Analysis of Pneumonia Model via Efficient Computing Techniques

Pneumonia is a highly transmissible disease in children.According to the World Health Organization(WHO),the most affected regions include south Asia and sub-Saharan Africa.Worldwide,15%of pediatric deaths can be attributed to pneumonia.Computing techniques have a significant role in science,engineering,and many other fields.In this study,we focused on the efficiency of numerical techniques via computer programs.We studied the dynamics of the pneumonia-like infections of epidemic models using numerical techniques.We discuss two types of analysis:dynamical and numerical.The dynamical analysis included positivity,boundedness,local stability,reproduction number,and equilibria of the model.We also discusswell-known computing techniques including Euler,Runge Kutta,and non-standard finite difference(NSFD)for the model.The non-standard finite difference(NSFD)technique shows convergence to the true equilibrium points of the model for any time step size.However,Euler and Runge Kutta do not work well over large time intervals.Computing techniques are the suitable tool for crosschecking the theoretical analysis of the model.

SPECTRAL TECHNIQUES AND SOFT COMPUTING

Soft Computing denotes a set of paradigma related to cognitive modelling, which in the last years have been intensively studied, since important synergy effects among members of this set have been disclosed. Because of this, Soft Computing has emerged as an environment to effectively work with red world complex problems. Fuzzy Logic, Genetic Algorithms and Neural Networks are possibly the best known representatives of Soft Computing. In this paper we show how Spectral Techniques may help to further study these subjects or to improve their performance. The name Spectral Techniques comprises Methods and Applications based on Abstract Harmonic Analysis.

New Trends in the Modeling of Diseases Through Computational Techniques

The computational techniques are a set of novel problem-solving methodologies that have attracted wider attention for their excellent performance.The handling strategies of real-world problems are artificial neural networks(ANN),evolutionary computing(EC),and many more.An estimated fifty thousand to ninety thousand new leishmaniasis cases occur annually,with only 25%to 45%reported to the World Health Organization(WHO).It remains one of the top parasitic diseases with outbreak and mortality potential.In 2020,more than ninety percent of new cases reported to World Health Organization(WHO)occurred in ten countries:Brazil,China,Ethiopia,Eritrea,India,Kenya,Somalia,South Sudan,Sudan,and Yemen.The transmission of visceral leishmaniasis is studied dynamically and numerically.The study included positivity,boundedness,equilibria,reproduction number,and local stability of the model in the dynamical analysis.Some detailed methods like Runge Kutta and Euler depend on time steps and violate the physical relevance of the disease.They produce negative and unbounded results,so in disease dynamics,such developments have no biological significance;in other words,these results are meaningless.But the implicit nonstandard finite difference method does not depend on time step,positive,bounded,dynamic and consistent.All the computational techniques and their results were compared using computer simulations.

Partition region-based suppressed fuzzy C-means algorithm

Aimed at the problem that the traditional suppressed fuzzy C-means clustering algorithms ignore the real needs of different objects, applying the same suppressed parameter for modifying membership degrees of all the objects, a novel partition region-based suppressed fuzzy C-means clustering algorithm with better capacity of adaptability and robustness is proposed in this paper. The model based on the real needs of different objects is built, making it clear to decide whether to proceed with further determination; in addition, the external user-defined suppressed parameter is automatically selected according to the intrinsic structural characteristic of each dataset, making the proposed method become robust to the fluctuations in the incoming dataset and initial conditions. Experimental results show that the proposed method is more robust than its counterparts and overcomes the weakness of the original suppressed clustering algorithm in most cases.

