Fritillaria cirrhosa D.Don(Liliaceae)is an endangered perennial bulbous plant and its dry bulb is a valuable med-icinal material with antitussive and expectorant effects.Nevertheless,lack of resources and expensive pr...Fritillaria cirrhosa D.Don(Liliaceae)is an endangered perennial bulbous plant and its dry bulb is a valuable med-icinal material with antitussive and expectorant effects.Nevertheless,lack of resources and expensive prices make it difficult to meet clinical needs.This study presents a regeneration system aimed at overcoming the challenge of inadequate supply in F.cirrhosa,focusing on:(1)callus induction,(2)bulblets and adventitious bud induction,and(3)artificial seed production.Callus development was achieved in 84.93%on Murashige and Skoog(MS)medium fortified with 1.0 mg·L^(-1) picloram.The optimal medium for callus differentiation into regenerated bulb-lets was MS medium supplemented with 1.0 mg·L^(-1)6-benzyladenine(6-BA)and 0.2 mg·L^(-1)α-naphthaleneacetic acid(NAA).Subsequently,bulblets and adventitious buds were induced from regenerated bulblet sections cul-tured on MS medium fortified with 0.3 mg·L^(-1)6-BA+1.0 mg·L^(-1)2,4-dichlorophenoxyacetic acid(2,4-D),2.0 mg·L^(-1)6-BA+0.5 mg·L^(-1),and the induction rates were 88.17%and 84.24%,respectively.The regenerated bulblets were transplanted into a substrate of humus soil,river sand,and pearlite(1:1:1)after low-temperature treatment.The germination rate was 42.80%after culture for 30 days.Regenerated bulblets were used for encap-sulations in liquid MS medium containing 3%sucrose(w/v)+0.5 mg·L^(-1) NAA+2.0 mg·L^(-1)6-BA+3%sodium alginate(w/v)with a 10 min exposure to 2%CaCl_(2).Under non-aseptic conditions,the germination rate reached 81.67%,while the rooting rate was 20.56%after 45 days.The capsule added 1.0 g·L^(-1) carbendazim and 1.0 g·L^(-1) activated carbon was the best component of artificial seeds.This study successfully established an efficient regen-eration system for the rapid propagation of F.cirrhosa,involving in vitro bulblet regeneration and artificial seed production.This method introduces a novel approach for efficient breeding and germplasm preservation,making it suitable for large-scale industrial resource production.展开更多
Agriculture plays a crucial role in the economy,and there is an increasing global emphasis on automating agri-cultural processes.With the tremendous increase in population,the demand for food and employment has also i...Agriculture plays a crucial role in the economy,and there is an increasing global emphasis on automating agri-cultural processes.With the tremendous increase in population,the demand for food and employment has also increased significantly.Agricultural methods traditionally used to meet these requirements are no longer ade-quate,requiring solutions to issues such as excessive herbicide use and the use of chemical fertilizers.Integration of technologies such as the Internet of Things,wireless communication,machine learning,artificial intelligence(AI),and deep learning shows promise in addressing these challenges.However,there is a lack of comprehensive documentation on the application and potential of AI in improving agricultural input efficiency.To address this gap,a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed,Scopus,Goo-gle Scholar,Web of Science,and Science Direct for relevant articles.Out of 327 initially identified articles,180 were deemed pertinent,focusing primarily on AI’s potential in enhancing yield through better management of nutrients,water,and weeds.Taking into account researchfindings worldwide,we found that AI technologies could assist farmers by providing recommendations on the optimal nutrients to enhance soil quality and deter-mine the best time for irrigation or herbicide application.The present status of AI-driven automation in agricul-ture holds significant promise for optimizing agricultural input utilization and reducing resource waste,particularly in the context of three pillars of crop management,i.e.,nutrient,irrigation,and weed management.展开更多
A deep-learning-based framework is proposed to predict the impedance response and underlying electrochemical behavior of the reversible protonic ceramic cell(PCC) across a wide variety of different operating condition...A deep-learning-based framework is proposed to predict the impedance response and underlying electrochemical behavior of the reversible protonic ceramic cell(PCC) across a wide variety of different operating conditions.Electrochemical impedance spectra(EIS) of PCCs were first acquired under a variety of opera ting conditions to provide a dataset containing 36 sets of EIS spectra for the model.An artificial neural network(ANN) was then trained to model the relationship between the cell operating condition and EIS response.Finally,ANN model-predicted EIS spectra were analyzed by the distribution of relaxation times(DRT) and compared to DRT spectra obtained from the experimental EIS data,enabling an assessment of the accumulative errors from the predicted EIS data vs the predicted DRT.We show that in certain cases,although the R^(2)of the predicted EIS curve may be> 0.98,the R^(2)of the predicted DRT may be as low as~0.3.This can lead to an inaccurate ANN prediction of the underlying time-resolved electrochemical response,although the apparent accuracy as evaluated from the EIS prediction may seem acceptable.After adjustment of the parameters of the ANN framework,the average R^(2)of the DRTs derived from the predicted EIS can be improved to 0.9667.Thus,we demonstrate that a properly tuned ANN model can be used as an effective tool to predict not only the EIS,but also the DRT of complex electrochemical systems.展开更多
In recent years,cloud computing has provided a Software As A Service(SaaS)platform where the software can be reused and applied to fulfill compli-cated user demands according to specific Quality of Services(QoS)constrai...In recent years,cloud computing has provided a Software As A Service(SaaS)platform where the software can be reused and applied to fulfill compli-cated user demands according to specific Quality of Services(QoS)constraints.The user requirements are formulated as a workflow consisting of a set of tasks.However,many services may satisfy the functionality of each task;thus,searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard problem.This work will introduce a hybrid Artificial Bee Colony(ABC)with a Cuckoo Search(CS)algorithm to untangle service composition problem.The ABC is a well-known metaheuristic algorithm that can be applied when dealing with different NP-hard problems with an outstanding record of performance.However,the ABC suffers from a slow convergence problem.Therefore,the CS is used to overcome the ABC’s limitations by allowing the abandoned bees to enhance their search and override the local optimum.The proposed hybrid algorithm has been tested on 19 datasets and then compared with two standard algorithms(ABC and CS)and three state-of-the-art swarm-based composition algorithms.In addition,extensive parameter study experiments were conducted to set up the proposed algorithm’s parameters.The results indicate that the proposed algorithm outperforms the standard algorithms in the three comparison criteria(bestfitness value,averagefitness value,and average execution time)overall datasets in 30 different runs.Furthermore,the proposed algorithm also exhibits better performance than the state–of–the–art algorithms in the three comparison criteria over 30 different runs.展开更多
