المؤلفون: Alohali, Manal Abdullah, Saleem, Nasir, Rhouma, Delel, Medani, Mohamed, Elmannai, Hela, Bourouis, Sami

المصدر: IEEE Access ; page 1-1 ; ISSN 2169-3536

الاتاحة: http://dx.doi.org/10.1109/access.2024.3444596
http://xplorestaging.ieee.org/ielx8/6287639/6514899/10637381.pdf?arnumber=10637381

المؤلفون: Khedher, NidhalBen, Ijaz, Nouman, Medani, Mohamed, Barghout, Kamal, Abu-Libdeh, Nidal

المصدر: Case Studies in Thermal Engineering ; volume 58, page 104421 ; ISSN 2214-157X

الاتاحة: https://doi.org/10.1016/j.csite.2024.104421
https://api.elsevier.com/content/article/PII:S2214157X24004520?httpAccept=text/xml
https://api.elsevier.com/content/article/PII:S2214157X24004520?httpAccept=text/plain

المؤلفون: Khedher, Nidhal Ben, Ijaz, Nouman, Medani, Mohamed, Barghout, Kamal, Abu-Libdeh, Nidal

المساهمون: Deanship of Scientific Research, King Khalid University

المصدر: Case Studies in Thermal Engineering ; volume 57, page 104317 ; ISSN 2214-157X

الاتاحة: http://dx.doi.org/10.1016/j.csite.2024.104317
https://api.elsevier.com/content/article/PII:S2214157X24003484?httpAccept=text/xml
https://api.elsevier.com/content/article/PII:S2214157X24003484?httpAccept=text/plain

المؤلفون: Waqas, M., Ashiq, M., Kausar, M.S., Khan, S.U., Hejazi, Hala A., Abdullaev, Sherzod, Medani, Mohamed

المساهمون: Deanship of Scientific Research, King Khalid University

المصدر: Case Studies in Thermal Engineering ; volume 57, page 104237 ; ISSN 2214-157X

الاتاحة: http://dx.doi.org/10.1016/j.csite.2024.104237
https://api.elsevier.com/content/article/PII:S2214157X24002685?httpAccept=text/xml
https://api.elsevier.com/content/article/PII:S2214157X24002685?httpAccept=text/plain

المؤلفون: Hussain, Syed Modassir, Majeed, Aaqib, Ijaz, Nouman, Omer, Abdoalrahman S.A., Khan, Ilyas, Medani, Mohamed, Ben Khedher, Nidhal

المساهمون: Deanship of Scientific Research, King Khalid University

المصدر: Alexandria Engineering Journal ; volume 94, page 339-353 ; ISSN 1110-0168

الاتاحة: https://doi.org/10.1016/j.aej.2024.03.034
https://api.elsevier.com/content/article/PII:S1110016824002515?httpAccept=text/xml
https://api.elsevier.com/content/article/PII:S1110016824002515?httpAccept=text/plain

المؤلفون: Dash, Sanjit Kumar, Dash, Sweta, Mahapatra, Satyajit, Mohanty, Sachi Nandan, Khan, M. Ijaz, Medani, Mohamed, Abdullaev, Sherzod, Gupta, Manish

المساهمون: Deanship of Scientific Research, King Khalid University

المصدر: Egyptian Informatics Journal ; volume 25, page 100450 ; ISSN 1110-8665

الاتاحة: http://dx.doi.org/10.1016/j.eij.2024.100450
https://api.elsevier.com/content/article/PII:S1110866524000136?httpAccept=text/xml
https://api.elsevier.com/content/article/PII:S1110866524000136?httpAccept=text/plain

المؤلفون: Hamam, Haneen, Awan, Aziz Ullah, Medani, Mohamed, Alroobaea, Roobaea, Bukhari, S. A. H. S., Fathima, Dowlath

المصدر: Modern Physics Letters B; 12/30/2024, Vol. 38 Issue 36, p1-20, 20p

مصطلحات موضوعية: ALGEBRAIC equations, DIFFERENTIAL equations, COLORECTAL cancer, POPULATION dynamics, MATHEMATICAL models

المؤلفون: Zhu, Wei, Fang, Liming, Ye, Xia, Medani, Mohamed, Escorcia-Gutierrez, José

المصدر: https://www.sciencedirect.com/science/article/pii/S0010482523010168?via%3Dihub.

