Solution: The use of artificial neural networks makes it possible to build a handwritten digit recognition system to accurately interpret the digits that a person draws. For this, a convolutional neural network (CNN) is used to recognize numbers on a piece of paper. This network has a HASYv2 dataset comprising 168,000 images of 369 different classifications.
Uses: In addition to papers, a handwritten digit recognition system can read mathematical symbols and handwriting styles from photos, touchscreen devices, and other sources. This software has various applications such as authenticating bank checks, reading completed forms, and taking quick notes.
Lane Line Detection
Goal: To create a system that can connect with autonomous vehicles and line-following robots to help them detect lane lines on a road in real-time.
Problem: Without a doubt, autonomous vehicles are innovative technologies that use Deep Learning techniques and algorithms. They have created new opportunities in the automotive sector and reduced the need for a human driver.
However, if the machine driving a self-driving car is not properly trained, it may lead to hazards and accidents on the road. When training the machine, one of the steps is to make the system learn to detect lanes in the road so that it does not veer into another lane or collide with other vehicles.
Solution: To solve this problem, build a system using Computer Vision concepts in Python. It will help self-driving vehicles correctly detect lane lines and ensure it is driving on the road where they should be, without risking others.
You can use the OpenCV library – an optimized library that focuses on real-time usage like this for detecting lane lines. The library includes Java, Python, and C++ interfaces that support Windows, macOS, Linux, Android, and iOS platforms.
In addition, it is imperative to find the markings on either side of a route. You can use computer vision techniques in Python to find traffic lanes for self-driving cars to drive on. You also need to find the white marking on one lane and mask the rest of the objects with frame masking and NumPy arrays. Nest, the Hough line transformation is applied to finally detect track lines. Additionally, you can use other computer vision methods such as color threshold to identify lane lines.
Uses: Lane line detection is used in real-time by autonomous vehicles like cars and line-following robots. It is also useful in the gaming industry for racing cars.
Pneumonia Detection
Goal: Build an AI system using convolutional neural networks (CNN) and Python that can detect pneumonia from a patient’s X-ray images
Problem: Pneumonia is still a threat, claiming lives in many countries. The problem is that X-ray images are taken to detect diseases like pneumonia, cancer, tumors, etc., in general, which can provide poor visibility and make the assessment ineffective. But if proper treatment is followed, mortality can be significantly reduced.
In addition, the position, shape, and size of pneumonia may differ significantly, with its target outline becoming largely vague. This increases detection and accuracy issues. This leads us to develop technology that can identify pneumonia early with optimal accuracy to give appropriate treatment and save lives.
Solution: The software solution will be trained with massive details about pneumonia or other diseases. When users share their health-related issues and symptoms, the software can process the information and compare it to its database for possibilities related to those details. It can use data mining to provide the most accurate disease-matching patient details.
In this way, a patient’s disease can be detected and he can receive appropriate treatment. And to design the software, you need to determine the most effective CNN model analytically and comparatively to achieve pneumonia detection from X-ray images using feature extraction. Then comes the presentation of the different models with their classifiers to propose the most suitable classifier and evaluate the best CNN model to check its performance.
Uses: This AI project is beneficial to the healthcare field to detect diseases like pneumonia, heart disease, etc., and provide medical consultation to patients.
Chatbots
Objective: To build a Chatbot use Python to integrate it into a website or application
Problem: Consumers need great service when using an app or website. If they have a question they can’t find an answer to, they may drop out of the app. So, if you’re building a website or an app, you need to provide the best quality of service to your users so you don’t lose them and impact your bottom line.
Solution: A chatbot is an application that can enable automatic conversation between bots (AI) and a human via text or speech like Alexa. It is available 24/7 to help users with their queries, navigate them, personalize user experience, drive sales, and provide more in-depth insights into customer behavior and needs to help you shape your products and services.
For this AI project, you can use a simple version of a chatbot that you can find on many websites. Identify their basic structure to start building a similar one. Once you have completed a simple chatbot, you can move on to more advanced ones.
To create a chatbot, AI concepts such as natural language processing (NLP) are used to enable algorithms and computers to understand human interactions across different languages and process that data. It breaks down audio signals and human text and then analyzes and converts the data into machine-understandable language. You will also need different tools, packages, and pre-trained voice recognition tools to create an intelligent and responsive chatbot.
Uses: Chatbots are very useful in the corporate sector for customer service, IT help desk, sales, marketing, and HR. Industries from e-commerce, Edtech, and real estate to finance and tourism use chatbots. Big brands like Amazon (Alexa), Spotify, Marriott International, Pizza Hut, Mastercard, and many more are leveraging chatbots.
