– 2016

1. Glycerol-based deep eutectic solvents: Physical properties. Journal of Molecular Liquids 215, 98-103. 
2. Functionalization of CNTs surface with Phosphonuim based deep eutectic solvents for arsenic removal from water. Applied Surface Science 389, 216-226.
3. Lead removal from water by Choline chloride based deep eutectic solvents functionalized Carbon nanotubes, Journal of Molecular Liquids 222, 883-894.

-2017

1. Allyl triphenyl phosphonium bromide based DES-functionalized carbon nanotubes for the removal of mercury from water. Chemosphere, 167, 44-52. doi:http://dx.doi.org/10.1016/j. chemosphere. 2016.09.133
2. Novel deep eutectic solvent-functionalized carbon nanotubes adsorbents for mercury removal from water. Journal of Colloid and Interface Science, 497 (2017) 413-421.
3. N,N-diethylethanolammonium chloride based DES-functionalized carbon nanotubes for arsenic removal from aqueous solution. 74 (2017) 163–173. Desalination and Water Treatment,
4. Computational investigation of the microstructural characteristics and physical properties of glycerol-based deep eutectic solvents. Journal of Molecular Modeling, 23(10), 277.
5. Hybriding carbon nanotubes with powder activated carbon for efficient Bisphenol A removal from water: The optimum growth and adsorption conditions. Desalination and Water Treatment. doi: 10.5004/dwt.2017.21451, 95 (2017) 128–143
6. The modelling of lead removal from water by deep eutectic solvents functionalized CNTs: Artificial neural network (ANN) approach. Water Science and Technology. 76 (9), 2413-2426
7. The modelling of arsenic removal from water by deep eutectic solvents functionalized CNTs: Artificial neural network (ANN) approach. Desalination and Water Treatment. doi:10.5004/dwt.2017.21538, 94 (2017) 189–

–2018

1. “Optimization of the Synthesis of Superhydrophobic Carbon Nanomaterials by Chemical Vapor Deposition.” Scientific Reports, 8(1), 2778. (Nature).
2. Growth and optimization of carbon nanotubes in powder activated carbon by chemical vapor deposition: An efficient sorbent for removal of methylene blue from aqueous solution. Environmental Technology.
3. The formation of hybrid carbon nanomaterial by chemical vapor deposition: An efficient adsorbent for enhanced adsorptive removal of methylene blue from aqueous solution. Water Science and Technology.
4. BTPC-Based DES-Functionalized CNTs for A s 3+ Removal from Water: NARX Neural Network Approach. Journal of Environmental Engineering, 144(8), 04018070.l, ASCE.
5. Adsorption of 2,4-dichlorophenol from water using deep eutectic solvents-functionalized carbon nanotubes. Desalination and Water Treatment. 116 (2018) 214–231, doi:10.5004/dwt.2018.22485.

–2019

1. Artificial Neural Network Approach for Modelling of Mercury Ions Removal from Water Using Functionalized CNTs with Deep Eutectic Solvent. International journal of molecular sciences, 20(17), p.4206.
2. Lead removal from water using DES functionalized CNTs: ANN modeling approach. Desalination and water treatment, 150, pp.105-113.
3. Review on heavy metal adsorption processes by carbon nanotubes. Journal of Cleaner Production, 230, 783-793. doi:https://doi.org/10.1016/j.jclepro.2019.05.154
4. Growth and optimization of carbon nanotubes in powder activated carbon for an efficient removal of methylene blue from aqueous solution. Environmental technology, 40(18), pp.2400-2415.
5. Prediction of Compressive Strength of High-Performance Concrete: Hybrid Artificial Intelligence Technique. In International Conference on Applied Computing to Support Industry: Innovation and Technology (pp. 323-335). Springer, Cham.
6. Mercury removal from water using deep eutectic solvents-functionalized multi walled carbon nanotubes: Nonlinear autoregressive network with an exogenous input neural network approach. Environmental Progress & Sustainable Energy, 38(6), e13261. doi:10.1002/ep.13261

2020

1. PLA/MMT-TiO2 Bionanocomposites: Chemical Structure and Surface Wettability. In Defect and Diffusion Forum (Vol. 398, pp. 136-139). Trans Tech Publications Ltd.
2. Tensile and Morphology Properties of PLA/MMT-TiO2 Bionanocomposites. In Defect and Diffusion Forum (Vol. 398, pp. 131-135). Trans Tech Publications Ltd.

