Kooperativer Bibliotheksverbund

UID:

Format: 1 Online-Ressource (vi, 383 Seiten) , Illustrationen, Diagramme

ISBN: 9783030114794

Series Statement: Smart innovation, systems and technologies volume 136

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-11478-7

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-11480-0

Language: English

Subjects: Computer Science

Keywords: Deep learning ; Maschinelles Lernen ; Anwendungssystem

DOI: 10.1007/978-3-030-11479-4

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: X, 218 p. 75 illus., 60 illus. in color. , online resource.

Edition: 1st ed. 2022.

ISBN: 9789811691584

Series Statement: Studies in Big Data, 103

Content: Currently, machine learning is playing a pivotal role in the progress of genomics. The applications of machine learning are helping all to understand the emerging trends and the future scope of genomics. This book provides comprehensive coverage of machine learning applications such as DNN, CNN, and RNN, for predicting the sequence of DNA and RNA binding proteins, expression of the gene, and splicing control. In addition, the book addresses the effect of multiomics data analysis of cancers using tensor decomposition, machine learning techniques for protein engineering, CNN applications on genomics, challenges of long noncoding RNAs in human disease diagnosis, and how machine learning can be used as a tool to shape the future of medicine. More importantly, it gives a comparative analysis and validates the outcomes of machine learning methods on genomic data to the functional laboratory tests or by formal clinical assessment. The topics of this book will cater interest to academicians, practitioners working in the field of functional genomics, and machine learning. Also, this book shall guide comprehensively the graduate, postgraduates, and Ph.D. scholars working in these fields.

Note: Local and global characterization of genomic data -- DNA sequencing using RNN -- Deep learning to study functional activities of DNA sequence -- Autoencoders for gene clastering -- Dimension reduction in gene expression using deep learning -- To predict DNA methylation states using deep learning -- Transfer learning in genomics -- CNN model to analyze gene expression images -- Gene expression Prediction using advanced machine learning -- Predicting splicing regulation using deep learning -- Transcription factor binding site prediction using deep learning -- Deep learning for prediction of structural classification of proteins -- Prediction of secondary strucure of RNA using advanced machine learning and deep learning -- Deep learning for pepositioning of drug and pharmacogenomics.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9789811691577

Additional Edition: Printed edition: ISBN 9789811691591

Additional Edition: Printed edition: ISBN 9789811691607

Language: English

Subjects: Computer Science , Biology

DOI: 10.1007/978-981-16-9158-4

URL: https://doi.org/10.1007/978-981-16-9158-4

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: XII, 210 p. 122 illus., 96 illus. in color. , online resource.

Edition: 1st ed. 2023.

ISBN: 9789819937844

Series Statement: Studies in Big Data, 129

Content: This book provides state-of-the-art coverage of deep learning applications in image analysis. The book demonstrates various deep learning algorithms that can offer practical solutions for various image-related problems; also how these algorithms are used by scientists and scholars in industry and academia. This includes autoencoder and deep convolutional generative adversarial network in improving classification performance of Bangla handwritten characters, dealing with deep learning-based approaches using feature selection methods for automatic diagnosis of covid-19 disease from x-ray images, imbalance image data sets of classification, image captioning using deep transfer learning, developing a vehicle over speed detection system, creating an intelligent system for video-based proximity analysis, building a melanoma cancer detection system using deep learning, plant diseases classification using AlexNet, dealing with hyperspectral images using deep learning, chest x-ray image classification of pneumonia disease using efficient net and inceptionv3. The book also addresses the difficulty of implementing deep learning in terms of computation time and the complexity of reasoning and modelling different types of data where information is currently encoded. Each chapter has the application of various new or existing deep learning models such as Deep Neural Network (DNN) and Deep Convolutional Neural Networks (DCNN). The detailed utilization of deep learning packages that are available in MATLAB, Python and R programming environments have also been discussed, therefore, the readers will get to know about the practical implementation of deep learning as well. The content of this book is presented in a simple and lucid style for professionals, nonprofessionals, scientists, and students interested in the research area of deep learning applications in image analysis.

