Artificial Intelligence Research Center (AIRC)

1. Abasi, A. K., Makhadmeh, S. N., Al-Betar, M. A., Alomari, O. A., Awadallah, M. A., Alyasseri, Z. A. A., . . . Hadjouni, M. (2022). Lemurs optimizer: A new metaheuristic algorithm for global optimization. Applied Sciences (Switzerland), 12(19) doi:10.3390/app121910057
2. Abdalkareem, Z. A., Al-Betar, M. A., Amir, A., Ehkan, P., Hammouri, A. I., & Salman, O. H. (2022). Discrete flower pollination algorithm for patient admission scheduling problem. Computers in Biology and Medicine, 141 doi:10.1016/j.compbiomed.2021.105007
3. 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, 11(3), 445-469. doi:10.1007/s12553-021-00547-5
4. 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 doi:10.1155/2022/5974634
5. Abed-alguni, B. H., Alawad, N. A., Al-Betar, M. A., & Paul, D. (2022). Opposition-based sine cosine optimizer utilizing refraction learning and variable neighborhood search for feature selection. Applied Intelligence, doi:10.1007/s10489-022-04201-z
6. Abu Doush, I., Al-Betar, M. A., Awadallah, M. A., Alyasseri, Z. A. A., Makhadmeh, S. N., & El-Abd, M. (2022). Island neighboring heuristics harmony search algorithm for flow shop scheduling with blocking. Swarm and Evolutionary Computation, 74 doi:10.1016/j.swevo.2022.101127
7. Abu Doush, I., Awadallah, M. A., Al-Betar, M. A., Alomari, O. A., Makhadmeh, S. N., Abasi, A. K., & Alyasseri, Z. A. A. (2023). Archive-based coronavirus herd immunity algorithm for optimizing weights in neural networks. Neural Computing and Applications, doi:10.1007/s00521-023-08577-y
8. Abu-Doush, I., Ahmed, B., Awadallah, M. A., Al-Betar, M. A., & Rababaah, A. R. (2023). Enhancing multilayer perceptron neural network using archive-based harris hawks optimizer to predict gold prices. Journal of King Saud University - Computer and Information Sciences, 35(5) doi:10.1016/j.jksuci.2023.101557
9. Ahmed, I. E., Mehdi, R., & Mohamed, E. A. (2023). The role of artificial intelligence in developing a banking risk index: An application of adaptive neural network-based fuzzy inference system (ANFIS). Artificial Intelligence Review, doi:10.1007/s10462-023-10473-9
10. Al-Betar, M. A. (2021). Island-based harmony search algorithm for non-convex economic load dispatch problems. Journal of Electrical Engineering and Technology, 16(4), 1985-2015. doi:10.1007/s42835-021-00758-w
11. Al-Betar, M. A., Abasi, A. K., Al-Naymat, G., Arshad, K., & Makhadmeh, S. N. (2023). Optimization of scientific publications clustering with ensemble approach for topic extraction. Scientometrics, 128(5), 2819-2877. doi:10.1007/s11192-023-04674-w
12. Al-Betar, M. A., Awadallah, M. A., Doush, I. A., Alomari, O. A., Abasi, A. K., Makhadmeh, S. N., & Alyasseri, Z. A. A. (2022). Boosting the training of neural networks through hybrid metaheuristics. Cluster Computing, doi:10.1007/s10586-022-03708-x
13. Al-Betar, M. A., Awadallah, M. A., Makhadmeh, S. N., Doush, I. A., Zitar, R. A., Alshathri, S., & Abd Elaziz, M. (2023). A hybrid harris hawks optimizer for economic load dispatch problems. Alexandria Engineering Journal, 64, 365-389. doi:10.1016/j.aej.2022.09.010
14. Al-Betar, M. A., Awadallah, M. A., Zitar, R. A., & Assaleh, K. (2022). Economic load dispatch using memetic sine cosine algorithm. Journal of Ambient Intelligence and Humanized Computing, doi:10.1007/s12652-022-03731-1
15. Aldeeb, B. A., Azmi Al-Betar, M., Md Norwawi, N., Alissa, K. A., Alsmadi, M. K., Hazaymeh, A. A., & Alzaqebah, M. (2022). Hybrid intelligent water drops algorithm for examination timetabling problem. Journal of King Saud University - Computer and Information Sciences, 34(8), 4847-4859. doi:10.1016/j.jksuci.2021.06.016
16. Alkareem Alyasseri, Z. A., Al-Betar, M. A., Awadallah, M. A., Makhadmeh, S. N., Abasi, A. K., Doush, I. A., & Alomari, O. A. (2022). A hybrid flower pollination with β-hill climbing algorithm for global optimization. Journal of King Saud University - Computer and Information Sciences, 34(8), 4821-4835. doi:10.1016/j.jksuci.2021.06.015
17. Alkhraisat, H., Dalbah, L. M., Al-Betar, M. A., Awadallah, M. A., Assaleh, K., & Deriche, M. (2023). Size optimization of truss structures using improved grey wolf optimizer. IEEE Access, 11, 13383-13397. doi:10.1109/ACCESS.2023.3243164
18. 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, 46(9), 8721-8740. doi:10.1007/s13369-021-05646-z
19. 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. doi:10.1504/IJAHUC.2021.112981
20. Al-Okaily, A., Teoh, A. P., Al-Okaily, M., Iranmanesh, M., & Al-Betar, M. A. (2023). The efficiency measurement of business intelligence systems in the big data-driven economy: A multidimensional model. Information Discovery and Delivery, doi:10.1108/IDD-01-2022-0008
21. 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 doi:10.1016/j.knosys.2021.107034
22. Alsoruji, G. S., Sadoun, A. M., Elaziz, M. A., Al-Betar, M. A., Abdallah, A. W., & Fathy, A. (2023). On the prediction of the mechanical properties of ultrafine grain al-TiO2 nanocomposites using a modified long-short term memory model with beluga whale optimizer. Journal of Materials Research and Technology, 23, 4075-4088. doi:10.1016/j.jmrt.2023.01.212
23. 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 doi:10.1016/j.knosys.2021.107629
24. Alyasseri, Z. A. A., Al-Betar, M. A., Doush, I. A., Awadallah, M. A., Abasi, A. K., Makhadmeh, S. N., . . . Zitar, R. A. (2022). Review on COVID-19 diagnosis models based on machine learning and deep learning approaches. Expert Systems, 39(3) doi:10.1111/exsy.12759
25. Alyasseri, Z. A. A., Alomari, O. A., Al-Betar, M. A., Makhadmeh, S. N., Doush, I. A., Awadallah, M. A., . . . Elnagar, A. (2022). Recent advances of bat-inspired algorithm, its versions and applications. Neural Computing and Applications, 34(19), 16387-16422. doi:10.1007/s00521-022-07662-y
26. Alyasseri, Z. A. A., Alomari, O. A., Makhadmeh, S. N., Mirjalili, S., Al-Betar, M. A., Abdullah, S., . . . Abasi, A. K. (2022). EEG channel selection for person identification using binary grey wolf optimizer. IEEE Access, 10, 10500-10513. doi:10.1109/ACCESS.2021.3135805
27. Alyasseri, Z. A. A., Alomari, O. A., Papa, J. P., Al-Betar, M. A., Abdulkareem, K. H., Mohammed, M. A., . . . Khuwuthyakorn, P. (2022). EEG channel selection based user identification via improved flower pollination algorithm. Sensors, 22(6) doi:10.3390/s22062092
28. Alyasseri, Z. A. A., Khader, A. T., Al-Betar, M. A., Yang, X. -., Mohammed, M. A., Abdulkareem, K. H., . . . Razzak, I. (2023). Multi-objective flower pollination algorithm: A new technique for EEG signal denoising. Neural Computing and Applications, 35(11), 7943-7962. doi:10.1007/s00521-021-06757-2
29. Awadallah, M. A., Al-Betar, M. A., Braik, M. S., Hammouri, A. I., Doush, I. A., & Zitar, R. A. (2022). An enhanced binary rat swarm optimizer based on local-best concepts of PSO and collaborative crossover operators for feature selection. Computers in Biology and Medicine, 147 doi:10.1016/j.compbiomed.2022.105675
30. Awadallah, M. A., Al-Betar, M. A., Doush, I. A., Makhadmeh, S. N., & Al-Naymat, G. (2023). Recent versions and applications of sparrow search algorithm. Archives of Computational Methods in Engineering, doi:10.1007/s11831-023-09887-z
31. 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 neighborhood shapes for optimization. Expert Systems with Applications, 194 doi:10.1016/j.eswa.2021.116431
32. Awadallah, M. A., Hammouri, A. I., Al-Betar, M. A., Braik, M. S., & Elaziz, M. A. (2022). Binary horse herd optimization algorithm with crossover operators for feature selection. Computers in Biology and Medicine, 141 doi:10.1016/j.compbiomed.2021.105152
33. Braik, M., Awadallah, M., Al-Betar, M. A., & Al-Hiary, H. (2023). Enhanced whale optimization algorithm-based modeling and simulation analysis for industrial system parameter identification. Journal of Supercomputing, doi:10.1007/s11227-023-05215-1
34. Braik, M., Hammouri, A., Atwan, J., Al-Betar, M. A., & Awadallah, M. A. (2022). White shark optimizer: A novel bio-inspired meta-heuristic algorithm for global optimization problems. Knowledge-Based Systems, 243 doi:10.1016/j.knosys.2022.108457
35. Braik, M. S., Awadallah, M. A., Al-Betar, M. A., Hammouri, A. I., & Zitar, R. A. (2023). A non-convex economic load dispatch problem using chameleon swarm algorithm with roulette wheel and levy flight methods. Applied Intelligence, doi:10.1007/s10489-022-04363-w
36. Dalbah, L. M., Al-Betar, M. A., Awadallah, M. A., & Zitar, R. A. (2022). A modified coronavirus herd immunity optimizer for capacitated vehicle routing problem. Journal of King Saud University - Computer and Information Sciences, 34(8), 4782-4795. doi:10.1016/j.jksuci.2021.06.013
