Heating in the ocean has continued in 2024 in response to increased greenhouse gas concentrations in the atmosphere,despite the transition from an El Ni?o to neutral conditions. In 2024, both global sea surface temper...Heating in the ocean has continued in 2024 in response to increased greenhouse gas concentrations in the atmosphere,despite the transition from an El Ni?o to neutral conditions. In 2024, both global sea surface temperature(SST) and upper2000 m ocean heat content(OHC) reached unprecedented highs in the historical record. The 0–2000 m OHC in 2024exceeded that of 2023 by 16 ± 8 ZJ(1 Zetta Joules = 1021 Joules, with a 95% confidence interval)(IAP/CAS data), which is confirmed by two other data products: 18 ± 7 ZJ(CIGAR-RT reanalysis data) and 40 ± 31 ZJ(Copernicus Marine data,updated to November 2024). The Indian Ocean, tropical Atlantic, Mediterranean Sea, North Atlantic, North Pacific, and Southern Ocean also experienced record-high OHC values in 2024. The global SST continued its record-high values from2023 into the first half of 2024, and declined slightly in the second half of 2024, resulting in an annual mean of 0.61°C ±0.02°C(IAP/CAS data) above the 1981–2010 baseline, slightly higher than the 2023 annual-mean value(by 0.07°C ±0.02°C for IAP/CAS, 0.05°C ± 0.02°C for NOAA/NCEI, and 0.06°C ± 0.11°C for Copernicus Marine). The record-high values of 2024 SST and OHC continue to indicate unabated trends of global heating.展开更多
In this study,the impact of the training sample selection method on the performance of fuzzy-based Possibilistic c-means(PCM)and Noise Clustering(NC)classifiers were examined and mapped the cumin and fennel rabi crop....In this study,the impact of the training sample selection method on the performance of fuzzy-based Possibilistic c-means(PCM)and Noise Clustering(NC)classifiers were examined and mapped the cumin and fennel rabi crop.Two training sample selection approaches that have been investigated in this study are“mean”and“individual sample as mean”.Both training sample techniques were applied to the PCM and NC classifiers to classify the two indices approach.Both approaches have been studied to decrease spectral information in temporal data processing.The Modified Soil Adjusted Vegetation Index 2(MSAVI-2)and Class-Based Sensor Independent Modified Soil Adjusted Vegetation Index-2(CBSI-MSAVI-2)have been considered to minimize soil background effects,enhancing vegetation detection accuracy,particularly in areas with sparse vegetation cover.The MMD(MeanMembership Difference)and RMSE(RootMean Square Error)approaches were used to measure the study’s accuracy.To illustrate that the classifier successfully describes classes,cluster validity(SSE)was also performed,and the variance parameter was computed to handle heterogeneity within cumin and fennel crop fields.For the calculation of RMSE,Sentinel-2 data was used as classified,whereas PlanetScope satellite data was utilized as the reference data set.The best result was obtained using the NC classifier with“individual sample as mean”using CBSI-MSAVI-2 temporal indices.For Fuzziness Factor(m)=1.1,the RMSE,MMD,Variance,and SSE values for the NC classifier using“individual sample as mean”on the CBSI-MSAVI-2 temporal indices for cumin were 0.00098,0.00162,0.02857,and 0.97143,respectively and for fennel were 0.00025,0.00248,0.10420,and 3.54286,respectively.展开更多
Ordinary AFM probes'characters prevent the AFM' s application in various scopes. Carbon nanotubes represent ideal AFM probe materials for their higher aspect ratio, larger Young's modulus, unique chemical ...Ordinary AFM probes'characters prevent the AFM' s application in various scopes. Carbon nanotubes represent ideal AFM probe materials for their higher aspect ratio, larger Young's modulus, unique chemical structure, and well-defined electronic property. Carbon nanotube AFM probes are obtained by using a new method of attaching carbon nanotubes to the end of ordinary AFM probes, and are then used for doing AFM experiments. These experiments indicated that carbon nanotube probes have higher elastic deformation, higher resolution and higher durability. And it was also found that carbon nanotube probes ean accurately reflect the morphology of deep narrow gaps, while ordinary probes can not reflect.展开更多
Inflammatory bowel disease affects a substantial number of women in their reproductive years. Pregnancy presents a number of challenges for clinicians and patients; the health of the baby needs to be balanced with the...Inflammatory bowel disease affects a substantial number of women in their reproductive years. Pregnancy presents a number of challenges for clinicians and patients; the health of the baby needs to be balanced with the need to maintain remission in the mother. Historically, treatments for Crohn’s disease (CD) were often discontinued during the pregnancy, or nursing period, due to concerns about teratogenicity. Fortunately, observational data has reported the relative safety of many agents used to treat CD, including 5-aminosalicylic acid, thiopurines, and tumor necrosis factor. Data on the long-term development outcomes of children exposed to these therapies in utero are still limited. It is most important that physicians educate the patient regarding the optimal time to conceive, discuss the possible risks, and together decide on the best management strategy.展开更多
