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dr. S. Abdiesfandani (Asad)

About Me

Asad Abdi is currently in a Researcher position at University of Twente, Netherlands. He received M.Tech and PhD degrees in software engineering and computer science from JNTU, India and University of Malaya (UM), Malaysia, respectively. He held a post-doctoral position and a RA position at UTM, Malaysia and the (UM), respectively. He published several papers in various ISI journals. His research interests include Text Mining, Natural Language Processing; Artificial Intelligence, Machine learning.

Expertise

Engineering & Materials Science
Linguistics
Redundancy
Semantics
Students
Syntactics
Mathematics
Multi-Document Summarization
Question Answering System
Social Sciences
Semantics

Research

   Research in an academic environment provides a unique opportunity to solve problems and vision the technology of the future. During my study as a bachelor, master, PhD student, research assistant, post-doc researcher and senior researcher, I have found that the ability to connect real-world applications with theoretical models is crucial to successful research. This includes abstracting a problem to its essence and then devising effective techniques for its solution. In my experience, many different problems, share some common characteristics. Understanding these inherent characteristics has enabled us to tackle problems at deeper levels and enables us to develop better solutions.

Machin learning and Deep Learning-based method —Artificial Intelligence (AI) is a branch of computer science where the idea is to simulate the human brain. In other words, it is a method to do a task that is potentially easy for humans, but difficult for the machines. Machine learning (ML) is a subclass of artificial intelligence. Its main task is to extract key features from data to cope with complex problems and handle large collections of data. Deep Learning is a new area of Machine Learning research, which has been introduced to move Machine Learning closer to one of its original goals: Artificial Intelligence. Learning can be supervised, semi-supervised or unsupervised.

Text data is becoming ubiquitous in all segments of life. On the other hands, a huge amount of text data are generated every day using apps messages, social media, forums, news publishing platforms and many other channels. Therefore, because of the large volumes of text data as well as the highly unstructured data source, NLP can be applied to handle large volumes of text data and structure highly unstructured data source.

Natural Language Processing (NLP) — is a field that combines linguistics, cognitive science, statistical machine learning and other computer science areas to compile intelligent computer systems that can understand human languages. NLP has various applications, among which are text similarity, question answering, sentiment analysis, automatic summarization, search engines, etc. Furthermore, there are different level of natural language: morphological, lexical, pragmatic, phonology, semantic, syntactic, discourse. Moreover, there are various approaches to NLP such as symbolic, statistical, connectionist, rule-based, knowledge-based and machine learning-based. Also, there are several open sources NLP that are used for fundamental NLP tasks, such as syntactic parsing, part-of-speech (POS) tagging, named entity recognition (NER), etc.

In particular, my research interests in computer science include:
• Artificial intelligence, Machine learning and Deep Learning.
• Text mining, Natural Language Processing (NLP),
• Data mining
The central focus of my research is to bring together all areas above and design methods/algorithms for various problems on text data mining. Over the last decade, I contributed to a wide range of topics in NLP. Meanwhile, I enjoy collaborating with scientists and domain experts of different background for interdisciplinary research in data science.
Research accomplishments — over the past years, my research interests and contributions cover different aspects of NLP and data mining, as demonstrated below:

1) Natural Language Processing (NLP) and Artificial intelligence

2) Deep Learning and machine learning for Natural Language Processing

3) Artificial Intelligence approach to Data Science

Publications

Recent
Abdi, A., Idris, N., Alguliyev, R. M., & Aliguliyev, R. M. (2018). Bibliometric Analysis of IP&M Journal (1980-2015). Journal of Scientometric Research, 7(1), 54-62. https://doi.org/10.5530/jscires.7.1.8
Abdi, A., Idris, N., & Ahmad, Z. (2018). QAPD: an ontology-based question answering system in the physics domain. Soft computing, 22, 213–230. https://doi.org/10.1007/s00500-016-2328-2
Abdi, A., Shamsuddin, S. M., & Aliguliyev, R. M. (2018). QMOS: Query-based multi-documents opinion-oriented summarization. Information processing & management, 54(2), 318-338. https://doi.org/10.1016/j.ipm.2017.12.002
Abdi, A., Shamsuddin, S. M., Hasan, S., & Piran, J. (2018). Machine learning-based multi-documents sentiment-oriented summarization using linguistic treatment. Expert systems with applications, 109(1), 66-85. https://doi.org/10.1016/j.eswa.2018.05.010
Alguliyev, R. M., Aliguliyev, R. M., Isazade, N. R. , Abdi, A., & Idris, N. (2017). A model for text summarization. International Journal of Intelligent Information Technologies, 13(1). https://doi.org/10.4018/IJIIT.2017010104
Abdi, A., Shamsuddin, S. M., Idris, N., Alguliev, R. M., & Aliguliyev, R. M. (2017). A linguistic treatment for automatic external plagiarism detection. Knowledge-based systems, 135, 135-146. https://doi.org/10.1016/j.knosys.2017.08.008
Abdiesfandani, S., Idris, N., Alguliyev, R. M., & Aliguliyev, R. M. (2017). Query-based multi-documents summarization using linguistic knowledge and content word expansion. Soft computing, 21, 1785–1801. https://doi.org/10.1007/s00500-015-1881-4

Google Scholar Link

Courses Academic Year  2021/2022

Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.
 

Courses Academic Year  2020/2021

Projects

 

Contact Details

Visiting Address

University of Twente
Drienerlolaan 5
7522 NB Enschede
The Netherlands

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Mailing Address

University of Twente
P.O. Box 217
7500 AE Enschede
The Netherlands

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