37. Essa, F. A., Elaziz, M. A., Al-Betar, M. A., & Elsheikh, A. H. (2023). Performance prediction of a reverse osmosis unit using an optimized long short-term memory model by hummingbird optimizer. Process Safety and Environmental Protection, 169, 93-106. doi:10.1016/j.psep.2022.10.071
38. Gu, Z., Xiong, G., Fu, X., Mohamed, A. W., Al-Betar, M. A., Chen, H., & Chen, J. (2023). Extracting accurate parameters of photovoltaic cell models via elite learning adaptive differential evolution. Energy Conversion and Management, 285 doi:10.1016/j.enconman.2023.116994
39. Habuza, T., Zaki, N., Mohamed, E. A., & Statsenko, Y. (2022). Deviation from model of normal aging in alzheimer's disease: Application of deep learning to structural MRI data and cognitive tests. IEEE Access, 10, 53234-53249. doi:10.1109/ACCESS.2022.3174601
40. Kassaymeh, S., Abdullah, S., Al-Betar, M. A., & Alweshah, M. (2022). Salp swarm optimizer for modeling the software fault prediction problem. Journal of King Saud University - Computer and Information Sciences, 34(6), 3365-3378. doi:10.1016/j.jksuci.2021.01.015
41. Kassaymeh, S., Abdullah, S., Al-Betar, M. A., Alweshah, M., Al-Laham, M., & Othman, Z. (2022). Self-adaptive salp swarm algorithm for optimization problems. Soft Computing, 26(18), 9349-9368. doi:10.1007/s00500-022-07280-9
42. 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, 53(6), 4451-4487. doi:10.1007/s11063-021-10607-6
43. Kassaymeh, S., Al-Laham, M., Al-Betar, M. A., Alweshah, M., Abdullah, S., & Makhadmeh, S. N. (2022). Backpropagation neural network optimization and software defect estimation modelling using a hybrid salp swarm optimizer-based simulated annealing algorithm. Knowledge-Based Systems, 244 doi:10.1016/j.knosys.2022.108511
44. Kassaymeh, S., Alweshah, M., Al-Betar, M. A., Hammouri, A. I., & Al-Ma'aitah, M. A. (2023). Software effort estimation modeling and fully connected artificial neural network optimization using soft computing techniques. Cluster Computing, doi:10.1007/s10586-023-03979-y
45. Li, C., Xiong, G., Fu, X., Mohamed, A. W., Yuan, X., Al-Betar, M. A., & Suganthan, P. N. (2022). Takagi–Sugeno fuzzy based power system fault section diagnosis models via genetic learning adaptive GSK algorithm. Knowledge-Based Systems, 255 doi:10.1016/j.knosys.2022.109773
46. Mafarja, M., Thaher, T., Al-Betar, M. A., Too, J., Awadallah, M. A., Abu Doush, I., & Turabieh, H. (2023). Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning. Applied Intelligence, doi:10.1007/s10489-022-04427-x
47. 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. Journal of Supercomputing, 78(9), 11794-11829. doi:10.1007/s11227-022-04325-6
48. Makhadmeh, S. N., Abasi, A. K., Al-Betar, M. A., Awadallah, M. A., Doush, I. A., Alyasseri, Z. A. A., & Alomari, O. A. (2022). A novel link-based multi-objective grey wolf optimizer for appliances energy scheduling problem. Cluster Computing, 25(6), 4355-4382. doi:10.1007/s10586-022-03675-3
49. Makhadmeh, S. N., Al-Betar, M. A., Abasi, A. K., Awadallah, M. A., Doush, I. A., Alyasseri, Z. A. A., & Alomari, O. A. (2023). Recent advances in butterfly optimization algorithm, its versions and applications. Archives of Computational Methods in Engineering, 30(2), 1399-1420. doi:10.1007/s11831-022-09843-3
50. 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 (Switzerland), 10(4), 1-35. doi:10.3390/electronics10040447
51. Makhadmeh, S. N., Al-Betar, M. A., Assaleh, K., & Kassaymeh, S. (2022). A hybrid white shark equilibrium optimizer for power scheduling problem based IoT. IEEE Access, 10, 132212-132231. doi:10.1109/ACCESS.2022.3229434
52. Makhadmeh, S. N., Al-Betar, M. A., Awadallah, M. A., Abasi, A. K., Alyasseri, Z. A. A., Doush, I. A., . . . Mohammed, M. A. (2022). A modified coronavirus herd immunity optimizer for the power scheduling problem. Mathematics, 10(3) doi:10.3390/math10030315
53. Makhadmeh, S. N., Alomari, O. A., Mirjalili, S., Al-Betar, M. A., & Elnagar, A. (2022). Recent advances in multi-objective grey wolf optimizer, its versions and applications. Neural Computing and Applications, 34(22), 19723-19749. doi:10.1007/s00521-022-07704-5
54. Mostafa, R. R., Gaheen, M. A., Abd ElAziz, M., Al-Betar, M. A., & Ewees, A. A. (2023). An improved gorilla troops optimizer for global optimization problems and feature selection. Knowledge-Based Systems, 269 doi:10.1016/j.knosys.2023.110462
55. Nabih, M., Ghoneimi, A., Bakry, A., Chelloug, S. A., Al-Betar, M. A., & Abd Elaziz, M. (2023). Rock physics analysis from predicted poisson's ratio using RVFL based on wild geese algorithm in scarab gas field in WDDM concession, egypt. Marine and Petroleum Geology, 147 doi:10.1016/j.marpetgeo.2022.105949
56. Abbas, F., Yasmin, M., Fayyaz, M., Elaziz, M. A., Lu, S., & Abd El-Latif, A. A. (2021). Gender classification using proposed CNN-based model and ant colony optimization. Mathematics, 9(19) doi:10.3390/math9192499
57. Abd Elaziz, M., Abualigah, L., Ibrahim, R. A., & Attiya, I. (2021). IoT workflow scheduling using intelligent arithmetic optimization algorithm in fog computing. Computational Intelligence and Neuroscience, 2021 doi:10.1155/2021/9114113
58. Abd Elaziz, M., Abualigah, L., Issa, M., & Abd El-Latif, A. A. (2023). Optimal parameters extracting of fuel cell based on gorilla troops optimizer. Fuel, 332 doi:10.1016/j.fuel.2022.126162
59. Abd Elaziz, M., Abu-Donia, H. M., Hosny, R. A., Hazae, S. L., & Ibrahim, R. A. (2022). Improved evolutionary-based feature selection technique using extension of knowledge based on the rough approximations. Information Sciences, 594, 76-94. doi:10.1016/j.ins.2022.01.026
60. Abd Elaziz, M., Almodfer, R., Ahmadianfar, I., Ibrahim, I. A., Mudhsh, M., Abualigah, L., . . . Yousri, D. (2022). Static models for implementing photovoltaic panels characteristics under various environmental conditions using improved gradient-based optimizer. Sustainable Energy Technologies and Assessments, 52 doi:10.1016/j.seta.2022.102150
61. Abd Elaziz, M., Al-qaness, M. A. A., Dahou, A., Ibrahim, R. A., & El-Latif, A. A. A. (2023). Intrusion detection approach for cloud and IoT environments using deep learning and capuchin search algorithm. Advances in Engineering Software, 176 doi:10.1016/j.advengsoft.2022.103402
62. Abd Elaziz, M., Chelloug, S., Alduailij, M., & Al-qaness, M. A. A. (2023). Boosted reptile search algorithm for engineering and optimization problems. Applied Sciences (Switzerland), 13(5) doi:10.3390/app13053206
63. 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) doi:10.3390/e23111383
64. Abd Elaziz, M., Dahou, A., Orabi, D. A., Alshathri, S., Soliman, E. M., & Ewees, A. A. (2023). A hybrid multitask learning framework with a fire hawk optimizer for arabic fake news detection. Mathematics, 11(2) doi:10.3390/math11020258
65. Abd Elaziz, M., Ghoneimi, A., Elsheikh, A. H., Abualigah, L., Bakry, A., & Nabih, M. (2022). Predicting shale volume from seismic traces using modified random vector functional link based on transient search optimization model: A case study from netherlands north sea. Natural Resources Research, 31(3), 1775-1791. doi:10.1007/s11053-022-10049-4
66. Abd Elaziz, M., Mabrouk, A., Dahou, A., & Chelloug, S. A. (2022). Medical image classification utilizing ensemble learning and levy flight-based honey badger algorithm on 6G-enabled internet of things. Computational Intelligence and Neuroscience, 2022 doi:10.1155/2022/5830766
67. Abd Elaziz, M., Ouadfel, S., Abd El-Latif, A. A., & Ali Ibrahim, R. (2022). Feature selection based on modified bio-inspired atomic orbital search using arithmetic optimization and opposite-based learning. Cognitive Computation, 14(6), 2274-2295. doi:10.1007/s12559-022-10022-6
68. Abd Elaziz, M., Ouadfel, S., & Ibrahim, R. A. (2023). Boosting capuchin search with stochastic learning strategy for feature selection. Neural Computing and Applications, doi:10.1007/s00521-023-08400-8
69. Abd Elaziz, M., Ewees, A. A., Yousri, D., Abualigah, L., & Al-qaness, M. A. A. (2022). Modified marine predators algorithm for feature selection: Case study metabolomics. Knowledge and Information Systems, 64(1), 261-287. doi:10.1007/s10115-021-01641-w
70. Abualigah, L., Almotairi, K. H., Al-qaness, M. A. A., Ewees, A. A., Yousri, D., Elaziz, M. A., & Nadimi-Shahraki, M. H. (2022). Efficient text document clustering approach using multi-search arithmetic optimization algorithm. Knowledge-Based Systems, 248 doi:10.1016/j.knosys.2022.108833
71. Abualigah, L., Almotairi, K. H., Elaziz, M. A., Shehab, M., & Altalhi, M. (2022). Enhanced flow direction arithmetic optimization algorithm for mathematical optimization problems with applications of data clustering. Engineering Analysis with Boundary Elements, 138, 13-29. doi:10.1016/j.enganabound.2022.01.014
