Makine Öğrenmesi Yöntemleri İle Günümüz Ve Geleceğe Yönelik Meslek Tahminlerinin Değerlendirilmesi : Türkiye'den Ampirik Deliller

Karaahmetoğlu, Ebru; Ersöz, Süleyman; Türker, Ahmet Kürşad; Ateş, Volkan; Inal, Ali Firat

doi:10.2339/politeknik.985534

Cited by 2 publications

(1 citation statement)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are different approaches in the literature, where the more popular ones are those addressing data analytics and machine learning tools [63]. Anyway, predictive maintenance consists of data mining to formulate machine learning models in order to gain knowledge with the aim of predicting the state of health of machinery [64]. Some popular methods of predictive maintenance are condition-based maintenance (CBM), prognosis and health management (PHM) and remaining useful life (RUL) [65].…”

Section: Fault Diagnosis and Detectionmentioning

confidence: 99%

Study on Data Center Network Topologies for Monitoring Data using Edge/Fog Computing

ROİG,

ALCARAZ,

GILLY

et al. 2023

Journal of Polytechnic

View full text Add to dashboard Cite

The election of an appropriate data center network topology is key when dealing with surveillance and monitoring processes, such as those devoted to obtaining relevant data for sensor data fusion in any type of remote computing environment so as to perform fault diagnosis and prevention. In this paper, some of the most commonly used topologies to interconnect nodes within a data center bound to edge/fog computing, representing either switch-centric ones or server-centric ones, are reviewed and analyzed from a statistical point of view in order to measure their performance, resulting in server-centric ones doing it better.

show abstract

Section: Fault Diagnosis and Detectionmentioning

confidence: 99%

Study on Data Center Network Topologies for Monitoring Data using Edge/Fog Computing

ROİG,

ALCARAZ,

GILLY

et al. 2023

Journal of Polytechnic

View full text Add to dashboard Cite

show abstract

Applying Toroidal k-ary Grids for Optimizing Edge Data Centers

ROİG,

ALCARAZ,

GILLY

et al. 2023

Journal of Polytechnic

View full text Add to dashboard Cite

IoT deployments are growing exponentially, leading to a huge increase in edge computing facilities. In order to cope with such a demand, data centers need to get customized for the specific requirements of edge computing, such as a small number of physical servers and the ability to scale and unscale according to the traffic flows running at a given time. In this context, artificial intelligence plays a key part as it may anticipate when traffic throughput will increase or otherwise by scrutinizing current traffic whilst considering other factors like historical data and network baselines. In this paper, a dynamic framework is outlined based on toroidal k-ary grids so as to organize and optimize small data centers, allowing them to increase or decrease according to the current and predicted capacity of IoT-generated traffic flows.

show abstract

Makine Öğrenmesi Yöntemleri İle Günümüz Ve Geleceğe Yönelik Meslek Tahminlerinin Değerlendirilmesi : Türkiye'den Ampirik Deliller

Abstract: Highlights Analyzing documents with text mining methods.  Applying machine learning algorithms with the proposed models.  Performance comparisions with the popular evaluation metrics.

Cited by 2 publications

References 36 publications

Study on Data Center Network Topologies for Monitoring Data using Edge/Fog Computing

Study on Data Center Network Topologies for Monitoring Data using Edge/Fog Computing

Applying Toroidal k-ary Grids for Optimizing Edge Data Centers

Contact Info

Product

Resources

About