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Fulltext Computer-assisted quantification of CD3+ T cells in follicular lymphoma

Abas FS, Shana'ah A, Christian B, Hasserjian R, Louissaint A, Pennell M, et al.

Cytometry A, 2017 06;91(6):609-621.
PMID: 28110507 DOI: 10.1002/cyto.a.23049

The advance of high resolution digital scans of pathology slides allowed development of computer based image analysis algorithms that may help pathologists in IHC stains quantification. While very promising, these methods require further refinement before they are implemented in routine clinical setting. Particularly critical is to evaluate algorithm performance in a setting similar to current clinical practice. In this article, we present a pilot study that evaluates the use of a computerized cell quantification method in the clinical estimation of CD3 positive (CD3+) T cells in follicular lymphoma (FL). Our goal is to demonstrate the degree to which computerized quantification is comparable to the practice of estimation by a panel of expert pathologists. The computerized quantification method uses entropy based histogram thresholding to separate brown (CD3+) and blue (CD3-) regions after a color space transformation. A panel of four board-certified hematopathologists evaluated a database of 20 FL images using two different reading methods: visual estimation and manual marking of each CD3+ cell in the images. These image data and the readings provided a reference standard and the range of variability among readers. Sensitivity and specificity measures of the computer's segmentation of CD3+ and CD- T cell are recorded. For all four pathologists, mean sensitivity and specificity measures are 90.97 and 88.38%, respectively. The computerized quantification method agrees more with the manual cell marking as compared to the visual estimations. Statistical comparison between the computerized quantification method and the pathologist readings demonstrated good agreement with correlation coefficient values of 0.81 and 0.96 in terms of Lin's concordance correlation and Spearman's correlation coefficient, respectively. These values are higher than most of those calculated among the pathologists. In the future, the computerized quantification method may be used to investigate the relationship between the overall architectural pattern (i.e., interfollicular vs. follicular) and outcome measures (e.g., overall survival, and time to treatment). © 2017 International Society for Advancement of Cytometry.

Matched MeSH terms: Entropy
Construction of a composite hospital admission index using the aggregated weights of criteria

Nor Hasliza Mat Desa, Maznah Mat Kasim, Abdul Aziz Jemain

Sains Malaysiana, 2015;44:239-247.

The issue of age difference in hospital admission should be given special attention since it affects the structure of hospital care and treatments. Patients of different age groups should be given different priority in service provision. Due to crucial time and limited resources, healthcare managers need to make wise decisions in identifying priorities in age of admission. This paper aimed to propose a construction of a daily composite hospital admission index (CHAI) as an indicator that captures relevant information about the overall performance of hospital admission over time. It involves five different age groups of total patients admitted to seven major public hospitals in the Klang Valley, Malaysia for respiratory and cardiovascular diseases for a period of three years, 2008 - 2010. The criteria weights were predetermined by aggregating the subjective weight based on rank ordered centroid (ROC) method and objective weight based on entropy - kernel method. The highest and lowest scores of CHAI were marked, while the groups of patients were prioritized according to the criteria weight ranking orders.

Matched MeSH terms: Entropy
Data mining framework for identification of myocardial infarction stages in ultrasound: A hybrid feature extraction paradigm (PART 2)

Sudarshan VK, Acharya UR, Ng EY, Tan RS, Chou SM, Ghista DN

Comput Biol Med, 2016 Apr 1;71:241-51.
PMID: 26897481 DOI: 10.1016/j.compbiomed.2016.01.029

