Displaying publications 1 - 20 of 56 in total

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  1. Dehdasht G, Ferwati MS, Zin RM, Abidin NZ
    PLoS One, 2020;15(2):e0228746.
    PMID: 32023306 DOI: 10.1371/journal.pone.0228746
    Successful implementation of the lean concept as a sustainable approach in the construction industry requires the identification of critical drivers in lean construction. Despite this significance, the number of in-depth studies toward understanding the considerable drivers of lean construction implementation is quite limited. There is also a shortage of methodologies for identifying key drivers. To address these challenges, this paper presents a list of all essential drivers within three aspects of sustainability (social, economic, and environmental) and proposes a novel methodology to rank the drivers and identify the key drivers for successful and sustainable lean construction implementation. In this regard, the entropy weighted Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was employed in this research. Subsequently, an empirical study was conducted within the Malaysian construction industry to demonstrate the proposed method. Moreover, sensitivity analysis and comparison with the existing method were engaged to validate the stability and accuracy of the achieved results. The significant results obtained in this study are as follows: presenting, verifying and ranking of 63 important drivers; identifying 22 key drivers; proposing an MCDM model of key drivers. The outcomes show that the proposed method in this study is an effective and accurate tool that could help managers make better decisions.
    Matched MeSH terms: Entropy*
  2. Abdul Razak F, Jensen HJ
    PLoS One, 2014;9(6):e99462.
    PMID: 24955766 DOI: 10.1371/journal.pone.0099462
    'Causal' direction is of great importance when dealing with complex systems. Often big volumes of data in the form of time series are available and it is important to develop methods that can inform about possible causal connections between the different observables. Here we investigate the ability of the Transfer Entropy measure to identify causal relations embedded in emergent coherent correlations. We do this by firstly applying Transfer Entropy to an amended Ising model. In addition we use a simple Random Transition model to test the reliability of Transfer Entropy as a measure of 'causal' direction in the presence of stochastic fluctuations. In particular we systematically study the effect of the finite size of data sets.
    Matched MeSH terms: Entropy*
  3. Ibrahim RW, Darus M
    Entropy (Basel), 2018 Sep 20;20(10).
    PMID: 33265810 DOI: 10.3390/e20100722
    In this paper, we study Tsallis' fractional entropy (TFE) in a complex domain by applying the definition of the complex probability functions. We study the upper and lower bounds of TFE based on some special functions. Moreover, applications in complex neural networks (CNNs) are illustrated to recognize the accuracy of CNNs.
    Matched MeSH terms: Entropy
  4. Al-Shamasneh AR, Jalab HA, Palaiahnakote S, Obaidellah UH, Ibrahim RW, El-Melegy MT
    Entropy (Basel), 2018 May 05;20(5).
    PMID: 33265434 DOI: 10.3390/e20050344
    Kidney image enhancement is challenging due to the unpredictable quality of MRI images, as well as the nature of kidney diseases. The focus of this work is on kidney images enhancement by proposing a new Local Fractional Entropy (LFE)-based model. The proposed model estimates the probability of pixels that represent edges based on the entropy of the neighboring pixels, which results in local fractional entropy. When there is a small change in the intensity values (indicating the presence of edge in the image), the local fractional entropy gives fine image details. Similarly, when no change in intensity values is present (indicating smooth texture), the LFE does not provide fine details, based on the fact that there is no edge information. Tests were conducted on a large dataset of different, poor-quality kidney images to show that the proposed model is useful and effective. A comparative study with the classical methods, coupled with the latest enhancement methods, shows that the proposed model outperforms the existing methods.
