Artificial Intelligence Project Ideas-2022

June 21, 2022

AI makes use of a wide range of ideas, methodologies, and technology. Machine learning, deep learning, neural networks, machine vision, cognitive computing, and natural language processing are some of the subfields.  Other AI-supporting technologies include graphics processing units GPUs, the Internet of Things (IoT), sophisticated algorithms, and API. 

Learning theory alone is insufficient. That is why students and professionals are encouraged to try and complete artificial intelligence projects. From tracking artificial intelligence trends to getting their hands dirty on projects, 

In this blog, we have jotted down a list of project ideas that includes suggestions for both students/professionals who are already familiar with the industry. 

Students/professionals will be able to assess themselves, gain hands-on experience, and construct a portfolio to demonstrate that they are industry-ready by producing any AI-based project from the list below.

Ideas for Artificial Intelligence(AI) Projects in 2022

1. Sentiment Analysis 

Sentiment analysis is the technique of assessing consumers' emotions. Their feelings might be classified as good, negative, or neutral. It is a fantastic project for learning how to conduct sentiment analysis, and it is frequently used nowadays. One of the most well-known machine learning initiatives. The reason for this is that every firm is attempting to understand their consumers' feelings; if customers are satisfied, they will stay. This project might provide a way to minimize customer turnover.

2. Speech Emotion Recognition

This is one of the most impressive machine learning projects. Audio data is used by the spoken emotion recognition system. It takes a segment of speech as input and identifies the emotions the speaker is expressing. You can recognize many emotions such as happiness, sadness, surprise, anger, and so on. This project might be useful for recognizing consumer emotions when on the phone with a call center.

3. Grocery Recommendation System

Collaborative filtering is an excellent strategy for identifying items that a user may be interested in based on the reactions of other users. A supermarket suggestion system would be a fantastic idea for helping shoppers discover what they want in their baskets. It is beneficial to people who intend to open a grocery store.

4. Project to Predict Heart Disease 

This initiative is advantageous from a medical standpoint since it is intended to give online medical advice and counseling to people suffering from cardiac ailments. Patients frequently complain about being unable to locate excellent doctors to assist their medical demands, which exacerbates their predicament. This heart disease prediction tool will help address the problem. The proposed web application would provide patients (users) with immediate access to the consultation and services of qualified medical specialists on heart disease-related issues. The application will be educated and provided information on a variety of various cardiac illnesses. Make the web platform such that users can share and discuss their heart-related difficulties amongst themselves.

5. Autonomous vehicle 

Self-driving automobiles are made possible by machine learning algorithms. They enable a vehicle to gather information about its surroundings from cameras and other sensors, analyze it, and decide what actions to take. Machine learning algorithms can even train automobiles to execute these activities on par with (or better than) people. Artificial intelligence is utilized to assist automobiles in crowded areas and on tough routes. When advanced mathematics and image recognition systems are required to detect automobiles from all sides, manage road conditions, speed, stop, and prevent collisions using AI simulation software and algorithms, this project may be very useful.

6. Windows Virtual Voice Assistant 

This is an intriguing Artificial Intelligence project proposal. Voice-based personal assistants are useful tools for streamlining daily activities. For example, you may use virtual voice assistants to search the Web for items/services, shop for things, create notes and set reminders, and much more. This voice-based virtual assistant can be created specifically for mobile or Windows users. By utilizing the voice command "open" a Windows or android user may open any application (Notepad, File Explorer, Google Chrome, etc.) they choose. You may also use the "write" voice command to compose essential messages.

7. Plagiarism Checker for Online Assignments 

This is one of the most important AI initiatives right now. Plagiarism is a severe problem that must be addressed and monitored. It refers to the practice of mindlessly duplicating someone else's work and passing it off as your own. Plagiarism is committed through paraphrasing texts, utilizing similar keywords, modifying sentence forms, and so forth. In this way, plagiarism is analogous to intellectual property theft. In this project, you will create a plagiarism detector that can detect similarities in text copies and calculate the proportion of plagiarism. Text mining technology can be utilized for this plagiarism detection. Users can register in your project by establishing a valid login id and password.

