BibTex RIS Kaynak Göster

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Yıl 2015, Cilt: 1 Sayı: 1, 33 - 52, 07.09.2015

Öz

In this paper, current social-based artificial intelligence optimization techniques that are general-purposed search and optimization techniques have been introduced for the first time as a whole and it has been shown that these techniques can be efficiently used as direct or indirect search algorithm within social network analysis such as link prediction, community discovery, sentimental analysis, text summarization, and etc

Kaynakça

  • Altunbey, F., Alatas, B., 2015. Overlapping community networks
  • optimization algorithm. International Journal of Computer Networks and Applications, 2(1): 12-19. in
  • social parliamentary Atashpaz-Gargari, E., Lucas, C., 2007. Imperialist competitive algorithm: an algorithm for optimization ınspired by ımperialistic Congress
  • Computation, CEC 2007, 4661-4667.
  • Evolutionary Borji, A., Gamidi, M., 2009. A new approach to global optimization motivated by parliamentary political competitions. Int. Journal of Innovative Computing, Information and Control, 5(6): 1643- 1653.
  • Bliss, C.A., Frank, M.R., Danforth, C.M., Dodds, P.S., 2014. An evolutionary algorithm approach to link prediction in dynamic social networks. Journal of Computational Science, 5(5): 750-764
  • Coello, C., Carlos, A., Becerra, R. L., 2003. Evolutionary
  • optimization using a cultural algorithm. In Swarm Intelligence Symposium, 2003. SIS'03. Proceedings of the 2003 IEEE, pp: 6-13.
  • multiobjective Coscia, M., Giannotti, F., Pedreschi, D., 2011. A discovery
  • networks. Statistical Analysis and Data Mining, 4(5): 512–546.
  • community complex methods in
  • Cui, Z. H., Shi, Z. Z., Zeng, J. C., 2010. Using emotional social
  • algorithm to direct orbits of chaotic systems. 1st International Conference on Swarm, Evoluation and Memetic Computation 389-395.
  • optimization (SEMCCO2010),
  • pp. Devi, S., Singla, S., 2012. Comparative analysis of
  • optimization algorithm & particle swarm
  • International Journal of Innovative Technology and Exploring Engineering (IJITEE), pp. 82-84.
  • emotional optimization
  • techniques. Duarte, A., Sánchez, A., Fernández, F., Montemayor, A.S., 2006. Improving ımage segmentation quality through effective region merging using a hierarchical
  • Pattern Recognition Letters, 27: 1239– 1251.
  • metaheuristic. 2003a. A software pipelining method based on hierarchical social algorithms. Proceedings of MISTA Conference, 1: 382-385.
  • Fernandez, F., Duarte, A., Sanchez, A., 2003b. Hierarchical social algorithms: A new metaheuristic for solving discrete bilevel optimization problems. Technical Report ESCET/URF - DTF/ UPM.
  • Fortunato, S., 2010. Community detection in graphs. Physics Reports 486(3): 75- 174.
  • Karaboğa, D., 2011. Yapay zekâ optimizasyon algoritmaları, Nobel Yayın Dağıtım, Ankara.
  • Osborn, A.F., 1957. Applied imagination, New York: Scribner.
  • Pooranian, Z., Shojafar, M., Abawajy, J. H., Singhal, M., 2013. GLOA: A new job scheduling
  • computing. International Journal of Interactive Multimedia and Artificial Intelligence, 2(1): 59-64. for
  • grid Rao, R.V., Patel, V., 2012. An ımproved teaching-learning-based
  • algorithm for solving unconstrained optimization
  • Iranica, pp. 710-720.
  • optimization problems.
  • Scientia Ramezani, F., Lotfi, S., 2013. Social-based algorithm
  • Computing, 13(5): 2837–2856.
  • Ray, T., Liew, K. M., 2003. Society and civilization: an optimization algorithm based on the simulation of social behavior. Evolutionary Computation, IEEE Transactions on, 7(4): 386-396.
  • Thammano, A., Moolwong, J., 2010. A new computational intelligence technique based on human group formation. Expert Systems with Applications, 37: 1628–1634.
  • Uçan, A., 2014, Otomatik duygu sözlüğü çevirimi kullanımı,
  • Hacettepe Üniversitesi, Fen Bilimleri Enstitüsü, Ankara. analizinde Yüksek Lisans
  • Tezi, Zhan, Z., Zhang, J., Shi, Y., Liu, H., 2012. A modified brain storm optimization. WCCI 2012 IEEE World Congress on Computational Intelligence, pp. 1-8.

