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• 1. Introduction and Scientific Goals
• 2. State of the Art: Grammatical Inference
• 3. State of the Art: Genetic Algorithms and Premature Convergence
• 4. Genetic Algorithms and Grammatical Inference
• 5. Performance Analysis of Genetic Algorithm for Grammatical Inference
• 6. Applications of Grammatical Inference Methods and Future Development.
• (source: Nielsen Book Data)

State of the Art on Grammatical Inference Using Evolutionary Method presents an approach for grammatical inference (GI) using evolutionary algorithms. Grammatical inference deals with the standard learning procedure to acquire grammars based on evidence about the language. It has been extensively studied due to its high importance in various fields of engineering and science. The book's prime purpose is to enhance the current state-of-the-art of grammatical inference methods and present new evolutionary algorithms-based approaches for context free grammar induction. The book's focus lies in the development of robust genetic algorithms for context free grammar induction. The new algorithms discussed in this book incorporate Boolean-based operators during offspring generation within the execution of the genetic algorithm. Hence, the user has no limitation on utilizing the evolutionary methods for grammatical inference.
(source: Nielsen Book Data)

Annotation "Java Programming"is an introductory level text that instils an understanding of basic concepts before gradually moving to advanced topics like swing, socket programming, JAVA native interface, remote method invocation and serialization. Programs are accompanied by complete explanations, and their output helps the reader better understand the logic behind them."

• Intro
• Contents
• About the Editors
• Firefly Algorithm and Deep Neural Network Approach for Intrusion Detection
• 1 Introduction
• 1.1 Research Goals and Contributions
• 1.2 Structure of the Paper
• 2 Theoretical Background and Literature Review
• 3 Proposed Method
• 3.1 Enhanced FA Metaheuristics
• 4 The eFA and DNN Framework for IDS Classification Experiments
• 5 Conclusion
• References
• Dimensionality Reduction Method for Early Detection of Dementia
• 1 Introduction
• 2 Related Works
• 3 Methodology
• 3.1 Materials and Subjects
• 3.2 Pre-processing

• 3.3 Feature Reduction
• 4 Result and Discussion
• 5 Conclusion
• References
• Prognostication in Retail World: Analysing Using Opinion Mining
• 1 Introduction
• 2 Literature Survey
• 3 Proposed Model
• 3.1 Sentimental Analysis
• 3.2 Classification Model
• 3.3 Techniques and Tools
• 4 Data
• 5 Evaluation Outcome
• 6 Conclusion
• References
• Impact of Resolution Techniques on Chlorophyll Fluorescence Wheat Images Using Classifier Models to Detect Nitrogen Deficiency
• 1 Introduction
• 2 Related Work
• 3 Experimental Setup
• 4 Results and Discussions
• 5 Conclusions

• 6 Conclusion
• References
• Saving Patterns and Investment Preferences: Prediction Through Machine Learning Approaches
• 1 Introduction
• 2 Review of Literature and Hypothesis Development
• 3 Data Analysis
• 3.1 Regression Equation and Data
• 4 Result Analysis and Discussion
• 4.1 Effect of Eight Independent Variables on the Saving Patterns
• 4.2 Effect of Eight Independent Variables on the Investment Preferences
• 4.3 Discussion
• 5 Conclusion
• References
• A Machine Learning Based Approach for Detection of Distributed Denial of Service Attacks
• 1 Introduction
• 2 Related Work

• 3 Proposed Framework for DDoS Attack Detection
• 3.1 Problem Definition
• 3.2 The Framework
• 3.3 Proposed Algorithm
• 3.4 Metrics for Performance Evaluation
• 4 Experimental Results
• 5 Conclusion and Future Work
• References
• Convolutional Neural Network Based Automatic Speech Recognition for Tamil Language
• 1 Introduction
• 2 Literature Review
• 3 Convolution Neural Network
• 4 Experimental Setup
• 4.1 Feature Extraction
• 4.2 Convolutional Neural Network Model
• 5 Result and Discussion
• 6 Conclusion
• References

The book presents a collection of peer-reviewed articles from the International Conference on Advances and Applications of Artificial Intelligence and Machine Learning - ICAAAIML 2020. The book covers research in artificial intelligence, machine learning, and deep learning applications in healthcare, agriculture, business, and security. This volume contains research papers from academicians, researchers as well as students. There are also papers on core concepts of computer networks, intelligent system design and deployment, real-time systems, wireless sensor networks, sensors and sensor nodes, software engineering, and image processing. This book will be a valuable resource for students, academics, and practitioners in the industry working on AI applications.
(source: Nielsen Book Data)

• 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.

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.