Matrix Technique Research Articles

An Enhanced Diabetic Foot Ulcer Classification Approach Using GLCM and Deep Convolution Neural Network

Diabetic Foot Ulcers (DFU) are considered to be a common complication of diabetes, usually resulting in the amputation of lower extremities. Therefore, diagnosing this disease at an early stage is necessary to avoid the accompanying treatment approach, and this results in a significant cost reduction for the patient. To achieve an early diagnosis of this disease, we need to classify a patient's skin as normal or abnormal. A classification process relies heavily on the extracted features. So, we proposed a new technique called CNN_GLCMNet for feature extraction. This technique relies on Convolution Neural Network (CNN) and the Gray-Level Co-Occurrence Matrix (GLCM) techniques to mine abstract features and second-order statistical texture features. Also, Singular Value Decomposition (SVD) is applied to reduce the dimensionality of the obtained features that result from CNN, Next, the GLCM method is applied to extract second-order statistical texture features. Then, these two kinds of features (abstract features and statistical features) are combined and used as input for the classifier. Two classification mechanisms have been adopted in the classification of images into normal and abnormal skin. First, the Deep Neural Network (DNN) classifier achieves the following performance evaluation metrics (accuracy 97.43%, recall 97.25%, specificity 97.59%, precision 97.53%, f1-score 97.38%). Second, the Support Vector Machine (SVM) classifier achieves the following performance evaluation metrics (accuracy 96.93%, recall 96.99%, specificity 96.94%, precision 96.76%, f1-score 96.85%). Since both classifiers have been validated against the DFU dataset using 10-fold cross-validation. The DNN classifiers with our new feature extraction technique achieve better results in terms of accuracy, specificity, precision, recall, and f1-score than in previous work. Furthermore, a comparison of DNN and SVM classifiers finds that DNN gives a better result according to performance metrics.

Facial Feature Extraction Using a Symmetric Inline Matrix-LBP Variant for Emotion Recognition.

With a large number of Local Binary Patterns (LBP) variants being currently used today, the significant and importance of visual descriptors in computer vision applications are prominent. This paper presents a novel visual descriptor, i.e., SIM-LBP. It employs a new matrix technique called the Symmetric Inline Matrix generator method, which acts as a new variant of LBP. The key feature that separates our variant from existing counterparts is that our variant is very efficient in extracting facial expression features like eyes, eye brows, nose and mouth in a wide range of lighting conditions. For testing our model, we applied SIM-LBP on the JAFFE dataset to convert all the images to its corresponding SIM-LBP transformed variant. These transformed images are then used to train a Convolution Neural Network (CNN) based deep learning model for facial expressions recognition (FER). Several performance evaluation metrics, i.e., recognition accuracy rate, precision, recall, and F1-score, were used to test mode efficiency in comparison with those using the traditional LBP descriptor and other LBP variants. Our model outperformed in all four matrices with the proposed SIM-LBP transformation on the input images against those of baseline methods. In comparison analysis with the other state-of-the-art methods, it shows the usefulness of the proposed SIM-LBP model. Our proposed SIM-LBP variant transformation can also be applied on facial images to identify a person's mental states and predict mood variations.

Understanding and Improving Current Risk Management Practices in Hospital Settings

A large number of patients in the healthcare industry have adverse events. Risk management has been implemented in hospitals to ensure patient safety. However, there is still a lot of room for improvement in current risk management practices. As a result, the purpose of this research is to better understand risk management practices in hospital settings and to make recommendations to improve them. While a questionnaire survey was created to understand current risk management applications, risk management literature was reviewed in order to comprehend and improve these risk management applications. The findings show that over 70% of practitioners and managers regard risk management as defining threats to patients, while only a minority agree on the ISO definition of risk. Furthermore, nearly half of practitioners and managers agree that risk assessment is more important than risk mitigation. To manage risks, participants mostly used Failure Mode and Effect Analysis (FMEA), brainstorming, and risk matrix techniques. Based on the results of the questionnaire and the literature review, risk management practices could be advanced by emphasizing safety culture, staff involvement, safety training, risk reporting systems, and risk management tools.

