The epidemiological associations between the prenatal perfluoroalkyl substances(PFASs)exposure and the reproductive outcomes remain controversial.A continuous evaluation is needed to combine the inconsistent results.I...The epidemiological associations between the prenatal perfluoroalkyl substances(PFASs)exposure and the reproductive outcomes remain controversial.A continuous evaluation is needed to combine the inconsistent results.In this study,we explored the associations between PFASs exposure and the low birth weight(LBW),preterm birth and small for gestational age(SGA).The quality of selected literature,quantitative estimates,publication bias and subgroup analysis were performed on the basis of 17 retrieved articles published before December 2020.The results showed a significant positive association between the perfluorooctane sulfonate(PFOS)exposure and the risk of LBW[Odds ratio(OR)=1.17;95%confidence interval(CI):1.01,1.36;heterogeneity:P=0.30,I2=17%].The positive association was also observed between the PFOS and the risk of preterm birth(OR=1.19;95%CI:1.01,1.39,P=0.007;I2=62%).There was a paucity of evidence regarding the negative effects of perfluorooctanoic acid(PFOA),perfluorohexanesulfonic acid(PFHxS)and perfluorononanoic acid(PFNA)on the pregnancy outcomes.The findings from the subgroup analysis(the sampling period,the birth gender and biologic specimens)did not substantially altered the results of the overall pooled estimate ORs.The increased prevalence of negative birth outcomes with gestational PFASs exposure warrants further explorations from biological process perspective.展开更多
BACKGROUND Left bundle branch pacing(LBBP)is a novel pacing modality of cardiac resynchronization therapy(CRT)that achieves more physiologic native ventricular activation than biventricular pacing(BiVP).AIM To explore...BACKGROUND Left bundle branch pacing(LBBP)is a novel pacing modality of cardiac resynchronization therapy(CRT)that achieves more physiologic native ventricular activation than biventricular pacing(BiVP).AIM To explore the validity of electromechanical resynchronization,clinical and echocardiographic response of LBBP-CRT.METHODS Systematic review and Meta-analysis were conducted in accordance with the standard guidelines as mentioned in detail in the methodology section.RESULTS In our analysis,the success rate of LBBP-CRT was determined to be 91.1%.LBBP CRT significantly shortened QRS duration,with significant improvement in echocardiographic parameters,including left ventricular ejection fraction,left ventricular end-diastolic diameter and left ventricular end-systolic diameter in comparison with BiVP-CRT.CONCLUSION A significant reduction in New York Heart Association class and B-type natriuretic peptide levels was also observed in the LBBP-CRT group vs BiVP-CRT group.Lastly,the LBBP-CRT cohort had a reduced pacing threshold at follow-up as compared to BiVP-CRT.展开更多
Plant disease classification based on digital pictures is challenging.Machine learning approaches and plant image categorization technologies such as deep learning have been utilized to recognize,identify,and diagnose...Plant disease classification based on digital pictures is challenging.Machine learning approaches and plant image categorization technologies such as deep learning have been utilized to recognize,identify,and diagnose plant diseases in the previous decade.Increasing the yield quantity and quality of rice forming is an important cause for the paddy production countries.However,some diseases that are blocking the improvement in paddy production are considered as an ominous threat.Convolution Neural Network(CNN)has shown a remarkable performance in solving the early detection of paddy leaf diseases based on its images in the fast-growing era of science and technology.Nevertheless,the significant CNN architectures construction is dependent on expertise in a neural network and domain knowledge.This approach is time-consuming,and high computational resources are mandatory.In this research,we propose a novel method based on Mutant Particle swarm optimization(MUT-PSO)Algorithms to search for an optimum CNN architecture for Paddy leaf disease classification.Experimentation results show that Mutant Particle swarm optimization Convolution Neural Network(MUTPSO-CNN)can find optimumCNNarchitecture that offers better performance than existing hand-crafted CNN architectures in terms of accuracy,precision/recall,and execution time.展开更多
In an era of unprecedented urbanization, population and industrial growth pressure is serious threat for the water management in Pakistan in present days. Water pollution from raw sewage, industrial wastes, and agricu...In an era of unprecedented urbanization, population and industrial growth pressure is serious threat for the water management in Pakistan in present days. Water pollution from raw sewage, industrial wastes, and agricultural runoff limited natural fresh water resources in the country. Human health is facing serious problems due to deteriorating drinking water quality. Current review paper provides an insight to the water quality problems in Pakistan with an attempt to emphasize the challenges of water laws enforcement. Although Pakistan has developed many water laws the state of implementation is dominant, intermediate pollution crises are still remaining. We could come to the conclusion that strictly enforcement is compulsory for water environment regulations in Pakistan. Moreover, it is necessary to establish a reliable risk assessment system for water quality, human health and ecological safety.展开更多
Image enhancement is a monumental task in the field of computer vision and image processing.Existing methods are insufficient for preserving naturalness and minimizing noise in images.This article discusses a techniqu...Image enhancement is a monumental task in the field of computer vision and image processing.Existing methods are insufficient for preserving naturalness and minimizing noise in images.This article discusses a technique that is based on wavelets for optimizing images taken in low-light.First,the V channel is created by mapping an image’s RGB channel to the HSV color space.Second,the acquired V channel is decomposed using the dual-tree complex wavelet transform(DT-CWT)in order to recover the concentrated information within its high and low-frequency subbands.Thirdly,an adaptive illumination boost technique is used to enhance the visibility of a low-frequency component.Simultaneously,anisotropic diffusion is used to mitigate the high-frequency component’s noise impact.To improve the results,the image is reconstructed using an inverse DT-CWT and then converted to RGB space using the newly calculated V.Additionally,images are white-balanced to remove color casts.Experiments demonstrate that the proposed approach significantly improves outcomes and outperforms previously reported methods in general.展开更多
