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International Journal of Smart Engineering System Design is a peer-reviewed journal published by Taylor & Francis, covering Neural Networks and Applications, Fuzzy Logic and Control Systems, Fault Detection and Control Systems.
| Publisher | Taylor & Francis |
|---|---|
| Reference style | Author–year (Chicago, T&F) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." International Journal of Smart Engineering System Design 12 (3): 45–58.
Formats any DOI in International Journal of Smart Engineering System Design style. No sign-up. |
| Publishes research in | Neural Networks and Applications Fuzzy Logic and Control Systems Fault Detection and Control Systems Multi-Criteria Decision Making Face and Expression Recognition |
| ISSN | 1025-5818 |
| h-index | 13 |
| i10-index | 21 |
| Total citations | 520 |
| Top institutions publishing here | Engineering Systems (United States) |
| Journal website | www.tandfonline.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
There has been a growing interest in applying neural networks and technical analysis indicators for predicting future stock behavior. However, previous studies have not practically evaluated the predictive power of technical indicators by employing neural networks as a decision maker to uncover the underlying nonlinear pattern of these indicators. The objective of this paper is…
Individual component reliability can often be estimated from degradation signals. In this paper, we examine the utility of the wavelet transform in preprocessing degradation signals for online reliability estimation. Wavelet preprocessing facilitates examination of degradation signals in both the time- and frequency-domains, simultaneously. Neural networks are used for forecasting the degradation signals (or a transformation…
The Bayes error rate gives a statistical lower bound on the error achievable for a given classification problem and the associated choice of features. By reliably estimating this rate, one can assess the usefulness of the feature set that is being used for classification. Moreover, by comparing the accuracy achieved by a given classifier with…
Rapidly growing needs for networking in the Internet and in local Intranets make network management increasingly important in today's computer world. We propose using learning techniques to predict network congestion problems before they start impacting the performance of services. In this paper, we focus on using a simple feed-forward neural network to predict severe congestion…
A novel semi-supervised clustering algorithm is proposed that synergizes the benefits of supervised and unsupervised learning methods. Data are clustered using an unsupervised learning technique biased toward producing clusters as pure as possible in terms of class distribution. These clusters can then be used to predict the class of future points. For example, in database…
