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1. |
Record Nr. |
UNISALENTO991003591159707536 |
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Autore |
Colautti, Arturo |
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Titolo |
Fidelia : romanzo / di Arturo Colautti |
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Pubbl/distr/stampa |
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Edizione |
[5 ed. riv.] |
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Descrizione fisica |
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Collana |
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Romantica mondiale Sonzogno |
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Disciplina |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNINA9910523784203321 |
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Autore |
Melin Patricia |
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Titolo |
Nature-inspired Optimization of Type-2 Fuzzy Neural Hybrid Models for Classification in Medical Diagnosis / / by Patricia Melin, Ivette Miramontes, German Prado Arechiga |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
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ISBN |
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Edizione |
[1st ed. 2022.] |
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Descrizione fisica |
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1 online resource (134 pages) |
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Collana |
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SpringerBriefs in Computational Intelligence, , 2625-3712 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Biomedical engineering |
Engineering - Data processing |
Artificial intelligence |
Computational Intelligence |
Biomedical Engineering and Bioengineering |
Data Engineering |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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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. |
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