Artificial Intelligence Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Artificial Intelligence ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Artificial Intelligence Projects
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DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework
This project focuses on predicting obesity in children and teenagers using artificial intelligence. It collects health data like height, weight, diet, and activity levels to give personalized feedback. The system, called DeepHealthNet, can make accurate predictions even with limited daily data. It helps identify health risks early and guide adolescents to make better lifestyle choices. -
EAG-RS: A Novel Explainability-Guided ROI-Selection Framework for ASD Diagnosis via Inter-Regional Relation Learning
This project focuses on using brain scans to detect autism. It studies how different regions of the brain interact in complex ways. The method finds important brain areas that help distinguish autistic patients from others. The approach is explainable, meaning it shows why it makes each diagnosis and works better than earlier methods. -
AIROGS Artificial Intelligence for Robust Glaucoma Screening Challenge
This project focuses on using artificial intelligence to detect glaucoma from eye images. The AI learns from a very large set of photos from many patients and clinics. It is designed to handle poor-quality or unusual images that usually make screening hard. The results show that AI can match experts in spotting glaucoma and could make early eye disease detection easier and faster. -
DeepHealthNet Adolescent Obesity Prediction System Based on a Deep Learning Framework
This project focuses on predicting obesity in teenagers using artificial intelligence. It collects health data like height, weight, activity, and diet to make personalized predictions. The system helps teens understand their health risks and suggests timely actions. It works well even with limited daily data and gives more accurate results for both boys and girls. -
LYSTO The Lymphocyte Assessment Hackathon and Benchmark Dataset
This project, called LYSTO, organized a fast-paced competition to count immune cells called lymphocytes in cancer tissue images. Participants had only a few hours to develop methods to analyze colon, breast, and prostate cancer samples. The results showed some methods matched expert pathologists in accuracy. The dataset and evaluation tools are now available online for further research. -
A Human-Centered Learning and Teaching Framework Using Generative Artificial Intelligence for Self-Regulated Learning Development Through Domain Knowledge Learning in K–12 Settings
This project explores how generative AI tools can improve learning and teaching. It proposes a framework that helps students use AI to focus, engage, and reflect on their learning. Teachers also learn to use AI to design lessons that meet individual student needs. The approach was tested with Chinese language writing for primary students and a teacher training program, showing improved student engagement and teacher confidence. -
A Review of Image-Based Food Recognition and Volume Estimation Artificial Intelligence Systems
This project reviews how smartphone apps can automatically analyze the food we eat. It explains how images of meals can be used to estimate nutrients and portion sizes. The study compares existing methods, showing their strengths and weaknesses. It also suggests ways to improve these systems for healthier lifestyles. -
AI in the Context of Complex Intelligent Systems: Engineering Management Consequences
This project studies how artificial intelligence changes complex systems and how engineers manage them. It shows that AI makes systems more adaptable and unpredictable. The research looks at design, boundaries, modeling, learning, and system behavior. It suggests new ways for engineers to design and manage intelligent systems while keeping them safe and reliable. -
Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities
This project focuses on using artificial intelligence to help manage diabetes. It explains the best ways to build and test machine learning models for diabetes care. The work highlights important features like accuracy, personalization, and clear results. It also provides tools and data resources for researchers to create better algorithms. -
Artificial Intelligence-Enabled Business Model Innovation: Competencies and Roles of Top Management
This study looks at how top managers can help their companies use artificial intelligence to create new business models. Researchers interviewed 47 professionals to understand what skills and roles managers need. They found that managers need certain abilities and must take on specific roles to guide AI-driven innovation. The work helps explain how leadership shapes AI-based business growth. -
Assessing Interdisciplinary Research Within an Emerging Technology Network: A Novel Approach Based on Patents in the Field of Bioplastics
This project studies how research from different fields combines to create new technologies, using bioplastics as an example. It analyzes patents to show how knowledge from various areas connects and forms networks. The work helps researchers and companies understand and plan interdisciplinary projects. It also guides policymakers to support research that leads to innovative technologies. -
E-BabyNet: Enhanced Action Recognition of Infant Reaching in Unconstrained Environments
This project develops a smart computer system called E-babyNet to detect when infants reach for objects. It uses video data to track the movements of babies’ hands and the objects they touch. The system can accurately find the start and end of each reaching action. It works well even in home or clinic settings and reduces false detections. -
How Artificial Intelligence Drives Sustainable Frugal Innovation: A Multitheoretical Perspective
This project studies how companies can use AI to improve innovation while using fewer resources, especially during disruptions like pandemics or political issues. It looks at innovation that is good for the economy, society, and the environment. The researchers identify the most important factors for successfully combining AI with sustainable frugal innovation. Their findings can help businesses stay competitive and sustainable in challenging times. -
