Machine Learning and Artificial Intelligence Project Ideas

Machine Learning and Artificial Intelligence Project is a complex process which requires in-depth research but as we are in this field for past 18years our research team has much experience in it. We understand the hardships that will be faced by scholars for doctoral process. Various services like research proposal development, literature review, data collection and thesis writing are accommodated in our service. We have done many projects by combining ML and AI. By combining various algorithms and methods we finish of the task as per your needs.

Here we provide a range of complications and applications by gaining various research ideas that span various fields of Machine Learning (ML) and Artificial Intelligence (AI).

Beginner Level:

1. Spam Detector using Naive Bayes:

• We utilize Naive Bayes method to execute a spam filter for emails or messages.

2. Handwritten Digit Classification:

• To construct a classifier, we utilize MNIST dataset, and we also utilize a simple neural network that identifies handwritten digits.

3. Movie Recommender System

• By utilizing collaborative filtering, our work built a recommendation system which recommends movies to users on the basis of their watching history.

4. Chatbot with Rule-Based AI:

• Our work uses a rule-based AI framework by constructing a simple chatbot which answers FAQs for industries or websites.

Intermediate Level:

1. Sentiment Analysis of social media:

• We identify the sentiment of posts or tweets to calculate the public point of view on exact topics by executing ML techniques.

2. Object Detection with TensorFlow:

• To detect and find various objects in images, we utilize TensorFlow or PyTorch by constructing an object detection framework.

3. Predictive Analytics for Healthcare:

• Based on the medical record of the patient, we forecast the onset of disease by utilizing machine learning methods.

4. Stock Market Prediction using Time Series Analysis:

• Our work forecasts stock prices on the basis of previous data by executing a time series framework.

Advanced Level:

1. Deep Reinforcement Learning for Gaming:

• To play and enhance a specific video game, our work executes a deep reinforcement learning framework.

2. Video Recognition for Command & Control:

• We identify spoken instructions and achieve a movement that is related to virtual supporters like Siri or Alexa.

3. Real-Time Traffic Analysis with Computer Vision:

• In our work we identify traffic patterns and offer actual-time traffic-jam details by utilizing computer vision methods.

4. AI for Predictive Maintenance in Manufacturing:

• Our work forecasts a machine which is likely to fail or requires presevence by using sensor data from tools.

Cutting Edge Research:

1. Generative Adversarial Networks for Art Creation:

• By utilizing GAN, we built an AI that produces new parts of art or music.

2. Neural Style Transfer:

• We execute an AI which can handle the creative style of one image to the content of another (e.g., create our photo look like a Van Gogh painting).

3. AI for Drug Discovery:

• To forecast which chemical compounds will be powerful as new drugs, we utilize Machine Learning methods.

4. Cross-Lingual Information Retrieval:

• We recognize and extract information between various languages by constructing an AI framework.

Societal Impact:

1. AI for Energy Efficiency:

• By utilizing AI to forecast and control consumption to optimize the usage of energy in smart houses or buildings.

2. AI in Agriculture for Crop Prediction:

• We forecast crop yields, plant disease identification and provide recommendations for farmers by using ML methods.

3. AI for Disaster Response and Recovery:

• We identify satellite imagery for disaster management such as recognizing areas that will be mostly affected by natural disaster by executing ML methods.

4. AI-Powered Educational Tools:

• To aid students to learn new titles and adjust them to their way of learning, we construct an AI supporter.

5. Fairness and Bias Detection in AI systems:

• We identify and reduce biases in AI decision-making procedure by constructing tools.

We frequently modify the difficult of our research. For example, A simpler version of an improved project will be tried by a beginner. While a knowledgeable expert will improve more characters or utilize more knowledgeable methods to improve a beginner-level project. 

            The selection of projects will range with our interests and aims, as well as the existing data and resources. In addition, we utilize ethical considerations, mainly for projects with societal impact, to make sure that AI frameworks are fair, transparent and respectful of privacy.

For all levels we guide scholars in their projects, who face research challenges. We also assure that you meet a high grade in al our research work. Research support is given for scholars to build their career in academics and development globally.