In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fi...In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fields,including computer games,smart homes,expression analysis,gesture recognition,surveillance films,depression therapy,patientmonitoring,anxiety,and others,have brought attention to its significant academic and commercial importance.This study emphasizes research that has only employed facial images for face expression recognition(FER),because facial expressions are a basic way that people communicate meaning to each other.The immense achievement of deep learning has resulted in a growing use of its much architecture to enhance efficiency.This review is on machine learning,deep learning,and hybrid methods’use of preprocessing,augmentation techniques,and feature extraction for temporal properties of successive frames of data.The following section gives a brief summary of assessment criteria that are accessible to the public and then compares them with benchmark results the most trustworthy way to assess FER-related research topics statistically.In this review,a brief synopsis of the subject matter may be beneficial for novices in the field of FER as well as seasoned scholars seeking fruitful avenues for further investigation.The information conveys fundamental knowledge and provides a comprehensive understanding of the most recent state-of-the-art research.展开更多
Regulatory authorities create a lot of legislation that must be followed.These create complex compliance requirements and time-consuming processes to find regulatory non-compliance.While the regulations establish rule...Regulatory authorities create a lot of legislation that must be followed.These create complex compliance requirements and time-consuming processes to find regulatory non-compliance.While the regulations establish rules in the relevant areas,recommendations and best practices for compliance are not generally mentioned.Best practices are often used to find a solution to this problem.There are numerous governance,management,and security frameworks in Information Technology(IT)area to guide businesses to run their processes at a much more mature level.Best practice maps can used to map another best practice,and users can adapt themselves by the help of this relation maps.These maps are created generally by an expert judgment or topdown relationship analysis.These methods are subjective and easily creates inconsistencies.In order to have an objective and statistical relationships map,we propose a Latent Semantic Analysis(LSA)based modal to generate a specific relatedness correlation map.We created a relatedness map of a banking regulation to a best practice.We analyzed 224 statements of this regulation in relation to Control Objectives for Information Technologies(Cobit)2019’s 1202 activities.Furthermore,we support our LSA results with MCDM analysis methods;Fuzzy Analytics Hierarchy Process(FAHP)to prioritize our criteria and,WASPAS(Weighted Aggregated Sum Product Assessment Method)to compare similarity results of regulation and Cobit activity pairs.Instead of the subjective methods for mapping best practices and regulations,this study suggests creating relatedness maps supported by the objectivity of LSA.展开更多
Biometric verification has become essential to authenticate the individuals in public and private places.Among several biometrics,iris has peculiar features and its working mechanism is complex in nature.The recent de...Biometric verification has become essential to authenticate the individuals in public and private places.Among several biometrics,iris has peculiar features and its working mechanism is complex in nature.The recent developments in Machine Learning and Deep Learning approaches enable the development of effective iris recognition models.With this motivation,the current study introduces a novel Chaotic Krill Herd with Deep Transfer Learning Based Biometric Iris Recognition System(CKHDTL-BIRS).The presented CKHDTL-BIRS model intends to recognize and classify iris images as a part of biometric verification.To achieve this,CKHDTL-BIRS model initially performs Median Filtering(MF)-based preprocessing and segmentation for iris localization.In addition,MobileNetmodel is also utilized to generate a set of useful feature vectors.Moreover,Stacked Sparse Autoencoder(SSAE)approach is applied for classification.At last,CKH algorithm is exploited for optimization of the parameters involved in SSAE technique.The proposed CKHDTL-BIRS model was experimentally validated using benchmark dataset and the outcomes were examined under several aspects.The comparison study results established the enhanced performance of CKHDTL-BIRS technique over recent approaches.展开更多
Cyber-attacks on cyber-physical systems(CPSs)resulted to sensing and actuation misbehavior,severe damage to physical object,and safety risk.Machine learning(ML)models have been presented to hinder cyberattacks on the ...Cyber-attacks on cyber-physical systems(CPSs)resulted to sensing and actuation misbehavior,severe damage to physical object,and safety risk.Machine learning(ML)models have been presented to hinder cyberattacks on the CPS environment;however,the non-existence of labelled data from new attacks makes their detection quite interesting.Intrusion Detection System(IDS)is a commonly utilized to detect and classify the existence of intrusions in the CPS environment,which acts as an important part in secure CPS environment.Latest developments in deep learning(DL)and explainable artificial intelligence(XAI)stimulate new IDSs to manage cyberattacks with minimum complexity and high sophistication.In this aspect,this paper presents an XAI based IDS using feature selection with Dirichlet Variational Autoencoder(XAIIDS-FSDVAE)model for CPS.The proposed model encompasses the design of coyote optimization algorithm(COA)based feature selection(FS)model is derived to select an optimal subset of features.Next,an intelligent Dirichlet Variational Autoencoder(DVAE)technique is employed for the anomaly detection process in the CPS environment.Finally,the parameter optimization of the DVAE takes place using a manta ray foraging optimization(MRFO)model to tune the parameter of the DVAE.In order to determine the enhanced intrusion detection efficiency of the XAIIDS-FSDVAE technique,a wide range of simulations take place using the benchmark datasets.The experimental results reported the better performance of the XAIIDSFSDVAE technique over the recent methods in terms of several evaluation parameters.展开更多
