Displaying publications 21 - 40 of 51 in total

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  1. Al-Qazzaz NK, Ali SHBM, Ahmad SA, Islam MS, Escudero J
    Med Biol Eng Comput, 2018 Jan;56(1):137-157.
    PMID: 29119540 DOI: 10.1007/s11517-017-1734-7
    Stroke survivors are more prone to developing cognitive impairment and dementia. Dementia detection is a challenge for supporting personalized healthcare. This study analyzes the electroencephalogram (EEG) background activity of 5 vascular dementia (VaD) patients, 15 stroke-related patients with mild cognitive impairment (MCI), and 15 control healthy subjects during a working memory (WM) task. The objective of this study is twofold. First, it aims to enhance the discrimination of VaD, stroke-related MCI patients, and control subjects using fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR); second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects. Nineteen channels were recorded and analyzed using the independent component analysis and wavelet analysis (ICA-WT) denoising technique. Using ANOVA, linear spectral power including relative powers (RP) and power ratio were calculated to test whether the EEG dominant frequencies were slowed down in VaD and stroke-related MCI patients. Non-linear features including permutation entropy (PerEn) and fractal dimension (FD) were used to test the degree of irregularity and complexity, which was significantly lower in patients with VaD and stroke-related MCI than that in control subjects (ANOVA; p ˂ 0.05). This study is the first to use fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR) dimensionality reduction technique with EEG background activity of dementia patients. The impairment of post-stroke patients was detected using support vector machine (SVM) and k-nearest neighbors (kNN) classifiers. A comparative study has been performed to check the effectiveness of using FNPAQR dimensionality reduction technique with the SVM and kNN classifiers. FNPAQR with SVM and kNN obtained 91.48 and 89.63% accuracy, respectively, whereas without using the FNPAQR exhibited 70 and 67.78% accuracy for SVM and kNN, respectively, in classifying VaD, stroke-related MCI, and control patients, respectively. Therefore, EEG could be a reliable index for inspecting concise markers that are sensitive to VaD and stroke-related MCI patients compared to control healthy subjects.
    Matched MeSH terms: Entropy
  2. Alsabery AI, Ishak MS, Chamkha AJ, Hashim I
    Entropy (Basel), 2018 May 03;20(5).
    PMID: 33265426 DOI: 10.3390/e20050336
    The problem of entropy generation analysis and natural convection in a nanofluid square cavity with a concentric solid insert and different temperature distributions is studied numerically by the finite difference method. An isothermal heater is placed on the bottom wall while isothermal cold sources are distributed along the top and side walls of the square cavity. The remainder of these walls are kept adiabatic. Water-based nanofluids with Al 2 O 3 nanoparticles are chosen for the investigation. The governing dimensionless parameters of this study are the nanoparticles volume fraction ( 0 ≤ ϕ ≤ 0.09 ), Rayleigh number ( 10 3 ≤ R a ≤ 10 6 ) , thermal conductivity ratio ( 0.44 ≤ K r ≤ 23.8 ) and length of the inner solid ( 0 ≤ D ≤ 0.7 ). Comparisons with previously experimental and numerical published works verify a very good agreement with the proposed numerical method. Numerical results are presented graphically in the form of streamlines, isotherms and local entropy generation as well as the local and average Nusselt numbers. The obtained results indicate that the thermal conductivity ratio and the inner solid size are excellent control parameters for an optimization of heat transfer and Bejan number within the fully heated and partially cooled square cavity.
    Matched MeSH terms: Entropy
  3. 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
  4. Sharma M, Agarwal S, Acharya UR
    Comput Biol Med, 2018 09 01;100:100-113.
