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Proceedings of International Conference on Trends in Computational and Cognitive Engineering [electronic resource] : proceedings of TCCE 2020 / M. Shamim Kaiser, Anirban Bandyopadhyay, Mufti Mahmud, Kanad Ray, editors.

This book presents various computational and cognitive modeling approaches in the areas of health, education, finance, environment, engineering, commerce, and industry. It is a collection of selected conference papers presented at the International Conference on Trends in Computational and Cognitive...

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Bibliographic Details
Online Access: Full Text (via Springer)
Corporate Author: International Conference on Trends in Computational and Cognitive Engineering Online
Other Authors: Kaiser, M. Shamim, Bandyopadhyay, Anirban, Mahmud, Mufti, Ray, Kanad
Other title:TCCE 2020.
Format: Electronic Conference Proceeding eBook
Language:English
Published: Singapore : Springer, 2021.
Series:Advances in intelligent systems and computing ; 1309.
Subjects:
Computational intelligence > Congresses.
User-centered system design > Congresses.
Computational intelligence.
User-centered system design.
Conference papers and proceedings.
Table of Contents:
  • Intro
  • Organization
  • Chief Patron
  • Conference Co-chairs
  • Steering Committee
  • Advisory Committee
  • Organizing Committee
  • Technical Program Committee Chairs
  • Technical Program Committee
  • Preface
  • Contents
  • About the Editors
  • Artificial Intelligence and Soft Computing
  • Bangla Real-Word Error Detection and Correction Using Bidirectional LSTM and Bigram Hybrid Model
  • 1 Introduction
  • 2 Related Work
  • 3 Methodology
  • 3.1 Dataset Details
  • 3.2 Bigram Generation and Probability Estimation
  • 3.3 Bidirectional LSTM Approach
  • 4 Tools and Technology.
  • 5 Experimental Result
  • 6 Conclusion
  • References
  • Quantitative Analysis of Deep CNNs for Multilingual Handwritten Digit Recognition
  • 1 Introduction
  • 2 Recognition Methods
  • 2.1 Convolutional Neural Network (CNN)
  • 2.2 Deep CNN
  • 3 Experimental Results and Discussion
  • 3.1 Dataset Description
  • 3.2 Discussion
  • 4 Conclusion
  • References
  • Performance Analysis of Machine Learning Approaches in Software Complexity Prediction
  • 1 Introduction
  • 2 Literature Review
  • 3 Research Methodology
  • 3.1 Research Questions
  • 3.2 Proposed Research Framework
  • 3.3 Dataset Collection.
  • 3.4 Dataset Cleaning and Analysis
  • 3.5 Machine Learning Classifiers and Evaluation Metrics
  • 4 Result and Discussion
  • 4.1 Correlation Results
  • 4.2 Performance Results
  • 5 Conclusion
  • References
  • Bengali Abstractive News Summarization (BANS): A Neural Attention Approach
  • 1 Introduction
  • 2 Related Work
  • 3 Dataset
  • 4 Model Architecture
  • 5 Result and Discussion
  • 5.1 Quantitative Evaluation
  • 5.2 Qualitative Evaluation
  • 6 Conclusion
  • References
  • Application of Feature Engineering with Classification Techniques to Enhance Corporate Tax Default Detection Performance.
  • 1 Introduction
  • 2 Proposed Methodology
  • 2.1 Dataset Description and Transformation
  • 2.2 Feature Selection
  • 2.3 Classification
  • 2.4 Evaluation Metrics
  • 3 Experimental Result
  • 3.1 Experimental Analysis of Primary Tax Default Dataset
  • 3.2 Experimental Analysis on Transformed Tax Default Dataset
  • 3.3 Wilcoxon Signed-Ranks Significant Test
  • 4 Discussion
  • 5 Conclusion and Future Work
  • References
  • PRCMLA: Product Review Classification Using Machine Learning Algorithms
  • 1 Introduction
  • 2 Related Work
  • 3 Proposed Approach and Model
  • 3.1 Proposed Web Crawler.
  • 3.2 Data Preprocessing
  • 3.3 Feature Extraction
  • 3.4 Classification Model
  • 3.5 Evaluation Criteria
  • 4 Experiment and Results
  • 4.1 Grabbing Data Using Web Crawler
  • 4.2 Dataset
  • 4.3 Result Analysis
  • 5 Conclusion
  • References
  • Handwritten Bangla Character Recognition Using Deep Convolutional Neural Network: Comprehensive Analysis on Three Complete Datasets
  • 1 Introduction
  • 2 Related Work
  • 3 Methodology and System Architecture
  • 4 Data Processing and Experiment
  • 4.1 Data Processing
  • 4.2 Experiment
  • 5 Result Analysis
  • 5.1 Accuracy Analysis
  • 5.2 Loss Analysis.

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