In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results i...In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results in software devel-opment is challenging.Thus,accurate estimation of software development efforts is always a concern for many companies.In this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artificial neural network(ANN).An artificial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model equation.Three state-of-the-art publicly available datasets are used for experiments.The backpropagation feed-forward procedure used a training set by iteratively processing and training a neural network.The proposed model is tested on the test set.The estimated effort is compared with the actual effort value.Experimental results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy.展开更多
This paper theoretically introduced the feasibility of changing the vibration characteristics offlexible plates by using bio-inspired,extremely light,and powerful Pneumatic Artificial Muscle(PAM)actuators.Many structura...This paper theoretically introduced the feasibility of changing the vibration characteristics offlexible plates by using bio-inspired,extremely light,and powerful Pneumatic Artificial Muscle(PAM)actuators.Many structural plates or shells are typicallyflexible and show highvibration sensitivity.For this reason,this paper provides a way toachieve active vibrationcontrolfor suppressing the oscillations ofthese structuresto meet strict stability,safety,and comfort requirements.The dynamic behaviors of the designed plates are modeled by using thefinite element(FE)method.As is known,the output force vs.contraction curve of PAM is nonlinear generally.In this presentfinite element model,the maximum forces provided by PAM in different air pressure are adopted as controlling forces for applying for the plate.The non-linearity between the output force and displacement of PAM is avoided in this study.The dynamic behaviors of plates with several independent groups of controlling forces are observed and studied.The results show that the natural frequencies of the plate can be varying and the max amplitude decreases significantly if the controlling forces are applied.The present work also demonstrates the potential of the PAM actuators as valid means for damping out the vibration offlexible systems.展开更多
The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial ma...The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial masks,and maintaining safe social distancing have become crucial factors in keeping the virus at bay.Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus,the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly.Tofight the spread of this virus,technologically developed systems have become very useful.However,the implementation of an automatic,robust,continuous,and lightweight monitoring system that can be efficiently deployed on an embedded device still has not become prevalent in the mass community.This paper aims to develop an automatic system to simul-taneously detect social distance and face mask violation in real-time that has been deployed in an embedded system.A modified version of a convolutional neural network,the ResNet50 model,has been utilized to identify masked faces in peo-ple.You Only Look Once(YOLOv3)approach is applied for object detection and the DeepSORT technique is used to measure the social distance.The efficiency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system,Jetson Nano edge computing device,and smartphones,Android and iOS applications.Empirical results show that the implemented model can efficiently detect facial masks and social distance viola-tions with acceptable accuracy and precision scores.展开更多
To make full use of plant shellfibers(rice husk,walnut shell,chestnut shell),three kinds of wood-plastic com-posites of plant shellfibers and polyvinyl chloride(PVC)were prepared.X-ray diffraction analysis was carried o...To make full use of plant shellfibers(rice husk,walnut shell,chestnut shell),three kinds of wood-plastic com-posites of plant shellfibers and polyvinyl chloride(PVC)were prepared.X-ray diffraction analysis was carried out on three kinds of plant shellfibers to test their crystallinity.The aging process of the composites was conducted under 2 different conditions.One was artificial seawater immersion and xenon lamp irradiation,and the other one was deionized water spray and xenon lamp irradiation.The mechanical properties(tensile strength,flexural strength,impact strength),changes in color,water absorption,Fourier transform infrared spectroscopy(FTIR),and microstructures of the composites before and after the two aging experiments were analyzed.The results showed that the chestnut shell had the highest crystallinity,which was 42%.The chestnut shell/PVC composites had the strongest interface bonding,the least internal defects,and the best general mechanical properties among the three composites.Its tensile strength,bending strength and impact strength were 23.81 MPa,34.12 MPa,and 4.32 KJ·m^(-2),respectively.Comparing the two aging conditions,artificial seawater immersion and xenon lamp irradiation destroyed the quality of the combination of plant shellfibers and PVC,making the internal defects of the composites increase.This made the water absorption ability and changes in the color of the composites more obvious and led to a great decrease in the mechanical properties.The general mechanical properties of the chestnut shell/PVC composites were the best,but their water absorption ability changed more obviously.展开更多
.Organ-on-Chip(OoC)has emerged as a revolutionary approach to emulate human organ function-ality in vitro,offering unparalleled insights into physiological processes and disease modeling.The integration of artificial i....Organ-on-Chip(OoC)has emerged as a revolutionary approach to emulate human organ function-ality in vitro,offering unparalleled insights into physiological processes and disease modeling.The integration of artificial intelligence(AI)with OoC platforms presents a transformative synergy,combining the precision of microscale organ replication with the analytical prowess of intelligent algorithms,is emerging as a transforma-tive force in harnessing the full potential of OoC.This perspective investigates the multifaceted implications of integrating AI with OoC,examining its impact on biomedical research,acknowledging the synergistic po-tential that arises from combining the precision of microscale organ replication with the analytical capabilities of intelligent algorithms,and fostering a future where the intricate workings of the technology and biology.展开更多
Human Activity Recognition(HAR)has been made simple in recent years,thanks to recent advancements made in Artificial Intelligence(AI)techni-ques.These techniques are applied in several areas like security,surveillance,...Human Activity Recognition(HAR)has been made simple in recent years,thanks to recent advancements made in Artificial Intelligence(AI)techni-ques.These techniques are applied in several areas like security,surveillance,healthcare,human-robot interaction,and entertainment.Since wearable sensor-based HAR system includes in-built sensors,human activities can be categorized based on sensor values.Further,it can also be employed in other applications such as gait diagnosis,observation of children/adult’s cognitive nature,stroke-patient hospital direction,Epilepsy and Parkinson’s disease examination,etc.Recently-developed Artificial Intelligence(AI)techniques,especially Deep Learning(DL)models can be deployed to accomplish effective outcomes on HAR process.With this motivation,the current research paper focuses on designing Intelligent Hyperparameter Tuned Deep Learning-based HAR(IHPTDL-HAR)technique in healthcare environment.The proposed IHPTDL-HAR technique aims at recogniz-ing the human actions in healthcare environment and helps the patients in mana-ging their healthcare service.In addition,the presented model makes use of Hierarchical Clustering(HC)-based outlier detection technique to remove the out-liers.IHPTDL-HAR technique incorporates DL-based Deep Belief Network(DBN)model to recognize the activities of users.Moreover,Harris Hawks Opti-mization(HHO)algorithm is used for hyperparameter tuning of DBN model.Finally,a comprehensive experimental analysis was conducted upon benchmark dataset and the results were examined under different aspects.The experimental results demonstrate that the proposed IHPTDL-HAR technique is a superior per-former compared to other recent techniques under different measures.展开更多
