The understanding of water resource changes and a proper projection of their future availability are necessary elements of sustainable water planning.Monitoring GWS change and future water resource availability are cr...The understanding of water resource changes and a proper projection of their future availability are necessary elements of sustainable water planning.Monitoring GWS change and future water resource availability are crucial,especially under changing climatic conditions.Traditional methods for in situ groundwater well measurement are a significant challenge due to data unavailability.The present investigation utilized the Long Short Term Memory(LSTM)networks to monitor and forecast Terrestrial Water Storage Change(TWSC)and Ground Water Storage Change(GWSC)based on Gravity Recovery and Climate Experiment(GRACE)datasets from 2003-2025 for five basins of Saudi Arabia.An attempt has been made to assess the effects of rainfall,water used,and net budget modeling of groundwater.Analysis of GRACE-derived TWSC and GWSC estimates indicates that all five basins show depletion of water from 2003-2020 with a rate ranging from−5.88±1.2 mm/year to−14.12±1.2 mm/year and−3.5±1.5 to−10.7±1.5,respectively.Forecasting based on the developed LSTM model indicates that the investigated basins are likely to experience serious water depletion at rates ranging from−7.78±1.2 to−15.6±1.2 for TWSC and−4.97±1.5 to−12.21±1.5 for GWSC from 2020-2025.An interesting observation was a minor increase in rainfall during the study period for three basins.展开更多
Objective:To synthesize bio-inspired gold nanoparticles(AuNPs)using the leaf extract of Justicia adhatoda and evaluate the anti-cancer activity on human lung cancer cell line(A549).Methods:Synthesis of AuNPs was done ...Objective:To synthesize bio-inspired gold nanoparticles(AuNPs)using the leaf extract of Justicia adhatoda and evaluate the anti-cancer activity on human lung cancer cell line(A549).Methods:Synthesis of AuNPs was done using an aqueous leaf extract of Justicia adhatoda as a green route.The bio-synthesized AuNPs were confirmed and characterized by using various spectral studies such as UV-Vis spectrum,Scanning Electron Microscope with EDAX,Transmission Electron Microscope,Fourier Transmission Infrared Spectroscope analysis and Surface Enhanced Raman Spectroscopy.The cell viability was determined by MTT reduction assay.In addition,cytomorphology and the nuclear morphological study of A549 cell line was observed under fluorescence microscope.Results:UV-Vis spectrum showed surface plasmon resonance peak at 547 nm,scanning electron microscope and transmission electron microscope studies showed the monodispersed spherical shape and its average size in the range of 40.1 nm was noticed.Fourier Transmission Infrared Spectroscope analysis confirmed that the C=O group of amino acids of proteins had strong ability to bind with the surface of nanoparticle.Interestingly,our results also demonstrated inhibited proliferation of A549 cell line by MTT(IC50 value:80μg/mL).Cell morphology was observed and cell death was caused by apoptosis as revealed by propidium iodide staining.Conclusions:The current study proves the anticancer potential of bio-synthesized AuNPs.Thus,synthesized AuNPs can be used for the treatment of human lung cancer cell(A549)and it can be exploited for drug delivery in future.展开更多
COVID’19 has caused the entire universe to be in existential healthcrisis by spreading globally in the year 2020. The lungs infection is detected inComputed Tomography (CT) images which provide the best way to increa...COVID’19 has caused the entire universe to be in existential healthcrisis by spreading globally in the year 2020. The lungs infection is detected inComputed Tomography (CT) images which provide the best way to increasethe existing healthcare schemes in preventing the deadly virus. Nevertheless,separating the infected areas in CT images faces various issues such as lowintensity difference among normal and infectious tissue and high changes inthe characteristics of the infection. To resolve these issues, a new inf-Net (LungInfection Segmentation Deep Network) is designed for detecting the affectedareas from the CT images automatically. For the worst segmentation results,the Edge-Attention Representation (EAR) is optimized using AdaptiveDonkey and Smuggler Optimization (ADSO). The edges which are identifiedby the ADSO approach is utilized for calculating dissimilarities. An IFCM(Intuitionistic Fuzzy C-Means) clustering approach is applied for computingthe similarity of the EA component among the generated edge maps andGround-Truth (GT) edge maps. Also, a Semi-Supervised Segmentation(SSS) structure is designed using the Randomly Selected Propagation (RP)technique and Inf-Net, which needs only less number of images and unlabelleddata. Semi-Supervised Multi-Class Segmentation (SSMCS) is designed usinga Bi-LSTM (Bi-Directional Long-Short-Term-memory), acquires all theadvantages of the disease segmentation done using Semi Inf-Net and enhancesthe execution of multi-class disease labelling. The newly designed SSMCSapproach is compared with existing U-Net++, MCS, and Semi-Inf-Net.factors such as MAE (Mean Absolute Error), Structure measure, Specificity(Spec), Dice Similarity coefficient, Sensitivity (Sen), and Enhance-AlignmentMeasure are considered for evaluation purpose.展开更多