Real-time video surveillance system is commonly employed to aid security professionals in preventing crimes.The use of deep learning(DL)technologies has transformed real-time video surveillance into smart video survei...Real-time video surveillance system is commonly employed to aid security professionals in preventing crimes.The use of deep learning(DL)technologies has transformed real-time video surveillance into smart video surveillance systems that automate human behavior classification.The recognition of events in the surveillance videos is considered a hot research topic in the field of computer science and it is gaining significant attention.Human action recognition(HAR)is treated as a crucial issue in several applications areas and smart video surveillance to improve the security level.The advancements of the DL models help to accomplish improved recognition performance.In this view,this paper presents a smart deep-based human behavior classification(SDL-HBC)model for real-time video surveillance.The proposed SDL-HBC model majorly aims to employ an adaptive median filtering(AMF)based pre-processing to reduce the noise content.Also,the capsule network(CapsNet)model is utilized for the extraction of feature vectors and the hyperparameter tuning of the CapsNet model takes place utilizing the Adam optimizer.Finally,the differential evolution(DE)with stacked autoencoder(SAE)model is applied for the classification of human activities in the intelligent video surveillance system.The performance validation of the SDL-HBC technique takes place using two benchmark datasets such as the KTH dataset.The experimental outcomes reported the enhanced recognition performance of the SDL-HBC technique over the recent state of art approaches with maximum accuracy of 0.9922.展开更多
The Internet ofMedical Things(IoMT)and cloud-based healthcare applications,services are beneficial for better decision-making in recent years.Melanoma is a deadly cancer with a highermortality rate than other skin can...The Internet ofMedical Things(IoMT)and cloud-based healthcare applications,services are beneficial for better decision-making in recent years.Melanoma is a deadly cancer with a highermortality rate than other skin cancer types such as basal cell,squamous cell,andMerkel cell.However,detection and treatment at an early stage can result in a higher chance of survival.The classical methods of detection are expensive and labor-intensive.Also,they rely on a trained practitioner’s level,and the availability of the needed equipment is essential for the early detection of Melanoma.The current improvement in computer-aided systems is providing very encouraging results in terms of precision and effectiveness.In this article,we propose an improved region growing technique for efficient extraction of the lesion boundary.This analysis and detection ofMelanoma are helpful for the expert dermatologist.The CNN features are extracted using the pre-trained VGG-19 deep learning model.In the end,the selected features are classified by SVM.The proposed technique is gauged on openly accessible two datasets ISIC 2017 and PH2.For the evaluation of our proposed framework,qualitative and quantitative experiments are performed.The suggested segmentation method has provided encouraging statistical results of Jaccard index 0.94,accuracy 95.7%on ISIC 2017,and Jaccard index 0.91,accuracy 93.3%on the PH2 dataset.These results are notably better than the results of prevalent methods available on the same datasets.The machine learning SVMclassifier executes significantly well on the suggested feature vector,and the comparative analysis is carried out with existing methods in terms of accuracy.The proposed method detects and classifies melanoma far better than other methods.Besides,our framework gained promising results in both segmentation and classification phases.展开更多
Recent technological advancements have been used to improve the quality of living in smart cities.At the same time,automated detection of vehicles can be utilized to reduce crime rate and improve public security.On th...Recent technological advancements have been used to improve the quality of living in smart cities.At the same time,automated detection of vehicles can be utilized to reduce crime rate and improve public security.On the other hand,the automatic identification of vehicle license plate(LP)character becomes an essential process to recognize vehicles in real time scenarios,which can be achieved by the exploitation of optimal deep learning(DL)approaches.In this article,a novel hybrid metaheuristic optimization based deep learning model for automated license plate character recognition(HMODL-ALPCR)technique has been presented for smart city environments.The major intention of the HMODL-ALPCR technique is to detect LPs and recognize the characters that exist in them.For effective LP detection process,mask regional convolutional neural network(Mask-RCNN)model is applied and the Inception with Residual Network(ResNet)-v2 as the baseline network.In addition,hybrid sunflower optimization with butterfly optimization algorithm(HSFO-BOA)is utilized for the hyperparameter tuning of the Inception-ResNetv2 model.Finally,Tesseract based character recognition model is applied to effectively recognize the characters present in the LPs.The experimental result analysis of the HMODL-ALPCR technique takes place against the benchmark dataset and the experimental outcomes pointed out the improved efficacy of the HMODL-ALPCR technique over the recent methods.展开更多
Diabetic Retinopathy(DR)has become a widespread illness among diabetics across the globe.Retinal fundus images are generally used by physicians to detect and classify the stages of DR.Since manual examination of DR im...Diabetic Retinopathy(DR)has become a widespread illness among diabetics across the globe.Retinal fundus images are generally used by physicians to detect and classify the stages of DR.Since manual examination of DR images is a time-consuming process with the risks of biased results,automated tools using Artificial Intelligence(AI)to diagnose the disease have become essential.In this view,the current study develops an Optimal Deep Learning-enabled Fusion-based Diabetic Retinopathy Detection and Classification(ODL-FDRDC)technique.The intention of the proposed ODLFDRDC technique is to identify DR and categorize its different grades using retinal fundus images.In addition,ODL-FDRDC technique involves region growing segmentation technique to determine the infected regions.Moreover,the fusion of two DL models namely,CapsNet and MobileNet is used for feature extraction.Further,the hyperparameter tuning of these models is also performed via Coyote Optimization Algorithm(COA).Gated Recurrent Unit(GRU)is also utilized to identify DR.The experimental results of the analysis,accomplished by ODL-FDRDC technique against benchmark DR dataset,established the supremacy of the technique over existing methodologies under different measures.展开更多
