to solve the below mentioned Deep Nural Network assignment step by step: 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 Self-Organizing Maps 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 2. Identify Three Papers: Identify three significant journals which uses Deep Feedforward Neural Network / CNN / RNN / Transformer networks (any one has to be chosen). You can use transfer learning for CNN also. Journal should be from reputed sources like IEEE/Springer or ACM that focus on the application of CNN/RNN/Transformer networks in your chosen domain. Upload all three PDFs as individual files on Canvas. 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: Group Number, member names, and BITS IDs 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. Submission: Upload the table and comparison as one PDF (Filename: DomainName_GroupNumber). Expected Comparison Table (5 marks) Group Number, member names and BITS ID Domain PAPER 1 PAPER 2 PAPER 3 Title of the paper Authors Year of publication Architecture of Deep Learning (including the number of layers, types of layers, activation functions, and any unique features) How is the network helping the overall task? eg: feature engg or classification or regression or all Training procedures (e.g, training strategy, including optimization algorithms, learning rates, batch sizes, and regularization techniques) Evaluation / Performance metric used Name of Dataset used. If a public dataset, provide the URL. Conclusion: You must end the comparison with a proper conclusion highlighting your observations. Part B: Implementation (7 marks) Objective: Implement one of the papers chosen in Part A using Python TensorFlow Keras Libraries. Instructions: Implement the Paper: Utilise the methodologies or algorithms detailed in your chosen paper. Ensure that the URL for the dataset is mentioned clearly. Code Submission: Upload the Python .ipynb file. Download the .ipynb file as a PDF, ensuring all outputs are clearly displayed. ZIP files are not accepted. Assignment Template: Use the provided DNN_Assignment1_Template.ipynb file or Google Colab for your work. DNN_Assignment1_Template.ipynbDownload DNN_Assignment1_Template.ipynb File Naming Convention: DNN_assignment_1_group##. Libraries: Use TensorFlow/Keras Plagiarism & Late Submissions: Any plagiarism will result in zero marks. Late submissions incur a penalty of (-2) marks. Additional Instructions: Data need not be uploaded with the submission. Submit the updated Jupyter Notebook with outputs + the final .ipynb notebook file converted as PDF, with proper formatting and alignment. Incomplete output, misalignment, or lack of comments may result in mark deductions. If the given template is not followed, ZERO marks will be awarded. Journals can be chosen without any restrictions on impact factors or other indices. Select three research papers within a single domain, each employing a different algorithm (CNN, RNN, Transformers) for comparative analysis. If the dataset URL is not provided in the research papers, utilize datasets from publicly accessible resources. For any queries, use the discussion forum