In the insurance sector, a massive volume of data is being generatedon a daily basis due to a vast client base. Decision makers and businessanalysts emphasized that attaining new customers is costlier than retainingex...In the insurance sector, a massive volume of data is being generatedon a daily basis due to a vast client base. Decision makers and businessanalysts emphasized that attaining new customers is costlier than retainingexisting ones. The success of retention initiatives is determined not only bythe accuracy of forecasting churners but also by the timing of the forecast.Previous works on churn forecast presented models for anticipating churnquarterly or monthly with an emphasis on customers’ static behavior. Thispaper’s objective is to calculate daily churn based on dynamic variations inclient behavior. Training excellent models to further identify potential churningcustomers helps insurance companies make decisions to retain customerswhile also identifying areas for improvement. Thus, it is possible to identifyand analyse clients who are likely to churn, allowing for a reduction in thecost of support and maintenance. Binary Golden Eagle Optimizer (BGEO)is used to select optimal features from the datasets in a preprocessing step.As a result, this research characterized the customer’s daily behavior usingvarious models such as RFM (Recency, Frequency, Monetary), MultivariateTime Series (MTS), Statistics-based Model (SM), Survival analysis (SA),Deep learning (DL) based methodologies such as Recurrent Neural Network(RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU),and Customized Extreme Learning Machine (CELM) are framed the problemof daily forecasting using this description. It can be concluded that all modelsproduced better overall outcomes with only slight variations in performancemeasures. The proposed CELM outperforms all other models in terms ofaccuracy (96.4).展开更多
Traffic flow prediction in urban areas is essential in the IntelligentTransportation System (ITS). Short Term Traffic Flow (STTF) predictionimpacts traffic flow series, where an estimation of the number of vehicleswil...Traffic flow prediction in urban areas is essential in the IntelligentTransportation System (ITS). Short Term Traffic Flow (STTF) predictionimpacts traffic flow series, where an estimation of the number of vehicleswill appear during the next instance of time per hour. Precise STTF iscritical in Intelligent Transportation System. Various extinct systems aim forshort-term traffic forecasts, ensuring a good precision outcome which was asignificant task over the past few years. The main objective of this paper is topropose a new model to predict STTF for every hour of a day. In this paper,we have proposed a novel hybrid algorithm utilizing Principal ComponentAnalysis (PCA), Stacked Auto-Encoder (SAE), Long Short Term Memory(LSTM), and K-Nearest Neighbors (KNN) named PALKNN. Firstly, PCAremoves unwanted information from the dataset and selects essential features.Secondly, SAE is used to reduce the dimension of input data using onehotencoding so the model can be trained with better speed. Thirdly, LSTMtakes the input from SAE, where the data is sorted in ascending orderbased on the important features and generates the derived value. Finally,KNN Regressor takes information from LSTM to predict traffic flow. Theforecasting performance of the PALKNN model is investigated with OpenRoad Traffic Statistics dataset, Great Britain, UK. This paper enhanced thetraffic flow prediction for every hour of a day with a minimal error value.An extensive experimental analysis was performed on the benchmark dataset.The evaluated results indicate the significant improvement of the proposedPALKNN model over the recent approaches such as KNN, SARIMA, LogisticRegression, RNN, and LSTM in terms of root mean square error (RMSE)of 2.07%, mean square error (MSE) of 4.1%, and mean absolute error (MAE)of 2.04%.展开更多
Neutrosophy is the study of neutralities,which is an extension of discussing the truth of opinions.Neutrosophic logic can be applied to any field,to provide the solution for indeterminacy problem.Many of the real-worl...Neutrosophy is the study of neutralities,which is an extension of discussing the truth of opinions.Neutrosophic logic can be applied to any field,to provide the solution for indeterminacy problem.Many of the real-world data have a problem of inconsistency,indeterminacy and incompleteness.Fuzzy sets provide a solution for uncertainties,and intuitionistic fuzzy sets handle incomplete information,but both concepts failed to handle indeterminate information.To handle this complicated situation,researchers require a powerful mathematical tool,naming,neutrosophic sets,which is a generalised concept of fuzzy and intuitionistic fuzzy sets.Neutrosophic sets provide a solution for both incomplete and indeterminate information.It has mainly three degrees of membership such as truth,indeterminacy and falsity.Boolean values are obtained from the three degrees of membership by cut relation method.Data items which contrast from other objects by their qualities are outliers.The weighted density outlier detection method based on rough entropy calculates weights of each object and attribute.From the obtained weighted values,the threshold value is fixed to determine outliers.Experimental analysis of the proposed method has been carried out with neutrosophic movie dataset to detect outliers and also compared with existing methods to prove its performance.展开更多