In recent years,researchers in handwriting recognition analysis relating to indigenous languages have gained significant internet among research communities.The recent developments of artificial intelligence(AI),natur...In recent years,researchers in handwriting recognition analysis relating to indigenous languages have gained significant internet among research communities.The recent developments of artificial intelligence(AI),natural language processing(NLP),and computational linguistics(CL)find useful in the analysis of regional low resource languages.Automatic lexical task participation might be elaborated to various applications in the NLP.It is apparent from the availability of effective machine recognition models and open access handwritten databases.Arabic language is a commonly spoken Semitic language,and it is written with the cursive Arabic alphabet from right to left.Arabic handwritten Character Recognition(HCR)is a crucial process in optical character recognition.In this view,this paper presents effective Computational linguistics with Deep Learning based Handwriting Recognition and Speech Synthesizer(CLDL-THRSS)for Indigenous Language.The presented CLDL-THRSS model involves two stages of operations namely automated handwriting recognition and speech recognition.Firstly,the automated handwriting recognition procedure involves preprocessing,segmentation,feature extraction,and classification.Also,the Capsule Network(CapsNet)based feature extractor is employed for the recognition of handwritten Arabic characters.For optimal hyperparameter tuning,the cuckoo search(CS)optimization technique was included to tune the parameters of the CapsNet method.Besides,deep neural network with hidden Markov model(DNN-HMM)model is employed for the automatic speech synthesizer.To validate the effective performance of the proposed CLDL-THRSS model,a detailed experimental validation process takes place and investigates the outcomes interms of different measures.The experimental outcomes denoted that the CLDL-THRSS technique has demonstrated the compared methods.展开更多
Biometric-based authentication systems have attracted more attention than traditional authentication techniques such as passwords in the last two decades.Multiple biometrics such as fingerprint,palm,iris,palm vein and...Biometric-based authentication systems have attracted more attention than traditional authentication techniques such as passwords in the last two decades.Multiple biometrics such as fingerprint,palm,iris,palm vein and finger vein and other biometrics have been introduced.One of the challenges in biometrics is physical injury.Biometric of finger vein is of the biometrics least exposed to physical damage.Numerous methods have been proposed for authentication with the help of this biometric that suffer from weaknesses such as high computational complexity and low identification rate.This paper presents a novel method of scattering wavelet-based identity identification.Scattering wavelet extracts image features from Gabor wavelet filters in a structure similar to convolutional neural networks.What distinguishes this algorithm from other popular feature extraction methods such as deep learning methods,filter-based methods,statistical methods,etc.,is that this algorithm has very high skill and accuracy in differentiating similar images but belongs to different classes,even when the image is subject to serious damage such as noise,angle changes or pixel location,this descriptor still generates feature vectors in away thatminimizes classifier error.This improves classification and authentication.The proposed method has been evaluated using two databases Finger Vein USM(FV-USM)and Homologous Multimodal biometrics Traits(SDUMLA-HMT).In addition to having reasonable computational complexity,it has recorded excellent identification rates in noise,rotation,and transmission challenges.At best,it has a 98.2%identification rate for the SDUMLA-HMT database and a 96.1%identification rate for the FV-USM database.展开更多
Image translation plays a significant role in realistic image synthesis,entertainment tasks such as editing and colorization,and security including personal identification.In Edge GAN,the major contribution is attribu...Image translation plays a significant role in realistic image synthesis,entertainment tasks such as editing and colorization,and security including personal identification.In Edge GAN,the major contribution is attribute guided vector that enables high visual quality content generation.This research study proposes automatic face image realism from freehand sketches based on Edge GAN.We propose a density variant image synthesis model,allowing the input sketch to encompass face features with minute details.The density level is projected into non-latent space,having a linear controlled function parameter.This assists the user to appropriately devise the variant densities of facial sketches and image synthesis.Composite data set of Large Scale CelebFaces Attributes(ClebA),Labelled Faces in theWild(LFWH),Chinese University of Hong Kong(CHUK),and self-generated Asian images are used to evaluate the proposed approach.The solution is validated to have the capability for generating realistic face images through quantitative and qualitative results and human evaluation.展开更多
Prevention of cervical cancer becomes essential and is carried out by the use of Pap smear images.Pap smear test analysis is laborious and tiresome work performed visually using a cytopathologist.Therefore,automated c...Prevention of cervical cancer becomes essential and is carried out by the use of Pap smear images.Pap smear test analysis is laborious and tiresome work performed visually using a cytopathologist.Therefore,automated cervical cancer diagnosis using automated methods are necessary.This paper designs an optimal deep learning based Inception model for cervical cancer diagnosis(ODLIM-CCD)using pap smear images.The proposed ODLIM-CCD technique incorporates median filtering(MF)based pre-processing to discard the noise and Otsu model based segmentation process.Besides,deep convolutional neural network(DCNN)based Inception with Residual Network(ResNet)v2 model is utilized for deriving the feature vectors.Moreover,swallow swarm optimization(SSO)based hyperparameter tuning process is carried out for the optimal selection of hyperparameters.Finally,recurrent neural network(RNN)based classification process is done to determine the presence of cervical cancer or not.In order to showcase the improved diagnostic performance of the ODLIM-CCD technique,a series of simulations occur on benchmark test images and the outcomes highlighted the improved performance over the recent approaches with a superior accuracy of 0.9661.展开更多
