviglianiti A, Randazzo V, Pasero E, Vallan A. Noninvasive arterial blood pressure estimation using ABPNet and VITAL-ECG. I2MTC 2020 - Int Instrum Meas Technol Conf Proc. https:\u002F\u002Fdoi.org\u002F10.1109\u002FI2MTC43012.2020.9129361\nPaviglianiti A, Randazzo V, Cirrincione G, Pasero E. Neural recurrent approches to noninvasive blood pressure estimation. 2020 Int. Jt. Conf. Neural Networks\nHe X, Goubran RA, Liu XP. Evaluation of the correlation between blood pressure and pulse transit time. In: MeMeA 2013 - IEEE Int. Symp. Med. Meas. Appl. Proc. pp 17–20\nShriram R, Wakankar A, Daimiwal N, Ramdasi D. Continuous cuffless blood pressure monitoring based on PTT. In: ICBBT 2010 - 2010 Int. Conf. Bioinforma. Biomed. Technol. pp 51–55\nMa Y, Choi J, Hourlier-Fargette A, et al. Relation between blood pressure and pulse wave velocity for human arteries. Proc Natl Acad Sci U S A. 2018;115:11144–9.\nChua CP, Heneghan C. Continuous blood pressure monitoring using ECG and finger photoplethysmogram. In: Annu. Int. Conf. IEEE Eng. Med. Biol. - Proc. 2016.pp 5117–5120\nKurylyak Y, Lamonaca F, Grimaldi D. A neural network-based method for continuous blood pressure estimation from a PPG signal. In: Conf. Rec. - IEEE Instrum. Meas. Technol. Conf. 2013.pp 280–283\nMIMIC Database v1.0.0. https:\u002F\u002Fphysionet.org\u002Fcontent\u002Fmimicdb\u002F1.0.0\u002F. Accessed 6 Apr 2021\nMoody GB, Mark RG. A database to support development and evaluation of intelligent intensive care monitoring. Comput Cardiol 1996.0:657–660\nSenturk U, Yucedag I, Polat K. Repetitive neural network (RNN) based blood pressure estimation using PPG and ECG signals. ISMSIT 2018 - 2nd Int Symp Multidiscip Stud Innov Technol Proc. https:\u002F\u002Fdoi.org\u002F10.1109\u002FISMSIT.2018.8567071\nMIMIC II Databases. https:\u002F\u002Farchive.physionet.org\u002Fmimic2\u002F. Accessed 31 Aug 2020\nGoldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE. 2000 PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals Circulation 10.1161\u002F01.cir.101.23.e215\nGitHub - MIT-LCP\u002Fwfdb-python: Native Python WFDB package. https:\u002F\u002Fgithub.com\u002FMIT-LCP\u002Fwfdb-python. Accessed 6 Apr 2021\nThe WFDB Software Package. https:\u002F\u002Farchive.physionet.org\u002Fphysiotools\u002Fwfdb.shtml. Accessed 6 Apr 2021\nElgendi M, Norton I, Brearley M, Abbott D, Schuurmans D. Systolic peak detection in acceleration photoplethysmograms measured from emergency responders in tropical conditions. PLoS One 2013;8:e76585\nMaronna, Ricardo A., et al. Robust statistics: theory and methods (with R). John Wiley & Sons, 2019.\nHe K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition, 2016.\nGéron A. Hands-on machine learning with Scikit-Learn, Keras, And TensorFlow: concepts, tools, and techniques To Build Intelligent Systems, 2019.\nOord A van den, Dieleman S, Zen H, Simonyan K, Vinyals O, Graves A, Kalchbrenner N, Senior A, Kavukcuoglu K. WaveNet: a generative model for raw audio, 2016.\nBergmeir C, Benítez JM. On the use of cross-validation for time series predictor evaluation. Inf Sci (Ny). 2012;191:192–213.",{"EN":195},"Continuous vital signal monitoring is becoming more relevant in preventing diseases that afflict a large part of the world’s population; for this reason, healthcare equipment should be easy to wear and simple to use. Non-intrusive and non-invasive detection methods are a basic requirement for wearable medical devices, especially when these are used in sports applications or by the elderly for self-monitoring. Arterial blood pressure (ABP) is an essential physiological parameter for health monitoring. Most blood pressure measurement devices determine the systolic and diastolic arterial blood pressure through the inflation and the deflation of a cuff. This technique is uncomfortable for the user and may result in anxiety, and consequently affect the blood pressure and its measurement. The purpose of this paper is the continuous measurement of the ABP through a cuffless, non-intrusive approach. The approach of this paper is based on deep learning techniques where several neural networks are used to infer ABP, starting from photoplethysmogram (PPG) and electrocardiogram (ECG) signals. The ABP was predicted first by utilizing only PPG and then by using both PPG and ECG. Convolutional neural networks (ResNet and WaveNet) and recurrent neural networks (LSTM) were compared and analyzed for the regression task. Results show that the use of the ECG has resulted in improved performance for every proposed configuration. The best performing configuration was obtained with a ResNet followed by three LSTM layers: this led to a mean absolute error (MAE) of 4.118 mmHg on and 2.228 mmHg on systolic and diastolic blood pressures, respectively. The results comply with the American National Standards of the Association for the Advancement of Medical Instrumentation. ECG, PPG, and ABP measurements were extracted from the MIMIC database, which contains clinical signal data reflecting real measurements. The results were validated on a custom dataset created at Neuronica Lab, Politecnico di Torino.",{"EN":197},"A Comparison of Deep Learning Techniques for Arterial Blood Pressure Prediction",{"VOID":199},"10.1007\u002Fs12559-021-09910-0","PUBLICATION","VERIFIED","Auto Verify","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs12559-021-09910-0",[205,222,249,261,273],{"id":206,"sortIndex":207,"researcher":20,"roles":208,"affiliations":210,"properties":219},"9c3c4e84-0791-454c-b6f6-6c68d1711532",4,[209],"AUTHOR",[211],{"id":20,"sortIndex":21,"affiliation":212,"properties":20},{"id":213,"createTime":214,"updateTime":214,"relativeEntities":215,"slug":20,"properties":216,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"46540a83-ed22-4a81-823c-b4b61958b92f","2023-11-30T06:54:24.221+00:00",[],{"title":217},{"VI":218},"DET - Department of Electronics and Telecommunications, Politecnico Di Torino, Turin, Italy",{"title":220},{"VI":221},"Eros Pasero",{"id":223,"sortIndex":224,"researcher":20,"roles":225,"affiliations":226,"properties":246},"2e55e2cf-65f5-40d1-893b-c2cff140e1bc",3,[209],[227,235],{"id":20,"sortIndex":21,"affiliation":228,"properties":20},{"id":229,"createTime":230,"updateTime":230,"relativeEntities":231,"slug":20,"properties":232,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"82d856ad-e9e6-41bd-a6d6-b54bc7797635","2024-01-22T16:04:09.776+00:00",[],{"title":233},{"VI":234},"Lab. 