IEEE Machine Learning Projects based projects for Mtech,Btech, BE, MS and diploma students in Trichy

Machine Learning Projects for Engineering Students

    Galwin Tech is a machine learning project training institute in Bangalore that gives students the tools they need to refine their skills and put what they learn in the classroom into practise. Galwin Tech's fundamental idea is to create projects that students may use to get practical experience and develop thorough methodologies.

    Below is the list of latest IEEE artificial intelligence projects which are 2024 based for engineering students.

    Go through the abstracts shortlist the topic from the ieee list & you can fill the quick enquiry for further assistance on synopsis & the implementation details.

    The easiest technique to help students understand Machine Learning and its projects is to make the theme and purpose absolutely apparent. There are many initiatives available today that might inspire people to focus more on the projects. CITL has sufficient expertise in evaluating students' potential to open doors to better careers. For students in their last year who want to learn about cutting-edge technology, CITL offers a variety of machine learning programmes. Students that study certain subjects at CITL can feel proud of their accomplishments and gain confidence. At CITL, students can undergo a thorough makeover in order to alter their lifestyles. What could be better than a change in one's life to achieve the objective?

    IEEE machine learning Projects

    • Visualizing Wellness ML-Powered Heart Disease Prediction with GUI
    • Neural Network-based Corn Seed Quality Evaluation in Precision Farming
    • Optimizing Crop Yield: A Machine Learning Approach to Seed Recommendations
    • Improving Maternal Healthcare: Machine Learning Algorithms for Preeclampsia Prediction
    • Advanced Analytics in Vascular Health: Machine Learning Applications for Varicose Veins
    • Personalized Treatment Strategies for Leukemia Patients: An ML Perspective
    • Delicious Decoding: Machine Learning for Automated Food Recognition
    • Towards Sustainable Agriculture: Leaf Disease Classification using CNNs
    • A Comparative Analysis of Classification Techniques in Diabetic Retinopathy Detection
    • Smart Farming: Machine Learning Classification for Accurate Yield Estimation
    • Towards Timely Intervention: Alzheimer's Detection through Advanced Classification Models
    • Beyond Mammography: Enhancing Breast Cancer Detection through Classification Models
    • Predictive Maintenance in Sewage Systems: A Machine Learning Classification Approach
    • Machine Learning in Ophthalmology: Classifying Eye Tumors for Early Detection
    • Automated Tea Leaf Recognition: Enhancing Quality Control with Classification
    • Scoring the Unscored: Innovations in Subjective Answer Assessment using AI and NLP
    • Predicting Asthma Exacerbations: A Machine Learning Revolution in COPD Healthcare
    • Intelligent Battery Management: A Machine Learning Approach for Vehicles
    • Revolutionizing Eye Care: ODIR System for Automated Disease Recognition
    • Exploring the Power of Machine Learning in the Prognosis of Kidney Disorders
    • Stroke Risk Prediction with Machine Learning Techniques
    • Instagram Fake and Automated Account Detection
    • Analysis of Suicidal Thoughts in Tweets Using Machine Learning
    • Innovations in Healthcare: Machine Learning for Early Lung Cancer Detection
    • Election Results Prediction Using Machine Learning and Twitter
    • FakeNewsGuard: Machine Learning Defense Against Information Manipulation
    • Securing Online Reputation: A Machine Learning Approach to Identify and Combat Fake Reviews
    • Health Informatics: A Comprehensive Study on Disease Prediction Using ML and Symptom Analysis
    • Image Analysis and Classification for Sinus Diagnosis with ML
    • Defending Your Inbox: The Role of Artificial Intelligence in Email Spam Detection
    • GoldSage: Predicting Tomorrow's Gold Prices with Advanced Analytics
    • Towards Precision Medicine: Machine Learning for Pneumonia Prognostication
    • Enhancing Bone Injury Diagnosis: A Machine Learning Approach for Crack Detection
    • SpiceGuard: CNNs for Automated Detection of Cassia and Cinnamon
    • Enhanced Brain Tumor Diagnosis through Machine Learning on Medical Imaging
    • Diagnostic Precision: Machine Learning Applications in Identifying Kidney Diseases
    • CancerWise: Wisdom in Oral Cancer Detection through Machine Learning
    • MediMind Chatbot: Smart Health Conversations at Your Fingertips
    • Cyclone Prediction and Rainfall Prediction using Machine Learning
    • UPI Sentinel: A Machine Learning Shield Against Fraudulent Transactions

List of Machine Learning Project ideas

1. AFNN-R: An Effective and Accurate Phishing Websites Detection using Optimal Feature Selection and Random Forest Classifier

