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Fundamentals of neural networks

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The article was published on 1993-01-01 and is currently open access. It has received 1921 citations till now. The article focuses on the topics: Time delay neural network & Physical neural network.

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Proceedings ArticleDOI

Bitcoin price forecast via blockchain technology and artificial intelligence algorithms

TL;DR: Artificial intelligence algorithms were exploited, and based on last three years values of bitcoin forecasting was performed, and Bitcoin which has a huge market share in since nine years was performed.
Journal ArticleDOI

Integration of morphological analysis theory and artificial neural network approach for sustainable product design: a case study of portable vacuum cleaner

TL;DR: In this paper, an integrated morphological analysis theory and artificial neural network approach is presented for producing products in sustainable manner that caters to environment, economic and social aspects, as a result from the case study, the most sustainable of new portable vacuum cleaner models can be systematically selected.
Journal ArticleDOI

Visualisation of Multidimensional Objects and the Socio-Economical Impact to Activity in EC RTD Databases

TL;DR: The integration of non-classical methods (the self-organising map and graphs of domination) with classical ones (regress analysis and Sammon' mapping) increases the capacity of visual analysis and allows making more complete conclusions.
Proceedings ArticleDOI

Reducing the risk of heat stress using artificial neural networks based job-combination approach

TL;DR: In this paper, the authors designed and implemented a system to reduce the risk of heat stress, a recognized occupational health hazard (OHH), in two labor intensive industries using a job-combination approach.

Using Case-Based Reasoning for Spam Filtering

TL;DR: This thesis presents Email Classification Using Examples (ECUE) a content based approach to spam filtering that can handle the concept drift inherent in spam email and compares the case-based approach to an ensemble approach which is a more standard technique for handling concept drift.