Quality of Service (QoS) generally refers to measurable like latency and throughput, things that directly affect the user experience. Queuing (the most popular QoS tool) involves choosing the packets to be sent based ...Quality of Service (QoS) generally refers to measurable like latency and throughput, things that directly affect the user experience. Queuing (the most popular QoS tool) involves choosing the packets to be sent based on something other than arrival time. The Active queue management is important subject to manage this queue to increase the effectiveness of Transmission Control Protocol networks. Active queue management (AQM) is an effective means to enhance congestion control, and to achieve trade-off between link utilization and delay. The de facto standard, Random Early Detection (RED), and many of its variants employ queue length as a congestion indicator to trigger packet dropping. One of these enhancements of RED is FRED or Fair Random Early Detection attempts to deal with a fundamental aspect of RED in that it imposes the same loss rate on all flows, regardless of their bandwidths. FRED also uses per-flow active accounting, and tracks the state of active flows. FRED protects fragile flows by deterministically accepting flows from low bandwidth connections and fixes several shortcomings of RED by computing queue length during both arrival and departure of the packet. Unlike FRED, we propose a new scheme that used hazard rate estimated packet dropping function in FRED. We call this new scheme Enhancement Fair Random Early Detection. The key idea is that, with EFRED Scheme change packet dropping function, to get packet dropping less than RED and other AQM algorithms like ARED, REM, RED, etc. Simulations demonstrate that EFRED achieves a more stable throughput and performs better than current active queue management algorithms due to decrease the packets loss percentage and lowest in queuing delay, end to end delay and delay variation (JITTER).展开更多
Construction Industry operates relying on various key economic indicators.One of these indicators is material prices.On the other hand,cost is a key concern in all operations of the construction industry.In the uncert...Construction Industry operates relying on various key economic indicators.One of these indicators is material prices.On the other hand,cost is a key concern in all operations of the construction industry.In the uncertain conditions,reliable cost forecasts become an important source of information.Material cost is one of the key components of the overall cost of construction.In addition,cost overrun is a common problem in the construction industry,where nine out of ten construction projects face cost overrun.In order to carry out a successful cost management strategy and prevent cost overruns,it is very important to find reliable methods for the estimation of construction material prices.Material prices have a time dependent nature.In order to increase the foreseeability of the costs of construction materials,this study focuses on estimation of construction material indices through time series analysis.Two different types of analysis are implemented for estimation of the future values of construction material indices.The first method implemented was Autoregressive Integrated Moving Average(ARIMA),which is known to be successful in estimation of time series having a linear nature.The second method implementedwas Non-LinearAutoregressive Neural Network(NARNET)which is known to be successful in modeling and estimating of series with non-linear components.The results have shown that depending on the nature of the series,both these methods can successfully and accurately estimate the future values of the indices.In addition,we found out that Optimal NARNET architectures which provide better accuracy in estimation of the series can be identified/discovered as result of grid search on NARNET hyperparameters.展开更多
The aim of our work is to formulate and demonstrate the results of the normality, the Lipschitz continuity, of a nonlinear feedback system described by the monotone maximal operators and hemicontinuous, defined on rea...The aim of our work is to formulate and demonstrate the results of the normality, the Lipschitz continuity, of a nonlinear feedback system described by the monotone maximal operators and hemicontinuous, defined on real reflexive Banach spaces, as well as the approximation in a neighborhood of zero, of solutions of a feedback system [A,B] assumed to be non-linear, by solutions of another linear, This approximation allows us to obtain appropriate estimates of the solutions. These estimates have a significant effect on the study of the robust stability and sensitivity of such a system see <a href="#ref1">[1]</a> <a href="#ref2">[2]</a> <a href="#ref3">[3]</a>. We then consider a linear FS <img src="Edit_4629d4d0-bbb2-478d-adde-391efde3d1e0.bmp" alt="" />, and prove that, if <img src="Edit_435aae08-e821-4b4d-99d2-e2a2b47609c1.bmp" alt="" />;<img src="Edit_4fa030bc-1f97-4726-8257-ca8d00657aac.bmp" alt="" /> , with <img src="Edit_63ab4faa-ba40-45fe-8b8a-7a6caef91794.bmp" alt="" />the respective solutions of FS’s [A,B] and <img src="Edit_e78e2e6d-8934-4011-93eb-8b7eb52fa856.bmp" alt="" /> corresponding to the given (u,v) in <img src="Edit_0e18433c-8c7a-454f-8eec-6eb9fb69469a.bmp" alt="" /> . There exists,<img src="Edit_3dcd8afc-8cea-4c06-a920-e4148a5f793e.bmp" alt="" />, positive real constants such that, <img src="Edit_edb88446-3e39-4fe0-865a-114de701e78e.bmp" alt="" />. These results are the subject of theorems 3.1, <span style="font-size:10.0pt;font-family:;" "="">... </span>, 3.3. The proofs of these theorems are based on our lemmas 3.2, <span style="font-size:10.0pt;font-family:;" "="">... </span>, 3.5, devoted according to the hypotheses on A and B, to the existence of the inverse of the operator <em>I+BA</em> and <img src="Edit_2db1326b-cb5b-44cf-8d1f-df22bd6da45f.bmp" alt="" />. The results obtained and demonstrated along this document, present an extension in general Banach space of those in <a href="#ref4">[4]</a> on a Hilbert space <em>H</em> and those in <a href="#ref5">[5]</a> on a extended Hilbert space <img src="Edit_b70ce337-1812-4d4b-ae7d-a24da7e5b3cf.bmp" alt="" />.展开更多