Early expansion of infarcted zone after Acute Myocardial Infarction (AMI) has serious short and long-term consequences and contributes to increased mortality. Thus, identification of moderate and severe phases of AMI before leading to other catastrophic post-MI medical condition is most important for aggressive treatment and management. Advanced image processing techniques together with robust classifier using two-dimensional (2D) echocardiograms may aid for automated classification of the extent of infarcted myocardium. Therefore, this paper proposes novel algorithms namely Curvelet Transform (CT) and Local Configuration Pattern (LCP) for an automated detection of normal, moderately infarcted and severely infarcted myocardium using 2D echocardiograms. The methodology extracts the LCP features from CT coefficients of echocardiograms. The obtained features are subjected to Marginal Fisher Analysis (MFA) dimensionality reduction technique followed by fuzzy entropy based ranking method. Different classifiers are used to differentiate ranked features into three classes normal, moderate and severely infarcted based on the extent of damage to myocardium. The developed algorithm has achieved an accuracy of 98.99%, sensitivity of 98.48% and specificity of 100% for Support Vector Machine (SVM) classifier using only six features. Furthermore, we have developed an integrated index called Myocardial Infarction Risk Index (MIRI) to detect the normal, moderately and severely infarcted myocardium using a single number. The proposed system may aid the clinicians in faster identification and quantification of the extent of infarcted myocardium using 2D echocardiogram. This system may also aid in identifying the person at risk of developing heart failure based on the extent of infarcted myocardium.

Matched MeSH terms: Entropy
Fulltext Determination of Temperature-Dependent Coefficients of Viscosity and Surface Tension of Tamarind Seeds (Tamarindus indica L.) Polymer

Malviya R, Jha S, Fuloria NK, Subramaniyan V, Chakravarthi S, Sathasivam K, et al.

Polymers (Basel), 2021 Feb 18;13(4).
PMID: 33670569 DOI: 10.3390/polym13040610

The rheological properties of tamarind seed polymer are characterized for its possible commercialization in the food and pharmaceutical industry. Seed polymer was extracted using water as a solvent and ethyl alcohol as a precipitating agent. The temperature's effect on the rheological behavior of the polymeric solution was studied. In addition to this, the temperature coefficient, viscosity, surface tension, activation energy, Gibbs free energy, Reynolds number, and entropy of fusion were calculated by using the Arrhenius, Gibbs-Helmholtz, Frenkel-Eyring, and Eotvos equations, respectively. The activation energy of the gum was found to be 20.46 ± 1.06 kJ/mol. Changes in entropy and enthalpy were found to be 23.66 ± 0.97 and -0.10 ± 0.01 kJ/mol, respectively. The calculated amount of entropy of fusion was found to be 0.88 kJ/mol. A considerable decrease in apparent viscosity and surface tension was produced when the temperature was raised. The present study concludes that the tamarind seed polymer solution is less sensitive to temperature change in comparison to Albzia lebbac gum, Ficus glumosa gum and A. marcocarpa gum. This study also concludes that the attainment of the transition state of viscous flow for tamarind seed gum is accompanied by bond breaking. The excellent physicochemical properties of tamarind seed polymers make them promising excipients for future drug formulation and make their application in the food and cosmetics industry possible.

Matched MeSH terms: Entropy
Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies

Koh JEW, Acharya UR, Hagiwara Y, Raghavendra U, Tan JH, Sree SV, et al.

Comput Biol Med, 2017 05 01;84:89-97.
PMID: 28351716 DOI: 10.1016/j.compbiomed.2017.03.008

Vision is paramount to humans to lead an active personal and professional life. The prevalence of ocular diseases is rising, and diseases such as glaucoma, Diabetic Retinopathy (DR) and Age-related Macular Degeneration (AMD) are the leading causes of blindness in developed countries. Identifying these diseases in mass screening programmes is time-consuming, labor-intensive and the diagnosis can be subjective. The use of an automated computer aided diagnosis system will reduce the time taken for analysis and will also reduce the inter-observer subjective variabilities in image interpretation. In this work, we propose one such system for the automatic classification of normal from abnormal (DR, AMD, glaucoma) images. We had a total of 404 normal and 1082 abnormal fundus images in our database. As the first step, 2D-Continuous Wavelet Transform (CWT) decomposition on the fundus images of two classes was performed. Subsequently, energy features and various entropies namely Yager, Renyi, Kapoor, Shannon, and Fuzzy were extracted from the decomposed images. Then, adaptive synthetic sampling approach was applied to balance the normal and abnormal datasets. Next, the extracted features were ranked according to the significances using Particle Swarm Optimization (PSO). Thereupon, the ranked and selected features were used to train the random forest classifier using stratified 10-fold cross validation. Overall, the proposed system presented a performance rate of 92.48%, and a sensitivity and specificity of 89.37% and 95.58% respectively using 15 features. This novel system shows promise in detecting abnormal fundus images, and hence, could be a valuable adjunct eye health screening tool that could be employed in polyclinics, and thereby reduce the workload of specialists at hospitals.