مصطلحات موضوعية: RIME, Image segmentation, Multi-threshold, Meta-heuristic algorithms, Rényi's entropy, Brain tumor detection

وصف الملف: 18 páginas; application/pdf

Relation: Computers in Biology and Medicine; [1] E.-S.A. El-Dahshan, et al., Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm, Expert Syst. Appl. 41 (11) (2014) 5526–5545.; [2] G. Chen, et al., MTANS: multi-scale mean teacher combined adversarial network with shape-aware embedding for semi-supervised brain lesion segmentation, Neuroimage 244 (2021), 118568.; [3] G. Chen, et al., RFDCR: automated brain lesion segmentation using cascaded random forests with dense conditional random fields, Neuroimage 211 (2020), 116620.; [4] H. Saleem, A.R. Shahid, B. Raza, Visual interpretability in 3D brain tumor segmentation network, Comput. Biol. Med. 133 (2021), 104410.; [5] J. Nodirov, A.B. Abdusalomov, T.K. Whangbo, Attention 3D U-net with multiple skip connections for segmentation of brain tumor images, Sensors 22 (2022), https://doi.org/10.3390/s22176501.; [6] R. Sindhiya Devi, B. Perumal, M. Pallikonda Rajasekaran, A hybrid deep learning based brain tumor classification and segmentation by stationary wavelet packet transform and adaptive kernel fuzzy c means clustering, Adv. Eng. Software 170 (2022), 103146.; [7] Y. Zhuang, et al., An effective WSSENet-based similarity retrieval method of large lung CT image databases, KSII Transactions on Internet & Information Systems 16 (7) (2022).; [8] Y. Zhuang, N. Jiang, Y. Xu, Progressive distributed and parallel similarity retrieval of large CT image sequences in mobile telemedicine networks, Wireless Commun. Mobile Comput. 2022 (2022), 6458350.; [9] Z. Zhang, et al., Endoscope image mosaic based on pyramid ORB, Biomed. Signal Process Control 71 (2022), 103261.; [10] S. Lu, et al., Soft tissue feature tracking based on DeepMatching network, CMESComputer Modeling in Engineering & Sciences 136 (1) (2023).; [11] Y. Zhu, et al., Deep learning-based predictive identification of neural stem cell differentiation, Nat. Commun. 12 (1) (2021) 2614.; [12] S. Lu, et al., Iterative reconstruction of low-dose CT based on differential sparse, Biomed. Signal Process Control 79 (2023), 104204.; [13] N. Narappanawar, B.M. Rao, M. Joshi, Graph theory based segmentation of traced boundary into open and closed sub-sections, Comput. Vis. Image Understand. 115 (11) (2011) 1552–1558.; [14] A. Ahilan, et al., Segmentation by fractional order darwinian particle swarm optimization based multilevel thresholding and improved lossless prediction based compression algorithm for medical images, IEEE Access 7 (2019) 89570–89580.; [15] J. Michetti, et al., Influence of CBCT parameters on the output of an automatic edge-detection-based endodontic segmentation, Dentomaxillofacial Radiol. 44 (8) (2015).; [16] D. Zhang, et al., A region-based segmentation method for ultrasound images in HIFU therapy, Med. Phys. 43 (6) (2016) 2975–2989.; [17] X. Xia, Q. Liu, M.L. Huang, The use of artificial intelligence based magnifying image segmentation algorithm combined with endoscopy in early diagnosis and nursing of esophageal cancer patients, J. Med. Imaging Health Inform. 11 (4) (2021) 1306–1311.; [18] S. Zhao, et al., Boosted crow search algorithm for handling multi-threshold image problems with application to X-ray images of COVID-19, Expert Syst. Appl. 213 (2023), 119095.; [19] D. Zhao, et al., Ant colony optimization with horizontal and vertical crossover search: fundamental visions for multi-threshold image segmentation, Expert Syst. Appl. (2021) 167.; [20] Y. Zheng, et al., Sine-SSA-BP ship trajectory prediction based on chaotic mapping improved sparrow search algorithm, Sensors 23 (2) (2023) 704.; [21] B. Cao, et al., Multiobjective 3-D topology optimization of next-generation wireless data center network, IEEE Trans. Ind. Inf. 16 (5) (2019) 3597–3605.; [22] C. Min, et al., Trajectory optimization of an electric vehicle with minimum energy consumption using inverse dynamics model and servo constraints, Mech. Mach. Theor. 181 (2023), 105185.; [23] B. Cao, et al., Applying graph-based differential grouping for multiobjective largescale optimization, Swarm Evol. Comput. 53 (2020), 100626.; [24] B. Cao, et al., Diversified personalized recommendation optimization based on mobile data, IEEE Trans. Intell. Transport. Syst. 22 (4) (2020) 2133–2139.; [25] B. Li, et al., A distributionally robust optimization based method for stochastic model predictive control, IEEE Trans. Automat. Control 67 (11) (2021) 5762–5776.; [26] X. Xu, et al., Multi-objective robust optimisation model for MDVRPLS in refined oil distribution, Int. J. Prod. Res. 60 (22) (2022) 6772–6792.; [27] B. Cao, et al., Large-scale many-objective deployment optimization of edge servers, IEEE Trans. Intell. Transport. Syst. 22 (6) (2021) 3841–3849.; [28] X. Liu, et al., Federated neural architecture search for medical data security, IEEE Trans. 