Note: Classification and segmentation of images using deep learning -- Image reconstruction, image super-resolution and image synthesis by deep learning techniques -- Deep learning for cancer images -- Deep Learning in Gastrointestinal Endoscopy -- Tumor detection using deep learning -- Deep learning for image analysis using multimodality fusion -- Image quality recognition methods inspired by deep learning -- Advanced Deep Learning methods in computer vision with 3D data -- Deep Learning models to solve the task of MOT(Multiple Object Tracking) -- Deep learning techniques for semantic segmentation of images -- Applications of deep learning for image forensics -- Human action recognition using deep learning -- Application of deep learning in satellite image classification and segmentation.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9789819937837

Additional Edition: Printed edition: ISBN 9789819937851

Additional Edition: Printed edition: ISBN 9789819937868

Language: English

DOI: 10.1007/978-981-99-3784-4

URL: https://doi.org/10.1007/978-981-99-3784-4

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (VI, 384 Seiten)

ISBN: 9789811084768

Series Statement: Studies in Big Data 44

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-10-8475-1

Language: English

Subjects: Computer Science

Keywords: Big Data ; Technik

DOI: 10.1007/978-981-10-8476-8

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 online resource (631 pages) : , illustrations (some color), tables

ISBN: 0-12-811319-7 , 0-12-811318-9

Note: Front Cover -- Handbook of Neural Computation -- Copyright -- Contents -- Contributors -- About the Editors -- 1 Gravitational Search Algorithm With Chaos -- 1.1 Introduction -- 1.2 Gravitational Search Algorithm -- 1.3 Chaotic Maps for GSA -- 1.3.1 Chaotic Maps -- 1.3.2 Integrating Chaotic Maps With GSA -- 1.4 Experimental Results and Discussion -- 1.4.1 Search Performance Analysis -- 1.4.2 Convergence Analysis -- 1.5 CGSA for Engineering Design Problems -- 1.5.1 Welded Beam Design -- 1.5.2 Pressure Vessel Design -- 1.6 Conclusion -- References -- 2 Textures and Rough Sets -- 2.1 Introduction -- 2.2 Fuzzy Lattices -- The Unit Interval [0, 1] -- 2.3 Texture Spaces -- Products -- Fuzzy Set Texture -- Direlations -- The Composition of Direlations -- Complemented Direlations -- Sections and Presections -- 2.4 Rough Sets -- Textures and Rough Sets -- 2.5 De nability -- 2.6 Order Preserving Functions -- 2.7 Approximation Spaces and Information Systems -- 2.8 Conclusion -- References -- 3 Hydrological Time Series Forecasting Using Three Different Heuristic Regression Techniques -- 3.1 Introduction -- 3.2 Methods -- 3.2.1 Least-Square Support Vector Regression -- 3.2.2 Multivariate Adaptive Regression Spline -- 3.2.3 M5 Model Tree -- 3.3 Applications and Results -- 3.4 Conclusion -- References -- 4 A Re ection on Image Classi cations for Forest Ecology Management: Towards Landscape Mapping and Monitoring -- 4.1 Introduction -- 4.2 Background -- 4.2.1 De nitions in Remote Sensing Community -- 4.2.2 Importance of Using Remote Sensing Tools -- 4.2.3 Types of Remote Sensing Data -- 4.2.4 Uncertainty Assessments of Remote Sensing Data -- 4.2.4.1 Thematic Data Collection in Field -- 4.2.4.2 Classi cation Accuracy of LULC Maps -- 4.3 Image Classi cation for Forest Cover Mapping -- 4.3.1 Mapping Methodologies -- 4.3.1.1 Approaches with Manual Interpretation. , 4.3.1.2 Approaches Relying on Automated Classi cation -- 4.3.1.2.1 Parametric Methods -- 4.3.1.2.2 Non-parametric Methods -- 4.3.2 Emerging Ensemble Classi ers -- 4.3.2.1 Random Forest Classi ers -- 4.3.2.1.1 From