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Öğe Dense Region Detection of Streaming Social Media Datasets using Blockchain-based Secure Computation(Institute of Electrical and Electronics Engineers Inc., 2022) Ecemis, Alper; Dokuz, Ahmet Sakir; Celik, MeteThe popularity of blockchain technology is increasing day by day, and it has a widespread usage as a reliable database structure. Blockchain has been utilized across a wide range of industries, such as IoT, social media, robotics, government, supply chain, and healthcare. As the usage of blockchain is emerged in social media studies, the problem of storing and analyzing streaming social media data in a safe, anonymous, and reliable manner is emerged. Within the scope of this problem, in this study, the adaptation of the streaming social media dataset to the blockchain systems and analysis of this data are carried out. A new algorithm of Dense Region Detection Algorithm (DRDA) is proposed to detect dense social regions of urban areas. Streaming data are collected from New York City, and the proposed DRDA is performed by miners to determine the dense regions of New York. As a result, dense regions with intensive tweet sharing in New York are detected and discussed. © 2022 IEEE.Öğe Location-based optimal sizing of hybrid renewable energy systems using deterministic and heuristic algorithms(Wiley, 2021) Demolli, Halil; Dokuz, Ahmet Sakir; Ecemis, Alper; Gokcek, MuratThe application of renewable energy sources in electrical energy generation is becoming widespread due to the decrease of installation costs and the increase of environmental concerns. Hybrid power generation systems are advantageous to meet the load demand, but optimal sizing is the main concern for having a cost-effective system based on given load demand and techno-economic indicators. This paper proposes a deterministic algorithm and utilizes genetic and artificial bee colony (ABC) optimization algorithms for optimal sizing of PV/battery and PV/WT/battery hybrid systems with minimum levelized cost of electricity (LCOE) constraint for two locations, Nigde and Bozcaada, in Turkey. The loss of power supply probability (LPSP) is used to build a reliable system and to make sure that the system produces required energy. Experimental results showed that optimal sizing of each location is different due to different wind and solar characteristics of locations. PV/battery model is more suitable for Nigde location with 1.22% LPSP and 0.1514 [$/kWh] LCOE, while PV/WT/battery model is more cost-efficient for Bozcaada location with 1.952% LPSP and 0.0872 [$/kWh] LCOE. Time performances of the algorithms are also investigated. It has been seen that the ABC algorithm has better performance and less execution time. This study demonstrated that heuristic algorithms are more applicable than deterministic algorithms, due to fast discovery of optimal solutions for hybrid renewable energy systems.Öğe Using machine learning algorithms for predicting real estate values in tourism centers(Springer, 2023) Alkan, Tansu; Dokuz, Yesim; Ecemis, Alper; Bozdas, Asli; Durduran, S. SavasAlong with the development of technology in recent years, artificial intelligence (machine learning) techniques that perform operations, such as learning, classification, association, optimization, and prediction, have started to be used on data on real estate according to the criteria affecting the value. Using artificial intelligence (machine learning) techniques, valuation processes are performed objectively and scientifically. In this study, machine learning techniques were employed to balance the real estate market, affected by the tourism sector in Alanya district of Antalya province, Turkey, and examine changes in value objectively and scientifically. First, the criteria affecting the real estate value were determined as structural and spatial, and data on real estate were obtained from the online real estate website. Then, the values of the real estate in the selected application area were predicted using machine learning algorithms (k-nearest neighbors, random forest, and support vector machines). Unlike studies in the literature, algorithm-based valuation using machine learning algorithms was performed instead of mathematical modeling. When analyzed for performance metrics, the best result was achieved with the support vector machines algorithm (0.73). Objective methods should be used to balance the exorbitant differences between real estate values, to regulate market conditions and to carry out a real estate valuation process free from speculative effects in coastal areas where tourism factor is effective. This study indicated the applicability of algorithm-based machine learning techniques in real estate valuation.Öğe Wind power forecasting based on daily wind speed data using machine learning algorithms(Pergamon-Elsevier Science Ltd, 2019) Demolli, Halil; Dokuz, Ahmet Sakir; Ecemis, Alper; Gokcek, MuratWind energy is a significant and eligible source that has the potential for producing energy in a continuous and sustainable manner among renewable energy sources. However, wind energy has several challenges, such as initial investment costs, the stationary property of wind plants, and the difficulty in finding wind-efficient energy areas. In this study, long-term wind power forecasting was performed based on daily wind speed data using five machine learning algorithms. We proposed a method based on machine learning algorithms to forecast wind power values efficiently. We conducted several case studies to reveal performances of machine learning algorithms. The results showed that machine learning algorithms could be used for forecasting long-term wind power values with respect to historical wind speed data. Furthermore, the results showed that machine learning-based models could be applied to a location different from model-trained locations. This study demonstrated that machine learning algorithms could be successfully used before the establishment of wind plants in an unknown geographical location whether it is logical by using the model of a base location.