Recently, many researchers have used nature inspired metaheuristicalgorithms due to their ability to perform optimally on complex problems. Tosolve problems in a simple way, in the recent era bat algorithm has becomef...Recently, many researchers have used nature inspired metaheuristicalgorithms due to their ability to perform optimally on complex problems. Tosolve problems in a simple way, in the recent era bat algorithm has becomefamous due to its high tendency towards convergence to the global optimummost of the time. But, still the standard bat with random walk has a problemof getting stuck in local minima. In order to solve this problem, this researchproposed bat algorithm with levy flight random walk. Then, the proposedBat with Levy flight algorithm is further hybridized with three differentvariants of ANN. The proposed BatLFBP is applied to the problem ofinsulin DNA sequence classification of healthy homosapien. For classificationperformance, the proposed models such as Bat levy flight Artificial NeuralNetwork (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) arecompared with the other state-of-the-art algorithms like Bat Artificial NeuralNetwork (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distributionback propagation (BatGDBP), in-terms of means squared error (MSE) andaccuracy. From the perspective of simulations results, it is show that theproposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185,and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on WL5.While on WL10 the proposed BatLFANN achieved 99.89899% accuracy withMSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853%accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracywith MSE of 0.006738 which achieve better accuracy as compared to the otherhybrid models.展开更多
Spray cooling is an effective tool to dissipate high heat fluxes from hot surfaces. This paper thoroughly investigates the effects of spray parameters on the cooling time and cooling rate under varying inlet pressure ...Spray cooling is an effective tool to dissipate high heat fluxes from hot surfaces. This paper thoroughly investigates the effects of spray parameters on the cooling time and cooling rate under varying inlet pressure using water as the coolant. Cylindrical samples of stainless steel with constant diameter, D = 25 mm, and thickness δ: 8.5 mm, 13 mm, 17.5 mm and 22 mm were investigated. Critical droplet diameter to achieve an ultrafast cooling rate of 300°C/s was estimated by using analytical model for samples of varying thickness. At an inlet pressure of 0.8 MPa, maximum cooling rates of 424.2°C/s, 502.81°C/s and 573.1°C/s were achieved for wall super heat ΔT = 600°C, 700°C and 800°C respectively.展开更多
A rapid and simple process has been developed for the recovery of antimony metal from stibnite ore of Kharan area (Balochistan) of Pakistan. The ore was characterized by X-ray diffraction (XRD) technique and found to ...A rapid and simple process has been developed for the recovery of antimony metal from stibnite ore of Kharan area (Balochistan) of Pakistan. The ore was characterized by X-ray diffraction (XRD) technique and found to contain 66% stibnite i.e. antimony sulfide (Sb2S3). The process parameters for the extraction of antimony were optimized on laboratory scale by varying reaction temperature from 900℃ - 1000℃, reaction time from 20 - 80 minutes and flux concentration was varied from 5 - 25 weight percent of the reaction mixture. The metal thus recovered with optimum conditions was evaluated by X-ray fluorescence (XRF) technique and was found to be more than 94% pure. The recovery yield calculated on the basis of stibnite present in the ore was 98.52%.展开更多
Object detection in images has been identified as a critical area of research in computer vision image processing.Research has developed several novel methods for determining an object’s location and category from an...Object detection in images has been identified as a critical area of research in computer vision image processing.Research has developed several novel methods for determining an object’s location and category from an image.However,there is still room for improvement in terms of detection effi-ciency.This study aims to develop a technique for detecting objects in images.To enhance overall detection performance,we considered object detection a two-fold problem,including localization and classification.The proposed method generates class-independent,high-quality,and precise proposals using an agglomerative clustering technique.We then combine these proposals with the relevant input image to train our network on convolutional features.Next,a network refinement module decreases the quantity of generated proposals to produce fewer high-quality candidate proposals.Finally,revised candidate proposals are sent into the network’s detection process to determine the object type.The algorithm’s performance is evaluated using publicly available the PASCAL Visual Object Classes Challenge 2007(VOC2007),VOC2012,and Microsoft Common Objects in Context(MS-COCO)datasets.Using only 100 proposals per image at intersection over union((IoU)=0.5 and 0.7),the proposed method attains Detection Recall(DR)rates of(93.17%and 79.35%)and(69.4%and 58.35%),and Mean Average Best Overlap(MABO)values of(79.25%and 62.65%),for the VOC2007 and MS-COCO datasets,respectively.Besides,it achieves a Mean Average Precision(mAP)of(84.7%and 81.5%)on both VOC datasets.The experiment findings reveal that our method exceeds previous approaches in terms of overall detection performance,proving its effectiveness.展开更多
