Artificial Intelligence Research Center (AIRC)

Introduction

The Artificial Intelligence Research Center (AIRC) at Ajman University was set-up in 2021 to strengthen the research related to Artificial Intelligence (AI) conducted by our faculty and students associated with the AI Graduate Program. The objective of this Center is to nurture and promote research, innovation and entrepreneurship in the area of Artificial Intelligence.

The AIRC consolidates our experience in the fields of AI, Robotics, evolutionary computation and Biomedical Data Science.

The aim of the AIRC is to achieve its objectives by fostering collaboration between professional research groups in the areas of AI, robotics, and biomedical engineering.

AIRC objectives

  1. Conduct cutting-edge AI related research in cooperation with multiple AU colleges
  2. To engage in impactful projects with the industry and the government including AI technology transfer
  3. To collaborate with external entities and partners in research projects and teaching
  4. To offer state-of-the-art courses, workshops & training for both the AU community and the wider society
  5. To provide a platform for incubators that supports AI-based entrepreneurships
  6. To conduct outreach programs for students to facilitate AI skill development
  7. To provide a research environment for graduate students to conduct research in cooperation with affiliated faculty and staff members
  8. To organize local/international events such as AI Hackathon
  9. Disseminate expertise and knowledge in the fields of AI, ML, and robotics

Key areas of research in AIRC

Evolutionary Computation:

 Evolutionary Computation uses AI-enabled optimization algorithms to study evolution in nature. It has the potential to help us find answers to a myriad of complex problems in various scientific and medical fields.

At AIRC, we conduct high quality research in the field of Evolutionary Computation by addressing several complex real-world problems using optimization algorithms.

Machine Learning and Deep Learning:

Machine learning provides systems with the ability to learn and improve from experience without being explicitly programmed. Machine learning focuses on developing computer programs that can access data and use it to learn for themselves.

The learning process begins with data acquisition and collection to find specific patterns in the data and make better decisions. Machine learning's primary aim is to make computers learn automatically without human intervention.

Data Science:

Data Science is an interdisciplinary field that utilizes computing technology to derive obvious and non-obvious relationships in data by developing the appropriate scientific algorithms and implement these methods to extract useful knowledge or insights from the data.

Data science is one of the most intensively researched areas within the field of AI and machine learning. The research carried out by AIRC will be at the cutting edge of new developments in this field.

Apart from the above, the AIRC also focusses on research in the areas of Robotics & Machine Vision and Natural Language Processing.

AIRC’s Vision & Mission

AIRC’s Vision

To become a leading center for AI-related research in the Arab region by making impactful research contributions in this field.

Our Mission

To nurture talent and an ecosystem of innovation in all areas related to AI and Machine Learning, with the active involvement and cooperation of industry and society in the UAE. To conduct impactful applied research in AI and ML and to foster strong industry academic synergy for AI adoption.

Research Groups and areas of interest.

The following research groups within the AIRC will conduct fundamental and applied research in the respective subject areas:

1- Deep Learning/Machine Learning research group.

2- Data Science research group.

3- Robotics and Machine Vision research group.

4- NLP and Speech Recognition research group.

5- Evolutionary computation research group

AIRC Team Members

Head of AIRC

Associate Professor
m.albetar@ajman.ac.ae
06 705 5183
Ajman Campus
 

AIRC Members

Vice Chancellor for Academic Affairs
k.assaleh@ajman.ac.ae
06 705 6565
Ajman Campus
Dean of Graduate Studies & Research
k.arshad@ajman.ac.ae
06 705 6027
Ajman Campus
Acting Dean
m.nasor@ajman.ac.ae
06 705 6762
Ajman Campus
Associate Professor, BSDA Program Coordinator
g.alnaymat@ajman.ac.ae
06 705 5178
Ajman Campus
Associate Professor
r.mehdi@ajman.ac.ae
06 705 6603
Ajman Campus
Assistant Professor
elfadil.abdalla@ajman.ac.ae
06 705 6041
Ajman Campus
Professor in Artificial Intelligence
m.deriche@ajman.ac.ae
06 705 5423
Ajman Campus
Associate Professor
q.yaseen@ajman.ac.ae
--
Ajman Campus
Associate Professor
s.fraihat@ajman.ac.ae
06 705 5420
Ajman Campus
 

