Summary
Education
Skills
Projects
MSc Dissertation (Ongoing)
Timeline
Generic
Moses Babalola

Moses Babalola

Hove,UK

Summary

Motivated graduate student in computer science with a strong academic background. Proficient in programming languages and data analysis, eager to contribute to cutting-edge research in machine learning, image processing, and natural language processing.

Education

Master of Science - Artificial Intelligence & Adaptive Systems

University of Sussex
Brighton
09.2022 -

Skills

  • Programming Languages: Python, MATLAB
  • Machine Learning: Supervised learning, unsupervised learning, classification, regression, clustering
  • Image Processing: Image enhancement, filtering, segmentation, feature extraction
  • Natural Language Processing (NLP): Text preprocessing, tokenization, part-of-speech tagging, sentiment analysis
  • Data Analysis: Data manipulation, data cleaning, data visualization (NumPy, Pandas, Matplotlib)
  • Statistical Analysis: Hypothesis testing, regression analysis, data modeling
  • Problem-Solving: Analytical thinking, problem-solving skills
  • Research and Documentation: Research methodology, data analysis, report writing
  • Teamwork and Collaboration: Effective team collaboration, idea contribution
  • Communication: Verbal and written communication skills, presentations, reports

Projects

 Automatic Detection of Dry Bean Seeds (Machine Learning Project)

  • Developed a system for the automatic detection of 7 types of dry bean seeds using data captured with a high-resolution camera.
  • Explored and implemented three models (Support Vector Machine, k-Nearest Neighbors, and Random Forest) for classification.
  • Employed GridSearchCV for hyperparameter tuning and model selection, choosing the best-performing model.
  • Conducted a comparative analysis between the selected best model and an Artificial Neural Network (ANN) to evaluate their performance.

MSc Dissertation (Ongoing)

Human Activity Recognition Based On Movement Representation Learning

  • Leveraging existing body movement datasets (EmoPain@Home, ETRI Activity 3D, NTU RGB+D) for activity recognition in chronic pain physical rehabilitation.
  • Applying representation learning techniques and developing a neural network-based model for automatic recognition of protective behaviors and contextual information.
  • Contribution to the field of human activity recognition and its applications in chronic pain rehabilitation technology.

Timeline

Master of Science - Artificial Intelligence & Adaptive Systems

University of Sussex
09.2022 -

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