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    Editorial
    Editorial: Quality Assurance and Workflow Optimization for the Diagnosis and Treatment of Head and Neck Cancer
    (Frontiers Media Sa, 2024) Elicin, Olgun; Hosal, Sefik
    [No Abstract Available]
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    Conference Object
    Citation - Scopus: 9
    Cognitive Radio and Its Applications in the New Trend of Communication System: a Review
    (Institute of Electrical and Electronics Engineers Inc., 2022) Al-Sudani,H.; Thabit,A.A.; Dalveren,Y.
    Spectrum efficiency decay due to high demand for high data rate and growing technologies, tens of billions of connected devices need to provide by the services wirelessly causing a sharp drop in spectral efficiency and high-power consumption. The software-defined technologies represent one of the most important enabling keys to 5G and beyond networks, which designed to host all emerging technologies in heterogeneous networks. Cognitive radio (CR) is a software-defined radio (SDR) and a magical tool to relieve spectrum scarcity and reduce the consumed power for communication. This paper surveys the detection techniques integrated with artificial neural networks (ANN) in heterogeneous networks to address a future work to accelerate the establishment of reconfigurable software-defined technologies and to succor the spectrum. It is found that wireless sensor network (WSN) and the internet of things (IoT) are expected to be the most influencers of the spectrum's solidity which have led the authors to conclude interesting future work. © 2022 IEEE.
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    Article
    Citation - WoS: 17
    Citation - Scopus: 23
    Regarding Solid Oxide Fuel Cells Simulation Through Artificial Intelligence: a Neural Networks Application
    (Mdpi, 2019) Baldinelli, Arianna; Barelli, Linda; Bidini, Gianni; Bonucci, Fabio; Iskenderoglu, Feride Cansu
    Because of their fuel flexibility, Solid Oxide Fuel Cells (SOFCs) are promising candidates to coach the energy transition. Yet, SOFC performance are markedly affected by fuel composition and operative parameters. In order to optimize SOFC operation and to provide a prompt regulation, reliable performance simulation tools are required. Given the high variability ascribed to the fuel in the wide range of SOFC applications and the high non-linearity of electrochemical systems, the implementation of artificial intelligence techniques, like Artificial Neural Networks (ANNs), is sound. In this paper, several network architectures based on a feedforward-backpropagation algorithm are proposed and trained on experimental data-set issued from tests on commercial NiYSZ/8YSZ/LSCF anode supported planar button cells. The best simulator obtained is a 3-hidden layer ANN (25/22/18 neurons per layer, hyperbolic tangent sigmoid as transfer function, obtained with a gradient descent with adaptive learning rate backpropagation). This shows high accuracy (RMS = 0.67% in the testing phase) and successful application in the forecast of SOFC polarization behaviour in two additional experiments (RMS in the order of 3% is scored, yet it is reduced to about 2% if only the typical operating current density range of real application is considered, from 300 to 500 mA.cm(-2)). Therefore, the neural tool is suitable for system simulation codes/software whether SOFC operating parameters agree with the input ranges (anode feeding composition 0-48%(vol) H-2, 0-38%(vol) CO, 0-45%(vol) CH4, 9-32%(vol) CO2, 0-54%(vol) N-2, specific equivalent hydrogen flow-rate per unit cell active area 10.8-23.6 mL.min(-1).cm(-2), current density 0-1300 mA.cm(-2) and temperature 700-800 degrees C).
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    Review
    Citation - WoS: 1
    Citation - Scopus: 2
    Bias in human data: A feedback from social sciences
    (Wiley Periodicals, inc, 2023) Takan, Savas; Ergun, Duygu; Yaman, Sinem Getir; Kilincceker, Onur
    The fairness of human-related software has become critical with its widespread use in our daily lives, where life-changing decisions are made. However, with the use of these systems, many erroneous results emerged. Technologies have started to be developed to tackle unexpected results. As for the solution to the issue, companies generally focus on algorithm-oriented errors. The utilized solutions usually only work in some algorithms. Because the cause of the problem is not just the algorithm; it is also the data itself. For instance, deep learning cannot establish the cause-effect relationship quickly. In addition, the boundaries between statistical or heuristic algorithms are unclear. The algorithm's fairness may vary depending on the data related to context. From this point of view, our article focuses on how the data should be, which is not a matter of statistics. In this direction, the picture in question has been revealed through a scenario specific to "vulnerable and disadvantaged" groups, which is one of the most fundamental problems today. With the joint contribution of computer science and social sciences, it aims to predict the possible social dangers that may arise from artificial intelligence algorithms using the clues obtained in this study. To highlight the potential social and mass problems caused by data, Gerbner's "cultivation theory" is reinterpreted. To this end, we conduct an experimental evaluation on popular algorithms and their data sets, such as Word2Vec, GloVe, and ELMO. The article stresses the importance of a holistic approach combining the algorithm, data, and an interdisciplinary assessment.This article is categorized under:Algorithmic Development > Statistics