Matched MeSH terms: Entropy
Digital Mammograms with Image Enhancement Techniques for Breast Cancer Detection: A Systematic Review

Suradi SH, Abdullah KA

Curr Med Imaging, 2021 Jan 26.
PMID: 33504312 DOI: 10.2174/1573405617666210127101101

BACKGROUND: Digital mammograms with appropriate image enhancement techniques will improve breast cancer detection, and thus increase the survival rates. The objectives of this study were to systematically review and compare various image enhancement techniques in digital mammograms for breast cancer detection.
METHODS: A literature search was conducted with the use of three online databases namely, Web of Science, Scopus, and ScienceDirect. Developed keywords strategy was used to include only the relevant articles. A Population Intervention Comparison Outcomes (PICO) strategy was used to develop the inclusion and exclusion criteria. Image quality was analyzed quantitatively based on peak signal-noise-ratio (PSNR), Mean Squared Error (MSE), Absolute Mean Brightness Error (AMBE), Entropy, and Contrast Improvement Index (CII) values.
RESULTS: Nine studies with four types of image enhancement techniques were included in this study. Two studies used histogram-based, three studies used frequency-based, one study used fuzzy-based and three studies used filter-based. All studies reported PSNR values whilst only four studies reported MSE, AMBE, Entropy and CII values. Filter-based was the highest PSNR values of 78.93, among other types. For MSE, AMBE, Entropy, and CII values, the highest were frequency-based (7.79), fuzzy-based (93.76), filter-based (7.92), and frequency-based (6.54) respectively.
CONCLUSION: In summary, image quality for each image enhancement technique is varied, especially for breast cancer detection. In this study, the frequency-based of Fast Discrete Curvelet Transform (FDCT) via the UnequiSpaced Fast Fourier Transform (USFFT) shows the most superior among other image enhancement techniques.

Matched MeSH terms: Entropy
Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis

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
Fulltext Dual-tree complex wavelet packet transform for voice pathology analysis

Farah Nazlia Che Kassim, Muthusamy, Hariharan, Vijean, Vikneswaran, Zulkapli Abdullah, Rokiah Abdullah

Pertanika Journal of Science & Technology, 2020;28(3):839-858.
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
Fulltext Dynamics of Influenza A (H5N1) virus protein sequence diversity

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
Fulltext Entropy Generation Analysis and Natural Convection in a Nanofluid-Filled Square Cavity with a Concentric Solid Insert and Different Temperature Distributions

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
Fulltext Entropy and Gaussian Filter-Based Adaptive Active Contour for Segmentation of Skin Lesions

Mustafa S, Iqbal MW, Rana TA, Jaffar A, Shiraz M, Arif M, et al.

Comput Intell Neurosci, 2022;2022:4348235.
PMID: 35909861 DOI: 10.1155/2022/4348235

Malignant melanoma is considered one of the deadliest skin diseases if ignored without treatment. The mortality rate caused by melanoma is more than two times that of other skin malignancy diseases. These facts encourage computer scientists to find automated methods to discover skin cancers. Nowadays, the analysis of skin images is widely used by assistant physicians to discover the first stage of the disease automatically. One of the challenges the computer science researchers faced when developing such a system is the un-clarity of the existing images, such as noise like shadows, low contrast, hairs, and specular reflections, which complicates detecting the skin lesions in that images. This paper proposes the solution to the problem mentioned earlier using the active contour method. Still, seed selection in the dynamic contour method has the main drawback of where it should start the segmentation process. This paper uses Gaussian filter-based maximum entropy and morphological processing methods to find automatic seed points for active contour. By incorporating this, it can segment the lesion from dermoscopic images automatically. Our proposed methodology tested quantitative and qualitative measures on standard dataset dermis and used to test the proposed method's reliability which shows encouraging results.

