Kooperativer Bibliotheksverbund

UID:

Format: XV, 813 p. 448 illus., 268 illus. in color. , online resource.

Edition: 1st ed. 2021.

ISBN: 9789813340879

Series Statement: Algorithms for Intelligent Systems,

Content: This book is a compilation of peer-reviewed papers presented at the International Conference on Machine Intelligence and Data Science Applications, organized by the School of Computer Science, University of Petroleum & Energy Studies, Dehradun, on September 4 and 5, 2020. The book starts by addressing the algorithmic aspect of machine intelligence which includes the framework and optimization of various states of algorithms. Variety of papers related to wide applications in various fields like image processing, natural language processing, computer vision, sentiment analysis, and speech and gesture analysis have been included with upfront details. The book concludes with interdisciplinary applications like legal, health care, smart society, cyber physical system and smart agriculture. The book is a good reference for computer science engineers, lecturers/researchers in machine intelligence discipline and engineering graduates.

Note: Probabilistic Machine Learning Using Social Network Analysis -- Prioritization of Disaster Recovery Aspects Implementing DEMATEL Technique -- A Study on ML Based Predictive Modelling for Pick Profiling at Distribution Centers -- Analysis of Computational Intelligence Techniques in Smart Cities -- Proposed end to end automated e-voting through blockchain technology to increase voter's turnout -- Future of Data Generated by Interactive Media.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9789813340862

Additional Edition: Printed edition: ISBN 9789813340886

Additional Edition: Printed edition: ISBN 9789813340893

Language: English

DOI: 10.1007/978-981-33-4087-9

URL: https://doi.org/10.1007/978-981-33-4087-9

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (XII, 802 p. 360 illus., 300 illus. in color)

Edition: 1st ed. 2023

ISBN: 9789819959747

Series Statement: Lecture Notes in Electrical Engineering 1078

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9959-73-0

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9959-75-4

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9959-76-1

Language: English

DOI: 10.1007/978-981-99-5974-7

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (XII, 814 p. 417 illus., 288 illus. in color)

Edition: 1st ed. 2022

ISBN: 9789811948312

Series Statement: Lecture Notes in Electrical Engineering 925

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1948-30-5

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1948-32-9

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1948-33-6

Language: English

DOI: 10.1007/978-981-19-4831-2

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 online resource (782 pages)

Edition: 1st ed.