    PMID: 29990643 DOI: 10.1016/j.compbiomed.2018.06.011
    Obstructive sleep apnea (OSA) is a sleep disorder caused due to interruption of breathing resulting in insufficient oxygen to the human body and brain. If the OSA is detected and treated at an early stage the possibility of severe health impairment can be mitigated. Therefore, an accurate automated OSA detection system is indispensable. Generally, OSA based computer-aided diagnosis (CAD) system employs multi-channel, multi-signal physiological signals. However, there is a great need for single-channel bio-signal based low-power, a portable OSA-CAD system which can be used at home. In this study, we propose single-channel electrocardiogram (ECG) based OSA-CAD system using a new class of optimal biorthogonal antisymmetric wavelet filter bank (BAWFB). In this class of filter bank, all filters are of even length. The filter bank design problem is transformed into a constrained optimization problem wherein the objective is to minimize either frequency-spread for the given time-spread or time-spread for the given frequency-spread. The optimization problem is formulated as a semi-definite programming (SDP) problem. In the SDP problem, the objective function (time-spread or frequency-spread), constraints of perfect reconstruction (PR) and zero moment (ZM) are incorporated in their time domain matrix formulations. The global solution for SDP is obtained using interior point algorithm. The newly designed BAWFB is used for the classification of OSA using ECG signals taken from the physionet's Apnea-ECG database. The ECG segments of 1 min duration are decomposed into six wavelet subbands (WSBs) by employing the proposed BAWFB. Then, the fuzzy entropy (FE) and log-energy (LE) features are computed from all six WSBs. The FE and LE features are classified into normal and OSA groups using least squares support vector machine (LS-SVM) with 35-fold cross-validation strategy. The proposed OSA detection model achieved the average classification accuracy, sensitivity, specificity and F-score of 90.11%, 90.87% 88.88% and 0.92, respectively. The performance of the model is found to be better than the existing works in detecting OSA using the same database. Thus, the proposed automated OSA detection system is accurate, cost-effective and ready to be tested with a huge database.
    Matched MeSH terms: Entropy
  5. Molinari F, Raghavendra U, Gudigar A, Meiburger KM, Rajendra Acharya U
    Med Biol Eng Comput, 2018 Sep;56(9):1579-1593.
    PMID: 29473126 DOI: 10.1007/s11517-018-1792-5
    Atherosclerosis is a type of cardiovascular disease which may cause stroke. It is due to the deposition of fatty plaque in the artery walls resulting in the reduction of elasticity gradually and hence restricting the blood flow to the heart. Hence, an early prediction of carotid plaque deposition is important, as it can save lives. This paper proposes a novel data mining framework for the assessment of atherosclerosis in its early stage using ultrasound images. In this work, we are using 1353 symptomatic and 420 asymptomatic carotid plaque ultrasound images. Our proposed method classifies the symptomatic and asymptomatic carotid plaques using bidimensional empirical mode decomposition (BEMD) and entropy features. The unbalanced data samples are compensated using adaptive synthetic sampling (ADASYN), and the developed method yielded a promising accuracy of 91.43%, sensitivity of 97.26%, and specificity of 83.22% using fourteen features. Hence, the proposed method can be used as an assisting tool during the regular screening of carotid arteries in hospitals. Graphical abstract Outline for our efficient data mining framework for the characterization of symptomatic and asymptomatic carotid plaques.
    Matched MeSH terms: Entropy
  6. 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
  7. 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
  8. Wong YM, Show PL, Wu TY, Leong HY, Ibrahim S, Juan JC
    J Biosci Bioeng, 2019 Feb;127(2):150-159.
    PMID: 30224189 DOI: 10.1016/j.jbiosc.2018.07.012
    Bio-hydrogen production from wastewater using sludge as inoculum is a sustainable approach for energy production. This study investigated the influence of initial pH and temperature on bio-hydrogen production from dairy wastewater using pretreated landfill leachate sludge (LLS) as an inoculum. The maximum yield of 113.2 ± 2.9 mmol H2/g chemical oxygen demand (COD) (12.8 ± 0.3 mmol H2/g carbohydrates) was obtained at initial pH 6 and 37 °C. The main products of volatile fatty acids were acetate and butyrate with the ratio of acetate:butyrate was 0.4. At optimum condition, Gibb's free energy was estimated at -40 kJ/mol, whereas the activation enthalpy and entropy were 65 kJ/mol and 0.128 kJ/mol/l, respectively. These thermodynamic quantities suggest that bio-hydrogen production from dairy wastewater using pretreated LLS as inoculum was effective and efficient. In addition, genomic and bioinformatics analyses were performed in this study.
