Mr. Shahaboddin Shamshirband
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Mr. Shahaboddin Shamshirband

Senior Lecturer
University of Malaya, Malaysia

Highest Degree
Ph.D. Student in Computer Science from University of Malaya, Malaysia

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Biography

Mr. Shahaboddin Shamshirband is currently working as Lecturer at University of Malaya Department of Computer System & Technology Malaysia. He is Ph.D. Student in Computer Science from same University. He is also serving as Research Scientist, Security Research Group (SECReg) at University of Malaya, Department of Civil Engineering Kuala Lumpur, Malaysia. He also appointed as Lecturer at Islamic Azad University Mashhad Branch, Department of Software Engineering. He is member of Institute of Electrical and Electronics Engineers. His main area of interest related to Network security, Expert system, Fuzzy system, Machine learning, Game theory, and Health care system. He has published 1 book, 21 research articles in journals, and 1 conference proceeding as author/co-author.

Area of Interest:

Computer Sciences
100%
Network Security
62%
Machine Learning
90%
Wireless Sensor Network
75%
Cloud Computing
55%

Research Publications in Numbers

Books
0
Chapters
0
Articles
0
Abstracts
0

Selected Publications

  1. Marjani, M., F. Nasaruddin, A. Gani and S. Shamshirband, 2018. Measuring transaction performance based on storage approaches of native XML database. Meas., 114: 91-101.
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  2. Zaji, A.H., H. Bonakdari and S. Shamshirband, 2017. Standard equations for predicting the discharge coefficient of a modified high-performance side weir. Sci. Iranica, 10.24200/sci.2017.4198.
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  3. Yusoff, Z., A. Kamsin, S. Shamshirband and A.T. Chronopoulos, 2017. A survey of educational games as interaction design tools for affective learning: Thematic analysis taxonomy. Educ. Inf. Technol., 10.1007/s10639-017-9610-5.
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  4. Petkovic, D., S. Shamshirband, A. Kamsin, M. Lee, O. Anicic and V. Nikolić, 2017. Corrigendum to “Survey of the most influential parameters on the wind farm net present value (NPV) by adaptive neuro-fuzzy approach” [Renew. Sustain. Energy Rev. 57C (2016) 1270-78]. Renewable Sustainable Energy Rev., 74: 1405-1405.
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  5. Nikpay, F., R.B. Ahmad, B.D. Rouhani, M.N.R. Mahrin and S. Shamshirband, 2017. An effective enterprise architecture implementation methodology. Inf. Syst. E-Bus. Manage., 15: 927-962.
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  6. Nazhad, S.H.H., M.M. Lotfinejad, M. Danesh, R. Amin and S. Shamshirband, 2017. A comparison of the performance of some extreme learning machine empirical models for predicting daily horizontal diffuse solar radiation in a region of southern Iran. Int. J. Remote Sens., 38: 6894-6909.
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  7. Nazhad, S.H.H., M. Shojafar, S. Shamshirband and M. Conti, 2017. An efficient routing protocol for the QoS support of large-scale Manets. Int. J. Commun. Syst., 10.1002/dac.3384.
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  8. Moosavi, S.A., M. Jalali, N. Misaghian, S. Shamshirband and M.H. Anisi, 2017. Community detection in social networks using user frequent pattern mining. Knowl. Inf. Syst., 51: 159-186.
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  9. Monzavi, M., M.H.S.A. Murad, M. Rahnama and S. Shamshirband, 2017. Historical path of traditional and modern idea of 'conscious universe'. Qual. Quantity, 51: 1183-1195.
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  10. Mojumder, J.C., H.C. Ong, W.T. Chong, N. Izadyar and S. Shamshirband, 2017. The intelligent forecasting of the performances in PV/T collectors based on soft computing method. Renewable Sustainable Energy Rev., 72: 1366-1378.
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  11. Misaghian, N., S. Shamshirband, D. Petkovic, M. Gocic and K. Mohammadi, 2017. Predicting the reference evapotranspiration based on tensor decomposition. Theor. Appl. Climatol., 130: 1099-1109.
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  12. Khan, S., B. Nazir, I.A. Khan, S. Shashirband and A.T. Chronopoulos, 2017. Load balancing in grid computing: Taxonomy, trends and opportunities. J. Network Comput. Applic., 88: 99-111.
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  13. Khan, F.G., B. Montrucchio, B. Jan, A.N. Khan and W. Jadoon et al., 2017. An optimized magnetostatic field solver on GPU using open computing language. Concurrency Comput. Pract. Experience, Vol. 29. 10.1002/cpe.3981.
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  14. Khan, A.N., M. Ali, F.G. Khan, I.A. Khan, W. Jadoon, S. Shamshirband and A.T. Chronopoulos, 2017. A comparative study and workload distribution model for re-encryption schemes in a mobile cloud computing environment. Int. J. Commun. Syst., Vol. 30. 10.1002/dac.3308.
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  15. Kalantari, A., A. Kamsin, S. Shamshirband, A. Gani, H. Alinejad-Rokny and A.T. Chronopoulos, 2017. Computational intelligence approaches for classification of medical data: State-of-the-art, future challenges and research directions. Neurocomputing, 10.1016/j.neucom.2017.01.126.
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  16. Kalantari, A., A. Kamsin, H.S. Kamaruddin, N.A. Ebrahim, A. Gani, A. Ebrahimi and S. Shamshirband, 2017. A bibliometric approach to tracking big data research trends. J. Big Data, Vol. 4. 10.1186/s40537-017-0088-1.
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  17. Jan, B., F.G. Khan, B. Montrucchio, A.T. Chronopoulos, S. Shamshirband and A.N. Khan, 2017. Introducing ToPe-FFT: An OpenCL-based FFT library targeting GPUs. Concurrency Comput. Pract. Experience, Vol. 29. 10.1002/cpe.4256.
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  18. Jabbarpour, M.R., H. Zarrabi, R.H. Khokhar, S. Shamshirband and K.K.R. Choo, 2017. Applications of computational intelligence in vehicle traffic congestion problem: A survey. Soft Comput., 10.1007/s00500-017-2492-z.
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  19. Hosseinzadeh-Bandbafha, H., A. Nabavi-Pelesaraei and S. Shamshirband, 2017. Investigations of energy consumption and greenhouse gas emissions of fattening farms using artificial intelligence methods. Environ. Prog. Sustainable Energy, 36: 1546-1559.
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  20. Hossain, M., S. Mekhilef, M. Danesh, L. Olatomiwa and S. Shamshirband, 2017. Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems. J. Cleaner Prod., 167: 395-405.
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  21. Ghorbani, M.A., S. Shamshirband, D.Z. Haghi, A. Azani, H. Bonakdari and I. Ebtehaj, 2017. Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point. Soil Tillage Res., 172: 32-38.
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  22. Ghazvinei, P.T., S. Shamshirband, S. Motamedi, H.H. Darvishi and E. Salwana, 2017. Performance investigation of the dam intake physical hydraulic model using support vector machine with a discrete wavelet transform algorithm. Comput. Electron. Agric., 140: 48-57.
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  23. Faiz, M., N.B. Anuar, A.W.A. Wahab, S. Shamshirband and A.T. Chronopoulos, 2017. Source camera identification: A distributed computing approach using Hadoop. J. Cloud Comput., Vol. 6. 10.1186/s13677-017-0088-x.
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  24. Esfahani, J.A., M.R. Safaei, M. Goharimanesh, L.R. De Oliveira, M. Goodarzi, S. Shamshirband and E.P. Bandarra Filho, 2017. Comparison of experimental data, modelling and non-linear regression on transport properties of mineral oil based nanofluids. Powder Technol., 317: 458-470.