Rapid increase in the large quantity of industrial data,Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation,data sensing and collection,real-time data processing,and high request ar...Rapid increase in the large quantity of industrial data,Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation,data sensing and collection,real-time data processing,and high request arrival rates.The classical intrusion detection system(IDS)is not a practical solution to the Industry 4.0 environment owing to the resource limitations and complexity.To resolve these issues,this paper designs a new Chaotic Cuckoo Search Optimiza-tion Algorithm(CCSOA)with optimal wavelet kernel extreme learning machine(OWKELM)named CCSOA-OWKELM technique for IDS on the Industry 4.0 platform.The CCSOA-OWKELM technique focuses on the design of feature selection with classification approach to achieve minimum computation complex-ity and maximum detection accuracy.The CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique,which incorpo-rates the concepts of chaotic maps with CSOA.Besides,the OWKELM technique is applied for the intrusion detection and classification process.In addition,the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunflower optimization(SFO)algorithm.The utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better performance.In order to guarantee the supreme performance of the CCSOA-OWKELM technique,a wide range of experiments take place on two benchmark datasets and the experimental outcomes demonstrate the promis-ing performance of the CCSOA-OWKELM technique over the recent state of art techniques.展开更多
In several fields like financial dealing,industry,business,medicine,et cetera,Big Data(BD)has been utilized extensively,which is nothing but a collection of a huge amount of data.However,it is highly complicated alon...In several fields like financial dealing,industry,business,medicine,et cetera,Big Data(BD)has been utilized extensively,which is nothing but a collection of a huge amount of data.However,it is highly complicated along with time-consuming to process a massive amount of data.Thus,to design the Distribution Preserving Framework for BD,a novel methodology has been proposed utilizing Manhattan Distance(MD)-centered Partition Around Medoid(MD–PAM)along with Conjugate Gradient Artificial Neural Network(CG-ANN),which undergoes various steps to reduce the complications of BD.Firstly,the data are processed in the pre-processing phase by mitigating the data repetition utilizing the map-reduce function;subsequently,the missing data are handled by substituting or by ignoring the missed values.After that,the data are transmuted into a normalized form.Next,to enhance the classification performance,the data’s dimensionalities are minimized by employing Gaussian Kernel(GK)-Fisher Discriminant Analysis(GK-FDA).Afterwards,the processed data is submitted to the partitioning phase after transmuting it into a structured format.In the partition phase,by utilizing the MD-PAM,the data are partitioned along with grouped into a cluster.Lastly,by employing CG-ANN,the data are classified in the classification phase so that the needed data can be effortlessly retrieved by the user.To analogize the outcomes of the CG-ANN with the prevailing methodologies,the NSL-KDD openly accessible datasets are utilized.The experiential outcomes displayed that an efficient result along with a reduced computation cost was shown by the proposed CG-ANN.The proposed work outperforms well in terms of accuracy,sensitivity and specificity than the existing systems.展开更多
Agriculture plays a vital role in the Indian economy.Crop recommen-dation for a specific region is a tedious process as it can be affected by various variables such as soil type and climatic parameters.At the same time...Agriculture plays a vital role in the Indian economy.Crop recommen-dation for a specific region is a tedious process as it can be affected by various variables such as soil type and climatic parameters.At the same time,crop yield prediction was based on several features like area,irrigation type,temperature,etc.The recent advancements of artificial intelligence(AI)and machine learning(ML)models pave the way to design effective crop recommendation and crop pre-diction models.In this view,this paper presents a novel Multimodal Machine Learning Based Crop Recommendation and Yield Prediction(MMML-CRYP)technique.The proposed MMML-CRYP model mainly focuses on two processes namely crop recommendation and crop prediction.At the initial stage,equilibrium optimizer(EO)with kernel extreme learning machine(KELM)technique is employed for effectual recommendation of crops.Next,random forest(RF)tech-nique was executed for predicting the crop yield accurately.For reporting the improved performance of the MMML-CRYP system,a wide range of simulations were carried out and the results are investigated using benchmark dataset.Experi-mentation outcomes highlighted the significant performance of the MMML-CRYP approach on the compared approaches with maximum accuracy of 97.91%.展开更多
Soil salinity is a serious land degradation issue in agriculture.It is a major threat to agriculture productivity.Extra irrigation water is applied to leach down the salts from the root zone of the plants in the form ...Soil salinity is a serious land degradation issue in agriculture.It is a major threat to agriculture productivity.Extra irrigation water is applied to leach down the salts from the root zone of the plants in the form of a Leaching fraction(LF)of irrigation water.For the leaching process to be effective,the LF of irriga-tion water needs to be adjusted according to the environmental conditions and soil salinity level in the form of Evapotranspiration(ET)rate.The relationship between environmental conditions and ET rate is hard to be defined by a linear relationship and data-driven Machine learning(ML)based decisions are required to determine the calibrated Evapotranspiration(ETc)rate.ML-assisted ETc is pro-posed to adjust the LF according to the ETc and soil salinity level.A regression model is proposed to determine the ETc rate according to the prevailing tempera-ture,humidity,and sunshine,which would be used to determine the smart LF according to the ETc and soil salinity level.The proposed model is trained and tested against the Blaney Criddle method of Reference evapotranspiration(ETo)determination.The validation of the model from the test dataset reveals the accu-racy of the ML model in terms of Root mean squared errors(RMSE)are 0.41,Mean absolute errors(MAE)are 0.34,and Mean squared errors(MSE)are 0.28 mm day-1.The applications of the proposed solution in a real-time environ-ment show that the LF by the proposed solution is more effective in reducing the soil salinity as compared to the traditional process of leaching.展开更多