    Matched MeSH terms: Entropy
  5. Kapitaniak T, Mohammadi SA, Mekhilef S, Alsaadi FE, Hayat T, Pham VT
    Entropy (Basel), 2018 Sep 05;20(9).
    PMID: 33265759 DOI: 10.3390/e20090670
    In this paper, we introduce a new, three-dimensional chaotic system with one stable equilibrium. This system is a multistable dynamic system in which the strange attractor is hidden. We investigate its dynamic properties through equilibrium analysis, a bifurcation diagram and Lyapunov exponents. Such multistable systems are important in engineering. We perform an entropy analysis, parameter estimation and circuit design using this new system to show its feasibility and ability to be used in engineering applications.
    Matched MeSH terms: Entropy
  6. Jaffari R, Hashmani MA, Reyes-Aldasoro CC
    Sensors (Basel), 2021 Apr 16;21(8).
    PMID: 33923472 DOI: 10.3390/s21082803
    The segmentation of power lines (PLs) from aerial images is a crucial task for the safe navigation of unmanned aerial vehicles (UAVs) operating at low altitudes. Despite the advances in deep learning-based approaches for PL segmentation, these models are still vulnerable to the class imbalance present in the data. The PLs occupy only a minimal portion (1-5%) of the aerial images as compared to the background region (95-99%). Generally, this class imbalance problem is addressed via the use of PL-specific detectors in conjunction with the popular class balanced cross entropy (BBCE) loss function. However, these PL-specific detectors do not work outside their application areas and a BBCE loss requires hyperparameter tuning for class-wise weights, which is not trivial. Moreover, the BBCE loss results in low dice scores and precision values and thus, fails to achieve an optimal trade-off between dice scores, model accuracy, and precision-recall values. In this work, we propose a generalized focal loss function based on the Matthews correlation coefficient (MCC) or the Phi coefficient to address the class imbalance problem in PL segmentation while utilizing a generic deep segmentation architecture. We evaluate our loss function by improving the vanilla U-Net model with an additional convolutional auxiliary classifier head (ACU-Net) for better learning and faster model convergence. The evaluation of two PL datasets, namely the Mendeley Power Line Dataset and the Power Line Dataset of Urban Scenes (PLDU), where PLs occupy around 1% and 2% of the aerial images area, respectively, reveal that our proposed loss function outperforms the popular BBCE loss by 16% in PL dice scores on both the datasets, 19% in precision and false detection rate (FDR) values for the Mendeley PL dataset and 15% in precision and FDR values for the PLDU with a minor degradation in the accuracy and recall values. Moreover, our proposed ACU-Net outperforms the baseline vanilla U-Net for the characteristic evaluation parameters in the range of 1-10% for both the PL datasets. Thus, our proposed loss function with ACU-Net achieves an optimal trade-off for the characteristic evaluation parameters without any bells and whistles. Our code is available at Github.
    Matched MeSH terms: Entropy
  7. Soundirarajan M, Pakniyat N, Sim S, Nathan V, Namazi H
    Technol Health Care, 2021;29(1):99-109.
    PMID: 32568131 DOI: 10.3233/THC-192085
    BACKGROUND: Human facial muscles react differently to different visual stimuli. It is known that the human brain controls and regulates the activity of the muscles.

