<doi_batch xmlns="http://www.crossref.org/schema/4.4.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" version="4.4.0"><head><doi_batch_id>77d3e8a9-f853-44f8-bb6d-b3009da29e86</doi_batch_id><timestamp>20210218104412947</timestamp><depositor><depositor_name>wsea</depositor_name><email_address>mdt@crossref.org</email_address></depositor><registrant>MDT Deposit</registrant></head><body><journal><journal_metadata language="en"><full_title>WSEAS TRANSACTIONS ON MATHEMATICS</full_title><issn media_type="print">1109-2769</issn><doi_data><doi>10.37394/23206</doi><resource>http://wseas.org/wseas/cms.action?id=4051</resource></doi_data></journal_metadata><journal_issue><publication_date media_type="online"><month>2</month><day>7</day><year>2020</year></publication_date><publication_date media_type="print"><month>2</month><day>7</day><year>2020</year></publication_date><journal_volume><volume>19</volume><doi_data><doi>10.37394/23206.2020.19</doi><resource>http://wseas.org/wseas/cms.action?id=23185</resource></doi_data></journal_volume></journal_issue><journal_article language="en"><titles><title>Complex Generalised Fuzzy Soft Set and its Application</title></titles><contributors><person_name sequence="first" contributor_role="author"><given_name>Abd Ulzeez M. J. S.</given_name><surname>Alkouri</surname><affiliation>Mathematics Department, Science College, Ajloun National University, Ajloun, JORDAN</affiliation></person_name></contributors><jats:abstract xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1"><jats:p>Human knowledge and mentality of experts may be changed with the time making the time a very important factor to the decision-makers. Therefore, different decisions for exact problem can be made by decision-makers in different times. We introduce here a new mathematical tool called complex generalized fuzzy soft set (CGFSS), which is a combination of the concept of generalized fuzzy soft set (GFSS) and complex fuzzy set (CFS). The importance of CGFSS may be appeared in the ability to convey the parametric nature in the concept of GFSS that happening periodically without losing the full meaning of human knowledge. While the uncertainty values lie in GFSS may be affected by different factors/phases/levels, CGFSS represents two values for each parameter (i) the degree of membership “belongingness of uncertainty and periodicity for elements in universe of discourse” and (ii) the degree of uncertainty and periodicity for the possibility of such belongingness which are represented by using complex membership form. Some CGFSS’s basic operations and its properties are introduced with the definition of relation on this tool and its application to illustrate the novelty of CGFSS in the decision-making problem. Finally, a comparison between several uncertainty sets and CGFSS is illustrated.</jats:p></jats:abstract><publication_date media_type="online"><month>6</month><day>9</day><year>2020</year></publication_date><publication_date media_type="print"><month>6</month><day>9</day><year>2020</year></publication_date><pages><first_page>323</first_page><last_page>333</last_page></pages><ai:program xmlns:ai="http://www.crossref.org/AccessIndicators.xsd" name="AccessIndicators"><ai:free_to_read start_date="2020-06-09"/><ai:license_ref applies_to="am" start_date="2020-06-09">https://www.wseas.org/multimedia/journals/mathematics/2020/a645106-1232.pdf</ai:license_ref></ai:program><archive_locations><archive name="Portico"/></archive_locations><doi_data><doi>10.37394/23206.2020.19.32</doi><resource>https://www.wseas.org/multimedia/journals/mathematics/2020/a645106-1232.pdf</resource></doi_data><citation_list><citation key="ref0"><doi>10.3390/math8050707</doi><unstructured_citation>Tran Thi Ngan, Luong Thi Hong Lan,  Mumtaz Ali,  Dan  Tamir,  Le  Hoang  SON,  Tran  Manh Tuan,  Naphtali RISHE  and  Abe Kandel.  