8. Banking Robot 

This is a fantastic Artificial Intelligence project idea for beginners. This AI project entails developing a banking bot that uses artificial intelligence algorithms to evaluate customer inquiries in order to comprehend their message and take the right action. It is a bank-specific application where customers may ask inquiries about their accounts, loans, credit cards, and so on. This is the project to add to your CV if you're seeking a solid AI project.

The banking bot is a mobile application for Android. It is taught, like a chatbot, to process users' queries/requests and comprehend what services or information they are searching for. The bot will interact with users in the same way that a person would. So, regardless of how you ask a question, the bot can answer it and, if necessary, escalate it to human executives.

9. Web Pattern Navigation for Online Marketing Campaigns 

In today's digital world, the majority of individuals find it impossible to go about their everyday lives without using the internet. Because of the extensive usage of the internet, various options for product promotion and service supply have arisen. With billions of people utilizing the internet, it's vital to target the right customer for product marketing or to provide a service that fulfills their needs. The best method to understand customer behavior is to analyze their online navigation habits. Using Web-mining techniques, you may extract valuable information about the interests and needs of your customers.

10. DrugBank: Measuring Distances Between Medical Entities 

This is another medical industry project for your portfolio and a more technical one as well. For the healthcare business, medical data processing is a complex task. They are having difficulty quantifying medical things such as illnesses, physical parts, drugs, symptoms, and so on. This AI-powered project provides an AI-powered method for estimating the distance between pharmaceuticals that have similar names, descriptions, targets, and chemical compositions. To compute the distance based on the aforesaid features, we must first represent the drugs in a vector space model, followed by an examination of textual, semantic, and chemical similarities.

The purpose of textual similarity is to identify the degree to which two texts are similar. Text fields like description, indication, and pharmacodynamics are concatenated to accomplish natural language processing. The NLP process includes techniques such as eliminating stop words, converting to lowercase, and computing the frequency-inverse document frequency (TF-IDF). The dimension of the vector space model is then lowered using Latent Semantic Indexing (LSI). Finally, the distance matrix is computed using the Euclidean distance. Then, inside a semantic space, we can employ a knowledge base to quantify semantic similarity.


If you have the correct coaching, perspective, and study material, learning AI may be pretty simple. We are confident that these initiatives will assist you in expanding your knowledge of artificial intelligence. And by now, you must have realized how strong AI is by looking at the diversity of projects here.

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June 29, 2022

Project Management for AI-ML-DL Projects

Managing a project properly is one of the factors behind its completion and subsequent success. The same can be said for any artificial intelligence (AI)/machine learning (ML)/deep learning (DL) project. Moreover, efficient management in this segment holds even more prominence as it requires continuous testing before delivering the final product.

An efficient project manager will ensure that there is ample time from the concept to the final product so that a client’s requirements are met without any delays and issues.

How is Project Management Done For AI, ML or DL Projects?

As already established, efficient project management is of great importance in AI/ML/DL projects. So, if you are planning to move into this field as a professional, here are some tips –

  • Identifying the problem-

The first step toward managing an AI project is the identification of the problem. What are we trying to solve or what outcome do we desire? AI is a means to receive the outcome that we desire. Multiple solutions are chosen on which AI solutions are built.

  • Testing whether the solution matches the problem-

After the problem has been identified, then testing the solution is done. We try to find out whether we have chosen the right solution for the problem. At this stage, we can ideally understand how to begin with an artificial intelligence or machine learning or deep learning project. We also need to understand whether customers will pay for this solution to the problem.

AI and ML engineers test this problem-solution fit through various techniques such as the traditional lean approach or the product design sprint. These techniques help us by analysing the solution within the deadline easily.

  • Preparing the data and managing it-

If you have a stable customer base for your AI, ML or DL solutions, then begin the project by collecting data and managing it. We begin by segregating the available data into unstructured and structured forms. It is easy to do the division of data in small and medium companies. It is because the amount of data is less. However, other players who own big businesses have large amounts of data to work on. Data engineers use all the tools and techniques to organise and clean up the data.

