Deep Learning Project List with Abstract and Source Code - 21/22
Deep learning is branch of machine learning which basically mimics the behavior of a human brain like how the neurons are connected with each other and the way they interact and share information which will allow the model to precisely identify or categorize the object it is trying to learn. Deep Learning is a subset of Machine Learning(A technique to achieve AI through algorithm trained data) which in turn is a subset of AI ( A technique to mimic human behavior) Deep learning is inspired by the structure of the human brain in terms of deep learning the human brain is called as Artificial Neural Network(ANN). It has three or more neural network layers.
We have selected quite interesting projects in Deep Learning and tried to explain each project in detail with step by step execution where every individual irrespective of their educational background can learn and execute this projects by themselves.
Project List with Step by Step execution
| S.No | Project Code | DL Projects | Action |
|---|---|---|---|
| 1 | TYTDL1004 | Classifying the Flowers of Iris Dataset using Deep Neural Networks | DETAILS |
| 2 | TYTDL1005 | Predicting the lung cancer in patients by Deep Neural Networks using CT Scan Images | DETAILS |
| 3 | TYTDL1008 | Customer churn prediction on Financial Dataset using Artificial Neural Network | DETAILS |
| 4 | TYTDL1009 | How likely a person is about to make Bank Turnover, Estimating it uisng Artificial Neural Network. | DETAILS |
| 5 | TYTDL1010 | Deep learning based dog breed identification using Convolutional Neural Networks with flask app | DETAILS |
| 6 | TYTDL1012 | Classifying the Brain Tumors using CNN with Front End Framework | DETAILS |
| 7 | TYTDL1013 | Identifying type of music (genre) using deep learning | DETAILS |
| 8 | TYTDL1014 | Hand Written Digits Classification using Deep Neural Network. | DETAILS |
| 9 | TYTDL1015 | Nifty price movement prediction using LSTM | DETAILS |
| 10 | TYTDL1019 | Classifying the type of Flowers using Convolutional Neural Networks | DETAILS |
| 11 | TYTDL1020 | Detection of Retinal pigmentosa in paediatric age patients using CNN with Tkinter Framework | DETAILS |
| 12 | TYTDL1022 | Transfer Learning Based Kidney Stone Prediction in Patients, using RESNET50 | DETAILS |
| 13 | TYTDL1023 | Future Stock Trend Estimation using LSTM and Deployed on Streamlit Application | DETAILS |
| 14 | TYTDL1024 | Covid 19 Pandemic Prediction using LSTM Network | DETAILS |
| 15 | TYTDL1025 | Identifying Defects in the Various Fabrics using Convolutional Neural Networks | DETAILS |
| 16 | TYTDL1026 | Pot Hole Detection on the roads using Transfer Learning (Resnet 50) | DETAILS |
| 17 | TYTDL1028 | Hand Gesture Recognition Model using Deep Learning | DETAILS |
| 18 | TYTDL1031 | Deep Fake Video Detection using Deep Learning with Tkinter GUI | DETAILS |
| 19 | TYTDL1034 | Identifying the lane the vehicle is travelling. | DETAILS |
Here are some project ideas for Deep Learning
- Image Classification: Train a model to classify images into different categories, such as animals, objects, or scenes.
- Object Detection: Train a model to detect and locate objects in images or video.
- Natural Language Processing: Train a model to perform tasks such as translation, summarization, or text classification.
- Generative Models: Train a model to generate new data, such as images, text, or music.
- Reinforcement Learning: Train a model to make decisions and take actions in a simulated or real-world environment.
- Speech Recognition: Train a model to transcribe and translate spoken language into written text.
- Time Series Forecasting: Train a model to predict future values based on past data, such as stock prices or weather patterns.
- Anomaly Detection: Train a model to identify unusual patterns or events in data, such as fraudulent activity or equipment failures.
- Recommendation Systems: Train a model to suggest items or content to users based on their past behaviour or preferences.
- Image Generation: Train a model to generate new images based on a set of input images or a specified theme.
- Sentiment Analysis: Train a model to classify text as positive, negative, or neutral in sentiment.
- Text Generation: Train a model to generate new text based on a set of input text or a specified theme.
- Style Transfer: Train a model to transfer the style of one image or piece of text to another.
- Image Super-Resolution: Train a model to increase the resolution of an image while maintaining its content and quality.
- Adversarial Attacks: Train a model to generate adversarial examples that can fool other machine learning models.
- Structured Data Prediction: Train a model to make predictions on structured data, such as in a spreadsheet or database.
- Unsupervised Learning: Train a model to discover patterns and relationships in a dataset without the use of labelled examples.
- Transfer Learning: Train a model to perform a task using pre-trained weights from another model as a starting point, potentially improving performance and reducing training time.
- Graph Neural Networks: Train a model to perform tasks on graph data structures, such as social network analysis or protein-protein interaction prediction.