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Y, Dong G, Han J, Wah BW, Wang J. Multi-dimensional regression analysis of time-series data streams. In: International Conference on VLDB. 2002. p 323–34.\nWeigend AS. Time series prediction: forecasting the future and understanding the past. 2018.\nCook RD. Detection of influential observation in linear regression. Technometrics. 1977;19(1):15–8.\nBates DM, Watts DG. Nonlinear regression analysis and its applications. 1981.\nRendle S. Factorization machines. In: Proceedings of the 2010 IEEE International Conference on Data Mining, ICDM ’10. IEEE Computer Society, 2010. p 995–1000.\nCordell HJ, Clayton DG. A unified stepwise regression procedure for evaluating the relative effects of polymorphisms within a gene using case\u002Fcontrol or family data: application to HLA in type 1 diabetes. Am J Hum Genet. 2002;70(1):124–41.\nHoerl AE, Kennard RW. Ridge regression: biased estimation for nonorthogonal problems. Technometrics a Journal of Stats for the Physical Chemical & Engineering Sciences. 2000;42.\nHans C. Bayesian lasso regression. Biometrika. 2009;96(4):835–45.\nHui Z, Trevor H. Regression shrinkage and selection via the elastic net, with applications to microarrays. JR Stat Soc Ser B. 2003;67:301–20.\nScott M. Six approaches to calculating standardized logistic regression coefficients. Am Stat. 2004;58(4):364–364.\nPrasad AM, Iverson LR, Liaw A. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems. 2006;9:181–99.\nCherkassky V, Ma Y. Practical selection of svm parameters and noise estimation for svm regression. Neural Netw. 2004;17(1):113–26.\nSpecht DF. The general regression neural network-rediscovered. Neural Netw. 1993;6(7):1033–4.\nWerbos JP. Backpropagation through time: what it does and how to do it. Proc IEEE. 1990;78(10):1550–60.\nWilliams RJ, Zipser D. A learning algorithm for continually running fully recurrent neural networks. Neural Comput. 1998;1(2).\nSepp H. Untersuchungen zu dynamischen neuronalen Netzen, vol 1 [Master’s thesis]. Institut fur Informatik, Technische Universitat, Munchen; 1991. p 1–150.\nMozer MC. Induction of multiscale temporal structure. Morgan Kaufmann Publishers Inc. 1997.\nGf A, Schmidhuber J, Cummins F. Learning to forget: continual prediction with LSTM. In: Istituto Dalle Molle Di Studi Sull Intelligenza Artificiale. 1999.\nHochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80.\nGers FA, Schraudolph NN, Schmidhuber J. Learning precise timing with lstm recurrent networks. J Mach Learn Res. 2003;3(1):115–43.\nPérez-Ortiz JA, Gers FA, Eck D, Schmidhuber J. Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets. Neural Netw. 2003;16(2):241–50.\nGraves A, Schmidhuber J. Framewise phoneme classification with bidirectional LSTM networks. In: IEEE International Joint Conference on Neural Networks. 2005.\nXu Y, Chhim L, Zheng B, Nojima Y. Stacked deep learning structure with bidirectional long-short term memory for stock market prediction. In: International Conference on Neural Computing for Advanced Applications. Springer; 2020. p 447–60.\nChen Q, Zhang W, Lou Y. Forecasting stock prices using a hybrid deep learning model integrating attention mechanism, multi-layer perceptron, and bidirectional long-short term memory neural network. IEEE Access. 2020;PP(99):1–1.\nLu W, Li J, Wang J, Qin L. A CNN-BILSTM-AM method for stock price prediction. Neural Comput Appl. 2021;33(10):4741–53.\nLai G, Chang WC, Yang Y, Liu H. Modeling long- and short-term temporal patterns with deep neural networks. In: International ACM SIGIR Conference on Research and Development in Information Retrieval. 2018.\nShih SY, Sun FK, Lee HY. Temporal pattern attention for multivariate time series forecasting. Mach Learn. 2019;108(8–9):1421–41.\nNama S, Saha AK. A bio-inspired multi-population-based adaptive backtracking search algorithm. Cogn Comput. 2022;14(2):900–25.\nMartínez-Cagigal V, Santamaría-Vázquez E, H Roberto. Brain-computer interface channel selection optimization using meta-heuristics and evolutionary algorithms. Appl Soft Comput. 2022;115:108176.\nNawaz MS, Nawaz MZ, Hasan O, Fournier-Viger P, Sun M. Proof searching and prediction in HOL4 with evolutionary\u002Fheuristic and deep learning techniques. Appl Intell. 2021;51:1580–1601.\nHochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80.\nCho K, Van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning phrase representations using RNN encoder-decoder for statistical machine translation. Comput Sci. 2014.\nSchuster M, Paliwal KK. Bidirectional recurrent neural networks. IEEE Trans Signal Process. 1997;45(11):2673–81.\nBand SS, Mohammadzadeh A, Csiba P, Mosavi A, Varkonyi-Koczy AR. Voltage regulation for photovoltaics-battery-fuel systems using adaptive group method of data handling neural networks (GMDH-NN). IEEE Access. 2020.\nIvakhnenko AG. Sorting methods for modeling and clusterization (survey of GMDH papers for the years 1983–1988). The present stage of GMDH development. Soviet Journal of Automation and Information Sciences (English translation of Avtomatyka). 1988;21(4).\nYang CH, Liao MY, Chen PL, Huang MT, Huang CW, Huang JS, Chung JB. Constructing financial distress prediction model using group method of data handling technique. In: International Conference on Machine Learning & Cybernetics. 2009.\nXu L, Lu XW, Xiao BJ, Qi L, Enhong C, Xiaoyi J, Bin L. Probabilistic SVM classifier ensemble selection based on GMDH-type neural network. Pattern Recogn. 2020;106:107373.\nXu L, Lu B, Xiao J, Liu Q, Chen E, Wang X, Tang Y. Multiple graph kernel learning based on GMDH-type neural network. Information Fusion. 2021;66:100–10.\nRadman A, Suandi SA. BILSTM regression model for face sketch synthesis using sequential patterns. Neural Comput Appl. 2021;33:12689–702.