Educational Blockchain

This project is aimed at early prediction of Parkinson’s disease through computerized analysis of online handwriting samples. While a number of clinical evaluations are carried out to diagnose the Parkinson’s disease, most of these tests are effective only once the disease is at a relatively advanced stage. Studies have shown that analysis of handwriting can be used as a valuable tool for prediction of Parkinson’s disease at very early stages. In the proposed research, we have employed raw signals captured by a digitizer tablet while attempting several writing and drawing templates, to compute various kinematic, temporal, and pen-based attributes. In addition to these online dynamic attributes, static offline visual features are also extracted using state-of-the-art Convolutional Neural Networks (CNNs). The developed tools allow practitioners to collect handwriting and drawing samples of subjects and employ a combination of different types of features (online and offline) for the analysis and prediction.

A system where multiple educational institutes or universities can collaborate to offer courses. A student enrolled in one university can take courses offered at other universities and this information will be shared among the universities on a blockchain to create a consolidated student transcript. Blockchain technology can serve as the foundation for creating. Each participating university can host its courses and credentials on the blockchain, making them accessible to all other universities within the network. A standardized protocol that enables interoperability between universities. Each participating university can host its courses and credentials on the blockchain, making them accessible to all other universities within the network. This eliminates the need for students to enroll in a single institution and instead allows them to choose courses from various universities seamlessly. Blockchain can store academic credentials, such as certificates, degrees, and transcripts, in a secure and tamper-proof manner. Students can build a comprehensive academic record on the blockchain, which they can share with employers or other educational institutions as needed. This enhances the portability and authenticity of their educational qualifications.

Project Team

Key Researchers

Dr. Fatima Khalique

Research Assistants

Qadeer Hashir

Publications

Moetesum, M., Diaz, M., Masroor, U., Siddiqi, I., & Vessio, G. (2022). A survey of visual and procedural handwriting analysis for neuropsychological assessment. Neural Computing and Applications, 1-18.
Diaz, M., Moetesum, M., Siddiqi, I., & Vessio, G. (2021). Sequence-based dynamic handwriting analysis for Parkinson’s disease detection with one-dimensional convolutions and BiGRUs. Expert Systems with Applications, 168, 114405.
Moetesum, M., Siddiqi, I., Javed, F., & Masroor, U. (2020, September). Dynamic Handwriting Analysis for Parkinson’s Disease Identification using C-BiGRU Model. In 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR) (pp. 115-120). IEEE.