18 (2022) 5628–5636.; [29] Y. Zheng, et al., An optimal bp neural network track prediction method based on a ga–aco hybrid algorithm, J. Mar. Sci. Eng. 10 (10) (2022) 1399.; [30] L. Qian, et al., A new method of inland water ship trajectory prediction based on long short-term memory network optimized by genetic algorithm, Appl. Sci. 12 (8) (2022) 4073.; [31] X. Zhang, Z. Wang, Z. Lu, Multi-objective load dispatch for microgrid with electric vehicles using modified gravitational search and particle swarm optimization algorithm, Appl. Energy 306 (2022), 118018.; [32] A.A. Heidari, et al., Harris hawks optimization: algorithm and applications, Future Generat. Comput. Syst. 97 (2019) 849–872.; [33] H. Chen, et al., Slime mould algorithm: a comprehensive review of recent variants and applications, Int. J. Syst. Sci. (2022) 1–32.; [34] S. Li, et al., Slime mould algorithm: a new method for stochastic optimization, Future Generat. Comput. Syst. 111 (2020) 300–323.; [35] S. Mirjalili, J.S. Dong, A. Lewis, Nature-inspired Optimizers: Theories, Literature Reviews and Applications, Springer, 2019, 811.; [36] R. Storn, K. Price, Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim. 11 (4) (1997) 341–359.; [37] J. Tu, et al., The colony predation algorithm, JBE 18 (3) (2021) 674–710.; [38] I. Ahmadianfar, et al., INFO: an efficient optimization algorithm based on weighted mean of vectors, Expert Syst. Appl. (2022), 116516.; [39] I. Ahmadianfar, et al., RUN beyond the Metaphor: an Efficient Optimization Algorithm Based on Runge Kutta Method, Expert Systems with Applications, 2021, 115079.; [40] H. Su, et al., RIME: A Physics-Based Optimization, Neurocomputing, 2023.; [41] Y. Yang, et al., Hunger games search: visions, conception, implementation, deep analysis, perspectives, and towards performance shifts, Expert Syst. Appl. 177 (2021), 114864.; [42] S. Zhao, et al., Performance optimization of salp swarm algorithm for multithreshold image segmentation: comprehensive study of breast cancer microscopy, Comput. Biol. Med. 139 (2021), 105015.; [43] S. Hao, et al., Performance optimization of water cycle algorithm for multilevel lupus nephritis image segmentation, Biomed. Signal Process Control 80 (2023), 104139.; [44] H. Su, et al., RIME: a physics-based optimization, Neurocomputing 532 (2023) 183–214.; [45] S. García, et al., Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power, Inf. Sci. 180 (10) (2010) 2044–2064.; [46] J. Derrac, et al., A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms, Swarm Evol. Comput. 1 (1) (2011) 3–18.; [47] Q. Huynh-Thu, M. Ghanbari, Scope of validity of PSNR in image/video quality assessment, Electron. Lett. 44 (13) (2008), 800-U35.; [48] Z. Wang, et al., Image quality assessment: from error visibility to structural similarity, IEEE Trans. Image Process. 13 (4) (2004) 600–612.; [49] J. Liang, et al., TransConver: transformer and convolution parallel network for developing automatic brain tumor segmentation in MRI images, Quant. Imag. Med. Surg. 12 (2021).; [50] M.U. Rehman, et al., BrainSeg-net: brain tumor MR image segmentation via enhanced encoder–decoder network, Diagnostics 11 (2021), https://doi.org/ 10.3390/diagnostics11020169.; [51] J. Zhang, et al., Attention Gate ResU-Net for Automatic MRI Brain Tumor Segmentation, IEEE Access, 2020, 1-1.; [52] T. Dhamija, et al., Semantic segmentation in medical images through transfused convolution and transformer networks, Appl. Intell. 53 (1) (2023) 1132–1148.; [53] J. Zhang, et al., Inter-slice context residual learning for 3D medical image segmentation, IEEE Trans. Med. Imag. 40 (2) (2021) 661–672.; [54] C.-W. Lin, Y. Hong, J. Liu, Aggregation-and-Attention Network for brain tumor segmentation, BMC Med. Imag. 21 (1) (2021) 109.; [55] T. Zhang, et al., A brain tumor image segmentation method based on quantum entanglement and wormhole behaved particle swarm optimization, Front. Med. 9 (2022), 794126.; [56] H. Su, et al., Horizontal and vertical search artificial bee colony for image segmentation of COVID-19 X-ray images, Comput. Biol. Med. 142 (2022), 105181.; [57] A. Qi, et al., Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation, Comput. Biol. Med. 148 (2022), 105810.; [58] H. Nematzadeh, et al., Ensemble-based genetic algorithm explainer with automized image segmentation: a case study on melanoma detection dataset, Comput. Biol. Med. 155 (2023), 106613.; [59] M. Abdel-Basset, et al., HWOA: a hybrid whale optimization algorithm with a novel local minima avoidance method for multi-level thresholding color image segmentation, Expert Syst. Appl. 190 (2022), 116145.; [60] L. Ren, et al., Gaussian kernel probability-driven slime mould algorithm with new movement mechanism for multi-level image segmentation, Measurement 192 (2022), 110884.; [61] A.S. Abutaleb, Automatic thresholding of gray-level pictures using twodimensional entropy, Comput. Vis. Graph Image Process 47 (1) (1989) 22–32.; [62] S. Borjigin, P.K. Sahoo, Color image segmentation based on multi-level Tsallis–Havrda–Charvat ´ entropy and 2D histogram using PSO algorithms, Pattern Recogn. 