The immediate international spread of severe acute respiratory syn-drome revealed the potential threat of infectious diseases in a closely integrated and interdependent world.When an outbreak occurs,each country must ...The immediate international spread of severe acute respiratory syn-drome revealed the potential threat of infectious diseases in a closely integrated and interdependent world.When an outbreak occurs,each country must have a well-coordinated and preventative plan to address the situation.Information and Communication Technologies have provided innovative approaches to dealing with numerous facets of daily living.Although intelligent devices and applica-tions have become a vital part of our everyday lives,smart gadgets have also led to several physical and psychological health problems in modern society.Here,we used an artificial intelligence AI-based system for disease prediction using an Artificial Neural Network(ANN).The ANN improved the regularization of the classification model,hence increasing its accuracy.The unconstrained opti-mization model reduced the classifier’s cost function to obtain the lowest possible cost.To verify the performance of the intelligent system,we compared the out-comes of the suggested scheme with the results of previously proposed models.The proposed intelligent system achieved an accuracy of 0.89,and the miss rate 0.11 was higher than in previously proposed models.展开更多
The development of non-linear loads at consumers has significantly impacted power supply systems.Since,the poor power quality has been found in the three-phase distribution system due to unbalanced loads,harmonic curre...The development of non-linear loads at consumers has significantly impacted power supply systems.Since,the poor power quality has been found in the three-phase distribution system due to unbalanced loads,harmonic current,undesired voltage regulation,and extreme reactive power demand.To overcome this issue,Distributed STATicCOMpensator(DSTATCOM)is implemented.DSTATCOM is a shunt-connected Voltage Source Converter(VSC)that has been utilized in distribution networks to balance the bus voltage in terms of enhancing reactive power control and power factor.DSTATCOM can provide both rapid and continuous capacitive and inductive mode compensation.A rectified resistive and inductive load eliminates current harmonics in a three-phase power supply.The synchronous fundamental DQ frame is a time-domain approach developed from three-phase system space vector transformations has been designed using MATLAB/Simulink.The DQ theory is used to produce the reference signal for the Pulse Width Modulation(PWM)generator.In addition,a traditional Propor-tional Integral Derivative(PID)controller is designed and compared with pro-posed soft computing approaches such as Fuzzy–PID and Artificial Neural Network(ANN-PID)and compared accurate reference current determination for Direct Current(DC)bus through DC link.An Analytical explores the pro-posed control strategies given to establish superior outcomes.Finally,total harmo-nic distortion analysis should be taken for performance analysis of the proposed system with IEEE standards.展开更多
Recognizing signs and fonts of prehistoric language is a fairly difficult job that requires special tools.This stipulation make the dispensation period over-riding,difficult and tiresome to calculate.This paper present ...Recognizing signs and fonts of prehistoric language is a fairly difficult job that requires special tools.This stipulation make the dispensation period over-riding,difficult and tiresome to calculate.This paper present a technique for recognizing ancient south Indian languages by applying Artificial Neural Network(ANN)associated with Opposition based Grey Wolf Optimization Algorithm(OGWA).It identifies the prehistoric language,signs and fonts.It is an apparent from the ANN system that arbitrarily produced weights or neurons linking various layers play a significant role in its performance.For adaptively determining these weights,this paper applies various optimization algorithms such as Opposition based Grey Wolf Optimization,Particle Swarm Optimization and Grey Wolf Opti-mization to the ANN system.Performance results are illustrated that the proposed ANN-OGWO technique achieves superior accuracy over the other techniques.In test case 1,the accuracy value of OGWO is 94.89%and in test case 2,the accu-racy value of OGWO is 92.34%,on average,the accuracy of OGWO achieves 5.8%greater accuracy than ANN-GWO,10.1%greater accuracy than ANN-PSO and 22.1%greater accuracy over conventional ANN technique.展开更多
Coronavirus(COVID-19 or SARS-CoV-2)is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 countries.The rapid spread of COVID-19 has caused a global health emergency and resu...Coronavirus(COVID-19 or SARS-CoV-2)is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 countries.The rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its transmission.There is a signifi-cant increase in the number of patients infected,resulting in a lack of test resources and kits in most countries.To overcome this panicked state of affairs,researchers are looking forward to some effective solutions to overcome this situa-tion:one of the most common and effective methods is to examine the X-radiation(X-rays)and computed tomography(CT)images for detection of Covid-19.How-ever,this method burdens the radiologist to examine each report.Therefore,to reduce the burden on the radiologist,an effective,robust and reliable detection system has been developed,which may assist the radiologist and medical specia-list in effective detecting of COVID.We proposed a deep learning approach that uses readily available chest radio-graphs(chest X-rays)to diagnose COVID-19 cases.The proposed approach applied transfer learning to the Deep Convolutional Neural Network(DCNN)model,Inception-v4,for the automatic detection of COVID-19 infection from chest X-rays images.The dataset used in this study contains 1504 chest X-ray images,504 images of COVID-19 infection,and 1000 normal images obtained from publicly available medical repositories.The results showed that the proposed approach detected COVID-19 infection with an overall accuracy of 99.63%.展开更多
Melanoma is of the lethal and rare types of skin cancer.It is curable at an initial stage and the patient can survive easily.It is very difficult to screen all skin lesion patients due to costly treatment.Clinicians ar...Melanoma is of the lethal and rare types of skin cancer.It is curable at an initial stage and the patient can survive easily.It is very difficult to screen all skin lesion patients due to costly treatment.Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of melanoma.These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease.The trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reflections,air bubbles,and oils on almost all images.Active contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular shapes.An entropy and morphology-based automated mask selection is pro-posed for the active contour method.The proposed method can improve the overall segmentation along with the boundary of melanoma images.In this study,features have been extracted to perform the classification on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been combined.Therefore,hybrid fused texture features;such as local,color feature as global,shape features,and Artificial neural network(ANN)as classifiers have been proposed for the categorization of the malignant and non-malignant.Experimentations had been carried out on datasets Dermis,DermQuest,and PH2.The results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.展开更多
基金funded by the National Key Research and Development Program of China(2018YFC1706101)the Science and Technology Program of Sichuan Province,China(2021YFS0045).