Electrically connected optical metasurfaces with high efficiencies are crucial for developing spatiotemporal metadevices with ultrahigh spatial and ultrafast temporal resolutions.While efficient metal–insulator–meta...Electrically connected optical metasurfaces with high efficiencies are crucial for developing spatiotemporal metadevices with ultrahigh spatial and ultrafast temporal resolutions.While efficient metal–insulator–metal(MIM)metasurfaces containing discretized meta-atoms require additional electrodes,Babinet-inspired slot-antenna-based plasmonic metasurfaces suffer from low efficiencies and limited phase coverage for copolarized optical fields.Capitalizing on the concepts of conventional MIM and slot-antenna metasurfaces,we design and experimentally demonstrate a new type of optical reflective metasurfaces consisting of mirrorcoupled slot antennas(MCSAs).By tuning the dimensions of rectangular-shaped nanoapertures atop a dielectric-coated gold mirror,we achieve efficient phase modulation within a sufficiently large range of 320 deg and realize functional phase-gradient metadevices for beam steering and beam splitting in the near-infrared range.The fabricated samples show(22%2%)diffraction efficiency for beam steering and(17%1%)for beam splitting at the wavelength of 790 nm.The considered MCSA configuration,dispensing with auxiliary electrodes,offers an alternative and promising platform for electrically controlled reflective spatiotemporal metasurfaces.展开更多
Gait refers to a person’s particular movements and stance while moving around.Although each person’s gait is unique and made up of a variety of tiny limb orientations and body positions,they all have common characte...Gait refers to a person’s particular movements and stance while moving around.Although each person’s gait is unique and made up of a variety of tiny limb orientations and body positions,they all have common characteristics that help to define normalcy.Swiftly identifying such characteristics that are difficult to spot by the naked eye,can help in monitoring the elderly who require constant care and support.Analyzing silhouettes is the easiest way to assess and make any necessary adjustments for a smooth gait.It also becomes an important aspect of decision-making while analyzing and monitoring the progress of a patient during medical diagnosis.Gait images made publicly available by the Chinese Academy of Sciences(CASIA)Gait Database was used in this study.After evaluating using the CASIA B and C datasets,this paper proposes a Convolutional Neural Network(CNN)and a CNN Long Short-TermMemory Network(CNN-LSTM)model for classifying the gait silhouette images.Transfer learningmodels such as MobileNetV2,InceptionV3,Visual Geometry Group(VGG)networks such as VGG16 and VGG19,Residual Networks(ResNet)like the ResNet9 and ResNet50,were used to compare the efficacy of the proposed models.CNN proved to be the best by achieving the highest accuracy of 94.29%.This was followed by ResNet9 and CNN-LSTM,which arrived at 93.30%and 87.25%accuracy,respectively.展开更多
Cloud computing becomes an important application development platform for processing user data with high security.Service providers are accustomed to providing storage centers outside the trusted location preferred by...Cloud computing becomes an important application development platform for processing user data with high security.Service providers are accustomed to providing storage centers outside the trusted location preferred by the data owner.Thus,ensuring the security and confidentiality of the data while processing in the centralized network is very difficult.The secured key transmission between the sender and the receiver in the network is a huge challenge in managing most of the sensitive data transmission among the cloud network.Intruders are very active over the network like real authenticated user to hack the personal sensitive data,such as bank balance,health data,personal data,and confidential documents over the cloud network.In this research,a secured key agreement between the sender and the receiver using Kerberos authentication protocol with fingerprint is proposed to ensure security in M-Healthcare.Conditions of patients are monitored using wireless sensor devices and are then transferred to the server.Kerberos protocol helps in avoiding unnecessary communication of authenticated data over the cloud network.Biometric security process is a procedure with the best security in most of the authentication field.Trust node is responsible in carrying data packets from the sender to the receiver in the cloud network.The Kerberos protocol is used in trust node to ensure security.Secured communication between the local health center and the healthcare server is ensured by using a fingerprint feature called minutiae form,which refers to the fingerprint image of both sender and receiver.The computational and communicational cost of the proposed system is lesser when compared with other existing authentication methods.展开更多
Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information quickly.The use of cloud computing has realized the rapid increase ...Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information quickly.The use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing efficiently.This cloud is nowadays highly affected by internal threats of the user.Sensitive applications such as banking,hospital,and business are more likely affected by real user threats.An intruder is presented as a user and set as a member of the network.After becoming an insider in the network,they will try to attack or steal sensitive data during information sharing or conversation.The major issue in today's technological development is identifying the insider threat in the cloud network.When data are lost,compromising cloud users is difficult.Privacy and security are not ensured,and then,the usage of the cloud is not trusted.Several solutions are available for the external security of the cloud network.However,insider or internal threats need to be addressed.In this research work,we focus on a solution for identifying an insider attack using the artificial intelligence technique.An insider attack is possible by using nodes of weak users’systems.They will log in using a weak user id,connect to a network,and pretend to be a trusted node.Then,they can easily attack and hack information as an insider,and identifying them is very difficult.These types of attacks need intelligent solutions.A machine learning approach is widely used for security issues.To date,the existing lags can classify the attackers accurately.This information hijacking process is very absurd,which motivates young researchers to provide a solution for internal threats.In our proposed work,we track the attackers using a user interaction behavior pattern and deep learning technique.The usage of mouse movements and clicks and keystrokes of the real user is stored in a database.The deep belief neural network is designed using a restricted Boltzmann machine(RBM)so that the layer of RBM communicates with the previous and subsequent layers.The result is evaluated using a Cooja simulator based on the cloud environment.The accuracy and F-measure are highly improved compared with when using the existing long short-term memory and support vector machine.展开更多
A novel image reversible data-hiding scheme based on primitive and varying radix numerical model is presented in this article.Using varying radix,variable sum of data may be embedded in various pixels of images.This s...A novel image reversible data-hiding scheme based on primitive and varying radix numerical model is presented in this article.Using varying radix,variable sum of data may be embedded in various pixels of images.This scheme is made adaptive using the correlation of the neighboring pixels.Messages are embedded as blocks of non-uniform length in the high-frequency regions of the rhombus mean interpolated image.A higher amount of data is embedded in the high-frequency regions and lesser data in the low-frequency regions of the image.The size of the embedded data depends on the statistics of the pixel distribution in the cover image.One of the major issues in reversible data embedding,the location map,is minimized because of the interpolation process.This technique,which is actually LSB matching,embeds only the residuals of modulo radix into the LSBs of each pixel.No attacks on this RDH technique will be able to decode the hidden content in the marked image.The proposed scheme delivers a prominent visual quality despite high embedding capacity.Experimental tests carried out on over 100 natural image data sets and medical images show an improvement in results compared to the existing schemes.Since the algorithm is based on the variable radix number system,it is more resistant to most of the steganographic attacks.The results were compared with a higher embedding capacity of up to 1.5 bpp reversible schemes for parameters like Peak Signal-to-Noise Ratio(PSNR),Embedding Capacity(EC)and Structural Similarity Index Metric(SSIM).展开更多
