Intro
ICICC-2023 Steering Committee Members
Preface
Contents
Editors and Contributors
Joint Identification and Clustering Using Deep Learning Techniques
1 Introduction
1.1 Contributions
2 Methods
2.1 BlazePose
2.2 Keypoint RCNN
2.3 MMPose
3 Clustering of Joints
4 Results
4.1 Limitations of the Proposed Work
5 Conclusion and Future Scope
References
Comparative Analysis of Deep Learning with Different Optimization Techniques for Type 2 Diabetes Mellitus Detection Using Gene Expression Data
1 Introduction
2 Literature Survey

3 Hybrid DL-Based Various Optimization Algorithms
3.1 Data Acquisition
3.2 Data Transformation by YJ
3.3 Selection of Features by Jaya-DOA
3.4 Data Augmentation
3.5 T2DM Detection Using Hybrid DL
4 Results and Discussion
4.1 Experimental Setup
4.2 Dataset Description
4.3 Performance Metrics
4.4 Comparative Methods
4.5 Comparative Analysis
4.6 Comparative Discussion
5 Conclusion
References
Differential Analysis of MOOC Models for Increasing Retention and Evaluation of the Performance of Proposed Model
1 Introduction
2 Literature Review

3 Methodology
4 Result
5 Tabulation of Comparing the Performance of Concerned Research Model with Earlier Studies
6 Conclusion
References
Deep Convolutional Neural Networks Network with Transfer Learning for Image-Based Malware Analysis
1 Introduction
2 Related Works
3 Proposed Work Implementation
3.1 Convolution Neural Network
3.2 Transfer Learning
3.3 VGG-16
3.4 Inception-V3
3.5 ResNet-50
4 Dataset
5 Architecture
6 Results and Discussion
7 Conclusion and Future Work
References

Analysis of Network Failure Detection Using Machine Learning in 5G Core Networks
1 Introduction
2 Related Work
3 Proposed Work
4 Experiment Results and Discussion
5 Conclusion
References
MVR Delay: Establishing Self-organizing Virtual Backhaul for Trusty, Reliable, and Timely Emergency Message Dissemination in VANET
1 Introduction
2 Related Work
3 Self-organizing Virtual Backhaul
4 Proposed Solution
5 Results
5.1 Packet Delivery Ratio by Varying the Node Density
5.2 Packet Delivery Ratio by Varying the Speed
5.3 Average Delay
5.4 Average Cost

5.5 Fake Message Detection Accuracy
5.6 Stability of Virtual Backhaul
6 Conclusion
References
Machine Learning Algorithms for Prediction of Mobile Phone Prices
1 Introduction
2 Literature Review
3 Materials and Methods
3.1 Dataset
3.2 Preprocessing
3.3 Model Building
4 Experimental Results and Discussion
5 Conclusion
References
Customized CNN for Traffic Sign Recognition Using Keras Pre-Trained Models
1 Introduction
2 Literature Survey
3 Proposed Methodology
3.1 Approach
3.2 Block Diagram
3.3 Dataset
4 Experimental Setup and Results