Network Quality Assessment in Heterogeneous Wireless Settings: An Optimization Approach

The identification of an effective network which can efficiently meet the service requirements of the target,while maintaining ultimate performance at an increased level is significant and challenging in a fully interconnected wireless medium.The wrong selection can contribute to unwanted situations like frustrated users,slow service,traffic congestion issues,missed and/or interrupted calls,and wastefulness of precious network components.Conventional schemes estimate the handoff need and cause the network screening process by a single metric.The strategies are not effective enough because traffic characteristics,user expectations,network terminology and other essential device metrics are not taken into account.This article describes an intelligent computing technique based on Multiple-Criteria Decision-Making(MCDM)approach developed based on integrated Fuzzy AHP-TOPSIS which ensures flexible usability and maximizes the experience of end-users in miscellaneous wireless settings.In different components the handover need is assessed and the desired network is chosen.Further,fuzzy sets provide effective solutions to address decision making problems where experts counter uncertainty to make a decision.The proposed research endeavor will support designers and developers to identify,select and prioritize best attributes for ensuring flexible usability in miscellaneous wireless settings.The results of this research endeavor depict that this proposed computational procedure would be the most conversant mechanism for determining the usability and experience of end-users.

Determination of Istanbul geoid using GNSS/levelling and valley cross levelling data

The Istanbul GPS Triangulation Network(IGTN) and the Istanbul Levelling Network(ILN),established in2006,provide data for the determination of a local GNSS/levelling geoid model.These networks’ measurements were done separately on both the Asian and European sides of the Bosphorus Strait in the vicinity of Istanbul.To connect these regions for those networks,a Valley Cross Levelling(VCL) data set,acquired in 1986 and 2004,was used.The use of this VCL data set was challenging in calculating the Istanbul geoid model,primarily because of its errors.In this study,this challenge was overcome through newly collected VCL data in 2010,allowing for the readjustment of the ILN and the newly collected VCL data set.The Istanbul geoid model was computed using soft computing techniques including the adaptive-network-based fuzzy inference system(ANFIS) and the artificial neural networks(ANNs).The resulting Istanbul GNSS/levelling geoid model is shown to be more reliable when compared with the model computed using conventional interpolation techniques.

Crowd evacuation simulation model with soft computing optimization techniques:a systematic literature review

Crowd evacuation simulation is an essential element when it comes to planning and preparation in evacuation management.This paper presents the survey based on systematic literature review(SLR)technique that aims to identify the crowd evacuation under microscopic model integrated with soft computing technique from previous works.In the review process,renowned databases were searched to retrieve the primary articles and total 38 studies were thoroughly studied.The researcher has identified the potential optimization factors in simulating crowd evacuation and research gaps based on acquired issues,limitation and challenges in this domain.The results of this SLR will serve as a guideline for the researchers that have same interest to develop better and effective crowd evacuation simulation model.The future direction from this SLR also suggests that there is a potential to hybrid the model with softcomputing optimization focusing on latest nature-inspired algorithms in improving the crowd evacuation model.

Spatial gradient distributions of thermal shock-induced damage to granite

In this study,we attempted to investigate the spatial gradient distributions of thermal shock-induced damage to granite with respect to associated deterioration mechanisms.First,thermal shock experiments were conducted on granite specimens by slowly preheating the specimens to high temperatures,followed by rapid cooling in tap water.Then,the spatial gradient distributions of thermal shock-induced damage were investigated by computed tomography(CT)and image analysis techniques.Finally,the influence of the preheating temperature on the spatial gradients of the damage was discussed.The results show that the thermal shock induced by rapid cooling can cause more damage to granite than that induced by slow cooling.The thermal shock induced by rapid cooling can cause spatial gradient distributions of the damage to granite.The damage near the specimen surface was at a maximum,while the damage inside the specimen was at a minimum.In addition,the preheating temperature can significantly influence the spatial gradient distributions of the thermal shock-induced damage.The spatial gradient distribution of damage increased as the preheating temperature increased and then decreased significantly over 600
C.When the preheating temperature was sufficiently high(e.g.800
C),the gradient can be ignored.

A worldwide SPT-based soil liquefaction triggering analysis utilizing gene expression programming and Bayesian probabilistic method

In this context,two different approaches of soil liquefaction evaluation using a soft computing technique based on the worldwide standard penetration test(SPT) databases have been studied.Gene expression programming(GEP) as a gray-box modeling approach is used to develop different deterministic models in order to evaluate the occurrence of soil liquefaction in terms of liquefaction field performance indicator(LI) and factor of safety(FS) in logistic regression and classification concepts.The comparative plots illustrate that the classification concept-based models show a better performance than those based on logistic regression.In the probabilistic approach,a calibrated mapping function is developed in the context of Bayes’ theorem in order to capture the failure probabilities(PL) in the absence of the knowledge of parameter uncertainty.Consistent results obtained from the proposed probabilistic models,compared to the most well-known models,indicate the robustness of the methodology used in this study.The probability models provide a simple,but also efficient decision-making tool in engineering design to quantitatively assess the liquefaction triggering thresholds.