72. Abualigah, L., Al-Okbi, N. K., Elaziz, M. A., & Houssein, E. H. (2022). Boosting marine predators algorithm by salp swarm algorithm for multilevel thresholding image segmentation. Multimedia Tools and Applications, 81(12), 16707-16742. doi:10.1007/s11042-022-12001-3
73. Abualigah, L., Diabat, A., Altalhi, M., & Elaziz, M. A. (2022). Improved gradual change-based harris hawks optimization for real-world engineering design problems. Engineering with Computers, doi:10.1007/s00366-021-01571-9
74. Abualigah, L., Diabat, A., Svetinovic, D., & Elaziz, M. A. (2022). Boosted harris hawks gravitational force algorithm for global optimization and industrial engineering problems. Journal of Intelligent Manufacturing, doi:10.1007/s10845-022-01921-4
75. Abualigah, L., Elaziz, M. A., Khasawneh, A. M., Alshinwan, M., Ibrahim, R. A., Al-qaness, M. A. A., . . . Gandomi, A. H. (2022). Meta-heuristic optimization algorithms for solving real-world mechanical engineering design problems: A comprehensive survey, applications, comparative analysis, and results. Neural Computing and Applications, 34(6), 4081-4110. doi:10.1007/s00521-021-06747-4
76. Abualigah, L., Elaziz, M. A., Sumari, P., Geem, Z. W., & Gandomi, A. H. (2022). Reptile search algorithm (RSA): A nature-inspired meta-heuristic optimizer. Expert Systems with Applications, 191 doi:10.1016/j.eswa.2021.116158
77. Abualigah, L., Elaziz, M. A., Sumari, P., Khasawneh, A. M., Alshinwan, M., Mirjalili, S., . . . Gandomi, A. H. (2022). Black hole algorithm: A comprehensive survey. Applied Intelligence, 52(10), 11892-11915. doi:10.1007/s10489-021-02980-5
78. Abualigah, L., Elaziz, M. A., Yousri, D., Al-qaness, M. A. A., Ewees, A. A., & Zitar, R. A. (2022). Augmented arithmetic optimization algorithm using opposite-based learning and lévy flight distribution for global optimization and data clustering. Journal of Intelligent Manufacturing, doi:10.1007/s10845-022-02016-w
79. Abualigah, L., Ewees, A. A., Al-qaness, M. A. A., Elaziz, M. A., Yousri, D., Ibrahim, R. A., & Altalhi, M. (2022). Boosting arithmetic optimization algorithm by sine cosine algorithm and levy flight distribution for solving engineering optimization problems. Neural Computing and Applications, 34(11), 8823-8852. doi:10.1007/s00521-022-06906-1
80. Abualigah, L., Zitar, R. A., Almotairi, K. H., Hussein, A. M., Elaziz, M. A., Nikoo, M. R., & Gandomi, A. H. (2022). Wind, solar, and photovoltaic renewable energy systems with and without energy storage optimization: A survey of advanced machine learning and deep learning techniques. Energies, 15(2) doi:10.3390/en15020578
81. Adel, H., Dahou, A., Mabrouk, A., Elaziz, M. A., Kayed, M., El-Henawy, I. M., . . . Ali, A. A. (2022). Improving crisis events detection using DistilBERT with hunger games search algorithm. Mathematics, 10(3) doi:10.3390/math10030447
82. Ahmed, I., Dahou, A., Chelloug, S. A., Al-Qaness, M. A. A., & Elaziz, M. A. (2022). Feature selection model based on gorilla troops optimizer for intrusion detection systems. Journal of Sensors, 2022 doi:10.1155/2022/6131463
83. Almodfer, R., Abd Elaziz, M., Alshathri, S., Abualigah, L., Mudhsh, M., Shahzad, K., & Issa, M. (2022). Improving parameter estimation of fuel cell using honey badger optimization algorithm. Frontiers in Energy Research, 10 doi:10.3389/fenrg.2022.875332
84. Almodfer, R., Mudhsh, M., Alshathri, S., Yousri, D., Abualigah, L., Hassan, O. F., & Abd Elaziz, M. (2022). Chaotic honey badger algorithm for single and double photovoltaic cell/module. Frontiers in Energy Research, 10 doi:10.3389/fenrg.2022.1011887
85. Almodfer, R., Mudhsh, M., Chelloug, S., Shehab, M., Abualigah, L., & Abd Elaziz, M. (2022). Quantum mutation reptile search algorithm for global optimization and data clustering. Human-Centric Computing and Information Sciences, 12 doi:10.22967/HCIS.2022.12.030
86. Almodfer, R., Zayed, M. E., Elaziz, M. A., Aboelmaaref, M. M., Mudhsh, M., & Elsheikh, A. H. (2022). Modeling of a solar-powered thermoelectric air-conditioning system using a random vector functional link network integrated with jellyfish search algorithm. Case Studies in Thermal Engineering, 31 doi:10.1016/j.csite.2022.101797
87. Alnaimy, M. A., Shahin, S. A., Afifi, A. A., Ewees, A. A., Junakova, N., Balintova, M., & Abd Elaziz, M. (2022). Spatio prediction of soil capability modeled with modified RVFL using aptenodytes forsteri optimization and digital soil assessment technique. Sustainability (Switzerland), 14(22) doi:10.3390/su142214996
88. Al-Qaness, M. A. A., Dahou, A., Elaziz, M. A., & Helmi, A. M. (2023). Multi-ResAtt: Multilevel residual network with attention for human activity recognition using wearable sensors. IEEE Transactions on Industrial Informatics, 19(1), 144-152. doi:10.1109/TII.2022.3165875
89. Al-qaness, M. A. A., Dahou, A., Ewees, A. A., Abualigah, L., Huai, J., Abd Elaziz, M., & Helmi, A. M. (2023). ResInformer: Residual transformer-based artificial time-series forecasting model for PM2.5 concentration in three major chinese cities. Mathematics, 11(2) doi:10.3390/math11020476
90. Al-qaness, M. A. A., Ewees, A. A., Abualigah, L., AlRassas, A. M., Thanh, H. V., & Abd Elaziz, M. (2022). Evaluating the applications of dendritic neuron model with metaheuristic optimization algorithms for crude-oil-production forecasting. Entropy, 24(11) doi:10.3390/e24111674
91. Al-qaness, M. A. A., Ewees, A. A., Elaziz, M. A., & Samak, A. H. (2022). Wind power forecasting using optimized dendritic neural model based on seagull optimization algorithm and aquila optimizer. Energies, 15(24) doi:10.3390/en15249261
92. Al-qaness, M. A. A., Ewees, A. A., Fan, H., Abualigah, L., & Elaziz, M. A. (2022). Boosted ANFIS model using augmented marine predator algorithm with mutation operators for wind power forecasting. Applied Energy, 314 doi:10.1016/j.apenergy.2022.118851
93. Al-qaness, M. A. A., Ewees, A. A., Fan, H., Abualigah, L., Elsheikh, A. H., & Abd Elaziz, M. (2023). Wind power prediction using random vector functional link network with capuchin search algorithm. Ain Shams Engineering Journal, doi:10.1016/j.asej.2022.102095
94. Al-qaness, M. A. A., Ewees, A. A., Fan, H., AlRassas, A. M., & Abd Elaziz, M. (2022). Modified aquila optimizer for forecasting oil production. Geo-Spatial Information Science, 25(4), 519-535. doi:10.1080/10095020.2022.2068385
95. Al-qaness, M. A. A., Ewees, A. A., Thanh, H. V., AlRassas, A. M., & Abd Elaziz, M. (2022). An optimized neuro-fuzzy system using advance nature-inspired aquila and salp swarm algorithms for smart predictive residual and solubility carbon trapping efficiency in underground storage formations. Journal of Energy Storage, 56 doi:10.1016/j.est.2022.106150
96. Al-qaness, M. A. A., Ewees, A. A., Thanh, H. V., AlRassas, A. M., Dahou, A., & Elaziz, M. A. (2023). Predicting CO2 trapping in deep saline aquifers using optimized long short-term memory. Environmental Science and Pollution Research, 30(12), 33780-33794. doi:10.1007/s11356-022-24326-5
97. Al-qaness, M. A. A., Helmi, A. M., Dahou, A., & Elaziz, M. A. (2022). The applications of metaheuristics for human activity recognition and fall detection using wearable sensors: A comprehensive analysis. Biosensors, 12(10) doi:10.3390/bios12100821
98. AlRassas, A. M., Al-qaness, M. A. A., Ewees, A. A., Ren, S., Sun, R., Pan, L., & Abd Elaziz, M. (2022). Advance artificial time series forecasting model for oil production using neuro fuzzy-based slime mould algorithm. Journal of Petroleum Exploration and Production Technology, 12(2), 383-395. doi:10.1007/s13202-021-01405-w
99. Alshathri, S., Abd Elaziz, M., Yousri , D., Hassan , O. F., & Ibrahim , R. A. (2022). Quantum chaotic honey badger algorithm for feature selection. Electronics (Switzerland), 11(21) doi:10.3390/electronics11213463
100. Al-Shourbaji, I., Helian, N., Sun, Y., Alshathri, S., & Elaziz, M. A. (2022). Boosting ant colony optimization with reptile search algorithm for churn prediction. Mathematics, 10(7) doi:10.3390/math10071031
101. Al-Shourbaji, I., Kachare, P. H., Alshathri, S., Duraibi, S., Elnaim, B., & Elaziz, M. A. (2022). An efficient parallel reptile search algorithm and snake optimizer approach for feature selection. Mathematics, 10(13) doi:10.3390/math10132351
102. Amran, G. A., Wang, S., Al-qaness, M. A. A., Mohsan, S. A. H., Abbas, R., Ghaleb, E., . . . Abd Elaziz, M. (2022). Efficient and secure WiFi signal booster via unmanned aerial vehicles WiFi repeater based on intelligence based localization swarm and blockchain. Micromachines, 13(11) doi:10.3390/mi13111924
103. 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. doi:10.1016/j.future.2021.09.032
104. Attiya, I., Abualigah, L., Alshathri, S., Elsadek, D., & Elaziz, M. A. (2022). Dynamic jellyfish search algorithm based on simulated annealing and disruption operators for global optimization with applications to cloud task scheduling. Mathematics, 10(11) doi:10.3390/math10111894