The current advancement in cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT)transformed the traditional healthcare system into smart healthcare.Healthcare services could be enhanced by incorp...The current advancement in cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT)transformed the traditional healthcare system into smart healthcare.Healthcare services could be enhanced by incorporating key techniques like AI and IoT.The convergence of AI and IoT provides distinct opportunities in the medical field.Fall is regarded as a primary cause of death or post-traumatic complication for the ageing population.Therefore,earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary support.Lately,the emergence of IoT,AI,smartphones,wearables,and so on making it possible to design fall detection(FD)systems for smart home care.This article introduces a new Teamwork Optimization with Deep Learning based Fall Detection for IoT Enabled Smart Healthcare Systems(TWODLFDSHS).The TWODL-FDSHS technique’s goal is to detect fall events for a smart healthcare system.Initially,the presented TWODL-FDSHS technique exploits IoT devices for the data collection process.Next,the TWODLFDSHS technique applies the TWO with Capsule Network(CapsNet)model for feature extraction.At last,a deep random vector functional link network(DRVFLN)with an Adam optimizer is exploited for fall event detection.A wide range of simulations took place to exhibit the enhanced performance of the presentedTWODL-FDSHS technique.The experimental outcomes stated the enhancements of the TWODL-FDSHS method over other models with increased accuracy of 98.30%on the URFD dataset.展开更多
基金supported by the National Key R&D Program of China (Grant No.2023YFF0806500)the International Partnership Program of the Chinese Academy of Sciences (Grant No.060GJHZ2024064MI)+10 种基金the Chinese Academy of Sciences and the National Research Council of Italy Scientific Cooperative Programmethe new Cornerstone Science Foundation through the XPLORER PRIZEthe National Key Scientific and Technological Infrastructure project “Earth System Science Numerical Simulator Facility” (Earth Lab), and Ocean Negative Carbon Emissions (ONCE)sponsored by the US National Science Foundationsupported by the Young Talent Support Project of Guangzhou Association for Science and Technologythe Open Research Cruise NORC2022-10+NORC2022-303 supported by NSFC shiptime Sharing Projects 42149910supported by NASA Awards 80NSSC17K0565, 80NSSC21K1191, and 80NSSC22K0046by the Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program of the U.S.Department of Energy’s Office of Biological & Environmental Research (BER) via National Science Foundation IA 1947282supported by NOAA (Grant No.NA19NES4320002 to CISESS-MD at the University of Maryland)supported by the Austrian Science Fund (P33177)ESA (contract ref.4000145298/24/I-LR)。
文摘Heating in the ocean has continued in 2024 in response to increased greenhouse gas concentrations in the atmosphere,despite the transition from an El Ni?o to neutral conditions. In 2024, both global sea surface temperature(SST) and upper2000 m ocean heat content(OHC) reached unprecedented highs in the historical record. The 0–2000 m OHC in 2024exceeded that of 2023 by 16 ± 8 ZJ(1 Zetta Joules = 1021 Joules, with a 95% confidence interval)(IAP/CAS data), which is confirmed by two other data products: 18 ± 7 ZJ(CIGAR-RT reanalysis data) and 40 ± 31 ZJ(Copernicus Marine data,updated to November 2024). The Indian Ocean, tropical Atlantic, Mediterranean Sea, North Atlantic, North Pacific, and Southern Ocean also experienced record-high OHC values in 2024. The global SST continued its record-high values from2023 into the first half of 2024, and declined slightly in the second half of 2024, resulting in an annual mean of 0.61°C ±0.02°C(IAP/CAS data) above the 1981–2010 baseline, slightly higher than the 2023 annual-mean value(by 0.07°C ±0.02°C for IAP/CAS, 0.05°C ± 0.02°C for NOAA/NCEI, and 0.06°C ± 0.11°C for Copernicus Marine). The record-high values of 2024 SST and OHC continue to indicate unabated trends of global heating.
文摘In this study,the impact of the training sample selection method on the performance of fuzzy-based Possibilistic c-means(PCM)and Noise Clustering(NC)classifiers were examined and mapped the cumin and fennel rabi crop.Two training sample selection approaches that have been investigated in this study are“mean”and“individual sample as mean”.Both training sample techniques were applied to the PCM and NC classifiers to classify the two indices approach.Both approaches have been studied to decrease spectral information in temporal data processing.The Modified Soil Adjusted Vegetation Index 2(MSAVI-2)and Class-Based Sensor Independent Modified Soil Adjusted Vegetation Index-2(CBSI-MSAVI-2)have been considered to minimize soil background effects,enhancing vegetation detection accuracy,particularly in areas with sparse vegetation cover.The MMD(MeanMembership Difference)and RMSE(RootMean Square Error)approaches were used to measure the study’s accuracy.To illustrate that the classifier successfully describes classes,cluster validity(SSE)was also performed,and the variance parameter was computed to handle heterogeneity within cumin and fennel crop fields.For the calculation of RMSE,Sentinel-2 data was used as classified,whereas PlanetScope satellite data was utilized as the reference data set.The best result was obtained using the NC classifier with“individual sample as mean”using CBSI-MSAVI-2 temporal indices.For Fuzziness Factor(m)=1.1,the RMSE,MMD,Variance,and SSE values for the NC classifier using“individual sample as mean”on the CBSI-MSAVI-2 temporal indices for cumin were 0.00098,0.00162,0.02857,and 0.97143,respectively and for fennel were 0.00025,0.00248,0.10420,and 3.54286,respectively.