Numerical Simulation of ATPS Parachute Transient Dynamics Using Fluid-Structure Interaction Method

In order to simulate and analyze the dynamic characteristics of the parachute from advanced tactical parachute system(ATPS),a nonlinear finite element algorithm and a preconditioning finite volume method are employed and developed to construct three dimensional parachute fluid-structure interaction(FSI)model.Parachute fabric material is represented by membrane-cable elements,and geometrical nonlinear algorithm is employed with wrinkling technique embedded to simulate the large deformations of parachute structure by applying the NewtonRaphson iteration method.On the other hand,the time-dependent flow surrounding parachute canopy is simulated using preconditioned lower-upper symmetric Gauss-Seidel(LU-SGS)method.The pseudo solid dynamic mesh algorithm is employed to update the flow-field mesh based on the complex and arbitrary motion of parachute canopy.Due to the large amount of computation during the FSI simulation,massage passing interface(MPI)parallel computation technique is used for all those three modules to improve the performance of the FSI code.The FSI method is tested to simulate one kind of ATPS parachutes to predict the parachute configuration and anticipate the parachute descent speeds.The comparison of results between the proposed method and those in literatures demonstrates the method to be a useful tool for parachute designers.

Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

Water level predictions in the river,lake and delta play an important role in flood management.Every year Mekong River delta of Vietnam is experiencing flood due to heavy monsoon rains and high tides.Land subsidence may also aggravate flooding problems in this area.Therefore,accurate predictions of water levels in this region are very important to forewarn the people and authorities for taking timely adequate remedial measures to prevent losses of life and property.There are so many methods available to predict the water levels based on historical data but nowadays Machine Learning(ML)methods are considered the best tool for accurate prediction.In this study,we have used surface water level data of 18 water level measurement stations of the Mekong River delta from 2000 to 2018 to build novel time-series Bagging based hybrid ML models namely:Bagging(RF),Bagging(SOM)and Bagging(M5P)to predict historical water levels in the study area.Performances of the Bagging-based hybrid models were compared with Reduced Error Pruning Trees(REPT),which is a benchmark ML model.The data of 19 years period was divided into 70:30 ratio for the modeling.The data of the period 1/2000 to 5/2013(which is about 70%of total data)was used for the training and for the period 5/2013 to 12/2018(which is about 30%of total data)was used for testing(validating)the models.Performance of the models was evaluated using standard statistical measures:Coefficient of Determination(R2),Root Mean Square Error(RMSE)and Mean Absolute Error(MAE).Results show that the performance of all the developed models is good(R2>0.9)for the prediction of water levels in the study area.However,the Bagging-based hybrid models are slightly better than another model such as REPT.Thus,these Bagging-based hybrid time series models can be used for predicting water levels at Mekong data.

Adaptive Fuzzy Logic Controller for Harmonics Mitigation Using Particle Swarm Optimization