\nGupta B, Prakasam P, Velmurugan T. Integrated BERT embeddings, BILSTM-BIGRU and 1-D CNN model for binary sentiment classification analysis of movie reviews. Multimed Tools Appl. 2022;81(23):33067–86.",{"EN":334},"The input for stock market prediction is usually a period of stock price data with time series characteristics, which will keep changing over time and have more complex background relationships. How to effectively mine and fuse multiple heterogeneous data of the stock market is difficult to be handled by traditional recurrent neural networks (RNN). To solve this problem, we divide the regression model into an encoder and decoder structure. In this paper, we first use RNN technique for missing value complementation, then use the fusion model of bidirectional gate recurrent unit (BiGRU) and bidirectional long short-term memory network (BiLSTM) as the encoder to extract. Finally, the group method of data handling (GMDH) model is used as a decoder to obtain stock market prediction results based on the time series data features. A deep heuristic evolutionary regression model (BBGMDH) based on the fusion of BiGRU and BiLSTM is proposed by the above process. We have conducted extensive experiments on four real stock data, and the results show that BBGMDH significantly outperforms existing methods, verifying the effectiveness of the encoding-decoding stepwise regression model in stock price prediction tasks. The reason is that the encoding layer utilizes the powerful time series data processing technology of RNN to effectively extract the hidden features of stock data, and the decoding layer utilizes the GMDH heuristic evolutionary computation method to simulate the “genetic mutation selection evolution” process of an organism for the regression task of stock market prediction, making full use of their complementary properties. We provide a new solution to the regression prediction problem.",{"EN":336},"Deep Heuristic Evolutionary Regression Model Based on the Fusion of BiGRU and BiLSTM",{"VOID":338},"10.1007\u002Fs12559-023-10135-6","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12559-023-10135-6",[341,358,370,387,399,415],{"id":342,"sortIndex":237,"researcher":20,"roles":343,"affiliations":344,"properties":355},"fdd94088-7f48-412c-acb3-2fe7c3e1b6aa",[209],[345],{"id":20,"sortIndex":21,"affiliation":346,"properties":20},{"id":347,"createTime":348,"updateTime":349,"relativeEntities":350,"slug":351,"properties":352,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"1f26f065-879a-4772-a515-3949ac8a288a","2024-04-08T16:22:30.977+00:00","2024-06-19T19:05:57.615+00:00",[],"School-of-Artificial-Intelligence-and-Big-Data-Hefei-University-Hefei-China",{"title":353},{"VI":354},"School of Artificial Intelligence 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feature reduction system based on Discriminative Common Vector is presented and evaluated in this paper. The validation of this system was made with three databases, first one is DNA markers and the other two are The ORL Database of Face and The Yale Face Database. Moreover, a supervised classification system has been implemented with three different classifiers, achieving the best success rates with Support Vector Machines using Radial Basis Function kernel and a one-versus-all multiclass approach. The study shows clearly how our approach reduces the number of features and load times, keeping or improving the level of discrimination.",{"EN":480},"Reducing Features Using Discriminative Common Vectors",{"VOID":482},"10.1007\u002Fs12559-010-9059-y",[484],"EN","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12559-010-9059-y",[487,505,517,529],{"id":488,"sortIndex":21,"researcher":20,"roles":489,"affiliations":490,"properties":500},"e725c592-fe2b-470b-8ac6-222a187125a1",[],[491],{"id":20,"sortIndex":21,"affiliation":492,"properties":20},{"id":493,"createTime":494,"updateTime":494,"relativeEntities":495,"slug":496,"properties":497,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"11fdcf7f-138f-4861-9f33-98569c7fc8da","2024-04-11T04:47:04.375+00:00",[],"Signals-and-Communications-Department-IDeTIC-University-of-Las-Palmas-de-Gran-Canaria-Las-Palmas-de-Gran-Can%C3%A1ria-Spain",{"title":498},{"EN":499},"Signals and Communications Department, IDeTIC, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canária, Spain",{"title":501,"email":503},{"EN":502},"Carlos M. 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Dimensional reduction based on independent component analysis for content based image retrieval. In: International joint conference on artificial intelligence; 2009. pp. 741–745.",{"id":20,"text":576,"url":20,"identifiers":20},"Shiqing Z, Zhijin Z. Dimensionality reduction-based phoneme recognition. In: 9th international conference on signal processing; 2008. pp. 667–670.",{"id":20,"text":578,"url":20,"identifiers":20},"Duda RO, Hart PE, Stork DG. Pattern classification. 2nd ed. New York: Wiley Interscience; 2000.",{"id":20,"text":580,"url":20,"identifiers":20},"Cevikalp H, Neamtu M, Barkana A. The kernel common vector method: a novel nonlinear subspace classifier for pattern recognition. IEEE Trans Syst Man Cybern Part B. 2007;37(4):937–51.",{"id":20,"text":582,"url":20,"identifiers":20},"Cevikalp H, Neamtu M, Wilkes M. Discriminative Common Vector method with kernels. IEEE Trans Neural Netw. 2006;17(6):1832–8.",{"id":20,"text":584,"url":20,"identifiers":20},"Cristianini N, Shawe-Taylor J. An introduction to support vector machines. Cambridge: Cambridge University Press; 2000.",{"id":20,"text":586,"url":20,"identifiers":20},"Joachims T. SVM_light support vector machine. Department of Computer Science, Cornell University, Version: 6.02. http:\u002F\u002Fsvmlight.joachims.org\u002F (2009). Last visit: 30 Mar 2009.",{"id":20,"text":588,"url":20,"identifiers":20},"Mora-Urpí J, Arroyo C. Sobre origen y diversidad en pejibaye. Serie Técnica Pejibaye (Guilielma). Technical report, Ed. University of Costa Rica; 1996. Vol. 5, No 1, pp. 18–25.",{"id":20,"text":590,"url":20,"identifiers":20},"Ferrer M, Eguiarte LE, Montana C. Genetic structure and outcrossing rates in Flourensia cernua (Asteraceae) growing at different densities in the South-western Chihuahuan Desert. Ann Bot. 2004;94:419–26.",{"id":20,"text":592,"url":20,"identifiers":20},"http:\u002F\u002Fwww.uk.research.att.com\u002Ffacedatabase.html (2009). Last visit: 30 Mar 2009.",{"id":20,"text":594,"url":20,"identifiers":20},"http:\u002F\u002Fcvc.yale.edu (2009). Last visit: 30 Mar 2009.",{"id":20,"text":596,"url":20,"identifiers":20},"Mitchell