H. Tolga Kahraman

A novel model for web-based adaptive educational hypermedia systems: SAHM (supervised adaptive hypermedia model)

Existing adaptive educational hypermedia systems focus on preparing adaptive educational environments for students and educators meeting their needs. In an educational adaptive hypermedia (AH) application, important problems faced by application developers (educators) are to create and update the domain model accurately. This study presents a new reference model and its concept for web‐based adaptive educational hypermedia called SAHM (supervised adaptive hypermedia model). The new model redefines the storage layer of existing AH reference models and helps to solve the problems encountered in AH applications. With the help of this proposed model, application developers might develop a domain model of AH applications easily, effectively, and successfully. It is expected that the new SAHM would provide a new direction in designing new and more adaptive systems and new applications.

Symbiotic organisms search optimization algorithm for economic/ emission dispatch problem in power systems

This paper presents symbiotic organisms search (SOS) algorithm to solve economic emission load dispatch (EELD) problem for thermal generators in power systems. The basic objective of the EELD is to minimize both minimum operating costs and emission levels, while satisfying the load demand and all equality–inequality constraints. In other research direction, this multi-objective problem is converted into single-objective function by using price penalty factor approach in order to solve it with SOS. The proposed algorithm has been implemented on various test cases, with different constraints and various cost curve nature. In order to see the effectiveness of the proposed algorithm, its results are compared to those reported in the recent literature. The results of the algorithms indicate that SOS gives good results in both systems and very competitive with the state of the art for the solution of the EELD problems.

Symbiotic organisms search algorithm for dynamic economic dispatch with valve-point effects

Abstract In this study, symbiotic organisms search (SOS) algorithm is proposed to solve the dynamic economic dispatch with valve-point effects problem, which is one of the most important problems of the modern power system. Some practical constraints like valve-point effects, ramp rate limits and prohibited operating zones have been considered as solutions. Proposed algorithm was tested on five different test cases in 5 units, 10 units and 13 units systems. The obtained results have been compared with other well-known metaheuristic methods reported before. Results show that proposed algorithm has a good convergence and produces better results than other methods.

Design of an Intelligent Decision Making System for a Travelling Wave Ultrasonic Motor

In this study, an intelligent decision making system which determines the compatibility of operating parameters has been designed for the travelling wave ultrasonic motor (TWUSM). The system designed converts input parameters and operating temperature into useful data by rule-based inference mechanism and these data are evaluated in Naïve Bayes Classifier. The proposed decision making system gives effective results in the compatibility determination of operating parameters for speed stability of the TWUSM.

An intelligent approach for speed stability analysis of a travelling wave ultrasonic motor based on genetic k-NN algorithm

Driving voltage, driving frequency, phase difference and operating temperature are the parameters which affect the speed stability of a travelling wave ultrasonic motor (TWUSM). The weight coefficients of these parameters should be determined for the purpose of ensuring the speed stability of a TWUSM with a maximum level under different operating conditions. In this paper, a novel approach is proposed for the speed stability analysis of the TWUSM using genetic k-nearest neighbor algorithm (k-NN) and the speed stability classes of new test observations are achieved accurately. Furthermore, the genetic k-NN algorithm is compared with the classic k-NN algorithm in terms of prediction accuracy using Euclidean, Manhattan and Minkowski distance metrics. As a result of experimental studies, it is shown that the TWUSM parameters weighted by the genetic k-NN algorithm increase the speed stability of the TWUSM significantly and the genetic k-NN algorithm outperforms the classic k-NN algorithm for all of distance metrics.

Novel User Modeling Approaches for Personalized Learning Environments

Modeling user knowledge and creating user profiles not only for special web-based social media but also for complex and mixed personalized learning environments are important research challenges. The key component for adaptation is the user’s knowledge model. This paper introduces fuzzy metric (FM)-based novel and efficient similarity measurement method and adaptive artificial neural network (AANN) and artificial bee colony (ABC)-based knowledge classification approaches for personalized learning environments. For this purpose, FM-based method has been developed to measure distances more efficiently among the users and their knowledge model using the web logs/session data. In addition, a novel knowledge classifier based on ABC and AANN having combined with the generic object model has been developed for user modeling strategies and user modeling server of adaptive educational electric course (AEEC). Finally, the approaches have been tested to compare the classification performance of the user modeling methods developed for user modeling task. The experimental results have shown that proposed methods have improved similarity measurements considerably and decreased the misclassifications in user modeling processes. Thus, powerful user modeling approaches have been presented to the literature. It is expected that the approaches introduced in this article can be a reference to others researches and to develop more adaptive and personalized web applications in future.

Application of Symbiotic Organisms Search Algorithm to solve various economic load dispatch problems

This paper proposes the application of Symbiotic Organisms Search (SOS) Algorithm to solve the various Economic Load Dispatch (ELD) problems. Both classical ELD problem which has smooth fuel cost function and nonconvex ELD problem which has nonconvex and discontinuous fuel cost function due to considering of some practical constraints like valve point effects, ramp rate limits and prohibited generating zones have been solved in the study. Three different test cases have been used to show the efficiency and reliability of the proposed algorithm. 38-unit test system has been used for classical ELD and 3-unit and 15-unit test systems have been used for nonconvex ELD problem. Results have been compared to various heuristic methods reported before in the literature and they show that proposed algorithm converges to the global optimum in early iterations and can produce superior results than others in the solution of ELD problems which have both smooth and nonconvex and discontinuous fuel cost function.

A comperative study on novel machine learning algorithms for estimation of energy performance of residential buildings

This study aims to improve the energy performance of residential buildings. heating load (HL) and cooling load (CL) are considered as a measure of heating ventilation and air conditioning (HVAC) system in this process. In order to achive an effective estimation, hybrid machine learning algorithms including, artificial bee colony-based k-nearest neighbor (abc-knn), genetic algorithm-based knn (ga-knn), adaptive artificial neural network with genetic algorithm (ga-ann) and adaptive ann with artificial bee colony (abc-ann) are used. Results are compared classical knn and ann methods. Thence, relations between input and target parameters are defined and performance of well-known classical knn and ann is improved substantialy.

An Intelligent Decision Support Tool for a Travelling Wave Ultrasonic Motor Based on k-Nearest Neighbor Algorithm

Driving frequency, amplitude and phase difference of two-phase sinusoidal voltages are the input parameters which have influence on speed stability of travelling wave ultrasonic motors (TWUSMs).These parameters are also time-varying due to the variations in operating temperature. In addition, a complete mathematical model of the TWUSM has not been derived yet. Owing to these reasons, a machine learning approach is required for determining the compatibility of operating parameters related to speed stability of TWUSMs. For this purpose, an intelligent decision support tool has been designed for TWUSMs in this study. The input parameters such as driving frequency, amplitude, phase difference of two-phase sinusoidal voltages and operating temperature were evaluated by the k-nearest neighbor algorithm in the decision support tool. The results have shown that the proposed tool provides effective results in the compatibility determination of operating parameters related to speed stability of TWUSMs.