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  25. Ebtehaj, I., H. Bonakdari, S. Shamshirband, Z. Ismail and R. Hashim, 2017. New approach to estimate velocity at limit of deposition in storm sewers using vector machine coupled with firefly algorithm. J. Pipeline Syst. Eng. Pract., Vol. 8. 10.1061/(ASCE)PS.1949-1204.0000252.
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  26. Bardestani, S., M. Givehchi, E. Younesi, S. Sajjadi, S. Shamshirband and D. Petkovic, 2017. Predicting turbulent flow friction coefficient using ANFIS technique. Signal Image Video Process., 11: 341-347.
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  27. Ardabili, S.F., B. Najafi, H. Ghaebi, S. Shamshirband and A. Mostafaeipour, 2017. A novel enhanced exergy method in analyzing HVAC system using soft computing approaches: A case study on mushroom growing hall. J. Build. Eng., 13: 309-318.
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  28. Amin, R., L. Aijun, M.U. Khan, S. Shamshirband and A. Kamsin, 2017. An adaptive trajectory tracking control of four rotor hover vehicle using extended normalized radial basis function network. Mech. Syst. Signal Process., 83: 53-74.
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  29. Al-Janabi, S., I. Al-Shourbaji, M. Shojafar and S. Shamshirband, 2017. Survey of main challenges (security and privacy) in wireless body area networks for healthcare applications. Egypt. Inf. J., 18: 113-122.
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  30. Zaji, A.H., H. Bonakdari, S.R. Khodashenas and S. Shamshirband, 2016. Firefly optimization algorithm effect on support vector regression prediction improvement of a modified labyrinth side weir's discharge coefficient. Appl. Math. Comput., 274: 14-19.
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  31. Zaji, A.H., H. Bonakdari and S. Shamshirband, 2016. Support vector regression for modified oblique side weirs discharge coefficient prediction. Flow Meas. Instrum., 51: 1-7.
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  32. Trajkovic, S., O. Kisi, M. Markus, H. Tabari, M. Gocic and S. Shamshirband, 2016. Hydrological hazards in a changing environment: Early warning, forecasting and impact assessment. Adv. Meteorol., Vol. 2016. 10.1155/2016/2752091.
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  33. Toghroli, A., M. Suhatril, Z. Ibrahim, M. Safa, M. Shariati and S. Shamshirband, 2016. Potential of soft computing approach for evaluating the factors affecting the capacity of steel-concrete composite beam. J. Intell. Manuf., 10.1007/s10845-016-1217-y.
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  34. Soleymani, S.A., S. Goudarzi, M.H. Anisi, W.H. Hassan and M.Y.I. Idris et al., 2016. A novel method to water level prediction using RBF and FFA. Water Resour. Manage., 30: 3265-3283.
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  35. Siddiqa, A., I.A.T. Hashem, I. Yaqoob, M. Marjani, S. Shamshirband, A. Gani and F. Nasaruddin, 2016. A survey of big data management: Taxonomy and state-of-the-art. J. Network Comput. Applic., 71: 151-166.
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  36. Shuja, J., A. Gani, S. Shamshirband, R.W. Ahmad and K. Bilal, 2016. Sustainable cloud data centers: A survey of enabling techniques and technologies. Renewable Sustainable Energy Rev., 62: 195-214.
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  37. Shojafar, M., M. Kardgar, A.A.R. Hosseinabadi, S. Shamshirband and A. Abraham, 2016. TETS: A Genetic-Based Scheduler in Cloud Computing to Decrease Energy and Makespan. In: Hybrid Intelligent Systems. Abraham, A., S. Han, S. Al-Sharhan and H. Liu (Ed.). Springer, Switzerland., ISBN: 978-3-319-27220-7, pp: 103-115..
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  38. Shenify, M., A.S. Danesh, M. Gocic, R.S. Taher and A.W.A. Wahab et al., 2016. Precipitation estimation using support vector machine with discrete wavelet transform. Water Resour. Manage., 30: 641-652.
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  39. Shamshirband, S., L. Banjanovic-Mehmedovic, I. Bosankic, S. Kasapovic and A.W.B.A. Wahab, 2016. Adaptive neuro-fuzzy determination of the effect of experimental parameters on vehicle agent speed relative to vehicle intruder. Plos One, Vol. 11. 10.1371/journal.pone.0155697.
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  40. Shamshirband, S., K. Mohammadi, J. Piri, D. Petkovic and A. Karim, 2016. Hybrid auto-regressive neural network model for estimating global solar radiation in bandar Abbas, Iran. Environ. Earth Sci., Vol. 75. 10.1007/s12665-015-4970-x.
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  41. Shamshirband, S., K. Mohammadi, H. Khorasanizadeh, L. Yee, M. Lee, D. Petkovic and E. Zalnezhad, 2016. Estimating the diffuse solar radiation using a coupled support vector machine-wavelet transform model. Renewable Sustainable Energy Rev., 56: 428-435.
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  42. Shamshirband, S., K. Mohammadi, C.W. Tong, M. Zamani, S. Motamedi and C. Sudheer, 2016. A hybrid SVM-FFA method for prediction of monthly mean global solar radiation. Theor. Appl. Climatol., 125: 53-65.
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  43. Shamshirband, S., K. Mohammadi, C.W. Tong, D. Petkovic and E. Porcu et al., 2016. Erratum to: Application of extreme learning machine for estimation of wind speed distribution. Climate Dyn., 46: 2025-2025.
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  44. Shamshirband, S., K. Mohammadi, C.W. Tong, D. Petkovic and E. Porcu et al., 2016. Application of extreme learning machine for estimation of wind speed distribution. Climate Dyn., 46: 1893-1907.
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  45. Shamshirband, S., H. Bonakdari, A.H. Zaji, D. Petkovic and S. Motamedi, 2016. Improved side weir discharge coefficient modeling by adaptive neuro-fuzzy methodology. KSCE J. Civil Eng., 20: 2999-3005.
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  46. Shamshirband, S., A. Keivani, K. Mohammadi, M. Lee, S.H.A. Hamid and D. Petkovic, 2016. Assessing the proficiency of adaptive neuro-fuzzy system to estimate wind power density: Case study of Aligoodarz, Iran. Renewable Sustainable Energy Rev., 59: 429-435.
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  47. Saybani, M.R., T.Y. Wah, S.R. Aghabozorgi, S. Shamshirband, M.L.M. Kiah and V.E. Balas, 2016. Diagnosing breast cancer with an improved artificial immune recognition system. Soft Comput., 20: 4069-4084.
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  48. Saybani, M.R., S. Shamshirband, S. Golzari, T.Y. Wah, A. Saeed, M.L.M. Kiah and V.E. Balas, 2016. RAIRS2 a new expert system for diagnosing tuberculosis with real-world tournament selection mechanism inside artificial immune recognition system. Med. Biol. Eng. Comput., 54: 385-399.
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  49. Sajjadi, S., S. Shamshirband, M. Alizamir, L. Yee and Z. Mansor ET AL., 2016. Extreme learning machine for prediction of heat load in district heating systems. Energy Build., 122: 222-227.
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  50. Sajjadi, B., A.A.A. Raman, R. Parthasarathy and S. Shamshirband, 2016. Sensitivity analysis of catalyzed-transesterification as a renewable and sustainable energy production system by adaptive neuro-fuzzy methodology. J. Taiwan Inst. Chem. Eng., 64: 47-58.
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  51. Saberi, A., S. Motamedi, S. Shamshirband, C.L. Kausel and D. Petkovic et al., 2016. Evaluating the legibility of decorative arabic scripts for Sultan Alauddin mosque using an enhanced soft-computing hybrid algorithm. Comput. Hum. Behav., 55: 127-144.
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  52. Roy, C., S. Motamedi, R. Hashim, S. Shamshirband and D. Petkovic, 2016. A comparative study for estimation of wave height using traditional and hybrid soft-computing methods. Environ. Earth Sci., Vol. 75. 10.1007/s12665-015-5221-x.