    Matched MeSH terms: Entropy
  9. Azareh A, Rahmati O, Rafiei-Sardooi E, Sankey JB, Lee S, Shahabi H, et al.
    Sci Total Environ, 2019 Mar 10;655:684-696.
    PMID: 30476849 DOI: 10.1016/j.scitotenv.2018.11.235
    Gully erosion susceptibility mapping is a fundamental tool for land-use planning aimed at mitigating land degradation. However, the capabilities of some state-of-the-art data-mining models for developing accurate maps of gully erosion susceptibility have not yet been fully investigated. This study assessed and compared the performance of two different types of data-mining models for accurately mapping gully erosion susceptibility at a regional scale in Chavar, Ilam, Iran. The two methods evaluated were: Certainty Factor (CF), a bivariate statistical model; and Maximum Entropy (ME), an advanced machine learning model. Several geographic and environmental factors that can contribute to gully erosion were considered as predictor variables of gully erosion susceptibility. Based on an existing differential GPS survey inventory of gully erosion, a total of 63 eroded gullies were spatially randomly split in a 70:30 ratio for use in model calibration and validation, respectively. Accuracy assessments completed with the receiver operating characteristic curve method showed that the ME-based regional gully susceptibility map has an area under the curve (AUC) value of 88.6% whereas the CF-based map has an AUC of 81.8%. According to jackknife tests that were used to investigate the relative importance of predictor variables, aspect, distance to river, lithology and land use are the most influential factors for the spatial distribution of gully erosion susceptibility in this region of Iran. The gully erosion susceptibility maps produced in this study could be useful tools for land managers and engineers tasked with road development, urbanization and other future development.
    Matched MeSH terms: Entropy
  10. Aimie-Salleh N, Malarvili MB, Whittaker AC
    Med Biol Eng Comput, 2019 Jun;57(6):1229-1245.
    PMID: 30734153 DOI: 10.1007/s11517-019-01958-3
    Adverse childhood experiences have been suggested to cause changes in physiological processes and can determine the magnitude of the stress response which might have a significant impact on health later in life. To detect the stress response, biomarkers that represent both the Autonomic Nervous System (ANS) and Hypothalamic-Pituitary-Adrenal (HPA) axis are proposed. Among the available biomarkers, Heart Rate Variability (HRV) has been proven as a powerful biomarker that represents ANS. Meanwhile, salivary cortisol has been suggested as a biomarker that reflects the HPA axis. Even though many studies used multiple biomarkers to measure the stress response, the results for each biomarker were analyzed separately. Therefore, the objective of this study is to propose a fusion of ANS and HPA axis biomarkers in order to classify the stress response based on adverse childhood experience. Electrocardiograph, blood pressure (BP), pulse rate (PR), and salivary cortisol (SCort) measures were collected from 23 healthy participants; 11 participants had adverse childhood experience while the remaining 12 acted as the no adversity control group. HRV was then computed from the ECG and the HRV features were extracted. Next, the selected HRV features were combined with the other biomarkers using Euclidean distance (ed) and serial fusion, and the performance of the fused features was compared using Support Vector Machine. From the result, HRV-SCort using Euclidean distance achieved the most satisfactory performance with 80.0% accuracy, 83.3% sensitivity, and 78.3% specificity. Furthermore, the performance of the stress response classification of the fused biomarker, HRV-SCort, outperformed that of the single biomarkers: HRV (61% Accuracy), Cort (59.4% Accuracy), BP (78.3% accuracy), and PR (53.3% accuracy). From this study, it was proven that the fused biomarkers that represent both ANS and HPA (HRV-SCort) able to demonstrate a better classification performance in discriminating the stress response. Furthermore, a new approach for classification of stress response using Euclidean distance and SVM named as ed-SVM was proven to be an effective method for the HRV-SCort in classifying the stress response from PASAT. The robustness of this method is crucial in contributing to the effectiveness of the stress response measures and could further be used as an indicator for future health. Graphical abstract ᅟ.
    Matched MeSH terms: Entropy
  11. Behkami S, Zain SM, Gholami M, Khir MFA
    Food Chem, 2019 Oct 01;294:309-315.