92 (2019) 107–118.; [63] J. Luo, Y. Yang, B. Shi, Multi-threshold image segmentation of 2D otsu based on improved adaptive differential evolution algorithm, Dianzi Yu Xinxi Xuebao/ Journal of Electronics and Information Technology 41 (8) (2019) 2017–2024.; [64] S. Zhao, et al., Multilevel threshold image segmentation with diffusion association slime mould algorithm and Renyi’s entropy for chronic obstructive pulmonary disease, Comput. Biol. Med. 134 (2021), 104427.; [65] B. Coll, J.-M. Morel, A Non-local Algorithm for Image Denoising, 2005, pp. 60–65, vol. 2; [66] B. Coll, J.-M. Morel, A review of image denoising algorithms, with a new one, SIAM Journal on Multiscale Modeling and Simulation 4 (2005).; [67] A. R’eny, On Measures of Entropy and Information. Symposium on Mathematics Statistics and Probabilities, 1961, pp. 547–561.; [68] A.F. Kamaruzaman, et al., Levy flight algorithm for optimization problems-a literature review, Appl. Mech. Mater. 421 (2013) 496–501.; [69] S. Mirjalili, et al., Whale optimization algorithm: theory, literature review, and application in designing photonic crystal filters, in: Studies in Computational Intelligence, 2020, pp. 219–238.; [70] D. Simon, Biogeography-based optimization, IEEE Trans. Evol. Comput. 12 (6) (2008) 702–713.; [71] A.A. Heidari, et al., An Enhanced Associative Learning-Based Exploratory Whale Optimizer for Global Optimization, Neural Computing and Applications, 2019.; [72] S. Mirjalili, Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm, Knowl. Base Syst. 89 (2015) 228–249.; [73] X.-S. Yang, Firefly algorithms for multimodal optimization, in: Stochastic Algorithms: Foundations and Applications: 5th International Symposium, SAGA 2009, Sapporo, Japan, October 26-28, 2009. Proceedings 5, Springer, 2009.; [74] X.-S. Yang, A new metaheuristic bat-inspired algorithm, in: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), 2010, pp. 65–74.; [75] S. Gupta, K. Deep, A hybrid self-adaptive sine cosine algorithm with opposition based learning, Expert Syst. Appl. 119 (2019) 210–230.; [76] H. Nenavath, R.K. Jatoth, Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking, Applied Soft Computing Journal 62 (2018) 1019–1043.; [77] S. Li, et al., Slime mould algorithm: a new method for stochastic optimization, FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 111 (2020) 300–323.; [78] M. Tubishat, et al., Improved whale optimization algorithm for feature selection in Arabic sentiment analysis, Appl. Intell. 49 (5) (2019) 1688–1707.; [79] J.J. Liang, et al., Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE Trans. Evol. Comput. 10 (3) (2006) 281–295.; [80] S. Mirjalili, et al., Salp Swarm Algorithm: a bio-inspired optimizer for engineering design problems, Adv. Eng. Software 114 (2017) 163–191.; [81] J. Xing, et al., Boosting whale optimizer with quasi-oppositional learning and Gaussian barebone for feature selection and COVID-19 image segmentation, Journal of bionic engineering 20 (2) (2023) 797–818.; [82] X. Wang, et al., Crisscross Harris hawks optimizer for global tasks and feature selection, JBE 20 (3) (2023) 1153–1174.; [83] J. Xia, et al., Adaptive barebones salp swarm algorithm with quasi-oppositional learning for medical diagnosis systems: a comprehensive analysis, JBE 19 (1) (2022) 240–256.; [84] J. Xia, et al., Generalized oppositional moth flame optimization with crossover strategy: an approach for medical diagnosis, JBE 18 (4) (2021) 991–1010.; [85] C. Lin, et al., Double mutational salp swarm algorithm: from optimal performance design to analysis, JBE 20 (1) (2023) 184–211.; [86] L. Hu, et al., An intelligent prognostic system for analyzing patients with paraquat poisoning using arterial blood gas indexes, J. Pharmacol. Toxicol. Methods 84 (2017) 78–85.; [87] H. Zhang, et al., Differential evolution-assisted salp swarm algorithm with chaotic structure for real-world problems, Eng. Comput. 39 (3) (2023) 1735–1769.; [88] X. Yu, et al., Synergizing the enhanced RIME with fuzzy K-nearest neighbor for diagnose of pulmonary hypertension, Comput. Biol. Med. 165 (2023), 107408.; 18; 166; https://hdl.handle.net/11323/13926; Corporación Universidad de la Costa; https://repositorio.cuc.edu.co/

