In the current study,applying simultaneously electroporation and silver nanoparticles(SNPs)are considered.Moreover,one restriction normally assigned to such nanoparticles is their side effects on the vital organs of the body.To mitigate such deleterious effects,it is better to use lower dosages of them.However,this can result in a decline in the technique's effectiveness.To compensate for the lower dose of SNPs,one can use secondary method like electroporation to deliver SNPs directly into the cells and reinforces the effect of electric pulses due to the high electrical conductivity of SNPs while having a minimal cytotoxicity effect on normal cells that are not treated with electroporation.In the present study,synergism effects of both procedures(SNPs and electroporation)experimentally and theoretically are considered to investigate the property of each technique in increasing the performance with respect to both procedures'limitations.To investigate more,adaptive neuro-fuzzy inference system(ANFIS)is used to predict the percent cell viability of cancerous cells affected by both procedures by considering amplitude and duration as inputs affecting on change of cell viability as an output.The results obtained from both experimental and simulation procedures showed that the maximum synergism between nanoparticles and electric pulses was recorded at 700V/cm strength and 100μs duration.Also,Results indicated high correlation between observed and predicted data(r2=0.88).Moreover,the calculated root mean square error for the results of the ANFIS model was equal to 1.1.This implies that the model has practical value and can estimate the percent cell viability of cancerous cells influenced by both procedures with varying electric field amplitude and duration.This method can be proposed for other biophysical or drug delivery applications to save time and resources by utilizing the previous experimental data rather than performing more experiments.
The distinct characteristics of photovoltaic generators related to power and current present a complex problem in terms of opti-mizing their power output.To tackle this,maximum-power-point tracking techniques such as the adaptive neuro-fuzzy inference system are frequently utilized for their swift adaptability and reduced fluctuations.In addition,the backstepping controller is often selected to handle both linear and non-linear systems due to its exceptional reliability.The purpose of this research is to propose an innovative method that merges the adaptive neuro-fuzzy inference system and backstepping controller to refine the tracking of the optimal power point and to bolster the stability of the photovoltaic system in the face of unpredictable scenarios,such as those pre-sented by the Ropp irradiance examination,which utilizes a single-ended primary inductor converter as a stage for power electronics adaptation.Simulations conducted using MATLAB®/Simulink®demonstrate that the combination of adaptive neuro-fuzzy inference system and backstepping controller achieves an impressive efficiency of 99.6%and exhibits fast,robust,and accurate responses com-pared with other algorithms such as artificial neural networks combined with the backstepping controller and conventional perturb and observe algorithm.
ESystems based on EHRs(Electronic health records)have been in use for many years and their amplified realizations have been felt recently.They still have been pioneering collections of massive volumes of health data.Duplicate detections involve discovering records referring to the same practical components,indicating tasks,which are generally dependent on several input parameters that experts yield.Record linkage specifies the issue of finding identical records across various data sources.The similarity existing between two records is characterized based on domain-based similarity functions over different features.De-duplication of one dataset or the linkage of multiple data sets has become a highly significant operation in the data processing stages of different data mining programmes.The objective is to match all the records associated with the same entity.Various measures have been in use for representing the quality and complexity about data linkage algorithms,and many other novel metrics have been introduced.An outline of the problem existing in themeasurement of data linkage and de-duplication quality and complexity is presented.This article focuses on the reprocessing of health data that is horizontally divided among data custodians,with the purpose of custodians giving similar features to sets of patients.The first step in this technique is about an automatic selection of training examples with superior quality from the compared record pairs and the second step involves training the reciprocal neuro-fuzzy inference system(RANFIS)classifier.Using the Optimal Threshold classifier,it is presumed that there is information about the original match status for all compared record pairs(i.e.,Ant Lion Optimization),and therefore an optimal threshold can be computed based on the respective RANFIS.Febrl,Clinical Decision(CD),and Cork Open Research Archive(CORA)data repository help analyze the proposed method with evaluated benchmarks with current techniques.
