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Conference Object Citation - Scopus: 9Cognitive 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.Review Citation - WoS: 1Citation - Scopus: 2Bias in human data: A feedback from social sciences(Wiley Periodicals, inc, 2023) Takan, Savas; Ergun, Duygu; Yaman, Sinem Getir; Kilincceker, OnurThe 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