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  53. Regrain, E., P. Regnault, C. Kirtley, S. Shamshirband, A. Chays, F.C. Boyer and R. Taiar, 2016. Impact of multi-task on symptomatic patient affected by chronical vestibular disorders. Acta Bioeng. Biomech., 18: 123-129.
    CrossRef  |  PubMed  |  Direct Link  |  
  54. Qolipour, M., A. Mostafaeipour, S. Shamshirband, O. Alavi, H. Goudarzi and D. Petkovic, 2016. Evaluation of wind power generation potential using a three hybrid approach for households in Ardebil province, Iran. Energy Conv. Manage., 118: 295-305.
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  55. Piri, J., K. Mohammadi, S. Shamshirband and S. Akib, 2016. Assessing the suitability of hybridizing the Cuckoo optimization algorithm with ANN and ANFIS techniques to predict daily evaporation. Environ. Earth Sci., Vol. 75. 10.1007/s12665-015-5058-3.
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  56. Petkovic, D., S. Shamshirband, N.B. Anuar, A.Q.M. Sabri, Z.B.A. Rahman and N.D. Pavlovic, 2016. Input displacement neuro-fuzzy control and object recognition by compliant multi-fingered passively adaptive robotic gripper. J. Intell. Rob. Syst., 82: 177-187.
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  57. Petkovic, D., M. Gocic, S. Shamshirband, S.N. Qasem and S. Trajkovic, 2016. Particle swarm optimization-based radial basis function network for estimation of reference evapotranspiration. Theor. Appl. Climatol., 125: 555-563.
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  58. Petkovic, D., A.S. Danesh, M. Dadkhah, N. Misaghian, S. Shamshirband, E. Zalnezhad and N.D. Pavlovic, 2016. Adaptive control algorithm of flexible robotic gripper by extreme learning machine. Rob. Comput. Integrated Manuf., 37: 170-178.
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  59. Nikpay, F., R. Ahmad, B.D. Rouhani and S. Shamshirband, 2016. A systematic review on post-implementation evaluation models of enterprise architecture artefacts. Inf. Syst. Front., 10.1007/s10796-016-9716-0.
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  60. Nikolic, V., S. Motamedi, S. Shamshirband, D. Petkovic, C. Sudheer and M. Arif, 2016. Extreme learning machine approach for sensorless wind speed estimation. Mechatron., 34: 78-83.
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  61. Naji, S., S. Shamshirband, H. Basser, U.J. Alengaram, M.Z. Jumaat and M. Amirmojahedi, 2016. Soft computing methodologies for estimation of energy consumption in buildings with different envelope parameters. Energy Effic., 9: 435-453.
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  62. Naji, S., A. Keivani, S. Shamshirband, U.J. Alengaram, M.Z. Jumaat, Z. Mansor and M. Lee, 2016. Estimating building energy consumption using extreme learning machine method. Energy, 97: 506-516.
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  63. Nahvi, B., J. Habibi, K. Mohammadi, S. Shamshirband and O.S. Al Razgan, 2016. Using self-adaptive evolutionary algorithm to improve the performance of an extreme learning machine for estimating soil temperature. Comput. Electron. Agric., 124: 150-160.
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  64. Mostafaeipour, A., M. Khayyami, A. Sedaghat, K. Mohammadi, S. Shamshirband, M.A. Sehati and E. Gorakifard, 2016. Evaluating the wind energy potential for hydrogen production: A case study. Int. J. Hydrogen Energy, 41: 6200-6210.
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  65. Mojumder, J.C., H.C. Ong, W.T. Chong and S. Shamshirband, 2016. Application of support vector machine for prediction of electrical and thermal performance in PV/T system. Energy Build., 111: 267-277.
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  66. Mohammadian, E., S. Motamedi, S. Shamshirband, R. Hashim, R. Junin, C. Roy and A. Azdarpour, 2016. Application of extreme learning machine for prediction of aqueous solubility of carbon dioxide. Environ. Earth Sci., Vol. 75. 10.1007/s12665-015-4798-4.
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  67. Mohammadi, K., S. Shamshirband, D. Petkovic and H. Khorasanizadeh, 2016. Determining the most important variables for diffuse solar radiation prediction using adaptive neuro-fuzzy methodology; Case study: City of Kerman, Iran. Renewable Sustainable Energy Rev., 53: 1570-1579.
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  68. Mohammadi, K., S. Shamshirband, A.S. Danesh, M.S. Abdullah and M. Zamani, 2016. Temperature-based estimation of global solar radiation using soft computing methodologies. Theor. Appl. Climatol., 125: 101-112.
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  69. Mohammadi, K., S. Shamshirband, A. Kamsin, P.C. Lai and Z. Mansor, 2016. Identifying the most significant input parameters for predicting global solar radiation using an ANFIS selection procedure. Renewable Sustainable Energy Rev., 63: 423-434.
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  70. Mohammadi, K., H. Khorasanizadeh, S. Shamshirband and C.W. Tong, 2016. Influence of introducing various meteorological parameters to the Angstrom-Prescott model for estimation of global solar radiation. Environ. Earth Sci., Vol. 75. 10.1007/s12665-015-4871-z.
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  71. Moghaddam, T.B., M. Soltani, H.S. Shahraki, S. Shamshirband, N.B.M. Noor and M.R. Karim, 2016. The use of SVM-FFA in estimating fatigue life of polyethylene terephthalate modified asphalt mixtures. Meas., 90: 526-533.
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  72. Mazinani, I., Z.B. Ismail, S. Shamshirband, A.M. Hashim, M. Mansourvar and E. Zalnezhad, 2016. Estimation of tsunami bore forces on a coastal bridge using an extreme learning machine. Entropy, Vol. 18. 10.3390/e18050167.
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  73. Khaki, M.R.D., B. Sajjadi, A.A.A. Raman, W.M.A.W. Daud and S. Shmshirband, 2016. Sensitivity analysis of the photoactivity of Cu-TiO2/ZnO during advanced oxidation reaction by adaptive neuro-fuzzy selection technique. Meas., 77: 155-174.
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  74. Kazemi, S.M.R., E. Hadavandi, S. Shamshirband and S. Asadi, 2016. A novel evolutionary-negative correlated mixture of experts model in tourism demand estimation. Comput. Hum. Behav., 64: 641-655.
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  75. Kariminia, S., S. Shamshirband, R. Hashim, A. Saberi, D. Petkovic, C. Roy and S. Motamedi, 2016. A simulation model for visitors' thermal comfort at urban public squares using non-probabilistic binary-linear classifier through soft-computing methodologies. Energy, 101: 568-580.
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  76. Kariminia, S., S. Motamedi, S. Shamshirband, R. Hashim, C. Roy and D. Petkovic, 2016. Determination of parameters affecting thermal sensations using support vector machine coupled with firefly algorithm. J. Therm. Biol., .
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  77. Kariminia, S., S. Motamedi, S. Shamshirband, D. Petkovic, C. Roy and R. Hashim, 2016. Adaptation of ANFIS model to assess thermal comfort of an urban square in moderate and dry climate. Stochastic Environ. Res. Risk Assess., 30: 1189-1203.
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  78. Jovic, S., A.S. Danesh, E. Younesi, O. Anicic, D. Petkovic and S. Shamshirband, 2016. Forecasting of underactuated robotic finger contact forces by support vector regression methodology. Int. J. Pattern Recognit. Artif. Intell., Vol. 30. 10.1142/S0218001416590199.
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  79. Hashim, R., C. Roy, S. Shamshirband, S. Motamedi, A. Fitri, D. Petkovic and K.I. Song, 2016. Estimation of wind-driven coastal waves near a mangrove forest using adaptive neuro-fuzzy inference system. Water Resour. Manage., 30: 2391-2404.
