LITMIR.BIZ

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target="_blank" rel="nofollow" href="#ulink_9de695a4-8a35-57a0-afa4-0b9125838a40">8.2 Literature Survey 8.3 Methodology 8.4 Dataset 8.5 Evaluation 8.6 Conclusion References 9 Image Classification by Reinforcement Learning With Two-State Q-Learning 9.1 Introduction 9.2 Proposed Approach 9.3 Datasets Used 9.4 Experimentation 9.5 Conclusion References 10 Design and Development of Neural-Fuzzy Control Model for Computer-Based Control Systems in a Multivariable Chemical Process 10.1 Introduction 10.2 Distributed Control System 10.3 Fuzzy Logic 10.4 Artificial Neural Network 10.5 Neuro-Fuzzy 10.6 Case Study 10.7 Software Implementation on Graphical User Interface 10.8 Results and Discussion 10.9 Discussion 10.10 Conclusion 10.11 Scope for Future Work References Appendix 10.1 MATLAB Simulation Configuration Using Sugeno Appendix 10.2 MATLAB Window Displaying Desired Training-Data Fed to Neuro-Fuzzy Model. Appendix 10.3 MATLAB Window Displaying Checking-Data Fed to Neuro-Fuzzy Model. 11 Artificial Neural Network in the Manufacturing Sectors 11.1 Introduction 11.2 Optimization 11.3 Artificial Neural Network: Optimization of Mechanical Systems 11.4 ANN vs. Human Brain 11.5 Architecture of Artificial Neural Networks 11.6 Learning Algorithm(s) 11.7 Different Type of Data 11.8 Case Study: Hard Machining of EN 31 Steel 11.9 Advantages of Using ANN in Manufacturing Sectors 11.10 Disadvantages of Using ANN in Manufacturing Sectors 11.11 Applications 11.12 Conclusions 11.13 Future Scope of ANN in Manufacturing Sectors References 12 Speech-Based Multilingual Translation Framework 12.1 Introduction 12.2 Literature Survey 12.3 Phases of ASR 12.4 Modules of ASR 12.5 Speech Database for ASR 12.6 Developing ASR 12.7 Performance of ASR 12.8 Application Areas 12.9 Conclusion and Future Work References 13 Text Summarization: A Technical Overview and Research Perspectives 13.1 Introduction 13.2 Summarization Techniques 13.3 Evaluating Summaries 13.4 Datasets and Results 13.5 Future Research Directions 13.6 Conclusion References 14 Democratizing Sentiment Analysis of Twitter Data Using Google Cloud Platform and BigQuery 14.1 Introduction 14.2 Literature Review 14.3 Understanding the Google Cloud Platform 14.4 Using BigQuery in the Google Cloud Console 14.5 Sentiment Analysis 14.6 Turning to Google BigQuery Analysis 14.7 Proposed Method 14.8 Experimental Setup and Results 14.9 Conclusion References 15 A Review of Topic Modeling and Its Application 15.1 Introduction 15.2 Objective of Topic Modeling

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