Breast cancer(BC)is the most common cause of women’s deaths worldwide.The mammography technique is the most important modality for the detection of BC.To detect abnormalities in mammographic images,the Breast Imaging...Breast cancer(BC)is the most common cause of women’s deaths worldwide.The mammography technique is the most important modality for the detection of BC.To detect abnormalities in mammographic images,the Breast Imaging Reporting and Data System(BI-RADs)is used as a baseline.The correct allocation of BI-RADs categories for mammographic images is always an interesting task,even for specialists.In this work,to detect and classify the mammogram images in BI-RADs,a novel hybrid model is presented using a convolutional neural network(CNN)with the integration of a support vector machine(SVM).The dataset used in this research was collected from different hospitals in the Qassim health cluster of Saudi Arabia.The collection of all categories of BI-RADs is one of the major contributions of this paper.Another significant contribution is the development of a hybrid approach through the integration of CNN and SVM.The proposed hybrid approach uses three CNN models to obtain ensemble CNN model results.This ensemble model saves the values to integrate them with SVM.The proposed system achieved a classification accuracy,sensitivity,specificity,precision,and F1-score of 93.6%,94.8%,96.9%,96.6%,and 95.7%,respectively.The proposed model achieved better performance compared to previously available methods.展开更多
Brain tumor is one of the most dreadful worldwide types of cancer and affects people leading to death.Magnetic resonance imaging methods capture skull images that contain healthy and affected tissue.Radiologists check...Brain tumor is one of the most dreadful worldwide types of cancer and affects people leading to death.Magnetic resonance imaging methods capture skull images that contain healthy and affected tissue.Radiologists checked the affected tissue in the slice-by-slice manner,which was timeconsuming and hectic task.Therefore,auto segmentation of the affected part is needed to facilitate radiologists.Therefore,we have considered a hybrid model that inherits the convolutional neural network(CNN)properties to the support vector machine(SVM)for the auto-segmented brain tumor region.The CNN model is initially used to detect brain tumors,while SVM is integrated to segment the tumor region correctly.The proposed method was evaluated on a publicly available BraTS2020 dataset.The statistical parameters used in this work for the mathematical measures are precision,accuracy,specificity,sensitivity,and dice coefficient.Overall,our method achieved an accuracy value of 0.98,which is most prominent than existing techniques.Moreover,the proposed approach is more suitable for medical experts to diagnose the early stages of the brain tumor.展开更多
The precise brain tumor diagnosis is critical and shows a vital role in the medical support for treating tumor patients.Manual brain tumor segmentation for cancer analysis from many Magnetic Resonance Images(MRIs)crea...The precise brain tumor diagnosis is critical and shows a vital role in the medical support for treating tumor patients.Manual brain tumor segmentation for cancer analysis from many Magnetic Resonance Images(MRIs)created in medical practice is a problematic and timewasting task for experts.As a result,there is a critical necessity for more accurate computeraided methods for early tumor detection.To remove this gap,we enhanced the computational power of a computer-aided system by proposing a finetuned Block-Wise Visual Geometry Group19(BW-VGG19)architecture.In this method,a pre-trained VGG19 is fine-tuned with CNN architecture in the block-wise mechanism to enhance the system`s accuracy.The publicly accessible Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)dataset collected from 2005 to 2020 from different hospitals in China has been used in this research.Our proposed method is simple and achieved an accuracy of 0.98%.We compare our technique results with the existing Convolutional Neural network(CNN),VGG16,and VGG19 approaches.The results indicate that our proposed technique outperforms the best results associated with the existing methods.展开更多
The actuaries always look for heavy-tailed distributions to model data relevant to business and actuarial risk issues.In this article,we introduce a new class of heavy-tailed distributions useful for modeling data in ...The actuaries always look for heavy-tailed distributions to model data relevant to business and actuarial risk issues.In this article,we introduce a new class of heavy-tailed distributions useful for modeling data in financial sciences.A specific sub-model form of our suggested family,named as a new extended heavy-tailed Weibull distribution is examined in detail.Some basic characterizations,including quantile function and raw moments have been derived.The estimates of the unknown parameters of the new model are obtained via the maximum likelihood estimation method.To judge the performance of the maximum likelihood estimators,a simulation analysis is performed in detail.Furthermore,some important actuarial measures such as value at risk and tail value at risk are also computed.A simulation study based on these actuarial measures is conducted to exhibit empirically that the proposed model is heavy-tailed.The usefulness of the proposed family is illustrated by means of an application to a heavy-tailed insurance loss data set.The practical application shows that the proposed model is more flexible and efficient than the other six competing models including(i)the two-parameter models Weibull,Lomax and Burr-XII distributions(ii)the three-parameter distributions Marshall-Olkin Weibull and exponentiated Weibull distributions,and(iii)a well-known four-parameter Kumaraswamy Weibull distribution.展开更多