文摘Fritillaria cirrhosa D.Don(Liliaceae)is an endangered perennial bulbous plant and its dry bulb is a valuable med-icinal material with antitussive and expectorant effects.Nevertheless,lack of resources and expensive prices make it difficult to meet clinical needs.This study presents a regeneration system aimed at overcoming the challenge of inadequate supply in F.cirrhosa,focusing on:(1)callus induction,(2)bulblets and adventitious bud induction,and(3)artificial seed production.Callus development was achieved in 84.93%on Murashige and Skoog(MS)medium fortified with 1.0 mg·L^(-1) picloram.The optimal medium for callus differentiation into regenerated bulb-lets was MS medium supplemented with 1.0 mg·L^(-1)6-benzyladenine(6-BA)and 0.2 mg·L^(-1)α-naphthaleneacetic acid(NAA).Subsequently,bulblets and adventitious buds were induced from regenerated bulblet sections cul-tured on MS medium fortified with 0.3 mg·L^(-1)6-BA+1.0 mg·L^(-1)2,4-dichlorophenoxyacetic acid(2,4-D),2.0 mg·L^(-1)6-BA+0.5 mg·L^(-1),and the induction rates were 88.17%and 84.24%,respectively.The regenerated bulblets were transplanted into a substrate of humus soil,river sand,and pearlite(1:1:1)after low-temperature treatment.The germination rate was 42.80%after culture for 30 days.Regenerated bulblets were used for encap-sulations in liquid MS medium containing 3%sucrose(w/v)+0.5 mg·L^(-1) NAA+2.0 mg·L^(-1)6-BA+3%sodium alginate(w/v)with a 10 min exposure to 2%CaCl_(2).Under non-aseptic conditions,the germination rate reached 81.67%,while the rooting rate was 20.56%after 45 days.The capsule added 1.0 g·L^(-1) carbendazim and 1.0 g·L^(-1) activated carbon was the best component of artificial seeds.This study successfully established an efficient regen-eration system for the rapid propagation of F.cirrhosa,involving in vitro bulblet regeneration and artificial seed production.This method introduces a novel approach for efficient breeding and germplasm preservation,making it suitable for large-scale industrial resource production.
文摘Agriculture plays a crucial role in the economy,and there is an increasing global emphasis on automating agri-cultural processes.With the tremendous increase in population,the demand for food and employment has also increased significantly.Agricultural methods traditionally used to meet these requirements are no longer ade-quate,requiring solutions to issues such as excessive herbicide use and the use of chemical fertilizers.Integration of technologies such as the Internet of Things,wireless communication,machine learning,artificial intelligence(AI),and deep learning shows promise in addressing these challenges.However,there is a lack of comprehensive documentation on the application and potential of AI in improving agricultural input efficiency.To address this gap,a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed,Scopus,Goo-gle Scholar,Web of Science,and Science Direct for relevant articles.Out of 327 initially identified articles,180 were deemed pertinent,focusing primarily on AI’s potential in enhancing yield through better management of nutrients,water,and weeds.Taking into account researchfindings worldwide,we found that AI technologies could assist farmers by providing recommendations on the optimal nutrients to enhance soil quality and deter-mine the best time for irrigation or herbicide application.The present status of AI-driven automation in agricul-ture holds significant promise for optimizing agricultural input utilization and reducing resource waste,particularly in the context of three pillars of crop management,i.e.,nutrient,irrigation,and weed management.
基金funding from the National Natural Science Foundation of China,China(12172104,52102226)the Shenzhen Science and Technology Innovation Commission,China(JCYJ20200109113439837)the Stable Supporting Fund of Shenzhen,China(GXWD2020123015542700320200728114835006)。
文摘A deep-learning-based framework is proposed to predict the impedance response and underlying electrochemical behavior of the reversible protonic ceramic cell(PCC) across a wide variety of different operating conditions.Electrochemical impedance spectra(EIS) of PCCs were first acquired under a variety of opera ting conditions to provide a dataset containing 36 sets of EIS spectra for the model.An artificial neural network(ANN) was then trained to model the relationship between the cell operating condition and EIS response.Finally,ANN model-predicted EIS spectra were analyzed by the distribution of relaxation times(DRT) and compared to DRT spectra obtained from the experimental EIS data,enabling an assessment of the accumulative errors from the predicted EIS data vs the predicted DRT.We show that in certain cases,although the R^(2)of the predicted EIS curve may be> 0.98,the R^(2)of the predicted DRT may be as low as~0.3.This can lead to an inaccurate ANN prediction of the underlying time-resolved electrochemical response,although the apparent accuracy as evaluated from the EIS prediction may seem acceptable.After adjustment of the parameters of the ANN framework,the average R^(2)of the DRTs derived from the predicted EIS can be improved to 0.9667.Thus,we demonstrate that a properly tuned ANN model can be used as an effective tool to predict not only the EIS,but also the DRT of complex electrochemical systems.
