Artificial Intelligence Research Center (AIRC)

Introduction

The Artificial Intelligence Research Center (AIRC) at Ajman University was set-up in 2020 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                       AIRC Members

Associate Professor
m.albetar@ajman.ac.ae
06 705 5183
Ajman Campus
Vice Chancellor for Academic Affairs
k.assaleh@ajman.ac.ae
06 705 6565
Ajman Campus
Dean of Research and Graduate Studies
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
Full- Time Research Associate
06 705 5435
Ajman Campus
Associate Professor, Acting HOD Mechanical Engineering
a.imran@ajman.ac.ae
06 705 6782
Ajman Campus
Assistant Professor in Mechanical Engineering
m.shah@ajman.ac.ae
--
Ajman Campus
 

 

Associated members

Dean
s.momani@ajman.ac.ae
06 705 6446
Ajman Campus
Dean
s.senok@ajman.ac.ae
06 705 6397
Ajman Campus
Professor, Head of Basic Medical Sciences Department
a.stadlin@ajman.ac.ae
06 705 5330
Ajman Campus
Professor
g.cao@ajman.ac.ae
06 705 5154
Ajman Campus
 

 

Name

Affiliation and Position

AU Title

Dr. Mohammed A. Awadallah

Al Aqsa University, Palestine (Dean, Associate Professor)

Adjunct Research Associate

Prof. Muhammad Ali Imran

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

Adjunct Research Professor

Dr. Mohamed Abd Elaziz

 Zagazig University

Adjunct Research Associate

Eng. Lamees Mohammad Dalbah

Ajman University (Master of AI)

Research Associate

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.