COVID-19 has caused severe health complications and produced a substantial adverse economic impact around the world.Forecasting the trend of COVID-19 infections could help in executing policies to effectively reduce t...COVID-19 has caused severe health complications and produced a substantial adverse economic impact around the world.Forecasting the trend of COVID-19 infections could help in executing policies to effectively reduce the number of new cases.In this study,we apply the decomposition and ensemble model to forecast COVID-19 confirmed cases,deaths,and recoveries in Pakistan for the upcoming month until the end of July.For the decomposition of data,the Ensemble Empirical Mode Decomposition(EEMD)technique is applied.EEMD decomposes the data into small components,called Intrinsic Mode Functions(IMFs).For individual IMFs modelling,we use the Autoregressive Integrated Moving Average(ARIMA)model.The data used in this study is obtained from the official website of Pakistan that is publicly available and designated for COVID-19 outbreak with daily updates.Our analyses reveal that the number of recoveries,new cases,and deaths are increasing in Pakistan exponentially.Based on the selected EEMD-ARIMA model,the new confirmed cases are expected to rise from 213,470 to 311,454 by 31 July 2020,which is an increase of almost 1.46 times with a 95%prediction interval of 246,529 to 376,379.The 95%prediction interval for recovery is 162,414 to 224,579,with an increase of almost two times in total from 100802 to 193495 by 31 July 2020.On the other hand,the deaths are expected to increase from 4395 to 6751,which is almost 1.54 times,with a 95%prediction interval of 5617 to 7885.Thus,the COVID-19 forecasting results of Pakistan are alarming for the next month until 31 July 2020.They also confirm that the EEMD-ARIMA model is useful for the short-term forecasting of COVID-19,and that it is capable of keeping track of the real COVID-19 data in nearly all scenarios.The decomposition and ensemble strategy can be useful to help decision-makers in developing short-term strategies about the current number of disease occurrences until an appropriate vaccine is developed.展开更多
In computer vision,convolutional neural networks have a wide range of uses.Images representmost of today’s data,so it’s important to know how to handle these large amounts of data efficiently.Convolutional neural ne...In computer vision,convolutional neural networks have a wide range of uses.Images representmost of today’s data,so it’s important to know how to handle these large amounts of data efficiently.Convolutional neural networks have been shown to solve image processing problems effectively.However,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher accuracy.This technique is time consuming and requires a lot of work and domain knowledge.Designing a convolutional neural network architecture is a classic NP-hard optimization challenge.On the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and inconvenient.Various approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random selection.To address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized hyperparameters.This study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN model.In addition,different types and parameter ranges of existing genetic algorithms are used.Acomparative study was conducted with various state-of-the-art methodologies and algorithms.Experiments have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature.展开更多
Abnormal growth of brain tissues is the real cause of brain tumor.Strategy for the diagnosis of brain tumor at initial stages is one of the key step for saving the life of a patient.The manual segmentation of brain tu...Abnormal growth of brain tissues is the real cause of brain tumor.Strategy for the diagnosis of brain tumor at initial stages is one of the key step for saving the life of a patient.The manual segmentation of brain tumor magnetic resonance images(MRIs)takes time and results vary significantly in low-level features.To address this issue,we have proposed a ResNet-50 feature extractor depended on multilevel deep convolutional neural network(CNN)for reliable images segmentation by considering the low-level features of MRI.In this model,we have extracted features through ResNet-50 architecture and fed these feature maps to multi-level CNN model.To handle the classification process,we have collected a total number of 2043 MRI patients of normal,benign,and malignant tumor.Three model CNN,multi-level CNN,and ResNet-50 based multi-level CNN have been used for detection and classification of brain tumors.All the model results are calculated in terms of various numerical values identified as precision(P),recall(R),accuracy(Acc)and f1-score(F1-S).The obtained average results are much better as compared to already existing methods.This modified transfer learning architecture might help the radiologists and doctors as a better significant system for tumor diagnosis.展开更多
Assistive devices for disabled people with the help of Brain-Computer Interaction(BCI)technology are becoming vital bio-medical engineering.People with physical disabilities need some assistive devices to perform thei...Assistive devices for disabled people with the help of Brain-Computer Interaction(BCI)technology are becoming vital bio-medical engineering.People with physical disabilities need some assistive devices to perform their daily tasks.In these devices,higher latency factors need to be addressed appropriately.Therefore,the main goal of this research is to implement a real-time BCI architecture with minimum latency for command actuation.The proposed architecture is capable to communicate between different modules of the system by adopting an automotive,intelligent data processing and classification approach.Neuro-sky mind wave device has been used to transfer the data to our implemented server for command propulsion.Think-Net Convolutional Neural Network(TN-CNN)architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data classification.Data collection and processing are the responsibility of the central integrated server for system load minimization.Testing of implemented architecture and deep learning model shows excellent results.The proposed system integrity level was the minimum data loss and the accurate commands processing mechanism.The training and testing results are 99%and 93%for custom model implementation based on TN-CNN.The proposed real-time architecture is capable of intelligent data processing unit with fewer errors,and it will benefit assistive devices working on the local server and cloud server.展开更多
The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients worldwide.Even in this technol-ogy-based arena,brain tumor images with proper labeling and acquisi...The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients worldwide.Even in this technol-ogy-based arena,brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original ones.The artificially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented data.To over-come the highlighted issue,a Generative Adversarial Network(GAN)deep learn-ing technique in which two neural networks compete to become more accurate in creating artificially realistic data for MRI images.The GAN network contains mainly two parts known as generator and discriminator.Commonly,a generator is the convolutional neural network,and a discriminator is the deconvolutional neural network.In this research,the publicly accessible Contrast-Enhanced Mag-netic Resonance Imaging(CE-MRI)dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been used.Our proposed method is simple and achieved an accuracy of 96%.We compare our technique results with the existing results,indicating that our proposed technique outper-forms the best results associated with the existing methods.展开更多