ISBN: 981-9959-74-8

Series Statement: Lecture Notes in Electrical Engineering Series ; v.1078

Note: Intro -- Contents -- About the Editors -- Development of Big Data Dimensionality Reduction Methods for Effective Data Transmission and Feature Enhancement Algorithms -- 1 Introduction -- 2 Works -- 3 Objectives -- 4 Proposed Dimensionality Reduction Method -- 5 Analysis of the Obtained Results -- 6 Conclusion -- References -- IndianFood-7: Detecting Indian Food Items Using Deep Learning-Based Computer Vision -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Preparation -- 3.2 Our Experimentation on Object Detection Models -- 4 Results -- 5 Conclusion -- References -- Prediction of Protein-Protein Interaction Using Support Vector Machine Based on Spatial Distribution of Amino Acids -- 1 Introduction -- 2 Experimental Setup -- 3 Methodology -- 3.1 Data Set -- 3.2 Feature Representation -- 3.3 Support Vector Machines (SVM) -- 4 Results and Discussion -- 4.1 Evaluation Metrics -- 4.2 Performance of Proposed Model -- 4.3 Proposed Model Comparison Against Various Predictors -- 5 Conclusion -- References -- A Computational Comparison of VGG16 and XceptionNet for Mango Plant Disease Recognition -- 1 Introduction -- 2 Methodology and Dataset -- 2.1 Architecture of the Proposed System -- 2.2 Dataset Description -- 2.3 Data Pre-processing -- 2.4 Models Used -- 2.5 Training and Compiling the Model -- 3 Result and Analysis -- 4 Conclusion -- References -- Generate Artificial Human Faces with Deep Convolutional Generative Adversarial Network (DCGAN) Machine Learning Model -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Experimental Setup -- 3.2 Dataset Description -- 3.3 Model Description -- 4 Results -- 5 Future Scope and Conclusion -- References -- Robust Approach for Person Identification Using Three-Triangle Concept -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Block Diagram of Recommended System. , 3.2 Algorithm Used -- 4 Circuit Layout -- 5 Interfacing of Components -- 6 Experimental Results -- 7 Conclusions -- 8 Future Scope -- References -- COVID-19 Disease Detection Using Explainable AI -- 1 Introduction -- 2 Explainable Artificial Intelligence -- 3 Dataset Description -- 4 Approach to the Proposed System -- 4.1 Support Vector Machine -- 4.2 Convolutional Neural Networks -- 4.3 ResNet50 -- 4.4 Implementation of Explainable AI -- 5 Proposed Methodology -- 6 Results -- 7 Conclusion and Future Scope -- References -- Towards Helping Visually Impaired People to Navigate Outdoor -- 1 Introduction -- 1.1 Convolutional Neural Network -- 1.2 Visual Geometry Group -- 2 Literature -- 3 Methodology -- 3.1 Create the Dataset -- 3.2 Applying Existing Approach -- 3.3 Analyzing the Existing Approach -- 3.4 Detect Objects in Image -- 3.5 Train and Test the Model -- 3.6 Analyzing the Results -- 4 Experimentation -- 5 Conclusion and Future Work -- References -- An Analysis of Deployment Challenges for Kubernetes: A NextGen Virtualization -- 1 Introduction -- 2 Origin, History of Kubernetes, and the Community Behind -- 3 Related Works -- 3.1 Literature Review -- 3.2 Objective -- 4 Deployment of Application in Kubernetes Cluster in Public Cloud -- 4.1 Survey to Examine Kubernetes Impact -- 5 Analysis of Deployment Failure Strategies and Measures -- 6 Result Analysis -- 7 Result Analysis -- 8 Conclusion -- References -- A New Task Offloading Scheme for Geospatial Fog Computing Environment Using M/M/C Queueing Approach -- 1 Introduction -- 2 Related Work -- 3 Establishing the Model -- 4 Numerical and Simulation Examples -- 5 Conclusions and Future Work -- References -- Face Recognition Using Deep Neural Network with MobileNetV3-Large -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Dataset -- 3.2 Pre-processing -- 3.3 MobileNetV3Large Model. , 3.4 Hyperparameter Tuning -- 4 Result -- 5 Conclusion -- References -- Detection of BotNet Using Extreme Learning Machine Tuned by Enhanced Sine Cosine Algorithm -- 1 Introduction -- 2 Background and Related Work -- 2.1 BotNet and DDOS -- 2.2 Extreme Learning Machine -- 2.3 Population-Based Metaheuristics -- 3 Proposed Method -- 3.1 Suggested Improved SCA -- 4 Experiments and Discussion -- 4.1 Dataset Description, Pre-processing and Evaluation Metrics -- 4.2 Research Findings and Comparative Analysis -- 5 Conclusion -- References -- Cloud Services Management Using LSTM-RNN -- 1 Introduction -- 2 Related Work -- 3 Proposed Model -- 3.1 Forecast Utilizing LSTM-RNN -- 3.2 Workload Prediction Using LSTM Pseudocode -- 4 Result -- 5 Conclusion and Future Scope -- References -- Detection of Various Types of Thyroid-Related Disease Using Machine Learning -- 1 Introduction -- 2 Related