Phishing attack is now a big threat to people’s daily life and networking environment. Through disguising illegal URLs as legitimate ones, attackers can induce users to visit the phishing URLs to get private information and other benefits. Effective methods of detecting phishing websites are urgently needed to alleviate the threats posed by phishing attacks. The objective of our proposed approach is to develop a model for effective and accurate phishing website prediction and to avoid some useless or small impact features and falling into the problem of over-fitting. FVV, Feature Validity Value is firstly introduced to evaluate the impact of sensitive features. Optimal feature selection algorithm, this algorithm calculates the FFV values of all features of the input URLs and their relevant websites at first. Then, a threshold is set to select sensitive features to construct an optimal feature vector. Through this algorithm, many useless and small influence features are pruned. Due to no disturbance from these redundant features, the over-fitting problem of the underlying neural network is alleviated. Meanwhile, this algorithm is also able to reduce the time cost of the process of phishing websites detection. The selected optimal features are used to train the underlying neural network and, finally, an optimal classifier (Random Forest Classifier) is constructed to detect the phishing websites.


Credit card frauds are easy and friendly targets. E-commerce and many other online sites have increased the online payment modes, increasing the risk for online frauds. Increase in fraud rates, researchers started using different machine learning methods to detect and analyse frauds in online transactions. The main aim of the paper is to design and develop a novel fraud detection method for Streaming Transaction Data, with an objective, to analyse the past transaction details of the customers and extract the behavioural patterns. Where cardholders are clustered into different groups based on their transaction amount. Then using sliding window strategy, to aggregate the transaction made by the cardholders from different groups so that the behavioural pattern of the groups can be extracted respectively. Later different classifiers are trained over the groups separately. And then the classifier with better rating score can be chosen to be one of the best methods to predict frauds. Thus, followed by a feedback mechanism to solve the problem of concept drift. In this paper, we worked with European credit card fraud dataset.

3. Harvest Smart Solutions Smart Crop Yield Prediction For Sustainable Agriculture

Crop recommendation plays a crucial role in ensuring food security and improving the livelihoods of farmers. With the global population continuing to grow, the demand for food is increasing, making it essential to produce crops efficiently and sustainably. Crop recommendation systems can help farmers make informed decisions about which crops to grow based on the soil characteristics, climate, and other relevant factors. By using data-driven approaches, these systems can optimize crop yields and minimize the use of inputs such as water and fertilizers, leading to cost savings for farmers and reducing the environmental impact of agriculture.Moreover, crop recommendation systems can help mitigate the effects of climate change by identifying crop varieties that are more resilient to changing weather patterns. In this paper, we propose a seed suggestion system using the Decision Tree algorithm that addresses these issues.The system uses a hybrid similarity metric that combines content-based and collaborative filtering techniques. The number of neighbors used is also dynamically adjusted based on the user's level of engagement with the platform. evaluate the performance of our system using a dataset from a popular e-commerce platform. Our results show that the proposed system outperforms traditional Decision tree-based systems in terms of recommendation accuracy and diversity. We also conduct a user study to evaluate the usability of our system, and the results show that users find our system intuitive and easy to use.


The urgent need for accurate precipitation forecasts is met by the Rain Prediction Project, which employs state-of-the-art machine learning techniques. Obtaining all historical weather data, including pertinent meteorological variables, is the first step in the project. By carefully selecting a solid data set using stringent data preprocessing methods, feature engineering, and temporal dynamics consideration, the models' dependability is guaranteed.In order to improve the accuracy of point precipitation predictions, this study contrasts and compares XG-Boost and Decision Tree, two popular machine learning algorithms. The study employs rigorous preprocessing techniques to ensure the dataset's integrity by enhancing historical weather data using precipitation records and meteorological variables. By carefully dividing the time for each algorithm, unique models are created, and hyperparameter tuning then enhances the each model's capacity for prediction.The urgent need for accurate precipitation forecasts is met by the Rain Prediction Project, which employs state-of-the-art machine learning techniques. Obtaining all historical weather data, including pertinent meteorological variables, is the first step in the project. By carefully selecting a solid data set using stringent data preprocessing methods, feature engineering, and temporal dynamics consideration, the models' dependability is guaranteed. One use of machine learning algorithms that employs multiple techniques, including Random Forest and XG-Boost, is rain prediction.


The proliferation of fake news on the internet poses a significant challenge to society, undermining the credibility of information and threatening democratic processes. In response, this project aims to develop a web application for fake news detection utilizing Natural Language Processing (NLP) algorithms. The application, built using Python Flask, will allow users to submit news articles or URLs, which will then be analyzed using various NLP techniques to determine their authenticity. The system will employ feature extraction, sentiment analysis, and machine learning classifiers such as Support Vector Machines (SVM) or Naive Bayes to distinguish between genuine and fake news articles. Additionally, the application will provide users with informative visualizations and explanations regarding the factors contributing to the classification decision, enhancing transparency and user trust. By empowering users to verify the authenticity of news content, this project aims to mitigate the spread of misinformation and foster a more informed online discourse.