The brain of humans and other organisms is affected in various ways through the electromagneticfield(EMF)radiations generated by mobile phones and cell phone towers.Morphological variations in the brain are caused by t...The brain of humans and other organisms is affected in various ways through the electromagneticfield(EMF)radiations generated by mobile phones and cell phone towers.Morphological variations in the brain are caused by the neurological changes due to the revelation of EMF.Cellular level analysis is used to measure and detect the effect of mobile radiations,but its utilization seems very expensive,and it is a tedious process,where its analysis requires the preparation of cell suspension.In this regard,this research article proposes optimal broadcast-ing learning to detect changes in brain morphology due to the revelation of EMF.Here,Drosophila melanogaster acts as a specimen under the revelation of EMF.Automatic segmentation is performed for the brain to attain the microscopic images from the prejudicial geometrical characteristics that are removed to detect the effect of revelation of EMF.The geometrical characteristics of the brain image of that is microscopic segmented are analyzed.Analysis results reveal the occur-rence of several prejudicial characteristics that can be processed by machine learn-ing techniques.The important prejudicial characteristics are given to four varieties of classifiers such as naïve Bayes,artificial neural network,support vector machine,and unsystematic forest for the classification of open or nonopen micro-scopic image of D.melanogaster brain.The results are attained through various experimental evaluations,and the said classifiers perform well by achieving 96.44%using the prejudicial characteristics chosen by the feature selection meth-od.The proposed system is an optimal approach that automatically identifies the effect of revelation of EMF with minimal time complexity,where the machine learning techniques produce an effective framework for image processing.展开更多
Ad hoc mobile cloud computing networks are affected by various issues,like delay,energy consumption,flexibility,infrastructure,network lifetime,security,stability,data transition,and link accomplishment.Given the issu...Ad hoc mobile cloud computing networks are affected by various issues,like delay,energy consumption,flexibility,infrastructure,network lifetime,security,stability,data transition,and link accomplishment.Given the issues above,route failure is prevalent in ad hoc mobile cloud computing networks,which increases energy consumption and delay and reduces stability.These issues may affect several interconnected nodes in an ad hoc mobile cloud computing network.To address these weaknesses,which raise many concerns about privacy and security,this study formulated clustering-based storage and search optimization approaches using cross-layer analysis.The proposed approaches were formed by cross-layer analysis based on intrusion detection methods.First,the clustering process based on storage and search optimization was formulated for clustering and route maintenance in ad hoc mobile cloud computing networks.Moreover,delay,energy consumption,network lifetime,and link accomplishment are highly addressed by the proposed algorithm.The hidden Markov model is used to maintain the data transition and distributions in the network.Every data communication network,like ad hoc mobile cloud computing,faces security and confidentiality issues.However,the main security issues in this article are addressed using the storage and search optimization approach.Hence,the new algorithm developed helps detect intruders through intelligent cross layer analysis with theMarkov model.The proposed model was simulated in Network Simulator 3,and the outcomes were compared with those of prevailing methods for evaluating parameters,like accuracy,end-to-end delay,energy consumption,network lifetime,packet delivery ratio,and throughput.展开更多
This research article proposes an automatic frame work for detectingCOVID -19 at the early stage using chest X-ray image. It is an undeniable factthat coronovirus is a serious disease but the early detection of the vi...This research article proposes an automatic frame work for detectingCOVID -19 at the early stage using chest X-ray image. It is an undeniable factthat coronovirus is a serious disease but the early detection of the virus presentin human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in needof right and even rich technology for its early detection. The proposed deeplearning model analysis the pixels of every image and adjudges the presence ofvirus. The classifier is designed in such a way so that, it automatically detectsthe virus present in lungs using chest image. This approach uses an imagetexture analysis technique called granulometric mathematical model. Selectedfeatures are heuristically processed for optimization using novel multi scaling deep learning called light weight residual–atrous spatial pyramid pooling(LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPPUnet technique has a higher level of contracting solution by extracting majorlevel of image features. Moreover, the corona virus has been detected usinghigh resolution output. In the framework, atrous spatial pyramid pooling(ASPP) method is employed at its bottom level for incorporating the deepmulti scale features in to the discriminative mode. The architectural workingstarts from the selecting the features from the image using granulometricmathematical model and the selected features are optimized using LightRESASPP-Unet. ASPP in the analysis of images has performed better than theexisting Unet model. The proposed algorithm has achieved 99.6% of accuracyin detecting the virus at its early stage.展开更多