Robust Output Tracking of Boolean Control Networks over Finite Time

With an increase in tracking time, the operating cost of the controller will increase accordingly. Considering the biological applications of Boolean control networks (BCNs), it is necessary to study the control problem of BCNs over finite time. In this paper, we study the output tracking problem of a BCN with disturbance inputs in a given finite time. First, the logical form of BCNs is transformed into an algebraic form using the semi-tensor product (STP) method. Then, the robust output tracking problems of a reference output trajectory and the outputs of a reference system over finite time are transformed into the robust reachability problem of the BCNs. Based on the truth matrix technique, two necessary and sufficient conditions are provided for the trackability of the reference outputs over finite time. Moreover, two algorithms are proposed to design the controllers in the case of the traceable outputs. It should be pointed out that the truth matrix method we used here has some unique features, including its simple computation and concise expression. Finally, two illustrative examples are presented to demonstrate the results in this paper.

A machine learning approach for skin disease detection and classification using image segmentation

Skin diseases are common health problems around the world. The perils of the infections are invisible, which cause physical health distress as well as initiate mental depression. In addition, it sometimes leads to skin cancer in severe cases. Subsequently, diagnosing skin diseases from clinical images is one of the foremost challenging tasks in medical image analysis. Moreover, when performed manually by medical experts, diagnosing skin diseases is time-intensive and subjective. As a result, both patients and dermatologists require automatic skin disease prediction, which makes the treatments plan faster. In this work, we introduce a digital hair removal technique based on morphological filtering such as Black-Hat transformation and inpainting algorithm and then apply Gaussian filtering to de-blur or denoise the images. In addition, we apply the automatic Grabcut segmentation technique to segment out the affected lesions. For extracting underlying input patterns from the skin images, we apply the Gray Level Co-occurrence Matrix (GLCM) and statistical features techniques. Three computationally efficient machine learning techniques, Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) classifiers are applied using the extracted features for effectively classifying the skin images as melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis (AK), benign keratosis (BKL), dermatofibroma (DF), vascular lesion (VASC), and Squamous cell carcinoma (SCC). The models are validated using two standard datasets ISIC 2019 challenge and HAM10000. SVM performs slightly better than the other two classifiers. We have also compared our work with state-of-the-art methods.

Comparison of Assessments of Urban Areas Using the Flood Vulnerability and Potential Flood Damage Methods

Recently, urban flood damage has rapidly increased due to climate change, rapid urbanization, and frequent guerrilla heavy rains. Therefore, a quantitative flood damage analysis technique is required for effective policy regarding flooding in urban areas. In this study, the flood vulnerability and potential flood damage (PFD) methods, which are representative methods for analyzing the degree of flood damage in urban areas, were applied. In addition, the results of each analysis method were compared and analyzed using the flood prevention capacity-damage (PCD) matrix technique, which intuitively shows that even if many existing flood protection facilities are built, flood damage can be large when there is insufficient management or heavy rainfall exceeding the design rainfall occurs. The two existing flood damage techniques estimated Seocho-gu to have low flood vulnerability due to high financial independence and many flood damage protection facilities. However, application of the PCD matrix resulted in classifying Seocho-gu as highly vulnerable to flooding. These conflicting results thus demonstrate that a more detailed review and analysis are necessary when establishing a flood protection policy.

Optical anisotropy composition of benign and malignant prostate tissues revealed by Mueller-matrix imaging.

