Title 1: Anomaly Detection of Malicious Energy Usage in Smart Factories using Deep Neural Network
Target: KICS 2012
Title 2: Deep Learning-Based Energy Consumption Prediction for Smart Factory
Target: ETFA 2021
Title 3: Efficient Deep Transfer Learning for Scada Vulnerability and Detection
Target: MILCOM 2021
Title 4: Efficient and Real-Time Embedded Algorithm for Energy Consumption Forecasting in Smart Factories
Target: IEEE Industrial Informatics
Title 5: Reliability Analysis of Vulnerability Detection in SCADA Systems using Machine Learning
Target: IEEE Industrial Informatics
Title 1
1. Plagiarism check done with 8%
2. Submitted for KICS Conference 2021
Title 2
1. Abstract reviewed based on result
2. Conclusion
Title 3
1. Review of title
2. Introduction
3. Related works
4. Survey of related papers
Title 4
1. Completed Introduction
2. Related works
3. Continue survey of related papers
4. Started data preprocessing on smart factory energy dataset
Title 5
1. Survey of related literatures
Title 2
1. Conduct plagiarism check
2. Review entire paper for English and any other correction
3. To be submitted for ETFA 2021 against May 28, 2021 timeline
Title 3
1. Methodology
2. Continue with survey of related papers
3. Start dataset pre-processing
Title 4
1. Start training dataset with Machine Learning
2. Continue survey of related papers
3. System Methodology
Title 5
1. To continue with survey of related literatures
Smart Grid Energy for Energy Consumption Prediction Using LSTM Model in Industrial Environment
Reviewed and compared Machine learning Algorithms for Smart Energy Forecasting to enable choice of proposed algorithm
To continue with pre-processing and training of smart factory Energy dataset on proposed model
To conclude and submit Title 2
To conclude Titles 3 and 4
To continue with Machine Learning
To attend at least one NSL approved Domestic Conference
To attend at least one International Conference
To submit at least two International Journals