With the rapid growth of internet based services and the data generated on these services are attracted by the attackers to intrude the networking services and information.Based on the characteristics of these intrude...With the rapid growth of internet based services and the data generated on these services are attracted by the attackers to intrude the networking services and information.Based on the characteristics of these intruders,many researchers attempted to aim to detect the intrusion with the help of automating process.Since,the large volume of data is generated and transferred through network,the security and performance are remained an issue.IDS(Intrusion Detection System)was developed to detect and prevent the intruders and secure the network systems.The performance and loss are still an issue because of the features space grows while detecting the intruders.In this paper,deep clustering based CNN have been used to detect the intruders with the help of Meta heuristic algorithms for feature selection and preprocessing.The proposed system includes three phases such as preprocessing,feature selection and classification.In the first phase,KDD dataset is preprocessed by using Binning normalization and Eigen-PCA based discretization method.In second phase,feature selection is performed by using Information Gain based Dragonfly Optimizer(IGDFO).Finally,Deep clustering based Convolutional Neural Network(CCNN)classifier optimized with Particle Swarm Optimization(PSO)identifies intrusion attacks efficiently.The clustering loss and network loss can be reduced with the optimization algorithm.We evaluate the proposed IDS model with the NSL-KDD dataset in terms of evaluation metrics.The experimental results show that proposed system achieves better performance compared with the existing system in terms of accuracy,precision,recall,f-measure and false detection rate.展开更多
In order to research brain problems using MRI,PET,and CT neuroimaging,a correct understanding of brain function is required.This has been considered in earlier times with the support of traditional algorithms.Deep lea...In order to research brain problems using MRI,PET,and CT neuroimaging,a correct understanding of brain function is required.This has been considered in earlier times with the support of traditional algorithms.Deep learning process has also been widely considered in these genomics data processing system.In this research,brain disorder illness incliding Alzheimer’s disease,Schizophrenia and Parkinson’s diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional methods.Moeover,deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks(DBN).Images are stored in a secured manner by using DNA sequence based on JPEG Zig Zag Encryption algorithm(DBNJZZ)approach.The suggested approach is executed and tested by using the performance metric measure such as accuracy,root mean square error,Mean absolute error and mean absolute percentage error.Proposed DBNJZZ gives better performance than previously available methods.展开更多
Brain medical image classification is an essential procedure in Computer-Aided Diagnosis(CAD)systems.Conventional methods depend specifically on the local or global features.Several fusion methods have also been devel...Brain medical image classification is an essential procedure in Computer-Aided Diagnosis(CAD)systems.Conventional methods depend specifically on the local or global features.Several fusion methods have also been developed,most of which are problem-distinct and have shown to be highly favorable in medical images.However,intensity-specific images are not extracted.The recent deep learning methods ensure an efficient means to design an end-to-end model that produces final classification accuracy with brain medical images,compromising normalization.To solve these classification problems,in this paper,Histogram and Time-frequency Differential Deep(HTF-DD)method for medical image classification using Brain Magnetic Resonance Image(MRI)is presented.The construction of the proposed method involves the following steps.First,a deep Convolutional Neural Network(CNN)is trained as a pooled feature mapping in a supervised manner and the result that it obtains are standardized intensified pre-processed features for extraction.Second,a set of time-frequency features are extracted based on time signal and frequency signal of medical images to obtain time-frequency maps.Finally,an efficient model that is based on Differential Deep Learning is designed for obtaining different classes.The proposed model is evaluated using National Biomedical Imaging Archive(NBIA)images and validation of computational time,computational overhead and classification accuracy for varied Brain MRI has been done.展开更多
基金The authors extend their appreciation to the deputyship for Research&Innovation,Ministry of Education in Saudi Arabia,for funding this research work through the project number(IFP-2020-14).