A Mueller matrix imaging approach is employed to disclose the three-dimensional composition framework of optical anisotropy within cancerous biotissues. Visualized by the Mueller matrix technique spatial architecture of optical anisotropy of tissues is characterised by high-order statistical moments. Thus, quantitative analysis of the spatial distribution of optical anisotropy, such as linear and circular birefringence and dichroism, is revealed by using high-order statistical moments, enabling definitively discriminate prostate adenoma and carcinoma. The developed approach provides greater (>90%) accuracy of diagnostic achieved by using either the 3-rd or 4-th order statistical moments of the linear anisotropy parameters. Noticeable difference is observed between prostate adenoma and carcinoma tissue samples in terms of the extinction coefficient and the degree of depolarisation. Juxtaposition to other optical diagnostic modalities demonstrates the greater accuracy of the approach described herein, paving the way for its wider application in cancer diagnosis and tissue characterization.

Promosi Kesehatan dengan Media Power Point dan Film Animasi Pendek dalam Upaya Pemeliharaan Kesehatan Gigi dan Mulut

Introduction: Oral and dental health is important in the life of every individual, including children, because damaged and untreated teeth will cause pain, and mastication disorders and can interfere with other body health. One of the most common dental health problems in children is dental caries. Dental disease is a very disturbing problem in school children because it not only causes pain but also transmits infections to other parts of the body, resulting in decreased productivity. This condition will certainly reduce the frequency of children's attendance at school, interfere with learning concentration, and affect appetite and food intake so that it can affect nutritional status. Objectives: To find out how the students of TPA Miftahul Jannah, Kota Depok in 2021 know about dental and oral health, determine priority issues and carry out effective health promotion. Method: This research is a study to find out the problem points in dental and oral health and carry out appropriate health promotion. The population in this study were TPA Miftahul Jannah students, Depok City in 2021 with a sampling technique, namely a total sampling of 30 respondents. The data collection used is the researcher's interview with the interview question sheet to the respondents. Determination of priority problems using the Criteria Matrix Technique. Results: Results Based on research from 30 respondents from Miftahul Jannah TPA students in Depok-West Java, as many as 16 respondents (53.3%) had never checked their teeth every 6 months for the last 1 month. This is due to a lack of knowledge about the importance of maintaining dental and oral health. Conclusion: It was concluded that the priority problem point was never having a dental check-up every 6 months related to oral health maintenance. So the health promotion that the researchers did was the use of powerpoint slides and videos to effectively increase respondents' knowledge, which was marked by increased motivation and behavior to maintain dental hygiene.

GIS-based identification of potential watershed recharge zones using analytic hierarchy process in Sikkim Himalayan region

Identification of groundwater recharge zone is an important factor for water resource management in any area. The present study investigates the potential water recharge zones in the Sikkim state, a mountain region of the Eastern Himalayas. To identify potential watershed recharge zone in the region, a combination of geographic information system (GIS)-based weighted overlay index (WOI) tools and analytic hierarchy process (AHP) pairwise matrix techniques was applied. The delineated watershed recharge sites were classified into five different potential zones. The results show that the majority of the study area (54.22%) was under moderate recharge potential zone. Some of the suitable recharge potential sites are forest-covered regions; therefore, the construction of surface check dams could be a suitable recharge method; it will escalate the discharge in springs and also help to make them perineal. This study provides first-hand information on the groundwater recharge potential of East Sikkim, where the populace depends largely on spring water discharge. Also, it is useful in selecting areas for digging staggered contour trenches, pits, and other structures to recharge the spring water and improve the watershed management system.

A new approach based on shifted Vieta-Fibonacci-quasilinearization technique and its convergence analysis for nonlinear third-order Emden–Fowler equation with multi-singularity

A shifted Vieta-Fibonacci (SVF) matrix technique is presented to find the solution of third-order Emden–Fowler equations with nonlinearity and multi-singularity. To develop an efficient approach, the quasilinearization method (QLM) is first applied to the underlying model problem and then a collocation scheme based on the SVF functions is applied to the resultant sequence of linearized equations. The application of SFV-QLM yields an algebraic system of linear equations to be solved iteratively. A comprehensive error analysis is presented to show that the SVF series solution is convergent in the weighted L2 and L∞ norms. Finally, numerical experiments confirm the theoretical results and the applicability as well as the practicability of SVF-QLM algorithm. The consumed computational time for our proposed algorithm is provided. The proposed algorithm has numerous merits. It is not only effortless to implement, but also highly accurate and computationally efficient.