TM. Machine learning. New York: McGraw-Hill; 1997.",{"id":20,"text":598,"url":20,"identifiers":20},"Bishop CM. Neural networks for pattern recognition. Oxford: Oxford University Press; 1996.",{"id":20,"text":600,"url":20,"identifiers":20},"Travieso CM, Botella P, Alonso JB, Ferrer MA. Discriminative Common Vector for face identification. In: 43rd IEEE international carnahan conference on security technology; 2009. pp. 134–138.",{"id":20,"text":602,"url":20,"identifiers":20},"Briceño JC, Travieso CM, Ferrer MA, Alonso JB, Vargas F. Off-line signature recognition based on contour parameterization. In: 12th international conference on computer aided system theory; 2009. pp. 118–120.",{"id":20,"text":604,"url":20,"identifiers":20},"Gonzalez RC, Wood RE. Digital image processing. Reading: Addison-Wesley; 2002.",{"id":20,"text":606,"url":20,"identifiers":20},"Hyvärinen A, Karhunen J, Oja E. Independent component analysis. New York: Wiley; 2001.",{"id":608,"createTime":609,"updateTime":610,"relativeEntities":611,"slug":612,"properties":613,"entityType":200,"verifyStatus":201,"verifyTime":622,"verifyNote":202,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":623,"fullTextUrl":20,"authors":624,"publicationType":286,"publisherRelationship":675,"citationCount":20,"citationInfo":20,"publishDate":709,"publishYear":710,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":323},"f4f612c2-880e-47ea-a21e-b7665dabe559","2024-01-01T16:32:23.024+00:00","2025-01-25T23:54:08.848+00:00",[],"Negotiating-over-Mobile-Phones-Calling-or-Being-Called-Can-Make-the-Difference",{"references":614,"abstract":616,"title":618,"doi":620},{"VOID":615},"Aarts H, Custers R, Marien H. Preparing and motivating behavior outside of awareness. Science. 2008;319(5870):1639.\nAbraham C, Michie S. 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Gender and negotiator competitiveness: a meta-analysis. Organ Behav Hum Decis Process. 1998;76(1):1–29.\nWilson T. The power of social psychological interventions. Science. 2006;313(5791):1251–2.",{"EN":617},"Mobile phones pervade our everyday life like no other technology, but the effects they have on one-to-one conversations are still relatively unknown. This paper focuses on how mobile phones influence negotiations, i.e., on discussions where two parties try to reach an agreement starting from opposing preferences. The experiments involve 60 pairs of unacquainted individuals (120 subjects). They must make a “yes” or “no” decision on whether several objects increase the chances of survival in a polar environment or not. When the participants disagree about a given object (one says “yes” and the other says “no”), they must try to convince one another and reach a common decision. Since the subjects discuss via phone, one of them (selected randomly) calls while the other is called. The results show that the caller convinces the receiver in 70 % of the cases (\n                  \n                    \n                  \n                  $$p$$\n                  \n                    \n                  \n                 value = 0.005 according to a two-tailed binomial test). Gender, age, personality and conflict handling style, measured during the experiment, fail in explaining such a persuasiveness difference. Calling or being called appears to be the most important factor behind the observed result.",{"EN":619},"Negotiating over Mobile Phones: Calling or Being Called Can Make the Difference",{"VOID":621},"10.1007\u002Fs12559-014-9267-y","2025-01-25T23:54:08.847+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12559-014-9267-y",[625,640,663],{"id":626,"sortIndex":237,"researcher":20,"roles":627,"affiliations":628,"properties":637},"ce2e11f6-a6e3-4330-9c48-757f0f5a2c2c",[209],[629],{"id":20,"sortIndex":21,"affiliation":630,"properties":20},{"id":631,"createTime":632,"updateTime":632,"relativeEntities":633,"slug":20,"properties":634,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"9380c731-2285-4237-854a-9443928a653b","2024-01-08T14:50:46.443+00:00",[],{"title":635},{"VI":636},"School of Computing Science, University of Glasgow, Glasgow, UK",{"title":638},{"VI":639},"Hugues Salamin",{"id":641,"sortIndex":21,"researcher":20,"roles":642,"affiliations":643,"properties":660},"d375d5d5-097d-4da0-85cd-2f6af6f4b22a",[209],[644,649],{"id":20,"sortIndex":21,"affiliation":645,"properties":20},{"id":631,"createTime":632,"updateTime":632,"relativeEntities":646,"slug":20,"properties":647,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":648},{"VI":636},{"id":650,"sortIndex":237,"affiliation":651,"properties":659},"d6c3e8ec-0c99-48ed-8cfc-4c13a96b4b76",{"id":652,"createTime":653,"updateTime":653,"relativeEntities":654,"slug":655,"properties":656,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"b17ac24d-21ff-4263-ab18-11ae430c6150","2024-04-07T05:03:51.191+00:00",[],"Idiap-Research-Institute-Martigny-Switzerland",{"title":657},{"VI":658},"Idiap Research Institute, Martigny, Switzerland",{},{"title":661},{"VI":662},"Alessandro Vinciarelli",{"id":664,"sortIndex":275,"researcher":20,"roles":665,"affiliations":666,"properties":672},"93d13159-b43c-4dab-9ced-08843249ca41",[209],[667],{"id":20,"sortIndex":21,"affiliation":668,"properties":20},{"id":631,"createTime":632,"updateTime":632,"relativeEntities":669,"slug":20,"properties":670,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":671},{"VI":636},{"title":673},{"VI":674},"Anna 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IEEE Trans Evol Comput. 2008;12:702–13.\nMafarja M, Mirjalili S. Whale optimization approaches for wrapper feature selection. Appl Soft Comput J. 2018;62:441–53.\nMafarja M, Aljarah I, Heidari AA, Hammouri AI, Faris H, Al-Zoubi AM, et al. Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems. Knowl Based Syst. 2018;145:25–45.\nArora S, Singh H, Sharma M, Sharma S, Anand P. A new hybrid algorithm based on grey wolf optimization and crow search algorithm for unconstrained function optimization and feature selection. IEEE Access. 