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  80. Hashim, R., C. Roy, S. Motamedi, S. Shamshirband, D. Petkovic, M. Gocic and S.C. Lee, 2016. Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology. Atmos. Res., 171: 21-30.
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  81. Hashim, R., C. Roy, S. Motamedi, S. Shamshirband and D. Petkovic, 2016. Selection of climatic parameters affecting wave height prediction using an enhanced Takagi-Sugeno-based fuzzy methodology. Renewable Sustainable Energy Rev., 60: 246-257.
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  82. Gocic, M., S. Shamshirband, Z. Razak, D. Petkovic, C. Sudheer and S. Trajkovic, 2016. Long-term precipitation analysis and estimation of precipitation concentration index using three support vector machine methods. Adv. Meteorol., Vol. 2016. 10.1155/2016/7912357.
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  83. Gani, A., A. Siddiqa, S. Shamshirband and F. Hanum, 2016. A survey on indexing techniques for big data: Taxonomy and performance evaluation. Knowl. Inf. Syst., 46: 241-284.
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  84. Danesh, A.S., R. Ahmad, S. Shamshirband and S.M. Zargarnataj, 2016. Towards a highly customizable framework for release planning process. Tech. Gazette, 23: 1777-1785.
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  85. Danesh, A.S., R. Ahmad, S. Shamshirband and S.M. Zargarnataj, 2016. PBRP: Pattern-based approach for software release planning. Asia Life Sci., 25: 479-506.
  86. Dadkhah, M., S. Shamshirband and A.W. Abdul Wahab, 2016. A hybrid approach for phishing web site detection. Electron. Lib., 34: 927-944.
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  87. Dadkhah, M., M.D. Jazi, M.S. Mobarakeh, S. Shamshirband, X. Wang and S. Raste, 2016. An overview of phishing attacks and their detection techniques. Int. J. Internet Protoc. Technol., 9: 187-195.
    CrossRef  |  Direct Link  |  
  88. Cojbasic, Z., D. Petkovic, S. Shamshirband, C.W. Tong and C. Sudheer et al., 2016. Surface roughness prediction by extreme learning machine constructed with abrasive water jet. Precis. Eng., 43: 86-92.
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  89. Chong, W.T., A. Al-Mamoon, S.C. Poh, L.H. Saw, S. Shamshirband and J.C. Mojumder, 2016. Sensitivity analysis of heat transfer rate for smart roof design by adaptive neuro-fuzzy technique. Energy Build., 124: 112-119.
    CrossRef  |  Direct Link  |  
  90. Bindal, P., U. Bindal, C.W. Lin, N.H.A. Kasim and T.S.A. Ramasamy et al., 2016. Neuro-fuzzy method for predicting the viability of stem cells treated at different time-concentration conditions. Technol. Health Care, 10.3233/THC-170922.
    CrossRef  |  Direct Link  |  
  91. Amirmojahedi, M., K. Mohammadi, S. Shamshirband, A.S. Danesh, A. Mostafaeipour and A. Kamsin, 2016. A hybrid computational intelligence method for predicting dew point temperature. Environ. Earth Sci., Vol. 75. 10.1007/s12665-015-5135-7.
    CrossRef  |  Direct Link  |  
  92. Amin, R., L. Aijun and S. Shamshirband, 2016. A review of quadrotor UAV: Control methodologies and performance evaluation. Int. J. Autom. Control, 10: 87-103.
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  93. Al-Shammari, E.T., K. Mohammadi, A. Keivani, S.H. Ab Hamid, S. Akib, S. Shamshirband and D. Petkovic, 2016. Prediction of daily dewpoint temperature using a model combining the support vector machine with firefly algorithm. J. Irrig. Drain. Eng., Vol. 142. .
    Direct Link  |  
  94. Al-Shammari, E.T., D. Petkovic, A.S. Danesh, S. Shamshirband, M. Issa and L. Zentner, 2016. Neuro-fuzzy estimation of passive robotic joint safe velocity with embedded sensors of conductive silicone rubber. Mech. Syst. Signal Process., 72: 486-498.
    CrossRef  |  Direct Link  |  
  95. Al-Shammari, E.T., A. Keivani, S. Shamshirband, A. Mostafaeipour, L. Yee, D. Petkovic and C. Sudheer, 2016. Prediction of heat load in district heating systems by support vector machine with firefly searching algorithm. Energy, 95: 266-273.
    CrossRef  |  Direct Link  |  
  96. Aghbashlo, M., S. Shamshirband, M. Tabatabaei, L. Yee and Y.N. Larimi, 2016. The use of ELM-WT (extreme learning machine with wavelet transform algorithm) to predict exergetic performance of a DI diesel engine running on diesel/biodiesel blends containing polymer waste. Energy, 94: 443-456.
    CrossRef  |  Direct Link  |  
  97. Afifi, F., N.B. Anuar, S. Shamshirband and K.K.R. Choo, 2016. DyHAP: Dynamic hybrid ANFIS-PSO approach for predicting mobile malware. Plos One, Vol. 11. 10.1371/journal.pone.0162627.
    CrossRef  |  PubMed  |  Direct Link  |  
  98. Abdi, E., R. Taiar, S. Shamshirband, P. Renault and D. Sifaki-Pistolla et al., 2016. Perspectives of support vector regression for static posturographic assessment of patients with cognitive impairment. Int. J. Ser. Multidiscip. Res., 2: 1-13.
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  99. Zalnezhad, E., A.M.S. Hamouda, G. Faraji and S. Shamshirband, 2015. TiO2 nanotube coating on stainless steel 304 for biomedical applications. Ceram. Int., 41: 2785-2793.
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  100. Zaji, A.H., H. Bonakdari, S. Shamshirband and S.N. Qasem, 2015. Potential of particle swarm optimization based radial basis function network to predict the discharge coefficient of a modified triangular side weir. Flow Meas. Instrum., 45: 404-407.
    CrossRef  |  Direct Link  |  
  101. Shuib, L., S. Shamshirband and M.H. Ismail, 2015. A review of mobile pervasive learning: Applications and issues. Comput. Human Behav., 46: 239-244.
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  102. Sharifipour, M., H. Bonakdari, A.H. Zaji and S. Shamshirband, 2015. Numerical investigation of flow field and flowmeter accuracy in open-channel junctions. Eng. Applic. Comput. Fluid Mech., 9: 280-290.
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  103. Shamshirband, S., M. Shojafar, A.R. Hosseinabadi, M. Kardgar, M.M. Nasir and R. Ahmad, 2015. OSGA: Genetic-based open-shop scheduling with consideration of machine maintenance in small and medium enterprises. Ann. Oper. Res., 229: 743-758.
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  104. Shamshirband, S., M. Gocic, D. Petkovic, H. Javidnia, S.H. Ab Hamid, Z. Mansor and S.N. Qasem, 2015. Clustering project management for drought regions determination: A case study in Serbia. Agric. Forest Meteorol., 200: 57-65.
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  105. Shamshirband, S., K. Mohammadi, L. Yee, D. Petkovic and A. Mostafaeipour, 2015. A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation. Renewable Sustainable Energy Rev., 52: 1031-1042.
    CrossRef  |  Direct Link  |  
  106. Shamshirband, S., K. Mohammadi, H.L. Chen, G.N. Samy, D. Petkovic and C. Ma, 2015. Daily global solar radiation prediction from air temperatures using kernel extreme learning machine: A case study for Iran. J. Atmos. Solar-Terr. Phys., 134: 109-117.
    CrossRef  |  Direct Link  |  
  107. Shamshirband, S., D. Petkovic, N.T. Pavlovic, C. Sudheer, T.A. Altameem and A. Gani, 2015. Support vector machine firefly algorithm based optimization of lens system. Appl. Optics, 54: 37-45.