- Computer Vision for Medical Imaging: Train a model to classify and detect abnormalities in medical images, such as X-rays or CT scans.
- Video Analysis: Train a model to perform tasks on video data, such as classification, object detection, or activity recognition.
- Audio Analysis: Train a model to perform tasks on audio data, such as speech recognition, music classification, or audio event detection.
- Robotics: Train a model to control a robot or make decisions in a simulated or real-world environment.
- Game Playing: Train a model to play a game, such as chess or go, at a high level.
- Fraud Detection: Train a model to identify fraudulent activity in financial transactions or other data.
- Cybersecurity: Train a model to identify and classify cyber threats, such as malware or network intrusions.
- Predictive Maintenance: Train a model to predict when equipment is likely to fail, allowing for preventative maintenance to be scheduled.
- Energy Forecasting: Train a model to predict energy consumption or production based on past data and other factors.
- Traffic Flow Prediction: Train a model to predict traffic flow and congestion on roads and highways.
- Environmental Monitoring: Train a model to identify and classify environmental factors, such as air or water quality, based on sensor data.
- Stock Prediction: Train a model to predict stock prices or other financial indicators based on historical data and other factors.
- Customer Churn Prediction: Train a model to predict when a customer is likely to stop using a product or service, allowing for preventative action to be taken.
- Demand Forecasting: Train a model to predict demand for a product or service based on past data and other factors.
- Product Recommendation: Train a model to recommend products or services to users based on their past purchases or browsing history.
- Supply Chain Optimization: Train a model to optimise the flow of goods and materials through a supply chain based on past data and other factors.
- Predictive Maintenance for Manufacturing: Train a model to predict when equipment is likely to fail in a manufacturing setting, allowing for preventative maintenance to be scheduled.
- Predictive Quality Control: Train a model to predict the quality of a product or process based on past data and other factors.
- Predictive Asset Management: Train a model to predict the performance and reliability of assets, such as machinery or equipment, based on past data and other factors.
- Predictive Process Optimization: Train a model to optimise the performance of a process, such as a production line, based on past data and other factors.
- Predictive Maintenance for Buildings: Train a model to predict when equipment or systems in a building are likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Transportation: Train a model to predict when equipment, such as vehicles or aircraft, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Infrastructure: Train a model to predict when equipment or systems in infrastructure, such as bridges or pipelines, are likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Energy: Train a model to predict when equipment in the energy industry, such as wind turbines or oil rigs, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Telecommunications: Train a model to predict when equipment in the telecommunications industry, such as cell towers or routers, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Agriculture: Train a model to predict when equipment in the agriculture industry, such as tractors or irrigation systems, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Construction: Train a model to predict when equipment in the construction industry, such as cranes or bulldozers, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Marine: Train a model to predict when equipment in the marine industry, such as ships or offshore platforms, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Aerospace: Train a model to predict when equipment in the aerospace industry, such as aircraft or satellites, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Military: Train a model to predict when equipment in the military, such as tanks or aircraft, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Government: Train a model to predict when equipment in government agencies, such as police cars or fire trucks, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Healthcare: Train a model to predict when equipment in the healthcare industry, such as medical devices or hospital beds, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Education: Train a model to predict when equipment in the education industry, such as computers or projectors, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Retail: Train a model to predict when equipment in the retail industry, such as point-of-sale systems or refrigerators, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Hospitality: Train a model to predict when equipment in the hospitality industry, such as elevators or HVAC systems, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Food and Beverage: Train a model to predict when equipment in the food and beverage industry, such as ovens or fryers, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Chemical: Train a model to predict when equipment in the chemical industry, such as reactors or pumps, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Biotechnology: Train a model to predict when equipment in the biotechnology industry, such as incubators or centrifuges, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Pharmaceutical: Train a model to predict when equipment in the pharmaceutical industry, such as sterilizers or mixers, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Consumer Goods: Train a model to predict when equipment in the consumer goods industry, such as appliances or electronics, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Manufacturing Services: Train a model to predict when equipment in the manufacturing services industry, such as machining or fabrication, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Business Services: Train a model to predict when equipment in the business services industry, such as printers or copiers, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Financial Services: Train a model to predict when equipment in the financial services industry, such as ATMs or servers, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Professional Services: Train a model to predict when equipment in the professional services industry, such as computers or printers, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Information Technology: Train a model to predict when equipment in the information technology industry, such as servers or routers, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Software: Train a model to predict when equipment in the software industry, such as servers or storage systems, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Telecommunications Services: Train a model to predict when equipment in the telecommunications services industry, such as cell towers or switches, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Transportation Services: Train a model to predict when equipment in the transportation services industry, such as vehicles or aircraft, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Wholesale: Train a model to predict when equipment in the wholesale industry, such as forklifts or pallet jacks, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Retail Trade: Train a model to predict when equipment in the retail trade industry, such as point-of-sale systems or