基金Sponsored by the National Natural Science Foundation of China (Grant No. 50202006)the Multidisciline Scientific Research Foundation of Harbin Institute of Technology (Grant No. HIT. MD. 2001.04)
文摘Ordinary AFM probes'characters prevent the AFM' s application in various scopes. Carbon nanotubes represent ideal AFM probe materials for their higher aspect ratio, larger Young's modulus, unique chemical structure, and well-defined electronic property. Carbon nanotube AFM probes are obtained by using a new method of attaching carbon nanotubes to the end of ordinary AFM probes, and are then used for doing AFM experiments. These experiments indicated that carbon nanotube probes have higher elastic deformation, higher resolution and higher durability. And it was also found that carbon nanotube probes ean accurately reflect the morphology of deep narrow gaps, while ordinary probes can not reflect.
文摘Inflammatory bowel disease affects a substantial number of women in their reproductive years. Pregnancy presents a number of challenges for clinicians and patients; the health of the baby needs to be balanced with the need to maintain remission in the mother. Historically, treatments for Crohn’s disease (CD) were often discontinued during the pregnancy, or nursing period, due to concerns about teratogenicity. Fortunately, observational data has reported the relative safety of many agents used to treat CD, including 5-aminosalicylic acid, thiopurines, and tumor necrosis factor. Data on the long-term development outcomes of children exposed to these therapies in utero are still limited. It is most important that physicians educate the patient regarding the optimal time to conceive, discuss the possible risks, and together decide on the best management strategy.
文摘In the insurance sector, a massive volume of data is being generatedon a daily basis due to a vast client base. Decision makers and businessanalysts emphasized that attaining new customers is costlier than retainingexisting ones. The success of retention initiatives is determined not only bythe accuracy of forecasting churners but also by the timing of the forecast.Previous works on churn forecast presented models for anticipating churnquarterly or monthly with an emphasis on customers’ static behavior. Thispaper’s objective is to calculate daily churn based on dynamic variations inclient behavior. Training excellent models to further identify potential churningcustomers helps insurance companies make decisions to retain customerswhile also identifying areas for improvement. Thus, it is possible to identifyand analyse clients who are likely to churn, allowing for a reduction in thecost of support and maintenance. Binary Golden Eagle Optimizer (BGEO)is used to select optimal features from the datasets in a preprocessing step.As a result, this research characterized the customer’s daily behavior usingvarious models such as RFM (Recency, Frequency, Monetary), MultivariateTime Series (MTS), Statistics-based Model (SM), Survival analysis (SA),Deep learning (DL) based methodologies such as Recurrent Neural Network(RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU),and Customized Extreme Learning Machine (CELM) are framed the problemof daily forecasting using this description. It can be concluded that all modelsproduced better overall outcomes with only slight variations in performancemeasures. The proposed CELM outperforms all other models in terms ofaccuracy (96.4).
文摘Traffic flow prediction in urban areas is essential in the IntelligentTransportation System (ITS). Short Term Traffic Flow (STTF) predictionimpacts traffic flow series, where an estimation of the number of vehicleswill appear during the next instance of time per hour. Precise STTF iscritical in Intelligent Transportation System. Various extinct systems aim forshort-term traffic forecasts, ensuring a good precision outcome which was asignificant task over the past few years. The main objective of this paper is topropose a new model to predict STTF for every hour of a day. In this paper,we have proposed a novel hybrid algorithm utilizing Principal ComponentAnalysis (PCA), Stacked Auto-Encoder (SAE), Long Short Term Memory(LSTM), and K-Nearest Neighbors (KNN) named PALKNN. Firstly, PCAremoves unwanted information from the dataset and selects essential features.Secondly, SAE is used to reduce the dimension of input data using onehotencoding so the model can be trained with better speed. Thirdly, LSTMtakes the input from SAE, where the data is sorted in ascending orderbased on the important features and generates the derived value. Finally,KNN Regressor takes information from LSTM to predict traffic flow. Theforecasting performance of the PALKNN model is investigated with OpenRoad Traffic Statistics dataset, Great Britain, UK. This paper enhanced thetraffic flow prediction for every hour of a day with a minimal error value.An extensive experimental analysis was performed on the benchmark dataset.The evaluated results indicate the significant improvement of the proposedPALKNN model over the recent approaches such as KNN, SARIMA, LogisticRegression, RNN, and LSTM in terms of root mean square error (RMSE)of 2.07%, mean square error (MSE) of 4.1%, and mean absolute error (MAE)of 2.04%.