Work -- 3 Proposed System -- 3.1 Dataset -- 3.2 Exploratory Data Analysis -- 3.3 Data Preprocessing -- 3.4 Training Phase -- 3.5 Testing the Model -- 4 Results and Discussion -- 5 Conclusion -- References -- Implementation of WSN in the Smart Hanger to Facilitate MRO Operations on Aircraft Fuselage Using Machine Learning -- 1 Introduction -- 2 Acquisition and Dataset -- 2.1 Complexity of Model and Training Dataset -- 2.2 Learning from Imbalanced Data -- 3 Existing Machine Learning Approaches -- 3.1 DNN (Deep Neural Networks) -- 3.2 Support Vector Machine (SVM) -- 3.3 Algorithmic Approach Using Minimal Data: Few-Shot Learning -- 4 State of the Art Method Evaluation -- 4.1 Experiment -- 4.2 Result -- 5 Proposed Approach -- 6 Conclusion and Prospects -- References -- Wi-Fi Controlled Smart Robot for Objects Tracking and Counting -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Wi-Fi Controlled Smart Robot Through Web Server. , 3.2 Proposed Methodology for Color Detection -- 3.3 Object Tracking for Counting Objects -- 4 Results and Discussion -- 5 Conclusion -- References -- Speech Recognition for Kannada Using LSTM -- 1 Introduction -- 2 Literature Review -- 3 Overview of LSTM and Kaldi -- 3.1 Markov Models -- 3.2 RNN -- 3.3 LSTM -- 3.4 Kaldi -- 4 Methodology -- 4.1 Audio Data Collection -- 4.2 Text Data Pre-processing -- 4.3 Feature Extraction and Preparing Language Models -- 4.4 Experiments with Monophone, Triphone Models -- 4.5 Experiments with DNN and LSTM -- 5 Results -- 6 Conclusion -- References -- Computer Vision-Based Smart Helmet with Voice Assistant for Increasing Driver Safety -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Deep Learning Model Development -- 3.2 Rear-End Collision Warning System -- 3.3 Deployment of Deep Learning Model via Server -- 3.4 Architecture of Helmet Software -- 3.5 Building Helmet (Hardware) Prototype -- 3.6 Comparison and Analysis of Models -- 4 Result -- 5 Conclusion and Future Scope -- References -- Predicting Aging Related Bugs with Automated Feature Selection Techniques in Cloud Oriented Softwares -- 1 Research Motivation and Aim -- 1.1 Imbalanced Data -- 1.2 High Dimensional Data -- 2 Related Work -- 3 Research Contributions -- 4 Research Framework -- 4.1 Automated Bug Report Extraction -- 4.2 Feature Selection Techniques -- 4.3 Datasets -- 4.4 Software Metrics -- 4.5 Imbalance Mitigation Procedure-SMOTE -- 4.6 Machine Learning Classifiers -- 4.7 Performance Measures -- 5 Experimental Setup -- 6 Results and Discussions -- 6.1 Detailed Analysis of Feature Ranking -- 6.2 Relative Comparison of Techniques -- 7 Conclusion -- 8 Threats to Validity and Future Work -- References -- Time Series Analysis of Crypto Currency Using ARIMAX -- 1 Introduction -- 2 Literature Review -- 3 Methodology. , 4 Components Taken into Consideration -- 4.1 Factors that Affect Crypto Currency -- 4.2 Dataset Used -- 4.3 ARIMAX Algorithm -- 5 Experimental Setup -- 5.1 Cointegrated Pair -- 5.2 Selection of Features -- 5.3 Building the Model -- 6 Result Analysis -- 7 Conclusion -- References -- A Machine Learning Approach Towards Prediction of User's Responsiveness to Notifications with Best Device Identification for Notification Delivery -- 1 Introduction -- 2 Related Work -- 3 Proposed System Architecture -- 3.1 Notification Module -- 3.2 User Identification Module -- 3.3 Active Device and Proximity Detection Module -- 3.4 Privacy and Access Control Module -- 3.5 Intelligent Delivery System Module -- 3.6 Notification Storage Bucket -- 4 Predicting User's Responsiveness to Notifications -- 4.1 Dataset -- 4.2 Predicting User's Responsiveness Using Machine Learning -- 5 Results and Discussions -- 6 Conclusion and Future Work -- References -- Real-Time Full Body Tracking for Life-Size Telepresence -- 1 Introduction -- 2 Related Work -- 3 Material and Method -- 3.1 Full-Body Tracking -- 3.2 Background Removal -- 3.3 Remote User Setting -- 4 Results and Discussion -- 5 Conclusion -- References -- Solar Power Generation Forecasting Using Deep Learning -- 1 Introduction -- 2 Use of Artificial Intelligence in Predicting Data -- 3 Methodology -- 3.1 Data Collection -- 3.2 Pre-processing -- 3.3 Split Data into Train and Testing Sets -- 3.4 Data Standardization -- 3.5 Building Model -- 3.6 Training Model -- 4 Model Building and Implementation -- 5 Model Evaluation -- 6 Results -- 7 Conclusion -- References -- Applications of Big Five Personality Test in Job Performance -- 1 Introduction -- 1.1 Dimensions of Job Performance -- 1.2 Personality Model with Five Traits -- 2 Literature Review -- 3 Objectives of the Study -- 4 Research Design -- 4.1 Measuring Instruments. , 5 Data Analysis Technique Employed.