The collocation spectral method with domain decomposition for radiative heat transfer in two-dimensional enclosures

In this paper, the collocation spectral method (CSM) combined with domain decomposition method is developed to solve the radiative transfer equation in two-dimensional irregular domains. Three benchmark problems consist of the square enclosure, the L-shaped enclosure and the square enclosure with a centered obstruction are solved and compared with the published data to validate the ability of present developed method. The comparison shows good agreements and indicates that the method has a good accuracy for all problems. Then, the performances of influence matrix technique and iterative substructuring technique to exchange the radiative information between subdomains are compared. It is found that, in the serial computations, the computational cost of influence matrix technique is hundreds of times more expensive than that of iterative substructuring technique. The high cost of influence matrix technique is due to that the radiative intensity is a high dimensional variable with angular dependence, and tremendous subproblems have to be solved to construct the influence matrix. Finally, the modified CSM with domain decomposition, in which the radiative intensity is decomposed into three components, the real wall-related one, the virtual shared interface-related one, and the medium-related one, is proposed. The first two components are solved analytically, and the last one is still solved by the CSM. With such treatments, the computational cost is slightly increased, but the ray effect originated from step-change temperature of medium or step-change optical parameters can be effectively mitigated. Besides, the modified CSM with domain decomposition can also avoid the ray effect due to shadowing singularities. But it should be noted that, in such case, the ray effect due to inhomogeneous spatial distribution of source term may emerge. In conclusion, the CSM combined with domain decomposition is a good alternative method for thermal radiation calculation in complex geometry which can be decomposed into regular subdomains.

A fully coupled, fully implicit simulation method for unsteady flames using Jacobian approximation and clustering

Jacobian clustering is proposed to reduce the computational cost associated with matrix operations encountered in the Newton iteration in fully coupled, fully implicit schemes for unsteady reactive flow simulations with detailed chemistry. The iterative solver is based on the Lower-Upper Symmetric Gauss-Seidel (LUSGS) algorithm and sparse matrix technique. The evaluation and sparse Lower-Upper (LU) factorization of the diagonal block of the system Jacobian are performed within clusters rather than individual cells for Computational Fluid Dynamics (CFD) simulations. Cells that are close in the state space are clustered to provide the averaged states for calculating the Jacobians of chemical source terms and transport fluxes. The cells retrieve the factorized sparse matrices from the belonging clusters to perform the necessary iterations. For the purpose of clustering, the spatial dependency of transport Jacobian in the diagonal block is eliminated. To further reduce the computational cost, the sparsity of chemical Jacobian is augmented by removing the insignificant matrix elements. The method is tested in various one-dimensional hydrocarbon flames with both the second order Crank-Nicolson scheme and a third order implicit Runge-Kutta scheme. Various chemical mechanisms with 9 to 111 species are used to test the performance of the iterative solver. Fast convergence of Newton iteration is achieved, and the formal order of accuracy is demonstrated with Jacobian clustering. The overall costs of evaluation and factorization of the block diagonal Jacobian are negligible compared to the cost of calculating transport fluxes and chemical source terms. The averaged costs of Jacobian evaluation, LU factorization and Newton iteration, all increase only linearly with the number of chemical species. The fully coupled, fully implicit Crank-Nicolson scheme with Jacobian clustering shows 4 to 42 times speedup in computational time compared to the decoupled implicit scheme with Strang operator splitting. Jacobian clustering is promising to increase the computational efficiency of high order fully coupled, fully implicit schemes for unsteady reactive flow simulations with detailed chemistry.