文摘Neutrosophy is the study of neutralities,which is an extension of discussing the truth of opinions.Neutrosophic logic can be applied to any field,to provide the solution for indeterminacy problem.Many of the real-world data have a problem of inconsistency,indeterminacy and incompleteness.Fuzzy sets provide a solution for uncertainties,and intuitionistic fuzzy sets handle incomplete information,but both concepts failed to handle indeterminate information.To handle this complicated situation,researchers require a powerful mathematical tool,naming,neutrosophic sets,which is a generalised concept of fuzzy and intuitionistic fuzzy sets.Neutrosophic sets provide a solution for both incomplete and indeterminate information.It has mainly three degrees of membership such as truth,indeterminacy and falsity.Boolean values are obtained from the three degrees of membership by cut relation method.Data items which contrast from other objects by their qualities are outliers.The weighted density outlier detection method based on rough entropy calculates weights of each object and attribute.From the obtained weighted values,the threshold value is fixed to determine outliers.Experimental analysis of the proposed method has been carried out with neutrosophic movie dataset to detect outliers and also compared with existing methods to prove its performance.
基金funded by the Villum Fonden(Award in Technical and Natural Sciences 2019 and Grant No.37372)Danmarks Frie Forskningsfond(Grant No.1134-00010B)+1 种基金support from the European Union’s Horizon 2020 Research and Innovation Program under the Marie Skłodowska-Curie Action(Grant Agreement No.713694)support from the China Scholarship Council(Grant No.202108330079).
文摘Electrically connected optical metasurfaces with high efficiencies are crucial for developing spatiotemporal metadevices with ultrahigh spatial and ultrafast temporal resolutions.While efficient metal–insulator–metal(MIM)metasurfaces containing discretized meta-atoms require additional electrodes,Babinet-inspired slot-antenna-based plasmonic metasurfaces suffer from low efficiencies and limited phase coverage for copolarized optical fields.Capitalizing on the concepts of conventional MIM and slot-antenna metasurfaces,we design and experimentally demonstrate a new type of optical reflective metasurfaces consisting of mirrorcoupled slot antennas(MCSAs).By tuning the dimensions of rectangular-shaped nanoapertures atop a dielectric-coated gold mirror,we achieve efficient phase modulation within a sufficiently large range of 320 deg and realize functional phase-gradient metadevices for beam steering and beam splitting in the near-infrared range.The fabricated samples show(22%2%)diffraction efficiency for beam steering and(17%1%)for beam splitting at the wavelength of 790 nm.The considered MCSA configuration,dispensing with auxiliary electrodes,offers an alternative and promising platform for electrically controlled reflective spatiotemporal metasurfaces.
文摘Gait refers to a person’s particular movements and stance while moving around.Although each person’s gait is unique and made up of a variety of tiny limb orientations and body positions,they all have common characteristics that help to define normalcy.Swiftly identifying such characteristics that are difficult to spot by the naked eye,can help in monitoring the elderly who require constant care and support.Analyzing silhouettes is the easiest way to assess and make any necessary adjustments for a smooth gait.It also becomes an important aspect of decision-making while analyzing and monitoring the progress of a patient during medical diagnosis.Gait images made publicly available by the Chinese Academy of Sciences(CASIA)Gait Database was used in this study.After evaluating using the CASIA B and C datasets,this paper proposes a Convolutional Neural Network(CNN)and a CNN Long Short-TermMemory Network(CNN-LSTM)model for classifying the gait silhouette images.Transfer learningmodels such as MobileNetV2,InceptionV3,Visual Geometry Group(VGG)networks such as VGG16 and VGG19,Residual Networks(ResNet)like the ResNet9 and ResNet50,were used to compare the efficacy of the proposed models.CNN proved to be the best by achieving the highest accuracy of 94.29%.This was followed by ResNet9 and CNN-LSTM,which arrived at 93.30%and 87.25%accuracy,respectively.
文摘Cloud computing becomes an important application development platform for processing user data with high security.Service providers are accustomed to providing storage centers outside the trusted location preferred by the data owner.Thus,ensuring the security and confidentiality of the data while processing in the centralized network is very difficult.The secured key transmission between the sender and the receiver in the network is a huge challenge in managing most of the sensitive data transmission among the cloud network.Intruders are very active over the network like real authenticated user to hack the personal sensitive data,such as bank balance,health data,personal data,and confidential documents over the cloud network.In this research,a secured key agreement between the sender and the receiver using Kerberos authentication protocol with fingerprint is proposed to ensure security in M-Healthcare.Conditions of patients are monitored using wireless sensor devices and are then transferred to the server.Kerberos protocol helps in avoiding unnecessary communication of authenticated data over the cloud network.Biometric security process is a procedure with the best security in most of the authentication field.Trust node is responsible in carrying data packets from the sender to the receiver in the cloud network.The Kerberos protocol is used in trust node to ensure security.Secured communication between the local health center and the healthcare server is ensured by using a fingerprint feature called minutiae form,which refers to the fingerprint image of both sender and receiver.The computational and communicational cost of the proposed system is lesser when compared with other existing authentication methods.
