Harri's Hawk Optimization (HHO) algorithm manifests as a new meta-heuristic algorithm in literature. When we look at studies that have used with this algorithm, we can see that its results in test functions and in the solutions of some test functions in IEEE Congress on Evolutionary Computation (CEC) are much better compared to other heuristic and meta heuristic algorithm results. In this study, an algorithm has been developed which has been hybridized with the mutation operators of Differential Evolution (DE) to further improve the HHO algorithm. This algorithm is named as Hybrid Harris Hawk Optimization based on Differential Evolution (HHODE). Performance of the proposed HHODE algorithm has been first compared with HHO and then compared with the results of other algorithms which have been most commonly used in the literature. In this comparison process, the most commonly used test functions in the literature and some of the other test functions in CEC2005 and CEC2017 as a new application field, have been solved. When the results of the comparison of HHODE with other algorithms are analyzed, it is observed that the balance between the exploratory tendency and exploitative tendency of the algorithm is well consistent. Formula 1 ranking method is used in the order of HHODE according to HHO and other algorithms. When a general evaluation of HHODE was performed, it was found to be an even more powerful algorithm as a result of the combination of strong features of both HHO and DE. The optimal power flow (OPF) problem is one of the most important problems of the modern power system. The HHODE algorithm is proposed to solve the OPF problem, which is considered without valve-point effect and prohibited zones (1) and with prohibited zones (2) in this paper. The effectiveness of the HHODE hybrid algorithm is tested on modified IEEE 30bus test system. The result of HHODE algorithms are compared with other optimization algorithms in the literature.

Earning via real-time predictions with the experience in the visible trend directions of an investment instrument in the past requires a different perspective on charts. Indicators and formations within the scope of technical analysis constitute the most significant basis of this perspective. Those who can generate a high income in financial markets and even be more successful than large companies are actually the ones interpreting the data in a different way. In this study, a model which had never been encountered in the literature before, was designed through a different perspective on the same data, enabling the movements of an investment element over the 2D candlestick chart to be recognized as a ''Buy-Sell'' object respectively and to decide on the trend direction as a result. The model is trained by state-of-the-art, real-time object detection system (You Only Look Once) YOLO; for the training, one-year candlestick charts belonging to the stocks traded on Borsa İstanbul (BIST) between 2000-2018 were used. The model, which can make a ''Buy-Sell'' decision without the need for an additional time series except for the views on the visual candlestick charts, is promising in terms of its successful predictions. Its ultimate aim is to provide a foresight strengthening the ''Buy-Sell'' decisions to be made in the decision-making process following the other basic and technical analyses in addition to its stand-alone use in making investment decisions. The effect of this foresight on the success can clearly be seen on the test results received. In the results, the model was found to be successful by 85% while a 100% profit was generated. Besides, the model can be used for all the time series for which candlestick charts can be created.

Blockchain technology is a distributed data recording system developed to monitor and secure all encrypted transactions with the shortest identification. Blockchain technology made a name for itself in 2008. However, the real development of this technology has been realized with use of smart contracts in Blockchain technology. In the cryptocurrency world, a smart contract can be defined as an application or program running on the Blockchain. They operate as digital deals that have to comply with certain rules. These rules are predetermined by computer codes and then copied and implemented by the servers in the entire network. The use of the word "smart" as a term in smart contracts comes from the fact that it is not made manually and is realized digitally. In this study, it is aimed to contribute to future studies in the transformation of smart contracts into real "smart" contract structures by applying artificial intelligence algorithms.

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Summary Despite the close of a tumultuous 2020 and the start of 2021, connected devices will continue to shape the future of numerous industries, and businesses are confident that the Internet of Things (IoT) will play a key role in the future success of their trade. The growing Internet of Things (IoT) is connecting devices to a variety of sensors, applications, and other IoT elements to automate business processes and support human efficiencies in business and the home. WSN along with node localization algorithms can play a critical role in IoT applications. Nevertheless, in IoT applications, the context of real‐time location‐based services is gaining an overwhelming interest. To do this, several approaches are proposed in the recent literature based mainly on computational intelligence algorithms. This paper proposes a node localization algorithm based on swarm intelligence algorithms, that is, a hybrid Harris Hawks optimization based on differential evolution (HHODE).HHODE algorithm relies on Euclidian Distance as objective function to evaluate best‐fit coordinates of sensor nodes in a wireless sensor network. Moreover, several experimentations are performed depending on the network size, communication range of sensors, geographical distribution, and the beacon nodes' density to demonstrate the efficiency of the HHODE algorithm. Compared to Standard DE, HOO, PSO, and Bat Algorithm, HHODE shows higher performance with regard to node localization.