Multi-spectral, LiDAR, and Radar Remote Sensing Data -- 4.3.2.1.2 From Hyperspectral Data -- 4.3.2.1.3 From Multi-source Data -- 4.4 A Case Study in the Himalayan Region -- 4.5 Future Research Outlook -- References -- 5 An Intelligent Hybridization of ABC and LM Algorithms With Constraint Engineering Applications -- 5.1 Introduction -- 5.2 Brief Introduction of Optimization Methods -- 5.2.1 Arti cial Bee Colony Algorithm -- 5.2.2 Levenberg-Marquardt Algorithm -- 5.2.3 Proposed Hybrid Algorithm: ABC-LM -- 5.3 Numerical Results -- 5.3.1 Unconstrained Optimization of Benchmark Functions -- 5.3.1.1 Comparisons of ABC, LM, and ABC-LM Algorithms -- 5.3.1.2 Comparisons with Literature Works -- 5.3.2 Constrained Real-World Optimization Problems -- 5.3.2.1 Welded Beam Design -- 5.3.2.2 Pressure Vessel Design -- 5.3.2.3 Tension/Compression Spring Design -- 5.3.2.4 Multi-tool Turning Operation -- 5.4 Strengths and Limitations -- 5.5 Conclusion -- References -- 6 Network Intrusion Detection Model Based on Fuzzy-Rough Classi ers -- 6.1 Introduction -- Misuse Detection System -- Anomaly Based Intrusion Detection System -- 6.2 Related Work -- 6.3 Methodology -- K-Nearest Neighbor (KNN) -- 6.3.1 Fuzzy Set Theory -- 6.3.2 Rough Set Theory -- Example -- Example -- 6.3.3 Hybridization of Fuzzy-Rough Set Theory -- 6.3.4 Fuzzy Nearest Neighbor (FNN) Classi cation -- 6.3.5 Fuzzy-Rough Nearest Neighbor Algorithm (FRNN) -- 6.3.6 Fuzzy Ownership Algorithm -- 6.3.7 Vaguely Quanti ed Nearest Neighbors (VQNN) -- 6.3.8 Ordered Weighted Average Nearest Neighbors -- 6.4 Experimental Setup -- 6.4.1 NSL-KDD Data Set -- 6.4.2 Feature Selection Process -- Ant Search. , Random Search -- 6.4.3 Confusion Matrix -- 6.4.4 Cross-Validation -- 6.5 Result Analysis -- 6.6 Conclusions -- References -- 7 Ef cient System Reliability Analysis of Earth Slopes Based on Support Vector Machine Regression Model -- 7.1 Introduction -- 7.2 Adopted Methodologies -- 7.2.1 Deterministic Slope Stability Analysis -- 7.2.2 Reliability Analysis of Slope Using Critical Slip Surfaces -- 7.2.3 System Reliability Analysis of Slopes Using SVM-Based MCS -- 7.2.3.1 Support Vector Machine (SVM) Regression -- 7.2.3.2 Proposed Algorithm for System Reliability Analysis Using SVR-Based MCS -- 7.3 Illustrative Example and Results -- 7.3.1 Slope Description -- 7.3.2 Deterministic Analyses and Results -- 7.3.3 Reliability Analyses and Results -- 7.3.3.1 Reliability Analyses Based on the Probabilistic Critical Slip Surfaces -- 7.3.3.2 System Reliability Analyses -- 7.4 Summary and Conclusions -- Acknowledgments -- References -- 8 Predicting Short-Term Congested Traf c Flow on Urban Motorway Networks -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Research Methodology -- 8.3.1 Back-Propagation Neural Network -- Stochastic Gradient Descent -- 8.3.2 Neuro-Fuzzy -- Fuzzy Logic -- Neuro Fuzzy -- 8.3.2.1 Input Variable Unit -- 8.3.2.2 Parameter Tuning Unit -- 8.3.2.3 Traf c Flow Estimator -- 8.3.3 Deep Learning -- Deep Belief Networks -- 8.3.4 Random Forest -- 8.4 Performance Evaluation -- 8.4.1 Error Measurement -- 8.4.2 Model Sensitivity -- 8.5 Application Results -- 8.5.1 Error Measurement -- 8.5.2 Rank Sensitivity -- 8.6 Conclusions and Future Works -- Acknowledgments -- References -- 9 Object Categorization Using Adaptive Graph-Based Semi-supervised Learning -- 9.1 Introduction -- 9.2 Related Work: Graph Construction Methods -- K-Nearest Neighbor Method -- e-Ball Graph Method -- LLE Graph Construction -- l1 Graph Construction -- 9.3 Local Binary Patterns. , 9.4 Proposed LBP Graph Construction -- 9.4.1 Weighted Regularized Least Square Minimization -- 9.4.2 Two Phase WRLS (TPWRLS) -- 9.4.3 Difference Between TPWRLS Graph and Existing Graphs -- 9.5 Performance Evaluation -- 9.5.1 Graph-Based Label Propagation -- 9.5.2 Experimental Results -- 9.5.2.1 First group -- Computation Time -- 9.5.2.2 Second Group -- 9.6 Conclusion -- Acknowledgments -- References -- 10 Hemodynamic Model Inversion by Iterative Extended Kalman Smoother -- 10.1 Introduction -- 10.1.1 Outline -- 10.2 Methods -- 10.2.1 Extended Kalman Filter -- 10.2.2 Extended Kalman Smoother -- 10.2.3 Iteration Step -- 10.3 Dynamic Representation of the Hemodynamic Model System -- 10.4 Results -- 10.4.1 Simulation Results -- 10.4.1.1 Filtering Performance Overview -- 10.4.1.2 Iteration and Smoother Performance -- 10.4.1.3 IEKS Simulations -- 10.4.2 Discussion -- 10.5 Real Data Case Study -- 10.5.1 Test Procedure and Real Data -- 10.5.1.1 IEKS Method With Switched Parameter Variance -- 10.5.2 Discussion -- 10.5.2.1 Validation of the Results -- 10.5.2.2 Estimated Hemodynamic Variables -- 10.6 Conclusion -- Acknowledgments -- References -- 11 Improved Sparse Approximation Models for Stochastic Computations -- 11.1 Introduction -- 11.2 Fundamentals of HDMR, Kriging, LASSO, LAR, and FS -- 11.2.1 High-Dimensional Model Representation -- 11.2.2 Kriging -- 11.2.3 Least Absolute Shrinkage and Selection Operator (LASSO) -- 11.2.4 Least Angle Regression (LAR) -- 11.2.5 Forward Selection (FS) -- 11.3 Proposed Approaches -- 11.3.1 Proposed Approach 1 (PA1) -- 11.3.2 Proposed Approach 2 (PA2) -- 11.3.3 Proposed Approach 3 (PA3) -- 11.4 Numerical Examples -- 11.4.1 Problem Set 1: Analytical Test Functions -- 11.4.2 Problem Set 2: Practical Problems -- 11.4.2.1 Five-Storey Building Subjected to Lateral Load -- 11.4.2.2 Twenty-Five Bar Space Truss. , 11.5 Summary and Conclusions -- References -- 12 Symbol Detection in Multiple Antenna Wireless Systems via Ant Colony Optimization -- 12.1 Introduction -- 12.2 Overview of Ant Colony Optimization -- 12.3 Point-to-Point MIMO System Model -- 12.4 Traditional MIMO Detection Techniques -- 12.4.1 Zero Forcing Detector -- 12.4.2 Minimum Mean Squared Error Detector -- 12.4.3 Successive Interference Cancellation Detector -- 12.5 Ant Colony Optimization Based MIMO Detection -- 12.6 Results and Discussion -- 12.7 Conclusions -- References -- 13 Application of Particle Swarm Optimization to Solve Robotic Assembly Line Balancing Problems -- 13.1 Introduction -- 13.2 Straight Robotic Assembly Line Balancing Problems -- 13.2.1 Assumptions and Mathematical Model for Balancing Straight Robotic Assembly Line -- 13.2.2 Particle Swarm Optimization for Solving Straight Robotic Assembly Line Balancing Problems -- 13.2.2.1 Implementation of PSO -- Recursive Allocation Method (Cycle Time Calculation) -- Consecutive Allocation Method (Cycle Time Calculation) -- 13.2.3 Parameter Selection for Straight RALB Problem -- 13.2.4 Computational Results for Straight Robotic Assembly Line Balancing Problems -- 13.3 Robotic U-Shaped Assembly Line Balancing Problems -- 13.3.1 Assumptions and Mathematical Model for Balancing U-Shaped Robotic Assembly Line -- 13.3.2 Particle Swarm Optimization for Solving Robotic U-Shaped Assembly Line Balancing Problems -- 13.3.3 Differences Between Straight and U-Shaped Robotic Assembly Lines -- 13.3.4 Parameters for PSO to Solve RUALB Problems -- 13.3.5 Computational Results for RUALB Problems -- 13.4 Summary -- References -- 14 The Cuckoo Optimization Algorithm and Its Applications -- 14.1 Introduction -- 14.2 Cuckoo Optimization Algorithm -- 14.3 Applications of the Cuckoo Optimization Algorithm -- 14.3.1 Replacement of Obsolete Components. , 14.3.2 Machining Parameters.

