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Animations & Interaction

The Leeds Beckett University website occasionally uses animations to page elements and animated gifs to add impact to pages. If you wish to stop them from playing for accessibility purposes or for personal preference then please see the guidance below. Please note that browsers sometimes change how they operate, so please refer to your browser help files to find their latest information.
How do I turn off animations?

Chrome

Chrome users will need to use browser extensions. Animation Policy from Google will help you set your own preferences.

Microsoft Edge

There is currently no way to disable animations within Microsoft Edge. Users of Edge (Chromium) browser can install the Chrome extensions above and use them.

Internet Explorer

To stop animations on a single page you can just press the 'Esc' key. This will stop the animation on that page. If you wish to restart the animations you need to refresh the page. To permanently disable the playing of animated GIF images, open Internet Options > Advanced tab. Under the Multimedia section, uncheck the Play animations in webpage check-box, click Apply and Exit and then restart your computer.

Firefox

Type about:config in Firefox address bar and hit Enter to open Settings. Search for the image.animation_mode flag and change its value from Normal to None. Once you restart Firefox this will disable animations.

Dr Mohammad Shan-A-Khuda

Lecturer

With a PhD in Data Science from Leeds Beckett University and an MSc in multidisciplinary informatics from the University of Leeds, Mohammad is an astute computing and data science academic with extensive experience in dynamic, cross-disciplinary research and teaching environments.

Mohammad guides, collaborates with and fully prepares students within various specialities and disciplines, supporting their journey towards professional positions within the IT industry and further study. He excels within interdisciplinary and multidisciplinary frameworks, leads and collaborates on projects, enhances methods of teaching practice to meet the needs of an ever-changing subject area, and fosters sustained student progression.

Mohammad has developed a wide range of practical skills in students using various tools and environments, introducing them to key critical and theoretical issues in computing and data science. He enhances the research journey, applications, and outcomes, collaborates across subjects to enable comprehensive, real-world findings and bolsters the institution's profile and funding potential.

Current Teaching

Statistics in practice
Intelligent Systems and Machine Learning
Mathematics for Data Science
Objected Oriented Programming
Computer Programming
Fundamentals of Databases
Computing Systems
Data Structures and Algorithms
Advanced Engineering Mathematics
Simulation and Modelling

Research Interests

Mohammad's research interests include data analytics, visualisation, and modelling large datasets. His PhD thesis, Modelling fear of crime area variation of people in England, holistically examines the emotional and cognitive dimensions of people's fear of crime in England and considers a wide range of demographic and area-level measures, such as The English Indices of Deprivation using a novel methodology called Multilevel Modelling.

While working as a postdoctoral research fellow at Leeds Beckett University, Mohammad led the Characteristics of Victims of Cybercrime project. He presented the results of a comprehensive analysis of West Yorkshire Police (WYP) cybercrime data and evaluated the recognition of the knowledge generated and its impact on WYP.

Mohammad proposed and successfully supervised a series of MSc dissertation projects: Machine learning-based crime prediction, Visualisation of Road traffic accidents in the UK, and Machine Learning-based dementia prediction.