文摘The understanding of water resource changes and a proper projection of their future availability are necessary elements of sustainable water planning.Monitoring GWS change and future water resource availability are crucial,especially under changing climatic conditions.Traditional methods for in situ groundwater well measurement are a significant challenge due to data unavailability.The present investigation utilized the Long Short Term Memory(LSTM)networks to monitor and forecast Terrestrial Water Storage Change(TWSC)and Ground Water Storage Change(GWSC)based on Gravity Recovery and Climate Experiment(GRACE)datasets from 2003-2025 for five basins of Saudi Arabia.An attempt has been made to assess the effects of rainfall,water used,and net budget modeling of groundwater.Analysis of GRACE-derived TWSC and GWSC estimates indicates that all five basins show depletion of water from 2003-2020 with a rate ranging from−5.88±1.2 mm/year to−14.12±1.2 mm/year and−3.5±1.5 to−10.7±1.5,respectively.Forecasting based on the developed LSTM model indicates that the investigated basins are likely to experience serious water depletion at rates ranging from−7.78±1.2 to−15.6±1.2 for TWSC and−4.97±1.5 to−12.21±1.5 for GWSC from 2020-2025.An interesting observation was a minor increase in rainfall during the study period for three basins.
文摘Objective:To synthesize bio-inspired gold nanoparticles(AuNPs)using the leaf extract of Justicia adhatoda and evaluate the anti-cancer activity on human lung cancer cell line(A549).Methods:Synthesis of AuNPs was done using an aqueous leaf extract of Justicia adhatoda as a green route.The bio-synthesized AuNPs were confirmed and characterized by using various spectral studies such as UV-Vis spectrum,Scanning Electron Microscope with EDAX,Transmission Electron Microscope,Fourier Transmission Infrared Spectroscope analysis and Surface Enhanced Raman Spectroscopy.The cell viability was determined by MTT reduction assay.In addition,cytomorphology and the nuclear morphological study of A549 cell line was observed under fluorescence microscope.Results:UV-Vis spectrum showed surface plasmon resonance peak at 547 nm,scanning electron microscope and transmission electron microscope studies showed the monodispersed spherical shape and its average size in the range of 40.1 nm was noticed.Fourier Transmission Infrared Spectroscope analysis confirmed that the C=O group of amino acids of proteins had strong ability to bind with the surface of nanoparticle.Interestingly,our results also demonstrated inhibited proliferation of A549 cell line by MTT(IC50 value:80μg/mL).Cell morphology was observed and cell death was caused by apoptosis as revealed by propidium iodide staining.Conclusions:The current study proves the anticancer potential of bio-synthesized AuNPs.Thus,synthesized AuNPs can be used for the treatment of human lung cancer cell(A549)and it can be exploited for drug delivery in future.