Here we investigate to what extent X-ray absorption（XAS） and emission（XES） spectroscopy, the oxygen-oxygen radial distribution function and σ（1H） and σ(17O) NMR shielding can be represented by a common set of ...Here we investigate to what extent X-ray absorption（XAS） and emission（XES） spectroscopy, the oxygen-oxygen radial distribution function and σ（1H） and σ(17O) NMR shielding can be represented by a common set of model structures of liquid water. This is done by using a Monte Carlo-based fitting technique which fits the spectra based on a library of ～1400 precomputed spectra and assigns weights to contributions from different model structures. These are then used to reweight the contributions from the structures in the library to reveal classes of structures that are over-or under-represented in the library. The goal is to include different experimental data sets which are sensitive to different aspects of liquid water structure and thus narrow down which types of structures must exist in the real liquid.展开更多
文摘In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fields,including computer games,smart homes,expression analysis,gesture recognition,surveillance films,depression therapy,patientmonitoring,anxiety,and others,have brought attention to its significant academic and commercial importance.This study emphasizes research that has only employed facial images for face expression recognition(FER),because facial expressions are a basic way that people communicate meaning to each other.The immense achievement of deep learning has resulted in a growing use of its much architecture to enhance efficiency.This review is on machine learning,deep learning,and hybrid methods’use of preprocessing,augmentation techniques,and feature extraction for temporal properties of successive frames of data.The following section gives a brief summary of assessment criteria that are accessible to the public and then compares them with benchmark results the most trustworthy way to assess FER-related research topics statistically.In this review,a brief synopsis of the subject matter may be beneficial for novices in the field of FER as well as seasoned scholars seeking fruitful avenues for further investigation.The information conveys fundamental knowledge and provides a comprehensive understanding of the most recent state-of-the-art research.
文摘Regulatory authorities create a lot of legislation that must be followed.These create complex compliance requirements and time-consuming processes to find regulatory non-compliance.While the regulations establish rules in the relevant areas,recommendations and best practices for compliance are not generally mentioned.Best practices are often used to find a solution to this problem.There are numerous governance,management,and security frameworks in Information Technology(IT)area to guide businesses to run their processes at a much more mature level.Best practice maps can used to map another best practice,and users can adapt themselves by the help of this relation maps.These maps are created generally by an expert judgment or topdown relationship analysis.These methods are subjective and easily creates inconsistencies.In order to have an objective and statistical relationships map,we propose a Latent Semantic Analysis(LSA)based modal to generate a specific relatedness correlation map.We created a relatedness map of a banking regulation to a best practice.We analyzed 224 statements of this regulation in relation to Control Objectives for Information Technologies(Cobit)2019’s 1202 activities.Furthermore,we support our LSA results with MCDM analysis methods;Fuzzy Analytics Hierarchy Process(FAHP)to prioritize our criteria and,WASPAS(Weighted Aggregated Sum Product Assessment Method)to compare similarity results of regulation and Cobit activity pairs.Instead of the subjective methods for mapping best practices and regulations,this study suggests creating relatedness maps supported by the objectivity of LSA.
文摘Biometric verification has become essential to authenticate the individuals in public and private places.Among several biometrics,iris has peculiar features and its working mechanism is complex in nature.The recent developments in Machine Learning and Deep Learning approaches enable the development of effective iris recognition models.With this motivation,the current study introduces a novel Chaotic Krill Herd with Deep Transfer Learning Based Biometric Iris Recognition System(CKHDTL-BIRS).The presented CKHDTL-BIRS model intends to recognize and classify iris images as a part of biometric verification.To achieve this,CKHDTL-BIRS model initially performs Median Filtering(MF)-based preprocessing and segmentation for iris localization.In addition,MobileNetmodel is also utilized to generate a set of useful feature vectors.Moreover,Stacked Sparse Autoencoder(SSAE)approach is applied for classification.At last,CKH algorithm is exploited for optimization of the parameters involved in SSAE technique.The proposed CKHDTL-BIRS model was experimentally validated using benchmark dataset and the outcomes were examined under several aspects.The comparison study results established the enhanced performance of CKHDTL-BIRS technique over recent approaches.
文摘Cyber-attacks on cyber-physical systems(CPSs)resulted to sensing and actuation misbehavior,severe damage to physical object,and safety risk.Machine learning(ML)models have been presented to hinder cyberattacks on the CPS environment;however,the non-existence of labelled data from new attacks makes their detection quite interesting.Intrusion Detection System(IDS)is a commonly utilized to detect and classify the existence of intrusions in the CPS environment,which acts as an important part in secure CPS environment.Latest developments in deep learning(DL)and explainable artificial intelligence(XAI)stimulate new IDSs to manage cyberattacks with minimum complexity and high sophistication.In this aspect,this paper presents an XAI based IDS using feature selection with Dirichlet Variational Autoencoder(XAIIDS-FSDVAE)model for CPS.The proposed model encompasses the design of coyote optimization algorithm(COA)based feature selection(FS)model is derived to select an optimal subset of features.Next,an intelligent Dirichlet Variational Autoencoder(DVAE)technique is employed for the anomaly detection process in the CPS environment.Finally,the parameter optimization of the DVAE takes place using a manta ray foraging optimization(MRFO)model to tune the parameter of the DVAE.In order to determine the enhanced intrusion detection efficiency of the XAIIDS-FSDVAE technique,a wide range of simulations take place using the benchmark datasets.The experimental results reported the better performance of the XAIIDSFSDVAE technique over the recent methods in terms of several evaluation parameters.
