Generation of ROM (Recursive Object Model) diagrams from sentences.

The aim of this project is to develop the existing ROMA software to generate the ROM diagrams as accurately as possible. The ROMA software is being used to demonstrate natural language processing based on ROM generation principles. In order to generate correct ROM diagrams, this rule-based algorithm must be built to cover all patterns in English grammar. As a result, many words needed to be tested in this project to identify software flaws, and then appropriate rules were implemented to create and improve the software using Python language.

Developing NLP and AI methods to identify hospital adverse events.

The project considers the NLP technique, EBD, prepaid API's and AI algorithms using python to identify adverse events that relive the problems of chart review. This project includes preprocessing of the narrative electronic medical records to identify hospital adverse events, which is the main part of the project.

Optimizing ROM diagram.

The main objective of this project is to optimize the ROM diagram using a different optimization algorithm to achieve better and more accurate results.

Design methodologies review.

The main focus of this project is to have a better insight in the usability of every design methodology by analyzing them with the TASKS framework and EBD methodology. This project will help us to understand how a methodology could enable designers to do the design, and how incensing/decreasing knowledge, Skill, perceived workload and affect could impact on the mental stress and mental effort of the designer.

Analyzing human-centered design methodology with TASKS framework.

This research aimed to investigate the implementation barrier of Human-Centered Design (HCD) by using the TASKS framework. TASKS framework guides the identification of implementation barriers by investigating implementer workload, knowledge, skill, and affect. We will use a systematic literature approach to extract the TASKS element from Correlated papers.

3D mesh generation.

This project builds up a 3D mesh generation model based on previous work for real-world applications by using Python as a programming language.

An unbiased method for criteria identification in two-sided matching markets, An Environment-Based Design solution.

The first unbiased method to extract criteria of two-sided matching problems in the literature. This project aims to minimize all kinds of costs associated with the mismatch in two-sided matching markets by improving the quality of matches, starting from the first step of the matching process by using EBD, ROM and eLCA.

Team creativity.

This project intends to model team creativity using a causal loop diagram. The methodologies used are Vensim, word, and SPSS.

Human behavior and design methodology.

This project aims to promote the behavior of designers and other humans in the health sector.

Non-Parametric clustering algorithms.

The objective is to develop a clustering algorithm that is easy to use and widely applicable. More broadly, we want to have computers achieve comparable abilities as humans in essential perception tasks. Clustering is recognized as an important task in unsupervised learning. However, most existing clustering algorithms have specific scopes, and it's hard for users to choose an appropriate one for their purpose. We are developing a unified framework that allows users to adjust the core similarity function after investigating the properties of the exact problems at hand. In this way, the algorithm is not limited to some specific problems but is application-dependent and more flexible. The algorithm is initially inspired by some psychological principles. It is tested based on Python.

Inferring pilot trainee's cognitive and affective states from EEG signals.

This project deals with inferring pilot trainees’ cognitive control and workload from EEG signals with the help of data analysis tools.

Knowledge extraction from free text.

Identifying core engineering or medical concepts from the massive scientific text and building semantic networks for information retrieval support. Methodologies like EBD and ROM are the backbone of this project.