Additional Edition: Print version: Unhelkar, Bhuvan Advances and Applications of Artificial Intelligence and Machine Learning Singapore : Springer,c2023 ISBN 9789819959730

Language: English

UID:

Format: 1 online resource (230 pages)

ISBN: 0-12-822154-2

Note: Front Cover -- STATE OF THE ART ON GRAMMATICAL INFERENCE USING EVOLUTIONARY METHOD -- STATE OF THE ART ON GRAMMATICAL INFERENCE USING EVOLUTIONARY METHOD -- Copyright -- Dedication -- Contents -- Foreword -- Preface -- Acknowledgment -- Abbreviations -- 1 - Introduction and scientific goals -- 1.1 Introduction -- 1.2 Why grammatical inference is popular -- 1.3 Scientific goals: why this book? -- 2 - State of the art: grammatical inference -- 2.1 Introduction -- 2.2 Part 1. Preliminary definitions -- 2.2.1 Backus-Naur form -- 2.2.2 Grammars -- 2.2.3 Chomsky hierarchy of grammars -- 2.2.4 Major grammar definitions -- 2.2.4.1 Unrestricted grammars -- 2.2.4.2 Context-sensitive grammars -- 2.2.4.3 Context-free grammars -- 2.2.4.4 Regular grammars -- 2.2.4.5 Regular expression -- 2.2.4.6 Matrix grammars -- 2.2.4.7 Programmed grammars -- 2.2.4.8 Random context-free grammars -- 2.2.4.9 Valence grammars -- 2.2.4.10 Bag context grammars -- 2.3 Part 2. Introduction to learning algorithms -- 2.3.1 Identification of the limit -- 2.3.1.1 Strengths and weaknesses -- 2.3.2 Teacher and queries learning algorithm -- 2.3.2.1 Strengths and weaknesses -- 2.3.3 Probably Approximately Correct learning algorithm -- 2.3.3.1 Strengths and weaknesses -- 2.3.4 Neural network in learning algorithm -- 2.3.4.1 Strengths and weaknesses -- 2.3.5 Automatic DIstillation of structure algorithm -- 2.3.5.1 Strengths and weaknesses -- 2.3.6 EMILE -- 2.3.6.1 Strengths and weaknesses -- 2.3.7 e-Grammar Induction Drive by Simplicity -- 2.3.7.1 Strengths and weaknesses -- 2.3.8 Computation learning of natural language -- 2.3.8.1 Strengths and weaknesses -- 2.3.9 Context Distribution Clustering algorithm -- 2.3.9.1 Strengths and weaknesses -- 2.3.10 Language agent method -- 2.3.10.1 Strengths and weaknesses -- 2.3.11 Genetic algorithm-based learning approach. , 2.3.11.1 Strengths and weaknesses -- 2.3.12 Architecture for learning linguistic structure -- 2.3.12.1 Strengths and weaknesses -- 2.3.13 Alignment-based learning -- 2.3.13.1 Strengths and weaknesses -- 2.3.14 Improved tabular representation algorithm -- 2.3.14.1 Strengths and weaknesses -- 2.4 Comparison and discussion -- 2.5 What are the challenges with grammatical inference algorithms? -- 2.6 Summary -- References -- 3 - State of the art: genetic algorithms and premature convergence -- 3.1 Introduction -- 3.2 Factors affecting genetic algorithms -- 3.3 Theoretical framework -- 3.3.1 Schema theory -- 3.3.2 Markov chain theory -- 3.3.3 Statistical mechanics -- 3.4 Approaches to preventing premature convergence -- 3.4.1 Crowding method -- 3.4.2 Strengths and weaknesses -- 3.4.3 Incest prevention algorithm -- 3.4.3.1 Strengths and weaknesses -- 3.4.4 Scheduled sharing approach -- 3.4.4.1 Strengths and weaknesses -- 3.4.5 Migration model and nCUBE-based approach -- 3.4.5.1 Strengths and weaknesses -- 3.4.6 Cooperation-based approach -- 3.4.6.1 Strengths and weaknesses -- 3.4.7 Syntactic analysis of convergence -- 3.4.7.1 Strengths and weaknesses -- 3.4.8 Pygmy algorithm -- 3.4.8.1 Strengths and weaknesses -- 3.4.9 Adaptive probability-based approach -- 3.4.9.1 Strengths and weaknesses -- 3.4.10 Social disaster technique -- 3.4.10.1 Strengths and weaknesses -- 3.4.11 Island model genetic algorithm -- 3.4.11.1 