Conferences (presenter)

1. [Densities of Phosphonium and Ammonium Based Deep Eutectic Solvents]. Design for Scientific Renaissance DSR. (2014), Malaysia
2.  [Stable Molar Ratios of Novel Deep Eutectic Solvents and Their Physical Properties].  Design for Scientific Renaissance DSR. (2015), Malaysia  
3.  [Sensitivity Analysis of Profile Steel Sheeting Dry Board (PSSDB) Floor System Using Finite Element Method]. Design for Scientific Renaissance DSR. (2015) , Malaysia

 The impact of Super-Hydrophobic Coating Carbon Nanomaterials Membrane on the Performance of Membrane Distillation, Meeting on Nanotechnology, Principles and Application, Malaysia. (2018)

1. Seawater desalination by CNT-Modified composite Super-Hydrophobic Membrane in DCMD System, Meeting on Nanotechnology, Principles and Application, Malaysia. (2018)
2.  The Cytotoxicity and Apoptotic Activity of Magnesium Orotate Nanoparticles Towards Human Hepatoma Cell Line Hepg2, Meeting on Nanotechnology, Principles and Application, Malaysia. (2018)

1. Afan, H. A., Allawi, M. F., El-Shafie, A., Yaseen, Z. M., Ahmed, A. N., Malek, M. A., . . . Lai, S. H. J. S. R. (2020). Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting. 10(1), pp. 1-15.

2. Afan, H. A., El-shafie, A., Mohtar, W. H. M. W., & Yaseen, Z. M. J. J. o. H. (2016). Past, present and prospect of an Artificial Intelligence (AI) based model for sediment transport prediction. 541, pp. 902-913.

3. Afan, H. A., El-Shafie, A., Yaseen, Z. M., Hameed, M. M., Mohtar, W. H. M. W., & Hussain, A. J. W. r. m. (2015). ANN based sediment prediction model utilizing different input scenarios. 29(4), pp. 1231-1245.

4. Afan, H. A., Keshtegar, B., Mohtar, W. H. M. W., & El-Shafie, A. J. J. o. h. (2017). Harmonize input selection for sediment transport prediction. 552, pp. 366-375.

5. Ahmed, A. N., Othman, F. B., Afan, H. A., Ibrahim, R. K., Fai, C. M., Hossain, M. S., . . . Elshafie, A. J. J. o. H. (2019). Machine learning methods for better water quality prediction. 578, p 124084.

6. Al-Janabi, A. M. S., Ghazali, A. H., Ghazaw, Y. M., Afan, H. A., Al-Ansari, N., & Yaseen, Z. M. J. S. (2020). Experimental and numerical analysis for earth-fill dam seepage. 12(6), p 2490.

7. Al-Janabi, A. M. S., Ghazali, A. H., Yusuf, B., Sammen, S. S., Afan, H. A., Al-Ansari, N., . . . Yaseen, Z. M. J. A. S. (2020). Optimizing Height and Spacing of Check Dam Systems for Better Grassed Channel Infiltration Capacity. 10(11), p 3725.

8. Allawi, M. F., Binti Othman, F., Afan, H. A., Ahmed, A. N., Hossain, M., Fai, C. M., & El-Shafie, A. J. W. (2019). Reservoir evaporation prediction modeling based on artificial intelligence methods. 11(6), p 1226.

9. Banadkooki, F. B., Ehteram, M., Ahmed, A. N., Fai, C. M., Afan, H. A., Ridwam, W. M., . . . El-Shafie, A. J. S. (2019). Precipitation Forecasting Using Multilayer Neural Network and Support Vector Machine Optimization Based on Flow Regime Algorithm Taking into Account Uncertainties of Soft Computing Models. 11(23), p 6681.

10. Banadkooki, F. B., Ehteram, M., Ahmed, A. N., Teo, F. Y., Fai, C. M., Afan, H. A., . . . El-Shafie, A. J. N. R. R. (2020). Enhancement of Groundwater-Level Prediction Using an Integrated Machine Learning Model Optimized by Whale Algorithm. pp. 1-20.

11. Bapat, S. A., Aalders, K., Aaltonen, K., Aapro, M., Razek, A. A., Abdelzaher, E., . . . Abraham, J. (2019). Acknowledgement of reviewers 2018.