This paper presents a grid connected photovoltaic system (PV) with a proposed high voltage conversion ratio DC-DC converter which steps up the variable low input voltages of photovoltaic module to the required DC link...This paper presents a grid connected photovoltaic system (PV) with a proposed high voltage conversion ratio DC-DC converter which steps up the variable low input voltages of photovoltaic module to the required DC link voltage. This voltage is applied to an H-bridge inverter which converts DC voltage into AC voltage and a low pass filter is used to filter the output. By adjusting the duty ratio of switches in DC-DC converter, the magnitude of inverter’s output voltage is controlled. The frequency and phase synchronization are ensured by a feedback signal taken from the grid. In this way, inverter is synchronized and connected with the grid to meet the energy demand. The PV system has been designed and simulated.展开更多
基金National Natural Science Foundation of China(No.22006010)Shanghai Sailing Program,China(No.19YF1400500)。
文摘The epidemiological associations between the prenatal perfluoroalkyl substances(PFASs)exposure and the reproductive outcomes remain controversial.A continuous evaluation is needed to combine the inconsistent results.In this study,we explored the associations between PFASs exposure and the low birth weight(LBW),preterm birth and small for gestational age(SGA).The quality of selected literature,quantitative estimates,publication bias and subgroup analysis were performed on the basis of 17 retrieved articles published before December 2020.The results showed a significant positive association between the perfluorooctane sulfonate(PFOS)exposure and the risk of LBW[Odds ratio(OR)=1.17;95%confidence interval(CI):1.01,1.36;heterogeneity:P=0.30,I2=17%].The positive association was also observed between the PFOS and the risk of preterm birth(OR=1.19;95%CI:1.01,1.39,P=0.007;I2=62%).There was a paucity of evidence regarding the negative effects of perfluorooctanoic acid(PFOA),perfluorohexanesulfonic acid(PFHxS)and perfluorononanoic acid(PFNA)on the pregnancy outcomes.The findings from the subgroup analysis(the sampling period,the birth gender and biologic specimens)did not substantially altered the results of the overall pooled estimate ORs.The increased prevalence of negative birth outcomes with gestational PFASs exposure warrants further explorations from biological process perspective.
文摘BACKGROUND Left bundle branch pacing(LBBP)is a novel pacing modality of cardiac resynchronization therapy(CRT)that achieves more physiologic native ventricular activation than biventricular pacing(BiVP).AIM To explore the validity of electromechanical resynchronization,clinical and echocardiographic response of LBBP-CRT.METHODS Systematic review and Meta-analysis were conducted in accordance with the standard guidelines as mentioned in detail in the methodology section.RESULTS In our analysis,the success rate of LBBP-CRT was determined to be 91.1%.LBBP CRT significantly shortened QRS duration,with significant improvement in echocardiographic parameters,including left ventricular ejection fraction,left ventricular end-diastolic diameter and left ventricular end-systolic diameter in comparison with BiVP-CRT.CONCLUSION A significant reduction in New York Heart Association class and B-type natriuretic peptide levels was also observed in the LBBP-CRT group vs BiVP-CRT group.Lastly,the LBBP-CRT cohort had a reduced pacing threshold at follow-up as compared to BiVP-CRT.
基金The authors received funding source for this research activity under Multi-Disciplinary Research(MDR)Grant Vot H483 from Research Management Centre(RMC)office,Universiti Tun Hussein Onn Malaysia(UTHM).
文摘Plant disease classification based on digital pictures is challenging.Machine learning approaches and plant image categorization technologies such as deep learning have been utilized to recognize,identify,and diagnose plant diseases in the previous decade.Increasing the yield quantity and quality of rice forming is an important cause for the paddy production countries.However,some diseases that are blocking the improvement in paddy production are considered as an ominous threat.Convolution Neural Network(CNN)has shown a remarkable performance in solving the early detection of paddy leaf diseases based on its images in the fast-growing era of science and technology.Nevertheless,the significant CNN architectures construction is dependent on expertise in a neural network and domain knowledge.This approach is time-consuming,and high computational resources are mandatory.In this research,we propose a novel method based on Mutant Particle swarm optimization(MUT-PSO)Algorithms to search for an optimum CNN architecture for Paddy leaf disease classification.Experimentation results show that Mutant Particle swarm optimization Convolution Neural Network(MUTPSO-CNN)can find optimumCNNarchitecture that offers better performance than existing hand-crafted CNN architectures in terms of accuracy,precision/recall,and execution time.
文摘In an era of unprecedented urbanization, population and industrial growth pressure is serious threat for the water management in Pakistan in present days. Water pollution from raw sewage, industrial wastes, and agricultural runoff limited natural fresh water resources in the country. Human health is facing serious problems due to deteriorating drinking water quality. Current review paper provides an insight to the water quality problems in Pakistan with an attempt to emphasize the challenges of water laws enforcement. Although Pakistan has developed many water laws the state of implementation is dominant, intermediate pollution crises are still remaining. We could come to the conclusion that strictly enforcement is compulsory for water environment regulations in Pakistan. Moreover, it is necessary to establish a reliable risk assessment system for water quality, human health and ecological safety.