105. Attiya, I., Abualigah, L., Elsadek, D., Chelloug, S. A., & Abd Elaziz, M. (2022). An intelligent chimp optimizer for scheduling of IoT application tasks in fog computing. Mathematics, 10(7) doi:10.3390/math10071100
106. Attiya, I., Elaziz, M. A., Abualigah, L., Nguyen, T. N., & El-Latif, A. A. A. (2022). An improved hybrid swarm intelligence for scheduling IoT application tasks in the cloud. IEEE Transactions on Industrial Informatics, 18(9), 6264-6272. doi:10.1109/TII.2022.3148288
107. Dahou, A., Al-qaness, M. A. A., Abd Elaziz, M., & Helmi, A. (2022). Human activity recognition in IoHT applications using arithmetic optimization algorithm and deep learning. Measurement: Journal of the International Measurement Confederation, 199 doi:10.1016/j.measurement.2022.111445
108. Dahou, A., Chelloug, S. A., Alduailij, M., & Elaziz, M. A. (2023). Improved feature selection based on chaos game optimization for social internet of things with a novel deep learning model. Mathematics, 11(4) doi:10.3390/math11041032
109. Dahou, A., Mabrouk, A., Ewees, A. A., Gaheen, M. A., & Abd Elaziz, M. (2023). A social media event detection framework based on transformers and swarm optimization for public notification of crises and emergency management. Technological Forecasting and Social Change, 192 doi:10.1016/j.techfore.2023.122546
110. El Shinawi, A., Ibrahim, R. A., Abualigah, L., Zelenakova, M., & Elaziz, M. A. (2021). Enhanced adaptive neuro-fuzzy inference system using reptile search algorithm for relating swelling potentiality using index geotechnical properties: A case study at el sherouk city, egypt. Mathematics, 9(24) doi:10.3390/math9243295
111. Elaziz, M. A., Abdelrahman, N. S., Hassan, N. A., & Mohamed, M. O. (2022). Discrete generalized inverted exponential distribution: Case study color image segmentation. Mathematical Problems in Engineering, 2022 doi:10.1155/2022/3029932
112. Elaziz, M. A., Abualigah, L., Yousri, D., Oliva, D., Al-Qaness, M. A. A., Nadimi-Shahraki, M. H., . . . Ibrahim, R. A. (2021). Boosting atomic orbit search using dynamic-based learning for feature selection. Mathematics, 9(21) doi:10.3390/math9212786
113. Elaziz, M. A., Ahmadein, M., Ataya, S., Alsaleh, N., Forestiero, A., & Elsheikh, A. H. (2022). A quantum-based chameleon swarm for feature selection. Mathematics, 10(19) doi:10.3390/math10193606
114. Elaziz, M. A., Dahou, A., El-Sappagh, S., Mabrouk, A., & Gaber, M. M. (2022). AHA-AO: Artificial hummingbird algorithm with aquila optimization for efficient feature selection in medical image classification. Applied Sciences (Switzerland), 12(19) doi:10.3390/app12199710
115. Elaziz, M. A., Dahou, A., Mabrouk, A., Ibrahim, R. A., & Aseeri, A. O. (2023). Medical image classifications for 6G IoT-enabled smart health systems. Diagnostics, 13(5) doi:10.3390/diagnostics13050834
116. Elaziz, M. A., Ewees, A. A., Al-qaness, M. A. A., Abualigah, L., & Ibrahim, R. A. (2022). Sine–Cosine-barnacles algorithm optimizer with disruption operator for global optimization and automatic data clustering. Expert Systems with Applications, 207 doi:10.1016/j.eswa.2022.117993
117. Elaziz, M. A., Ewees, A. A., Al-qaness, M. A. A., Alshathri, S., & Ibrahim, R. A. (2022). Feature selection for high dimensional datasets based on quantum-based dwarf mongoose optimization. Mathematics, 10(23) doi:10.3390/math10234565
118. Elsheikh, A. H., El-Said, E. M. S., Abd Elaziz, M., Fujii, M., & El-Tahan, H. R. (2023). Water distillation tower: Experimental investigation, economic assessment, and performance prediction using optimized machine-learning model. Journal of Cleaner Production, 388 doi:10.1016/j.jclepro.2023.135896
119. Ewees, A. A., Abualigah, L., Yousri, D., Sahlol, A. T., Al-Qaness, M. A. A., Alshathri, S., & Elaziz, M. A. (2021). Modified artificial ecosystem-based optimization for multilevel thresholding image segmentation. Mathematics, 9(19) doi:10.3390/math9192363
120. Ewees, A. A., Algamal, Z. Y., Abualigah, L., Al-Qaness, M. A. A., Yousri, D., Ghoniem, R. M., & Elaziz, M. A. (2022). A cox proportional-hazards model based on an improved aquila optimizer with whale optimization algorithm operators. Mathematics, 10(8) doi:10.3390/math10081273
121. Ewees, A. A., Al-qaness, M. A. A., Abualigah, L., Algamal, Z. Y., Oliva, D., Yousri, D., & Elaziz, M. A. (2023). Enhanced feature selection technique using slime mould algorithm: A case study on chemical data. Neural Computing and Applications, 35(4), 3307-3324. doi:10.1007/s00521-022-07852-8
122. Ewees, A. A., Al-qaness, M. A. A., Abualigah, L., & Elaziz, M. A. (2022). HBO-LSTM: Optimized long short term memory with heap-based optimizer for wind power forecasting. Energy Conversion and Management, 268 doi:10.1016/j.enconman.2022.116022
123. Ewees, A. A., Al-Qaness, M. A. 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) doi:10.3390/math9182321
124. Fatani, A., Dahou, A., Al-Qaness, M. A. A., Lu, S., & Elaziz, M. A. (2022). Advanced feature extraction and selection approach using deep learning and aquila optimizer for iot intrusion detection system. Sensors, 22(1) doi:10.3390/s22010140
125. Helmi, A. M., Al-qaness, M. A. A., Dahou, A., & Abd Elaziz, M. (2023). Human activity recognition using marine predators algorithm with deep learning. Future Generation Computer Systems, 142, 340-350. doi:10.1016/j.future.2023.01.006
126. Hosny, R. A., Abd Elaziz, M., & Ali Ibrahim, R. (2022). Enhanced feature selection based on integration containment neighborhoods rough set approximations and binary honey badger optimization. Computational Intelligence and Neuroscience, 2022 doi:10.1155/2022/3991870
127. Ibrahim, R. A., Abualigah, L., Ewees, A. A., Al-Qaness, M. A. A., Yousri, D., Alshathri, S., & Elaziz, M. A. (2021). An electric fish-based arithmetic optimization algorithm for feature selection. Entropy, 23(9) doi:10.3390/e23091189
128. 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. doi:10.1109/ACCESS.2021.3111121
129. 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 doi:10.1016/j.eswa.2021.116063
130. Kamran, S., Safavi, H. R., Golmohammadi, M. H., Rezaei, F., Elaziz, M. A., Forestiero, A., & Lu, S. (2022). Maximizing sustainability in reservoir operation under climate change using a novel adaptive accelerated gravitational search algorithm. Water (Switzerland), 14(6) doi:10.3390/w14060905
131. Khurma, R. A., Aljarah, I., Sharieh, A., Elaziz, M. A., Damaševičius, R., & Krilavičius, T. (2022). A review of the modification strategies of the nature inspired algorithms for feature selection problem. Mathematics, 10(3) doi:10.3390/math10030464
132. Mabrouk, A., Dahou, A., Elaziz, M. A., Díaz Redondo, R. P., & Kayed, M. (2022). Medical image classification using transfer learning and chaos game optimization on the internet of medical things. Computational Intelligence and Neuroscience, 2022 doi:10.1155/2022/9112634
133. Mabrouk, A., Redondo, R. P. D., Dahou, A., Elaziz, M. A., & Kayed, M. (2022). Pneumonia detection on chest X-ray images using ensemble of deep convolutional neural networks. Applied Sciences (Switzerland), 12(13) doi:10.3390/app12136448
134. Mahaboob Basha, S., Neto, A. V. L., Menezes, J. W. M., Chelloug, S. A., Abd Elaziz, M., & De Albuquerque, V. H. C. (2022). Evaluation of weighted nuclear norm minimization algorithm for ultrasound image denoising. Wireless Communications and Mobile Computing, 2022 doi:10.1155/2022/3167717
135. Nadimi-Shahraki, M. H., Fatahi, A., Zamani, H., Mirjalili, S., Abualigah, L., & Elaziz, M. A. (2021). Migration-based moth-flame optimization algorithm. Processes, 9(12) doi:10.3390/pr9122276
136. Nadimi-Shahraki, M. H., Taghian, S., Mirjalili, S., Abualigah, L., Elaziz, M. A., & Oliva, D. (2021). Ewoa-opf: Effective whale optimization algorithm to solve optimal power flow problem. Electronics (Switzerland), 10(23) doi:10.3390/electronics10232975
137. Nadimi-Shahraki, M. H., Taghian, S., Mirjalili, S., Ewees, A. A., Abualigah, L., & Elaziz, M. A. (2021). Mtv-mfo: Multi-trial vector-based moth-flame optimization algorithm. Symmetry, 13(12) doi:10.3390/sym13122388
138. Najjar, I. M. R., Sadoun, A. M., Alsoruji, G. S., Elaziz, M. A., & Wagih, A. (2022). Predicting the mechanical properties of Cu–Al2O3 nanocomposites using machine learning and finite element simulation of indentation experiments. Ceramics International, 48(6), 7748-7758. doi:10.1016/j.ceramint.2021.11.322
139. Ouadfel, S., & Abd Elaziz, M. (2021). A multi-objective gradient optimizer approach-based weighted multi-view clustering. Engineering Applications of Artificial Intelligence, 106 doi:10.1016/j.engappai.2021.104480
140. Ouadfel, S., & Abd Elaziz, M. (2022). Efficient high-dimension feature selection based on enhanced equilibrium optimizer. Expert Systems with Applications, 187 doi:10.1016/j.eswa.2021.115882

