The scope of Deep Learning is playing a significant role in solving several challenges and is being implemented in various sectors as a result of the development of new artificial intelligence technologies. In other words, the human brain's biological neural network is what this technology tries to replicate. The practical approach will be more important for engineering students than theoretical understanding; we have a long list of projects that cover all application areas in a real-world setting. Let's look at some fascinating real-world deep learning project ideas that both professionals and amateurs can work on to test their expertise and gain more practical deep learning experience.

A kind of machine learning called deep learning uses artificial neural networks that are organised hierarchically to carry out particular ML tasks. Deep Learning networks learn from unstructured or unlabeled data using the unsupervised learning method. Artificial neural networks have a structure resembling a web of interconnected neuron nodes, precisely like the human brain.

Deep learning projects and examples in real life:

As new advances are being made in this domain, The more deep learning project ideas you try, the more experience you gain.

IEEE Deep learning projects

Self Driving Cars
News Aggregation and Fraud News Detection
Natural Language Processing
Virtual Assistants
Entertainment
Visual Recognition
Fraud Detection
Healthcare
Personalisations
Detecting Developmental Delay in Children
Colourisation of Black and White images
Adding sounds to silent movies
Automatic Machine Translation
Automatic Handwriting Generation
Automatic Game Playing
Language Translations
Pixel Restoration
Photo Descriptions
Demographic and Election Predictions
Natural language processing, computer vision, bioinformatics
Speech recognition, audio recognition, machine translation, social network filtering.

TOP 10 Advanced Deep Learning Project Ideas

1. Person age & gender recognition using deep learning techniques.

The cameras on modern smartphones now include AI. Even the gender and age of a person might be ascertained by them. Deep learning can be utilised for this, however in order to develop the model for detecting age and gender, we'll need a tonne of data.

2. Slumped Driving Detection of a driver using deep learning model.

Using a model of driver tiredness created using deep learning, it is possible to determine whether a motorist is napping. When used by drivers, this model will aid in preventing collisions.

3. Pose estimation for people

Human pose estimate is the practise of calculating a person's body alignment from different body joints. Snapchat employs posture assessment to ascertain where a person's head and eyes are located in order to apply a filter on them. In a manner similar to this, we may ascertain a person's stance and instantly apply filters to them.

4. computer vision technique for viability of quantifying the treatment for stroke patients a deep neural network approach

Training in limb rehabilitation aids stroke patients with hemiparalysis in recovering more quickly and living more comfortably. The patient's progress in rehabilitation needs to be communicated to doctors as well as patients. Compared to wearable sensors or deep cameras, the computer vision technology can be used to monitor rehabilitation more precisely and effectively since it can determine a patient's training action, movement trajectory, and activity status. With the exception of static measures of real-time behaviour, it is challenging to quantify the dynamic change of several training sessions to evaluate the progress of the rehabilitation in the clinic.

In this study, we proposed a computational approach to compare the change in motion of the upper limb. The positions of each joint point were then determined using Cartesian coordinates after the upper limb joint points were initially located using OpenPose to preprocess the video material. Second, we used the dynamic time warping algorithm to compute the similarity of the limb's lift angle and time across different training periods in order to assess the rehabilitation progress. The results show that our method, which has a bright future for use, can measure data and analyse the efficacy of rehabilitation actions using a simple monocular camera.

5. Yolo V4 & Yolo V5 approach for recognition of bird and drone.

We created this project to recognise drones because it is now challenging to spot them due to their popularity and increasing ubiquity. Concerns concerning physical infrastructure security, safety, and monitoring at airports have been raised in addition to their potential for use in malign activities. Numerous complaints have been made in recent months about various drone types being misused at airports, which has interfered with airline operations and left some individuals unable to identify the drones or birds. We implement this project using a deep learning-based approach in order to efficiently recognise and identify two different types of drones and birds. Evaluation of the proposed methodology utilising the generated image dataset demonstrates increased efficiency when compared to existing detection systems.

In addition, drones and birds are commonly confused for one another because to their similar physical traits and behaviours. The suggested method is able to distinguish between two different types of drones and tell them apart from birds, in addition to being able to determine whether drones are present or missing in a specific area.

