Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
Browse
3 results
Search Results
Conference Object Citation - Scopus: 2Moodle: Practical Advices for University Teachers(Springer Verlag service@springer.de, 2017-09-28) Pedersen, Jens Myrup; Kuran, Mehmet ŞükrüMoodle is a widely used Learning Management System, with a market share of 20% in the US/Canada and 65% in Europe. However, it is our experience that the system is too often used just as a website or repository for classical teaching material such as literature references, slides and problems for students to solve after the lectures, and that the fully potential of the platform is not exploited. In this paper we demonstrate some of the functionalities that university teachers can make use of to increase the learning experience of the students. For each of the features we demonstrate, we both show how it can be used, and give some didactic considerations. We have tested all of the presented features ourself in a blended learning course carried out as part of an Erasmus+ Strategic Partnership. © 2017 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 4Citation - Scopus: 3Green Supplier Selection by Using Fuzzy TOPSIS Method(World Scientific Publishing Co. Pte Ltd wspc@wspc.com.sg, 2016-08) Dogan, Ahmet; Söylemez, İsmet; Özcan, Uǧur; Stylemez, IsmetWith the increased environmental consciousness in customers, organizations took upon the task of redesigning their strategic goals in a more environment-sensitive way in order to fulfill their social obligations, to enable sustainability, to gain competitive advantage and to make the world more habitable. Because, the emerging conditions in the 21st century indicate that the traditional criteria -such as price, cost so on for supply chain management, supplier selection and performance measurement of suppliers are no more sufficient and there is the necessity of adding new criteria such as environmental matters. This paper deals with the problem of selecting green suppliers in an organization in Turkey that has operations in the field of accumulator. The aim is to select the greenest of 3 suppliers in Turkey, France and Bulgaria which supply the organization with the plastic material used in the production of accumulator. The problem is solved via fuzzy TOPSIS, which is a multi-criteria decision making method (MCDM), and the results are used to select the greenest supplier. © 2017 Elsevier B.V., All rights reserved.Article Citation - Scopus: 9Captain: A Testbed for Co-Simulation of Scalable Serverless Computing Environments for AIoT Enabled Predictive Maintenance in Industry 4.0(Institute of Electrical and Electronics Engineers Inc., 2025-08-15) Golec, Muhammed; Wu, Huaming; Ozturac, Ridvan; Kumar Parlikad, Ajith; Cuadrado Latasa, Felix; Gill, Sukhpal Singh; Uhlig, Steve; Cuadrado, Felix; Singh Gill, SukhpalThe massive amounts of data generated by the Industrial Internet of Things (IIoT) require considerable processing power, which increases carbon emissions and energy usage, and we need sustainable solutions to enable flexible manufacturing. Serverless computing shows potential for meeting this requirement by scaling idle containers to zero energy-efficiency and cost, but this will lead to a cold start delay. Most solutions rely on idle containers, which necessitates dynamic request time forecasting and container execution monitoring. Furthermore, Artificial Intelligence of Things (AIoT) can provide autonomous and sustainable solutions by combining IIoT with artificial intelligence (AI) to solve this problem. Therefore, we develop a new testbed, CAPTAIN, to facilitate AI-based co-simulation of scalable and flexible serverless computing in IIoT environments. The AI module in the CAPTAIN framework employs random forest (RF) and light gradient-boosting machine (LightGBM) models to optimize cold start frequency and prevent cold starts based on their prediction results. The proxy module additionally monitors the client-server network and constantly updates the AI module training dataset via a message queue. Finally, we evaluated the proxy module’s performance using a predictive maintenance-based real-world IIoT application and the AI module’s performance in a realistic serverless environment using a Microsoft Azure dataset. The AI module of the CAPTAIN outperforms baselines in terms of cold start frequency, computational time with 0.5 ms, energy consumption with 1161.0 joules, and CO2 emissions with 32.25e-05 gCO<inf>2</inf>. The CAPTAIN testbed provides a co-simulation of sustainable and scalable serverless computing environments for AIoT-enabled predictive maintenance in Industry 4.0. © 2025 Elsevier B.V., All rights reserved.