1. Aldhanhani, M., Al-Betar, M. A., & Makhadmeh, S. N. (2022). Business intelligence solution for covid-19 pandemic. Paper presented at the 2022 International Conference on Emerging Trends in Computing and Engineering Applications, ETCEA 2022 - Proceedings, doi:10.1109/ETCEA57049.2022.10009694
2. Alyasseri, Z. A. A., Al-Betar, M. A., Awadallah, M. A., Makhadmeh, S. N., Alomari, O. A., Abasi, A. K., & Doush, I. A. (2021). EEG feature fusion for person identification using efficient machine learning approach. Paper presented at the Proceedings - 2021 Palestinian International Conference on Information and Communication Technology, PICICT 2021, 97-102. doi:10.1109/PICICT53635.2021.00029
3. Fraihat, S., Innab, H., Makhadmeh, S. N., & Al-Betar, M. A. (2022). Business intelligence system for human resource management system. Paper presented at the 2022 International Conference on Emerging Trends in Computing and Engineering Applications, ETCEA 2022 - Proceedings, doi:10.1109/ETCEA57049.2022.10009861
4. Makhadmeh, S. N., Al-Betar, M. A., Abasi, A. K., Awadallah, M. A., Alkareem Alyasseri, Z. A., Alomari, O. A., & Doush, I. A. (2021). Wind driven optimization with smart home battery for power scheduling problem in smart home. Paper presented at the Proceedings - 2021 Palestinian International Conference on Information and Communication Technology, PICICT 2021, 82-87. doi:10.1109/PICICT53635.2021.00026
5. Mashaleh, A. S., Binti Ibrahim, N. F., Al-Betar, M. A., Mustafa, H. M. J., & Yaseen, Q. M. (2022). Detecting spam email with machine learning optimized with harris hawks optimizer (HHO) algorithm. Paper presented at the Procedia Computer Science, , 201(C) 659-664. doi:10.1016/j.procs.2022.03.087