    PMID: 31126468 DOI: 10.1016/j.foodchem.2019.05.060
    Spectra data from two instruments (UV-Vis/NIR and FT-NIR) consisting of three and one detectors, respectively, were employed in order to discriminate the geographical origin of milk as a way to detect adulteration. Initially, principal component analysis (PCA) was used to see if clusters of milk from different origins are formed. Separation between samples of different origins were not observed with PCA, hence, feed-forward multi-layer perceptron artificial neural network (MLP-ANN) models were designed. ANN models were developed by changing the number of input variables and the best models were chosen based on high values of generalized R-square and entropy R-square, as well as small values of root mean square error (RMSE), mean absolute deviation (Mean Abs. Dev), and -loglikelihood while considering 100% classification rate. Based on the results, whether the spectra data was collected from a single or three detector instrument the same clustering was observed based on geographical origin.
    Matched MeSH terms: Entropy
  12. Jawed S, Amin HU, Malik AS, Faye I
    PMID: 31133829 DOI: 10.3389/fnbeh.2019.00086
    This study analyzes the learning styles of subjects based on their electroencephalo-graphy (EEG) signals. The goal is to identify how the EEG features of a visual learner differ from those of a non-visual learner. The idea is to measure the students' EEGs during the resting states (eyes open and eyes closed conditions) and when performing learning tasks. For this purpose, 34 healthy subjects are recruited. The subjects have no background knowledge of the animated learning content. The subjects are shown the animated learning content in a video format. The experiment consists of two sessions and each session comprises two parts: (1) Learning task: the subjects are shown the animated learning content for an 8-10 min duration. (2) Memory retrieval task The EEG signals are measured during the leaning task and memory retrieval task in two sessions. The retention time for the first session was 30 min, and 2 months for the second session. The analysis is performed for the EEG measured during the memory retrieval tasks. The study characterizes and differentiates the visual learners from the non-visual learners considering the extracted EEG features, such as the power spectral density (PSD), power spectral entropy (PSE), and discrete wavelet transform (DWT). The PSD and DWT features are analyzed. The EEG PSD and DWT features are computed for the recorded EEG in the alpha and gamma frequency bands over 128 scalp sites. The alpha and gamma frequency band for frontal, occipital, and parietal regions are analyzed as these regions are activated during learning. The extracted PSD and DWT features are then reduced to 8 and 15 optimum features using principal component analysis (PCA). The optimum features are then used as an input to the k-nearest neighbor (k-NN) classifier using the Mahalanobis distance metric, with 10-fold cross validation and support vector machine (SVM) classifier using linear kernel, with 10-fold cross validation. The classification results showed 97% and 94% accuracies rate for the first session and 96% and 93% accuracies for the second session in the alpha and gamma bands for the visual learners and non-visual learners, respectively, for k-NN classifier for PSD features and 68% and 100% accuracies rate for first session and 100% accuracies rate for second session for DWT features using k-NN classifier for the second session in the alpha and gamma band. For PSD features 97% and 96% accuracies rate for the first session, 100% and 95% accuracies rate for second session using SVM classifier and 79% and 82% accuracy for first session and 56% and 74% accuracy for second session for DWT features using SVM classifier. The results showed that the PSDs in the alpha and gamma bands represent distinct and stable EEG signatures for visual learners and non-visual learners during the retrieval of the learned contents.
    Matched MeSH terms: Entropy
  13. Hussain J, Zhou K, Guo S, Khan A
    Sci Total Environ, 2020 Mar 16;723:137981.
    PMID: 32208210 DOI: 10.1016/j.scitotenv.2020.137981
    Chinese enterprises that conduct overseas investment projects encounter diverse challenges that emerge from political, economic, social, and environmental risks in the host countries. To better assess the overseas investment risks faced by Chinese enterprises, this study introduced and assessed novel aspects and an indicator system. Moreover, the "Technique for Order Preference by Similarity to Ideal Solution" (TOPSIS) method based on entropy weight was performed to generate a comprehensive assessment of China's foreign investment risk and natural resource potential in 63 "Belt & Road Initiative" (BRI) countries. This study aims to encourage Chinese enterprises to devise suitable overseas investment decision-making strategies concerning natural resource potential in host countries. A Geographic Information System (GIS) map was also created to assess the potential risks and opportunities for Chinese enterprises when making investment decisions in host countries. The findings indicate that the majority of countries in Central and Eastern Europe and other BRI countries such as Singapore, Malaysia, Nepal, Bhutan, Russia, Armenia, and the United Arab Emirates were the most suitable choices for Chinese enterprises engaging in overseas investment. Based on these results, Chinese enterprises could manage and execute BRI projects more effectively to minimise potential risks and maximise their investment benefits.