    OBJECTIVE: In this research, for the first time, we investigate how facial muscle reaction is related to the reaction of the human brain.

    METHODS: Since both electromyography (EMG) and electroencephalography (EEG) signals, as the features of muscle and brain activities, contain information, we benefited from the information theory and computed the Shannon entropy of EMG and EEG signals when subjects were exposed to different static visual stimuli with different Shannon entropies (information content).

    RESULTS: Based on the obtained results, the variations of the information content of the EMG signal are related to the variations of the information content of the EEG signal and the visual stimuli. Statistical analysis also supported the results indicating that the visual stimuli with greater information content have a greater effect on the variation of the information content of both EEG and EMG signals.

    CONCLUSION: This investigation can be further continued to analyze the relationship between facial muscle and brain reactions in case of other types of stimuli.

    Matched MeSH terms: Entropy
  8. Uddin J, Ghazali R, Deris MM
    PLoS One, 2017;12(1):e0164803.
    PMID: 28068344 DOI: 10.1371/journal.pone.0164803
    Clustering a set of objects into homogeneous groups is a fundamental operation in data mining. Recently, many attentions have been put on categorical data clustering, where data objects are made up of non-numerical attributes. For categorical data clustering the rough set based approaches such as Maximum Dependency Attribute (MDA) and Maximum Significance Attribute (MSA) has outperformed their predecessor approaches like Bi-Clustering (BC), Total Roughness (TR) and Min-Min Roughness(MMR). This paper presents the limitations and issues of MDA and MSA techniques on special type of data sets where both techniques fails to select or faces difficulty in selecting their best clustering attribute. Therefore, this analysis motivates the need to come up with better and more generalize rough set theory approach that can cope the issues with MDA and MSA. Hence, an alternative technique named Maximum Indiscernible Attribute (MIA) for clustering categorical data using rough set indiscernible relations is proposed. The novelty of the proposed approach is that, unlike other rough set theory techniques, it uses the domain knowledge of the data set. It is based on the concept of indiscernibility relation combined with a number of clusters. To show the significance of proposed approach, the effect of number of clusters on rough accuracy, purity and entropy are described in the form of propositions. Moreover, ten different data sets from previously utilized research cases and UCI repository are used for experiments. The results produced in tabular and graphical forms shows that the proposed MIA technique provides better performance in selecting the clustering attribute in terms of purity, entropy, iterations, time, accuracy and rough accuracy.
    Matched MeSH terms: Entropy
  9. Sharma M, Tan RS, Acharya UR
    Comput Biol Med, 2018 11 01;102:341-356.
    PMID: 30049414 DOI: 10.1016/j.compbiomed.2018.07.005
    Myocardial infarction (MI), also referred to as heart attack, occurs when there is an interruption of blood flow to parts of the heart, due to the acute rupture of atherosclerotic plaque, which leads to damage of heart muscle. The heart muscle damage produces changes in the recorded surface electrocardiogram (ECG). The identification of MI by visual inspection of the ECG requires expert interpretation, and is difficult as the ECG signal changes associated with MI can be short in duration and low in magnitude. Hence, errors in diagnosis can lead to delay the initiation of appropriate medical treatment. To lessen the burden on doctors, an automated ECG based system can be installed in hospitals to help identify MI changes on ECG. In the proposed study, we develop a single-channel single lead ECG based MI diagnostic system validated using noisy and clean datasets. The raw ECG signals are taken from the Physikalisch-Technische Bundesanstalt database. We design a novel two-band optimal biorthogonal filter bank (FB) for analysis of the ECG signals. We present a method to design a novel class of two-band optimal biorthogonal FB in which not only the product filter but the analysis lowpass filter is also a halfband filter. The filter design problem has been composed as a constrained convex optimization problem in which the objective function is a convex combination of multiple quadratic functions and the regularity and perfect reconstruction conditions are imposed in the form linear equalities. ECG signals are decomposed into six subbands (SBs) using the newly designed wavelet FB. Following to this, discriminating features namely, fuzzy entropy (FE), signal-fractal-dimensions (SFD), and renyi entropy (RE) are computed from all the six SBs. The features are fed to the k-nearest neighbor (KNN). The proposed system yields an accuracy of 99.62% for the noisy dataset and an accuracy of 99.74% for the clean dataset, using 10-fold cross validation (CV) technique. Our MI identification system is robust and highly accurate. It can thus be installed in clinics for detecting MI.