1. Al-Naymat, G., Khader, M., Al-Betar, M. A., Hriez, R., & Hadi, A. (2023). MR-VDENCLUE: Varying density clustering using MapReduce. doi:10.1007/978-3-031-16072-1_55

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.

1. Al-Betar, M. A., Abasi, A. K., Al-Naymat, G., Arshad, K., & Makhadmeh, S. N. (2023). Optimization of scientific publications clustering with ensemble approach for topic extraction. Scientometrics, 128(5), 2819-2877. doi:10.1007/s11192-023-04674-w
2. Alhijawi, B., & AL-Naymat, G. (2022). Novel positive multi-layer graph based method for collaborative filtering recommender systems. Journal of Computer Science and Technology, 37(4), 975-990. doi:10.1007/s11390-021-0420-2
3. Alian, M., & Al-Naymat, G. (2022). Questions clustering using canopy-K-means and hierarchical-K-means clustering. International Journal of Information Technology (Singapore), 14(7), 3793-3802. doi:10.1007/s41870-022-01012-w
4. Al-Naymat, G., Hussain, H., Al-Kasassbeh, M., & Al-Dmour, N. (2021). Accurate detection of network anomalies within SNMP-MIB data set using deep learning. International Journal of Computer Applications in Technology, 66(1), 74-85. doi:10.1504/IJCAT.2021.119606
5. Alsadoon, A., Al-Naymat, G., Osman, A. H., Alsinglawi, B., Maabreh, M., & Islam, M. R. (2023). DFCV: A framework for evaluation deep learning in early detection and classification of lung cancer. Multimedia Tools and Applications, doi:10.1007/s11042-023-15238-8
6. Awadallah, M. A., Al-Betar, M. A., Doush, I. A., Makhadmeh, S. N., & Al-Naymat, G. (2023). Recent versions and applications of sparrow search algorithm. Archives of Computational Methods in Engineering, doi:10.1007/s11831-023-09887-z
7. 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? International Journal of Management Education, 20(1) doi:10.1016/j.ijme.2021.100595