文摘Real-time video surveillance system is commonly employed to aid security professionals in preventing crimes.The use of deep learning(DL)technologies has transformed real-time video surveillance into smart video surveillance systems that automate human behavior classification.The recognition of events in the surveillance videos is considered a hot research topic in the field of computer science and it is gaining significant attention.Human action recognition(HAR)is treated as a crucial issue in several applications areas and smart video surveillance to improve the security level.The advancements of the DL models help to accomplish improved recognition performance.In this view,this paper presents a smart deep-based human behavior classification(SDL-HBC)model for real-time video surveillance.The proposed SDL-HBC model majorly aims to employ an adaptive median filtering(AMF)based pre-processing to reduce the noise content.Also,the capsule network(CapsNet)model is utilized for the extraction of feature vectors and the hyperparameter tuning of the CapsNet model takes place utilizing the Adam optimizer.Finally,the differential evolution(DE)with stacked autoencoder(SAE)model is applied for the classification of human activities in the intelligent video surveillance system.The performance validation of the SDL-HBC technique takes place using two benchmark datasets such as the KTH dataset.The experimental outcomes reported the enhanced recognition performance of the SDL-HBC technique over the recent state of art approaches with maximum accuracy of 0.9922.
文摘The Internet ofMedical Things(IoMT)and cloud-based healthcare applications,services are beneficial for better decision-making in recent years.Melanoma is a deadly cancer with a highermortality rate than other skin cancer types such as basal cell,squamous cell,andMerkel cell.However,detection and treatment at an early stage can result in a higher chance of survival.The classical methods of detection are expensive and labor-intensive.Also,they rely on a trained practitioner’s level,and the availability of the needed equipment is essential for the early detection of Melanoma.The current improvement in computer-aided systems is providing very encouraging results in terms of precision and effectiveness.In this article,we propose an improved region growing technique for efficient extraction of the lesion boundary.This analysis and detection ofMelanoma are helpful for the expert dermatologist.The CNN features are extracted using the pre-trained VGG-19 deep learning model.In the end,the selected features are classified by SVM.The proposed technique is gauged on openly accessible two datasets ISIC 2017 and PH2.For the evaluation of our proposed framework,qualitative and quantitative experiments are performed.The suggested segmentation method has provided encouraging statistical results of Jaccard index 0.94,accuracy 95.7%on ISIC 2017,and Jaccard index 0.91,accuracy 93.3%on the PH2 dataset.These results are notably better than the results of prevalent methods available on the same datasets.The machine learning SVMclassifier executes significantly well on the suggested feature vector,and the comparative analysis is carried out with existing methods in terms of accuracy.The proposed method detects and classifies melanoma far better than other methods.Besides,our framework gained promising results in both segmentation and classification phases.
文摘Recent technological advancements have been used to improve the quality of living in smart cities.At the same time,automated detection of vehicles can be utilized to reduce crime rate and improve public security.On the other hand,the automatic identification of vehicle license plate(LP)character becomes an essential process to recognize vehicles in real time scenarios,which can be achieved by the exploitation of optimal deep learning(DL)approaches.In this article,a novel hybrid metaheuristic optimization based deep learning model for automated license plate character recognition(HMODL-ALPCR)technique has been presented for smart city environments.The major intention of the HMODL-ALPCR technique is to detect LPs and recognize the characters that exist in them.For effective LP detection process,mask regional convolutional neural network(Mask-RCNN)model is applied and the Inception with Residual Network(ResNet)-v2 as the baseline network.In addition,hybrid sunflower optimization with butterfly optimization algorithm(HSFO-BOA)is utilized for the hyperparameter tuning of the Inception-ResNetv2 model.Finally,Tesseract based character recognition model is applied to effectively recognize the characters present in the LPs.The experimental result analysis of the HMODL-ALPCR technique takes place against the benchmark dataset and the experimental outcomes pointed out the improved efficacy of the HMODL-ALPCR technique over the recent methods.
文摘Diabetic Retinopathy(DR)has become a widespread illness among diabetics across the globe.Retinal fundus images are generally used by physicians to detect and classify the stages of DR.Since manual examination of DR images is a time-consuming process with the risks of biased results,automated tools using Artificial Intelligence(AI)to diagnose the disease have become essential.In this view,the current study develops an Optimal Deep Learning-enabled Fusion-based Diabetic Retinopathy Detection and Classification(ODL-FDRDC)technique.The intention of the proposed ODLFDRDC technique is to identify DR and categorize its different grades using retinal fundus images.In addition,ODL-FDRDC technique involves region growing segmentation technique to determine the infected regions.Moreover,the fusion of two DL models namely,CapsNet and MobileNet is used for feature extraction.Further,the hyperparameter tuning of these models is also performed via Coyote Optimization Algorithm(COA).Gated Recurrent Unit(GRU)is also utilized to identify DR.The experimental results of the analysis,accomplished by ODL-FDRDC technique against benchmark DR dataset,established the supremacy of the technique over existing methodologies under different measures.
