Introduction

In this tutorial, we are going to learn about Deep Learning Projects for Final year students. It contains all the beginner, intermediate and advanced level project ideas as well as an understanding of what is deep learning and the applications of deep learning.

What is Deep Learning?

Deep learning is basically the subset of machine learning, which is based on artificial intelligence neural networks. Day by day the scope of deep learning is rapidly expanding as it can mimic the human brain with its neural networks technology. These neural networks try to imitate the behavior of the human brain by matching the ability, allowing it to learn from a large volume of data and make accurate predictions from the given data which is opposed to machine learning which make use of simpler principles.

Deep learning has the ability to solve the problem of pattern recognition with less human interference, though deep learning has its roots in the 1950s, it has nowadays come into light with the increasing use of the technologies of Artificial Intelligence and machine learning. It is good for machine learning beginners to go through the suggested Deep learning project ideas to get a deep insight into machine learning, deep learning, and artificial intelligence.

Applications of Deep Learning

Deep Learning has come up with a lot of fascinating applications such as virtual assistants, Alexa, Google Assistant, self-driving cars, and fraud detection, a few years ago we have hardly imagined that we would come across these deep learning applications but it’s now the reality.

Deep learning finds its application in industries such as:

• Music
• Robotics
• Language Translation
• Advertising
• Entertainment
• Chatbots
• Computer Vision
• Virtual Assistants
• Customer Experience etc

We are trying to cover various deep learning final year projects through this article on Deep Learning Projects for the Final year.

Deep Learning Projects Ideas for Beginners

1. Human Face Detection

Human face Detection helps us to learn how to detect a particular object in the image and starts working with the detection of the object. This helps in identifying the people in the photographs or images as face detection is a computer vision problem. The deep learning algorithms have the datasets for face identification which has resulted in incredible results. An algorithm such as cascade classifiers is trained with positive sample views of the particular object and the arbitrary negative images of the same size, after it gets trained it is applied to the region of the image to detect the object. Hence the models help to accurately detect the face.

The GitHub link to for human face detection for this Final year deep learning project is here.

2. Cyber Attack Prediction

The cyber-attack is one of the most common problems of that small and large organization face and prediction of those cyber-attacks is one of the major challenges to face. Organizations largely depend on third-party Artificial intelligence solutions to prevent cyber attacks before they happen. As cyber-attacks are increasing exponentially this makes the existing detection mechanism insufficient which leads to the necessity to design more prediction models using a deep learning approach. Hence trained models labeled web-traffic data are built and they are used to predict whether the incoming data traffic is an attack or not.

The GitHub link contains the source code for the deep learning project idea for Cyber Attack prediction.

3. Covid-19 Detection in Lungs

The COVID-19 pandemic has made the need to develop novel computer-assisted diagnosis tools for the purpose of rapid and cost-effective screening in all those places where traditional testing techniques in massive amount is not feasible. One of the major challenges that are faced during this pandemic is the detection of the disease of the patients. The model of computer-assisted analysis of lung ultrasound image is built which has shown great potential in pulmonary condition diagnosis and is also used as an alternative for diagnosis of COVID-19 in a patient.

The GitHub link contains the source code for this deep learning final year project => Covid-19 Detection in Lungs.

4. Automated Attendance System

In schools and colleges, the students can easily manipulate the system by signing /taking attendance on another student’s behalf. Hence this becomes a major problem. Hence there must be a sure way of knowing whether the student is present in the class or not. This project is an extension of the human face detection system described above. The human face detection takes the student attendance. It compiles the database of the labeled student images after that you need to train a convolution neural network on the images.

The student’s face is scanned to record the attendance images the facial recognition system matches the faces to the face present in the database. This project needs to understand the database and web development before taking the project.

The GitHub link for this project is here.

5. Text Generator

Text generation is one of the interesting applications of deep learning. The challenging aspect of this project is that it is not easy to generate sequential data like the text as it requires an understanding of the context information. Here we are required to create the model that is responsible for generating the words that are in close association with each other based on the past datasets, that generated the word. The whole sentence may become meaningless if any of those words become wrong.

The GitHub link is here.

