Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithms. Группа авторов
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Conclusion and Future Scope References
12 8 Feature Optimized Machine Learning Framework for Unbalanced Bioassays 8.1 Introduction 8.2 Related Work 8.3 Proposed Work 8.4 Experimental 8.5 Result and Discussion 8.6 Conclusion References
13 9 Predictive Model and Theory of Interaction 9.1 Introduction 9.2 Related Work 9.3 Predictive Analytics Process 9.4 Predictive Analytics Opportunities 9.5 Classes of Predictive Analytics Models 9.6 Predictive Analytics Techniques 9.7 Dataset Used in Our Research 9.8 Methodology 9.9 Results 9.10 Discussion 9.11 Use of Predictive Analytics 9.12 Conclusion and Future Work References
14 10 Advancement in Augmented and Virtual Reality 10.1 Introduction 10.2 Proposed Methodology 10.3 Results 10.4 Conclusion References
15 11 Computer Vision and Image Processing for Precision Agriculture 11.1 Introduction 11.2 Computer Vision 11.3 Machine Learning 11.4 Computer Vision and Image Processing in Agriculture 11.5 Conclusion References
16 12 A Novel Approach for Low-Quality Fingerprint Image Enhancement Using Spatial and Frequency Domain Filtering Techniques 12.1 Introduction 12.2 Existing Works for the Fingerprint Ehancement 12.3 Design and Implementation of the Proposed Algorithm 12.4 Results and Discussion 12.5 Conclusion and Future Scope References
17 13 Elevate Primary Tumor Detection Using Machine Learning 13.1 Introduction 13.2 Related Works 13.3 Proposed Work 13.4 Experimental Investigation 13.5 Result and Discussion 13.6 Conclusion 13.7 Future Work References
18 14 Comparative Sentiment Analysis Through Traditional and Machine Learning-Based Approach 14.1 Introduction to Sentiment Analysis 14.2 Four Types of Sentiment Analyses 14.3 Working of SA System 14.4 Challenges Associated With SA System 14.5 Real-Life Applications of SA 14.6 Machine Learning Methods Used for SA 14.7 A Proposed Method 14.8 Results and Discussions 14.9 Conclusion References
19 15 Application of Artificial Intelligence and Computer Vision to Identify Edible Bird’s Nest 15.1 Introduction 15.2 Prior Work 15.3 Auto Grading of Edible Birds Nest 15.4 Experimental Results 15.5 Conclusion Acknowledgments References
20 16 Enhancement of Satellite and Underwater Image Utilizing Luminance Model by Color Correction Method 16.1 Introduction