    CrossRef  |  PubMed  |  Direct Link  |  
  108. Shamshirband, S., D. Petkovic, H. Javidnia and A. Gani, 2015. Sensor data fusion by support vector regression methodology: A comparative study. IEEE Sensors J., 15: 850-854.
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  109. Shamshirband, S., B. Khoshnevisan, M. Yousefi, E. Bolandnazar, N.B. Anuar, A.W.A. Wahab and S.U.R. Khan, 2015. A multi-objective evolutionary algorithm for energy management of agricultural systems: A case study in Iran. Renewable Sustainable Energy Rev., 44: 457-465.
    CrossRef  |  Direct Link  |  
  110. Shamshirband, S., B. Daghighi, N.B. Anuar, M.L.M. Kiah, A. Patel and A. Abraham, 2015. Co-FQL: Anomaly detection using cooperative fuzzy Q-learning in network. J. Intell. Fuzzy Syst., 28: 1345-1357.
    CrossRef  |  Direct Link  |  
  111. Shamshirband, S., A. Tavakkoli, C.B. Roy, S. Motamedi, K.I. Song, R. Hashim and S.M. Islam, 2015. Hybrid intelligent model for approximating unconfined compressive strength of cement-based bricks with odd-valued array of peat content (0-29%). Powder Technol., 284: 560-570.
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  112. Shamshirband, S., A. Malvandi, A. Karimipour, M. Goodarzi and M. Afrand et al., 2015. Performance investigation of micro- and nano-sized particle erosion in a 90 elbow using an ANFIS model. Powder Technol., 284: 336-343.
    CrossRef  |  Direct Link  |  
  113. Saybani, M.R., S. Shamshirband, S.G. Hormozi, T.Y. Wah and S. Aghabozorgi et al., 2015. Diagnosing tuberculosis with a novel support vector machine-based artificial immune recognition system. Iran. Red Crescent Med. J., Vol. 17. 10.5812/ircmj.17(4)2015.24557.
    CrossRef  |  Direct Link  |  
  114. Qureshi, M.A., R.M. Noor, S. Shamshirband, S. Parveen, M. Shiraz and A. Gani, 2015. A survey on obstacle modeling patterns in radio propagation models for vehicular ad hoc networks. Arabian J. Sci. Eng., 40: 1385-1407.
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  115. Protic, M., S. Shamshirband, D. Petkovic, A. Abbasi and M.L.M. Kiah et al., 2015. Forecasting of consumers heat load in district heating systems using the support vector machine with a discrete wavelet transform algorithm. Energy, 87: 343-351.
    CrossRef  |  Direct Link  |  
  116. Pourtousi, M., J.N. Sahu, P. Ganesan, S. Shamshirband and G. Redzwan, 2015. A combination of Computational Fluid Dynamics (CFD) and adaptive Neuro-fuzzy system (ANFIS) for prediction of the bubble column hydrodynamics. Powder Technol., 274: 466-481.
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  117. Piri, J., S. Shamshirband, D. Petkovic, C.W. Tong and M.H. ur Rehman, 2015. Prediction of the solar radiation on the earth using support vector regression technique. Infrared Phys. Technol., 68: 179-185.
    CrossRef  |  Direct Link  |  
  118. Petkovic, D., S. Shamshirband, N.B. Anuar, S. Naji, M.L.M. Kiah and A. Gani, 2015. Adaptive neuro-fuzzy evaluation of wind farm power production as function of wind speed and direction. Stochastic Environ. Res. Risk Assess., 29: 793-802.
    CrossRef  |  Direct Link  |  
  119. Petkovic, D., M. Protic, S. Shamshirband, S. Akib, M. Raos and D. Markovic, 2015. Evaluation of the most influential parameters of heat load in district heating systems. Energy Build., 104: 264-274.
    CrossRef  |  Direct Link  |  
  120. Petkovic, D., M. Gocic, S. Trajkovic, S. Shamshirband, S. Motamedi, R. Hashim and H. Bonakdari, 2015. Determination of the most influential weather parameters on reference evapotranspiration by adaptive neuro-fuzzy methodology. Comput. Electron. Agric., 114: 277-284.
    CrossRef  |  Direct Link  |  
  121. Petkovic, D., M. Arif, S. Shamshirband, E.H. Bani-Hani and D. Kiakojoori, 2015. Sensorless estimation of wind speed by soft computing methodologies: A comparative study. Inf., 26: 493-508.
    Direct Link  |  
  122. Olatomiwa, L., S. Mekhilef, S. Shamshirband, K. Mohammadi, D. Petkovic and C. Sudheer, 2015. A support vector machine-firefly algorithm-based model for global solar radiation prediction. Solar Energy, 115: 632-644.
    CrossRef  |  Direct Link  |  
  123. Olatomiwa, L., S. Mekhilef, S. Shamshirband and D. Petkovic, 2015. Potential of support vector regression for solar radiation prediction in Nigeria. Nat. Hazards, 77: 1055-1068.
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  124. Olatomiwa, L., S. Mekhilef, S. Shamshirband and D. Petkovic, 2015. Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria. Renewable Sustainable Energy Rev., 51: 1784-1791.
    CrossRef  |  Direct Link  |  
  125. Nikolic, V., S. Shamshirband, D. Petkovic, K. Mohammadi, Z. Cojbasic, T.A. Altameem and A. Gani, 2015. Wind wake influence estimation on energy production of wind farm by adaptive neuro-fuzzy methodology. Energy, 80: 361-372.
    CrossRef  |  Direct Link  |  
  126. Nikolic, V., D. Petkovic, S. Shamshirband and Z. Cojbasic, 2015. Adaptive neuro-fuzzy estimation of diffuser effects on wind turbine performance. Energy, 89: 324-333.
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  127. Motamedi, S., S. Shamshirband, D. Petkovic and R. Hashim, 2015. Application of adaptive neuro-fuzzy technique to predict the unconfined compressive strength of PFA-sand-cement mixture. Powder Technol., 278: 278-285.
    CrossRef  |  Direct Link  |  
  128. Mohammadi, K., S. Shamshirband, S. Motamedi, D. Petkovic, R. Hashim and M. Gocic, 2015. Extreme learning machine based prediction of daily dew point temperature. Comput. Electron. Agric., 117: 214-225.
    CrossRef  |  Direct Link  |  
  129. Mohammadi, K., S. Shamshirband, M.H. Anisi, K.A. Alam and D. Petkovic, 2015. Support vector regression based prediction of global solar radiation on a horizontal surface. Energy Conv. Manage., 91: 433-441.
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  130. Mohammadi, K., S. Shamshirband, L. Yee, D. Petkovic, M. Zamani and C. Sudheer, 2015. Predicting the wind power density based upon extreme learning machine. Energy, 86: 232-239.
    CrossRef  |  Direct Link  |  
  131. Mohammadi, K., S. Shamshirband, C.W. Tong, M. Arif, D. Petkovic and C. Sudheer, 2015. A new hybrid support vector machine-wavelet transform approach for estimation of horizontal global solar radiation. Energy Conv. Manage., 92: 162-171.
    CrossRef  |  Direct Link  |  
  132. Mohammadi, K., S. Shamshirband, C.W. Tong, K.A. Alam and D. Petkovic, 2015. Potential of adaptive neuro-fuzzy system for prediction of daily global solar radiation by day of the year. Energy Conv. Manage., 93: 406-413.
    CrossRef  |  Direct Link  |  
  133. Moghaddam, T.B., M. Soltani, M.R. Karim, S. Shamshirband, D. Petkovic and H. Baaj, 2015. Estimation of the rutting performance of polyethylene terephthalate modified asphalt mixtures by adaptive neuro-fuzzy methodology. Construct. Build. Mater., 96: 550-555.