Name

Affiliation and Position

AU Title

Eng. Lamees Mohammad Dalbah

Ajman University (Master of AI)

Research Associate

Dr. Mohammed A. Awadallah

Al Aqsa University, Palestine (Dean, Associate Professor)

Adjunct Research Associate

Dr. Sharif Naser Makhadmeh

University Sciences Malaysia (Teaching Assistant till 2019)

Adjunct Research Associate

Prof. Muhammad Ali Imran

University of Glasgow, UK (Dean University of Glasgow UESTC, Professor of Communication Systems)

Adjunct Research Professor

Dr. Solomon Senok

Ajman University

Associated members

Prof. Shaher Al Momani

Ajman University

Associated members

Dr. Alfreda Stadlin  Ajman University

Associated members

Dr. Ahmed Imran Ajman University

Associated members

Dr. Guangming Cao Ajman University

Associated members

Eng. Shaimaa Mahmood Mounir Kouka Ajman University (Master of AI) Research Associate

AIRC Publications

  1. Al-Betar, M. A., Hammouri, A. I., Awadallah, M. A., & Doush, I. A. (2020). Binary β-hill climbing optimizer with S-shape transfer function for feature selection. Journal of Ambient Intelligence and Humanized Computing, 1-29.

  2. Abdalkareem, Z. A., Amir, A., Al-Betar, M. A., Ekhan, P., & Hammouri, A. I. (2021). Healthcare scheduling in optimization context: a review. Health and Technology, 1-25.

  3. Al-Betar, M. A., Alyasseri, Z. A. A., Awadallah, M. A., & Doush, I. A. (2021). Coronavirus herd immunity optimizer (CHIO). Neural Computing and Applications, 33(10), 5011-5042.

  4. Al-Betar, M. A., Awadallah, M. A., Heidari, A. A., Chen, H., Al-Khraisat, H., & Li, C. (2021). Survival exploration strategies for harris hawks optimizer. Expert Systems with Applications, 168, 114243.

  5. Makhadmeh, S. N., Khader, A. T., Al-Betar, M. A., Naim, S., Abasi, A. K., & Alyasseri, Z. A. A. (2021). A novel hybrid grey wolf optimizer with min-conflict algorithm for power scheduling problem in a smart home. Swarm and Evolutionary Computation, 60, 100793.

  6. Abasi, A. K., Khader, A. T., Al-Betar, M. A., Naim, S., Alyasseri, Z. A. A., & Makhadmeh, S. N. (2021). An ensemble topic extraction approach based on optimization clusters using hybrid multi-verse optimizer for scientific publications. Journal of Ambient Intelligence and Humanized Computing, 12(2), 2765-2801.

  7. Al-Betar, M. A. (2021). Island-Based Harmony Search Algorithm for Non-convex Economic Load Dispatch Problems. Journal of Electrical Engineering & Technology, 1-31.

  8. Kassaymeh, S., Abdullah, S., Al-Betar, M. A., & Alweshah, M. (2021). Salp swarm optimizer for modeling the software fault prediction problem. Journal of King Saud University-Computer and Information Sciences.

  9. Kassaymeh, S., Abdullah, S., Al-Laham, M., Alweshah, M., Al-Betar, M. A., & Othman, Z. (2021). Salp Swarm Optimizer for Modeling Software Reliability Prediction Problems. Neural Processing Letters, 1-37.

  10. Almomani, A., Al-Nawasrah, A., Alauthman, M., Al-Betar, M. A., & Meziane, F. (2021). Botnet detection used fast-flux technique, based on adaptive dynamic evolving spiking neural network algorithm. International Journal of Ad Hoc and Ubiquitous Computing, 36(1), 50-65.