refrigerators, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Hospitality and Food Services: Train a model to predict when equipment in the hospitality and food services industry, such as kitchen appliances or HVAC systems, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Healthcare and Social Assistance: Train a model to predict when equipment in the healthcare and social assistance industry, such as medical devices or hospital beds, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Education Services: Train a model to predict when equipment in the education services industry, such as computers or projectors, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Professional, Scientific, and Technical Services: Train a model to predict when equipment in the professional, scientific, and technical services industry, such as computers or printers, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Management of Companies and Enterprises: Train a model to predict when equipment in the management of companies and enterprises industry, such as servers or storage systems, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Administrative and Support Services: Train a model to predict when equipment in the administrative and support services industry, such as office equipment or delivery vehicles, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Waste Management and Remediation Services: Train a model to predict when equipment in the waste management and remediation services industry, such as garbage trucks or recycling equipment, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Mining, Quarrying, and Oil and Gas Extraction: Train a model to predict when equipment in the mining, quarrying, and oil and gas extraction industry, such as drilling rigs or excavators, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Construction: Train a model to predict when equipment in the construction industry, such as cranes or bulldozers, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Manufacturing: Train a model to predict when equipment in the manufacturing industry, such as machining or fabrication, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Agriculture, Forestry, Fishing, and Hunting: Train a model to predict when equipment in the agriculture, forestry, fishing, and hunting industry, such as tractors or irrigation systems, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Utilities: Train a model to predict when equipment in the utilities industry, such as power plants or transmission lines, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Transportation and Warehousing: Train a model to predict when equipment in the transportation and warehousing industry, such as vehicles or aircraft, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Information: Train a model to predict when equipment in the information industry, such as servers or routers, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Finance and Insurance: Train a model to predict when equipment in the finance and insurance industry, such as ATMs or servers, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Real Estate and Rental and Leasing: Train a model to predict when equipment in the real estate and rental and leasing industry, such as elevators or HVAC systems, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Professional, Scientific, and Technical Services: Train a model to predict when equipment in the professional, scientific, and technical services industry, such as computers or printers, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Management of Companies and Enterprises: Train a model to predict when equipment in the management of companies and enterprises industry, such as servers or storage systems, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Administrative and Support Services: Train a model to predict when equipment in the administrative and support services industry, such as office equipment or delivery vehicles, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Waste Management and Remediation Services: Train a model to predict when equipment in the waste management and remediation services industry, such as garbage trucks or recycling equipment, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Educational Services: Train a model to predict when equipment in the educational services industry, such as computers or projectors, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Health Care and Social Assistance: Train a model to predict when equipment in the healthcare and social assistance industry, such as medical devices or hospital beds, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Arts, Entertainment, and Recreation: Train a model to predict when equipment in the arts, entertainment, and recreation industry, such as amusement park rides or theatre lighting, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Accommodation and Food Services: Train a model to predict when equipment in the accommodation and food services industry, such as kitchen appliances or HVAC systems, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Other Services (Except Public Administration): Train a model to predict when equipment in other services industries, such as personal care or repair and maintenance, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Public Administration: Train a model to predict when equipment in the public administration industry, such as police cars or fire trucks, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Agriculture, Forestry, Fishing, and Hunting: Train a model to predict when equipment in the agriculture, forestry, fishing, and hunting industry, such as tractors or irrigation systems, is likely to fail, allowing for preventative maintenance to be scheduled.
- Predictive Maintenance for Mining, Quarrying, and Oil and Gas Extraction: Train a model to predict when equipment in the mining, quarrying, and oil and gas extraction industry, such
Your Journey With Us..
Your ideas are implemented into reality. At each and every stage of the project development get the complete assistant from our technical developers. Also students who are looking to write and publish their paper in International Journal, we will help them to publish their paper/thesis.
Clear all your doubts here..
Get all the answers to your queries and start your journey with us now..
Hey! Click on the above degree which you're from. You can find the specified branches. Please select your branch and a new page will pop-up with the latest projects list. There you go.. You can select any title and mail / call us.
Time is a dependent factor. If we're going with a basic one it will take 2-3 days, for moderate level it will be 7-10 days, for Master's or Research level projects time taken is 30-45 days.
Once you come up with your idea or project title, we will provide you abstract or base paper for the detailed understanding. If you're ready to go with the title, we will be providing you end to end support which includes project execution, project training, a reference document, support until your final review.
Of course, any new feature or new ideas will be implemented as per your requirement.
Our Technical writers will be helping you to write the paper as per the journal which you would like to publish. Complete support will be provided until your research paper is published.
Want to learn and execute these projects? Searching for new project ideas? Here we go…. Find the interesting and mind boggling projects. TechieYan is highly recommended to get your project work done. Diploma /Btech /BE /Mtech /ME /MS students can get complete project support along with the project training certification