基金Supported by Teaching Team Project of Hubei Provincial Department of Education(203201929203)the Natural Science Foundation of Hubei Province(2021CFB316)+1 种基金New Generation Information Technology Innovation Project Ministry of Education(20202020ITA05022)Hundreds of Schools Unite with Hundreds of Counties-University Serving Rural Revitalization Science and Technology Support Action Plan(BXLBX0847)。
文摘Image enhancement is a monumental task in the field of computer vision and image processing.Existing methods are insufficient for preserving naturalness and minimizing noise in images.This article discusses a technique that is based on wavelets for optimizing images taken in low-light.First,the V channel is created by mapping an image’s RGB channel to the HSV color space.Second,the acquired V channel is decomposed using the dual-tree complex wavelet transform(DT-CWT)in order to recover the concentrated information within its high and low-frequency subbands.Thirdly,an adaptive illumination boost technique is used to enhance the visibility of a low-frequency component.Simultaneously,anisotropic diffusion is used to mitigate the high-frequency component’s noise impact.To improve the results,the image is reconstructed using an inverse DT-CWT and then converted to RGB space using the newly calculated V.Additionally,images are white-balanced to remove color casts.Experiments demonstrate that the proposed approach significantly improves outcomes and outperforms previously reported methods in general.
基金This research is supported by Tier-1 Research Grant, vote no. H938 by ResearchManagement Office (RMC), Universiti Tun Hussein Onn Malaysia and Ministry of Higher Education,Malaysia.
文摘Recently, many researchers have used nature inspired metaheuristicalgorithms due to their ability to perform optimally on complex problems. Tosolve problems in a simple way, in the recent era bat algorithm has becomefamous due to its high tendency towards convergence to the global optimummost of the time. But, still the standard bat with random walk has a problemof getting stuck in local minima. In order to solve this problem, this researchproposed bat algorithm with levy flight random walk. Then, the proposedBat with Levy flight algorithm is further hybridized with three differentvariants of ANN. The proposed BatLFBP is applied to the problem ofinsulin DNA sequence classification of healthy homosapien. For classificationperformance, the proposed models such as Bat levy flight Artificial NeuralNetwork (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) arecompared with the other state-of-the-art algorithms like Bat Artificial NeuralNetwork (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distributionback propagation (BatGDBP), in-terms of means squared error (MSE) andaccuracy. From the perspective of simulations results, it is show that theproposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185,and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on WL5.While on WL10 the proposed BatLFANN achieved 99.89899% accuracy withMSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853%accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracywith MSE of 0.006738 which achieve better accuracy as compared to the otherhybrid models.
文摘Spray cooling is an effective tool to dissipate high heat fluxes from hot surfaces. This paper thoroughly investigates the effects of spray parameters on the cooling time and cooling rate under varying inlet pressure using water as the coolant. Cylindrical samples of stainless steel with constant diameter, D = 25 mm, and thickness δ: 8.5 mm, 13 mm, 17.5 mm and 22 mm were investigated. Critical droplet diameter to achieve an ultrafast cooling rate of 300°C/s was estimated by using analytical model for samples of varying thickness. At an inlet pressure of 0.8 MPa, maximum cooling rates of 424.2°C/s, 502.81°C/s and 573.1°C/s were achieved for wall super heat ΔT = 600°C, 700°C and 800°C respectively.
文摘A rapid and simple process has been developed for the recovery of antimony metal from stibnite ore of Kharan area (Balochistan) of Pakistan. The ore was characterized by X-ray diffraction (XRD) technique and found to contain 66% stibnite i.e. antimony sulfide (Sb2S3). The process parameters for the extraction of antimony were optimized on laboratory scale by varying reaction temperature from 900℃ - 1000℃, reaction time from 20 - 80 minutes and flux concentration was varied from 5 - 25 weight percent of the reaction mixture. The metal thus recovered with optimum conditions was evaluated by X-ray fluorescence (XRF) technique and was found to be more than 94% pure. The recovery yield calculated on the basis of stibnite present in the ore was 98.52%.
基金funded by Huanggang Normal University,China,Self-type Project of 2021(No.30120210103)and 2022(No.2042021008).
文摘Object detection in images has been identified as a critical area of research in computer vision image processing.Research has developed several novel methods for determining an object’s location and category from an image.However,there is still room for improvement in terms of detection effi-ciency.This study aims to develop a technique for detecting objects in images.To enhance overall detection performance,we considered object detection a two-fold problem,including localization and classification.The proposed method generates class-independent,high-quality,and precise proposals using an agglomerative clustering technique.We then combine these proposals with the relevant input image to train our network on convolutional features.Next,a network refinement module decreases the quantity of generated proposals to produce fewer high-quality candidate proposals.Finally,revised candidate proposals are sent into the network’s detection process to determine the object type.The algorithm’s performance is evaluated using publicly available the PASCAL Visual Object Classes Challenge 2007(VOC2007),VOC2012,and Microsoft Common Objects in Context(MS-COCO)datasets.Using only 100 proposals per image at intersection over union((IoU)=0.5 and 0.7),the proposed method attains Detection Recall(DR)rates of(93.17%and 79.35%)and(69.4%and 58.35%),and Mean Average Best Overlap(MABO)values of(79.25%and 62.65%),for the VOC2007 and MS-COCO datasets,respectively.Besides,it achieves a Mean Average Precision(mAP)of(84.7%and 81.5%)on both VOC datasets.The experiment findings reveal that our method exceeds previous approaches in terms of overall detection performance,proving its effectiveness.
基金Authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia for this research through a project grant(NU/IFC/ENT/01/009)under the institutional Funding Committee at Najran University,Kingdom of Saudi Arabia.