  11. Abasi, A. K., Khader, A. T., Al-Betar, M. A., Naim, S., Makhadmeh, S. N., & Alyasseri, Z. A. A. (2021). A novel ensemble statistical topic extraction method for scientific publications based on optimization clustering. Multimedia Tools and Applications, 80(1), 37-82.

  12. Awadallah, M. A., Al-Betar, M. A., Hammouri, A. I., & Alomari, O. A. (2020). Binary JAYA algorithm with adaptive mutation for feature selection. Arabian Journal for Science and Engineering, 45(12), 10875-10890.

  13. Alyasseri, Z. A. A., Al‐Betar, M. A., Doush, I. A., Awadallah, M. A., Abasi, A. K., Makhadmeh, S. N., ... & Zitar, R. A. Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches. Expert Systems, e12759.

  14. Aldeeb, B. A., Al-Betar, M. A., Norwawi, N. M., Alissa, K. A., Alsmadi, M. K., Hazaymeh, A. A., & Alzaqebah, M. (2021). Hybrid intelligent water Drops algorithm for examination timetabling problem. Journal of King Saud University-Computer and Information Sciences.

  15. Ja’afar, S., Tubishat, M., Idris, M., Al-Betar, M. A., Alswaitti, M., Jarrah, H., Ismail, M. A., & Omar, M. S. (2021). Improved Sine Cosine Algorithm with Simulated Annealing and Singer Chaotic Map for Hadith Classification. Neural Computing and Applications.

  16. Raghda Fawzey Hriez, Ghazi Al-Naymat. A Framework to Capture the Dependency between Prerequisite and Advanced Courses in Higher Education. Journal of Computing in Higher Education (JCHE).

  17. Alhijawi, B., & Ghazi, A. N. Novel Positive Multi-Layer Graph-Based Method for Collaborative Filtering Recommender Systems. Journal of Computer Science and Technology.

  18. Alsadoon, A., Al-Naymat, G., Alsadoon, O. H., & Prasad, P. W. C. (2021). DDV: A Taxonomy for Deep Learning Methods in Detecting Prostate Cancer. Neural Processing Letters, 1-21.

  19. Bhagyashree Shah, Abeer Alsadoon, P.W.C. Prasad, Ghazi Al-Naymat & Azam Beg DPV: a taxonomy for utilizing deep learning as a prediction technique for various types of cancers detection. Multimedia Tools and Applications.

  20. Alhijawi, B., Al-Naymat, G., Obeid, N., & Awajan, A. (2021). Novel predictive model to improve the accuracy of collaborative filtering recommender systems. Information Systems, 96, 101670.

  21. Khader, M., & Al-Naymat, G. (2020). Density-based Algorithms for Big Data Clustering Using MapReduce Framework: A Comprehensive Study. ACM Computing Surveys (CSUR), 53(5), 1-38.

  22. Ghazi Al-Naymat, Hannan Hussien, Mouhammd Al-kasassbeh, Nidhal Aldmour. Accurate Detection of Network Anomalies within SNMP-MIB Dataset using Deep Learning. Int. J. of Computer Applications in Technology (IJCAT).

  23. Suleiman, D., Al-Zewairi, M., Etaiwi, W., & Al-Naymat, G. (2020). Empirical Evaluation of the Classification of Deep Learning under Big Data Processing Platforms. International Journal of Advanced Trends in Computer Science and Engineering, 9(5).

  24. D Suleiman, G Al-Naymat, M Itriq. Deep SMS Spam Detection using H2O Platform. International Journal of Advanced Trends in Computer Science and Engineering. Vol (9)5. 2020.

  25. Alian, M., Al-Naymat, G., & Ramadan, B. (2020). Arabic real time entity resolution using inverted indexing. Language Resources and Evaluation, 54(4), 921-941.