2019;7:26343–61.",{"EN":721},"Feature selection (FS) has the largest influence on the performance of machine learning methods. FS can remove the irrelevant and redundancy features from the data while preserving the same quality of increasing it. However, the traditional FS methods are time-consuming and can be stuck in local optima. So, the metaheuristic (MH) techniques are used to avoid these limitations since they have several operators that explore and exploit the search domain better than traditional methods. Besides these behaviors of MH, we present an improved atomic orbital search (IAOS) algorithm using a global search strategy that uses the operators of arithmetic optimization algorithm (AOA), which has proven a good exploration ability to provide a promising candidate solution. The opposite-based learning (OBL) is applied to enhance the initial population, which leads to enhancing the convergence rate towards the optimal solution. In addition, a dynamic photon rate is used to enhance the balance between exploration and exploitation. Finally, the sequential backward selection (SBS) is used as a local search strategy to improve the best solution, and this leads to obtaining a set of relevant features that increase the classification accuracy. To evaluate the performance of the presented IAOS-SBS as an FS method, a set of twenty UCI datasets is used; also, it is compared with other well-known FS methods. The results show the superiority of IAOS-SBS among the performance measures. Finally, it is concluded that IAOS-SBS can select fewer features with achieving high classification accuracy for most of the datasets utilized in the study. This indicates the use of OBL and SBS leads to enhancing the original AOS.",{"EN":723},"Feature Selection Based on Modified Bio-inspired Atomic Orbital Search Using Arithmetic Optimization and Opposite-Based Learning",{"VOID":725},"10.1007\u002Fs12559-022-10022-6","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12559-022-10022-6",[728,753,768,785],{"id":729,"sortIndex":275,"researcher":20,"roles":730,"affiliations":731,"properties":750},"838aa740-8695-4ae7-a5d0-aaa04735aed7",[209],[732,740],{"id":20,"sortIndex":21,"affiliation":733,"properties":20},{"id":734,"createTime":735,"updateTime":735,"relativeEntities":736,"slug":20,"properties":737,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"a50ae693-a1ab-4eca-8bcd-addf5afe8a01","2023-12-19T19:09:12.132+00:00",[],{"title":738},{"VI":739},"EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia",{"id":741,"sortIndex":237,"affiliation":742,"properties":749},"a4d38580-b145-47b0-af6f-b91119474f47",{"id":743,"createTime":744,"updateTime":744,"relativeEntities":745,"slug":20,"properties":746,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"601a91d9-53eb-4643-a560-0cb28189508c","2023-11-30T21:06:26.699+00:00",[],{"title":747},{"VI":748},"Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Shebin El-Koom, Egypt",{},{"title":751},{"VI":752},"Ahmed A. 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V, Kendall A, Cipolla R. 2015. 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IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009. p. 248–255. IEEE; 2009.\nEigen D, Fergus R. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. Proceedings of the IEEE International Conference on Computer Vision; 2015. p. 2650–2658.\nGarcia-Garcia A, Orts-Escolano S, Oprea S, Villena-Martinez V, Garcia-Rodriguez J. 2017. A review on deep learning techniques applied to semantic segmentation. arXiv:1704.06857.\nGeiger A, Lenz P, Stiller C, Urtasun R. Vision meets robotics: The kitti dataset. Int J Robot Res 2013;32(11):1231–1237.\nGros C. Cognitive computation with autonomously active neural networks: an emerging field. Cogn Comput 2009;1(1):77–90.\nHe K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 770–778.\nHubara I, Courbariaux M, Soudry D, El-Yaniv R, Bengio Y. Binarized neural networks. Advances in neural information processing systems; 2016. p. 4107–4115.\nIandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K. 2016. Squeezenet: Alexnet-level accuracy with 50x fewer parameters and \u003C 0.5 mb model size. arXiv:1602.07360.\nJia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T. 2014. Caffe: Convolutional architecture for fast feature embedding. arXiv:1408.5093.\nKingma D, Adam JB. 2014. A method for stochastic optimization. arXiv preprint. arXiv:1412.6980.\nKrizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems; 2012. p. 1097–1105.\nLe V, Brandt J, Lin Z, Bourdev L, Huang T. Interactive facial feature localization. Comput Vision–ECCV 2012;2012:679–692.\nLi H, Kadav A, Durdanovic I, Samet H, Graf HP. 2016. Pruning filters for efficient convnets. arXiv:1608.08710.\nLiu B, Wang M, Foroosh H, Tappen M, Pensky M. 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You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2016. p. 779–788.\nRoy A, Todorovic S. A multi-scale cnn for affordance segmentation in rgb images. European Conference on Computer Vision, Springer; 2016. p. 186–201.\nShelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 2017;39(4):640–651.\nShotton J, Johnson M, Cipolla R. Semantic texton forests for image categorization and segmentation. IEEE Conference on Computer vision and pattern recognition, 2008. CVPR 2008, IEEE; 2008. p. 1–8.\nSimonyan K, Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556.\nSmith BM, Li Z, Brandt J, Lin Z, Yang J. Exemplar-based face parsing. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2013. p. 3484–3491.\nSturgess P, Alahari K, Ladicky L, Torr PHS. Combining appearance and structure from motion features for road scene understanding. BMVC 2012-23rd British Machine Vision Conference. BMVA; 2009.\nSzegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition; 2015. p. 1–9.\nWang Y, Zhao Q, Bo W, Wang S, Zhang Y, Guo W, Feng Z. A real-time active pedestrian tracking system inspired by the human visual system. Cogn Comput 2016;8(1):39–51.\nWen G, Hou Z, Li H, Li D, Jiang L, Xun E. Ensemble of deep neural networks with probability-based fusion for facial expression recognition. Cogn Comput 2017;9(5):597–610.\nXie J, Lu Y, Zhu L, Chen X. Semantic image segmentation method with multiple adjacency trees and multiscale features. Cogn Comput 2017;9(2):168–179.\nFisher Y, Koltun V. 2015. Multi-scale context aggregation by dilated convolutions. arXiv:1511.07122.\nZeiler MD, Fergus R. Visualizing and understanding convolutional networks. European conference on computer vision, Springer; 2014. p. 818–833.