AI Lab

14/03/2023

In memory of Dr. Saeed Ul Hassan, published by the British Journal of Educational Technology (BJET)

Click on the article title to read more.

14/10/2022

Stanford’s list of world's top 2% most-cited scientists (2022) includes five from Information Technology University. We heartily congratulate our scientists for international recognition of their groundbreaking research at Information Technology University.

Stanford University scientists publish annual data of the top 2 percent of the world’s most-cited scientists in various categories, and the most recent data (published on October 10, 2022) includes five faculty members from Information Technology University.

The following ITU faculty members are ranked among the top 2% scientists in the field of Information & Communication Technologies (ICT):

Dr. Junaid Qadir, Professor, Electrical Engineering
Dr. Saeed Ul Hassan (late), Associate Professor, Computer Science
Dr. Faisal Kamiran, Associate Professor, Computer Science
Dr. Muhammad Qasim Mehmood, Associate Professor, Electrical Engineering
Dr. Muhammad Zubair, Associate Professor, Electrical Engineering

The Stanford ranking is based on the bibliometric information (citation metrics) contained in the Scopus database which includes more than 200,000 scientists. The database provides standardized information on citations, h-index, co-authorship-adjusted hm-index, citations to papers in different authorship positions, and a composite indicator.
Read more about the most-cited scientists at: https://elsevier.digitalcommonsdata.com/datasets/btchxktzyw/
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08/10/2022

We announce the acceptance of our manuscript entitled "Early prediction of learners at risk in self-paced education: A neural
network approach " in the prestigious Elsevier journal of "Expert Systems with Applications" IF: 8.665.

This work is done in collaboration with colleagues from Monash University Australia, KAU, and the MMU United Kingdom, led by Dr. Hajra Waheed from ITU and supervised by Dr. Saeed Ul Hassan.

This research addresses the demands of modern education and increase flexibility, many higher education institutions are
considering self-paced education programs. However, student retention is yet a widely recognized challenge faced in self-paced education. While many studies have examined the potential of the use of data about student interaction with learning technologies to predict student success, studies that focus on self-paced education are scarce. To address this gap in the literature, this paper reports on the findings of a study that has investigated the performance of a well-known deep learning technique i.e., Long Short-term Memory (LSTM), in the prediction of students at risk of failing a course offered in a self-paced mode of online education. The study has utilized a freely accessible Open University Learning Analytics Dataset comprising 22,437 students with 69% pass, and 31% failed instances. The deep LSTM shows the highest predictive power to classify between pass and fail students, compared to all other alternatives by achieving an accuracy of 84.57 %, precision of 82.24 % and recall of 79.43%. Interestingly, with only first five weeks of course activity log data used for training, the receiver operating characteristic based diagnostic accuracy of the LSTM algorithm is achieved up to 71 %, that outperforms almost all other conventional algorithms - despite trained on the complete dataset collected for the entire duration of the course i.e. up to 38 weeks. Furthermore, this study has also employed a shapely additive explanation model to identify the most important predictors of student retention, e.g., assessment submission and attempted quizzes.
This approach is essential in order to increase the interpretability of deep learning techniques and, thus, increase their potential to generate actionable insights.

15/09/2022

ITU’s Scientist Dr. Saeed Ul Hassan Wins Eugene Garfield Award for Innovation in Citation Analysis, Posthumously:

Former Associate Professor and Chairperson Department of Computer Science at ITU, Dr. Saeed Ul Hassan’s (Late), proposal for innovation in science communications using AI wins the 2022 award.

Clarivate Plc, a global leader in providing trusted information and insights to accelerate the pace of innovation, earlier awarded the Eugene Garfield Award for Innovation in Citation Analysis to the late Dr. Saeed Ul Hassan. The award was announced at the 26th International Conference on Science, Technology and Innovation Indicators (STI 2022) by Dr. Gali Halevi, Director of the Institute for Scientific Information (ISI)™ at Clarivate.

Adeel Asghar Bilal H.Qureshi M Salman Mubarik

08/09/2022

Director of AI Lab, Dr. Saeed Ul Hassan's proposal for innovation in science communications using AI wins the 2022 award.

http://ow.ly/yCq850KCwkK

21/08/2022

ITU's 3rd convocation, graduating team of AI Lab supervised by Dr. Saeed Ul Hassan.

3 PhD students and more than 10 MS students received their degrees at the convocation.

27/05/2021

AI Lab publishes a comprehensive review paper in Scientometrics, entitled "A Decade of In-text Citation Analysis based on Natural Language Processing and Machine Learning Techniques: An overview of empirical studies"

Abstract: In-text citation analysis is one of the most frequently used methods in research evaluation. We are seeing significant growth in citation analysis through bibliometric metadata, primarily due to the availability of citation databases such as the Web of Science, Scopus, Google Scholar, Microsoft Academic, and Dimensions. Due to better access to full-text publication corpora in recent years, information scientists have gone far beyond traditional bibliometrics by tapping into advancements in full-text data processing techniques to measure the impact of scientific publications in contextual terms. This has led to technical developments in citation classifications, citation sentiment analysis, citation summarisation, and citation-based recommendation. This article aims to narratively review the studies on these developments. Its primary focus is on publications that have used natural language processing and machine learning techniques to analyse citations.

20/05/2021

25/02/2021

Join us LIVE for a lively discussion with Dr. Saeed ul Hassan on 'Stepping into AI enabled Research Assessment using Social Media'.

The live session is the first session of a newly initiated series by ITU Lincoln Corner: ‘Changing Tomorrow: Pakistani Academics Shaping the World’.

The primary goal of the series is to showcase Pakistani academics doing cutting-edge research and shaping a new world in the process.

Dr. Saeed ul Hassan is Chairperson, Department of Computer Science and Director, Artificial Intelligence (AI) Lab at Information Technology University.
4:00 PM; Friday, February 26, 2021

Bilal H.Qureshi Saeed Ul Hassan Wishal Farid