  43. Abdalkareem, Z. A., Al-Betar, M. A., Amir, A., Ehkan, P., Hammouri, A. I., & Salman, O. H. (2021). Discrete flower pollination algorithm for patient admission problem. Computers in Biology and Medicine, 105007.
  44. Alweshah, M., Alkhalaileh, S., Al-Betar, M. A., & Bakar, A. A. (2021). Coronavirus herd immunity optimizer with greedy crossover for feature selection in medical diagnosis. Knowledge-Based Systems, 107629.
  45. Awadallah, M. A., Hammouri, A. I., Al-Betar, M. A., Braik, M. S., & Abd Elaziz, M. (2021). Binary Horse herd optimization algorithm with crossover operators for feature selection. Computers in Biology and Medicine, 105152.
  46. Aliyu, F., Sheltami, T., Deriche, M., & Nasser, N. (2022). Human Immune-Based Intrusion Detection and Prevention System for Fog Computing. Journal of Network and Systems Management, 30(1), 1-27.
  47. Saoud, L. S., Al-Marzouqi, H., & Deriche, M. (2021). Wind Speed Forecasting Using the Stationary Wavelet Transform and Quaternion Adaptive-Gradient Methods. IEEE Access, 9, 127356-127367.
  48. Awadallah, M. A., Al-Betar, M. A., Doush, I. A., Makhadmeh, S. N., Alyasseri, Z. A. A., Abasi, A. K., & Alomari, O. A. (2022). CCSA: Cellular Crow Search Algorithm with topological neighbourhood shapes for optimization. Expert Systems with Applications, 116431.
  49. Abdi Alkareem Alyasseri, Z., Alomari, O. A., Al-Betar, M. A., Awadallah, M. A., Hameed Abdulkareem, K., Abed Mohammed, M., ... & Rho, S. (2022). EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications. Computational Intelligence and Neuroscience, 2022.
  50. Alyasseri, Z. A. A., Khader, A. T., Al-Betar, M. A., Yang, X. S., Mohammed, M. A., Abdulkareem, K. H., ... & Razzak, I. (2022). Multi-objective flower pollination algorithm: a new technique for EEG signal denoising. Neural Computing and Applications, 1-20.
  51. Alweshah, M., Alkhalaileh, S., Al-Betar, M. A., & Bakar, A. A. (2022). Coronavirus herd immunity optimizer with greedy crossover for feature selection in medical diagnosis. Knowledge-Based Systems, 235, 107629.
  52. Al-Fawa'reh, M., Al-Fayoumi, M., Nashwan, S., & Fraihat, S. (2021). Cyber threat intelligence using PCA-DNN model to detect abnormal network behavior. Egyptian Informatics Journal.
  53. Durrani, U. K., Al Naymat, G., Ayoubi, R. M., Kamal, M. M., & Hussain, H. (2022). Gamified flipped classroom versus traditional classroom learning: Which approach is more efficient in business education?. The International Journal of Management Education, 20(1), 100595.
  54. Abualigah, L., Abd Elaziz, M., Sumari, P., Geem, Z. W., & Gandomi, A. H. (2022). Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer. Expert Systems with Applications, 191, 116158.
  55. Issa, M., Helmi, A. M., Elsheikh, A. H., & Abd Elaziz, M. (2022). A biological sub-sequences detection using integrated BA-PSO based on infection propagation mechanism: Case study COVID-19. Expert Systems with Applications, 189, 116063.
  56. Awadallah, M. A., Hammouri, A. I., Al-Betar, M. A., Braik, M. S., & Abd Elaziz, M. (2021). Binary Horse herd optimization algorithm with crossover operators for feature selection. Computers in biology and medicine, 105152.
  57. Anter, A. M., Abd Elaziz, M., & Zhang, Z. (2022). Real-time epileptic seizure recognition using Bayesian genetic whale optimizer and adaptive machine learning. Future Generation Computer Systems, 127, 426-434.
  58. Yousri, D., Abd Elaziz, M., Oliva, D., Abraham, A., Alotaibi, M. A., & Hossain, M. A. (2022). Fractional-order comprehensive learning marine predators algorithm for global optimization and feature selection. Knowledge-Based Systems, 235, 107603.
  59. Ouadfel, S., & Abd Elaziz, M. (2022). Efficient high-dimension feature selection based on enhanced equilibrium optimizer. Expert Systems with Applications, 187, 115882.
  60. Nadimi-Shahraki, M. H., Fatahi, A., Zamani, H., Mirjalili, S., Abualigah, L., & Abd Elaziz, M. (2021). Migration-Based Moth-Flame Optimization Algorithm. Processes, 9(12), 2276.
  61. Nadimi-Shahraki, M. H., Taghian, S., Mirjalili, S., Ewees, A. A., Abualigah, L., & Abd Elaziz, M. (2021). MTV-MFO: Multi-Trial Vector-Based Moth-Flame Optimization Algorithm. Symmetry, 13(12), 2388.
  62. Nadimi-Shahraki, M. H., Taghian, S., Mirjalili, S., Abualigah, L., Abd Elaziz, M., & Oliva, D. (2021). EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem. Electronics, 10(23), 2975.
  63. Elaziz, M. A., Abualigah, L., Yousri, D., Oliva, D., Al-Qaness, M. A., Nadimi-Shahraki, M. H., ... & Ali Ibrahim, R. (2021). Boosting Atomic Orbit Search Using Dynamic-Based Learning for Feature Selection. Mathematics, 9(21), 2786.
  64. Abd Elaziz, M., Dahou, A., Alsaleh, N. A., Elsheikh, A. H., Saba, A. I., & Ahmadein, M. (2021). Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm. Entropy, 23(11), 1383.