\nZeng Dan, Zhao Fan, Shen Wei, Ge Shiming. 2017. Compressing and accelerating neural network for facial point localization. Cognitive Computation.\nZhang R, Candra SA, Vetter K, Zakhor A. Sensor fusion for semantic segmentation of urban scenes. 2015 IEEE International Conference on Robotics and Automation (ICRA), IEEE; 2015. p. 1850–1857.\nZhao H, Shi J, Qi X, Wang X, Jia J. 2016. Pyramid scene parsing network. arXiv:1612.01105.\nZhao J, Chun D, Sun H, Liu X, Sun J. Biologically motivated model for outdoor scene classification. Cogn Comput 2015;7(1):20–33.\nZheng S, Jayasumana S, Romera-Paredes B, Vineet V, Zhizhong S, Dalong D, Huang C, Torr PHS. Conditional random fields as recurrent neural networks. Proceedings of the IEEE International Conference on Computer Vision; 2015. p. 1529–1537.\nZhou A, Yao A, Guo Y, Xu L, Chen Y. 2017. Incremental network quantization: Towards lossless cnns with low-precision weights. arXiv:1702.03044.\nZhou S, Wu Y, Ni Z, Zhou X, Wen H, Zou Y. 2016. Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv:1606.06160.",{"EN":862},"With the rapid development of deep learning techniques, semantic image segmentation has been considerably improved recently, which is viewed as the key problem of scene understanding in computer vision. These advances are built upon the capability of complex architectures for deep neural network. In this paper, we present a novel deep neural network architecture designed for semantic image segmentation. In order to improve the segmentation accuracy, we introduce a novel hierarchical dilation block to effectively enlarge the size of receptive field and enable multi-scale processing in fully convolutional neural network. Moreover, we exploit the technique of bypass and intermediate supervision to capture the context information during upsampling and refining coarse features. We have conducted extensive experiments on several popular semantic segmentation testbeds, including Cityscapes, CamVid, Kitti, and Helen facial datasets. The experimental results demonstrate that our proposed approach runs two times faster than the state-of-the-art method. Our full system is able to obtain realtime inference performance on 1080P images using a PC with single GPU. It executes a network forwarding at 200fps in our experiment while retaining high accuracy. Our proposed approach not only runs faster than the existing realtime methods but also performs on par with them.",{"EN":864},"Very Fast Semantic Image Segmentation Using Hierarchical Dilation and Feature Refining",{"VOID":866},"10.1007\u002Fs12559-017-9530-0","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12559-017-9530-0",[869,886,908],{"id":870,"sortIndex":275,"researcher":20,"roles":871,"affiliations":872,"properties":883},"223e6640-0e03-4bab-8d22-0913018c122a",[209],[873],{"id":20,"sortIndex":21,"affiliation":874,"properties":20},{"id":875,"createTime":876,"updateTime":877,"relativeEntities":878,"slug":879,"properties":880,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"1f75ff51-641b-4262-9764-e771635944e6","2024-02-05T18:19:19.934+00:00","2024-09-25T11:43:50.044+00:00",[],"College-of-Computer-Science-Zhejiang-University-Zhejiang-China",{"title":881},{"VI":882},"College 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Augmenting colonoscopy using extended and directional cyclegan for lossy image translation, in: IEEE Conference on Computer Vision and Pattern Recognition, 2020.",{"EN":1238},"A general trend of nuclei segmentation is the transition from two-dimensional to three-dimensional nuclei segmentation and from traditional image processing methods to data-driven cognitively inspired methods. Existing nuclei segmentation datasets do not meet this trend: They either do not contain enough samples for training the deep learning model or not contain challenging 3D structure. Thus, large-scale datasets are critically demanded for nuclei segmentation tasks. In this paper, we introduce a new benchmark nuclei segmentation dataset termed as Scaffold-A549 for 3D cell culture on bio-scaffold. The A549 human non-small cell lung cancer cells are seeded in the bio-scaffold for cell culture and the samples with different density of nuclei are captured using confocal laser scanning microscope at the first, third, and eighth culture day. A total of 21 3D images are collected containing more than 10,000 nucleus and each of the images containing more than 800 nucleus are annotated manually for evaluation. Scaffold-A549 presents one large, diverse, challenging, and publicly available dataset and can be widely used for the research on 3D unsupervised nuclei segmentation.",{"EN":1240},"Scaffold-A549: A Benchmark 3D Fluorescence Image Dataset for Unsupervised Nuclei 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In Thirty-Second AAAI Conference on Artificial Intelligence. 2018. pp. 1795–802. https:\u002F\u002Fdoi.org\u002F10.1109\u002FMIS.2017.4531228.\nCambria E, Poria S, Gelbukh A, Thelwall M. Sentiment analysis is a big suitcase. IEEE Intell Syst. 2017;32(6):74–80. https:\u002F\u002Fdoi.org\u002F10.1109\u002FMIS.2017.4531228.\nCambria E. Affective computing and sentiment analysis. IEEE Intell Syst. 2016;31(2):102–7. https:\u002F\u002Fdoi.org\u002F10.1109\u002FMIS.2016.31.\nCambria E, Li Y, Xing FZ, Poria S, Kwok K. SenticNet 6: Ensemble application of symbolic and subsymbolic ai for sentiment analysis. CIKM’20, Oct 20-24. 2020. pp. 105–14. https:\u002F\u002Fdoi.org\u002F10.1145\u002F3340531.3412003.\nDragoni M, Poria S, Cambria E. Ontosenticnet: A commonsense ontology for sentiment analysis. IEEE Intell Syst. 2018;33(3):77–85. https:\u002F\u002Fdoi.org\u002F10.1109\u002FMIS.2018.033001419.\nWeichselbraun A, Gindl S, Fischer F, Vakulenko S, Scharl A. 