$3$ Attempts Allowed

Available: Dec $16,20230$:$00$ until Jan $6,202423$:$59$Available: Dec $16,20230$:$00$ until Jan $6,202423$:$59$

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:

CO$2$ 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$).$