    Matched MeSH terms: Entropy
  14. Asghar MA, Khan MJ, Rizwan M, Mehmood RM, Kim SH
    Sensors (Basel), 2020 Jul 05;20(13).
    PMID: 32635609 DOI: 10.3390/s20133765
    Emotional awareness perception is a largely growing field that allows for more natural interactions between people and machines. Electroencephalography (EEG) has emerged as a convenient way to measure and track a user's emotional state. The non-linear characteristic of the EEG signal produces a high-dimensional feature vector resulting in high computational cost. In this paper, characteristics of multiple neural networks are combined using Deep Feature Clustering (DFC) to select high-quality attributes as opposed to traditional feature selection methods. The DFC method shortens the training time on the network by omitting unusable attributes. First, Empirical Mode Decomposition (EMD) is applied as a series of frequencies to decompose the raw EEG signal. The spatiotemporal component of the decomposed EEG signal is expressed as a two-dimensional spectrogram before the feature extraction process using Analytic Wavelet Transform (AWT). Four pre-trained Deep Neural Networks (DNN) are used to extract deep features. Dimensional reduction and feature selection are achieved utilising the differential entropy-based EEG channel selection and the DFC technique, which calculates a range of vocabularies using k-means clustering. The histogram characteristic is then determined from a series of visual vocabulary items. The classification performance of the SEED, DEAP and MAHNOB datasets combined with the capabilities of DFC show that the proposed method improves the performance of emotion recognition in short processing time and is more competitive than the latest emotion recognition methods.
    Matched MeSH terms: Entropy
  15. Hasan AM, Jalab HA, Ibrahim RW, Meziane F, Al-Shamasneh AR, Obaiys SJ
    Entropy (Basel), 2020 Sep 15;22(9).
    PMID: 33286802 DOI: 10.3390/e22091033
    Brain tumor detection at early stages can increase the chances of the patient's recovery after treatment. In the last decade, we have noticed a substantial development in the medical imaging technologies, and they are now becoming an integral part in the diagnosis and treatment processes. In this study, we generalize the concept of entropy difference defined in terms of Marsaglia formula (usually used to describe two different figures, statues, etc.) by using the quantum calculus. Then we employ the result to extend the local binary patterns (LBP) to get the quantum entropy LBP (QELBP). The proposed study consists of two approaches of features extractions of MRI brain scans, namely, the QELBP and the deep learning DL features. The classification of MRI brain scan is improved by exploiting the excellent performance of the QELBP-DL feature extraction of the brain in MRI brain scans. The combining all of the extracted features increase the classification accuracy of long short-term memory network when using it as the brain tumor classifier. The maximum accuracy achieved for classifying a dataset comprising 154 MRI brain scan is 98.80%. The experimental results demonstrate that combining the extracted features improves the performance of MRI brain tumor classification.
    Matched MeSH terms: Entropy
  16. Mardianingrum R, Yusuf M, Hariono M, Mohd Gazzali A, Muchtaridi M
    J Biomol Struct Dyn, 2020 Nov 06.
    PMID: 33155528 DOI: 10.1080/07391102.2020.1841031
    Estrogen receptor alpha (ERα) acts as the transcription factor and the main therapeutic target against breast cancer. One of the compounds that has been shown to act as an ERα is α-mangostin. However, it still has weaknesses due to its low solubility and low potent activity. In this study, α-mangostin was modified by substituting -OH group at C6 using benzoyl derivatives through a step by step in silico study, namely pharmacokinetic prediction (https://preadmet.bmdrc.kr/adme/), pharmacophore modeling (LigandScout 4.1), molecular docking simulation (AutoDock 4.2), molecular dynamics simulation (AMBER 16) and a binding free energy analysis using MM-PBSA method. From the computational studies, three compounds which are derived from α-mangostin (AMB-1 (-9.84 kcal/mol), AMB-2 (-6.80 kcal/mol) and AMB-10 (-12.42 kcal/mol)) have lower binding free energy than α-mangostin (-1.77 kcal/mol), as evidenced by the binding free energy calculation using the MM-PBSA method. They can then be predicted to have potent activities as ERα antagonists.Communicated by Ramaswamy H. Sarma.