文摘COVID’19 has caused the entire universe to be in existential healthcrisis by spreading globally in the year 2020. The lungs infection is detected inComputed Tomography (CT) images which provide the best way to increasethe existing healthcare schemes in preventing the deadly virus. Nevertheless,separating the infected areas in CT images faces various issues such as lowintensity difference among normal and infectious tissue and high changes inthe characteristics of the infection. To resolve these issues, a new inf-Net (LungInfection Segmentation Deep Network) is designed for detecting the affectedareas from the CT images automatically. For the worst segmentation results,the Edge-Attention Representation (EAR) is optimized using AdaptiveDonkey and Smuggler Optimization (ADSO). The edges which are identifiedby the ADSO approach is utilized for calculating dissimilarities. An IFCM(Intuitionistic Fuzzy C-Means) clustering approach is applied for computingthe similarity of the EA component among the generated edge maps andGround-Truth (GT) edge maps. Also, a Semi-Supervised Segmentation(SSS) structure is designed using the Randomly Selected Propagation (RP)technique and Inf-Net, which needs only less number of images and unlabelleddata. Semi-Supervised Multi-Class Segmentation (SSMCS) is designed usinga Bi-LSTM (Bi-Directional Long-Short-Term-memory), acquires all theadvantages of the disease segmentation done using Semi Inf-Net and enhancesthe execution of multi-class disease labelling. The newly designed SSMCSapproach is compared with existing U-Net++, MCS, and Semi-Inf-Net.factors such as MAE (Mean Absolute Error), Structure measure, Specificity(Spec), Dice Similarity coefficient, Sensitivity (Sen), and Enhance-AlignmentMeasure are considered for evaluation purpose.
文摘The brain of humans and other organisms is affected in various ways through the electromagneticfield(EMF)radiations generated by mobile phones and cell phone towers.Morphological variations in the brain are caused by the neurological changes due to the revelation of EMF.Cellular level analysis is used to measure and detect the effect of mobile radiations,but its utilization seems very expensive,and it is a tedious process,where its analysis requires the preparation of cell suspension.In this regard,this research article proposes optimal broadcast-ing learning to detect changes in brain morphology due to the revelation of EMF.Here,Drosophila melanogaster acts as a specimen under the revelation of EMF.Automatic segmentation is performed for the brain to attain the microscopic images from the prejudicial geometrical characteristics that are removed to detect the effect of revelation of EMF.The geometrical characteristics of the brain image of that is microscopic segmented are analyzed.Analysis results reveal the occur-rence of several prejudicial characteristics that can be processed by machine learn-ing techniques.The important prejudicial characteristics are given to four varieties of classifiers such as naïve Bayes,artificial neural network,support vector machine,and unsystematic forest for the classification of open or nonopen micro-scopic image of D.melanogaster brain.The results are attained through various experimental evaluations,and the said classifiers perform well by achieving 96.44%using the prejudicial characteristics chosen by the feature selection meth-od.The proposed system is an optimal approach that automatically identifies the effect of revelation of EMF with minimal time complexity,where the machine learning techniques produce an effective framework for image processing.
基金This research was supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist)and the Soonchunhyang University Research Fund.
文摘Ad hoc mobile cloud computing networks are affected by various issues,like delay,energy consumption,flexibility,infrastructure,network lifetime,security,stability,data transition,and link accomplishment.Given the issues above,route failure is prevalent in ad hoc mobile cloud computing networks,which increases energy consumption and delay and reduces stability.These issues may affect several interconnected nodes in an ad hoc mobile cloud computing network.To address these weaknesses,which raise many concerns about privacy and security,this study formulated clustering-based storage and search optimization approaches using cross-layer analysis.The proposed approaches were formed by cross-layer analysis based on intrusion detection methods.First,the clustering process based on storage and search optimization was formulated for clustering and route maintenance in ad hoc mobile cloud computing networks.Moreover,delay,energy consumption,network lifetime,and link accomplishment are highly addressed by the proposed algorithm.The hidden Markov model is used to maintain the data transition and distributions in the network.Every data communication network,like ad hoc mobile cloud computing,faces security and confidentiality issues.However,the main security issues in this article are addressed using the storage and search optimization approach.Hence,the new algorithm developed helps detect intruders through intelligent cross layer analysis with theMarkov model.The proposed model was simulated in Network Simulator 3,and the outcomes were compared with those of prevailing methods for evaluating parameters,like accuracy,end-to-end delay,energy consumption,network lifetime,packet delivery ratio,and throughput.