Recommendation System
Goal: Build a customer recommendation system for products, streaming videos and music, and more, with the help of ANN, data mining, machine learning, and programming.
Problem: Competition is high in everything from e-commerce to entertainment. And to stand out, you have to travel extra miles. If you’re offering something your target customer is looking for but you can’t afford to guide them to your store or recommend your offerings, you’re leaving a lot of money on the table.
Solution: Using a recommendation system can effectively drive more visitors to your site or app. You may have noticed that e-commerce platforms like Amazon offer product recommendations that you have researched somewhere on the internet. When you open your Facebook or Instagram, you see similar products. This is how a recommendation system works.
To build this system, you need browsing history, customer behavior, and implicit data. Data mining and machine learning skills are required to produce the most suitable product recommendations based on customer interests. And you will also need to program in R, Java, or Python and take advantage of artificial neural networks.
Uses: Recommender systems find huge applications in e-commerce stores like Amazon, eBay, video streaming services like Netflix and YouTube, music streaming services like Spotify, and more. It increases product reach, the number of leads and customers, visibility across various channels, and overall profitability.
Fire Detection
Objective: Build a fire detection system using CNN for tasks related to computer vision and image classification
Problem: Fires in residential and commercial buildings are dangerous. If the fire is not detected in time, it can cause massive loss of life and property. Forest fires are more and more frequent; therefore, regular monitoring is necessary to preserve wildlife and natural resources.
Solution: Building a system that can detect indoor and outdoor fire at an early stage and with its exact location can help put it out before it can cause damage. The fire detection system is improved with a surveillance camera.
For this, AI techniques like CNN and computer vision and tools like OpenCV are used. It needs sophisticated image processing and cloud computing. The system can be designed to analyze images from video cameras for visible and infrared light. It must also identify smoke, differentiate it from fog, and alert people quickly.
Uses: AI-powered fire detection can be used to detect forest fires to preserve natural resources, flora, and fauna, as well as in homes and business buildings.
Voice-Based Virtual Assistant
Purpose: To create an application with voice capabilities to help users
Problem: The web is vast with many products and services that customers may feel overwhelmed by. Also, people are busy and need help in various areas even for their daily tasks.
Solution: Today, voice-based virtual assistants are demanded to simplify the lives of users. People can use these apps like Alexa and Siri for entertainment purposes, search for products and services online, and perform daily tasks for better productivity.
To build this system, NLP is used to understand human language. The system will hear the voice, convert it to machine language, and save the commands to its database. It will also identify the user’s intent to perform the task accordingly and can use text-to-speech or text-to-speech tools.
Uses: Voice-enabled virtual assistants are used to search the Internet for relevant items, play music, movies, and videos for entertainment, set reminders, write quick notes, turn household appliances on and off, etc.
Plagiarism Checker
Goal: To create a system that can check a document for plagiarism or duplication using AI
Problem: Content duplication is a disease, which needs to be monitored and eradicated. For businesses, this leads to reputational damage and poor search engine rankings. People can also be penalized for plagiarism, due to copyright. Therefore, it is necessary to identify plagiarized content for businesses and educational institutions.
Solution: AI concepts are used to create a plagiarism checker tool to detect duplicates in a document. In this project, Python Flask or text mining can be used to detect plagiarism using a vector database called Pinecone. It can also show plagiarism percentage.
Uses: The plagiarism checker has many benefits for content creators, bloggers, editors, publishers, writers, freelancers, and educators. They can use it to check if someone has stolen their work and use it, while editors can analyze an article submitted by a writer and identify if it is unique or copied from somewhere.
Facial Emotion Detection
Purpose: To create an application that can predict or identify human emotions through facial features using AI
Problem: Understanding human emotions is a challenge. There has been a lot of research for decades to understand facial emotions. Before the advent of AI, results were everywhere.
Solution: AI can help analyze human emotions through the face using concepts like Deep Learning and CNN. Deep learning can be used to create the software to identify facial expressions and interpret them by detecting basic emotions in humans in real-time such as happiness, sadness, fear, anger, surprise, disgust, neutrality, etc.
The system will be made capable of extracting facial features and classifying expressions. CNN can do this and will also distinguish between bad and good emotions to detect an individual’s behavior and thought patterns.
Uses: Facial emotion detection systems can be used by robots to enhance human interaction and provide appropriate assistance to users. They can also help autistic children, and blind people, monitor warning signs for driver safety, etc.