1. Al-Omoush, R., Fraihat, S., Al-Naymat, G., & Awad, M. (2022). Design and implementation of business intelligence framework for a global online retail business. Paper presented at the 2022 International Conference on Emerging Trends in Computing and Engineering Applications, ETCEA 2022 - Proceedings, doi:10.1109/ETCEA57049.2022.10009688
2. Dalbah, L. M., Ali, S., & Al-Naymat, G. (2022). An interactive dashboard for predicting bank customer attrition. Paper presented at the 2022 International Conference on Emerging Trends in Computing and Engineering Applications, ETCEA 2022 - Proceedings, doi:10.1109/ETCEA57049.2022.10009818

1. Al-Naymat, G., Khader, M., Al-Betar, M. A., Hriez, R., & Hadi, A. (2023). MR-VDENCLUE: Varying density clustering using MapReduce. doi:10.1007/978-3-031-16072-1_55

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.

1. Al-Fawa'reh, M., Al-Fayoumi, M., Nashwan, S., & Fraihat, S. (2022). Cyber threat intelligence using PCA-DNN model to detect abnormal network behavior. Egyptian Informatics Journal, 23(2), 173-185. doi:10.1016/j.eij.2021.12.001
2. Awad, M., Fraihat, S., Salameh, K., & Al Redhaei, A. (2022). Examining the suitability of NetFlow features in detecting IoT network intrusions. Sensors, 22(16) doi:10.3390/s22166164
3. Fraihat, M., Fraihat, S., Awad, M., & AlKasassbeh, M. (2022). An efficient enhanced k-means clustering algorithm for best offer prediction in telecom. International Journal of Electrical and Computer Engineering, 12(3), 2931-2943. doi:10.11591/ijece.v12i3.pp2931-2943

1. Al-Omoush, R., Fraihat, S., Al-Naymat, G., & Awad, M. (2022). Design and implementation of business intelligence framework for a global online retail business. Paper presented at the 2022 International Conference on Emerging Trends in Computing and Engineering Applications, ETCEA 2022 - Proceedings, doi:10.1109/ETCEA57049.2022.10009688
2. Awad, M., Al Redhaei, A., & Fraihat, S. (2022). Using business intelligence to analyze road traffic accidents. Paper presented at the ACM International Conference Proceeding Series, 83-92. doi:10.1145/3551504.3551507
3. Fraihat, S., Innab, H., Makhadmeh, S. N., & Al-Betar, M. A. (2022). Business intelligence system for human resource management system. Paper presented at the 2022 International Conference on Emerging Trends in Computing and Engineering Applications, ETCEA 2022 - Proceedings, doi:10.1109/ETCEA57049.2022.10009861

None

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.

1. Akbari, M. A., Zare, M., Azizipanah-abarghooee, R., Mirjalili, S., & Deriche, M. (2022). The cheetah optimizer: A nature-inspired metaheuristic algorithm for large-scale optimization problems. Scientific Reports, 12(1) doi:10.1038/s41598-022-14338-z
2. Alanazi, M., Alanazi, A., Akbari, M. A., Deriche, M., & Memon, Z. A. (2023). A non-simulation-based linear model for analytical reliability evaluation of radial distribution systems considering renewable DGs. Applied Energy, 342 doi:10.1016/j.apenergy.2023.121153
3. Al-Betar, M. A., Awadallah, M. A., Zitar, R. A., & Assaleh, K. (2022). Economic load dispatch using memetic sine cosine algorithm. Journal of Ambient Intelligence and Humanized Computing, doi:10.1007/s12652-022-03731-1
4. Alfarhan, M., Deriche, M., & Maalej, A. (2022). Robust concurrent detection of salt domes and faults in seismic surveys using an improved UNet architecture. IEEE Access, 10, 39424-39435. doi:10.1109/ACCESS.2020.3043973
5. 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) doi:10.1007/s10922-021-09616-6
6. Alkhraisat, H., Dalbah, L. M., Al-Betar, M. A., Awadallah, M. A., Assaleh, K., & Deriche, M. (2023). Size optimization of truss structures using improved grey wolf optimizer. IEEE Access, 11, 13383-13397. doi:10.1109/ACCESS.2023.3243164
7. 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, 46(9), 8721-8740. doi:10.1007/s13369-021-05646-z
8. Amin, A., Mahmood, A., Khan, A. R., Arshad, K., Assaleh, K., & Zoha, A. (2023). A two-stage multi-agent EV charging coordination scheme for maximizing grid performance and customer satisfaction. Sensors, 23(6) doi:10.3390/s23062925
9. Iqbal, N., Deriche, M., AlRegib, G., & Khan, S. (2023). Blind curvelet-based denoising of seismic surveys in coherent and incoherent noise environments. Arabian Journal for Science and Engineering, doi:10.1007/s13369-023-07836-3
10. Makhadmeh, S. N., Al-Betar, M. A., Assaleh, K., & Kassaymeh, S. (2022). A hybrid white shark equilibrium optimizer for power scheduling problem based IoT. IEEE Access, 10, 132212-132231. doi:10.1109/ACCESS.2022.3229434