文摘Breast cancer(BC)is the most common cause of women’s deaths worldwide.The mammography technique is the most important modality for the detection of BC.To detect abnormalities in mammographic images,the Breast Imaging Reporting and Data System(BI-RADs)is used as a baseline.The correct allocation of BI-RADs categories for mammographic images is always an interesting task,even for specialists.In this work,to detect and classify the mammogram images in BI-RADs,a novel hybrid model is presented using a convolutional neural network(CNN)with the integration of a support vector machine(SVM).The dataset used in this research was collected from different hospitals in the Qassim health cluster of Saudi Arabia.The collection of all categories of BI-RADs is one of the major contributions of this paper.Another significant contribution is the development of a hybrid approach through the integration of CNN and SVM.The proposed hybrid approach uses three CNN models to obtain ensemble CNN model results.This ensemble model saves the values to integrate them with SVM.The proposed system achieved a classification accuracy,sensitivity,specificity,precision,and F1-score of 93.6%,94.8%,96.9%,96.6%,and 95.7%,respectively.The proposed model achieved better performance compared to previously available methods.
基金Authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia for funding this research through a Project(NU/IFC/ENT/01/014)under the institutional funding committee at Najran University,Kingdom of Saudi Arabia.
文摘Brain tumor is one of the most dreadful worldwide types of cancer and affects people leading to death.Magnetic resonance imaging methods capture skull images that contain healthy and affected tissue.Radiologists checked the affected tissue in the slice-by-slice manner,which was timeconsuming and hectic task.Therefore,auto segmentation of the affected part is needed to facilitate radiologists.Therefore,we have considered a hybrid model that inherits the convolutional neural network(CNN)properties to the support vector machine(SVM)for the auto-segmented brain tumor region.The CNN model is initially used to detect brain tumors,while SVM is integrated to segment the tumor region correctly.The proposed method was evaluated on a publicly available BraTS2020 dataset.The statistical parameters used in this work for the mathematical measures are precision,accuracy,specificity,sensitivity,and dice coefficient.Overall,our method achieved an accuracy value of 0.98,which is most prominent than existing techniques.Moreover,the proposed approach is more suitable for medical experts to diagnose the early stages of the brain tumor.
基金Authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia for funding this research through a project(NU/IFC/ENT/01/014)under the institutional funding committee at Najran University,Kingdom of Saudi Arabia。
文摘The precise brain tumor diagnosis is critical and shows a vital role in the medical support for treating tumor patients.Manual brain tumor segmentation for cancer analysis from many Magnetic Resonance Images(MRIs)created in medical practice is a problematic and timewasting task for experts.As a result,there is a critical necessity for more accurate computeraided methods for early tumor detection.To remove this gap,we enhanced the computational power of a computer-aided system by proposing a finetuned Block-Wise Visual Geometry Group19(BW-VGG19)architecture.In this method,a pre-trained VGG19 is fine-tuned with CNN architecture in the block-wise mechanism to enhance the system`s accuracy.The publicly accessible Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)dataset collected from 2005 to 2020 from different hospitals in China has been used in this research.Our proposed method is simple and achieved an accuracy of 0.98%.We compare our technique results with the existing Convolutional Neural network(CNN),VGG16,and VGG19 approaches.The results indicate that our proposed technique outperforms the best results associated with the existing methods.
文摘The actuaries always look for heavy-tailed distributions to model data relevant to business and actuarial risk issues.In this article,we introduce a new class of heavy-tailed distributions useful for modeling data in financial sciences.A specific sub-model form of our suggested family,named as a new extended heavy-tailed Weibull distribution is examined in detail.Some basic characterizations,including quantile function and raw moments have been derived.The estimates of the unknown parameters of the new model are obtained via the maximum likelihood estimation method.To judge the performance of the maximum likelihood estimators,a simulation analysis is performed in detail.Furthermore,some important actuarial measures such as value at risk and tail value at risk are also computed.A simulation study based on these actuarial measures is conducted to exhibit empirically that the proposed model is heavy-tailed.The usefulness of the proposed family is illustrated by means of an application to a heavy-tailed insurance loss data set.The practical application shows that the proposed model is more flexible and efficient than the other six competing models including(i)the two-parameter models Weibull,Lomax and Burr-XII distributions(ii)the three-parameter distributions Marshall-Olkin Weibull and exponentiated Weibull distributions,and(iii)a well-known four-parameter Kumaraswamy Weibull distribution.
文摘COVID-19 has caused severe health complications and produced a substantial adverse economic impact around the world.Forecasting the trend of COVID-19 infections could help in executing policies to effectively reduce the number of new cases.In this study,we apply the decomposition and ensemble model to forecast COVID-19 confirmed cases,deaths,and recoveries in Pakistan for the upcoming month until the end of July.For the decomposition of data,the Ensemble Empirical Mode Decomposition(EEMD)technique is applied.EEMD decomposes the data into small components,called Intrinsic Mode Functions(IMFs).For individual IMFs modelling,we use the Autoregressive Integrated Moving Average(ARIMA)model.The data used in this study is obtained from the official website of Pakistan that is publicly available and designated for COVID-19 outbreak with daily updates.Our analyses reveal that the number of recoveries,new cases,and deaths are increasing in Pakistan exponentially.Based on the selected EEMD-ARIMA model,the new confirmed cases are expected to rise from 213,470 to 311,454 by 31 July 2020,which is an increase of almost 1.46 times with a 95%prediction interval of 246,529 to 376,379.The 95%prediction interval for recovery is 162,414 to 224,579,with an increase of almost two times in total from 100802 to 193495 by 31 July 2020.On the other hand,the deaths are expected to increase from 4395 to 6751,which is almost 1.54 times,with a 95%prediction interval of 5617 to 7885.Thus,the COVID-19 forecasting results of Pakistan are alarming for the next month until 31 July 2020.They also confirm that the EEMD-ARIMA model is useful for the short-term forecasting of COVID-19,and that it is capable of keeping track of the real COVID-19 data in nearly all scenarios.The decomposition and ensemble strategy can be useful to help decision-makers in developing short-term strategies about the current number of disease occurrences until an appropriate vaccine is developed.