Translator Application
Objective: To build a translation application using artificial intelligence
Problem: There are thousands of languages spoken in the world. Although English is a global language, not everyone understands it in all parts of the world. And if you want to do business with someone from other countries who speaks a language you don’t understand, that’s problematic. Also, if you travel to other countries, you may experience similar issues.
Solution: If you can translate what others are saying or have written, it will help you connect deeply with them. For this, you can use a translator such as Google Translate. However, you can make your own app from starch using AI.
For this, you can use NLP and transformer models. A transformer will extract features from a sentence to determine each word and its meaning that can give the full meaning of a sentence. It will encode and decode the end of the word to end. To do this, loading a pre-trained Python-based transformer model will help. You can also use the GluonNLP library and then load and test the datasets.
Uses: Translator app is used to translate different languages for purposes like business, travel, blogging, etc.
Advanced AI projects
Resume Parser
Goal: To create software using AI that can sift through many resumes and help users choose the right one for them
Problem: In recruitment, professionals spend a considerable amount of time sifting through many resumes, one by one, manually to find suitable candidates for a position. It is time-consuming and inefficient. Although it can be automated through keyword research, it has many drawbacks. Candidates who know this procedure will add many more keywords to be shortlisted, while others will be rejected even if they have the required skills.
Solution: Skimming through a large number of resumes and finding the right person for a position can be automated using a resume parser. It will help you do this efficiently, saving time and effort while allowing you to choose candidates with the required skills.
AI and ML can help you build the app to choose a suitable candidate while filtering out the rest. To do this, you can use the resume dataset on Kaggle with two columns – resume information and job title. You can also use NLTK – a Python-based library – to create skill-matching clustering algorithms.
Application: A resume parser is used for the recruitment process and can be used by companies and educational institutions.
Face Recognition App
Goal: To build an app with facial recognition capability using ANN, CNN, ML, and deep learning
Issue: Identity theft issues are serious with growing cybersecurity risks that can infiltrate systems and data. This can lead to privacy issues, data leaks, and reputational damage to individuals and businesses.
Solution: Biometrics like facial features are unique, so organizations and individuals can use them to protect their systems and data. Facial recognition systems can help verify a user, ensuring that only authorized and authenticated users can access a system, network, facility, or data.
You need advanced ML algorithms, mathematical functions, and 3D image processing and recognition techniques to create this solution.
Uses: It is used in smartphones and other devices as a security lock and organizational facilities and systems to ensure privacy and data security. It is also used by Identity and Access Management (IAM) vendors, the defense industry, etc.
Games
Objective: To create video games using AI concepts
Problem: The video game industry is booming and gamers are getting more and more advanced. Therefore, there is a constant need to evolve and deliver interesting games that stand out while continuing to increase your sales.
Solution: AI concepts are used to create various game applications such as chess, snake games, racing cars, procedural games, etc. It can use many skills such as chatbots, voice recognition, NLP, image processing, data mining, CNN, machine learning, and many more to create a realistic video game.
Application: AI is used to create various video games like AlphaGo, Deep Blue, FEAR, Halo, etc.
Sales Predictor
Goal: To create software that can predict sales for businesses
Problem: Companies that deal in many products have difficulty managing and tracking the turnover of each product. They also struggle to track inventory and make sold-out products available again. As a result, they may fail to deliver entitlement products to users, which degrades the customer experience.
Solution: Creating a sales prediction tool can help you predict the average sales figure daily, weekly, or monthly. This way, you can understand your product performance and stock more items in time to meet customer demands.
To do this, you can use skills such as machine learning algorithms, data analysis, big data, and more to enable the software to predict sales accurately.
Uses: It is used by e-commerce stores, retailers, distributors, and other companies dealing with mass products.
Automation System
Goal: To create a software solution that can automate certain tasks for productivity
Problem: Repeated manual labor takes time. These are not only tedious, but they also take away from productivity. Therefore, a system must be built that can automate different tasks such as call scheduling, attendance taking, auto-correction, transaction processing, etc.
Solution: Using AI allows you to create software that can automate such tasks to help improve user productivity and free up time for more critical tasks. It can also be designed to provide timely notifications so that you can complete tasks on time. And building this system requires skills like NLP, facial recognition, computer vision, etc.
Application: Automation using AI is widely used to create productivity tools for businesses of all sizes and across various industries in banking, finance, healthcare, education, and manufacturing. the making.
Conclusion
I hope you find these AI projects interesting to work with and expand your knowledge of artificial intelligence and other related concepts like data science, machine learning, NLP, etc. It will also help you sharpen your skills in programming and using tools and technologies in the project.
