Intro
ICDAM-2023 Steering Committee Members
Preface
Contents
Editors and Contributors
Enhancing Computational Thinking Based on Virtual Robot of Artificial Intelligence Modeling in the English Language Classroom
1 Introduction
2 Method
2.1 Participants
2.2 Questionnaire
2.3 The Procedure of Robotics Instructional Design (RID) in English Class
3 Results and Discussions
3.1 Questionnaire and Observation Results of CT Through Virtual Robotics of Artificial Intelligence
3.2 Discussion
4 Conclusions
References

Software Change Prediction Model Using Ensemble Learning
1 Introduction
2 Data Collection
2.1 Data Acquisition
2.2 Extraction of Objective Oriented Metrics
2.3 Assessment of Changes Made Between Two Versions
2.4 Dealing with Data Imbalance
3 Training of Base Classifiers
3.1 Selection of Classifiers
3.2 Training Base Classifiers
4 Implementation of Prediction Network
5 Aggregation of the Outcome
6 Results and Analysis
6.1 AUC Score
6.2 Precision Result
7 Conclusion
References

Discerning Monkeypox from Other Viruses of the Poxviridae Family in a Deep Learning Paradigm
1 Introduction
1.1 History of Monkeypox
1.2 Need for Monkeypox Detection
1.3 Machine Learning and AI for Monkeypox Detection and a Brief History for the Researches Using ML
1.4 Proposed Method
2 Recent Works in the Field
3 Proposed Methodology
3.1 Models Used
3.2 Image Preprocessing, Augmentation and Data Separation
3.3 Basic Flow and Classification
3.4 Ensemble
4 Dataset Used
5 Metrics Used for Evaluation of the Research
6 Results
7 Conclusion
References

An Exploratory Study to Classify Brain Tumor Using Convolutional Neural Networks
1 Introduction
1.1 Overview
1.2 Motivation
1.3 Organization of the Paper
1.4 Main Contributions
2 Background Study
2.1 Convolutional Neural Network
2.2 Brain Tumor
2.3 Magnetic Resonance Imaging
3 Literature Survey
4 Proposed Methodology
4.1 Dataset
4.2 Image Preprocessing
4.3 Model Training
4.4 Model Deployment
5 Results
6 Comparative Study
7 Conclusion
References
Skin Cancer Detection with Edge Devices Using YOLOv7 Deep CNN
1 Introduction

2 Related Work
3 Methodology
3.1 Data Description
3.2 Transfer Learning Approach
3.3 Experimental Setup
4 Result and Discussion
5 Conclusion
References
Effective Image Captioning Using Multi-layer LSTM with Attention Mechanism
1 Introduction
2 Related Works
3 Proposed Methodology
3.1 Encoder-Decoder Architecture
3.2 Feature Extraction
3.3 Text Preprocessing
3.4 Attention Mechanism
3.5 Language Modeling
4 Experimental Setup
4.1 Dataset
4.2 Evaluation Metric
4.3 Hyperparameters Used
5 Results and Discussion
6 Conclusion
References