A web‐based tool for teaching neural network concepts

Uğur, Aybars; Kinaci, Ahmet Cumhur

doi:10.1002/cae.20184

Cited by 18 publications

(15 citation statements)

References 6 publications

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“…Here, inputs are multiplied with the weight values, and sum of them is used by a transfer function. The output by the transfer function is output of the neuron [69,[71][72][73]. Thanks to such connections more and more along artificial neurons, bigger models of ANN can be obtained [69,73].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Time Series Prediction With a Hybrid System Formed by Artificial Neural Network and Cognitive Development Optimization Algorithm

Köse

Arslan

2018

Scientia Iranica

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Time series prediction is a remarkable research interest, which is widely followed by scientists / researchers. Because many fields include analyzing processes over such time series, different kinds of approaches, methods, and techniques are often employed in order to achieve alternative prediction ways. It seems that Artificial Intelligence oriented solutions have strong potential on providing effective and accurate prediction approaches in even most complicated time series structures. In the sense of the explanations, this study aims to technique introduced by the authors. Here, it has been aimed to predict different kinds of time series, by using the introduced system / approach. In this way it has been possible to discuss about application potential of the hybrid system and report findings related to its success on prediction. The authors believe that the study has been a good chance to support the literature with an alternative solution approach and see potential of a newly developed, Artificial Intelligence oriented optimization algorithm on different applications.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Time Series Prediction With a Hybrid System Formed by Artificial Neural Network and Cognitive Development Optimization Algorithm

Köse

Arslan

2018

Scientia Iranica

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“…At this point, inputs of a typical artificial neuron are multiplied by connection weights, and summation of these products is then fed through a chosen transfer function. The result obtained from the transfer function is the output of the artificial neuron . By forming connections between artificial neurons, ANNs, which have specific number of inputs and output(s) to ensure needed mathematical calculation structures, is then obtained.…”

Section: Foundationsmentioning

confidence: 99%

Optimization of self‐learning in Computer Engineering courses: An intelligent software system supported by Artificial Neural Network and Vortex Optimization Algorithm

Köse

Arslan

2017

Comp Applic In Engineering

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Self-learning process is an important factor that enables learners to improve their own educational experiences when they are away of face-to-face interactions with the teacher. A well-designed selflearning activity process supports both learners and teachers to achieve educational objectives rapidly. Because of this, there has always been a remarkable trend on developing alternative self-learning approaches. In this context, this study is based on two essential objectives. Firstly, it aims to introduce an intelligent software system, which optimizes and improves computer engineering students' self-learning processes. Secondly, it aims to improve computer engineering students' self-learning during the courses. As general, the software system introduced here evaluates students' intelligence levels according to the Theory of Multiple Intelligences and supports their learning via accurately chosen materials provided over the software interface. The evaluation mechanism of the system is based on a hybrid Artificial Intelligence approach formed by an Artificial Neural Network, and an optimization algorithm called as Vortex Optimization Algorithm (VOA). The system is usable for especially technical courses taught at computer engineering departments of universities and makes it easier to teach abstract subjects. For having idea about success of the system, it has been tested with students and positive results on optimizing and improving self-learning have been obtained. Additionally, also a technical evaluation has been done previously, in order to see if the VOA is a good choice to be used in the system. It can be said that the whole obtained results encourage the authors to continue to future works. ß 2017 Wiley Periodicals, Inc. Comput Appl Eng Educ 25:142-156, 2017; View this article online at wileyonlinelibrary.com/journal/cae;

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“…The commercial tools generally address a widespread area rather than a specific area, so extra efforts are needed to apply to a specific area. [5][6][7][8][9][10][11][12][13]. An educational tool that demonstrates useful ANNs' algorithms and structures, such as Incremental Back Propagation, Incremental Back Propagation with momentum, Back Propagation, and Back Propagation with momentum, have been developed by Bayindir and et al [14].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Tool for ANN Based Power Factor Correction

Sesveren¹,

Bayındır

2013

2013 12th International Conference on Machine Learning and Applications

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This paper describes a case study of a power factor correction technique based on the Artificial Neural Network (ANN). In order to accelerate the training process of ANNs, four learning algorithm, Incremental Back Propagation (IBP), Batch Back Propagation (BBP), Resilient Back Propagation (RBP), and Quick Back Propagation (QBP), were modeled and software that has a graphic user interface was developed. Using the developed software, the training actions of ANNs can be performed according to the inputs. Results show that the developed software can be used as a visual educational tool for training power factor correction using a synchronous motor.

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A web‐based tool for teaching neural network concepts

Cited by 18 publications

References 6 publications

Time Series Prediction With a Hybrid System Formed by Artificial Neural Network and Cognitive Development Optimization Algorithm

Time Series Prediction With a Hybrid System Formed by Artificial Neural Network and Cognitive Development Optimization Algorithm

Optimization of self‐learning in Computer Engineering courses: An intelligent software system supported by Artificial Neural Network and Vortex Optimization Algorithm

An Intelligent Tool for ANN Based Power Factor Correction

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