Sosyal Ağ Analizi İçin Sosyal Tabanlı Yapay Zekâ Optimizasyon Algoritmalarının İncelenmesi

Yıl 2015, Cilt: 1 Sayı: 1, 33 - 52, 07.09.2015

Öz

Bu makalede, genel amaçlı arama ve optimizasyon tekniklerinden sosyal tabanlı güncel yapay zekâ optimizasyon algoritmaları, ilk kez bir bütün olarak tanıtılmış ve sosyal ağ analizinde bağlantı tahmini, topluluk keşfi, duygu analizi, metin özetleme vb. alanlarda etkili olarak dolaylı ya da direkt çözüm algoritması olarak kullanılabileceği belirtilmiştir.

Kaynakça

  • Altunbey, F., Alatas, B., 2015. Overlapping community networks
  • optimization algorithm. International Journal of Computer Networks and Applications, 2(1): 12-19. in
  • social parliamentary Atashpaz-Gargari, E., Lucas, C., 2007. Imperialist competitive algorithm: an algorithm for optimization ınspired by ımperialistic Congress
  • Computation, CEC 2007, 4661-4667.
  • Evolutionary Borji, A., Gamidi, M., 2009. A new approach to global optimization motivated by parliamentary political competitions. Int. Journal of Innovative Computing, Information and Control, 5(6): 1643- 1653.
  • Bliss, C.A., Frank, M.R., Danforth, C.M., Dodds, P.S., 2014. An evolutionary algorithm approach to link prediction in dynamic social networks. Journal of Computational Science, 5(5): 750-764
  • Coello, C., Carlos, A., Becerra, R. L., 2003. Evolutionary
  • optimization using a cultural algorithm. In Swarm Intelligence Symposium, 2003. SIS'03. Proceedings of the 2003 IEEE, pp: 6-13.
  • multiobjective Coscia, M., Giannotti, F., Pedreschi, D., 2011. A discovery
  • networks. Statistical Analysis and Data Mining, 4(5): 512–546.
  • community complex methods in
  • Cui, Z. H., Shi, Z. Z., Zeng, J. C., 2010. Using emotional social
  • algorithm to direct orbits of chaotic systems. 1st International Conference on Swarm, Evoluation and Memetic Computation 389-395.
  • optimization (SEMCCO2010),
  • pp. Devi, S., Singla, S., 2012. Comparative analysis of
  • optimization algorithm & particle swarm
  • International Journal of Innovative Technology and Exploring Engineering (IJITEE), pp. 82-84.
  • emotional optimization
  • techniques. Duarte, A., Sánchez, A., Fernández, F., Montemayor, A.S., 2006. Improving ımage segmentation quality through effective region merging using a hierarchical
  • Pattern Recognition Letters, 27: 1239– 1251.
  • metaheuristic. 2003a. A software pipelining method based on hierarchical social algorithms. Proceedings of MISTA Conference, 1: 382-385.
  • Fernandez, F., Duarte, A., Sanchez, A., 2003b. Hierarchical social algorithms: A new metaheuristic for solving discrete bilevel optimization problems. Technical Report ESCET/URF - DTF/ UPM.
  • Fortunato, S., 2010. Community detection in graphs. Physics Reports 486(3): 75- 174.
  • Karaboğa, D., 2011. Yapay zekâ optimizasyon algoritmaları, Nobel Yayın Dağıtım, Ankara.
  • Osborn, A.F., 1957. Applied imagination, New York: Scribner.
  • Pooranian, Z., Shojafar, M., Abawajy, J. H., Singhal, M., 2013. GLOA: A new job scheduling
  • computing. International Journal of Interactive Multimedia and Artificial Intelligence, 2(1): 59-64. for
  • grid Rao, R.V., Patel, V., 2012. An ımproved teaching-learning-based
  • algorithm for solving unconstrained optimization
  • Iranica, pp. 710-720.
  • optimization problems.
  • Scientia Ramezani, F., Lotfi, S., 2013. Social-based algorithm
  • Computing, 13(5): 2837–2856.
  • Ray, T., Liew, K. M., 2003. Society and civilization: an optimization algorithm based on the simulation of social behavior. Evolutionary Computation, IEEE Transactions on, 7(4): 386-396.
  • Thammano, A., Moolwong, J., 2010. A new computational intelligence technique based on human group formation. Expert Systems with Applications, 37: 1628–1634.
  • Uçan, A., 2014, Otomatik duygu sözlüğü çevirimi kullanımı,
  • Hacettepe Üniversitesi, Fen Bilimleri Enstitüsü, Ankara. analizinde Yüksek Lisans
  • Tezi, Zhan, Z., Zhang, J., Shi, Y., Liu, H., 2012. A modified brain storm optimization. WCCI 2012 IEEE World Congress on Computational Intelligence, pp. 1-8.
Toplam 38 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Feyza Altunbey