  • Choosing the algorithm for the problem-

To keep the blog simple, we will try not to mention the technical side of AI algorithms in the content here. There are different types of algorithms which depend on the type of machine learning technique we employ. If it is the supervised learning model, then the classification helps us in labelling the project and the regression helps us predict the quantity. A data engineer can choose from any of the popular algorithms like the Naïve Bayes classification or the random forest algorithm. If the unsupervised learning model is used, then clustering algorithms are used.

  • Training the algorithm-

For training algorithms, one needs to use various AI techniques, which are done through software developed by programmers. While most of the job is done in Python, nowadays, JavaScript, Java, C++ and Julia are also used. So, a developmental team is set up at this stage. These developers make a minimum threshold that is able to generate the necessary statistics to train the algorithm.  

  • Deployment of the project-

After the project is completed, then we come to its deployment. It can either be deployed on a local server or the Cloud. So, data engineers see if the local GPU or the Cloud GPU are in order. And, then they deploy the code along with the required dashboard to view the analytics.

Final Words-

To sum it up, this is a generic overview of how a project management system should work for AI/ML/DL projects. However, a point to keep in mind here is that this is not a universal process. The particulars will alter according to a specific project. 

Reference Links:,product%20on%20the%20right%20platform.

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June 29, 2022

Top 7 AI & ML start-ups in Telecom Industry in India

With the multiple technological advancements witnessed by India as a country in the last few years, deep learning, machine learning and artificial intelligence have come across as futuristic technologies that will lead to the improved management of data hungry workloads.


The availability of artificial intelligence and machine learning in almost all industries today, including the telecom industry in India, has helped change the way of operational management for many existing businesses and startups that are the exclusive service providers in India.


In addition to that, the awareness and popularity of cloud GPU servers or other GPU cloud computing mediums have encouraged AI and ML startups in the telecom industry in India to take up their efficiency a notch higher by combining these technologies with cloud computing GPU. Let us look into the 7 AI and ML startups in the telecom industry in India 2022 below.


Top AI and ML Startups in Telecom Industry 

With 5G being the top priority for the majority of companies in the telecom industry in India, the importance of providing network affordability for everyone around the country has become the sole mission. Technologies like artificial intelligence and machine learning are the key digital transformation techniques that can change the way networks rotates in the country. The top startups include the following:


Founded in 2021, Wiom is a telecom startup using various technologies like deep learning and artificial intelligence to create a blockchain-based working model for internet delivery. It is an affordable scalable model that might incorporate GPU cloud servers in the future when data flow increases. 


As one of the companies that are strongly driven by data and unique state-of-the-art solutions for revenue generation and cost optimization, TechVantage is a startup in the telecom industry that betters the user experiences for leading telecom heroes with improved media generation and reach, using GPU cloud online


As one of the strongest performers is the customer analytics solutions, Manthan is a supporting startup in India in the telecom industry. It is an almost business assistant that can help with leveraging deep analytics for improved efficiency. For denser database management, NVIDIA A100 80 GB is one of their top choices. 


Just as NVIDIA is known as a top GPU cloud provider, NetraDyne can be named as a telecom startup, even if not directly. It aims to use artificial intelligence and machine learning to increase road safety which is also a key concern for the telecom providers, for their field team. It assists with fleet management. 

KeyPoint Tech

This AI- and ML-driven startup is all set to combine various technologies to provide improved technology solutions for all devices and platforms. At present, they do not use any available cloud GPU servers but expect to experiment with GPU cloud computing in the future when data inflow increases.



Actively known to resolve customer communication, it is also considered to be a startup in the telecom industry as it facilitates better communication among customers for increased engagement and satisfaction. 


An AI startup in Chennai, Facilio is a facility operation and maintenance solution that aims to improve the machine efficiency needed for network tower management, buildings, machines, etc.