Effect of Vaccination and Culling on the Dynamics of Rabies Transmission from Stray Dogs to Domestic Dogs

In this paper, the population dynamics of rabies-infected dogs are studied. The mathematical model is constructed by dividing the dog population into two categories: stray dogs and domestic dogs. On the other hand, the rabies virus is likely to spread in both populations. In the current model, disease-controlling strategies such as vaccination and culling are applied, and their impact is studied. Both subpopulations of susceptible individuals are vaccinated to control disease spread. The current study assumes that stray dogs can transmit rabies to domestic dogs but not the other way around. Because domestic dogs are under the control of their owners, they are well vaccinated. The model is medically and analytically correct because the findings are idealistic and limited. The next-generation matrix technique is used to compute the effective reproductive amount, and also, each parameter is subjected to sensitivity analysis. The equilibrium point free from disease is discovered, demonstrating that it was asymptotically steady locally and globally. A conditionally global asymptotically stable point of endemic equilibrium is also discovered using the Lyapunov function method. The numerical simulation, which makes use of approximations for parameter values, shows that the most efficient method for avoiding rabies transmission is a combination of vaccination and the culling of infected stray dogs. Using MATLAB’s ode45, this numerical simulation investigation was carried out. Our early findings indicated that the annual dog birth rate is a critical factor in influencing the occurrence of rabies. In the body of the paper, the findings and discussion are organized logically.

Numerical modeling on age-based study of coronavirus transmission

Ethiopia is one of the countries highly affected by the COVID-19 pandemic in Africa. A new study has looked at the transmission dynamics of the outbreak in Ethiopia based on the age categories of the infected individuals. Infected individuals are divided into three age categories (less than 14 years (I), 15–54 years (I), and above 54 years I). Chebyshev polynomial of the fourth kind is used to convert a fractional system into an algebraic system of equations, which is then solved using iterative methods. The fundamental reproduction number is calculated using the next-generation matrix technique, and the disease-free and endemic equilibrium points, as well as their stability, are carefully studied. We calculated the parameters and starting conditions provided for our model using data from the Ethiopian Public Health Institute (EPHI) and the Ethiopian Ministry of Health from 29 February 2021, to 7 June 2021.

Polarized induced phase grating in a quantized four-level graphene monolayer system.

We discuss the electromagnetically induced grating (EIG) and electromagnetically induced phase grating (EIPG) in a four-level quantized graphene monolayer system. By using the density matrix technique and perturbation theory, we first obtain the self-Kerr nonlinear susceptibility of the graphene system; afterwards, we study the amplitude and phase modulations of the probe light. We discovered that the EIG and EIPG can be found by controlling the elliptically polarized coupling fields that interact with the monolayer graphene system. Owing to the phase modulation of the transmitted light beam, we recognized that the probe strength can also additionally switch from zeroth-order to high-order diffraction. Moreover, we found that the diffraction performance of the grating may be adjusted through tuning the polarization of the coupling light.

Clustering of differentials in CRAFT with correlation matrices

CRAFT is an substitution-permutation network tweakable block cipher proposed at fast software encryption 2019 by Beierle et al., which is designed to optimize the efficient protection against differential fault analysis (DFA) attacks. In this paper, the full round differential characteristics for CRAFT block cipher are given. A new method on counting the number of differentials by using correlation matrix is given. We can compute the number of all optimal characteristics or suboptimum differentials with the same input difference and output difference by hand. We explore the multiple differential trails and compute the probability of differential characteristic by using the multiplication of correlation matrices. Our work complements automatic search methods for the best differential with a careful manual analysis. Since the automatic search method is stranded by storage and search space limitations, which will cause a computer to crash as the number of search rounds increases. Thanks to the correlation matrix technique, we are able to find differential distinguishers for 9-round of the cipher with the probability of at least 2 − 40.68 + 2 − 48.60 ${2}^{-40.68}+{2}^{-48.60}$ . Moreover, we can construct differential distinguisher covers more rounds based on the 9-round differential distinguishers. As one of its typical application, we propose the differential characteristics for the full-round CRAFT which ensure that the probability of each round is optimal. Besides, we explore the clustering effect on the full round by exhibiting a class of high probability characteristics for 9-round. In general, we obtain a good understanding of the propagation of differences for CRAFT due to its algebraic structure.

