Artificial Intelligence and Machine Learning Projects

In application field and in complicated projects, Artificial Intelligence (AI) and Machine Learning (ML) works are significantly differed. We suggested various project concepts varying from easiest to advance among several fields, we are ready to guide to if you are struck up in any stages of your research work. So, without any hesitation contact us to get excellent research ideas. Low cost and confidential is guaranteed as it stands as our key ethics:

Beginner Projects:

  1. Email Spam Categorization:
  • Aim: We categorize the emails as not spam or spam.
  • Data: Publicly available datasets such as Enron Corpus are utilized by us.
  1. Movie Suggestion Model:
  • Aim: Our aim is to suggest movies related to the user’s searching or previous history.
  • Data: Initially, we can make use of MovieLens dataset.
  1. House Price Forecasting:
  • Aim: By considering various factors including location, number of rooms and dimension, we forecast the house price by employing regression methods.
  • Data: In our work, Boston Housing dataset is used.
  1. Handwritten Digit Recognition:
  • Aim: From the analysis of image data, handwritten digits (0-9) are recognized by us.
  • Data: An appropriate dataset for this type of task is MNIST dataset.

Intermediate Projects:

  1. Sentiment Analysis of social media:
  • Aim: By evaluating posts or tweets, we analyze the sentiment.
  • Data: For this, Reddit comments or Twitter API datasets are used in our research.
  1. Image based Object identification:
  • Aim: We detect and localize various objects by examining images.
  • Tools: Various methods such as SSD or YOLO can utilized with tools like PyTorch or TensorFlow.
  1. Chatbot with Natural Language Processing:
  • Aim: For a particular field such as customer service, we develop a Chatbot.
  • Tools: NLP libraries are employed by us such as spaCy or NLTK.
  1. Credit Scoring Framework:
  • Aim: For the credit card users, the possibility of default is forecasted by us.
  • Data: We utilized Kaggle competitions or publicly available credit default datasets.

Advanced projects:

  1. Automatic Driving Car Simulation:
  • Aim: Our goal is to navigate the car to drive autonomously in a virtual platform.
  • Tools: Python with libraries such as Car Learning to Act (CARLA) are employed in our approach.

  1. Generative Adversarial Networks (GANs) for Art Development:
  • Aim: By learning from a paintings-based dataset, we create novel artworks.
  • Tools: We used tools like PyTorch or TensorFlow with GAN techniques.
  1. Drug Discover Utilizing Bioinformatics:
  • Aim: Biological characteristics of compounds are forecasted in our project.
  • Data: For that, we utilized PubChem or ChEMBL based datasets.
  1. Speech Emotion Recognition:
  • Aim: By examining the spoken tone, the emotions are identified by us.
  • Data: In this, Emo-DB or RAVDESS datasets are used.
  • Tools: Librosa is employed in our study for feature extraction process and DL frameworks are used with CNNs or LSTM.
  1. Real-Time Object Monitoring:
  • Aim: By using video data, we monitor the objects.
  • Tools: Various methods are used by us including OpenCV, YOLO or DeepSORT.
  1. AI for Healthcare (For instance: Disease Forecasting):
  • Aim: The severity of the disease is forecasted in our work by utilizing patient’s data.
  • Data: We make use of electronic health records that are stick to data security rules.

Cross-Disciplinary Projects:

  1. AI for Farming (Precision Farming):
  • Aim: To identify pests or forecast crop production, the satellite imagery is utilized by us.
  • Data: Drone imagery datasets or satellite images are employed.
  1. AI in Retail (Customer Behavior Analysis):
  • Aim: We optimize product positioning and production by examining the behavior of customers.
  • Data: For this, loyalty program data and customer purchase information are used.
  1. Machine Learning for Renewable Energy Prediction:
  • Aim: The energy outcomes from renewable factors such as solar or wind are forecasted in our research.
  • Data: Here, we utilized energy creation data and previous weather data.
  1. Natural Language processing for Legal Documents:
  • Aim: The main points in the legal documents are retrieved and described by us.
  • Tools: We employed NLP based methods such as summarization method and entity recognition method.
  1. AI based Fitness Coach:
  • Aim: A framework is created by us to offer an actual time review on workouts through the use of pose estimation methods.
  • Tools: Our approach utilizes post estimation methods such as OpenPose.

The following are the common procedural steps for every above-mentioned project:

  • Problem Description:
  • Data Gathering and Preprocessing:
  • Exploratory Data Analysis
  • Model Chosen and Training
  • Model Evaluation and Tuning
  • Deployment
  • Tracking and Maintenance

We should have to consider some common regulations based on data security when proceeding with AI or ML tasks and must be cautious of possible unfairness in our framework. From various perspectives, we concentrate on every project like enhancing model’s efficiency, scalability, understandability and user reviews. Paper publishing is carried out well by our publishing team, we publish your paper in leading and reputed journals like SCI, IEEE, SCOPUS, ELSEVIERetc.

 Artificial Intelligence and Machine Learning Thesis Topics

If you are new to research and looking for help in machine learning you are on the right place, we help researchers by guiding the right topic that is in trend.

  1. Integration of multimodal data for large-scale rapid agricultural land evaluation using machine learning and deep learning approaches
  2. Application of classification machine learning algorithms for characterizing nutrient transport in a clay plain agricultural watershed
  3. Classification of agriculture farm machinery using machine learning and internet of things
  4. Use and adaptations of machine learning in big data—Applications in real cases in agriculture
  5. Towards applicability of machine learning techniques in agriculture and energy sector
  6. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review
  7. Detecting crop health using machine learning techniques in smart agriculture system
  8. Analysis of demand forecasting of agriculture using machine learning algorithm
  9. Development potential of nano enabled agriculture projected using machine learning
  10. Applying machine learning to extract new knowledge in precision agriculture applications
  11. From machine learning to deep learning in agriculture–the quantitative review of trends
  12. Machine learning techniques in wireless sensor network-based precision agriculture
  13. Machine learning methods for crop yield prediction and climate change impact assessment in agriculture
  14. Optimization of Pesticides Spray on Crops in Agriculture using Machine Learning
  15. Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review
  16. Automation in agriculture by machine and deep learning techniques: A review of recent developments
  17. Machine learning algorithms for modelling groundwater level changes in agricultural regions of the US
  18. A hybrid machine learning approach for estimating the water-use efficiency and yield in agriculture
  19. Supervised machine learning approach for crop yield prediction in agriculture sector
  20. Land suitability assessment and agricultural production sustainability using machine learning models
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