Strengths and weaknesses -- 3.4.12 Shifting balance theory in dynamic environment -- 3.4.12.1 Strengths and weaknesses -- 3.4.13 Random offspring generation approach -- 3.4.13.1 Strengths and weaknesses -- 3.4.14 Chaos operator-based approach -- 3.4.14.1 Strengths and weaknesses -- 3.4.15 Self-adaptive selection pressure steering approach -- 3.4.15.1 Strengths and weaknesses -- 3.4.16 Multicombinative strategy -- 3.4.16.1 Strengths and weaknesses. , 3.4.17 Genetic algorithm using self-organizing maps -- 3.4.17.1 Strengths and weaknesses -- 3.4.18 Age-layered population structure approach -- 3.4.18.1 Strengths and weaknesses -- 3.4.19 Number structuring approach -- 3.4.19.1 Strengths and weaknesses -- 3.4.20 Hybrid particle swarm optimization and genetic algorithm -- 3.4.20.1 Strengths and weaknesses -- 3.4.21 Selective mutation-based approach -- 3.4.21.1 Strengths and weaknesses -- 3.4.22 Elite mating pool genetic algorithm -- 3.4.22.1 Strengths and weaknesses -- 3.4.23 Dynamic application of reproduction operator -- 3.4.23.1 Strengths and weaknesses -- 3.4.24 Hybrid strategy using elite mating pool genetic algorithm and dynamic application reproduction operator -- 3.4.24.1 Strengths and weaknesses -- 3.4.25 Frequency crossover with nine different mutations -- 3.4.25.1 Strengths and weaknesses -- 3.5 Classifications and analyses -- 3.6 Challenges with the genetic algorithm -- 3.7 Summary -- References -- Further reading -- 4 - Genetic algorithms and grammatical inference -- 4.1 Introduction -- 4.2 Bit-mask oriented genetic algorithm -- 4.3 Bit-masking oriented data structure -- 4.4 Reproduction operators: crossover and mutation mask fill -- 4.4.1 Crossover operators -- 4.4.1.1 Cut crossover mask-fill operator -- 4.4.1.2 Bit-by-bit mask-fill crossover -- 4.4.1.3 Local cut crossover mask-fill operator -- 4.4.2 Mutation operator -- 4.4.2.1 Mutation mask-fill operator -- 4.5 New offspring generation -- 4.6 Genetic algorithm implemented for grammar induction -- 4.7 Maintaining regularity and generalization and minimum description length principle -- 4.8 Grammatical inference and minimum description length principle -- 4.9 Summary -- References -- Further reading -- 5 - Performance analysis of genetic algorithm for grammatical inference -- 5.1 Introduction -- 5.2 Simulation model and test languages. , 5.3 Parameter selection and tuning -- 5.3.1 Taguchi design for parameter quantification -- 5.4 Performance analysis of proposed bit masking-oriented genetic algorithm -- 5.4.1 Testing for premature convergence -- 5.4.1.1 Statistical test -- 5.4.2 Comparison with global optimization algorithms -- 5.4.2.1 Particle swarm optimization algorithm -- 5.4.2.2 Simulated annealing -- 5.4.2.3 Results and discussion -- 5.4.2.4 Statistical test -- 5.4.3 Comparison with other grammatical inference algorithms -- 5.4.3.1 The minimum description length principle in grammatical inference: an example -- 5.4.3.2 Results and discussions -- 5.4.3.3 Statistical test -- 5.5 Summary -- References -- 6 - Applications of grammatical inference methods and future development -- 6.1 Introduction -- 6.2 Application of grammatical inference method -- 6.3 Opportunities for future research -- 6.3.1 Handling noisy data -- 6.3.2 Dealing with natural languages -- 6.3.3 Developing a convergence model -- 6.3.4 Evolutionary algorithms for domain-specific languages -- 6.3.5 Categorizing the power of reproduction operators -- 6.3.6 Exploring opportunities in software testing -- References -- Subject index -- Author index -- Back Cover.