12. Ehteram, M., Afan, H. A., Dianatikhah, M., Ahmed, A. N., Ming Fai, C., Hossain, M. S., . . . Elshafie, A. J. W. (2019). Assessing the predictability of an improved ANFIS model for monthly streamflow using lagged climate indices as predictors. 11(6), p 1130.

13. Ehteram, M., Ahmed, A. N., Fai, C. M., Afan, H. A., & El-Shafie, A Preparing a Zone Map of Solar Radiation Considering the Air Quality based on a Multi-objective Shark Algorithm, an Adaptive Neuro-fuzzy Inference System and the Taguchi Model.

14. Ehteram, M., Ahmed, A. N., Fai, C. M., Afan, H. A., & El-Shafie, A. J. E. (2019). Accuracy enhancement for zone mapping of a solar radiation forecasting based multi-objective model for better Management of the Generation of renewable energy. 12(14), p 2730.

15. Ehteram, M., Ahmed, A. N., Ling, L., Fai, C. M., Latif, S. D., Afan, H. A., . . . El-Shafie, A. J. W. (2020). Pipeline Scour Rates Prediction-Based Model Utilizing a Multilayer Perceptron-Colliding Body Algorithm. 12(3), p 902.

16. Ehteram, M., Binti Koting, S., Afan, H. A., Mohd, N. S., Malek, M., Ahmed, A. N., . . . El-Shafie, A. J. A. S. (2019). New evolutionary algorithm for optimizing hydropower generation considering multireservoir systems. 9(11), p 2280.

17. Ehteram, M., El-Shafie, A. H., Hin, L. S., Othman, F., Koting, S., Karami, H., . . . Zawawi, B. J. A. S. (2019). Toward bridging future irrigation deficits utilizing the shark algorithm integrated with a climate change model. 9(19), p 3960.

18. Ehteram, M., Singh, V. P., Ferdowsi, A., Mousavi, S. F., Farzin, S., Karami, H., . . . Kisi, O. J. P. o. (2019). An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration. 14(5)

19. Elzwayie, A., Afan, H. A., Allawi, M. F., El-Shafie, A. J. E. S., & Research, P. (2017). Heavy metal monitoring, analysis and prediction in lakes and rivers: state of the art. 24(13), pp. 12104-12117.

20. Elzwayie, A., El-Shafie, A., Yaseen, Z. M., Afan, H. A., Allawi, M. F. J. N. C., & Applications. (2017). RBFNN-based model for heavy metal prediction for different climatic and pollution conditions. 28(8), pp. 1991-2003.

21. Fayaed, S. S., Fiyadh, S. S., Khai, W. J., Ahmed, A. N., Afan, H. A., Ibrahim, R. K., . . . Binti Jaafar, W. Z. J. S. (2019). Improving Dam and Reservoir Operation Rules Using Stochastic Dynamic Programming and Artificial Neural Network Integration Model. 11(19), p 5367.

22. Ghazvinian, H., Mousavi, S.-F., Karami, H., Farzin, S., Ehteram, M., Hossain, M. S., . . . Ros, F. C. J. P. o. (2019). Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction. 14(5)

23. Hameed, M., Sharqi, S. S., Yaseen, Z. M., Afan, H. A., Hussain, A., Elshafie, A. J. N. C., & Applications. (2017). Application of artificial intelligence (AI) techniques in water quality index prediction: a case study in tropical region, Malaysia. 28(1), pp. 893-905.

24. Ho, J. Y., Afan, H. A., El-Shafie, A. H., Koting, S. B., Mohd, N. S., Jaafar, W. Z. B., . . . Mohtar, W. H. M. W. J. J. o. H. (2019). Towards a time and cost effective approach to water quality index class prediction. 575, pp. 148-165.

25. Ibrahim, R. K., Fiyadh, S. S., AlSaadi, M. A., Hin, L. S., Mohd, N. S., Ibrahim, S., . . . Elshafie, A. J. M. (2020). Feedforward Artificial Neural Network-Based Model for Predicting the Removal of Phenolic Compounds from Water by Using Deep Eutectic Solvent-Functionalized CNTs. 25(7), p 1511.

26. Keshtegar, B., Allawi, M. F., Afan, H. A., & El-Shafie, A. J. W. R. M. (2016). Optimized river stream-flow forecasting model utilizing high-order response surface method. 30(11), pp. 3899-3914.

27. Khan, F. A., Khan, T. M. A., Ahmed, A. N., Afan, H. A., Sherif, M., Sefelnasr, A., & El-Shafie, A. J. E. (2020). Complex Extreme Sea Levels Prediction Analysis: Karachi Coast Case Study. 22(5), p 549.