    Matched MeSH terms: Entropy
  10. Abdul Aziz Jemain
    This paper offers a technique to create a development index among districts. To determine the weight for each criterion in the index entropy theory was used. Two approaches for criteria normalization were also suggested. The data obtained from 1991 census conducted in Peninsular Malaysia were utilized as illustration.
    Kertas ini mencadangkan teknik pembinaan indeks kemajuan daerah. Teori entropy digunakan untuk menentukan pemberat bagi kriteria yang digunakan dalam pembinaan indeks. Dua pendekatan menormalkan data turut dicadangkan. Contoh pembinaan indeks dikemukakan berdasarkan kemudahan asas yang terdapat di daerah-daerah di Semenanjung Malaysia seperti yang diperoleh berdasarkan banci tahun 1991.
    Matched MeSH terms: Entropy
  11. Khan NS, Shah Q, Bhaumik A, Kumam P, Thounthong P, Amiri I
    Sci Rep, 2020 03 10;10(1):4448.
    PMID: 32157121 DOI: 10.1038/s41598-020-61172-2
    Buongiorno's nanofluid model is followed to study the bioconvection in two stretchable rotating disks with entropy generation. Similarity transformations are used to handle the problem equations for non-dimensionality. For the simulation of the modeled equations, Homotopy Analysis Method is applied. The biothermal system is explored for all the embedded parameters whose effects are shown through different graphs. There exists interesting results due to the effects of different parameters on different profiles. Radial velocity decreases with increasing stretching and magnetic field parameters. Temperature increases with Brownian motion and thermophoresis parameters. Nanoparticles concentration decreases on increasing Lewis number and thermophoresis parameter while motile gyrotactic microorganisms profile increases with increasing Lewis and Peclet numbers. Convergence of the solution is found and good agreement is obtained when the results are compared with published work.
    Matched MeSH terms: Entropy
  12. Ali BH, Sulaiman N, Al-Haddad SAR, Atan R, Hassan SLM, Alghrairi M
    Sensors (Basel), 2021 Sep 27;21(19).
    PMID: 34640773 DOI: 10.3390/s21196453
    One of the most dangerous kinds of attacks affecting computers is a distributed denial of services (DDoS) attack. The main goal of this attack is to bring the targeted machine down and make their services unavailable to legal users. This can be accomplished mainly by directing many machines to send a very large number of packets toward the specified machine to consume its resources and stop it from working. We implemented a method using Java based on entropy and sequential probabilities ratio test (ESPRT) methods to identify malicious flows and their switch interfaces that aid them in passing through. Entropy (E) is the first technique, and the sequential probabilities ratio test (SPRT) is the second technique. The entropy method alone compares its results with a certain threshold in order to make a decision. The accuracy and F-scores for entropy results thus changed when the threshold values changed. Using both entropy and SPRT removed the uncertainty associated with the entropy threshold. The false positive rate was also reduced when combining both techniques. Entropy-based detection methods divide incoming traffic into groups of traffic that have the same size. The size of these groups is determined by a parameter called window size. The Defense Advanced Research Projects Agency (DARPA) 1998, DARPA2000, and Canadian Institute for Cybersecurity (CIC-DDoS2019) databases were used to evaluate the implementation of this method. The metric of a confusion matrix was used to compare the ESPRT results with the results of other methods. The accuracy and f-scores for the DARPA 1998 dataset were 0.995 and 0.997, respectively, for the ESPRT method when the window size was set at 50 and 75 packets. The detection rate of ESPRT for the same dataset was 0.995 when the window size was set to 10 packets. The average accuracy for the DARPA 2000 dataset for ESPRT was 0.905, and the detection rate was 0.929. Finally, ESPRT was scalable to a multiple domain topology application.
    Matched MeSH terms: Entropy
  13. Kamal SM, Dawi NM, Namazi H
    Technol Health Care, 2021;29(6):1109-1118.
    PMID: 33749623 DOI: 10.3233/THC-202744
    BACKGROUND: Walking like many other actions of a human is controlled by the brain through the nervous system. In fact, if a problem occurs in our brain, we cannot walk correctly. Therefore, the analysis of the coupling of brain activity and walking is very important especially in rehabilitation science. The complexity of movement paths is one of the factors that affect human walking. For instance, if we walk on a path that is more complex, our brain activity increases to adjust our movements.