Deep Learning Projects for Intermediate level

6. Traffic Sign Classification

This project helps in the understanding of image classification. It is quite fascinating when the machines are able to identify the traffic signs based on a given image or picture. It’s a process of automatically recognizing the traffic sign, speed limit signs, yields, etc that enables us to build smart cars. This project is useful for all autonomous vehicles and it also adds value to your portfolio.

The GitHub link => Traffic Sign Classification.

7. Drowsy Driver Detection System

Drowsy Driver Detection System is a project that is designed to prevent fatal accidents happening on the road. One of the major causes of accidents on road is the drowsiness of the drivers. They become sleepy or tired and may fall asleep either to long-distance travel, stress, or lack of sleep. By developing a drowsy detection, we are hoping to avoid and reduce such accidents. This project has made use of Python, Open CV, and Keras to detect the closed eyes of the driver and set an alarm if they fall asleep

The GitHub link => Drowsy Driver Detection System.

8. Crop Disease Detection System

The technology is growing day by day and is also evolved much in terms of agriculture, while there is also the issue that needs to be considered is plant disease detection. With the help of deep learning and neural networks, models are built to determine whether the plant is healthy or has some disease. Convolutional Neural Networks (CNN) are being used to create a crop disease detection model. The model uses photos to forecast illness in crops and to detect and identify the disease.

The GitHub link => Crop Disease Detection System

9. Gender Recognition using Voice

In gender recognition using voice, machines are taught to identify between the male and female voices. They are just needed to provide the audio clips with the male and female voices. Deep learning models such as multilayer Perceptron help to recognize gender. The data of male and female voices are passed through the classifying models to identify the voices.

The GitHub link => Gender Recognition using Voice

10. Automatic Music Generation

Many of us are very fond of listening to music, but what if I told you that you can music automatically? In deep learning, it is possible to generate small tracks of music automatically with less human intervention. This project has made use of WaveNet deep learning generative model for raw audio to generate the music automatically.

The GitHub link => Automatic Music Generation.

Deep Learning Projects for Advanced level

11. Fake News Detection Project

There is a variety of news that we come across through various platforms or chat applications such as WhatsApp, but how to recognize which are real and which are fraudulent? To attain this goal to identify the fishy news by just looking at the text three deep learning architectures are proposed and later tested over two datasets (Fake news corpus and TI-CNN), obtaining state-of-the-art results.

• LSTM (Long Short Term Memory) Based architecture
• CNN (Convolutional Neural Network) Based architecture
• BERT (Bidirectional Encoder Representations from transformers) Based architecture

The GitHub => Fake News Detection

12. Language Translator using Deep Learning

While traveling from one location to another we find it difficult with the people over there if they are having different mother tongues. Machine Translation (MT) mainly focuses on translation from one language to another. The Neural Machine Translation(NMT) model of deep learning is one of the most effective methods that take text input in one language and translate it into another language. In this project, the RNN sequence is used for language translation.

The GitHub Link=> Language Translator using Deep Learning.

13. Lane Detection and Assistance System

Lane Detection and assistance is a concern that mainly comes with automatic driving cars. In this project, lanenet model is used for real-time lane detection. Here, you will be implementing a Deep Neural Network for real-time lane detection using Tensorflow, based on an IEEE IV conference article. This model uses an encoder-decoder stage, a binary semantic segmentation stage, and instance semantic segmentation using a discriminative loss function for real-time lane detection.

The GitHub link is Lane Detection and Assistance System

14. Human Pose Detection

Deep learning has made it possible for Human Pose Detection when they pose in front of the camera while taking pictures. There are various algorithms proposed To estimate the pose they work by identifying the component parts and trying to understand their connections. Some of the real-world applications are Activity Recognition, Motion Capture and Augmented Reality, Training Robots, etc that help in identifying the human pose.

The GitHub link => Human Pose Detection

15. Variational Autoencoders

Another application of deep learning in building VAE or Variational Autoencoders. They have an encoder and decoder built inside them which helps to generate new data that is very similar to data used while training. They use Neural Networks to generate data, instead of drawing conclusions from it.

The GitHub Link=> Variational Autoencoders

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