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Res. 7(Jan):1–30, 2006.",{"EN":1380},"The dramatic growth of the Web has motivated researchers to extract knowledge from enormous repositories and to exploit the knowledge in myriad applications. In this study, we focus on natural language processing (NLP) and, more concretely, the emerging field of affective computing to explore the automation of understanding human emotions from texts. This paper continues previous efforts to utilize and adapt affective techniques into different areas to gain new insights. This paper proposes two novel feature extraction methods that use the previous sentic computing resources AffectiveSpace and SenticNet. These methods are efficient approaches for extracting affect-aware representations from text. In addition, this paper presents a machine learning framework using an ensemble of different features to improve the overall classification performance. Following the description of this approach, we also study the effects of known feature extraction methods such as TF-IDF and SIMilarity-based sentiment projectiON (SIMON). We perform a thorough evaluation of the proposed features across five different datasets that cover radicalization and hate speech detection tasks. To compare the different approaches fairly, we conducted a statistical test that ranks the studied methods. The obtained results indicate that combining affect-aware features with the studied textual representations effectively improves performance. We also propose a criterion considering both classification performance and computational complexity to select among the different methods.",{"EN":1382},"An Ensemble Method for Radicalization and Hate Speech Detection Online Empowered by Sentic Computing",{"VOID":1384},"10.1007\u002Fs12559-021-09845-6","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12559-021-09845-6",[1387,1404],{"id":1388,"sortIndex":21,"researcher":20,"roles":1389,"affiliations":1390,"properties":1401},"4486a1f3-04be-4299-9a2a-b8b4af002de3",[209],[1391],{"id":20,"sortIndex":21,"affiliation":1392,"properties":20},{"id":1393,"createTime":1394,"updateTime":1395,"relativeEntities":1396,"slug":1397,"properties":1398,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"03de10a1-a8fc-4501-a8af-1e6a2996d995","2024-02-05T23:44:52.126+00:00","2025-06-11T17:10:42.967+00:00",[],"Intelligent-Systems-Group-Universidad-Polit%C3%A9cnica-de-Madrid-Madrid-Spain",{"title":1399},{"VI":1400},"Intelligent Systems Group, Universidad Politécnica de Madrid, Madrid, Spain",{"title":1402},{"VI":1403},"Oscar Araque",{"id":1405,"sortIndex":237,"researcher":20,"roles":1406,"affiliations":1407,"properties":1413},"b7d91183-812b-411c-a0b8-24dc8886fcdb",[209],[1408],{"id":20,"sortIndex":21,"affiliation":1409,"properties":20},{"id":1393,"createTime":1394,"updateTime":1395,"relativeEntities":1410,"slug":1397,"properties":1411,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1412},{"VI":1400},{"title":1414},{"VI":1415},"Carlos A. Iglesias",{"url":1385,"publisher":1417,"properties":1445},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1418,"slug":10,"properties":1419,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1423,"manageAffiliations":1424,"indexDatabases":1425,"url":112,"thumbnailPath":20,"statistic":1440,"gsStatistic":20,"type":180,"analyzePriority":20},[],{"issn":1420,"eissn":1421,"title":1422},{"VOID":13},{"VOID":15},{"EN":17},[],[],[1426,1433],{"id":72,"indexDatabase":1427,"url":85,"indexYears":86,"academicFieldIds":1432,"indexDatabaseRanking":91},{"id":74,"createTime":75,"updateTime":76,"relativeEntities":1428,"label":1429,"description":1430,"key":82,"publicationTags":1431,"standard":20},[],{"EN":79,"VI":79},{"EN":79,"VI":81},[84],[88,89,90],{"id":93,"indexDatabase":1434,"url":108,"indexYears":20,"academicFieldIds":1439,"indexDatabaseRanking":20},{"id":95,"createTime":96,"updateTime":97,"relativeEntities":1435,"label":1436,"description":1437,"key":104,"publicationTags":1438,"standard":20},[],{"EN":100,"VI":100},{"VI":102,"EN":103},[106,107],[110,111],{"impactFactor":21,"impactFactorByYear":1441,"i10Index":127,"i10IndexLast5Year":128,"totalPublication":129,"totalPublicationByYear":1442,"totalCitation":145,"totalCitationByYear":1443,"totalCitationPerPublication":162,"totalCitationPerPublicationByYear":1444,"hindexLast5Year":161,"hindex":161},{"2012":115,"2013":116,"2014":117,"2015":118,"2016":119,"2017":120,"2018":121,"2019":122,"2020":123,"2021":124,"2022":125,"2023":126},{"2009":131,"2010":132,"2011":133,"2012":134,"2013":135,"2014":136,"2015":137,"2016":136,"2017":138,"2018":139,"2019":140,"2020":136,"2021":141,"2022":142,"2023":143,"2024":144},{"2009":147,"2010":148,"2011":149,"2012":133,"2013":150,"2014":151,"2015":152,"2016":153,"2017":154,"2018":155,"2019":156,"2020":157,"2021":158,"2022":159,"2023":160,"2024":161},{"2009":164,"2010":165,"2011":166,"2012":167,"2013":168,"2014":169,"2015":170,"2016":171,"2017":172,"2018":173,"2019":174,"2020":175,"2021":176,"2022":177,"2023":178,"2024":179},{"volume":1446,"pages":1447},{"VOID":318},{"VOID":1448},"48-61","2021-02-16",{"id":1451,"createTime":1452,"updateTime":1453,"relativeEntities":1454,"slug":1455,"properties":1456,"entityType":200,"verifyStatus":201,"verifyTime":1453,"verifyNote":202,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1465,"fullTextUrl":20,"authors":1466,"publicationType":286,"publisherRelationship":1506,"citationCount":20,"citationInfo":20,"publishDate":1540,"publishYear":1541,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":323},"6a436bd5-4c10-4644-ac48-bf3774b2ef4c","2024-02-05T21:32:15.316+00:00","2025-02-11T23:43:14.918+00:00",[],"Gender-Classification-by-Means-of-Online-Uppercase-Handwriting-A-Text-Dependent-Allographic-Approach",{"references":1457,"abstract":1459,"title":1461,"doi":1463},{"VOID":1458},"Faundez-Zanuy M, Hussain A, Mekyska J, Sesa-Nogueras E, Monte-Moreno E, Esposito A, Chetouani M, Garre-Olmo J, Abel A, Smekal Z, Lopez-de-Ipiña K. 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Psychol Rev. 1910;17(3):205–16.\nBinet A. Les révélations de l’écriture d’après un controle scientifique. Paris: Félix Alcan, Éditeur; 1906.\nNewhall SM. Sex differences in handwriting. J Appl Psychol. 1926;10(2):151–61.\nYoung PT. Sex differences in handwriting. J Appl Psychol. 1931;15(5):486–98.\nKinder JS. A new investigation of judgments on the sex of handwriting. J Educ Psychol. 1926;17(5):341–4.\nBroom ME, Thompson B, Bouton MT. Sex differences in handwriting. J Appl Psychol. 1929;13(2):159–66.\nTenwolde H. More on sex differences in handwriting. J Appl Psychol. 1934;18(5):705–10.\nFluckiger FA, Tripp CA, Weinberg GH. A review of experimental research in graphology, 1933–1960. Percept Mot Skills. 1961;12(1):67–90.