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Article Citation - WoS: 12Citation - Scopus: 12A Systematic Approach To Optimizing Energy-Efficient Automated Systems With Learning Models for Thermal Comfort Control in Indoor Spaces(Mdpi, 2023) Erisen, SerdarEnergy-efficient automated systems for thermal comfort control in buildings is an emerging research area that has the potential to be considered through a combination of smart solutions. This research aims to explore and optimize energy-efficient automated systems with regard to thermal comfort parameters, energy use, workloads, and their operation for thermal comfort control in indoor spaces. In this research, a systematic approach is deployed, and building information modeling (BIM) software and energy optimization algorithms are applied at first to thermal comfort parameters, such as natural ventilation, to derive the contextual information and compute the building performance of an indoor environment with Internet of Things (IoT) technologies installed. The open-source dataset from the experiment environment is also applied in training and testing unique black box models, which are examined through the users' voting data acquired via the personal comfort systems (PCS), thus revealing the significance of Fanger's approach and the relationship between people and their surroundings in developing the learning models. The contextual information obtained via BIM simulations, the IoT-based data, and the building performance evaluations indicated the critical levels of energy use and the capacities of the thermal comfort control systems. Machine learning models were found to be significant in optimizing the operation of the automated systems, and deep learning models were momentous in understanding and predicting user activities and thermal comfort levels for well-being; this can optimize energy use in smart buildings.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.Article Citation - WoS: 17Citation - Scopus: 23Regarding Solid Oxide Fuel Cells Simulation Through Artificial Intelligence: a Neural Networks Application(Mdpi, 2019) Baldinelli, Arianna; Barelli, Linda; Bidini, Gianni; Bonucci, Fabio; Iskenderoglu, Feride CansuBecause 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).Article Citation - Scopus: 88Is Chatgpt Accurate and Reliable in Answering Questions Regarding Head and Neck Cancer?(Frontiers Media SA, 2023) Kuşcu,O.; Pamuk,A.E.; Sütay Süslü,N.; Hosal,S.Background and objective: Chat Generative Pre-trained Transformer (ChatGPT) is an artificial intelligence (AI)-based language processing model using deep learning to create human-like text dialogue. It has been a popular source of information covering vast number of topics including medicine. Patient education in head and neck cancer (HNC) is crucial to enhance the understanding of patients about their medical condition, diagnosis, and treatment options. Therefore, this study aims to examine the accuracy and reliability of ChatGPT in answering questions regarding HNC. Methods: 154 head and neck cancer-related questions were compiled from sources including professional societies, institutions, patient support groups, and social media. These questions were categorized into topics like basic knowledge, diagnosis, treatment, recovery, operative risks, complications, follow-up, and cancer prevention. ChatGPT was queried with each question, and two experienced head and neck surgeons assessed each response independently for accuracy and reproducibility. Responses were rated on a scale: (1) comprehensive/correct, (2) incomplete/partially correct, (3) a mix of accurate and inaccurate/misleading, and (4) completely inaccurate/irrelevant. Discrepancies in grading were resolved by a third reviewer. Reproducibility was evaluated by repeating questions and analyzing grading consistency. Results: ChatGPT yielded “comprehensive/correct” responses to 133/154 (86.4%) of the questions whereas, rates of “incomplete/partially correct” and “mixed with accurate and inaccurate data/misleading” responses were 11% and 2.6%, respectively. There were no “completely inaccurate/irrelevant” responses. According to category, the model provided “comprehensive/correct” answers to 80.6% of questions regarding “basic knowledge”, 92.6% related to “diagnosis”, 88.9% related to “treatment”, 80% related to “recovery – operative risks – complications – follow-up”, 100% related to “cancer prevention” and 92.9% related to “other”. There was not any significant difference between the categories regarding the grades of ChatGPT responses (p=0.88). The rate of reproducibility was 94.1% (145 of 154 questions). Conclusion: ChatGPT generated substantially accurate and reproducible information to diverse medical queries related to HNC. Despite its limitations, it can be a useful source of information for both patients and medical professionals. With further developments in the model, ChatGPT can also play a crucial role in clinical decision support to provide the clinicians with up-to-date information. Copyright © 2023 Kuşcu, Pamuk, Sütay Süslü and Hosal.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]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 > StatisticsReview Citation - WoS: 17Citation - Scopus: 29Review of Modern Forest Fire Detection Techniques: Innovations in Image Processing and Deep Learning(Mdpi, 2024) Ozel, Berk; Alam, Muhammad Shahab; Khan, Muhammad UmerFire detection and extinguishing systems are critical for safeguarding lives and minimizing property damage. These systems are especially vital in combating forest fires. In recent years, several forest fires have set records for their size, duration, and level of destruction. Traditional fire detection methods, such as smoke and heat sensors, have limitations, prompting the development of innovative approaches using advanced technologies. Utilizing image processing, computer vision, and deep learning algorithms, we can now detect fires with exceptional accuracy and respond promptly to mitigate their impact. In this article, we conduct a comprehensive review of articles from 2013 to 2023, exploring how these technologies are applied in fire detection and extinguishing. We delve into modern techniques enabling real-time analysis of the visual data captured by cameras or satellites, facilitating the detection of smoke, flames, and other fire-related cues. Furthermore, we explore the utilization of deep learning and machine learning in training intelligent algorithms to recognize fire patterns and features. Through a comprehensive examination of current research and development, this review aims to provide insights into the potential and future directions of fire detection and extinguishing using image processing, computer vision, and deep learning.