28. Kumar, P., Lai, S. H., Wong, J. K., Mohd, N. S., Kamal, M. R., Afan, H. A., . . . El-Shafie, A. J. S. (2020). Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models. 12(11), p 4359.

29. Liu, S., Afan, H. A., Aldlemy, M. S., Al-Ansari, N., & Yaseen, Z. M. J. E. R. (2020). Energy analysis using carbon and metallic oxides-based nanomaterials inside a solar collector. 6, pp. 1373-1381.

30. Malik, A., Kumar, A., Ahmed, A. N., Fai, C. M., Afan, H. A., Sefelnasr, A., . . . El-Shafie, A. J. A. E. J. (2020). Application of non-parametric approaches to identify trend in streamflow during 1976–2007 (Naula watershed).

31. Osman, A., Afan, H. A., Allawi, M. F., Jaafar, O., Noureldin, A., Hamzah, F. M., . . . El-shafie, A. J. J. o. H. (2020). Adaptive Fast Orthogonal Search (FOS) algorithm for forecasting streamflow. p 124896.

32. Osman, A., Allawi, M. F., Afan, H. A., Noureldin, A., El-shafie, A. J. H., & Discussions, E. S. S. (2016). Acclimatizing Fast Orthogonal Search (FOS) Model for River Stream-flow Forecasting. pp. 1-28.

33. Othman, F., Alaaeldin, M., Seyam, M., Ahmed, A. N., Teo, F. Y., Ming Fai, C., . . . El-Shafie, A. J. E. A. o. C. F. M. (2020). Efficient river water quality index prediction considering minimal number of inputs variables. 14(1), pp. 751-763.

34. Pham, Q. B., Afan, H. A., Mohammadi, B., Ahmed, A. N., Linh, N. T. T., Vo, N. D., . . . El-Shafie10, A. Hybrid model to improve the river streamflow forecasting utilizing multi-layer perceptron-based intelligent water drop optimization algorithm.

35. Salman, S. A., Shahid, S., Afan, H. A., Shiru, M. S., Al-Ansari, N., & Yaseen, Z. M. J. S. (2020). Changes in Climatic Water Availability and Crop Water Demand for Iraq Region. 12(8), p 3437.

36. Sayl, K. N., Afan, H. A., Muhammad, N. S., ElShafie, A. J. H., & Discussions, E. S. S. (2017). Development of a Spatial Hydrologic Soil Map Using Spectral Reflectance Band Recognition and a Multiple-Output Artificial Neural Network Model. pp. 1-16.

37. Turgut, M. S., Turgut, O. E., Afan, H. A., & El-Shafie, A. J. J. o. H. (2019). A novel Master–Slave optimization algorithm for generating an optimal release policy in case of reservoir operation. 577, p 123959.

38. Valikhan-Anaraki, M., Mousavi, S.-F., Farzin, S., Karami, H., Ehteram, M., Kisi, O., . . . Ahmed, A. N. J. S. (2019). Development of a Novel Hybrid Optimization Algorithm for Minimizing Irrigation Deficiencies. 11(8), p 2337.

39. Valizadeh, N., Mirzaei, M., Allawi, M. F., Afan, H. A., Mohd, N. S., Hussain, A., & El-Shafie, A. J. N. H. (2017). Artificial intelligence and geo-statistical models for stream-flow forecasting in ungauged stations: state of the art. 86(3), pp. 1377-1392.

40. Wan Mohtar, W. H. M., Afan, H., El-Shafie, A., Bong, C. H. J., & Ab. Ghani, A. J. U. W. J. (2018). Influence of bed deposit in the prediction of incipient sediment motion in sewers using artificial neural networks. 15(4), pp. 296-302.

41. Yahya, A., Saeed, A., Ahmed, A. N., Binti Othman, F., Ibrahim, R. K., Afan, H. A., . . . Ehteram, M. J. W. (2019). Water quality prediction model based support vector machine model for Ungauged River catchment under dual scenarios. 11(6), p 1231.

42. Yaseen, Z., El-Shafie, A., & Afan, H. J. N. C. A., Malaysia. doi. (2015). RBFNN versus FFNN for daily river flow forecasting at Johor River. 10

43. Yaseen, Z. M., Afan, H. A., & Tran, M.-T. (2018). Beam-column joint shear prediction using hybridized deep learning neural network with genetic algorithm. IOP conference series: earth and environmental science.