Additional Edition: Print version: Pandey, Hari Mohan State of the Art on Grammatical Inference Using Evolutionary Method San Diego : Elsevier Science & Technology,c2021 ISBN 9780128221167

Language: English

UID:

Format: 1 online resource (230 pages)

ISBN: 0-12-822154-2

Note: Front Cover -- STATE OF THE ART ON GRAMMATICAL INFERENCE USING EVOLUTIONARY METHOD -- STATE OF THE ART ON GRAMMATICAL INFERENCE USING EVOLUTIONARY METHOD -- Copyright -- Dedication -- Contents -- Foreword -- Preface -- Acknowledgment -- Abbreviations -- 1 - Introduction and scientific goals -- 1.1 Introduction -- 1.2 Why grammatical inference is popular -- 1.3 Scientific goals: why this book? -- 2 - State of the art: grammatical inference -- 2.1 Introduction -- 2.2 Part 1. Preliminary definitions -- 2.2.1 Backus-Naur form -- 2.2.2 Grammars -- 2.2.3 Chomsky hierarchy of grammars -- 2.2.4 Major grammar definitions -- 2.2.4.1 Unrestricted grammars -- 2.2.4.2 Context-sensitive grammars -- 2.2.4.3 Context-free grammars -- 2.2.4.4 Regular grammars -- 2.2.4.5 Regular expression -- 2.2.4.6 Matrix grammars -- 2.2.4.7 Programmed grammars -- 2.2.4.8 Random context-free grammars -- 2.2.4.9 Valence grammars -- 2.2.4.10 Bag context grammars -- 2.3 Part 2. Introduction to learning algorithms -- 2.3.1 Identification of the limit -- 2.3.1.1 Strengths and weaknesses -- 2.3.2 Teacher and queries learning algorithm -- 2.3.2.1 Strengths and weaknesses -- 2.3.3 Probably Approximately Correct learning algorithm -- 2.3.3.1 Strengths and weaknesses -- 2.3.4 Neural network in learning algorithm -- 2.3.4.1 Strengths and weaknesses -- 2.3.5 Automatic DIstillation of structure algorithm -- 2.3.5.1 Strengths and weaknesses -- 2.3.6 EMILE -- 2.3.6.1 Strengths and weaknesses -- 2.3.7 e-Grammar Induction Drive by Simplicity -- 2.3.7.1 Strengths and weaknesses -- 2.3.8 Computation learning of natural language -- 2.3.8.1 Strengths and weaknesses -- 2.3.9 Context Distribution Clustering algorithm -- 2.3.9.1 Strengths and weaknesses -- 2.3.10 Language agent method -- 2.3.10.1 Strengths and weaknesses -- 2.3.11 Genetic algorithm-based learning approach. , 2.3.11.1 Strengths and weaknesses -- 2.3.12 Architecture for learning linguistic structure -- 2.3.12.1 Strengths and weaknesses -- 2.3.13 Alignment-based learning -- 2.3.13.1 Strengths and weaknesses -- 2.3.14 Improved tabular representation algorithm -- 2.3.14.1 Strengths and weaknesses -- 2.4 Comparison and discussion -- 2.5 What are the challenges with grammatical inference algorithms? -- 2.6 Summary -- References -- 3 - State of the art: genetic algorithms and premature convergence -- 3.1 Introduction -- 3.2 Factors affecting genetic algorithms -- 3.3 Theoretical framework -- 3.3.1 Schema theory -- 3.3.2 Markov chain theory -- 3.3.3 Statistical mechanics -- 3.4 Approaches to preventing premature convergence -- 3.4.1 Crowding method -- 3.4.2 Strengths and weaknesses -- 3.4.3 Incest prevention algorithm -- 3.4.3.1 Strengths and weaknesses -- 3.4.4 Scheduled sharing approach -- 3.4.4.1 Strengths and weaknesses -- 3.4.5 Migration model and nCUBE-based approach -- 3.4.5.1 Strengths and weaknesses -- 3.4.6 Cooperation-based approach -- 3.4.6.1 Strengths and weaknesses -- 3.4.7 Syntactic analysis of convergence -- 3.4.7.1 Strengths and weaknesses -- 3.4.8 Pygmy algorithm -- 3.4.8.1 Strengths and weaknesses -- 3.4.9 Adaptive probability-based approach -- 3.4.9.1 Strengths and weaknesses -- 3.4.10 Social disaster technique -- 3.4.10.1 Strengths and weaknesses -- 3.4.11 Island model genetic algorithm -- 