01905nam 2200433 n 450 99639237340331620200824121016.0(CKB)4940000000103875(EEBO)2240854600(UnM)99848764e(UnM)99848764(EXLCZ)99494000000010387519920116d1623 uy |engurbn||||a|bb|The praise of hemp-seed[electronic resource] With the voyage of Mr. Roger Bird and the writer hereof in a boat of brown-paper, from London to Quinborough in Kent. As also, a farewell to the matchlesse deceased Mr. Thomas Coriat. Concluding with the commendations of the famous riuer of Thames. By Iohn Taylor. The contents of the booke are in the next leafe before the preamble. The profits arising by hempseed are cloathing, food, fishing, shipping, pleasure, profit, iustice, whippingPrinted at London [By E. Allde] for Henry Gosson, and are to be sold [by E. Wright?] at Christ-Church gate1623[12], 36 pIn verse.Printer's and publisher's names from STC.The words "cloathing .. whipping." are bracketed together on the title page.A variant of STC 23789.Reproduction of the original in the Bodleian Library.eebo-0014HempPoetryEarly works to 1800MarijuanaPoetryEarly works to 1800EnglandDescription and travelPoetryEarly works to 1800Thames River (England)PoetryEarly works to 1800HempMarijuanaTaylor John1580-1653.1000995Cu-RivESCu-RivESCStRLINWaOLNBOOK996392373403316The praise of hemp-seed2340244UNISA01025nam a22002651i 450099100359115970753620040725155501.0040802s1936 it |||||||||||||||||ita b13097179-39ule_instARCHE-104978ExLBiblioteca InterfacoltàitaA.t.i. Arché s.c.r.l. Pandora Sicilia s.r.l.853.912Colautti, Arturo203858Fidelia :romanzo /di Arturo Colautti5 ed. riv.Milano :Sonzogno,1936380 p. ;18 cmRomantica mondiale Sonzogno.b1309717902-04-1405-08-04991003591159707536LE008 TS M I 30512008000451769le008-E0.00-no 00000.i1486812x30-10-08LE002 Fondo Giudici E 134212002000217479le002C. 1-E0.00-no 00000.i1373090305-08-04Fidelia306595UNISALENTOle008le00205-08-04ma -itait 0206976nam 22006735 450 991052378420332120251113192454.03-030-82219-210.1007/978-3-030-82219-4(CKB)4100000011995869(MiAaPQ)EBC6694620(Au-PeEL)EBL6694620(PPN)257352228(OCoLC)1265460630(DE-He213)978-3-030-82219-4(EXLCZ)99410000001199586920210806d2022 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierNature-inspired Optimization of Type-2 Fuzzy Neural Hybrid Models for Classification in Medical Diagnosis /by Patricia Melin, Ivette Miramontes, German Prado Arechiga1st ed. 2022.Cham :Springer International Publishing :Imprint: Springer,2022.1 online resource (134 pages)SpringerBriefs in Computational Intelligence,2625-37123-030-82218-4 Intro -- Preface -- Contents -- 1 Introduction to Soft Computing Applied in Medicine -- References -- 2 Theory of Soft Computing and Medical Terms -- 2.1 Hybrid Systems -- 2.1.1 Artificial Neural Networks -- 2.1.2 Type-1 Fuzzy Systems -- 2.1.3 Type-2 Fuzzy Logic -- 2.1.4 Optimization -- 2.1.5 CSO -- 2.1.6 CSA -- 2.1.7 FPA -- 2.1.8 BSA -- 2.2 Blood Pressure -- 2.2.1 Hypertension -- 2.2.2 Heart Rate -- 2.2.3 Nocturnal Blood Pressure Profile -- 2.2.4 Ambulatory Blood Pressure Monitoring -- 2.2.5 Framingham Heart Study -- 2.2.6 Cardiovascular Risk -- References -- 3 Proposed Model to Obtain the Medical Diagnosis -- 3.1 Neuro-Fuzzy Hybrid Model -- 3.2 IT2FS for Classification of Heart Rate Level -- 3.3 IT2FS for Classification of Nocturnal Bloor Pressure Profile -- 4 Study Cases to Test the Optimization Performed in the Hybrid Model -- 4.1 Optimization of the Fuzzy System to Provide the Correct Classification of the Nocturnal Blood Pressure Profile -- 4.1.1 Design of a Fuzzy System for Classification of Nocturnal Blood Pressure Profile -- 4.1.2 Experimentation and Results -- 4.1.3 Statistical Test -- 4.2 Fuzzy System Optimization to Obtain the Heart Rate Level -- 4.2.1 Proposed Method for Optimization of the Heart Rate Fuzzy Classifier -- 4.2.2 Type-1 Fuzzy System Optimization Using the BSA -- 4.2.3 Design and Optimization of the IT2FS -- 4.2.4 Results Obtained from Optimizing