الاتاحة: https://hdl.handle.net/11323/13926
https://doi.org/10.1016/j.compbiomed.2023.107551
https://repositorio.cuc.edu.co/

المؤلفون: Medani Mohamed Ahmed

الاتاحة: https://dx.doi.org/10.13140/rg.2.2.14128.37127
https://rgdoi.net/10.13140/RG.2.2.14128.37127

المؤلفون: Balla, Minas Mohamed, Mergani, Adil, A. M. E. Medani, Mohamed Elamin, Abakar, Adam Dawoud

المصدر: Advances in Microbiology ; volume 12, issue 08, page 500-510 ; ISSN 2165-3402 2165-3410

الاتاحة: http://dx.doi.org/10.4236/aim.2022.128034
https://www.scirp.org/journal/doi.aspx?doi=10.4236/aim.2022.128034

المؤلفون: Balla, Minas Mohamed, Mergani, Adil, Medani, Mohamed Elamin A. M. E., Abakar, Adam Dawoud, Dafalla, Ameer Mohamed

المصدر: American Journal of Molecular Biology ; volume 12, issue 04, page 181-189 ; ISSN 2161-6620 2161-6663

الاتاحة: http://dx.doi.org/10.4236/ajmb.2022.124015
https://www.scirp.org/journal/doi.aspx?doi=10.4236/ajmb.2022.124015

المؤلفون: Ali, Sulafa, Medani, Mohamed Eamin A.M.E.