基金Ministry of Education in Saudi Arabia for funding this research work through the project number (IFPSAU-2021/01/17793)rch work through the project number (IFPSAU-2021/01/17793)。
文摘In recent years,cloud computing has provided a Software As A Service(SaaS)platform where the software can be reused and applied to fulfill compli-cated user demands according to specific Quality of Services(QoS)constraints.The user requirements are formulated as a workflow consisting of a set of tasks.However,many services may satisfy the functionality of each task;thus,searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard problem.This work will introduce a hybrid Artificial Bee Colony(ABC)with a Cuckoo Search(CS)algorithm to untangle service composition problem.The ABC is a well-known metaheuristic algorithm that can be applied when dealing with different NP-hard problems with an outstanding record of performance.However,the ABC suffers from a slow convergence problem.Therefore,the CS is used to overcome the ABC’s limitations by allowing the abandoned bees to enhance their search and override the local optimum.The proposed hybrid algorithm has been tested on 19 datasets and then compared with two standard algorithms(ABC and CS)and three state-of-the-art swarm-based composition algorithms.In addition,extensive parameter study experiments were conducted to set up the proposed algorithm’s parameters.The results indicate that the proposed algorithm outperforms the standard algorithms in the three comparison criteria(bestfitness value,averagefitness value,and average execution time)overall datasets in 30 different runs.Furthermore,the proposed algorithm also exhibits better performance than the state–of–the–art algorithms in the three comparison criteria over 30 different runs.
基金This work was supported by the Technology development Program of MSS[No.S3033853].
文摘In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results in software devel-opment is challenging.Thus,accurate estimation of software development efforts is always a concern for many companies.In this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artificial neural network(ANN).An artificial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model equation.Three state-of-the-art publicly available datasets are used for experiments.The backpropagation feed-forward procedure used a training set by iteratively processing and training a neural network.The proposed model is tested on the test set.The estimated effort is compared with the actual effort value.Experimental results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy.
基金supported by the Henan Provincial Science and Technology Research Project(222102220068).
文摘This paper theoretically introduced the feasibility of changing the vibration characteristics offlexible plates by using bio-inspired,extremely light,and powerful Pneumatic Artificial Muscle(PAM)actuators.Many structural plates or shells are typicallyflexible and show highvibration sensitivity.For this reason,this paper provides a way toachieve active vibrationcontrolfor suppressing the oscillations ofthese structuresto meet strict stability,safety,and comfort requirements.The dynamic behaviors of the designed plates are modeled by using thefinite element(FE)method.As is known,the output force vs.contraction curve of PAM is nonlinear generally.In this presentfinite element model,the maximum forces provided by PAM in different air pressure are adopted as controlling forces for applying for the plate.The non-linearity between the output force and displacement of PAM is avoided in this study.The dynamic behaviors of plates with several independent groups of controlling forces are observed and studied.The results show that the natural frequencies of the plate can be varying and the max amplitude decreases significantly if the controlling forces are applied.The present work also demonstrates the potential of the PAM actuators as valid means for damping out the vibration offlexible systems.
文摘The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial masks,and maintaining safe social distancing have become crucial factors in keeping the virus at bay.Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus,the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly.Tofight the spread of this virus,technologically developed systems have become very useful.However,the implementation of an automatic,robust,continuous,and lightweight monitoring system that can be efficiently deployed on an embedded device still has not become prevalent in the mass community.This paper aims to develop an automatic system to simul-taneously detect social distance and face mask violation in real-time that has been deployed in an embedded system.A modified version of a convolutional neural network,the ResNet50 model,has been utilized to identify masked faces in peo-ple.You Only Look Once(YOLOv3)approach is applied for object detection and the DeepSORT technique is used to measure the social distance.The efficiency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system,Jetson Nano edge computing device,and smartphones,Android and iOS applications.Empirical results show that the implemented model can efficiently detect facial masks and social distance viola-tions with acceptable accuracy and precision scores.
基金This study was supported by the financial support of Natural Science Research Projects in Higher Education Institutions in Jiangsu Province(No.18KJD430002).
文摘To make full use of plant shellfibers(rice husk,walnut shell,chestnut shell),three kinds of wood-plastic com-posites of plant shellfibers and polyvinyl chloride(PVC)were prepared.X-ray diffraction analysis was carried out on three kinds of plant shellfibers to test their crystallinity.The aging process of the composites was conducted under 2 different conditions.One was artificial seawater immersion and xenon lamp irradiation,and the other one was deionized water spray and xenon lamp irradiation.The mechanical properties(tensile strength,flexural strength,impact strength),changes in color,water absorption,Fourier transform infrared spectroscopy(FTIR),and microstructures of the composites before and after the two aging experiments were analyzed.The results showed that the chestnut shell had the highest crystallinity,which was 42%.The chestnut shell/PVC composites had the strongest interface bonding,the least internal defects,and the best general mechanical properties among the three composites.Its tensile strength,bending strength and impact strength were 23.81 MPa,34.12 MPa,and 4.32 KJ·m^(-2),respectively.Comparing the two aging conditions,artificial seawater immersion and xenon lamp irradiation destroyed the quality of the combination of plant shellfibers and PVC,making the internal defects of the composites increase.This made the water absorption ability and changes in the color of the composites more obvious and led to a great decrease in the mechanical properties.The general mechanical properties of the chestnut shell/PVC composites were the best,but their water absorption ability changed more obviously.
文摘.Organ-on-Chip(OoC)has emerged as a revolutionary approach to emulate human organ function-ality in vitro,offering unparalleled insights into physiological processes and disease modeling.The integration of artificial intelligence(AI)with OoC platforms presents a transformative synergy,combining the precision of microscale organ replication with the analytical prowess of intelligent algorithms,is emerging as a transforma-tive force in harnessing the full potential of OoC.This perspective investigates the multifaceted implications of integrating AI with OoC,examining its impact on biomedical research,acknowledging the synergistic po-tential that arises from combining the precision of microscale organ replication with the analytical capabilities of intelligent algorithms,and fostering a future where the intricate workings of the technology and biology.