基金This research was supported by the Researchers Supporting Program(TUMAProject-2021-27)Almaarefa University,Riyadh,Saudi Arabia.
文摘In computer vision,convolutional neural networks have a wide range of uses.Images representmost of today’s data,so it’s important to know how to handle these large amounts of data efficiently.Convolutional neural networks have been shown to solve image processing problems effectively.However,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher accuracy.This technique is time consuming and requires a lot of work and domain knowledge.Designing a convolutional neural network architecture is a classic NP-hard optimization challenge.On the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and inconvenient.Various approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random selection.To address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized hyperparameters.This study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN model.In addition,different types and parameter ranges of existing genetic algorithms are used.Acomparative study was conducted with various state-of-the-art methodologies and algorithms.Experiments have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature.
基金Authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia for funding this research through a project(NU/IFC/ENT/01/014)under the institutional funding committee at Najran University,Kingdom of Saudi Arabia.
文摘Abnormal growth of brain tissues is the real cause of brain tumor.Strategy for the diagnosis of brain tumor at initial stages is one of the key step for saving the life of a patient.The manual segmentation of brain tumor magnetic resonance images(MRIs)takes time and results vary significantly in low-level features.To address this issue,we have proposed a ResNet-50 feature extractor depended on multilevel deep convolutional neural network(CNN)for reliable images segmentation by considering the low-level features of MRI.In this model,we have extracted features through ResNet-50 architecture and fed these feature maps to multi-level CNN model.To handle the classification process,we have collected a total number of 2043 MRI patients of normal,benign,and malignant tumor.Three model CNN,multi-level CNN,and ResNet-50 based multi-level CNN have been used for detection and classification of brain tumors.All the model results are calculated in terms of various numerical values identified as precision(P),recall(R),accuracy(Acc)and f1-score(F1-S).The obtained average results are much better as compared to already existing methods.This modified transfer learning architecture might help the radiologists and doctors as a better significant system for tumor diagnosis.
基金Authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia for funding this research through a project(NU/IFC/ENT/01/014)under the institutional funding committee at Najran University,Kingdom of Saudi Arabia.
文摘Assistive devices for disabled people with the help of Brain-Computer Interaction(BCI)technology are becoming vital bio-medical engineering.People with physical disabilities need some assistive devices to perform their daily tasks.In these devices,higher latency factors need to be addressed appropriately.Therefore,the main goal of this research is to implement a real-time BCI architecture with minimum latency for command actuation.The proposed architecture is capable to communicate between different modules of the system by adopting an automotive,intelligent data processing and classification approach.Neuro-sky mind wave device has been used to transfer the data to our implemented server for command propulsion.Think-Net Convolutional Neural Network(TN-CNN)architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data classification.Data collection and processing are the responsibility of the central integrated server for system load minimization.Testing of implemented architecture and deep learning model shows excellent results.The proposed system integrity level was the minimum data loss and the accurate commands processing mechanism.The training and testing results are 99%and 93%for custom model implementation based on TN-CNN.The proposed real-time architecture is capable of intelligent data processing unit with fewer errors,and it will benefit assistive devices working on the local server and cloud server.
基金Authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia for funding this research through a project(NU/IFC/ENT/01/014)under the institutional funding committee at Najran University,Kingdom of Saudi Arabia.
文摘The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients worldwide.Even in this technol-ogy-based arena,brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original ones.The artificially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented data.To over-come the highlighted issue,a Generative Adversarial Network(GAN)deep learn-ing technique in which two neural networks compete to become more accurate in creating artificially realistic data for MRI images.The GAN network contains mainly two parts known as generator and discriminator.Commonly,a generator is the convolutional neural network,and a discriminator is the deconvolutional neural network.In this research,the publicly accessible Contrast-Enhanced Mag-netic Resonance Imaging(CE-MRI)dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been used.Our proposed method is simple and achieved an accuracy of 96%.We compare our technique results with the existing results,indicating that our proposed technique outper-forms the best results associated with the existing methods.
文摘This paper presents a grid connected photovoltaic system (PV) with a proposed high voltage conversion ratio DC-DC converter which steps up the variable low input voltages of photovoltaic module to the required DC link voltage. This voltage is applied to an H-bridge inverter which converts DC voltage into AC voltage and a low pass filter is used to filter the output. By adjusting the duty ratio of switches in DC-DC converter, the magnitude of inverter’s output voltage is controlled. The frequency and phase synchronization are ensured by a feedback signal taken from the grid. In this way, inverter is synchronized and connected with the grid to meet the energy demand. The PV system has been designed and simulated.