Ala Saleh AlluhaidanPushparajAnitha SubbappaVed Prakash MishraP.V.ChandrikaAnurika VaishSarthak Sengupta
It is one of the topics that have been studied extensively on maximum power point tracking(MPPT)recently.Traditional or soft computing methods are used for MPPT.Since soft computing approaches are more effective than traditional approaches,studies on MPPT have shifted in this direction.This study aims comparison of performance of seven meta-heuristic training algorithms in the neuro-fuzzy training for MPPT.The meta-heuristic training algorithms used are particle swarm optimization(PSO),harmony search(HS),cuckoo search(CS),artificial bee colony(ABC)algorithm,bee algorithm(BA),differential evolution(DE)and flower pollination algorithm(FPA).The antecedent and conclusion parameters of neuro-fuzzy are determined by these algorithms.The data of a 250 W photovoltaic(PV)is used in the applications.For effective MPPT,different neuro-fuzzy structures,different membership functions and different control parameter values are evaluated in detail.Related training algorithms are compared in terms of solution quality and convergence speed.The strengths and weaknesses of these algorithms are revealed.It is seen that the type and number of membership function,colony size,number of generations affect the solution quality and convergence speed of the training algorithms.As a result,it has been observed that CS and ABC algorithm are more effective than other algorithms in terms of solution quality and convergence in solving the related problem.
The immune system goes through a profound transformation during pregnancy,and certain unexpected maternal complications have been correlated to this transition.The ability to correctly examine,diagnoses,and predict pregnancy-hastened diseases via the available big data is a delicate problem since the range of information continuously increases and is scalable.Many approaches for disease diagnosis/classification have been established with the use of data mining concepts.However,such methods do not provide an appropriate classification/diagnosis model.Furthermore,single learning approaches are used to create the bulk of these systems.Classification issues may be made more accurate by combining predictions from many different techniques.As a result,we used the Ensembling of Neuro-Fuzzy(E-NF)method to perform a high-level classification of medical diseases.E-NF is a layered computational model with self-learning and self-adaptive capabilities to deal with specific problems,such as the handling of imprecise and ambiguous data that may lead to uncertainty concerns that specifically emerge during the classification stage.Preprocessing data,Training phase,Ensemble phase,and Testing phase make up the complete procedure for the suggested task.Data preprocessing includes feature extraction and dimensionality reduction.Besides such processes,the training phase includes the fuzzification process of medical data.Moreover,training of input data was done using four types of NF techniques:Fuzzy Adaptive Learning Control Network(FALCON),Adaptive Network-based Fuzzy Inference System(ANFIS),Self Constructing Neural Fuzzy Inference Network(SONFIN)and/Evolving Fuzzy Neural Network(EFuNN).Later,in the ensemble phase,all the NF methods’predicted outcomes are integrated,and finally,the test results are evaluated in the testing phase.The outcomes indicate that the method could predict impaired glucose tolerance,preeclampsia,gestational hypertensive abnormalities,bacteriuria,and iron deficiency anaemia better than the others.In addition,
Purpose-The Covid 19 prediction process is more indispensable to handle the spread and deathocurred rate because of Covid-19.However early and precise prediction of Covid-19 is more difcult because of different sizes and resolutions of input image Thus these challenges and problems experienced by traditional Covid-19 detection methods are considered as major motivation to develop JHBO-based DNFN.Design/methodology/approach-The major contribution of this research is to desigm an ffectualCovid-19 detection model using devised JHBObased DNFN,Here,the audio signal is considered as input for detecting Covid-19.The Gaussian filter is applied to input signal for removing the noises and then feature extraction is performed.The substantial features,like spectral rlloff.spectral bandwidth,Mel-frequency,cepstral coefficients (MFCC),spectral flatness,zero crossing rate,spectral centroid,mean square energy and spectral contract are extracted for further processing.Finally,DNFN is applied for detecting Covid 19 and the deep leaning model is trained by designed JHBO algorithm.Accordingly.the developed JHBO method is newly desigmed by inoorporating Honey Badger optimization Algorithm(HBA)and.Jaya algorithm.Findings-The performance of proposed hybrid optimization-based deep learming algorithm is estimated by meansof twoperformance metrics,namely testing accuracy,sensitivity and speificity of 09176,09218 and 09219.Research limitations/implications-The JHBO-based DNFN approach is developed for Covid-19 detection.The developed approach can be extended by including other hybrid optimization algorithms as well as other features can be extracted for further improving the detection performance.Practical implications-The proposed Covid-19 detection method is useful in various applications,like medical and so on,Originality/value-Developed JHBO-enabled DNFN for Covid-19 detection:An effective Covid-19 detection technique is introduced based on hybrid optimization-driven deep learning model The DNFN is used for detecting Covid-19,which cla
Jawad Ahmad DarKamal Kr SrivastavaSajaad Ahmad Lone