    OBJECTIVE: This study for the first time analyzed the coupling of walking paths and brain reaction from the information point of view.

    METHODS: We analyzed the Shannon entropy for electroencephalography (EEG) signals versus the walking paths in order to relate their information contents.

    RESULTS: According to the results, walking on a path that contains more information causes more information in EEG signals. A strong correlation (p= 0.9999) was observed between the information contents of EEG signals and walking paths. Our method of analysis can also be used to investigate the relation among other physiological signals of a human and walking paths, which has great benefits in rehabilitation science.

    Matched MeSH terms: Entropy
  14. Shi Y, Huang L, Soh AK, Weng GJ, Liu S, Redfern SAT
    Sci Rep, 2017 09 11;7(1):11111.
    PMID: 28894256 DOI: 10.1038/s41598-017-11633-y
    Electrocaloric (EC) materials show promise in eco-friendly solid-state refrigeration and integrable on-chip thermal management. While direct measurement of EC thin-films still remains challenging, a generic theoretical framework for quantifying the cooling properties of rich EC materials including normal-, relaxor-, organic- and anti-ferroelectrics is imperative for exploiting new flexible and room-temperature cooling alternatives. Here, we present a versatile theory that combines Master equation with Maxwell relations and analytically relates the macroscopic cooling responses in EC materials with the intrinsic diffuseness of phase transitions and correlation characteristics. Under increased electric fields, both EC entropy and adiabatic temperature changes increase quadratically initially, followed by further linear growth and eventual gradual saturation. The upper bound of entropy change (∆Smax) is limited by distinct correlation volumes (V cr ) and transition diffuseness. The linearity between V cr and the transition diffuseness is emphasized, while ∆Smax = 300 kJ/(K.m3) is obtained for Pb0.8Ba0.2ZrO3. The ∆Smax in antiferroelectric Pb0.95Zr0.05TiO3, Pb0.8Ba0.2ZrO3 and polymeric ferroelectrics scales proportionally with V cr-2.2, owing to the one-dimensional structural constraint on lattice-scale depolarization dynamics; whereas ∆Smax in relaxor and normal ferroelectrics scales as ∆Smax ~ V cr-0.37, which tallies with a dipolar interaction exponent of 2/3 in EC materials and the well-proven fractional dimensionality of 2.5 for ferroelectric domain walls.
    Matched MeSH terms: Entropy
  15. Wang S, Khan SA, Munir M, Alhajj R, Khan YA
    PLoS One, 2022;17(12):e0278236.
    PMID: 36548250 DOI: 10.1371/journal.pone.0278236
    Entropy is an alternative measure to calculate the risk, simplify the portfolios and equity risk premium. It has higher explanatory power than capital asset price model (CAPM) beta. The comparison of Entropy and CAPM beta provide in depth analysis about the explanatory power of the model that in turn help investor to make right investment decisions that minimizes risk. In this context, this study aims to compare Shannon and Rennyi Entropies with the CAPM beta for measuring the risk. Ordinary Least square approach has been utilized using a dataset of 67 enterprises registered in Pakistan Stock exchange. The comparative analysis of CAPM beta and entropy has been carried out with the R2 parameters. The result indicates that entropy has more explanatory power as compare to CAPM beta's explanatory power, and this turns out to be the best option to evaluate the risk performances. The result implies that an investor should make the best investment decision by choosing an enterprise that provide with good returns at minimum risk based on entropy technique.
    Matched MeSH terms: Entropy
  16. Hui TX, Kasim S, Aziz IA, Fudzee MFM, Haron NS, Sutikno T, et al.
    BMC Bioinformatics, 2024 Jan 12;25(1):23.
    PMID: 38216898 DOI: 10.1186/s12859-024-05632-w
    BACKGROUND: With the exponential growth of high-throughput technologies, multiple pathway analysis methods have been proposed to estimate pathway activities from gene expression profiles. These pathway activity inference methods can be divided into two main categories: non-Topology-Based (non-TB) and Pathway Topology-Based (PTB) methods. Although some review and survey articles discussed the topic from different aspects, there is a lack of systematic assessment and comparisons on the robustness of these approaches.

    RESULTS: Thus, this study presents comprehensive robustness evaluations of seven widely used pathway activity inference methods using six cancer datasets based on two assessments. The first assessment seeks to investigate the robustness of pathway activity in pathway activity inference methods, while the second assessment aims to assess the robustness of risk-active pathways and genes predicted by these methods. The mean reproducibility power and total number of identified informative pathways and genes were evaluated. Based on the first assessment, the mean reproducibility power of pathway activity inference methods generally decreased as the number of pathway selections increased. Entropy-based Directed Random Walk (e-DRW) distinctly outperformed other methods in exhibiting the greatest reproducibility power across all cancer datasets. On the other hand, the second assessment shows that no methods provide satisfactory results across datasets.

    CONCLUSION: However, PTB methods generally appear to perform better in producing greater reproducibility power and identifying potential cancer markers compared to non-TB methods.

    Matched MeSH terms: Entropy
  17. Zhang K, Ting HN, Choo YM
    Comput Methods Programs Biomed, 2024 Mar;245:108043.
    PMID: 38306944 DOI: 10.1016/j.cmpb.2024.108043
    BACKGROUND AND OBJECTIVE: Conflict may happen when more than one classifier is used to perform prediction or classification. The recognition model error leads to conflicting evidence. These conflicts can cause decision errors in a baby cry recognition and further decrease its recognition accuracy. Thus, the objective of this study is to propose a method that can effectively minimize the conflict among deep learning models and improve the accuracy of baby cry recognition.

    METHODS: An improved Dempster-Shafer evidence theory (DST) based on Wasserstein distance and Deng entropy was proposed to reduce the conflicts among the results by combining the credibility degree between evidence and the uncertainty degree of evidence. To validate the effectiveness of the proposed method, examples were analyzed, and applied in a baby cry recognition. The Whale optimization algorithm-Variational mode decomposition (WOA-VMD) was used to optimally decompose the baby cry signals. The deep features of decomposed components were extracted using the VGG16 model. Long Short-Term Memory (LSTM) models were used to classify baby cry signals. An improved DST decision method was used to obtain the decision fusion.