文摘In recent years,researchers in handwriting recognition analysis relating to indigenous languages have gained significant internet among research communities.The recent developments of artificial intelligence(AI),natural language processing(NLP),and computational linguistics(CL)find useful in the analysis of regional low resource languages.Automatic lexical task participation might be elaborated to various applications in the NLP.It is apparent from the availability of effective machine recognition models and open access handwritten databases.Arabic language is a commonly spoken Semitic language,and it is written with the cursive Arabic alphabet from right to left.Arabic handwritten Character Recognition(HCR)is a crucial process in optical character recognition.In this view,this paper presents effective Computational linguistics with Deep Learning based Handwriting Recognition and Speech Synthesizer(CLDL-THRSS)for Indigenous Language.The presented CLDL-THRSS model involves two stages of operations namely automated handwriting recognition and speech recognition.Firstly,the automated handwriting recognition procedure involves preprocessing,segmentation,feature extraction,and classification.Also,the Capsule Network(CapsNet)based feature extractor is employed for the recognition of handwritten Arabic characters.For optimal hyperparameter tuning,the cuckoo search(CS)optimization technique was included to tune the parameters of the CapsNet method.Besides,deep neural network with hidden Markov model(DNN-HMM)model is employed for the automatic speech synthesizer.To validate the effective performance of the proposed CLDL-THRSS model,a detailed experimental validation process takes place and investigates the outcomes interms of different measures.The experimental outcomes denoted that the CLDL-THRSS technique has demonstrated the compared methods.
基金This research is supported by Artificial Intelligence&Data Analytics Lab(AIDA)CCIS Prince Sultan University,Riyadh 11586 Saudi Arabia.
文摘Biometric-based authentication systems have attracted more attention than traditional authentication techniques such as passwords in the last two decades.Multiple biometrics such as fingerprint,palm,iris,palm vein and finger vein and other biometrics have been introduced.One of the challenges in biometrics is physical injury.Biometric of finger vein is of the biometrics least exposed to physical damage.Numerous methods have been proposed for authentication with the help of this biometric that suffer from weaknesses such as high computational complexity and low identification rate.This paper presents a novel method of scattering wavelet-based identity identification.Scattering wavelet extracts image features from Gabor wavelet filters in a structure similar to convolutional neural networks.What distinguishes this algorithm from other popular feature extraction methods such as deep learning methods,filter-based methods,statistical methods,etc.,is that this algorithm has very high skill and accuracy in differentiating similar images but belongs to different classes,even when the image is subject to serious damage such as noise,angle changes or pixel location,this descriptor still generates feature vectors in away thatminimizes classifier error.This improves classification and authentication.The proposed method has been evaluated using two databases Finger Vein USM(FV-USM)and Homologous Multimodal biometrics Traits(SDUMLA-HMT).In addition to having reasonable computational complexity,it has recorded excellent identification rates in noise,rotation,and transmission challenges.At best,it has a 98.2%identification rate for the SDUMLA-HMT database and a 96.1%identification rate for the FV-USM database.
基金The authors received no specific funding for this study.
文摘Image translation plays a significant role in realistic image synthesis,entertainment tasks such as editing and colorization,and security including personal identification.In Edge GAN,the major contribution is attribute guided vector that enables high visual quality content generation.This research study proposes automatic face image realism from freehand sketches based on Edge GAN.We propose a density variant image synthesis model,allowing the input sketch to encompass face features with minute details.The density level is projected into non-latent space,having a linear controlled function parameter.This assists the user to appropriately devise the variant densities of facial sketches and image synthesis.Composite data set of Large Scale CelebFaces Attributes(ClebA),Labelled Faces in theWild(LFWH),Chinese University of Hong Kong(CHUK),and self-generated Asian images are used to evaluate the proposed approach.The solution is validated to have the capability for generating realistic face images through quantitative and qualitative results and human evaluation.
文摘Prevention of cervical cancer becomes essential and is carried out by the use of Pap smear images.Pap smear test analysis is laborious and tiresome work performed visually using a cytopathologist.Therefore,automated cervical cancer diagnosis using automated methods are necessary.This paper designs an optimal deep learning based Inception model for cervical cancer diagnosis(ODLIM-CCD)using pap smear images.The proposed ODLIM-CCD technique incorporates median filtering(MF)based pre-processing to discard the noise and Otsu model based segmentation process.Besides,deep convolutional neural network(DCNN)based Inception with Residual Network(ResNet)v2 model is utilized for deriving the feature vectors.Moreover,swallow swarm optimization(SSO)based hyperparameter tuning process is carried out for the optimal selection of hyperparameters.Finally,recurrent neural network(RNN)based classification process is done to determine the presence of cervical cancer or not.In order to showcase the improved diagnostic performance of the ODLIM-CCD technique,a series of simulations occur on benchmark test images and the outcomes highlighted the improved performance over the recent approaches with a superior accuracy of 0.9661.