Atangana–Baleanu derivative-based fractional model of COVID-19 dynamics in Ethiopia

ABSTRACT The paper's main aim is to investigate the 2019 coronavirus disease in Ethiopia using a fractional-order mathematical model. It would also focus on the importance of fractional-order derivatives that may help us in modelling the system and understanding the effect of model parameters and fractional derivative orders on the approximate solutions of our model. A SELAIQHCR model is constructed using nonlinear differential equations in the Atangana–Baleanu non-integer operator in the Caputo sense. After that, the Chebyshev fourth kind spectral collocation method is used to change a fractional system to an algebraic system. Newton iterative technique is used to solve the converted system. The next-generation matrix technique is used to obtain the effective reproduction number. The COVID-19-free equilibrium point and endemic equilibrium point, solution positivity and boundedness, and their stability are all carefully done. The sensitivity of the effective reproduction value with respect to the key model parameters is discussed. The beginning values provided for our system were obtained using reports from the Ethiopian Public Health Institute from 29 February 2021 to 7 June 2021. The fundamental reproduction number is obtained with . The model's numerical solutions are represented graphically.

Comparative investigation between complex energy and transfer matrix techniques for quasi-energy states in heterostructure materials

A comparative analysis is performed to study the resonant tunneling through symmetric multiple double-barrier resonant tunneling heterostructure materials composed of nanoscale semiconductors ZnSe/BeTe, AlAs/GaAs, InGaAs/AlInAs, and AlGaAs/GaAs. Two techniques are applied in the present investigation for calculating the quasi-resonant energy states and their associated lifetimes in nanoscale double-barrier quantum well heterostructures. The first technique is based on the complex energy solution of the time-dependent Schrödinger equation with the time–energy uncertainty condition. The second one is employing the transfer matrix method. The quasi-resonant energies, lifetimes, and frequencies in nanoscale double-barrier quantum well heterostructures are calculated by both methods and compared. The validity and accuracy of both techniques are tested and compared extensively with various current numerical methods. Excellent agreements are obtained between our results and the available experimental and theoretical values. It is also found that the complex energy technique is recommended when handling the quasi-energy states and quasi-lifetimes of novel nanoscale devices.

Study of first-order corrections and phase transition of RN de-sitter Black Hole

In this paper, we study the phase transition and thermodynamics of Reissner–Nordstrom black hole. We compute the thermodynamics quantities such as Hawking temperature, Bekenstein entropy and heat capacity by using mathematical and graphical analysis. We obtain the corrected entropy to examine the effect of logarithmic corrections on the different thermodynamic quantities like pressure, Gibbs free energy, Helmholtz free energy, internal energy, enthalpy. Moreover, we investigate the stability of the system with the help of heat capacity and well known Hessian matrix technique. It is noted that the considered system is thermodynamically stable under these correction terms.

Data-driven fault monitoring for spacecraft control moment gyro with slice residual attention network

This paper studies the fault monitoring problem of a spacecraft control moment gyro (CMG) in complex environments based on the data-driven method. First, the wavelet denoising method and short-time Fourier transform (STFT) are utilized to preprocess the signal measured by an industrial personal computer (IPC) to obtain the frequency spectrum of each failure mode. Then, a slice residual attention network (SRAN) based on the ResNeXt model, attention mechanism, and random slice idea is proposed, which can fully capture the edge features of images while satisfying the learning efficiency. Furthermore, a set of comparative experiments are carried out to validate the ability of the proposed method, and the performance of SRAN is further verified under different datasets. Finally, based on the confusion matrix and t-SNE dimension reduction technique, the monitoring ability of SRAN for various faults is analyzed. Experimental results show that SRAN processes good fault monitoring capability and ideal robustness and can identify different fault degrees under the real-time fault monitoring scenario.