文摘Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information quickly.The use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing efficiently.This cloud is nowadays highly affected by internal threats of the user.Sensitive applications such as banking,hospital,and business are more likely affected by real user threats.An intruder is presented as a user and set as a member of the network.After becoming an insider in the network,they will try to attack or steal sensitive data during information sharing or conversation.The major issue in today's technological development is identifying the insider threat in the cloud network.When data are lost,compromising cloud users is difficult.Privacy and security are not ensured,and then,the usage of the cloud is not trusted.Several solutions are available for the external security of the cloud network.However,insider or internal threats need to be addressed.In this research work,we focus on a solution for identifying an insider attack using the artificial intelligence technique.An insider attack is possible by using nodes of weak users’systems.They will log in using a weak user id,connect to a network,and pretend to be a trusted node.Then,they can easily attack and hack information as an insider,and identifying them is very difficult.These types of attacks need intelligent solutions.A machine learning approach is widely used for security issues.To date,the existing lags can classify the attackers accurately.This information hijacking process is very absurd,which motivates young researchers to provide a solution for internal threats.In our proposed work,we track the attackers using a user interaction behavior pattern and deep learning technique.The usage of mouse movements and clicks and keystrokes of the real user is stored in a database.The deep belief neural network is designed using a restricted Boltzmann machine(RBM)so that the layer of RBM communicates with the previous and subsequent layers.The result is evaluated using a Cooja simulator based on the cloud environment.The accuracy and F-measure are highly improved compared with when using the existing long short-term memory and support vector machine.
文摘A novel image reversible data-hiding scheme based on primitive and varying radix numerical model is presented in this article.Using varying radix,variable sum of data may be embedded in various pixels of images.This scheme is made adaptive using the correlation of the neighboring pixels.Messages are embedded as blocks of non-uniform length in the high-frequency regions of the rhombus mean interpolated image.A higher amount of data is embedded in the high-frequency regions and lesser data in the low-frequency regions of the image.The size of the embedded data depends on the statistics of the pixel distribution in the cover image.One of the major issues in reversible data embedding,the location map,is minimized because of the interpolation process.This technique,which is actually LSB matching,embeds only the residuals of modulo radix into the LSBs of each pixel.No attacks on this RDH technique will be able to decode the hidden content in the marked image.The proposed scheme delivers a prominent visual quality despite high embedding capacity.Experimental tests carried out on over 100 natural image data sets and medical images show an improvement in results compared to the existing schemes.Since the algorithm is based on the variable radix number system,it is more resistant to most of the steganographic attacks.The results were compared with a higher embedding capacity of up to 1.5 bpp reversible schemes for parameters like Peak Signal-to-Noise Ratio(PSNR),Embedding Capacity(EC)and Structural Similarity Index Metric(SSIM).
基金The Deanship of Scientific Research (DSR)at King Abdulaziz University (KAU),Jeddah,Saudi Arabia has funded this project,under grant no.KEP-4-120-42.
文摘The current advancement in cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT)transformed the traditional healthcare system into smart healthcare.Healthcare services could be enhanced by incorporating key techniques like AI and IoT.The convergence of AI and IoT provides distinct opportunities in the medical field.Fall is regarded as a primary cause of death or post-traumatic complication for the ageing population.Therefore,earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary support.Lately,the emergence of IoT,AI,smartphones,wearables,and so on making it possible to design fall detection(FD)systems for smart home care.This article introduces a new Teamwork Optimization with Deep Learning based Fall Detection for IoT Enabled Smart Healthcare Systems(TWODLFDSHS).The TWODL-FDSHS technique’s goal is to detect fall events for a smart healthcare system.Initially,the presented TWODL-FDSHS technique exploits IoT devices for the data collection process.Next,the TWODLFDSHS technique applies the TWO with Capsule Network(CapsNet)model for feature extraction.At last,a deep random vector functional link network(DRVFLN)with an Adam optimizer is exploited for fall event detection.A wide range of simulations took place to exhibit the enhanced performance of the presentedTWODL-FDSHS technique.The experimental outcomes stated the enhancements of the TWODL-FDSHS method over other models with increased accuracy of 98.30%on the URFD dataset.
