Assignments:

Literature Exploration and Comparison

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.

List of potential application area:

$$ CO$2$ 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

$2.$ 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

$3.$ 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:

o Domain

o 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$)$

$$ Conclude: End the comparison with a proper conclusion highlighting your observations. Justify the choice of one paper over the others if one paper has to be implemented using Python

Expected Comparison Table

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.