Logic Connectives of Complex Fuzzy Sets. Romanian Journal of Information Science and Technology. 21 (4). 2018. 344–357. </unstructured_citation></citation><citation key="ref1"><doi>10.1109/tfuzz.2012.2226890</doi><unstructured_citation>Li  C.,  &amp;  Chiang  T.  W.  Complex  Neurofuzzy ARIMA Forecasting—A New Approach Using Complex  Fuzzy  Sets. IEEE  Transactions  on Fuzzy Systems. 21(3), 2013.567–584.</unstructured_citation></citation><citation key="ref2"><doi>10.3390/sym11030358</doi><unstructured_citation>Yousef    Al-Qudah,    Mazlan    Hassan,    and Nasruddin     Hassan.     Fuzzy     Parameterized Complex  Multi-Fuzzy  Soft  Expert  Set  Theory and    Its    Application    in    Decision-Making. Symmetry.11, 358. 2019.; doi:10.3390/sym11030358.  </unstructured_citation></citation><citation key="ref3"><unstructured_citation>P.  K.  Maji,  A.  R.  Roy,  and  R.  Biswas.  Fuzzy Soft  Sets. Journal  of  Fuzzy  Mathematics,  9, 2001.589-602.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>P.  Kmaji,  Etal. An  application of  soft  sets in  a decision-making problem. Comput. Math. Appl. 44. 2002.1077–1083. </unstructured_citation></citation><citation key="ref5"><unstructured_citation>P.  Kmaji,  Etal.  Soft  set  theory. Comput.  Math. Appl.45. 2003. 555–562. </unstructured_citation></citation><citation key="ref6"><unstructured_citation>D.  Molodtsov.  Soft  set  theory—First  results. Comput. Math. Appl.37. 1999. 19–31. </unstructured_citation></citation><citation key="ref7"><doi>10.1016/j.cam.2008.01.011</doi><unstructured_citation>Z. Kong, Etal. Comment on A Fuzzy Soft Set-Theoretic    Approach    to    Decision    Making Problems. J.  Comput.  Appl.  Math.  223.  2009. 540–542.</unstructured_citation></citation><citation key="ref8"><doi>10.1016/j.camwa.2009.12.006</doi><unstructured_citation>P.  Majumdar,  and  S.K.  Samanta.  Generalised Fuzzy  Soft  Sets. Computers  and  Mathematics with Applications. 59. 2010. 1425–1432.</unstructured_citation></citation><citation key="ref9"><doi>10.1155/2013/287382</doi><unstructured_citation>Alkouri, A., &amp; Salleh, A. Complex Atanassov’s Intuitionistic   Fuzzy   Relation.    Journal   of Abstract   and   Applied   Analysis,   Article   ID 287382. 2013. 18 pages, doi:10.1155/2013/287382. </unstructured_citation></citation><citation key="ref10"><unstructured_citation>Alkouri, A. &amp; Salleh, A. Complex Atanassov’s intuitionistic  fuzzy  Set. in  AIPConf.  Proc., International  conference  on  fundamental  and applied   sciences,   Kuala   Lampur,   Malaysia. 2012. 464-470; doi: 10. 1063/1.4757515. </unstructured_citation></citation><citation key="ref11"><doi>10.1063/1.4858782</doi><unstructured_citation>Alkouri And Salleh. Some   operations   on complex  Atanassov's  intuitionistic  fuzzy  sets. AIP  conference  proceedings.  1571,  (1):  2012. 987-993. </unstructured_citation></citation><citation key="ref12"><unstructured_citation>Alkouri And Salleh. Complex   fuzzy   soft multisets. AIP  Conference  Proceedings.  1614, (1): 2012. 955-961.</unstructured_citation></citation><citation key="ref13"><doi>10.3233/ifs-130923</doi><unstructured_citation>Alkouri And Salleh. Linguistic variable, hedges and  several  distances  on  complex  fuzzy  sets. Journal  of  Intelligent  &amp;  Fuzzy  Systems.  26. 2014.    2527–2535    DOI:10.3233/IFS-130923 IOS Press. </unstructured_citation></citation><citation key="ref14"><unstructured_citation>Lee,  K.  H. First  Course  on  Fuzzy  Theory  and Applications. Springer-Verlag, 2004. </unstructured_citation></citation><citation key="ref15"><doi>10.1007/978-3-642-20042-7_25</doi><unstructured_citation>Li,   C.   &amp;   Chiang,   T.-W.   Complex   Fuzzy Computing to Time Series Prediction-A Multi-Swarm  Pso  Learning  Approach,” ACIIDS, Lecture  Notes  In  Artificial  Intelligence.  6592:. 2011. 242–251.