    Matched MeSH terms: Entropy
  17. Farah Nazlia Che Kassim, Muthusamy, Hariharan, Vijean, Vikneswaran, Zulkapli Abdullah, Rokiah Abdullah
    MyJurnal
    Voice pathology analysis has been one of the useful tools in the diagnosis of the pathological voice, as the method is non-invasive, inexpensive, and can reduce the time required for the analysis. This paper investigates feature extraction based on the Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) using energy and entropy measures tested with two classifiers, k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM). Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database and Saarbruecken Voice Database (SVD) were used. Five datasets of voice samples were used from these databases, including normal and abnormal samples, Cysts, Vocal Nodules, Polyp, and Paralysis vocal fold. To the best of the authors’ knowledge, very few studies were done on multiclass classifications using specific pathology database. File-based and frame-based investigation for two-class and multiclass were considered. In the two-class analysis using the DT-CWPT with entropies, the classification accuracy of 100% and 99.94% was achieved for MEEI and SVD database respectively. Meanwhile, the classification accuracy for multiclass analysis comprised of 99.48% for the MEEI database and 99.65% for SVD database. The experimental results using the proposed features provided promising accuracy to detect the presence of diseases in vocal fold.
    Matched MeSH terms: Entropy
  18. 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
  19. 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*
  20. Abd Raman HS, Tan S, August JT, Khan AM
    PeerJ, 2020;7:e7954.
    PMID: 32518710 DOI: 10.7717/peerj.7954
    Background: Influenza A (H5N1) virus is a global concern with potential as a pandemic threat. High sequence variability of influenza A viruses is a major challenge for effective vaccine design. A continuing goal towards this is a greater understanding of influenza A (H5N1) proteome sequence diversity in the context of the immune system (antigenic diversity), the dynamics of mutation, and effective strategies to overcome the diversity for vaccine design.

    Methods: Herein, we report a comprehensive study of the dynamics of H5N1 mutations by analysis of the aligned overlapping nonamer positions (1-9, 2-10, etc.) of more than 13,000 protein sequences of avian and human influenza A (H5N1) viruses, reported over at least 50 years. Entropy calculations were performed on 9,408 overlapping nonamer position of the proteome to study the diversity in the context of immune system. The nonamers represent the predominant length of the binding cores for peptides recognized by the cellular immune system. To further dissect the sequence diversity, each overlapping nonamer position was quantitatively analyzed for four patterns of sequence diversity motifs: index, major, minor and unique.

    Results: Almost all of the aligned overlapping nonamer positions of each viral proteome exhibited variants (major, minor, and unique) to the predominant index sequence. Each variant motif displayed a characteristic pattern of incidence change in relation to increased total variants. The major variant exhibited a restrictive pyramidal incidence pattern, with peak incidence at 50% total variants. Post this peak incidence, the minor variants became the predominant motif for majority of the positions. Unique variants, each sequence observed only once, were present at nearly all of the nonamer positions. The diversity motifs (index and variants) demonstrated complex inter-relationships, with motif switching being a common phenomenon. Additionally, 25 highly conserved sequences were identified to be shared across viruses of both hosts, with half conserved to several other influenza A subtypes.

    Discussion: The presence of distinct sequences (nonatypes) at nearly all nonamer positions represents a large repertoire of reported viral variants in the proteome, which influence the variability dynamics of the viral population. This work elucidated and provided important insights on the components that make up the viral diversity, delineating inherent patterns in the organization of sequence changes that function in the viral fitness-selection. Additionally, it provides a catalogue of all the mutational changes involved in the dynamics of H5N1 viral diversity for both avian and human host populations. This work provides data relevant for the design of prophylactics and therapeutics that overcome the diversity of the virus, and can aid in the surveillance of existing and future strains of influenza viruses.

    Matched MeSH terms: Entropy
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