3.4.11.1 Strengths and weaknesses -- 3.4.12 Shifting balance theory in dynamic environment -- 3.4.12.1 Strengths and weaknesses -- 3.4.13 Random offspring generation approach -- 3.4.13.1 Strengths and weaknesses -- 3.4.14 Chaos operator-based approach -- 3.4.14.1 Strengths and weaknesses -- 3.4.15 Self-adaptive selection pressure steering approach -- 3.4.15.1 Strengths and weaknesses -- 3.4.16 Multicombinative strategy -- 3.4.16.1 Strengths and weaknesses. , 3.4.17 Genetic algorithm using self-organizing maps -- 3.4.17.1 Strengths and weaknesses -- 3.4.18 Age-layered population structure approach -- 3.4.18.1 Strengths and weaknesses -- 3.4.19 Number structuring approach -- 3.4.19.1 Strengths and weaknesses -- 3.4.20 Hybrid particle swarm optimization and genetic algorithm -- 3.4.20.1 Strengths and weaknesses -- 3.4.21 Selective mutation-based approach -- 3.4.21.1 Strengths and weaknesses -- 3.4.22 Elite mating pool genetic algorithm -- 3.4.22.1 Strengths and weaknesses -- 3.4.23 Dynamic application of reproduction operator -- 3.4.23.1 Strengths and weaknesses -- 3.4.24 Hybrid strategy using elite mating pool genetic algorithm and dynamic application reproduction operator -- 3.4.24.1 Strengths and weaknesses -- 3.4.25 Frequency crossover with nine different mutations -- 3.4.25.1 Strengths and weaknesses -- 3.5 Classifications and analyses -- 3.6 Challenges with the genetic algorithm -- 3.7 Summary -- References -- Further reading -- 4 - Genetic algorithms and grammatical inference -- 4.1 Introduction -- 4.2 Bit-mask oriented genetic algorithm -- 4.3 Bit-masking oriented data structure -- 4.4 Reproduction operators: crossover and mutation mask fill -- 4.4.1 Crossover operators -- 4.4.1.1 Cut crossover mask-fill operator -- 4.4.1.2 Bit-by-bit mask-fill crossover -- 4.4.1.3 Local cut crossover mask-fill operator -- 4.4.2 Mutation operator -- 4.4.2.1 Mutation mask-fill operator -- 4.5 New offspring generation -- 4.6 Genetic algorithm implemented for grammar induction -- 4.7 Maintaining regularity and generalization and minimum description length principle -- 4.8 Grammatical inference and minimum description length principle -- 4.9 Summary -- References -- Further reading -- 5 - Performance analysis of genetic algorithm for grammatical inference -- 5.1 Introduction -- 5.2 Simulation model and test languages. , 5.3 Parameter selection and tuning -- 5.3.1 Taguchi design for parameter quantification -- 5.4 Performance analysis of proposed bit masking-oriented genetic algorithm -- 5.4.1 Testing for premature convergence -- 5.4.1.1 Statistical test -- 5.4.2 Comparison with global optimization algorithms -- 5.4.2.1 Particle swarm optimization algorithm -- 5.4.2.2 Simulated annealing -- 5.4.2.3 Results and discussion -- 5.4.2.4 Statistical test -- 5.4.3 Comparison with other grammatical inference algorithms -- 5.4.3.1 The minimum description length principle in grammatical inference: an example -- 5.4.3.2 Results and discussions -- 5.4.3.3 Statistical test -- 5.5 Summary -- References -- 6 - Applications of grammatical inference methods and future development -- 6.1 Introduction -- 6.2 Application of grammatical inference method -- 6.3 Opportunities for future research -- 6.3.1 Handling noisy data -- 6.3.2 Dealing with natural languages -- 6.3.3 Developing a convergence model -- 6.3.4 Evolutionary algorithms for domain-specific languages -- 6.3.5 Categorizing the power of reproduction operators -- 6.3.6 Exploring opportunities in software testing -- References -- Subject index -- Author index -- Back Cover.