  26. A Al-Qerem, G Al-Naymat, M Alhasan. Model Improvement Through Comprehensive Preprocessing For Loan Default Prediction. International Journal of Scientific & Technology Research 9 (1), 2020.

  27. S Sawlha, G Al-Naymat. IoT data compression based on successive data grouping. Turkish Journal Of Electrical Engineering & Computer Sciences.

  28. Alomari, O. A., Makhadmeh, S. N., Al-Betar, M. A., Alyasseri, Z. A. A., Doush, I. A., Abasi, A. K., ... & Zitar, R. A. (2021). Gene selection for microarray data classification based on Gray Wolf Optimizer enhanced with TRIZ-inspired operators. Knowledge-Based Systems, 223, 107034.

  29. Makhadmeh, S. N., Al-Betar, M. A., Alyasseri, Z. A. A., Abasi, A. K., Khader, A. T., Damaševičius, R., ... & Abdulkareem, K. H. (2021). Smart Home Battery for the Multi-Objective Power Scheduling Problem in a Smart Home Using Grey Wolf Optimizer. Electronics, 10(4), 447.

  30. Mahmood, S. N., Ishak, A. J., Saeidi, T., Soh, A. C., Jalal, A., Imran, M. A. and Abbasi, Q. H. (2021) Full ground ultra-wideband wearable textile antenna for breast cancer and wireless area body network applications. Micromachines, 12(3), 322.

  31. Mollel, M. S. , Abubakar, A. I. , Öztürk, M., Kaijage, S., Kisangiri, M., Hussain, S. , Imran, M. A. and Abbasi, Q. H. (2021) A survey of machine learning applications to handover management in 5G and beyond. IEEE Access, 9, 45770 -45802.

  32. Suresh Kumar, S., Dashtipour, K., Abbasi, Q. H. , Imran, M. A.and Ahmad, W. (2021) A review on wearable and contactless sensing for COVID-19 with policy challenges. Frontiers in Communications and Networks, 2, 636293.

  33. Dashtipour, K., Taylor, W., Ansari, S. , Gogate, M., Zahid, A., Sambo, Y. , Hussain, A., Abbasi, Q. H. and Imran, M. A. (2021)Public perception of the fifth generation of cellular networks (5G) on social media. Frontiers in Big Data, 4, 640868.

  34. Ali, S. M., Sovuthy, C., Noghanian, S., Ali, Z., Abbasi, Q. H. , Imran, M. A. , Saeidi, T. and Socheatra, S. (2021) Design and evaluation of a flexible dual-band meander line monopole antenna for on- and off-body healthcare applications. Micromachines, 12(5), 475.

  35. Aman, W., Rahman, M. M. U., Abbas, H. T. , Khalid, M., Imran, M. A. , Alomainy, A. and Abbasi, Q. H. (2021)Securing the insecure: a first-line-of-defense for body-centric nanoscale communication systems operating in THz band. Sensors, 21(10), 3534.

  36. Rehman, M., Shah, R. A., Khan, M. B., AbuAli, N. A., Shah, S. A. , Yang, X., Alomainy, A., Imran, M. A. and Abbasi, Q. H. (2021) RF sensing based breathing patterns detection leveraging USRP devices. Sensors, 21(11), 3855.

  37. Attaullah, H., Anjum, A., Kanwal, T., Malik, S. U., Asheralieva, A., Malik, H., Zoha, A. , Arshad, K. and Imran, M. A. (2021) F-classify: fuzzy rule based classification method for privacy preservation of multiple sensitive attributes. Sensors, 21(14), 4933.

  38. Dashtipour, K., Taylor, W., Ansari, S. , Gogate, M., Zahid, A., Sambo, Y. , Hussain, A., Abbasi, Q. H. and Imran, M. A. (2021) Public perception of the fifth generation of cellular networks (5G) on social media. Frontiers in Big Data, 4, 640868.