An excessive use of non-linear devices in industry results in current harmonics that degrades the power quality with an unfavorable effect on power system performance.In this research,a novel control techniquebased Hybrid-Active Power-Filter(HAPF)is implemented for reactive power compensation and harmonic current component for balanced load by improving the Power-Factor(PF)and Total–Hormonic Distortion(THD)and the performance of a system.This work proposed a soft-computing technique based on Particle Swarm-Optimization(PSO)and Adaptive Fuzzy technique to avoid the phase delays caused by conventional control methods.Moreover,the control algorithms are implemented for an instantaneous reactive and active current(Id-Iq)and power theory(Pq0)in SIMULINK.To prevent the degradation effect of disturbances on the system’s performance,PS0-PI is applied in the inner loop which generate a required dc link-voltage.Additionally,a comparative analysis of both techniques has been presented to evaluate and validate the performance under balanced load conditions.The presented result concludes that the Adaptive Fuzzy PI controller performs better due to the non-linearity and robustness of the system.Therefore,the gains taken from a tuning of the PSO based PI controller optimized with Fuzzy Logic Controller(FLC)are optimal that will detect reactive power and harmonics much faster and accurately.The proposed hybrid technique minimizes distortion by selecting appropriate switching pulses for VSI(Voltage Source Inverter),and thus the simulation has been taken in SIMULINK/MATLAB.The proposed technique gives better tracking performance and robustness for reactive power compensation and harmonics mitigation.As a result of the comparison,it can be concluded that the PSO-basedAdaptive Fuzzy PI system produces accurate results with the lower THD and a power factor closer to unity than other techniques.

Sunflower leaf diseases detection using image segmentation based on particle swarm optimization

Sun flower(Helianthus annuus L.)is one of the important oil seed crops and potentially fit in agricultural system and oil production sector of India.Sunflower crop gets damaged by the impact of various diseases,insects and nematodes resulting in wide range of loss in production.Disease detection is possible through naked eye observation,but this method is unsuccessful when one has to monitor the large farms.As a solution to this problem,we developed and present a system for segmentation and classification of Sunflower leaf images.This research paper presents surveys conducted on different diseases classification techniques that can be used for sunflower leaf disease detection.Segmentation of Sunflower leaf images,which is an important aspect for disease classification,is done by using Particle swarm optimization algorithm.Satisfactory results have been given by the experiments done on leaf images.The average accuracy of classification of proposed algorithm is 98.0%compared to 97.6 and 92.7%reported in state-of-the-art methods.

Virtual Determination of Sex:Estimating Cut off Value of Digital Metric Traits of Foramen Magnum on Three-dimensional Computed Tomography with Receiver Operating Characteristic and Logistic Regression Analysis

Background:Radiological imaging plays a pivotal role in forensic anthropology.As have the imaging techniques advances,so have the digital skeletal measurements inched towards precision.Secular trends of the population keep on changing in modem times.Hence,finding the precise technique of bone measurement,with greater reproducibility,in modem population is always needed in making population specific biological profile.Aim and Objective:The aim of this study was to estimate the accuracy of the foramen magnum measurement,obtained by three dimensional multi-detector computed tomography using volume rendering technique with the cut off value of each variable,in sex determination of an individual.Materials and Methods:Two metric traits,an antero-posterior diameter(APD)and transverse diameter(TD),were measured digitally in an analysis of 130 radiological images having equal proportion of male and female samples.Foramen magnum index and area of foramen magnum(Area by Radinsky's[AR],Area by Teixeira5s[AT])were derived from APD and TD.Results:Descriptive statistical analysis,using unpaired t-test,showed significant higher value in males in all the variables.Using Pearson correlation analysis,maximum correlation was observed between area(AT and AR r=0.999)and between area and TD(AR r=0.955 and AT r=0.945 respectively).When used individually,TD had the highest predictive value(67.7%)for sex detennination among all the parameters followed by AT(65.4%)and AR(64.6%).Cutoff value of variables TD,AR and AT were 29.9 mm,841.80 mm2 and 849.70 mm2 respectively.Receiver operating characteristic curve predicted male and female sex with 96.2%and 89.2%accuracy respectively.The overall accuracy of the model was 92.7%.Conclusion:Measurements from 3D CT using volume rendering technique were precise,and the application of logistic regression analysis predicted the sex with more accuracy.

Event-by-Event Simulation of the Hanbury Brown-Twiss Experiment with Coherent Light

We present a computer simulation model for the Hanbury Brown-Twiss experiment that is entirely particle-based and reproduces the results of wave theory. Themodel is solely based on experimental facts, satisfies Einstein’s criterion of local causality and does not require knowledge of the solution of a wave equation. The simulationmodel is fully consistent with earlier work and provides another demonstration thatit is possible to give a particle-only description of wave phenomena, rendering theconcept of wave-particle duality superfluous.