Proceedings of International Conference on Computational Intelligence and Data Engineering ICCIDE 2020 /

Proceedings of International Conference on Computational Intelligence and Data Engineering [electronic resource] : ICCIDE 2020 / Nabendu Chaki, Jerzy Pejas, Nagaraju Devarakonda, Ram Mohan Rao Kovvur, editors.

This book is a collection of high-quality research work on cutting-edge technologies and the most-happening areas of computational intelligence and data engineering. It includes selected papers from the International Conference on Computational Intelligence and Data Engineering (ICCIDE 2020). It cov...

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Bibliographic Details
Online Access: Full Text (via Springer)
Corporate Author: International Conference on Computational Intelligence & Data Engineering Hyderabad, India
Other Authors: Chaki, Nabendu, Pejaś, Jerzy, 1954-, Devarakonda, Nagaraju, Kovvur, Ram Mohan Rao
Other title:ICCIDE 2020.
Format: Electronic Conference Proceeding eBook
Language:English
Published: Singapore : Springer, 2021.
Series:Lecture notes on data engineering and communications technologies ; v. 56.
Subjects:
Computational intelligence > Congresses.
Computational intelligence.
Conference papers and proceedings.
Table of Contents:
  • Intro
  • Preface
  • Contents
  • About the Editors
  • Autonomous Obstacle Avoidance Robot Using Regression
  • 1 Introduction
  • 2 Methodology
  • 3 Obstacle Avoidance Algorithm
  • 4 Implementing Obstacle Avoidance
  • 5 Results and Analysis
  • 6 Conclusion and Future Scope
  • References
  • ABC-BSRF: Artificial Bee Colony and Borderline-SMOTE RF Algorithm for Intrusion Detection System on Data Imbalanced Problem
  • 1 Introduction
  • 2 Related Work
  • 3 Outline of ABC Analysis, SMOTE, and Random Forests
  • 3.1 Feature Selection: ABC Analysis.
  • 3.2 Oversampling: Borderline Synthetic Minority Oversampling Technique (B-SMOTE) Through Random Forests
  • 4 Proposed Methodology
  • 5 Experiential Work and Results Inferred
  • 6 Conclusions and Perspectives
  • References
  • A Novel Two-Layer Feature Selection for Emotion Recognition with Body Movements
  • 1 Introduction
  • 2 Related Work
  • 3 Proposed Work
  • 4 Experimental Results
  • 5 Conclusion
  • References
  • Nearest Neighbors via a Hybrid Approach in Large Datasets: A Speed up
  • 1 Introduction
  • 2 Preliminaries
  • 2.1 Coresets
  • 2.2 K-Means
  • 2.3 Quadtree and kd-tree
  • 3 Proposed Work.
  • 4 Results
  • 5 Future Work
  • 6 Conclusion
  • References
  • Impact of Hyperparameters on Model Development in Deep Learning
  • 1 Introduction
  • 2 Literature Review
  • 3 Skopt: Library for Hyperparameter Optimization
  • 3.1 gp_minimize
  • 3.2 forest_minimize
  • 3.3 gbrt_minimize
  • 4 Environment
  • 4.1 Dataset
  • 5 Results and Discussions
  • 6 Conclusion
  • References
  • Strategies to Effectively Integrate Visualization with Active Learning in Computer Science Class
  • 1 Introduction
  • 2 Literature Review
  • 3 Active Learning
  • 3.1 Think Pair Share
  • 3.2 One Minute Paper.
  • 3.3 Collaborative Learning
  • 3.4 Learning by Teaching
  • 3.5 Infographics
  • 4 Visualization
  • 4.1 Visualization and Its Need
  • 4.2 How to Choose Appropriate Visualization for Your Topic
  • 5 Strategies for Integration of Visualization with Active Learning
  • 5.1 Predict
  • 5.2 Calculate
  • 5.3 Explain
  • 6 Results and Discussions
  • 7 Conclusion
  • References
  • Supervised Learning-Based Classifiers in Healthcare Decision-Making
  • 1 Introduction
  • 2 Classification Algorithms
  • 3 Methodology
  • 4 Performance Assessment
  • 5 Case Study
  • 5.1 Dataset Description
  • 5.2 Results.
  • 6 Conclusion and Future Works
  • References
  • Gridding and Supervised Segmentation Method for DNA Microarray Images
  • 1 Introduction
  • 2 Microarray Gridding
  • 3 Microarray Segmentation
  • 4 Experimental Results
  • 5 Conclusions
  • References
  • Predicting Breast Cancer Using Machine Learning
  • 1 Introduction
  • 2 Literature Survey
  • 3 Existing System
  • 4 Proposed System
  • 4.1 Dataset
  • 4.2 Methods
  • 4.3 Methodology
  • 5 Results
  • 6 Conclusion
  • References
  • Gender Identification Over Voice Sample Using Machine Learning
  • 1 Introduction
  • 2 Gender Classification Related Work.

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