the Heart Rate Fuzzy System -- 4.3 Optimization of the Modular Neural Network to Obtain the Trend of the Blood Pressure -- 4.3.1 Proposed Method for Optimization of the Modular Neural Network -- 4.3.2 Results of the Optimization Made to the Modular Neural Network -- 4.4 Optimization of the Artificial Neural Network Used to Obtain the Risk of Developing Hypertension.4.4.1 Proposed Method for the Optimization of the Monolithic Neural Network Used to Obtain the Risk of Developing Hypertension -- 4.4.2 Results Obtained from the Optimization -- 4.4.3 Z-test of FPA and ALO Versus Simple Enumeration Method -- 4.5 Optimization of the Modular Neural Network to Obtain the Risk of Developing a Cardiovascular Event -- 4.5.1 Proposed Method for Optimizing the Modular Network for the Risk of Developing a Cardiovascular Event -- 4.5.2 Experimentation and Results of the Optimization -- 4.6 Fuzzy Bird Swarm Algorithm -- 4.6.1 Proposed Method for the Dynamic Parameter Adjustment -- 4.6.2 Experiments and Results -- 4.6.3 Results -- 4.6.4 Statistical Test -- References -- 5 Conclusions of the Hybrid Medical Model -- Appendix A Knowledge Representation -- Type-1 Fuzzy System Knowledge Representation for Heart Rate Classification -- Inputs Variables -- Output Variable -- IT2FS Knowledge Representation Using Gaussian Membership Functions -- Inputs Variables -- Output Variable -- Knowledge Representation of the Fuzzy Classifier to Obtain the Nocturnal Blood Pressure Profile -- Inputs Variables -- Appendix B Graphical User Interface -- Index.This book describes the utilization of different soft computing techniques and their optimization for providing an accurate and efficient medical diagnosis. The proposed method provides a precise and timely diagnosis of the risk that a person has to develop a particular disease, but it can be adaptable to provide the diagnosis of different diseases. This book reflects the experimentation that was carried out, based on the different optimizations using bio-inspired algorithms (such as bird swarm algorithm, flower pollination algorithms, and others). In particular, the optimizations were carried out to design the fuzzy classifiers of the nocturnal blood pressure profile and heart rate level. In addition, to obtain the architecture that provides the best result, the neurons and the number of neurons per layers of the artificial neural networks used in the model are optimized. Furthermore, different tests were carried out with the complete optimized model. Another work that is presented in this book is the dynamic parameter adaptation of the bird swarm algorithm using fuzzy inference systems, with the aim of improving its performance. For this, different experiments are carried out, where mathematical functions and a monolithic neural network are optimized to compare the results obtained with the original algorithm. The book will be of interest for graduate students of engineering and medicine, as well as researchers and professors aiming at proposing and developing new intelligent models for medical diagnosis. In addition, it also will be of interest for people working on metaheuristic algorithms and their applications on medicine.SpringerBriefs in Computational Intelligence,2625-3712Computational intelligenceBiomedical engineeringEngineeringData processingArtificial intelligenceComputational IntelligenceBiomedical Engineering and BioengineeringData EngineeringArtificial IntelligenceComputational intelligence.Biomedical engineering.EngineeringData processing.Artificial intelligence.Computational Intelligence.Biomedical Engineering and Bioengineering.Data Engineering.Artificial Intelligence.610.1511322Melin Patricia762263Miramontes IvettePrado-Arechiga GermanMiAaPQMiAaPQMiAaPQBOOK9910523784203321Nature-Inspired Optimization of Type-2 Fuzzy Neural Hybrid Models for Classification in Medical Diagnosis2591574UNINA