Performance Analysis of AI Driven Sustainable Smart Education
Implementation Plan:
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Step 1: Initially, we collect and load data from Student Performance dataset.
Step 2: Then, we preprocess the data using Z-Score Normalization with PCA Analysis.
Step 3: Next, we split the data using Federated Averaging and train the data using Federated Learning-Based AI Model Training.
Step 4: Next, we analyze the AI-Driven Smart Classroom Engagement using Bi-LSTM with K-Means-A-PSO-Hierarchical FML methods.
Step 5: Next, we implement Deep Q-Network (DQN) reinforcement learning with Multi-Agent Deep Deterministic Policy Gradient (MADDPG) for Personalized Adaptive Learning & Decision-Making.
Step 6 : Finally, We plot performance metrics for the following
6.1: Number of Epochs vs. Accuracy (%)
6.2: Number of Epochs vs. Precision (%)
6.3: Number of Epochs vs. F1 score(%)
6.4: Number of Epochs vs. Recall (%)
Software Requirement:
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1. Development Tool: Python 3.12.9
2. Operating System: Windows 11 (64-bit)
Dataset Link:
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https://archive.ics.uci.edu/dataset/320/student+performance
Note:
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1) If the plan does not meet your requirements, provide detailed steps, parameters, models, or expected results in advance. Once implemented, changes won’t be possible without prior input; otherwise, we’ll proceed as per our implementation plan.
2) If the plan satisfies your requirement, Please confirm with us.
3) Project based on Simulation only, not a real time project.
4) If you have any changes in the Dataset , kindly provide before implementation.
5) Please understand that any modifications made to the confirmed implementation plan will not be made after the project development.
We perform with an Existing Approach Reference : 1 – Title: Federated Learning for Data Analytics in Education