Is Attention all You Need in Medical Image Analysis? A Review
This project reviews the use of hybrid models that combine CNNs and Transformers for medical image analysis. CNNs capture local details in images, while Transformers capture global patterns. By combining both, these models can better understand complex medical images. The study analyzes existing designs, their strengths, and future opportunities for improving medical diagnosis and research. -
Linking Digital Servitization and Industrial Sustainability Performance: A Configurational Perspective on Smart Solution Strategies
This project studies how manufacturing companies can help their customers become more sustainable using smart technologies. It looks at how artificial intelligence, focusing on outcomes, working with partners, and creating value together can improve customer performance. The researchers analyzed data from 180 Swedish firms to find strategies that work best. They identified five strategies, with one focused on using technology and partnerships to actively support customer operations. -
Mixed Reality and Artificial Intelligence: A Holistic Approach to Multimodal Visualization and Extended Interaction in Knee Osteotomy
This project developed Holoknee, a system that combines artificial intelligence and mixed reality to help plan knee surgery. Surgeons can see patient data and X-rays as holograms using a headset. Tests showed it can speed up planning and is useful for training. It may also assist surgeons during surgery. -
Multiclass Counterfactual Explanations Using Support Vector Data Description
This project focuses on making complex AI models easier to understand. It develops a method to show how small changes in data can change the model’s prediction. The approach finds multiple alternative scenarios to explain decisions clearly. The method was tested on real datasets and gave useful results for practical applications. -
Prerequisites for the Innovation Performance of Artificial Intelligence Laboratory: A Fuzzy-Set Qualitative Comparative Analysis
This project studies what makes AI research labs innovative. It looks at labs in universities, companies, and public institutions. The study finds that AI research needs strong computing resources and a supportive environment, while AI engineering work relies on good data and leading organizations. The results can help governments and lab managers improve AI innovation. -
Quantification of Hypsarrhythmia in Infantile Spasmatic EEG: A Large Cohort Study
This study focuses on helping doctors detect a brain disorder in infants called infantile spasms. The researchers analyzed brain wave recordings (EEG) from both affected and healthy babies. They found specific patterns in the signals that can reliably indicate the disorder. These patterns could be used to automatically diagnose infantile spasms, making the process faster and more accurate. -
Student Perceptions of ChatGPT Use in a College Essay Assignment: Implications for Learning, Grading, and Trust in Artificial Intelligence
This project studied how undergraduate engineering students used ChatGPT for an essay assignment. It looked at their experiences before and after using the tool. The study found that ChatGPT did not make writing easier but changed how students learned. Students found it helpful for learning, but they preferred teachers to oversee its use and did not trust it to grade work alone.
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How We Help You with Artificial Intelligence Projects
At UniPhD, we provide complete guidance and support for Artificial Intelligence ieee projects for MTech, ME, Master’s, and PhD students. Our team assists you at every stage from topic selection to coding, report writing, and result analysis.
We also help you choose a suitable IEEE base paper and guide you in developing your project using Python-based tools and frameworks such as TensorFlow, Keras, PyTorch, Scikit-learn, OpenCV, Flask, and Streamlit. In addition, we support implementation and simulation through platforms like MATLAB, Simulink, and NS2, depending on project requirements.
Our experts guide students all over India, including in Mumbai, Delhi, Bangalore, Hyderabad, Ahmedabad, Chennai, Kolkata, Pune, Jaipur, and Surat. We also assist students in the USA, UK, Canada, Australia, Singapore, Malaysia, and Thailand. They have extensive experience in computer science, electronics, electrical and all engineering domains.
Artificial Intelligence Thesis and Dissertation Writing
UniPhD has a team of experienced academic writers who specialize in Artificial Intelligence research and thesis development. We offer fast-track dissertation writing services to help you complete your Artificial Intelligence thesis or dissertation smoothly and on time.
Our M.E., M.Tech, Masters, MS abroad, and PhD theses are developed according to individual university guidelines and checked with plagiarism detection tools to ensure originality and quality.
Artificial Intelligence Research Paper Publishing Support
UniPhD provides complete support for research paper writing, editing, and proofreading to help you publish your work in reputed journals or conferences. We accept documents in Microsoft Word, RTF, or LaTeX formats and ensure your paper meets publication standards.
Project Synopsis and Presentation Support
We help you prepare your project synopsis, including the problem definition, objectives, and motivation for your dissertation. Our team also provides complete PPT, documentation, and tutorials to make your final presentation successful. You can also download complete project resources, including source code, a project report, a PPT, a tutorial, documentation, and a research paper for your Artificial Intelligence final year project.
Artificial Intelligence Research Support for PhD Scholars
UniPhD offers advanced Artificial Intelligence research projects designed specifically for PhD scholars. We provide end-to-end support for your research design, implementation, experimentation, and publication process.
Each project package includes comprehensive documentation, including the research proposal, complete source code, research guidance, documentation, research paper, and thesis writing support, helping you successfully complete your doctoral research and academic publications.