Language: English

UID:

Format: 1 online resource (xvi, 535 pages) : , illustrations

ISBN: 0-12-817773-X

Content: Predictive Modeling for Energy Management and Power Systems Engineering introduces readers to the cutting-edge use of big data and large computational infrastructures in energy demand estimation and power management systems. The book supports engineers and scientists who seek to become familiar with advanced optimization techniques for power systems designs, optimization techniques and algorithms for consumer power management, and potential applications of machine learning and artificial intelligence in this field. The book provides modeling theory in an easy-to-read format, verified with on-site models and case studies for specific geographic regions and complex consumer markets.

Additional Edition: ISBN 0-12-817772-1

Language: English

UID:

Format: 30 PDFs (xxvi, 618 pages)

ISBN: 9781522547679

Content: "This book explores the latest development of optimization techniques. It shows the application of optimization in new fields such as big data, artificial intelligence, etc. The application of hybrid optimization techniques and stochastic optimization are explored"--

Note: Chapter 1. A comparative study for locating critical failure surface in slope stability analysis via meta-heuristic approach -- Chapter 2. Adaptive refined-model-based approach for robust design optimization -- Chapter 3. An optimum tuning application of mass dampers considering soil-structure interaction: metaheuristic-based optimization of TMDs -- Chapter 4. Flood forecasting and uncertainty assessment using wavelet- and bootstrap-based neural networks -- Chapter 5. Grouping concept in optimum sizing of truss structures: optimization of truss structures -- Chapter 6. Hybrid data intelligent models and applications for water level prediction -- Chapter 7. Implementation of genetic-algorithm-based forecasting model to power system problems -- Chapter 8. Improvement of RSM prediction and optimization by using box-cox transformation: separation of colloidal contaminants from mineral processing effluents via electrocoagulation -- Chapter 9. Long-term degradation-based modeling and optimization framework -- Chapter 10. Multi-objective optimization of slope stability using wedge analysis and genetic algorithm -- Chapter 11. Multi-performance optimization in friction stir welding of aluminum alloy using response surface methodology -- Chapter 12. Multiscale modelling of daily suspended sediment load using MEMD-SLR coupled approach -- Chapter 13. Optimization of pile groups under vertical loads using metaheuristic algorithms -- Chapter 14. Optimization of the angle of twist of propeller using modified flower pollination algorithm -- Chapter 15. Optimization of windspeed prediction using an artificial neural network compared with a genetic programming model -- Chapter 16. Optimum design of reinforced concrete retaining walls -- Chapter 17. Predicting human actions using a hybrid of relieff feature selection and kernel-based extreme learning machine -- Chapter 18. Predictive modeling and optimization of cutting forces through RSM and taguchi techniques in the turning of ASTM b574 (Hastelloy c-22) -- Chapter 19. Robust design of helicopter rotor flaps using bat algorithm -- Chapter 20. Selection of representative feature training sets with self-organized maps for optimized time series modeling and prediction: application to forecasting daily drought conditions with ARIMA and neural network models -- Chapter 21. Soil cation exchange capacity predicted by learning from multiple modelling: forming multiple models run by SVM to learn from ANN and its hybrid with firefly algorithm -- Chapter 22. Usage of differential evolution algorithm in the calibration of parametric rainfall-runoff modeling -- Chapter 23. Whale optimization algorithm with wavelet mutation for the solution of optimal power flow problem. , Also available in print. , Mode of access: World Wide Web.

Additional Edition: Print version: ISBN 1522547665

Additional Edition: ISBN 9781522547662

Language: English

DOI: 10.4018/978-1-5225-4766-2

URL: Chapter PDFs via platform:

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: VI, 383 Seiten , Illustrationen, Diagramme

ISBN: 9783030114787

Series Statement: Smart innovation, systems and technologies Volume 136

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-11480-0

Additional Edition: Erscheint auch als Online-Ausgabe, eBook ISBN 978-3-030-11479-4

Language: English

Subjects: Computer Science

Keywords: Deep learning ; Maschinelles Lernen ; Anwendungssystem

UID:

Format: xxviii, 630 Seiten : , Diagramme, Illustrationen, Karten.

ISBN: 978-0-12-811318-9

Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-0-12-811319-6

Language: English

Subjects: Computer Science

Keywords: Aufsatzsammlung

UID:

Format: x, 218 Seiten : , Illustrationen, Diagramme.

ISBN: 978-981-16-9157-7

Series Statement: Studies in big data 103

Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-981-1691-58-4

Language: English

Subjects: Computer Science , Biology