Integer and fractional-order entropy analysis of earthquake data series

Lopes, António M.; Machado, J. A. Tenreiro

doi:10.1007/s11071-015-2231-x

Cited by 41 publications

(14 citation statements)

References 54 publications

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“…For instance, it has been advantageously used during the modeling of different diseases, such as human immunodeficiency virus (HIV) [34] and malaria [35]. In addition, FC-based analytical tools have outperformed classical techniques in geology [36,37], economics and finance [38,39], etc. Moreover, in our recent paper [20], we identified a high potential for the use of FC in the kinematic analysis of PD drawings.…”

Section: Introductionmentioning

confidence: 99%

Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting

et al. 2018

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Parkinson’s disease dysgraphia affects the majority of Parkinson’s disease (PD) patients and is the result of handwriting abnormalities mainly caused by motor dysfunctions. Several effective approaches to quantitative PD dysgraphia analysis, such as online handwriting processing, have been utilized. In this study, we aim to deeply explore the impact of advanced online handwriting parameterization based on fractional-order derivatives (FD) on the PD dysgraphia diagnosis and its monitoring. For this purpose, we used 33 PD patients and 36 healthy controls from the PaHaW (PD handwriting database). Partial correlation analysis (Spearman’s and Pearson’s) was performed to investigate the relationship between the newly designed features and patients’ clinical data. Next, the discrimination power of the FD features was evaluated by a binary classification analysis. Finally, regression models were trained to explore the new features’ ability to assess the progress and severity of PD. These results were compared to a baseline, which is based on conventional online handwriting features. In comparison with the conventional parameters, the FD handwriting features correlated more significantly with the patients’ clinical characteristics and provided a more accurate assessment of PD severity (error around 12%). On the other hand, the highest classification accuracy (ACC = 97.14%) was obtained by the conventional parameters. The results of this study suggest that utilization of FD in combination with properly selected tasks (continuous and/or repetitive, such as the Archimedean spiral) could improve computerized PD severity assessment.

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Section: Introductionmentioning

confidence: 99%

Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting

et al. 2018

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“…Figures 3-5 depict the three-dimensional maps generated by the MDS with CD, JSD and JSD α (α = 0.7). The value of α = 0.7 was chosen to obtain good discrimination between items [51]. For all cases, we observe that the points representing researchers form similar patterns, namely, those on the right-hand side of the charts.…”

Section: Comparing and Visualizing Scientific Output By Means Of Mdsmentioning

confidence: 97%

Fractional Jensen–Shannon Analysis of the Scientific Output of Researchers in Fractional Calculus

Machado

Lopes

2017

Entropy

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This paper analyses the citation profiles of researchers in fractional calculus. Different metrics are used to quantify the dissimilarities between the data, namely the Canberra distance, and the classical and the generalized (fractional) Jensen-Shannon divergence. The information is then visualized by means of multidimensional scaling and hierarchical clustering. The mathematical tools and metrics allow for direct comparison and visualization of researchers based on their relative positioning and on patterns displayed in two-or three-dimensional maps.

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“…In this case it yields the classical BoltzmannGibbs-Shannon formulation (3). The entropy S α was proven to be more sensitive to small variations in the statistical distributions of real-world phenomena [35,39]. There is a growth trend in b and S α .…”

Section: Visualization Of Global Terrorism Using Sspmentioning

confidence: 99%

Analysis of global terrorism dynamics by means of entropy and state space portrait

2016

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This paper studies the global terrorism dynamics over the period 1970-2014. Data about terrorist events are analyzed by means of several mathematical tools, namely fractal dimension, entropy, state space portrait and multidimensional scaling, that reflect the dynamics in time and space. In a first phase, we consider worldwide events and we unveil the space-time characteristics exhibited by the global terrorism statistics. In a second phase, we group the events into eight geographic regions, and we analyze terrorism dynamics in a regional perspective. Finally, in a third phase, we adopt a complementary analysis of global terrorism based on multidimensional scaling and clustering techniques. The proposed methodology reveals to support new directions for exploring terrorism data.

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Integer and fractional-order entropy analysis of earthquake data series

Cited by 41 publications

References 54 publications

Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting

Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting

Fractional Jensen–Shannon Analysis of the Scientific Output of Researchers in Fractional Calculus

Analysis of global terrorism dynamics by means of entropy and state space portrait

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