Classification applications with deep learning and machine learning technologies [electronic resource] / Laith Abualigah, editor.

Intro
Preface
Contents
Artocarpus Classification Technique Using Deep Learning Based Convolutional Neural Network
1 Introduction
2 Propose Deep Learning
2.1 Proposed Convolutional Neural Network (CNN) Architecture
2.2 Transfer Learning Model for Artocarpus Classification
2.3 Dataset
2.4 Augmentation
3 Performance Result
3.1 Experimental Setup
3.2 Performance of Proposed CNN Model
3.3 Accuracy Comparison
3.4 Model Performance Comparison
4 Conclusion
References
Rambutan Image Classification Using Various Deep Learning Approaches

1 Introduction
2 Literature Review
3 Proposed Deep Learning Method
3.1 CNN
3.2 Transfer Learning
3.3 Dataset
4 Performance Results and Recommendation
4.1 Convolutional Neural Network (CNN)
4.2 Transfer Learning Model
5 Concluding Remarks
References
Mango Varieties Classification-Based Optimization with Transfer Learning and Deep Learning Approaches
1 Introduction
2 Methodology
2.1 Dataset
2.2 Data Preparation
2.3 Proposed CNN Architecture
2.4 Transfer Learning Model
3 Experiment Result
3.1 CNN
3.2 Transfer Learning
3.3 Xception

3.4 Accuracy Comparison
4 Conclusion
References
Salak Image Classification Method Based Deep Learning Technique Using Two Transfer Learning Models
1 Introduction
2 Dataset
2.1 Dataset Description
2.2 Dataset Preparation
3 Proposed Deep Learning
3.1 CNN
3.2 VGG16
3.3 ResNet50
4 Performance Result
4.1 Experimental Setup
4.2 Effect of Kernel Size: CNN
4.3 Effect of Pool Size: CNN
4.4 Effect of Epoch
4.5 Effect of Optimizer
4.6 Effect of Learning Rate
4.7 Effect of Dense Layer

4.8 Effect of Fine-Tuning for Pre-trained Models (VGG16 and ResNet50)
4.9 Accuracy Comparison
5 Conclusion
References
Image Processing Identification for Sapodilla Using Convolution Neural Network (CNN) and Transfer Learning Techniques
1 Introduction
2 Literature Survey
3 Proposed Deep Learning for Sapodilla Recognition
3.1 The Proposed CNN Architecture
3.2 Transfer Learning Model
3.3 Dataset
3.4 Augmentation
4 Performance Result
4.1 Experimental Setup
4.2 Performance of Proposed CNN Model
4.3 Accuracy Comparison
5 Conclusion
References

Comparison of Pre-trained and Convolutional Neural Networks for Classification of Jackfruit Artocarpus integer and Artocarpus heterophyllus
1 Introduction
2 Literature Review
3 Methodology
3.1 Dataset
3.2 Data Preprocessing and Partition
3.3 Convolutional Neural Networks
3.4 Transfer Learning
4 Result and Discussion
5 Conclusion
References
Markisa/Passion Fruit Image Classification Based Improved Deep Learning Approach Using Transfer Learning
1 Introduction
2 Literature Survey
3 Proposed CNN Architecture for Passion Food Recognition