1. Almohammedi, A. M., Zerguine, A., Deriche, M., & Sait, S. M. (2022). Artificial bee colony DLMS beyond mean square error boundary in ad-hoc WSN. Paper presented at the Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022, 572-576. doi:10.1109/CICN56167.2022.10008253
2. Arbateni, K., & Deriche, M. (2022). Support vector machine for heart beats classification based on robust filtering. Paper presented at the 2022 19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022, 653-656. doi:10.1109/SSD54932.2022.9955703
3. Faraj, M. A., Maalej, B., Derbel, N., & Deriche, M. (2022). Modeling and robust computed torque control for lower limb exoskeleton contacting with ground. Paper presented at the 2022 19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022, 571-579. doi:10.1109/SSD54932.2022.9955781
4. Sharif, A., Liaqat, S., Arshad, K., Assaleh, K., & Ramzan, N. (2023). Inkjet printed passive RFID sensor for wetness detection of diapers. Paper presented at the 2023 International Microwave and Antenna Symposium, IMAS 2023, 172-175. doi:10.1109/IMAS55807.2023.10066932
5. Taib, Y., Qasim, M., Hayek, B. A., Kabalan, A., Shehieb, W., Yacoub, P., . . . Assaleh, K. (2022). Primary diagnosis of thyroid stimulating hormone using a non-invasive method. Paper presented at the Proceedings of the 2022 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022, 1-5. doi:10.1109/IoTaIS56727.2022.9975924

1. Suresh Kumar, S., Dashtipour, K., Gogate, M., Ahmad, J., Assaleh, K., Arshad, K., . . . Ahmad, W. (2022). Comparing the performance of different classifiers for posture detection. doi:10.1007/978-3-030-95593-9_17
2. Taha, A., Ge, Y., Taylor, W., Zoha, A., Assaleh, K., Arshad, K., . . . Imran, M. A. (2022). Indoor activity position and direction detection using software defined radios. doi:10.1007/978-3-030-95593-9_2
3. Taylor, W., Shah, S. A., Dashtipour, K., Le Kernec, J., Abbasi, Q. H., Assaleh, K., . . . Imran, M. A. (2022). Wireless sensing for human activity recognition using USRP. doi:10.1007/978-3-030-95593-9_5

1. Niu, Z., Awad, M. I., Shah, U. H., Boushaki, M. N., Zweiri, Y., Seneviratne, L., & Hussain, I. (2022). Towards safe physical human-robot interaction by exploring the rapid stiffness switching feature of discrete variable stiffness actuation. IEEE Robotics and Automation Letters, 7(3), 8084-8091. doi:10.1109/LRA.2022.3185366

1. Toubar, H., Hameed Shah, U., & Hussain, I. (2022). A compliant-rigid underwater manipulator for enhanced safety and precision performance. Paper presented at the ASCC 2022 - 2022 13th Asian Control Conference, Proceedings, 1505-1510. doi:10.23919/ASCC56756.2022.9828337

None

1. Al-Betar, M. A., Abasi, A. K., Al-Naymat, G., Arshad, K., & Makhadmeh, S. N. (2023). Optimization of scientific publications clustering with ensemble approach for topic extraction. Scientometrics, 128(5), 2819-2877. doi:10.1007/s11192-023-04674-w
2. Al-Quraan, M., Mohjazi, L., Bariah, L., Centeno, A., Zoha, A., Arshad, K., . . . Muhammad, M. (2023). Edge-native intelligence for 6G communications driven by federated learning: A survey of trends and challenges. IEEE Transactions on Emerging Topics in Computational Intelligence, , 1-23. doi:10.1109/TETCI.2023.3251404
3. Amin, A., Mahmood, A., Khan, A. R., Arshad, K., Assaleh, K., & Zoha, A. (2023). A two-stage multi-agent EV charging coordination scheme for maximizing grid performance and customer satisfaction. Sensors, 23(6) doi:10.3390/s23062925
4. Liaqat, S., Dashtipour, K., Rizwan, A., Usman, M., Shah, S. A., Arshad, K., . . . Ramzan, N. (2022). Personalized wearable electrodermal sensing-based human skin hydration level detection for sports, health and wellbeing. Scientific Reports, 12(1) doi:10.1038/s41598-022-07754-8

1. Sharif, A., Liaqat, S., Arshad, K., Assaleh, K., & Ramzan, N. (2023). Inkjet printed passive RFID sensor for wetness detection of diapers. Paper presented at the 2023 International Microwave and Antenna Symposium, IMAS 2023, 172-175. doi:10.1109/IMAS55807.2023.10066932

1. Suresh Kumar, S., Dashtipour, K., Gogate, M., Ahmad, J., Assaleh, K., Arshad, K., . . . Ahmad, W. (2022). Comparing the performance of different classifiers for posture detection. doi:10.1007/978-3-030-95593-9_17
2. Taha, A., Ge, Y., Taylor, W., Zoha, A., Assaleh, K., Arshad, K., . . . Imran, M. A. (2022). Indoor activity position and direction detection using software defined radios. doi:10.1007/978-3-030-95593-9_2

The AIRC at Ajman University is organizing the 5th International Conference on Artificial Intelligence Science and Applications in Industry and Society (CAISAIS 2026), which brings together international researchers and practitioners from academia and industry to exchange their latest results, advancements, and future directions across the different fields of artificial intelligence. The conference, which will be held in Ajman, UAE, as a hybrid event, covers a wide range of tracks, including machine learning and deep learning, computer vision, big data analytics, AI for security, AI for healthcare, mathematical optimization, and AI for IoT and Industry 4.0, and offers a unique opportunity to establish new collaborations between academia and industry. The conference is approved by IEEE.

The conference will be held over three days from November 25 to 27, 2026.

Prospective authors are invited to submit their papers before the final deadline of September 30, 2026.

For more information, please visit the official CAISAIS 2026 website. https://caisais26.ajman.ac.ae/en/welcome-to-caisais-2026

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

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 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​

Learn python language starting from basics to advanced topics and apply your knowledge in voice assistant applications. This workshop aims to teach python language to students without prior programming experience and deliver the basic programming principles such as operations, control structures, data types, etc. Also, it covers advanced topics in python such as dictionaries and list manipulation, creating and calling functions, and file manipulation. Our workshop includes practice and debugging sessions to enrich the students' knowledge and grasp of python and enhance their problem-solving skills. Finally, applying the knowledge of the course content in an innovative voice assistant application. At the end of the workshop, students have a comprehensive understanding of python and implement practical and innovative speech assistant project.

The Hands-On Python Workshop is prepared and presented by:

• Dr. Sharif Naser Makhadmeh
• Eng. Lamees Mohammad Dalbah

• Eng. Shaimaa Mahmood Mounir Kouka

This workshop is oriented around building a 3D rocket game using unity engine along with covering the basics of programming in unity with C# language, dives into the 3D game development fundamentals such as controlling objects in the game environment, scripting its functionality using C# language, simulating and applying physics laws in the game environment for a more realistic user experience. The aim of this workshop is to practice professionally creating 3D high-quality games efficiently using unity game engine with Graphical User Interface and deploy it for many platforms.

The Master Unity 3D Game Development Workshop is prepared and presented by:

• Dr. Sharif Naser Makhadmeh
• Eng. Lamees Mohammad Dalbah

• Eng. Shaimaa Mahmood Mounir Kouka