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  134. Mirzavand, M., B. Khoshnevisan, S. Shamshirband, O. Kisi, R. Ahmad and S. Akib, 2015. Evaluating groundwater level fluctuation by support vector regression and neuro-fuzzy methods: A comparative study. Nat. Hazards. 10.1007/s11069-015-1602-4.
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  135. Mazinani, I., Z.B. Ismail, S. Shamshirband and A. Mustafa, 2015. Application of extreme learning machine for estimation of tsunami bore forces on a coastal bridge. Entropy, Vol. 15. .
  136. Kisi, O., J. Shiri, S. Karimi, S. Shamshirband and S. Motamedi et al., 2015. A survey of water level fluctuation predicting in urmia lake using support vector machine with firefly algorithm. Appl. Math. Comput., 270: 731-743.
    Direct Link  |  
  137. Kiakojuri, D., S. Shamshirband, N.B. Anuar and J. Abdullah, 2015. Analysis of the social capital indicators by using DEMATEL approach: The case of Islamic Azad University. Qual. Quantity, 49: 1985-1995.
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  138. Khoshnevisan, B., E. Bolandnazar, S. Shamshirband, H.M. Shariati, N.B. Anuar and M.L.M. Kiah, 2015. Decreasing environmental impacts of cropping systems using life cycle assessment (LCA) and multi-objective genetic algorithm. J. Cleaner Prod., 86: 67-77.
    CrossRef  |  Direct Link  |  
  139. Khoshnevisan, B., E. Bolandnazar, S. Barak, S. Shamshirband, H. Maghsoudlou, T.A. Altameem and A. Gani, 2015. A clustering model based on an evolutionary algorithm for better energy use in crop production. Stochastic Environ. Res. Risk Assess., 29: 1921-1935.
    CrossRef  |  Direct Link  |  
  140. Khan, A.N., M.M. Kiah, M. Ali and S. Shamshirband, 2015. A cloud-manager-based re-encryption scheme for mobile users in cloud environment: A hybrid approach. J. Grid Comput., 13: 651-675.
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  141. Inayat, I., S.S. Salim, S. Marczak, M. Daneva and S. Shamshirband, 2015. A systematic literature review on agile requirements engineering practices and challenges. Comput. Human Behav., 51: 915-929.
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  142. Hosseinabadi, A.A.R., H. Siar, S. Shamshirband, M. Shojafar and M.H.N.M. Nasir, 2015. Using the gravitational emulation local search algorithm to solve the multi-objective flexible dynamic job shop scheduling problem in small and medium enterprises. Ann. Oper. Res., 229: 451-474.
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  143. Hooshyar, D., R.B. Ahmad, S. Shamshirband, M. Yousefi and S.J. Horng, 2015. A flowchart-based programming environment for improving problem solving skills of Cs minors in computer programming. Asia Life Sci., 24: 629-646.
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  144. Hooshyar, D., R.B. Ahmad, M.H.N.M. Nasir, S. Shamshirband and S.J. Horng, 2015. Flowchart-based programming environments for improving comprehension and problem-solving skill of novice programmers: a survey. Int. J. Adv. Intell. Paradigms, 7: 24-56.
    CrossRef  |  Direct Link  |  
  145. Hamedani, S.R., M. Liaqat, S. Shamshirband, O.S. Al-Razgan, E.T. Al-Shammari and D. Petkovic, 2015. Comparative study of soft computing methodologies for energy input-output analysis to predict potato production. Am. J. Potato Res., 92: 426-434.
    CrossRef  |  Direct Link  |  
  146. Gocic, M., S. Motamedi, S. Shamshirband, D. Petkovic, C. Sudheer, R. Hashim and M. Arif, 2015. Soft computing approaches for forecasting reference evapotranspiration. Comput. Electron. Agric., 113: 164-173.
    CrossRef  |  Direct Link  |  
  147. Farid, S., R. Ahmad, I.A. Niaz, M. Arif, S. Shamshirband and M.D. Khattak, 2015. Identification and prioritization of critical issues for the promotion of e-learning in Pakistan. Comput. Human Behav., 51: 161-171.
    CrossRef  |  Direct Link  |  
  148. Daghighi, B., M.L.M. Kiah, S. Shamshirband, S. Iqbal and P. Asghari, 2015. Key management paradigm for mobile secure group communications: Issues, solutions and challenges. Comput. Commun., 72: 1-16.
    CrossRef  |  Direct Link  |  
  149. Daghighi, B., M.L.M. Kiah, S. Shamshirband and M.H.U. Rehman, 2015. Toward secure group communication in wireless mobile environments: Issues, solutions and challenges. J. Network Comput. Appl., 50: 1-14.
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  150. Dadkhah, M., T. Sutikno and S. Shamshirband, 2015. Social network applications and free online mobile numbers: Real risk. Int. J. Electr. Comput. Eng., 5: 175-176.
    Direct Link  |  
  151. Dadkhah, M., A.M. Alharbi, M.H. Al-Khresheh, T. Sutikno, T. Maliszewski, M.D. Jazi and S. Shamshirband, 2015. Affiliation oriented journals: Don't worry about peer review if you have good affiliation. Int. J. Electr. Comput. Eng., 5: 621-625.
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  152. Dadkhah, M. and S. Shamshirband, 2015. An introduction to remote installation vulnerability in content management systems. Int. J. Secure Software Eng., 6: 52-63.
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  153. Chong, S.S., A.A. Aziz, S.W. Harun, H. Arof and S. Shamshirband, 2015. Application of multiple linear regression, central composite design and ANFIS models in dye concentration measurement and prediction using plastic optical fiber sensor. Measurement, 74: 78-86.
    CrossRef  |  Direct Link  |  
  154. Bayat, A.E., R. Junin, S. Shamshirband and W.T. Chong, 2015. Transport and retention of engineered Al2O3, TiO2 and SiO2 nanoparticles through various sedimentary rocks. Sci. Rep., Vol. 5. 10.1038/srep14264.
    CrossRef  |  PubMed  |  Direct Link  |  
  155. Bayat, A.E., R. Junin, R. Kharrat, S. Shamshirband, S. Akib and Z. Buang, 2015. Optimization of solvent composition and injection rate in vapour extraction process. J. Pet. Sci. Eng., 128: 33-43.
    CrossRef  |  Direct Link  |  
  156. Basser, H., R. Cheraghi, H. Karami, A. Ardeshir and M. Amirmojahedi et al., 2015. Modeling sediment transport around a rectangular bridge abutment. Environ. Fluid Mech., 15: 1105-1114.
    CrossRef  |  Direct Link  |  
  157. Basser, H., H. Karami, S. Shamshirband, S. Akib, M. Amirmojahedi, R. Ahmad and H. Javidnia, 2015. Hybrid ANFIS-PSO approach for predicting optimum parameters of a protective spur dike. Appl. Soft Comput., 30: 642-649.
    CrossRef  |  Direct Link  |  
  158. Altameem, T.A., V. Nikolic, S. Shamshirband, D. Petkovic, H. Javidnia, M.L.M. Kiah and A. Gani, 2015. Potential of support vector regression for optimization of lens system. Computer-Aided Design, 62: 57-63.
    CrossRef  |  Direct Link  |  
  159. Akib, S., S. Rahman, S. Shamshirband and D. Petkovic, 2015. Soft computing methodologies for estimation of bridge girder forces with perforations under tsunami wave loading. Bull. Earthquake Eng., 13: 935-952.
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  160. Abbasi, A., C.S. Woo and S. Shamshirband, 2015. Robust image watermarking based on Riesz transformation and IT2FLS. Meas., 74: 116-129.
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  161. Zakaria, R., O.Y. Sheng, K. Wern, S. Shamshirband, D. Petkovic and N.T. Pavlovic, 2014. Adaptive neuro-fuzzy evaluation of the tapered plastic multimode fiber-based sensor performance with and without silver thin film for different concentrations of calcium hypochlorite. IEEE Sens. J., 14: 3579-3584.