基金supported by Korea Institute for Advancement of Technology(KIAT)grant fundedthe Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist)the Soonchunhyang University Research Fund.
文摘Human Activity Recognition(HAR)has been made simple in recent years,thanks to recent advancements made in Artificial Intelligence(AI)techni-ques.These techniques are applied in several areas like security,surveillance,healthcare,human-robot interaction,and entertainment.Since wearable sensor-based HAR system includes in-built sensors,human activities can be categorized based on sensor values.Further,it can also be employed in other applications such as gait diagnosis,observation of children/adult’s cognitive nature,stroke-patient hospital direction,Epilepsy and Parkinson’s disease examination,etc.Recently-developed Artificial Intelligence(AI)techniques,especially Deep Learning(DL)models can be deployed to accomplish effective outcomes on HAR process.With this motivation,the current research paper focuses on designing Intelligent Hyperparameter Tuned Deep Learning-based HAR(IHPTDL-HAR)technique in healthcare environment.The proposed IHPTDL-HAR technique aims at recogniz-ing the human actions in healthcare environment and helps the patients in mana-ging their healthcare service.In addition,the presented model makes use of Hierarchical Clustering(HC)-based outlier detection technique to remove the out-liers.IHPTDL-HAR technique incorporates DL-based Deep Belief Network(DBN)model to recognize the activities of users.Moreover,Harris Hawks Opti-mization(HHO)algorithm is used for hyperparameter tuning of DBN model.Finally,a comprehensive experimental analysis was conducted upon benchmark dataset and the results were examined under different aspects.The experimental results demonstrate that the proposed IHPTDL-HAR technique is a superior per-former compared to other recent techniques under different measures.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP1/338/40)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R237)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Rapid increase in the large quantity of industrial data,Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation,data sensing and collection,real-time data processing,and high request arrival rates.The classical intrusion detection system(IDS)is not a practical solution to the Industry 4.0 environment owing to the resource limitations and complexity.To resolve these issues,this paper designs a new Chaotic Cuckoo Search Optimiza-tion Algorithm(CCSOA)with optimal wavelet kernel extreme learning machine(OWKELM)named CCSOA-OWKELM technique for IDS on the Industry 4.0 platform.The CCSOA-OWKELM technique focuses on the design of feature selection with classification approach to achieve minimum computation complex-ity and maximum detection accuracy.The CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique,which incorpo-rates the concepts of chaotic maps with CSOA.Besides,the OWKELM technique is applied for the intrusion detection and classification process.In addition,the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunflower optimization(SFO)algorithm.The utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better performance.In order to guarantee the supreme performance of the CCSOA-OWKELM technique,a wide range of experiments take place on two benchmark datasets and the experimental outcomes demonstrate the promis-ing performance of the CCSOA-OWKELM technique over the recent state of art techniques.
文摘In several fields like financial dealing,industry,business,medicine,et cetera,Big Data(BD)has been utilized extensively,which is nothing but a collection of a huge amount of data.However,it is highly complicated along with time-consuming to process a massive amount of data.Thus,to design the Distribution Preserving Framework for BD,a novel methodology has been proposed utilizing Manhattan Distance(MD)-centered Partition Around Medoid(MD–PAM)along with Conjugate Gradient Artificial Neural Network(CG-ANN),which undergoes various steps to reduce the complications of BD.Firstly,the data are processed in the pre-processing phase by mitigating the data repetition utilizing the map-reduce function;subsequently,the missing data are handled by substituting or by ignoring the missed values.After that,the data are transmuted into a normalized form.Next,to enhance the classification performance,the data’s dimensionalities are minimized by employing Gaussian Kernel(GK)-Fisher Discriminant Analysis(GK-FDA).Afterwards,the processed data is submitted to the partitioning phase after transmuting it into a structured format.In the partition phase,by utilizing the MD-PAM,the data are partitioned along with grouped into a cluster.Lastly,by employing CG-ANN,the data are classified in the classification phase so that the needed data can be effortlessly retrieved by the user.To analogize the outcomes of the CG-ANN with the prevailing methodologies,the NSL-KDD openly accessible datasets are utilized.The experiential outcomes displayed that an efficient result along with a reduced computation cost was shown by the proposed CG-ANN.The proposed work outperforms well in terms of accuracy,sensitivity and specificity than the existing systems.
文摘Agriculture plays a vital role in the Indian economy.Crop recommen-dation for a specific region is a tedious process as it can be affected by various variables such as soil type and climatic parameters.At the same time,crop yield prediction was based on several features like area,irrigation type,temperature,etc.The recent advancements of artificial intelligence(AI)and machine learning(ML)models pave the way to design effective crop recommendation and crop pre-diction models.In this view,this paper presents a novel Multimodal Machine Learning Based Crop Recommendation and Yield Prediction(MMML-CRYP)technique.The proposed MMML-CRYP model mainly focuses on two processes namely crop recommendation and crop prediction.At the initial stage,equilibrium optimizer(EO)with kernel extreme learning machine(KELM)technique is employed for effectual recommendation of crops.Next,random forest(RF)tech-nique was executed for predicting the crop yield accurately.For reporting the improved performance of the MMML-CRYP system,a wide range of simulations were carried out and the results are investigated using benchmark dataset.Experi-mentation outcomes highlighted the significant performance of the MMML-CRYP approach on the compared approaches with maximum accuracy of 97.91%.
基金funded by the Deanship of Scientific Research(DSR),King AbdulAziz University,Jeddah,Saudi Arabia under Grant No.(RG-11-611-43).