  39. Alkoffash, M. S., Awadallah, M. A., Alweshah, M., Zitar, R. A., Assaleh, K., & Al-Betar, M. A. (2021). A Non-convex Economic Load Dispatch Using Hybrid Salp Swarm Algorithm. Arabian Journal for Science and Engineering, 1-20.

  40. Alyasseri, Z. A. A., Al-Betar, M. A., Awadallah, M. A., Makhadmeh, S. N., Abasi, A. K., Doush, I. A., & Alomari, O. A. (2021). A Hybrid Flower Pollination with β-Hill Climbing Algorithm for Global Optimization. Journal of King Saud University-Computer and Information Sciences.

  41. Zitar, R. A., Al-Betar, M. A., Awadallah, M. A., Doush, I. A., & Assaleh, K. (2021). An Intensive and Comprehensive Overview of JAYA Algorithm, its Versions and Applications. Archives of Computational Methods in Engineering, 1-30.

  42. Dalbah, L. M., Al-Betar, M. A., Awadallah, M. A., & Zitar, R. A. (2021). A modified coronavirus herd immunity optimizer for capacitated vehicle routing problem. Journal of King Saud University-Computer and Information Sciences.

  1. Abasi, A. K., Khader, A. T., Al-Betar, M. A., Alyasseri, Z. A. A., Makhadmeh, S. N., Al-laham, M., & Naim, S. (2021). A Hybrid Salp Swarm Algorithm with β-Hill Climbing Algorithm for Text Documents Clustering. Evolutionary Data Clustering: Algorithms and Applications, 129.

  2. Alyasseri, Z. A. A., Abasi, A. K., Al-Betar, M. A., Makhadmeh, S. N., Papa, J. P., Abdullah, S., & Khader, A. T. (2021). EEG-Based Person Identification Using Multi-Verse Optimizer as Unsupervised Clustering Techniques. Evolutionary Data Clustering: Algorithms and Applications, 89.

  1. Doush, I. A., Al-Betar, M. A., Awadallah, M. A., Hammouri, A. I., & El-Abd, M. (2020, December). Island-based Modified Harmony Search Algorithm with Neighboring Heuristics Methods for Flow Shop Scheduling with Blocking. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 976-982). IEEE.

  2. Hraiz, S., Al-Naymat, G., & Awajan, A. (2020, November). A Novel Method to Verify the Search Results of Database Queries on Cloud Computing. In 2020 21st International Arab Conference on Information Technology (ACIT) (pp. 1-7). IEEE.

  3. Khader, M. S., & Al-Naymat, G. (2020, September). VDENCLUE: An Enhanced Variant of DENCLUE Algorithm. In Proceedings of SAI Intelligent Systems Conference (pp. 425-436). Springer, Cham.

  4. Khader, M. S., & Al-Naymat, G. (2020, September). Big Data Clustering Using MapReduce Framework: A Review. In Proceedings of SAI Intelligent Systems Conference (pp. 575-593). Springer, Cham.

  5. Hriez, R. F., Al-Naymat, G., & Awajan, A. (2021, April). An Effective Algorithm for Extracting Maximal Bipartite Cliques. In International Conference on Data Science, E-learning and Information Systems 2021 (pp. 76-81).

  6. Makhadmeh, S. N., Al-Betar, M. A., Abasi, A. K., Awadallah, A. A., Alyasseri, Z. A. A., Alomari, O. A., & Abu Doush, I. Wind Driven Optimization With Smart Home Battery for Power Scheduling Problem in Smart Home. In Third Palestinian International Conference on Information and Communication Technology (PICICT 2021).

  7. Mahfouz, K., Ali, S., Al-Betar, M. A., & Awadallah, A. A. Solving 0-1 Knapsack Problems Using Sine-Cosine Algorithm. In Third Palestinian International Conference on Information and Communication Technology (PICICT 2021).