    CrossRef  |  Direct Link  |  
  162. Zakaria, R., O.Y. Sheng, K. Wern, S. Shamshirband, A.W.A. Wahab, D. Petkovic and H. Saboohi, 2014. Examination of tapered plastic multimode fiber-based sensor performance with silver coating for different concentrations of calcium hypochlorite by soft computing methodologies: A comparative study. J. Opt. Soc. Am. A, 31: 1023-1030.
    CrossRef  |  PubMed  |  Direct Link  |  
  163. Shamshirband, S., S. Hessam, H. Javidnia, M. Amiribesheli and S. Vahdat et al., 2014. Tuberculosis disease diagnosis using artificial immune recognition system. Int. J. Med. Sci., 11: 508-514.
    CrossRef  |  PubMed  |  Direct Link  |  
  164. Shamshirband, S., N.B. Anuar, M.L.M. Kiah, V.A. Rohani, D. Petkovic, S. Misra and A.N. Khan, 2014. Co-FAIS: Cooperative fuzzy artificial immune system for detecting intrusion in wireless sensor networks. J. Network Comput. Applic., 42: 102-117.
    CrossRef  |  Direct Link  |  
  165. Shamshirband, S., N.B. Anuar, M. Laiha, M. Kiah and S. Misra, 2014. Anomaly detection using fuzzy q-learning algorithm. Acta Polytech. Hungarica, 11: 5-28.
  166. Shamshirband, S., J. Iqbal, D. Petkovic and M.A. Mirhashemi, 2014. Survey of four models of probability density functions of wind speed and directions by adaptive neuro-fuzzy methodology. Adv. Eng. Software, 76: 148-153.
    CrossRef  |  Direct Link  |  
  167. Shamshirband, S., D. Petkovic, Z. Cojbasic, V. Nikolic and N.B. Anuar et al., 2014. Adaptive neuro-fuzzy optimization of wind farm project net profit. Energy Conv. Manage., 80: 229-237.
    CrossRef  |  Direct Link  |  
  168. Shamshirband, S., D. Petkovic, R. Hashim, S. Motamedi and N.B. Anuar, 2014. An appraisal of wind turbine wake models by adaptive neuro-fuzzy methodology. Int. J. Electr. Power Energy Syst., 63: 618-624.
    CrossRef  |  Direct Link  |  
  169. Shamshirband, S., D. Petkovic, R. Hashim and S. Motamedi, 2014. Adaptive neuro-fuzzy methodology for noise assessment of wind turbine. Plos One, Vol. 9. 10.1371/journal.pone.0103414.
    CrossRef  |  Direct Link  |  
  170. Shamshirband, S., D. Petkovic, N.B. Anuar, M.L.M. Kiah and S. Akib et al., 2014. Sensorless estimation of wind speed by adaptive neuro-fuzzy methodology. Int. J. Electr. Power Energy Syst., 62: 490-495.
    CrossRef  |  Direct Link  |  
  171. Shamshirband, S., D. Petkovic, N.B. Anuar and A. Gani, 2014. Adaptive neuro-fuzzy generalization of wind turbine wake added turbulence models. Renewable Sustainable Energy Rev., 36: 270-276.
    CrossRef  |  Direct Link  |  
  172. Shamshirband, S., D. Petkovic, A. Amini, N.B. Anuar and V. Nikolic et al., 2014. Support vector regression methodology for wind turbine reaction torque prediction with power-split hydrostatic continuous variable transmission. Energy, 67: 623-630.
    CrossRef  |  Direct Link  |  
  173. Shamshirband, S., A. Patel, N.B. Anuar, M.L.M. Kiah and A. Abraham, 2014. Cooperative game theoretic approach using fuzzy Q-learning for detecting and preventing intrusions in wireless sensor networks. Eng. Applic. Artif. Intell., 32: 228-241.
    CrossRef  |  Direct Link  |  
  174. Shamshirband, S., A. Amini, N.B. Anuar, M.L.M. Kiah, Y.W. Teh and S. Furnell, 2014. D-FICCA: A density-based fuzzy imperialist competitive clustering algorithm for intrusion detection in wireless sensor networks. Measurement, 55: 212-226.
    CrossRef  |  Direct Link  |  
  175. Ramedani, Z., M. Omid, A. Keyhani, S. Shamshirband and B. Khoshnevisan, 2014. Potential of radial basis function based support vector regression for global solar radiation prediction. Renewable Sustainable Energy Rev., 39: 1005-1011.
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  176. Radzi, M.R.B.M., S. Shamshirband, S. Aghabozorgi, S. Misra, S. Akib and M.L.M. Kiah, 2014. Potential of support-vector regression for forecasting stream flow. Tech. Bull., 21: 1017-1024.
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  177. Petkovic, D., Z. Cojbasic, V. Nikolic, S. Shamshirband, M.L.M. Kiah, N.B. Anuar and A.W.A. Wahab, 2014. Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission. Energy, 64: 868-874.
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  178. Petkovic, D., S. Shamshirband, Z. Cojbasic, V. Nikolic, N.B. Anuar, A.Q.M. Sabri and S. Akib, 2014. Adaptive neuro-fuzzy estimation of building augmentation of wind turbine power. Comput. Fluids, 97: 188-194.
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  179. Petkovic, D., S. Shamshirband, N.T. Pavlovic, N.B. Anuar and M.L.M. Kiah, 2014. Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology. Opt. Spectrosc., 117: 121-131.
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  180. Petkovic, D., S. Shamshirband, N.B. Anuar, H. Saboohi and A.W.A. Wahab et al., 2014. An appraisal of wind speed distribution prediction by soft computing methodologies: A comparative study. Energy Conv. Manage., 84: 133-139.
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  181. Petkovic, D., S. Shamshirband, J. Iqbal, N.B. Anuar, N.D. Pavlovic and M.L.M. Kiah, 2014. Adaptive neuro-fuzzy prediction of grasping object weight for passively compliant gripper. Appl. Soft Comput., 22: 424-431.
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  182. Petkovic, D., S. Shamshirband, H. Saboohi, T.F. Ang, N.B. Anuar and N.D. Pavlovic, 2014. Support vector regression methodology for prediction of input displacement of adaptive compliant robotic gripper. Appl. Intell., 41: 887-896.
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  183. Petkovic, D., N.T. Pavlovic, S. Shamshirband, M.L.M. Kiah, N.B. Anuar and M.Y.I. Idris, 2014. Adaptive neuro-fuzzy estimation of optimal lens system parameters. Opt. Lasers Eng., 55: 84-93.
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  184. Omrani, E., B. Khoshnevisan, S. Shamshirband, H. Saboohi, N.B. Anuar and M.H.N.M. Nasir, 2014. Potential of radial basis function-based support vector regression for apple disease detection. Measurement, 55: 512-519.
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  185. Noh, S.M.C., S. Shamshirband, D. Petkovic, R. Penny and R. Zakaria, 2014. Adaptive neuro-fuzzy appraisal of plasmonic studies on morphology of deposited silver thin films having different thicknesses. Plasmonics, 9: 1189-1196.
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  186. Khoshnevisan, B., M.A. Rajaeifar, S. Clark, S. Shamahirband, N.B. Anuar, N.L.M. Shuib and A. Gani, 2014. Evaluation of traditional and consolidated rice farms in Guilan province, Iran, using life cycle assessment and fuzzy modeling. Sci. Total Environ., 481: 242-251.
    CrossRef  |  PubMed  |  Direct Link  |  
  187. Khan, A.N., M.M. Kiah, S.A. Madani, M. Ali and S. Shamshirband, 2014. Incremental proxy re-encryption scheme for mobile cloud computing environment. J. Supercomputing, 68: 624-651.
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  188. Khan, A.N., M.M. Kiah, M. Ali, S.A. Madani and S. Shamshirband, 2014. BSS: Block-based sharing scheme for secure data storage services in mobile cloud environment. J. Supercomput., 70: 946-976.