Bilal Alataş

Yayımlanma Tarihi 7 Eylül 2015
Gönderilme Tarihi 7 Eylül 2015
Yayımlandığı Sayı Yıl 2015 Cilt: 1 Sayı: 1

Kaynak Göster

APA Altunbey, F., & Alataş, B. (2015). Sosyal Ağ Analizi İçin Sosyal Tabanlı Yapay Zekâ Optimizasyon Algoritmalarının İncelenmesi. International Journal of Pure and Applied Sciences, 1(1), 33-52.
AMA Altunbey F, Alataş B. Sosyal Ağ Analizi İçin Sosyal Tabanlı Yapay Zekâ Optimizasyon Algoritmalarının İncelenmesi. International Journal of Pure and Applied Sciences. Eylül 2015;1(1):33-52.
Chicago Altunbey, Feyza, ve Bilal Alataş. “Sosyal Ağ Analizi İçin Sosyal Tabanlı Yapay Zekâ Optimizasyon Algoritmalarının İncelenmesi”. International Journal of Pure and Applied Sciences 1, sy. 1 (Eylül 2015): 33-52.
EndNote Altunbey F, Alataş B (01 Eylül 2015) Sosyal Ağ Analizi İçin Sosyal Tabanlı Yapay Zekâ Optimizasyon Algoritmalarının İncelenmesi. International Journal of Pure and Applied Sciences 1 1 33–52.
IEEE F. Altunbey ve B. Alataş, “Sosyal Ağ Analizi İçin Sosyal Tabanlı Yapay Zekâ Optimizasyon Algoritmalarının İncelenmesi”, International Journal of Pure and Applied Sciences, c. 1, sy. 1, ss. 33–52, 2015.
ISNAD Altunbey, Feyza - Alataş, Bilal. “Sosyal Ağ Analizi İçin Sosyal Tabanlı Yapay Zekâ Optimizasyon Algoritmalarının İncelenmesi”. International Journal of Pure and Applied Sciences 1/1 (Eylül 2015), 33-52.
JAMA Altunbey F, Alataş B. Sosyal Ağ Analizi İçin Sosyal Tabanlı Yapay Zekâ Optimizasyon Algoritmalarının İncelenmesi. International Journal of Pure and Applied Sciences. 2015;1:33–52.
MLA Altunbey, Feyza ve Bilal Alataş. “Sosyal Ağ Analizi İçin Sosyal Tabanlı Yapay Zekâ Optimizasyon Algoritmalarının İncelenmesi”. International Journal of Pure and Applied Sciences, c. 1, sy. 1, 2015, ss. 33-52.
Vancouver Altunbey F, Alataş B. Sosyal Ağ Analizi İçin Sosyal Tabanlı Yapay Zekâ Optimizasyon Algoritmalarının İncelenmesi. International Journal of Pure and Applied Sciences. 2015;1(1):33-52.

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