المصدر: Frontiers in Pediatrics ; volume 10 ; ISSN 2296-2360

الاتاحة: http://dx.doi.org/10.3389/fped.2022.793188
https://www.frontiersin.org/articles/10.3389/fped.2022.793188/full

المؤلفون: Elbahri, Hassan Mohammed Hassan, Abd-Elmaged, Hozifa Mohammed Ali, Abdulkarim, Mohamed, Ahmed, Mohammed Mubarak Mohammed, Medani, Mohamed Medani Elhag

المصدر: International Journal of Surgery Case Reports ; volume 99, page 107621 ; ISSN 2210-2612

الاتاحة: http://dx.doi.org/10.1016/j.ijscr.2022.107621
https://api.elsevier.com/content/article/PII:S2210261222008677?httpAccept=text/xml
https://api.elsevier.com/content/article/PII:S2210261222008677?httpAccept=text/plain

المؤلفون: Medani, Mohamed¹ (AUTHOR), Alsubai, Shtwai² (AUTHOR), Min, Hong³ (AUTHOR) hmin@gachon.ac.kr, Dutta, Ashit Kumar⁴ (AUTHOR) hmin@gachon.ac.kr, Anjum, Mohd⁵ (AUTHOR) mohdanjum@zhcet.ac.in

المصدر: Bioengineering (Basel). Jul2024, Vol. 11 Issue 7, p715. 21p.

مصطلحات موضوعية: *COMPUTER systems, *PREDICTION models, *EMOTIONS, *SCALABILITY, *FORECASTING

المؤلفون: Medani Mohamed Ahmed

الاتاحة: https://dx.doi.org/10.13140/rg.2.2.23661.81126
https://rgdoi.net/10.13140/RG.2.2.23661.81126

المؤلفون: Ramesh, Katta, Asogwa, Kanayo K., Lodhi, Ram Kishun, Khan, M. Ijaz, Medani, Mohamed, Gepreel, Khaled A., Abduvalieva, Dilsora

المساهمون: Taif University, Saudi Arabia, Project No

المصدر: Numerical Heat Transfer, Part A: Applications ; page 1-16 ; ISSN 1040-7782 1521-0634

الاتاحة: http://dx.doi.org/10.1080/10407782.2024.2345864
https://www.tandfonline.com/doi/pdf/10.1080/10407782.2024.2345864

المؤلفون: Ben Khedher, Nidhal, Mehryan, S.A.M., Medani, Mohamed, Selim, Mahmoud M., Elbashir, Nasrin B.M., Boujelbene, Mohamed

المساهمون: Deanship of Scientific Research, King Khalid University, Prince Sattam bin Abdulaziz University

المصدر: Applied Thermal Engineering ; volume 248, page 123121 ; ISSN 1359-4311

الاتاحة: http://dx.doi.org/10.1016/j.applthermaleng.2024.123121
https://api.elsevier.com/content/article/PII:S1359431124007890?httpAccept=text/xml
https://api.elsevier.com/content/article/PII:S1359431124007890?httpAccept=text/plain

المؤلفون: Medani Mohamed Ahmed

الاتاحة: https://dx.doi.org/10.13140/rg.2.2.12174.57928
https://rgdoi.net/10.13140/RG.2.2.12174.57928

المؤلفون: Medani Mohamed Ahmed

الاتاحة: https://dx.doi.org/10.13140/rg.2.2.16780.90249
https://rgdoi.net/10.13140/RG.2.2.16780.90249

المؤلفون: Medani, Mohamed Ahmed

الاتاحة: https://dx.doi.org/10.13140/rg.2.2.13425.45921
https://rgdoi.net/10.13140/RG.2.2.13425.45921