Diabetes is a serious condition that leads to high blood sugar and the prediction of this disease at an early stage is of great importance for reducing the risk of some significant diabetes complications. In this study, bagging and boosting approaches using six different decision tree-based (DTB) classifiers were implemented on experimental data for diabetes prediction. This paper also compares applied individual implementation, bagging, and boosting of DTB classifiers in terms of accuracy rates. The results indicate that the bagging and boosting approaches outperform the individual DTB classifiers, and real Adaptive Boosting (AdaBoost) and bagging using Naive Bayes Tree (NBTree) present the best accuracy score of 98.65%.
Since the harmful effects of climate warming on our planet were first observed, the use of renewable energy resources has been significantly increasing. Among the potential renewable energy sources, photovoltaic (PV) system installations keep continuously increasing world‐wide due to its economic and environmental contributions. Despite its significant benefits, the inherent variability of PV power generation due to meteorological parameters can cause power management/planning problems. Thus, forecasting of PV output data (directly or indirectly) in an accurate manner is a critical task to provide stability, reliability, and optimisation of the grid systems. In considering the literature reviewed, there are various research items utilizing PV output power forecasting. In this study, a systematic literature review based on the search of primary studies (published between 2010 and 2020), which forecast PV power generation using machine learning and deep learning methods, is reported. The studies are evaluated based on the PV material used, their approaches, generated outputs, data set used, and the performance evaluation methods. As a result, gaps and improvable points in the existing literature are revealed, and suggestions which include novelties are offered for future works.
Recently, there has been a growing interest in association rule mining (ARM) in various fields. However, standard ARM algorithms fail to discover rules for multitask problems as they do not consider task-oriented investigation and, therefore, they ignore the correlation among the tasks. Considering this situation, this paper proposes a novel algorithm, named multitask association rule miner (MTARM), that tends to jointly discover rules by considering multiple tasks. This paper also introduces two novel concepts: single-task rule and multiple-task rule. In the first phase of the proposed approach, highly frequent local rules (single-task rules) are explored for each task separately and then these local rules are combined to produce the global result (multitask rules) using a majority voting mechanism. Experiments were conducted on four different real-world multitask learning datasets. The experimental results indicated that the proposed MTARM approach discovers more information than that of traditional ARM algorithms by jointly considering the relationships among multiple tasks. This work is licensed under a Creative Commons Attribution 4.0 International License. 933 YILDIRIM TAŞER et al./Turk J Elec Eng & Comp Scisubset of them, are familiar to one mutual rule set with a small difference. The proposed approach can discover common association rules by responding to different tasks and by applying an algorithm to consider all the tasks collectively. Accordingly, the rule X → Y can have high support and confidence in a task. A rule may not be frequent in the entire dataset; however, it may be frequent in several specific tasks. Therefore, finding task-based frequent rules in an effective way is important. This paper proposes a novel algorithm, named multitask association rule miner (MTARM), that tends to discover rules by jointly considering multiple tasks. The proposed algorithm mines datasets that include task information. The MTARM approach consists of two phases. In the first phase, the algorithm discovers local frequent rules (single-task rules) from the data of each task separately, and in the second phase, it combines these local rules to produce the global result (multitask rules). The discovered multitask rules can be used to deploy information systems that support the execution of their associated task or to improve decision-making in applications.The main contributions and novelty of this study are threefold: (i) this is the first study that applies ARM to multitask problems; (ii) it proposes the MTARM approach, which attempts to find intertask rules based solely on the task identities and the observed data for each task; and, finally (iii) it also introduces two novel concepts: single-task rule and multitask rule. This paper also provides a brief survey of ARM as it is an active research area of data mining. In addition, it presents the results of experimental studies conducted on four different real-world datasets to demonstrate the capability of the proposed algorithm.The rest of the paper is organized...
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