  65. Ouadfel, S., & Abd Elaziz, M. (2021). A multi-objective gradient optimizer approach-based weighted multi-view clustering. Engineering Applications of Artificial Intelligence, 106, 104480.
  66. Abbas, F., Yasmin, M., Fayyaz, M., Elaziz, M. A., Lu, S., & El-Latif, A. A. A. (2021). Gender Classification Using Proposed CNN-Based Model and Ant Colony Optimization. Mathematics, 9(19), 2499.
  67. Ewees, A. A., Abualigah, L., Yousri, D., Sahlol, A. T., Al-qaness, M. A., Alshathri, S., & Elaziz, M. A. (2021). Modified Artificial Ecosystem-Based Optimization for Multilevel Thresholding Image Segmentation. Mathematics, 9(19), 2363.
  68. Ewees, A. A., Al-qaness, M. A., Abualigah, L., Oliva, D., Algamal, Z. Y., Anter, A. M., ... & Abd Elaziz, M. (2021). Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model. Mathematics, 9(18), 2321.
  69. Ibrahim, R. A., Abualigah, L., Ewees, A. A., Al-Qaness, M. A., Yousri, D., Alshathri, S., & Abd Elaziz, M. (2021). An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection. Entropy, 23(9), 1189.
  70. Najjar, I. M. R., Sadoun, A. M., Alsoruji, G. S., Abd Elaziz, M., & Wagih, A. (2021). Predicting the mechanical properties of Cu–Al2O3 nanocomposites using machine learning and finite element simulation of indentation experiments. Ceramics International.
  71. AlRassas, A. M., Al-Qaness, M. A., Ewees, A. A., Ren, S., Sun, R., Pan, L., & Abd Elaziz, M. (2021). Advance artificial time series forecasting model for oil production using neuro fuzzy-based slime mould algorithm. Journal of Petroleum Exploration and Production Technology, 1-13.
  72. Ibrahim, R. A., Yousri, D., Abd Elaziz, M., Alshathri, S., & Attiya, I. (2021). Fractional Calculus-Based Slime Mould Algorithm for Feature Selection Using Rough Set. IEEE Access, 9, 131625-131636.
  73. Yousri, D., AbdelAty, A. M., Al-qaness, M. A., Ewees, A. A., Radwan, A. G., & Abd Elaziz, M. (2021). Discrete fractional-order Caputo method to overcome trapping in local optima: Manta Ray Foraging Optimizer as a case study. Expert Systems with Applications, 116355.
  74. Makhadmeh, S. N., Abasi, A. K., & Al-Betar, M. A. (2022). Hybrid multi-verse optimizer with grey wolf optimizer for power scheduling problem in smart home using IoT. The Journal of Supercomputing, 1-36.
  75. Adel, H., Dahou, A., Mabrouk, A., Abd Elaziz, M., Kayed, M., El-Henawy, I.M., Alshathri, S. and Amin Ali, A., 2022. Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm. Mathematics, 10(3), p.447.
  76. Abualigah, L., Elaziz, M.A., Sumari, P., Khasawneh, A.M., Alshinwan, M., Mirjalili, S., Shehab, M., Abuaddous, H.Y. and Gandomi, A.H., 2022. Black hole algorithm: A comprehensive survey. Applied Intelligence, pp.1-24.
  77. Abualigah, L., Diabat, A., Altalhi, M. and Elaziz, M.A., 2022. Improved gradual change-based Harris Hawks optimization for real-world engineering design problems. Engineering with Computers, pp.1-41.
  78. Attiya, I.A., Abd Elaziz, M., Abualigah, L., Nguyen, T.N. and Abd El-Latif, A.A., 2022. An Improved Hybrid Swarm Intelligence for Scheduling IoT Application Tasks in the Cloud. IEEE Transactions on Industrial Informatics.
  79. Abd Elaziz, M., Abu-Donia, H.M., Hosny, R.A., Hazae, S.L. and Ibrahim, R.A., 2022. Improved Evolutionary-Based Feature Selection Technique Using Extension of Knowledge Based on the Rough Approximations. Information Sciences.
  80. Abualigah, L., Ewees, A.A., Al-qaness, M.A., Elaziz, M.A., Yousri, D., Ibrahim, R.A. and Altalhi, M., 2022. Boosting arithmetic optimization algorithm by sine cosine algorithm and levy flight distribution for solving engineering optimization problems. Neural Computing and Applications, pp.1-30.
  81. Abualigah, L., Almotairi, K.H., Abd Elaziz, M., Shehab, M. and Altalhi, M., 2022. Enhanced Flow Direction Arithmetic Optimization Algorithm for mathematical optimization problems with applications of data clustering. Engineering Analysis with Boundary Elements, 138, pp.13-29.
  82. Yousri, D., Mudhsh, M., Shaker, Y., Abualigah, L., Tag-Eldin, E., Abd Elaziz, M. and Allam, D., 2022. Modified Interactive Algorithm Based on Runge Kutta Optimizer for Photovoltaic Modeling: Justification under Partial Shading and Varied Temperature Conditions. IEEE Access.
  83. Rezaei, F., Safavi, H.R., Abd Elaziz, M., El-Sappagh, S.H.A., Al-Betar, M.A. and Abuhmed, T., 2022. An Enhanced Grey Wolf Optimizer with a Velocity-Aided Global Search Mechanism. Mathematics, 10(3), p.351.
  84. Fatani A, Dahou A, Al-Qaness MA, Lu S, Abd Elaziz M. Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System. Sensors. 2022 Jan;22(1):140.
  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


