Resource optimisation in cloud computing : comparative study of algorithms applied to recommendations in a big data analysis architecture

• Autorzy: Ndayikengurukiye Aristide , Ez-Zahout Abderrahmane , Aboubakr Akou , Charkaoui Youssef , Fouzia Omary

Identyfikatory

DOI

10.14313/JAMRIS/4-2021/28

• Języki publikacji: EN

Abstrakty

EN

Recommender systems (RS) have emerged as a means of providing relevant content to users, whether in social networking, health, education, or elections. Furthermore, with the rapid development of cloud computing, Big Data, and the Internet of Things (IoT), the component of all this is that elections are controlled by open and accountable, neutral, and autonomous election management bodies. The use of technology in voting procedures can make them faster, more efficient, and less susceptible to security breaches. Technology can ensure the security of every vote, better and faster automatic counting and tallying, and much greater accuracy. The election data were combined by different websites and applications. In addition, it was interpreted using many recommendation algorithms such as Machine Learning Algorithms, Vector Representation Algorithms, Latent Factor Model Algorithms, and Neighbourhood Methods and shared with the election management bodies to provide appropriate recommendations. In this paper, we conduct a comparative study of the algorithms applied in the recommendations of Big Data architectures. The results show us that the K-NN model works best with an accuracy of 96%. In addition, we provided the best recommendation system is the hybrid recommendation combined by content-based filtering and collaborative filtering uses similarities between users and items.

Słowa kluczowe

EN

cloud computing; Big Data; IoT; recommender system; KNN algorithm

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2021
• Tom: Vol. 15, No. 4
• Strony: 65--75
• Opis fizyczny: Bibliogr. 33 poz., rys.

Twórcy

autor

Ndayikengurukiye Aristide

• Mohammed V University, Rabat, Morocco

autor

Ez-Zahout Abderrahmane

• Mohammed V University, Rabat, Morocco

autor

Aboubakr Akou

• Mohammed V University, Rabat, Morocco

autor

Charkaoui Youssef

• Mohammed V University, Rabat, Morocco

autor

Fouzia Omary

• Mohammed V University, Rabat, Morocco

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