Part A: Literature Exploration and Comparison (8 marks) Objective: Explore a specific application within a specific domain, identify three significant papers, and conduct a comparative analysis. Steps 1. Choose an Application Area: Choose any one application area from the list given below. You can choose your own domain also. List of potential application area: CO2 Emission Prediction Cyclone prediction Traffic Flow Prediction Automatic music generation Energy Consumption Prediction Building Energy Optimization Waste Composition Analysis Predictive Air Quality Models Application for cancer detection Gender recognition using voice Content Recommendation with Transformers Medical Image Diagnosis Speech Recognition Speech Translation Emotion Recognition in Social Media Autonomous Navigation for Robots Gesture Recognition for Human-Computer Interaction Wildlife Classification Real-time Language Translation Human Activity Recognition from videos Expression Recognition from images

Expression Recognition from images 2. Identify Three Papers: Identify three significant journals which uses Deep learning CNN algorithm from IEEE journals.

3. Compare the architecture and methodologies used in the journals.

Create a Comparison Table: Compare the three papers and present your findings in a table with the following titles:

Domain PAPER 1, PAPER 2, PAPER 3 (with subheadings: Title, Authors, Year, Architecture of Deep Learning (including the number of layers, types of layers, activation functions, and any unique features). Network application (e.g., feature engineering, classification, regression), Training procedures (e. g, training strategy, including optimization algorithms, learning rates, batch sizes, and regularization techniques) Evaluation/Performance metric, Dataset used, URL if public dataset) [*Reference Comparison Table is given below] Conclude: End the comparison with a proper conclusion highlighting your observations. Justify the choice of one paper over the others for implementation in Part B.