AI Research Topics

How does AI work?

AI has the potential to quicken the leap of scientific finding and improve the quality of research conclusions. To gain a deeper knowledge of AI in science and we help our scholars to discover its life changing potentiality, we also encourage our customers to delve into Artificial Intelligence Research Topics by aiding in latest research topic which can even be customized.

Basic Steps in an AI System:

Data Collection

The starting point for most of the AI is machine learning here we work on data which can be in the form of images, text, mathematical measurements or other type of information that is digitally kept.

Data Preprocessing

Explanation will be given how we can make AI system to understand about eliminating errors, supervising missing values or changing the data into a form that is easier before its changed or altered.

Feature Extraction

We make use of AI systems to use the features which are exact to make decisions or predictions from the fresh data. For example, the distances between key points on a person’s face in a facial recognition system is the features might be included.

Model Training

A mathematical function is a model in AI that makes a calculation or conclusion based on its input features. At the time of the training phase, we adjust machine learning algorithm to minimalize the variance between the model’s calculations and the actual outcomes on a dataset.


Once we train our model, we need to calculate on unseen data to check its analytical power or decision-making capability. Metrics like accurateness, exactness, recollection may be used for this purpose.


The model will be trained and evaluated; it can be organized into a real-world application, which could be anything from a web service that mentions products to customers, by means of computer vision system we can recognize defective items on a production line.

Limitations and Challenges:

Artificial Intelligence (AI) is a speedily advancing field which has the potential to create an impact in many areas of our lives. However, in spite of its many advantages, there are also some limitations to the technology which we can overcome by our professional experts.

  1. Data Dependence: A large amount of data for training is required.
  2. Computational Power: A powerful hardware is needed under progressive models, mainly in deep learning.
  3. Explain capability: Under machine learning models deep learning models, are considered as “black boxes” as how they reach at detailed conclusions or calculations.
  4. Ethical and Social Problems: Right from data confidentiality to job displacement, AI offers us a wide range of ethical encounters that subjects to ongoing debate and research.

AI technologies has joined a huge range of uses in many areas right from education, health care and finance and many more. So, it’s necessary that we increase the computational power as algorithm will be more advanced. Don’t worry we got you covered in all your research endeavours while novel idea yet practical explanation will be given.

What are the key topics for Artificial intelligence?

Some of the key points to be considered under AI project are

  • 4 – Expert Systems: AI for Decision Support
  • Google Maps
  • Smart Assistants
  • Reinforcement learning
  • Robotics
  • Computer vision
  • Wearables
  • DeepMind
  • speech recognition
  • unsupervised learning
  • virtual assistants
  • Large-scale machine learning
  • Deep learning
  • Natural Language Processing
  • Collaborative systems
  • Crowdsourcing and human computation
  • Algorithmic game theory and computational social choice
  • Internet of Things (IoT)
  • Neuromorphic Computing
  • Snapchat Filters
  • Self-Driving Cars

What are some research topics that connect artificial intelligence to marketing?

Artificial intelligence (AI) and marketing are closely connected with each other there are several topics that can be listed by the connection of these two, offers you the effective yet customized results by making use of the correct algorithm and techniques.

Customer Separation and Personalization

  • Predictive Analytics of Client Value: To forecast customer value such as lifetime value, and purchasing patterns we can calculate by applying machine learning algorithms.
  • Personalized Recommendations: Here we investigate about improving reference systems by making use of AI techniques to provide more accurate suggestions.
  • Dynamic Pricing Strategies: We will be able to calculate the market conditions and consumer behaviour for real-time price alterations.

Natural Language Processing (NLP) and Sentiment Analysis

  • Chatbots for Customer Service: How can we handle enquiries by making customer service chatbots more casual is discussed.
  • Sentiment Analysis for Brand Monitoring: We can measure public sentiment about a brand or product by analysing customer analyses, social media notes or news articles.
  • Automated Content Creation: By using AI to create marketing copy, social media posts or articles.

Marketing Mix Displaying and Optimization

  • Campaign Optimization: Here our developers focus on how we could conduct research by using machine learning algorithms to improve ad placements, targeting, and offer strategies in real-time.
  • Attribution Modelling: By making use of advanced statistical models, we can exactly attribute changes to marketing channels.
  • Budget Allocation: To frame algorithms for a large marketing budget across diverse channels based on performance metrics.

Customer Journey Mapping

  • Predictive Analytics for Customer Journeys: We can calculate how consumers move through the marketing strategy.
  • Multichannel Marketing: Here we improve marketing strategies that extent multiple touchpoints for online and offline.

Market and Competitive Analysis

  • Market Trend Calculation: Market trends can be calculated here, which is based on several economic indicators and consumer behaviour metrics.
  • Competitive Intelligence: We can gather and analyse data on competitors by using AI algorithms.

Other Emerging Topics

  • Ethical Considerations: Some of the ethical considerations as data privacy, algorithmic partiality and transparency will be researched as AI gets more united into marketing strategies.
  • IoT and Marketing: For marketing perceptions and actions data from Internet of Things devices can be hold on.
  • Visual Recognition for social media: To analyse images and videos posted on social media for branding insights by using computer vision techniques.
  • Voice Search Optimization: We can optimize marketing policies for voice search and virtual assistants like Amazon’s Alexa or Google Assistant by conducting research.
  • Blockchain and Marketing: The discussion is laid on how blockchain technology effects digital marketing, from ad fraud prevention to consumer data management.

Thus, a wide set of opportunities is listed when AI meets marketing so we offer valuable understanding to our scholars by making correct ideas and algorithm in both the field.

Artificial Intelligence Research Projects 2023

What are some good AI projects?

Don’t worry we got you covered…. we also guide our research scholars in these topics while personalized topics are also encouraged as exhaustive research will be conducted by multiple revisions.

  1. Artificial Intelligence Hand Spatial Position Predictor Based on Data Gloves and Jetson Xavier NX
  2. Research on the Application of Artificial Intelligence Technology in Public Product Design of Intelligent Scenic Spot
  3. MOOC Teaching Platform System Based on Application of Artificial Intelligence
  4. Design of Humanity by the Concept of Artificial Personalities
  5. An Artificial Intelligence Based Rainfall Prediction Using LSTM and Neural Network
  6. Application of Artificial Intelligence in Military: From Projects View
  7. Application of FinTech, Machine learning and Artificial Intelligence in programmed decision making and the perceived benefits
  8. A Tool For Software Requirement Allocation Using Artificial Intelligence Planning
  9. Artificial Intelligence Methods for Automatic Music Transcription using Isolated Notes in Real-Time
  10. Application of artificial intelligence in computer aided instruction
  11. A 1.41mW on-chip/off-chip hybrid transposition table for low-power robust deep tree search in artificial intelligence SoCs
  12. Explainable Artificial Intelligence for Predictive Maintenance Applications
  13. Comparison of Artificial Intelligence Algorithms and Traditional Algorithms in Detector Neutron/Gamma Discrimination
  14. Introducing Artificial Intelligence Agents to the Empirical Measurement of Design Properties for Aspect Oriented Software Development
  15. A Systematic Review of Human–Computer Interaction and Explainable Artificial Intelligence in Healthcare with Artificial Intelligence Techniques
  16. Human Resource Management System Based on Artificial Intelligence
  17. Deployment of Differential Privacy for Application in Artificial Intelligence
  18. Autonomic mobile networks: The use of artificial intelligence in wireless communications
  19. DDoS detection and prevention based on artificial intelligence techniques
  20. The determination and analysis of factors affecting to student learning by artificial intelligence in higher education