In conclusion, the telecom industry in India is actively looking to improve the services provided to customers to ensure maximum customer satisfaction. From top-class networking solutions to better management of increasing databases using GPU cloud or other GPU online services to manage data hungry workloads efficiently, AI and MI-enabled solutions have taken the telecom industry by storm. Moreover, with the introduction of artificial intelligence and machine learning in this industry, the scope of innovation and improvement is higher than ever before.




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June 29, 2022

Top 7 AI Startups in Education Industry

The evolution of the global education system is an interesting thing to watch. The way this whole sector has transformed in the past decade can make a great case study on how modern technology like artificial intelligence (AI) makes a tangible difference in human life. 

In this evolution, edtech startups have played a pivotal role. And, in this write-up, you will get a chance to learn about some of them. So, read on to explore more.

Top AI Startups in the Education Industry-

Following is a list of education startups that are making a difference in the way this sector is transforming –

  1. Miko

Miko started its operations in 2015 in Mumbai, Maharashtra. Miko has made a companion for children. This companion is a bot which is powered by AI technology. The bot is able to perform an array of functions like talking, responding, educating, providing entertainment, and also understanding a child’s requirements. Additionally, the bot can answer what the child asks. It can also carry out a guided discussion for clarifying any topic to the child. Miko bots are integrated with a companion app which allows parents to control them through their Android and iOS devices. 

  1. iNurture

iNurture was founded in 2005 in Bengaluru, Karnataka. It provides universities assistance with job-oriented UG and PG courses. It offers courses in IT, innovation, marketing leadership, business analytics, financial services, design and new media, and design. One of its popular products is KRACKiN. It is an AI-powered platform which engages students and provides employment with career guidance. 

  1. Verzeo

Verzeo started its operations in 2018 in Bengaluru, Karnataka. It is a platform based on AI and ML. It provides academic programmes involving multi-disciplinary learning that can later culminate in getting an internship. These programmes are in subjects like artificial intelligence, machine learning, digital marketing and robotics.

  1. EnglishEdge 

EnglishEdge was founded in Noida in 2012. EnglishEdge provides courses driven by AI for getting skilled in English. There are several programmes to polish your English skills through courses provided online like professional edge, conversation edge, grammar edge and professional edge. There is also a portable lab for schools using smart classes for teaching the language. 

  1. CollPoll

CollPoll was founded in 2013 in Bengaluru, Karnataka. The platform is mobile- and web-based. CollPoll helps in managing educational institutions. It helps in the management of admission, curriculum, timetable, placement, fees and other features. College or university administrators, faculty and students can share opinions, ideas and information on a central server from their Android and iOS phones.

  1. Thinkster

Thinkster was founded in 2010 in Bengaluru, Karnataka. Thinkster is a program for learning mathematics and it is based on AI. The program is specifically focused on teaching mathematics to K-12 students. Students get a personalised experience as classes are conducted in a one-on-one session with the tutors of mathematics. Teachers can give scores for daily worksheets along with personalised comments for the improvement of students. The platform uses AI to analyse students’ performance. You can access the app through Android and iOS devices.

  1. ByteLearn 

ByteLearn was founded in Noida in 2020. ByteLean is an assistant driven by artificial intelligence which helps mathematics teachers and other coaches to tutor students on its platform. It provides students attention in one-on-one sessions. ByteLearn also helps students with personalised practice sessions.

Key Highlights

  • High demand for AI-powered personalised education, adaptive learning and task automation is steering the market.
  • Several AI segments such as speech and image recognition, machine learning algorithms and natural language processing can radically enhance the learning system with automatic performance assessment, 24x7 tutoring and support and personalised lessons.
  • As per the market reports of P&S Intelligence, the worldwide AI in the education industry has a valuation of $1.1 billion as of 2019.
  • In 2030, it is projected to attain $25.7 billion, indicating a 32.9% CAGR from 2020 to 2030.

Bottom Line

Rising reliability on smart devices, huge spending on AI technologies and edtech and highly developed learning infrastructure are the primary contributors to the growth education sector has witnessed recently. Notably, artificial intelligence in the education sector will expand drastically. However, certain unmapped areas require innovations.

With experienced well-coordinated teams and engaging ideas, AI education startups can achieve great success.

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