  8. Abu Khurma, R., A., Awadallah, A. A., & Aljarah, I. Binary Harris Hawks Optimization Filter Based Approach for Feature Selection. In Third Palestinian International Conference on Information and Communication Technology (PICICT 2021).

  9. Alyasseri, Z. A. A., Al-Betar, M. A., Awadallah, A. A., Makhadmeh, S. N., Abasi, A. K., Alomari, O. A., & Abu Doush, I. EEG Feature Fusion for Person Identification Using Efficient Machine Learning Approach. In Third Palestinian International Conference on Information and Communication Technology (PICICT 2021).

  10. Dalbah, L. M., Al-Betar, M. A., Awadallah, M. A., & Zitar, R. A. (2021). A Coronavirus Herd Immunity Optimization (CHIO) for Travelling salesman problem. In International Conference on Innovative Computing and Communication.

  11. Dalbah, L. M., Alshamsi, H. S., Al-Betar, M. A., & Awadallah, A. A. Solving Truss Structures Problem by Size Optimizing using Red Deer Algorithm. In Third Palestinian International Conference on Information and Communication Technology (PICICT 2021).

AIRC Activities

The 3rd International Workshop on Data-Driven Security (DDSW 2022)

The AIRC at Ajman University is organizing the 3rd International Workshop on Data-Driven Security (DDSW 2022) which focuses on using machine learning algorithms for cybersecurity solutions. The workshop, which will be held in Porto, Portugal, focuses on new security techniques that use machine learning, data mining and statistical analytical techniques to solve nowadays security challenges.

Prospective authors are invited to submit unpublished papers before December 20, 2021.

For more information, please visit the official DDSW website. https://www.ajman.ac.ae/en/ddsw


European, Asian, Middle Eastern, North African Conference on Management and Information Systems (EAMMIS)

In cooperation with Coventry University, UK, Bridges Foundation is organizing the EAMMIS 2022 that will take place on May 13-14, 2022. EAMMIS 2022 probes a deeper academic and scholarly interpretation through its theme. AIRC, from Ajman University, is one of the strategic sponsors of this venue and Dr Anjum Razzaque (Assoc. Prof. AIRC member, College of Engineering and Information Technology) is the EAMMIS 2022 conference chair; an international platform where researchers, academicians, practitioners, and industry professionals can present research findings that offer more profound insight into novel theories that embrace theoretical, managerial, and practical implications within the fields of management and information systems. At the EAMMIS conferences, participants can exchange new ideas and experiences for establishing research collaborations. In extensive consultation with the EAMMIS executive committee and the faculty of the organizing universities, the conclusive consensus is that it is best not to hold only a physical EAMMIS 2022, but a hybrid conference platform.

Call for papers: EAMMIS 2022 invite scholars, practitioners, and research students to submit and present their papers and findings to the EAMMIS 2022 conference on From the Internet of Things to the Internet of Ideas: The role of Artificial Intelligence to be held on May 13-14, 2022, a hybrid venue: online on MS Teams and at Coventry University, UK. EAMMIS conference invites theoretical and empirical papers that employ quantitative, qualitative, or critical methods. Authors should submit original, unpublished research papers and must not simultaneously submit to another journal or conference. All accepted papers will be published in the conference proceedings “From the Internet of Things to the Internet of Ideas: The role of Artificial Intelligence” as a volume of the “Lecture Notes in Networks and Systems” series published by Springer Verlag. Indexing by SCOPUS, SJR Q3, ISI Proceedings Clarivate Analytics WEB OF SCIENCE, EI-Compendex, DBLP, Google Scholar, and Springerlink.Authors can submit mansucript/s based on the following four tracks:

Theme 1: Innovation in the Digital Era

Theme 2: Sustainable Technologies, Artificial intelligence, and Internet of Ideas

Theme 3: Cybersecurity, Artificial Intelligence, and Internet of Things

Theme 4: Internet of Ideas, Artificial Intelligence, and the business systems of the future

For more information, please visit the official EAMMIS 2022 website: https://www.eammis.com/