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A General View of Big Data and Machine Learning

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Logistics 4.0 and Future of Supply Chains

Abstract

Nowadays, winds of digitalization have continuing blow in the world. No one cannot ignore these kinds of changes and is continuing to cause dramatic changes in our lives. Big data is one of the crucial parts of these changes. Nowadays, almost millions of people have become the main element providing the data flow. Furthermore, they are continuing to perform it voluntarily and willingly. As a result, companies, industries, and other stakeholders have started to encounter a very huge volume of data about their customers. However, most of the data are not be useful because they are not structured. This chapter is organized into two parts. In the first part, the big data concept was explained and its important elements were discussed. In the second stage, machine learning and its elements were presented information on some computational tools used for converting the unstructured data to structured data.

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References

  • Alan MA (2014) Karar Ağaçlarıyla Öğrenci Verilerinin Sınıflandırılması. Atatürk Üniversitesi İktisadi Ve İdari Bilimler Dergisi 28(4):101–112

    Google Scholar 

  • Aladağ ÇH (2019) Architecture selection in neural networks by statistical and machine learning. Orient J Comput Sci Technol 12(3):76–89

    Article  Google Scholar 

  • Atalay M, Çelik E (2017) Büyük Veri Analizinde Yapay Zekâ ve Makine Öğrenmesi Uygulamaları. Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 9(22):155–172

    Article  Google Scholar 

  • Aydemir E, Karagül K (2020) Solving a periodic capacitated vehicle routing problem using simulated annealing algorithm for a manufacturing company. Braz J Oper Prod Manag 17(1):1–13

    Article  Google Scholar 

  • Aytekin Ç, Sütcü CS, Özfidan U (2018) Text classification via decision trees algorithm customer comments case. J Int Soc Res 11(55):782–792

    Article  Google Scholar 

  • Cyganek B, Graña M, Krawczyk B, Kasprzak A, Porwik P, Walkowiak K, Woźniak M (2016) A survey of big data issues in electronic health record analysis. Appl Artif Intell 30(6):497–520

    Article  Google Scholar 

  • Çalış Boyacı A, Kayapınar Kaya S, Çetinyokuş T (2014) Veri Madenciliğinde Karar Ağacı Algoritmaları ile Bilgisayar ve İnternet Güvenliği Üzerine Bir Uygulama. Endüstri Mühendisliği Dergisi 25(3–4):2–19

    Google Scholar 

  • Dülger Ü (2015) Stratejik Büyük Veri Yönetiminin Yatırımlar Üzerindeki Etkileri. Yayınlanmamış Yüksek Lisans Tezi, İstanbul Üniversitesi Fen Bilimleri Enstitüsü

    Google Scholar 

  • Emel AG, Taşkın Ç (2002) Genetik Algoritmalar ve Uygulama Alanları. Uludağ Üniversitesi İİBF Dergisi 21(1):129–152

    Google Scholar 

  • Ghannadpour SF, Zandiyeh F. (2020) An adapted multi-objective genetic algorithm for solving the cash in transit vehicle routing problem with vulnerability estimation for risk quantification. Engineering applications of artificial intelligence, vol 96

    Google Scholar 

  • Gholamia R, Moradzadehb A, Malekic S, Amiric S, Hanachid J (2014) Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs. J Petrol Sci Eng 122(2014):643–656

    Article  Google Scholar 

  • Gülsün B, Tuzkaya G, Duman C (2009) Genetik Algoritmalar ile Tesis Yerleşimi Tasarımı ve Bir Uygulama. Doğuş Üniversitesi Dergisi 10(1):73–87

    Google Scholar 

  • Gümüşoğlu Ş, Erboy N, Özdağoğlu G (2013) Siparişe Dayalı Üretim için Ürün Gruplarının Oluşturulmasında Genetik Algoritma Tabanlı Bir Yaklaşım. Yönetim Ve Ekonomi Dergisi 20(2):259–284

    Google Scholar 

  • İşçi Ö, Korukoğlu S (2003) Genetik Algoritma Yaklaşımı ve Yöneylem Araştırmasında Bir Uygulama. Yönetim Ve Ekonomi 10(2):191–208

    Google Scholar 

  • Kavzoğlu T, Çölkesen İ (2010) Karar ağaçları ile Uydu Görüntülerinin Sınıflandırılması Kocaeli Örneği. Harita Teknolojileri Elektronik Dergisi 2(1):36–45

    Google Scholar 

  • Maind SB, Wankar P (2014) Research paper on basic of artificial neural network. Int J Recent Innov Trends Comput Commun 2(1):96–100. ISSN: 2321-8169

    Google Scholar 

  • Mayer-Schonberger V, Cukier K (2013) Big data: a revolution that will transform how we live, work and think. Houghton Mifflin Harcourt, Boston, Massachusetts.