44. Yaseen, Z. M., Allawi, M. F., Karami, H., Ehteram, M., Farzin, S., Ahmed, A. N., . . . Applications. (2019). A hybrid bat–swarm algorithm for optimizing dam and reservoir operation. 31(12), pp. 8807-8821.

45. Yaseen, Z. M., Ehteram, M., Hossain, M., Fai, C. M., Binti Koting, S., Mohd, N. S., . . . Zaini, N. J. S. (2019). A novel hybrid evolutionary data-intelligence algorithm for irrigation and power production management: Application to multi-purpose reservoir systems. 11(7), p 1953.

46. Yaseen, Z. M., El-Shafie, A., Afan, H. A., Hameed, M., Mohtar, W. H. M. W., Hussain, A. J. N. C., & Applications. (2016). RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia. 27(6), pp. 1533-1542

47. Yaseen, Z. M., El-Shafie, A., Jaafar, O., Afan, H. A., & Sayl, K. N. J. J. o. H. (2015). Artificial intelligence based models for stream-flow forecasting: 2000–2015. 530, pp. 829-844.

1. Publications

1.      Optimization Synthesis of Super-Hydrophobic Carbon Nanomaterials by Chemical Vapor Deposition. Scientific Reports, Nature. ISI index Q1(5. 5  impact factor).

2.      PVDF-co-HFP/superhydrophobic acetylene-based nanocarbon hybrid membrane for seawater desalination via DCMD, chemical engineering research and design. ISI index Q1 (2.795 impact factor).

3.      Hybriding carbon nanotubes with powder activated carbon for efficient Bisphenol A removal from water: The optimum growth and adsorption conditions. RSC Advances. RSC. DESALIN WATER TREATMANT.ISI index Q2 (1.7 impact factor).

4.      N,N-diethylethanolammonium chloride based DES-functionalized carbon nanotubes for arsenic removal from aqueous solution, DESALIN WATER TREATMANT.ISI index Q2 (1.7 impact factor).

5.      Embedded super-hydrophobic CNMs prepared by CVD technique with PVDF-co-HFP membrane for application in water desalination by DCMD. DESALIN WATER TREATMANT.ISI index Q2 (1.7 impact factor).

6.      Optimum super-hydrophobic surface nanocarbon-based membrane with improved anti-bacterial characteristics. Biotechnology Progress. ISI index Q2 (2.4. impact factor).

7.      A clean approach for functionalized carbon nanotubes by deep eutectic solvents and their performance in the adsorption of methyl orange from aqueous solution. Journal of environmental management. ISI index Q1 (4.4 impact factor).

8.      Synthesis and Characterization of Natural Extracted Precursor Date Palm Fibre-Based Activated Carbon for Aluminum Removal by RSM Optimization. Processes. ISI index Q3 (1.4 impact factor).

9.      Proficiency of Few-Layered Graphene Oxide Nanosheets as Promising Sorbents for Dye Pollution Management. Journal of Environmental Engineering. ISI index Q3 (1.6 impact factor).

10.   Probing the Effect of Gaseous Hydrocarbon Precursors on the Adsorptive Efficiency of Synthesized Carbon-Based Nanomaterials. The Journal of Engineering Research. Scopus index Q3(4.3 impact factor).

2 Conference

1.      Mustafa Mohammed aljumaily, Azrul A. Mutalib, Qahtan Adnan Altaee (2015) [Finite Elements Method For Ultimate Behaviour Of Plate Girder With Diagonal Stiffener]. Design for Scientific Renaissance DSR.

2.      Serving as Rapporteur of Graphene Malaysia 2016.

3.      Serving as Rapporteur of Nano Technology Malaysia 2017

1- بناء قواعد بيانات جغرافية للخصائص المناخية في محافظة للانبار، مجلة الفنون والاداب، العدد 11، 2016.

2- نمذجة وتحليل الخرائط المناخية لمحافظة الانبار: الاشعاع الشمسي ودرجة الحرارة انموذجا، مجلة اداب الفراهيدي، جامعة تكريت، العدد 236، 2016.

 “EVALUATION OF TRAFFIC OPERATION FOR RAMADI – FALLUJA MULTI-LANE RURAL HIGHWAY PAVEMENT MODEFIED”, مجلة كلية المعارف الجامعة (ISBN 1815-3364) 26 (1), pp 25-50