\nHodgins JH. Determination of sex from handwriting. Can Soc Forensic Sci J. 1971;4:124–32.\nHecker MR. The scientific examination of sex differences. 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Helsinki University of Technology: SOM toolbox. http:\u002F\u002Fwww.cis.hut.fi\u002Fsomtoolbox\u002F.\nFierrez J, Galbally J, Ortega-Garcia J, Freire MR, Alonso-Fernandez F, Ramos D, Toledano DT, Gonzalez-Rodriguez J, Siguenza JA, Garrido-Salas J, Anguiano E, Gonzalez-de-Rivera G, Ribalda R, Faundez-Zanuy M, Ortega JA, Cardeñoso-Payo V, Viloria A, Vivaracho CE, Moro QI, Igarza JJ, Sanchez J, Hernaez I, Orrite-Uruñuela C, Martinez-Contreras F, Gracia-Roche JJ. BiosecurID: a multimodal biometric database. Pattern Anal Appl. 2010;13(2):235–46.\nSiegel S, Castellan NJ Jr. Nonparametric statistics for the behavioral sciences. New York: Mcgraw-Hill Book Company; 1988.\nSesa-Nogueras E, Faundez-Zanuy M, Mekyska J. An information analysis of in-air and on-surface trajectories in online handwriting. Cogn Comput. 2012;4(2):195–205.\nEspinosa-Duró V, Faundez-Zanuy M, Mekyska J. Beyond cognitive signals. Cogn Comput. 2011;3(2):374–81.",{"EN":1460},"This paper presents a gender-classification schema based on online handwriting. Using samples acquired with a digital tablet that captures the dynamics of the writing, it classifies the writer as a male or a female. The method proposed is allographic, regarding strokes as the structural units of handwriting. Strokes performed while the writing device is not exerting any pressure on the writing surface, pen-up (in-air) strokes, are also taken into account. The method is also text-dependent meaning that training and testing is done with exactly the same text. Text-dependency allows classification be performed with very small amounts of text. Experimentation, performed with samples from the BiosecurID database, yields results that fall in the range of the classification averages expected from human judges. With only four repetitions of a single uppercase word, the average rate of well-classified writers is 68 %; with sixteen words, the rate rises to an average of 72.6 %. Statistical analysis reveals that the aforementioned rates are highly significant. In order to explore the classification potential of the pen-up strokes, these are also considered. Although in this case, results are not conclusive, and an outstanding average of 74 % of well-classified writers is obtained when information from pen-up strokes is combined with information from pen-down ones.",{"EN":1462},"Gender Classification by Means of Online Uppercase Handwriting: A Text-Dependent Allographic Approach",{"VOID":1464},"10.1007\u002Fs12559-015-9332-1","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12559-015-9332-1",[1467,1482,1494],{"id":1468,"sortIndex":275,"researcher":20,"roles":1469,"affiliations":1470,"properties":1479},"0d572fe0-12d7-440d-a101-8bdef645d9d2",[209],[1471],{"id":20,"sortIndex":21,"affiliation":1472,"properties":20},{"id":1473,"createTime":1474,"updateTime":1474,"relativeEntities":1475,"slug":20,"properties":1476,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"1e3734fb-58a6-4fa2-956d-e6119ec32426","2024-02-05T21:32:15.336+00:00",[],{"title":1477},{"VI":1478},"Escola Universitària Politècnica de Mataró, Mataró, Spain",{"title":1480},{"VI":1481},"Josep Roure-Alcobé",{"id":1483,"sortIndex":237,"researcher":20,"roles":1484,"affiliations":1485,"properties":1491},"d4b7c68c-1596-4925-8b2f-4df99a5b841b",[209],[1486],{"id":20,"sortIndex":21,"affiliation":1487,"properties":20},{"id":1473,"createTime":1474,"updateTime":1474,"relativeEntities":1488,"slug":20,"properties":1489,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1490},{"VI":1478},{"title":1492},{"VI":1493},"Marcos Faundez-Zanuy",{"id":1495,"sortIndex":21,"researcher":20,"roles":1496,"affiliations":1497,"properties":1503},"2d2786af-49e4-4d7c-bd5a-2428c9744d0f",[209],[1498],{"id":20,"sortIndex":21,"affiliation":1499,"properties":20},{"id":1473,"createTime":1474,"updateTime":1474,"relativeEntities":1500,"slug":20,"properties":1501,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1502},{"VI":1478},{"title":1504},{"VI":1505},"Enric Sesa-Nogueras",{"url":1465,"publisher":1507,"properties":1535},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1508,"slug":10,"properties":1509,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1513,"manageAffiliations":1514,"indexDatabases":1515,"url":112,"thumbnailPath":20,"statistic":1530,"gsStatistic":20,"type":180,"analyzePriority":20},[],{"issn":1510,"eissn":1511,"title":1512},{"VOID":13},{"VOID":15},{"EN":17},[],[],[1516,1523],{"id":72,"indexDatabase":1517,"url":85,"indexYears":86,"academicFieldIds":1522,"indexDatabaseRanking":91},{"id":74,"createTime":75,"updateTime":76,"relativeEntities":1518,"label":1519,"description":1520,"key":82,"publicationTags":1521,"standard":20},[],{"EN":79,"VI":79},{"EN":79,"VI":81},[84],[88,89,90],{"id":93,"indexDatabase":1524,"url":108,"indexYears":20,"academicFieldIds":1529,"indexDatabaseRanking":20},{"id":95,"createTime":96,"updateTime":97,"relativeEntities":1525,"label":1526,"description":1527,"key":104,"publicationTags":1528,"standard":20},[],{"EN":100,"VI":100},{"VI":102,"EN":103},[106,107],[110,111],{"impactFactor":21,"impactFactorByYear":1531,"i10Index":127,"i10IndexLast5Year":128,"totalPublication":129,"totalPublicationByYear":1532,"totalCitation":145,"totalCitationByYear":1533,"totalCitationPerPublication":162,"totalCitationPerPublicationByYear":1534,"hindexLast5Year":161,"hindex":161},{"2012":115,"2013":116,"2014":117,"2015":118,"2016":119,"2017":120,"2018":121,"2019":122,"2020":123,"2021":124,"2022":125,"2023":126},{"2009":131,"2010":132,"2011":133,"2012":134,"2013":135,"2014":136,"2015":137,"2016":136,"2017":138,"2018":139,"2019":140,"2020":136,"2021":141,"2022":142,"2023":143,"2024":144},{"2009":147,"2010":148,"2011":149,"2012":133,"2013":150,"2014":151,"2015":152,"2016":153,"2017":154,"2018":155,"2019":156,"2020":157,"2021":158,"2022":159,"2023":160,"2024":161},{"2009":164,"2010":165,"2011":166,"2012":167,"2013":168,"2014":169,"2015":170,"2016":171,"2017":172,"2018":173,"2019":174,"2020":175,"2021":176,"2022":177,"2023":178,"2024":179},{"volume":1536,"pages":1538},{"VOID":1537},"8",{"VOID":1539},"15-29","2015-05-01",2015]