Research Publications

The research at CDAM (Centre for Data Analytics and Modelling) focuses on the development and application of data analytics, modelling, and artificial intelligence (AI) techniques. These efforts span a wide range of disciplines, including statistics, mathematics, data science, and various application areas such as healthcare, marketing, agriculture, education, business, and finance among others. Explore our cutting-edge research areas below:

Our research has been published in leading journals and presented at international conferences, contributing to advancements in data science and AI applications.

Research Areas Overview

1. Data Analytics

 
  • ●Advanced Data Processing: Developing methods for cleaning, preprocessing, and transforming large datasets to extract meaningful insights.
  • ●Exploratory Data Analysis (EDA): Using statistical and visual techniques to uncover patterns, trends, and anomalies in data.
  • ●Predictive Analytics: Building models to forecast future outcomes based on historical data.
  • ●Real-Time Analytics: Researching techniques for analyzing streaming data in real-time for applications like IoT and finance.
  • ●Big Data Technologies: Exploring tools like Hadoop, Spark, and cloud-based platforms for handling massive datasets.

2.  Modelling

 
  • ●Mathematical Modelling: Creating mathematical frameworks to represent complex systems and phenomena
  • ●Statistical Modelling: Developing models to understand relationships between variables and make data-driven decisions.
  • ●Simulation Techniques: Using computational methods to simulate real-world scenarios for testing and optimization.
  • ●Optimization Models: Researching algorithms to solve optimization problems in logistics, supply chain, and resource allocation.
  • ●Stochastic Processes: Studying random processes and their applications in finance, biology, and engineering.

3. Artificial Intelligence (AI)

 
  • ●Machine Learning Algorithms :Developing and improving algorithms for supervised, unsupervised, and reinforcement learning.
  • ●Deep Learning: Researching neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) for tasks like image recognition and natural language processing.
  • ●Generative AI: Exploring generative models like GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders) for creative applications.
  • ●AI Ethics and Fairness: Investigating ethical considerations, bias mitigation, and fairness in AI systems.
  • ●AI in Healthcare:Applying AI techniques for disease prediction, medical imaging, and personalized treatment.

4. Interdisciplinary Applications

 
  • ●Healthcare and Biomedicine: Using data analytics and AI for disease diagnosis, drug discovery, and patient care optimization.
  • ●Finance and Economics:  Developing models for risk assessment, fraud detection, and market trend analysis.
  • ●Environmental Science: Applying data-driven approaches to climate modeling, pollution monitoring, and resource management.
  • ●Social Sciences:Analyzing social networks, human behavior, and public policy impacts using data science techniques.
  • ●Engineering and Manufacturing: Implementing predictive maintenance, quality control, and process optimization using AI and analytics.

5. Theoretical Foundations

 
  • ●Statistical Theory: Advancing the theoretical underpinnings of statistical methods and their applications.
  • ●Computational Mathematics: Researching numerical methods and algorithms for solving complex mathematical problems.
  • ●Algorithm Development:Designing efficient algorithms for data processing, machine learning, and optimization.
  • ●Data Privacy and Security:Developing techniques to ensure data confidentiality and integrity in analytics and AI systems
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6. Collaborative Research

 
  • ●Industry Partnerships:Collaborating with industries to solve real-world problems using data-driven approaches.
  • ●Academic Collaborations: Partnering with universities and research institutions to advance knowledge in data science and AI.
  • ●Interdisciplinary Projects:Working across disciplines to address complex challenges in areas like healthcare, sustainability, and technology.

Key Features of CDAM Research

 
  • ●Innovation:  Focus on cutting-edge techniques and technologies in data analytics and AI.
  • ● Impact: Addressing real-world challenges through applied research and practical solutions.
  • ●Collaboration: Building partnerships with academia, industry, and government organizations.
  • ●Training and Development:Mentoring researchers and students to build expertise in data science and AI
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