文摘This research article proposes an automatic frame work for detectingCOVID -19 at the early stage using chest X-ray image. It is an undeniable factthat coronovirus is a serious disease but the early detection of the virus presentin human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in needof right and even rich technology for its early detection. The proposed deeplearning model analysis the pixels of every image and adjudges the presence ofvirus. The classifier is designed in such a way so that, it automatically detectsthe virus present in lungs using chest image. This approach uses an imagetexture analysis technique called granulometric mathematical model. Selectedfeatures are heuristically processed for optimization using novel multi scaling deep learning called light weight residual–atrous spatial pyramid pooling(LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPPUnet technique has a higher level of contracting solution by extracting majorlevel of image features. Moreover, the corona virus has been detected usinghigh resolution output. In the framework, atrous spatial pyramid pooling(ASPP) method is employed at its bottom level for incorporating the deepmulti scale features in to the discriminative mode. The architectural workingstarts from the selecting the features from the image using granulometricmathematical model and the selected features are optimized using LightRESASPP-Unet. ASPP in the analysis of images has performed better than theexisting Unet model. The proposed algorithm has achieved 99.6% of accuracyin detecting the virus at its early stage.
基金The third and fourth authors were supported by the Project of Specific Research PrF UHK No.2101/2021 and Long-term development plan of UHK,University of Hradec Králové,Czech Republic.
文摘With the rapid growth of internet based services and the data generated on these services are attracted by the attackers to intrude the networking services and information.Based on the characteristics of these intruders,many researchers attempted to aim to detect the intrusion with the help of automating process.Since,the large volume of data is generated and transferred through network,the security and performance are remained an issue.IDS(Intrusion Detection System)was developed to detect and prevent the intruders and secure the network systems.The performance and loss are still an issue because of the features space grows while detecting the intruders.In this paper,deep clustering based CNN have been used to detect the intruders with the help of Meta heuristic algorithms for feature selection and preprocessing.The proposed system includes three phases such as preprocessing,feature selection and classification.In the first phase,KDD dataset is preprocessed by using Binning normalization and Eigen-PCA based discretization method.In second phase,feature selection is performed by using Information Gain based Dragonfly Optimizer(IGDFO).Finally,Deep clustering based Convolutional Neural Network(CCNN)classifier optimized with Particle Swarm Optimization(PSO)identifies intrusion attacks efficiently.The clustering loss and network loss can be reduced with the optimization algorithm.We evaluate the proposed IDS model with the NSL-KDD dataset in terms of evaluation metrics.The experimental results show that proposed system achieves better performance compared with the existing system in terms of accuracy,precision,recall,f-measure and false detection rate.
文摘In order to research brain problems using MRI,PET,and CT neuroimaging,a correct understanding of brain function is required.This has been considered in earlier times with the support of traditional algorithms.Deep learning process has also been widely considered in these genomics data processing system.In this research,brain disorder illness incliding Alzheimer’s disease,Schizophrenia and Parkinson’s diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional methods.Moeover,deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks(DBN).Images are stored in a secured manner by using DNA sequence based on JPEG Zig Zag Encryption algorithm(DBNJZZ)approach.The suggested approach is executed and tested by using the performance metric measure such as accuracy,root mean square error,Mean absolute error and mean absolute percentage error.Proposed DBNJZZ gives better performance than previously available methods.
文摘Brain medical image classification is an essential procedure in Computer-Aided Diagnosis(CAD)systems.Conventional methods depend specifically on the local or global features.Several fusion methods have also been developed,most of which are problem-distinct and have shown to be highly favorable in medical images.However,intensity-specific images are not extracted.The recent deep learning methods ensure an efficient means to design an end-to-end model that produces final classification accuracy with brain medical images,compromising normalization.To solve these classification problems,in this paper,Histogram and Time-frequency Differential Deep(HTF-DD)method for medical image classification using Brain Magnetic Resonance Image(MRI)is presented.The construction of the proposed method involves the following steps.First,a deep Convolutional Neural Network(CNN)is trained as a pooled feature mapping in a supervised manner and the result that it obtains are standardized intensified pre-processed features for extraction.Second,a set of time-frequency features are extracted based on time signal and frequency signal of medical images to obtain time-frequency maps.Finally,an efficient model that is based on Differential Deep Learning is designed for obtaining different classes.The proposed model is evaluated using National Biomedical Imaging Archive(NBIA)images and validation of computational time,computational overhead and classification accuracy for varied Brain MRI has been done.