    RESULTS: The proposed fusion method achieves an accuracy of 90.15% in classifying three types of baby cry. Improvement between 2.90% and 4.98% was obtained over the existing DST fusion methods. Recognition accuracy was improved by between 5.79% and 11.53% when compared to the latest methods used in baby cry recognition.

    CONCLUSION: The proposed method optimally decomposes baby cry signal, effectively reduces the conflict among the results of deep learning models and improves the accuracy of baby cry recognition.

    Matched MeSH terms: Entropy
  18. Goodarzi M, Safaei MR, Oztop HF, Karimipour A, Sadeghinezhad E, Dahari M, et al.
    ScientificWorldJournal, 2014;2014:761745.
    PMID: 24778601 DOI: 10.1155/2014/761745
    The effect of radiation on laminar and turbulent mixed convection heat transfer of a semitransparent medium in a square enclosure was studied numerically using the Finite Volume Method. A structured mesh and the SIMPLE algorithm were utilized to model the governing equations. Turbulence and radiation were modeled with the RNG k-ε model and Discrete Ordinates (DO) model, respectively. For Richardson numbers ranging from 0.1 to 10, simulations were performed for Rayleigh numbers in laminar flow (10⁴) and turbulent flow (10⁸). The model predictions were validated against previous numerical studies and good agreement was observed. The simulated results indicate that for laminar and turbulent motion states, computing the radiation heat transfer significantly enhanced the Nusselt number (Nu) as well as the heat transfer coefficient. Higher Richardson numbers did not noticeably affect the average Nusselt number and corresponding heat transfer rate. Besides, as expected, the heat transfer rate for the turbulent flow regime surpassed that in the laminar regime. The simulations additionally demonstrated that for a constant Richardson number, computing the radiation heat transfer majorly affected the heat transfer structure in the enclosure; however, its impact on the fluid flow structure was negligible.
    Matched MeSH terms: Entropy*
  19. Ravanfar SA, Razak HA, Ismail Z, Monajemi H
    Sensors (Basel), 2015;15(9):22750-75.
    PMID: 26371005 DOI: 10.3390/s150922750
    This paper reports on a two-step approach for optimally determining the location and severity of damage in beam structures under flexural vibration. The first step focuses on damage location detection. This is done by defining the damage index called relative wavelet packet entropy (RWPE). The damage severities of the model in terms of loss of stiffness are assessed in the second step using the inverse solution of equations of motion of a structural system in the wavelet domain. For this purpose, the connection coefficient of the scaling function to convert the equations of motion in the time domain into the wavelet domain is applied. Subsequently, the dominant components based on the relative energies of the wavelet packet transform (WPT) components of the acceleration responses are defined. To obtain the best estimation of the stiffness parameters of the model, the least squares error minimization is used iteratively over the dominant components. Then, the severity of the damage is evaluated by comparing the stiffness parameters of the identified model before and after the occurrence of damage. The numerical and experimental results demonstrate that the proposed method is robust and effective for the determination of damage location and accurate estimation of the loss in stiffness due to damage.
    Matched MeSH terms: Entropy
  20. Mujib Kamal S, Babini MH, Krejcar O, Namazi H
    Front Physiol, 2020;11:602027.
    PMID: 33324242 DOI: 10.3389/fphys.2020.602027
    Walking is an everyday activity in our daily life. Because walking affects heart rate variability, in this research, for the first time, we analyzed the coupling among the alterations of the complexity of walking paths and heart rate. We benefited from the fractal theory and sample entropy to evaluate the influence of the complexity of paths on the complexity of heart rate variability (HRV) during walking. We calculated the fractal exponent and sample entropy of the R-R time series for nine participants who walked on four paths with various complexities. The findings showed a strong coupling among the alterations of fractal dimension (an indicator of complexity) of HRV and the walking paths. Besides, the result of the analysis of sample entropy also verified the obtained results from the fractal analysis. In further studies, we can analyze the coupling among the alterations of the complexities of other physiological signals and walking paths.
    Matched MeSH terms: Entropy
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