6. RESNET50, GoogleNet, Squeezenet, and AlexNet for the diagnosis of grape fruit illness.

Crop diseases are quite widespread because of the continually changing climatic and environmental conditions. Crop diseases can affect the development and yield of the crops and are often difficult to control. To guarantee high output and excellent quality, accurate disease identification and prompt disease control techniques are crucial. The commonly grown grape vine in India is prone to a number of ailments that can damage the fruit, stem, and leaves. Sour rot, powdery mildew, healthy grapes, grey mould, and healthy grapes are fruit diseases that show early indicators. As a result, it is essential to develop an automated system that can be used to recognise various illness kinds and provide appropriate therapies.

7. Classification of Brain Tumors Using a Highly Accurate Attention-Based Convolutional Neural Network

One of the most frequent and hazardous cybercrime assaults is phishing. The information utilised by people and companies to execute transactions is the target of these assaults. Phishing websites use a variety of indicators in both their text and information that is based on web browsers. The goal of this work is to classify 30 variables, including data from phishing websites, using Extreme Learning Machines (ELM) on a database at UC Irvine. ELM had the highest accuracy of 95.34% when compared to other machine learning techniques used for results evaluation, including Support Vector Machine (SVM) and Naive Bayes (NB).

Testing on the dataset reveals that the CNN model significantly outperforms the other three models in terms of accuracy when it comes to classifying brain tumours, suggesting that the model's attention mechanism can attenuate the effect of contextually irrelevant information on the classification result. The online platform where the trained analytical model is used in this work is also user-friendly and appropriate for medical staff.

8. System for Detecting Sitting Posture Based on the Keras Framework

With a low-power embedded real-time sitting posture detection system in mind, a deep learning-based real-time sitting posture detection system is created. The technology measures the pressure of the human body in a sitting position using a thin-film pressure sensor. Following data collection and analysis, the system builds an analytical model using the Keras framework based on information about human body pressure in various sitting positions. To provide real-time data collection, analysis, and seating position recognition, burn the model onto the STM32 using cubemax. The MQTT protocol completes communication between the STM32 and the Android application by enabling the real-time detection and categorization of the sitting position as well as providing the necessary sitting posture correction cues.

9. SE-ResNeXt50 model to identify and control strawberry diseases and pests.

Studies on the identification and treatment of strawberry disease and pests are uncommon at the moment because there aren't enough high-quality open image datasets accessible. In light of this, databases were initially created using independent online and offline collections of 13 different common strawberry pests and diseases. As an alternative to the ResNet50 residual network model, the SE-ResNeXt50 model was created. To be more specific, 32 branches were defined, the attention mechanism, the squeeze and excitation module (SE), was also imported, and the Inception model was merged with the ResNet50 model to widen the network.

The complicated image background and information interference issues were resolved, which increased the model's identification efficiency and precision. The results showed that the accuracy of the SE-ResNeXt50 model, which reached 89.3%, was 8% higher than the ResNet50 model. The SEResNeXt50 model hit a plateau after 15 iterations, indicating successful identification. The SEResNeXt50 model also has excellent robustness and generalisation capabilities, better meeting the needs of strawberry growers, and was developed using data gathered in the real world. A WeChat mini-program for strawberry disease and pest identification was developed using the SE-ResNeXt50 model. This programme enables fruit growers to swiftly recognise strawberry pests and illnesses and obtain prevention suggestions, promoting the expansion of the strawberry industry.

10. Technology for detecting IoT intrusions based on deep learning

To address the problem of low accuracy of existing network intrusion detection models for multi-classification of intrusion behaviours and redundancy of data features, a network intrusion detection model incorporating convolutional neural networks and gated recurrent units is given. The problem of feature redundancy is resolved by using the random forest technique and Pearson correlation analysis. Following the extraction of the data's temporal features using TCN and GRU, the attention module is introduced to give the features varied weights, hence lowering overhead and improving model performance. Finally, the Softmax function is used to resolve the categorization issue. On the Bot-Lot dataset, a model with a 99.99% accuracy is examined in this study.

DEEP LEARNING PROJECTS FOR ENGINEERING STUDENTS IN 2022-2023

IEEE Deep Learning Project based projects for Mtech,Btech, BE, MS and diploma students in Trichy

Deep Learning Project for Engineering Students