文摘Soil salinity is a serious land degradation issue in agriculture.It is a major threat to agriculture productivity.Extra irrigation water is applied to leach down the salts from the root zone of the plants in the form of a Leaching fraction(LF)of irrigation water.For the leaching process to be effective,the LF of irriga-tion water needs to be adjusted according to the environmental conditions and soil salinity level in the form of Evapotranspiration(ET)rate.The relationship between environmental conditions and ET rate is hard to be defined by a linear relationship and data-driven Machine learning(ML)based decisions are required to determine the calibrated Evapotranspiration(ETc)rate.ML-assisted ETc is pro-posed to adjust the LF according to the ETc and soil salinity level.A regression model is proposed to determine the ETc rate according to the prevailing tempera-ture,humidity,and sunshine,which would be used to determine the smart LF according to the ETc and soil salinity level.The proposed model is trained and tested against the Blaney Criddle method of Reference evapotranspiration(ETo)determination.The validation of the model from the test dataset reveals the accu-racy of the ML model in terms of Root mean squared errors(RMSE)are 0.41,Mean absolute errors(MAE)are 0.34,and Mean squared errors(MSE)are 0.28 mm day-1.The applications of the proposed solution in a real-time environ-ment show that the LF by the proposed solution is more effective in reducing the soil salinity as compared to the traditional process of leaching.
文摘The immediate international spread of severe acute respiratory syn-drome revealed the potential threat of infectious diseases in a closely integrated and interdependent world.When an outbreak occurs,each country must have a well-coordinated and preventative plan to address the situation.Information and Communication Technologies have provided innovative approaches to dealing with numerous facets of daily living.Although intelligent devices and applica-tions have become a vital part of our everyday lives,smart gadgets have also led to several physical and psychological health problems in modern society.Here,we used an artificial intelligence AI-based system for disease prediction using an Artificial Neural Network(ANN).The ANN improved the regularization of the classification model,hence increasing its accuracy.The unconstrained opti-mization model reduced the classifier’s cost function to obtain the lowest possible cost.To verify the performance of the intelligent system,we compared the out-comes of the suggested scheme with the results of previously proposed models.The proposed intelligent system achieved an accuracy of 0.89,and the miss rate 0.11 was higher than in previously proposed models.
文摘The development of non-linear loads at consumers has significantly impacted power supply systems.Since,the poor power quality has been found in the three-phase distribution system due to unbalanced loads,harmonic current,undesired voltage regulation,and extreme reactive power demand.To overcome this issue,Distributed STATicCOMpensator(DSTATCOM)is implemented.DSTATCOM is a shunt-connected Voltage Source Converter(VSC)that has been utilized in distribution networks to balance the bus voltage in terms of enhancing reactive power control and power factor.DSTATCOM can provide both rapid and continuous capacitive and inductive mode compensation.A rectified resistive and inductive load eliminates current harmonics in a three-phase power supply.The synchronous fundamental DQ frame is a time-domain approach developed from three-phase system space vector transformations has been designed using MATLAB/Simulink.The DQ theory is used to produce the reference signal for the Pulse Width Modulation(PWM)generator.In addition,a traditional Propor-tional Integral Derivative(PID)controller is designed and compared with pro-posed soft computing approaches such as Fuzzy–PID and Artificial Neural Network(ANN-PID)and compared accurate reference current determination for Direct Current(DC)bus through DC link.An Analytical explores the pro-posed control strategies given to establish superior outcomes.Finally,total harmo-nic distortion analysis should be taken for performance analysis of the proposed system with IEEE standards.
文摘Recognizing signs and fonts of prehistoric language is a fairly difficult job that requires special tools.This stipulation make the dispensation period over-riding,difficult and tiresome to calculate.This paper present a technique for recognizing ancient south Indian languages by applying Artificial Neural Network(ANN)associated with Opposition based Grey Wolf Optimization Algorithm(OGWA).It identifies the prehistoric language,signs and fonts.It is an apparent from the ANN system that arbitrarily produced weights or neurons linking various layers play a significant role in its performance.For adaptively determining these weights,this paper applies various optimization algorithms such as Opposition based Grey Wolf Optimization,Particle Swarm Optimization and Grey Wolf Opti-mization to the ANN system.Performance results are illustrated that the proposed ANN-OGWO technique achieves superior accuracy over the other techniques.In test case 1,the accuracy value of OGWO is 94.89%and in test case 2,the accu-racy value of OGWO is 92.34%,on average,the accuracy of OGWO achieves 5.8%greater accuracy than ANN-GWO,10.1%greater accuracy than ANN-PSO and 22.1%greater accuracy over conventional ANN technique.
文摘Coronavirus(COVID-19 or SARS-CoV-2)is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 countries.The rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its transmission.There is a signifi-cant increase in the number of patients infected,resulting in a lack of test resources and kits in most countries.To overcome this panicked state of affairs,researchers are looking forward to some effective solutions to overcome this situa-tion:one of the most common and effective methods is to examine the X-radiation(X-rays)and computed tomography(CT)images for detection of Covid-19.How-ever,this method burdens the radiologist to examine each report.Therefore,to reduce the burden on the radiologist,an effective,robust and reliable detection system has been developed,which may assist the radiologist and medical specia-list in effective detecting of COVID.We proposed a deep learning approach that uses readily available chest radio-graphs(chest X-rays)to diagnose COVID-19 cases.The proposed approach applied transfer learning to the Deep Convolutional Neural Network(DCNN)model,Inception-v4,for the automatic detection of COVID-19 infection from chest X-rays images.The dataset used in this study contains 1504 chest X-ray images,504 images of COVID-19 infection,and 1000 normal images obtained from publicly available medical repositories.The results showed that the proposed approach detected COVID-19 infection with an overall accuracy of 99.63%.
文摘Melanoma is of the lethal and rare types of skin cancer.It is curable at an initial stage and the patient can survive easily.It is very difficult to screen all skin lesion patients due to costly treatment.Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of melanoma.These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease.The trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reflections,air bubbles,and oils on almost all images.Active contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular shapes.An entropy and morphology-based automated mask selection is pro-posed for the active contour method.The proposed method can improve the overall segmentation along with the boundary of melanoma images.In this study,features have been extracted to perform the classification on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been combined.Therefore,hybrid fused texture features;such as local,color feature as global,shape features,and Artificial neural network(ANN)as classifiers have been proposed for the categorization of the malignant and non-malignant.Experimentations had been carried out on datasets Dermis,DermQuest,and PH2.The results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.