AIRC Internal Projects


MR-VDENCLUE: Varying Density Clustering using MapReduce

The VDENCLUE is an enhanced variant of the DENCLUE algorithm capable of discovering clusters with varying densities. However, to compute an object density, VDENCLUE computes this object's influence from all other objects, which is repeated for each data object. Hence, incurring high computation overhead that is impractical for large datasets. This proposal introduces the first parallel variant of VDENCLUE (and DENCLUE) algorithm, an approximated variant of VDENCLUE, called MR-VDENCLUE. The MR-VDENCLUE uses the Locality-Sensitive Hashing (LSH) technique to partition the big dataset, performs local clustering using adaptive grid structure, and aggregates local results based on a new merging approach to generate the final approximated results. Besides discovering clusters with arbitrary shapes, MR-VDENCLUE will discover clusters with varying densities and scale-up to handle big datasets.



Extract Scientific Topics from Publications based on Machine Learning Methods in Top QS Ranking of United Arab Emirates Universities

The interest in defining theme structures in science (so-called topics) has grown over the past ten years in understanding new and historical ideas in scientific publications. In reality, the content of scientific publications can be represented in short sentences or phrases. Commonly, the topics are often done by researchers manually, for instance, when they create or update their online profile on various scientific platforms or submit an original manuscript to a journal. At present, an overwhelming number of scientific publications are published every day, making it difficult to get a complete overview of such studies using manual approaches. Hence, focusing on automatically extracting topics from scientific publications could be the best alternative. Until today, there is no intelligent system for extracting significant topics from the scientific publications in the United Arab Emirates academic especially for research institutions that are ranked in the QS World University Rankings. This project aims to propose an intelligent system for automatically extracting topics from scientific publications, enabling the researchers and decision-makers to obtain a comprehensive overview of multi-scale scientific publications to increase the opportunities for collaboration between the researchers in UAE universities common interest topics.  Technically, this system can be divided into two main tasks: (i) Text Document Clustering and (ii) Topic Extraction. To achieve the project aims, the main objectives will be addressed as (a) Collecting the scientific publications of UAE universities from the Scopus database of the last five years. b) Clustering the scientific publication's text using unsupervised clustering method c) Classifying the scientific publications based on the clustering results to extract the most important scientific topics for each cluster. d) Build a web-based system that can visualize the results of the topic extraction task.



The Effect of Smart grids and Smart Homes on the Power Grid in UAE Using Artificial Intelligence Technique

In the current decade of the electrical power sector, traditional power grids and their primitive systems became not able to meet the user requirements of power due to the multitude of appliances that require a huge amount of power and population growth, particularly in overcrowded countries, such as UAE. Therefore, alternative systems based on smart technologies, known as smart grids (SGs), are emerged to overcome such issues. The SG is an upgraded generation of the traditional power grid proposed to improve the grid systems and capacity, increasing the power supplier companies' (PSCs) profits. In addition, SG allows users to get advantages by rescheduling smart appliances' operation time and reducing electricity bills. The problem of scheduling smart appliances' operation time according to several constraints, is known as the power scheduling problem in smart home (PSPSH). Several artificial intelligence (AI) methods have been proposed to address PSPSH and find the best schedule optimally. The AI methods can achieve their benefits through an automatic smart system called artificial intelligence smart system. In this research project, a new approach is proposed for PSCs in UAE based on upgrading the traditional power grid to SG to achieve its benefits. In addition, an AISS is constructed to automatically and optimally address PSPSH using a recent robust AI method. The AI method is adapted to find the best schedule for appliances' operation time and address PSPSH optimally. Furthermore, the adapted AI method’s performance is improved to enhance results and achieve better schedules.


Events


LATEX workshop - The Gate for Professional Document

LaTeX, which is pronounced «Lah-tech» or «Lay-tech» (to rhyme with «blech» or «Bertolt Brecht»), is a document preparation system for high-quality typesetting. It is most often used for medium-to-large technical or scientific documents but it can be used for almost any form of publishing.

LaTeX is not a word processor! Instead, LaTeX encourages authors not to worry too much about the appearance of their documents but to concentrate on getting the right content.

The AIRC has organized a workshop, titled LATEX workshop - The Gate for Professional Document, for the MSAI students on 23/01/2022.

The AIRC has provided a participation certificate for the participants.


 The LATEX workshop - The Gate for Professional Document is prepared and presented by:

  • Dr. Sharif Naser Makhadmeh
  • Eng. Lamees Mohammad Dalbah
  • Eng. Shaimaa Mahmood Mounir Kouka

The main outlines of the LATEX workshop - The Gate for Professional Document are:

  1. Introduction
  2. Share and Download​
  3. Basic Structure​
  4. Text manipulation​
  5. Mathematical Expression​
  6. Figures
  7. Tables​
  8. Algorithms​
  9. References​
  10. Presentations​

Python and Data Engineering Workshop

Python is an open-source (free) programming language that is used in web programming, artificial intelligence, machine learning, data science, and many scientific applications. Learning Python allows the programmer to focus on solving problems, rather than focusing on syntax. Its relative size and simplified syntax give it an edge over languages like Java and C++, yet the abundance of libraries gives it the power needed to accomplish great things.

The AIRC has organized a new workshop for MSAI students at Ajman University, titled Python and Data Engineering Workshop, on 20/02/2022.

The AIRC has provided a participation certificate for the participants.


The Python and Data Engineering Workshop is prepared and presented by:

  • Dr. Sharif Naser Makhadmeh
  • Eng. Lamees Mohammad Dalbah
  • Eng. Shaimaa Mahmood Mounir Kouka

The main outlines of the Python and Data Engineering Workshop are:

    Python Basics​

  1. Introduction to Python​
  2. Starting with Colab​
  3. Add comment​
  4. Variables and simple data types​
  5. If Statements​
  6. Loops​
  7. Functions​

    Data Engineering ​

  1. What is preprocessing ​
  2. Loading the data​
  3. Pandas dataframe​
  4. Exploratory Data Analysis​
  5. Data Cleaning​
  6. Input-output separation​
  7. Data Transformation ​
  8. Data Reduction
  9. Data Visualization​