文摘Quality of Service (QoS) generally refers to measurable like latency and throughput, things that directly affect the user experience. Queuing (the most popular QoS tool) involves choosing the packets to be sent based on something other than arrival time. The Active queue management is important subject to manage this queue to increase the effectiveness of Transmission Control Protocol networks. Active queue management (AQM) is an effective means to enhance congestion control, and to achieve trade-off between link utilization and delay. The de facto standard, Random Early Detection (RED), and many of its variants employ queue length as a congestion indicator to trigger packet dropping. One of these enhancements of RED is FRED or Fair Random Early Detection attempts to deal with a fundamental aspect of RED in that it imposes the same loss rate on all flows, regardless of their bandwidths. FRED also uses per-flow active accounting, and tracks the state of active flows. FRED protects fragile flows by deterministically accepting flows from low bandwidth connections and fixes several shortcomings of RED by computing queue length during both arrival and departure of the packet. Unlike FRED, we propose a new scheme that used hazard rate estimated packet dropping function in FRED. We call this new scheme Enhancement Fair Random Early Detection. The key idea is that, with EFRED Scheme change packet dropping function, to get packet dropping less than RED and other AQM algorithms like ARED, REM, RED, etc. Simulations demonstrate that EFRED achieves a more stable throughput and performs better than current active queue management algorithms due to decrease the packets loss percentage and lowest in queuing delay, end to end delay and delay variation (JITTER).
基金supported by the Energy Cloud R&D Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT(2019M3F2A1073164)MSGSU BAP(2021-25).
文摘Construction Industry operates relying on various key economic indicators.One of these indicators is material prices.On the other hand,cost is a key concern in all operations of the construction industry.In the uncertain conditions,reliable cost forecasts become an important source of information.Material cost is one of the key components of the overall cost of construction.In addition,cost overrun is a common problem in the construction industry,where nine out of ten construction projects face cost overrun.In order to carry out a successful cost management strategy and prevent cost overruns,it is very important to find reliable methods for the estimation of construction material prices.Material prices have a time dependent nature.In order to increase the foreseeability of the costs of construction materials,this study focuses on estimation of construction material indices through time series analysis.Two different types of analysis are implemented for estimation of the future values of construction material indices.The first method implemented was Autoregressive Integrated Moving Average(ARIMA),which is known to be successful in estimation of time series having a linear nature.The second method implementedwas Non-LinearAutoregressive Neural Network(NARNET)which is known to be successful in modeling and estimating of series with non-linear components.The results have shown that depending on the nature of the series,both these methods can successfully and accurately estimate the future values of the indices.In addition,we found out that Optimal NARNET architectures which provide better accuracy in estimation of the series can be identified/discovered as result of grid search on NARNET hyperparameters.
文摘The aim of our work is to formulate and demonstrate the results of the normality, the Lipschitz continuity, of a nonlinear feedback system described by the monotone maximal operators and hemicontinuous, defined on real reflexive Banach spaces, as well as the approximation in a neighborhood of zero, of solutions of a feedback system [A,B] assumed to be non-linear, by solutions of another linear, This approximation allows us to obtain appropriate estimates of the solutions. These estimates have a significant effect on the study of the robust stability and sensitivity of such a system see <a href="#ref1">[1]</a> <a href="#ref2">[2]</a> <a href="#ref3">[3]</a>. We then consider a linear FS <img src="Edit_4629d4d0-bbb2-478d-adde-391efde3d1e0.bmp" alt="" />, and prove that, if <img src="Edit_435aae08-e821-4b4d-99d2-e2a2b47609c1.bmp" alt="" />;<img src="Edit_4fa030bc-1f97-4726-8257-ca8d00657aac.bmp" alt="" /> , with <img src="Edit_63ab4faa-ba40-45fe-8b8a-7a6caef91794.bmp" alt="" />the respective solutions of FS’s [A,B] and <img src="Edit_e78e2e6d-8934-4011-93eb-8b7eb52fa856.bmp" alt="" /> corresponding to the given (u,v) in <img src="Edit_0e18433c-8c7a-454f-8eec-6eb9fb69469a.bmp" alt="" /> . There exists,<img src="Edit_3dcd8afc-8cea-4c06-a920-e4148a5f793e.bmp" alt="" />, positive real constants such that, <img src="Edit_edb88446-3e39-4fe0-865a-114de701e78e.bmp" alt="" />. These results are the subject of theorems 3.1, <span style="font-size:10.0pt;font-family:;" "="">... </span>, 3.3. The proofs of these theorems are based on our lemmas 3.2, <span style="font-size:10.0pt;font-family:;" "="">... </span>, 3.5, devoted according to the hypotheses on A and B, to the existence of the inverse of the operator <em>I+BA</em> and <img src="Edit_2db1326b-cb5b-44cf-8d1f-df22bd6da45f.bmp" alt="" />. The results obtained and demonstrated along this document, present an extension in general Banach space of those in <a href="#ref4">[4]</a> on a Hilbert space <em>H</em> and those in <a href="#ref5">[5]</a> on a extended Hilbert space <img src="Edit_b70ce337-1812-4d4b-ae7d-a24da7e5b3cf.bmp" alt="" />.
基金supported by the Swedish Research Council (Grant No. 2015009559)
文摘Here we investigate to what extent X-ray absorption（XAS） and emission（XES） spectroscopy, the oxygen-oxygen radial distribution function and σ（1H） and σ(17O) NMR shielding can be represented by a common set of model structures of liquid water. This is done by using a Monte Carlo-based fitting technique which fits the spectra based on a library of ～1400 precomputed spectra and assigns weights to contributions from different model structures. These are then used to reweight the contributions from the structures in the library to reveal classes of structures that are over-or under-represented in the library. The goal is to include different experimental data sets which are sensitive to different aspects of liquid water structure and thus narrow down which types of structures must exist in the real liquid.