Additional Edition: Print version: Pandey, Hari Mohan State of the Art on Grammatical Inference Using Evolutionary Method San Diego : Elsevier Science & Technology,c2021 ISBN 9780128221167

Language: English

UID:

Format: 1 Online-Ressource (XII, 814 p. 417 illus., 288 illus. in color).

Edition: 1st ed. 2022

ISBN: 978-981-1948-31-2

Series Statement: Lecture Notes in Electrical Engineering 925

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1948-30-5

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1948-32-9

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1948-33-6

Language: English

DOI: 10.1007/978-981-19-4831-2

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (XII, 814 p. 417 illus., 288 illus. in color).

Edition: 1st ed. 2022

ISBN: 978-981-1948-31-2

Series Statement: Lecture Notes in Electrical Engineering 925

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1948-30-5

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1948-32-9

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1948-33-6

Language: English

DOI: 10.1007/978-981-19-4831-2

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: XII, 802 p. 360 illus., 300 illus. in color. , online resource.

Edition: 1st ed. 2023.

ISBN: 9789819959747

Series Statement: Lecture Notes in Electrical Engineering, 1078

Content: This volume comprises the select peer-reviewed proceedings of the International Conference on Advances and Applications of Artificial Intelligence and Machine Learning 2022 (ICAAAIML 2022). It aims to provide a comprehensive and broad-spectrum picture of state-of-the-art research and development in the areas of artificial intelligence, machine learning, deep learning, and their advanced applications in computer vision and blockchain. It also covers research in core concepts of computers, intelligent system design and deployment, real-time systems, WSN, sensors and sensor nodes, software engineering, image processing, and cloud computing. This volume will provide a valuable resource for those in academia and industry.

Note: Cloud computing and Virtualization -- Computer Network and Sensors -- Big Data and Data Mining -- Artificial Intelligence and its applications in Smart Education -- Cloud Computing enabling Technologies and their applications -- Image/ Video Processing -- Algorithm and Design -- IoT and Automation -- Software Engineering -- Soft Computing -- Machine Learning applications in Smart Healthcare, Manufacturing and Smart Transportation -- Image Processing applications in Smart Agriculture -- Security and Privacy Challenges and Data Analytics -- Challenges of Smart Cities and Future research directions -- Application of Artificial Intelligence and Machine Learning in production and Mechanical Engineering systems -- Applications of Artificial Intelligence and Machine learning in Biotechnology -- Infrastructure and Resource Development & Management using Artificial Intelligence and Machine learning.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9789819959730

Additional Edition: Printed edition: ISBN 9789819959754

Additional Edition: Printed edition: ISBN 9789819959761

Language: English

DOI: 10.1007/978-981-99-5974-7

URL: https://doi.org/10.1007/978-981-99-5974-7