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  189. Jahangirzadeh, A., S. Shamshirband, S. Aghabozorgi, S. Akib, H. Basser, N.B. Anuar and M.L.M. Kiah, 2014. A cooperative expert based support vector regression (Co-ESVR) system to determine collar dimensions around bridge pier. Neurocomputing, 140: 172-184.
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  190. Jahangirzadeh, A., S. Shamshirband, D. Petkovic, H. Basser, A. Sedaghat, S. Akib and H. Karami, 2014. Adaptive neuro-fuzzy estimation of the influence of slot on local scour at bridge pier groups. J. Coastal Conserv., 10.1007/s11852-014-0357-5.
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  191. Daryabeigi, E., A. Zafari, S. Shamshirband, N.B. Anuar and M.L.M. Kiah, 2014. Calculation of optimal induction heater capacitance based on the smart bacterial foraging algorithm. Int. J. Electr. Power Energy Syst., 61: 326-334.
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  192. Basser, H., S. Shamshirband, D. Petkovic, H. Karami, S. Akib and A. Jahangirzadeh, 2014. Adaptive neuro-fuzzy prediction of the optimum parameters of protective spur dike. Nat. Hazards, 73: 1439-1449.
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  193. Akib, S., S. Rahman and S. Shamshirband, 2014. Adaptive neuro-fuzzy estimation of bridge girder forces with perforations under Tsunami wave loading. J. Coastal Conserv., 10.1007/s11852-014-0356-6.
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  194. Shamshirband, S., N.B. Anuar, L.M. Kiah and A. Patel, 2013. An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique. Eng. Appl. Artif. Intell., 26: 2105-2127.
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  195. Petkovic, D., N.D. Pavlovic, S. Shamshirband and N.B. Anuar, 2013. Development of a new type of passively adaptive compliant gripper. Ind. Robot Int. J., 40: 610-623.
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  196. Feizollah, A., N.B. Anuar, R. Salleh, F. Amalina and S. Shamshirband, 2013. A study of machine learning classifiers for anomaly-based mobile botnet detection. Malaysian J. Comput. Sci., 26: 251-265.
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  197. Shamshirband, S. and A. Za`fari, 2012. Evaluation of the performance of intelligent spray networks based on fuzzy logic. Res. J. Recent Sci., 1: 77-81.
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  198. Shamshirband, S., 2012. A distributed approach for coordination between traffic lights based on Game theory. Int. J. Inf. Technol., 9: 148-153.
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  199. Za'fari, A. and S. Shamshirband, 2011. Simulation and analysis of the harmonic behavior of matrix converters as compared with conventional converters. Int. J. Phys. Sci., 6: 2818-2825.
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  200. Vaskas, A.S., S. Shamshirband, M. Gholami and M.A. Besheli, 2011. Information optimization for speaker recognition using correlation functions. Int. J. Phys. Sci., 6: 3398-3408.
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  201. Shamalizadeh, M.A., S. Shamshirband, M. Amiri and S. Kalantari, 2011. Security in wireless sensor networks based on service-oriented architecture. Aust. J. Basic Applied Sci., 5: 694-701.
  202. Lima, S.M. and S. Shamshirband, 2011. Hot symmetric nuclear and neutron matter properties in the Thomas-Fermi model. Int. J. Phys. Sci., 6: 2577-2585.
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  203. Kalantari, S., M.A. Besheli, Z.S. Daliri, S. Shamshirband and L.S. Ng, 2011. Routing in wireless sensor network based on soft computing technique. Scientific Res. Essays, 6: 4432-4441.
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  204. Hessam, S., S. Vahdat and S. Shamshirband, 2011. Factors affecting process orientation in Iranian social security organization's hospitals. Afr. J. Bus. Manage., 5: 11345-11351.
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  205. Daliri, Z.S., S. Shamshirband and M.A. Besheli, 2011. Railway security through the use of wireless sensor networks based on fuzzy logic. Int. J. Physical Sci., 6: 448-458.
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  206. Ali Za'fari and S. Shamshirband, 2011. Simulation and study of the harmonic behavior of matrix converters as compared with conventional converters. Int. J. Phys. Sci., 6: 2818-2825.
    Direct Link  |  
  207. Vaskas, A.S., S.A. Ghasemi, M. Gholami and S. Shamshirband, 2010. The text independent speaker recognition using modified group delay function analysis and correlation function. Aust. J. Basic Applied Sci., 4: 3880-3894.
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  208. Vaskas, A.S., A. Esfandiyari, S. Shamshirband, 2010. Modified Mfcc for speaker recognition. Aust. J. Basic Applied Sci., 4: 4357-4364.
  209. Shamshirband, S., S. Shirgahi, H. Setayeshi, 2010. Designing of rescue multi agent system based on soft computing techniques. Adv. Electr. Comput. Eng., 10: 79-83.
  210. Shamshirband, S., S. Kalantari, Z.S. Daliri and L.S. Ng, 2010. Expert security system in wireless sensor networks based on fuzzy discussion multi-agent systems. Scientific Res. Essays, 5: 3840-3849.
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  211. Shamshirband, S., S. Kalantari and Z. Bakhshandeh 2010. Designing a smart multi-agent system based on fuzzy logic to improve the gas consumption pattern. Sci. Res. Essays, 5: 592-605.
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  212. Shamshirband, S., H. Siar and S.H. Nabavi, 2010. Cooperation among agents at the scene of accident using combination of fuzzy logic and genetic algorithm. Aust. J. Basic Applied Sci., 4: 5550-5555.
  213. Nasiri, H., S. Shamshirband and M. Ehsani, 2010. Power consumption pattern based on fuzzy method. Int. J. Phys. Sci., 5: 2535-2542.
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  214. Heidari, M., S.H. Nabavi and S. Shamshirband 2010. Application of an adaptive neural-fuzzy system to establish a relationship among nonlinear phenomena in meteorology to obtain monthly rainfall. Proccedings of the International Conference on Software Technology and Engineering, October3-5, 2010, ICSTE INSPEC Accession -.
  215. Hajar, S., S.H. Nabavi and S. Shamshirband, 2010. Static task scheduling in cooperative distributed systems based on soft computing techniques. Aust. J. Basic Applied Sci., 4: 1518-1526.
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  216. Ghadikolaie, Z.O., S.J. Bazminabadi, S. Kalantari, Z.H. Line, S. Shamshirband, 2010. Control of crisis environments by the use of WSN structures and based on expert-SOA architecture. Adv. Mate. Res., Manufacturing Sci. Technol., 383-390: 4629-4633.
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  217. Saffariaman, S., S. Shamshirband, H. Shirgahi, M. Gholami and B. Kia, 2009. The effect of AntNet parameters on its performance. Sci. Res. Essays, 4: 159-166.
  218. Shirgahi, H., S. Shamshirband, H. Motameni and P. Valipour, 2008. A new approach for detection by movement of lips base on image processing and fuzzy decision. World Applied Sci. J., 3: 323-329.
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  219. Shamshirband, S.S., H. Shirgahi, M. Gholami and B. Kia, 2008. Coordination between traffic signals based on cooperative. World Applied Sci. J., 5: 525-530.
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  220. Shamshirband, S., 2007. A distributed approach for coordination between traffic lights based onnn_qlearning. Proccedings of the 12th Annual International. CSI Computer Conference (CSICC'2007), Febeury, 20-22, 2007, Tehran, Iran, -.
  221. Akbarzadeh, M.R., T.S. Setayeshi, S. Shamshirband, R. Ghaffari, N. Mohsenian-k, 2007. Signal traffic control with soft computing techniques. Proceedings of the Third Conference on Information, Knowledge and Technology, December 26-29, 2007, Mashhad, Iran, -.