Matched MeSH terms: Entropy
Fulltext Entropy-based financial asset pricing: Evidence from Pakistan

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
Exploring the interaction between the antiallergic drug, tranilast and human serum albumin: Insights from calorimetric, spectroscopic and modeling studies

Tayyab S, Zaroog MS, Feroz SR, Mohamad SB, Malek SN

Int J Pharm, 2015 Aug 1;491(1-2):352-8.
PMID: 26142245 DOI: 10.1016/j.ijpharm.2015.06.042

The interaction of tranilast (TRN), an antiallergic drug with the main drug transporter in human circulation, human serum albumin (HSA) was studied using isothermal titration calorimetry (ITC), fluorescence spectroscopy and in silico docking methods. ITC data revealed the binding constant and stoichiometry of binding as (3.21 ± 0.23) × 10(6)M(-1) and 0.80 ± 0.08, respectively, at 25°C. The values of the standard enthalpy change (ΔH°) and the standard entropy change (ΔS°) for the interaction were found as -25.2 ± 5.1 kJ mol(-1) and 46.9 ± 5.4 J mol(-1)K(-1), respectively. Both thermodynamic data and modeling results suggested the involvement of hydrogen bonding, hydrophobic and van der Waals forces in the complex formation. Three-dimensional fluorescence data of TRN-HSA complex demonstrated significant changes in the microenvironment around the protein fluorophores upon drug binding. Competitive drug displacement results as well as modeling data concluded the preferred binding site of TRN as Sudlow's site I on HSA.

Matched MeSH terms: Entropy
Fusion of heart rate variability and salivary cortisol for stress response identification based on adverse childhood experience

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
Identification of Distributed Denial of Services Anomalies by Using Combination of Entropy and Sequential Probabilities Ratio Test Methods

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
Indeks Kemajuan antara Daerah di Semenanjung Malaysia (Development Index among Districts in Peninsular Malaysia)

Abdul Aziz Jemain

Sains Malaysiana, 2005;34.

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
Information-based analysis of the relationship between brain and facial muscle activities in response to static visual stimuli

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
Information-based decoding of the coupling among human brain activity and movement paths

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
Investment risk and natural resource potential in "Belt & Road Initiative" countries: A multi-criteria decision-making approach

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
Iterative variational mode decomposition based automated detection of glaucoma using fundus images

Maheshwari S, Pachori RB, Kanhangad V, Bhandary SV, Acharya UR

Comput Biol Med, 2017 Sep 01;88:142-149.
PMID: 28728059 DOI: 10.1016/j.compbiomed.2017.06.017

Glaucoma is one of the leading causes of permanent vision loss. It is an ocular disorder caused by increased fluid pressure within the eye. The clinical methods available for the diagnosis of glaucoma require skilled supervision. They are manual, time consuming, and out of reach of common people. Hence, there is a need for an automated glaucoma diagnosis system for mass screening. In this paper, we present a novel method for an automated diagnosis of glaucoma using digital fundus images. Variational mode decomposition (VMD) method is used in an iterative manner for image decomposition. Various features namely, Kapoor entropy, Renyi entropy, Yager entropy, and fractal dimensions are extracted from VMD components. ReliefF algorithm is used to select the discriminatory features and these features are then fed to the least squares support vector machine (LS-SVM) for classification. Our proposed method achieved classification accuracies of 95.19% and 94.79% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the ophthalmologists in confirming their manual reading of classes (glaucoma or normal) using fundus images.

Matched MeSH terms: Entropy

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