Daniel T. Várkonyi

Daniel T. Várkonyi
Eötvös Loránd University · Department of Informatics

About

6
Publications
275
Reads
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4
Citations
Citations since 2017
5 Research Items
3 Citations
20172018201920202021202220230.00.51.01.52.02.53.0
20172018201920202021202220230.00.51.01.52.02.53.0
20172018201920202021202220230.00.51.01.52.02.53.0
20172018201920202021202220230.00.51.01.52.02.53.0

Publications

Publications (6)
Preprint
Full-text available
Time-Series Forecasting is a powerful data modeling discipline that analyzes historical observations to predict future values of a time-series. It has been utilized in numerous applications, including but not limited to economics, meteorology, and health. In this paper, we use time-series forecasting techniques to model and predict the future incid...
Preprint
Full-text available
Time-Series Forecasting is a powerful data modeling discipline that analyzes historical observations to predict future values of a time-series. It has been utilized in numerous applications, including but not limited to economics, meteorology, and health. In this paper, we use time-series forecasting techniques to model and predict the future incid...
Article
Honeybees are the most specialized insect pollinators and are critical not only for honey production but, also, for keeping the environmental balance by pollinating the flowers of a wide variety of crops. Recording and analyzing bee sounds became a fundamental part of recent initiatives in the development of so-called smart hives. The majority of r...
Article
Full-text available
Analysis of audio signals is widely used and very effective technique in several domains like health-care, transportation, and agriculture. In a general process the output of the feature extraction method results in huge number of relevant features which may be difficult to process. The number of features heavily correlates with the complexity of t...
Conference Paper
Full-text available
Extreme learning machine (ELM) is a special single-hidden layer feed-forward neural network (SLFN), with only one hidden layer and randomly chosen weights between the input layer and the hidden layer. The advantage of ELM is that only the weights between hidden layer and output layer need to be trained, therefore, the computational costs are much l...
Conference Paper
Anytime signal processing algorithms are to improve the overall performance of larger scale embedded digital signal processing (DSP) systems. In such systems there are cases where due to abrupt changes within the environment and/or the processing system temporal shortage of computational power and/or loss of some data may occur. It is an obvious re...

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