    Google Scholar 

  • Mjalli FS, Al-Asheh S, Alfadala HE (2007) Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance. J Environ Manag 83(3):329–338

    Article  Google Scholar 

  • Nabiyev VV (2012) Yapay Zekâ. Ankara: Seçkin Yayıncılık

    Google Scholar 

  • Namazkhan M, Albers C, Steg L (2020) A decision tree method for explaining household gas consumption: the role of building characteristics, socio-demographic variables, psychological factors and household behaviour. Renew Sustain Energy Rev 119:109542

    Google Scholar 

  • Ocak İ, Şeker ŞE (2013) Calculation of surface settlements caused by EPBM tunneling using artificial neural network SVM and Gaussian processes. Environ Earth Sci 70(3):1263–1276

    Article  Google Scholar 

  • Özköse H, Gencer C (2019) Proje Planlama ve Çizelgelemede Genetik Algoritma Tabanlı Bir Yöntem ile Kritik Yolun Proje Tamamlanma Zamanının tespiti ve Zaman Maliyet Analizi. Bartın Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi 10(20):278–300

    Google Scholar 

  • Özşahin Ş, Singer H (2019) Odun Yüzey Pürüzlülüğü Tahmininde Bir Yapay Sinir Ağı Modelinin Kullanılması. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi 7(3):1764–1777

    Article  Google Scholar 

  • Öztemel E (2003) Yapay Sinir Ağları. İstanbul: Papatya Yayıncılık

    Google Scholar 

  • Qureshi SA, Mirza SM, Arif M (2006) Fitness function evaluation for image reconstruction using binary genetic algorithm for parallel ray transmission tomography, emerging technologies. In: ICET’06 international conference. Islamabad, Pakistan, pp 196–201

    Google Scholar 

  • Sağıroğlu Ş, Koç, O(eds) (2017) Büyük Veri ve Açık Veri Analitiği, Yöntemler ve Uygulamalar. Ankara: Grafiker Yayınevi

    Google Scholar 

  • Spann T (2017) The physics of big data. Erişim: 22.05.2020. https://dzone.com/articles/the-physics-of-big-data/

  • Şahan AN (2020) Stratejik Yönetim Perspektifinden Sigortacılık Sektöründe Makine Öğrenmesi Algoritmaları ile Anomali tespiti. Yüksek Lisans Tezi, İstanbul Teknik Üniversitesi Fen Bilimleri Enstitüsü

    Google Scholar 

  • Şahin Y, Karagül K (2019) Gezgin Satıcı Probleminin Melez Akışkan Genetik Algoritma MAGA Kullanarak Çözümü. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 25(1):106–114

    Google Scholar 

  • Şahinarslan FV (2019) Makine Öğrenmesi Algoritmaları ile Nüfus Tahmini: Türkiye Örneği. Yüksek Lisans Tezi, İstanbul Teknik Üniversitesi Sosyal Bilimler Enstitüsü.

    Google Scholar 

  • URL 1: http://www.accaglobal.com. Access Date: 22.05.2020

  • URL 2: https://andressilvaa.tumblr.com/post/87206443764/big-data-refers-to-5vs-volume. Access Date: 22.05.2020

  • Venkatram K, Geetha MA (2017) Review on big data & analytics–concepts, philosophy, process, and applications. Cybernetics Inform Technol 17(2):3–27.

    Google Scholar 

  • Wei J, Chu X, Sun X-Y, Xu K, Deng H.-X, Chen J, Wei Z, Lei M (2019) Machine learning in materials science. InfoMat 1:338–358. https://doi.org/10.1002/inf2.12028

  • Yakıcı Ayan T (2008) Sabit Maliyetli Ulaştırma Problemi için Bir Genetik Algoritma. Gazi Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi 10(1):97–116

    Google Scholar 

  • Yılmaz B, Bülbül S, Atik M (2017) Büyük Verinin Big Data Muhasebe Üzerindeki Etkisi ve Muhasebeye Sağladığı Katkıların İncelenmesi. Kara Harp Okulu Bilim Dergisi 27(1):79–112

    Google Scholar 

  • Zhang L, He M, Shao S (2020) Machine learning for halide perovskite materials. Nano Energy 78:105380

    Google Scholar 

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Görçün, Ö., Küçükönder, H. (2022). A General View of Big Data and Machine Learning. In: İyigün, İ., Görçün, Ö.F. (eds) Logistics 4.0 and Future of Supply Chains. Accounting, Finance, Sustainability, Governance & Fraud: Theory and Application. Springer, Singapore. https://doi.org/10.1007/978-981-16-5644-6_4

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