</unstructured_citation></citation><citation key="ref16"><doi>10.1504/ijiids.2011.041325</doi><unstructured_citation>Li, C. &amp; Chiang, T.-W. Complex Fuzzy Model with PSO-RLSE Hybrid Learning Approach to Function Approximation. International Journal of Intelligent    Information    and    Database Systems. 5, (4). 2011. 409-430. </unstructured_citation></citation><citation key="ref17"><doi>10.1109/tfuzz.2012.2226890</doi><unstructured_citation>Li,  C.  &amp;  Chiang,  T.-W.  Complex  Neurofuzzy ARIMA  Forecasting  A  New  Approach  Using Complex  Fuzzy  Sets,” IEEE  Transactions  On Fuzzy Systems, 21, (3). 2013. 567-584. </unstructured_citation></citation><citation key="ref18"><doi>10.1007/978-3-642-12101-2_30</doi><unstructured_citation>Li, C. &amp; Chiang, T.-W. Complex Neuro-Fuzzy Self-Learning Approach to Function Approximation. Lecture   Notes   in   Artificial Intelligence. 5991. 2010. 289-299.</unstructured_citation></citation><citation key="ref19"><doi>10.1016/j.neucom.2012.04.011</doi><unstructured_citation>Li,  C., Wu,  T.  &amp;  Chan,  F.-T.  Self-Learning Complex  Neuro-Fuzzy  System  with  Complex Fuzzy  Sets  and  Its  Application  to  Adaptive Image  Noise  Canceling. Neurocomputing.  94, (1).2012. 121-139.</unstructured_citation></citation><citation key="ref20"><doi>10.1109/tfuzz.2011.2164084</doi><unstructured_citation>Ma, J., Zhang G. &amp; Lu, J. A Method for Multiple Periodic   Factor   Prediction   Problems   Using Complex  Fuzzy Sets. IEEE  Trans.  On Fuzzy. System. 20, (1).  2012. 32-45.</unstructured_citation></citation><citation key="ref21"><doi>10.1063/1.4980969</doi><unstructured_citation>Ganeshsree  Selvachandran,  Nisren  A.  Hafeed, and  Abdul  Razak  Salleh.  Complex  Fuzzy  Soft Expert Sets. AIP Conference Proceedings 1830, 070020. 2017. Doi: 10.1063/1.4980969. </unstructured_citation></citation><citation key="ref22"><doi>10.1109/tfuzz.2011.2164084</doi><unstructured_citation>Ma, J., Zhang G. &amp; Lu, J. A Method for Multiple Periodic   Factor   Prediction   Problems   Using Complex  Fuzzy  Sets. IEEE  Trans.  on  Fuzzy. System. 20, (1).  2012. 32-45. </unstructured_citation></citation><citation key="ref23"><doi>10.1109/91.995119</doi><unstructured_citation>Ramot,   D.,   Milo,   R.,   Friedman,   M.,   &amp;   A. Kandel, A.    Complex    Fuzzy    Sets. IEEE Transaction  on  Fuzzy  Systems10. 2002.  171-186. </unstructured_citation></citation><citation key="ref24"><doi>10.14419/ijet.v7i4.16976</doi><unstructured_citation>Yousef    Al-Qudah    and   Nasruddin    Hassan. Complex  Multi-Fuzzy  Relation  for  Decision Making    Using    Uncertain    Periodic    Data. International    Journal    of Engineering &amp; Technology. 7 (4). 2018. 2437-2445. </unstructured_citation></citation><citation key="ref25"><unstructured_citation>Zadeh,  A.  Fuzzy  Sets”. Inform.  Control.  8. 1965. 338-353.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>Zhang, G., Dillon, T. S., Cai, K. Y., Ma, J. &amp; Lu, J.  Operation Properties  and  Delta-Equalities  of Complex Fuzzy Sets,” International Journal of Approximate  Reasoning,  50,  (8).  2009.  1227-1249.</unstructured_citation></citation><citation key="ref27"><doi>10.1109/91.995119</doi><unstructured_citation>Ramot,  D.,  Milo,  R.,  Friedman,  M.  &amp; Kandel, A. Complex Fuzzy Sets.IEEE Transactions on Fuzzy Systems. 10, (2). 2002. 171-186. </unstructured_citation></citation><citation key="ref28"><unstructured_citation>K. Atanassov, Intuitionistic Fuzzy Sets, Physica-Verlag, Heidelberg. 1999. </unstructured_citation></citation><citation key="ref29"><doi>10.1007/s00521-015-2154-y</doi><unstructured_citation>M.    Ali    and    F. Smarandache, Complex neutrosophic    set. Neural    Computing    and Applications, 2017, 28. (7). 2017. 1817–1834.</unstructured_citation></citation></citation_list></journal_article></journal></body></doi_batch>