Objective:To examine the effect of an neurokinin 3 receptor(NK3R)agonist,senktide,on neuronal nitric oxide synthase(nNOS)activation in the median eminence-arcuate nucleus(ME-ARC)and preoptic area(POA)regions of the hy...Objective:To examine the effect of an neurokinin 3 receptor(NK3R)agonist,senktide,on neuronal nitric oxide synthase(nNOS)activation in the median eminence-arcuate nucleus(ME-ARC)and preoptic area(POA)regions of the hypothalamus across proestrus,diestrus,and ovariectomized states in female rats and its correlation with luteinizing hormone(LH)secretion.Methods:Adult female Sprague-Dawley rats were examined for proestrus and diestrus phases of the estrous cycle.Female rats were categorized into proestrus and diestrus groups,and each was further divided into four subgroups(n=4).In both the diestrus and proestrus categories,Group 1 was the control group.Groups 2,3,and 4 received senktide(100μg/kg-1),NK3R antagonist SB222200(10 mg/kg-1),and SB222200 followed by senktide,respectively.To evaluate the effect of sex steroids on NK3R agonist-induced nNOS activation,female rats underwent bilateral ovariectomy and were divided into four groups(n=3).Group 1 served as the control.Group 2 received a subcutaneous injection of 17β-estradiol 3-benzoate(E2,3μg/rat).Group 3 received E2 and progesterone(30μg/rat).Group 4 was administered senktide(100μg/kg).Female rats from each group were sacrificed,blood was collected for LH ELISA,and hypothalamic tissues were collected for Western blotting.Results:Senktide increased nNOS phosphorylation in the ME-ARC during both the proestrus and diestrus phases.In the POA,senktide increased nNOS phosphorylation only during the diestrus phase.In ovariectomized rats,senktide activated nNOS independent of sex steroid levels.Senktide also increased serum LH concentration in diestrus and ovariectomized female rats.Conclusions:Senktide,an NK3R agonist,activates nNOS in the POA and ME-ARC regions of the hypothalamus in a phase dependent manner.The activation of nNOS by senktide suggests a potential mechanism by which neurokinin B triggers nNOS activation in the ARC and POA regions and regulates GnRH/LH secretion.展开更多
White Blood Cell(WBC)cancer or leukemia is one of the serious cancers that threaten the existence of human beings.In spite of its prevalence and serious consequences,it is mostly diagnosed through manual practices.The...White Blood Cell(WBC)cancer or leukemia is one of the serious cancers that threaten the existence of human beings.In spite of its prevalence and serious consequences,it is mostly diagnosed through manual practices.The risks of inappropriate,sub-standard and wrong or biased diagnosis are high in manual methods.So,there is a need exists for automatic diagnosis and classification method that can replace the manual process.Leukemia is mainly classified into acute and chronic types.The current research work proposed a computer-based application to classify the disease.In the feature extraction stage,we use excellent physical properties to improve the diagnostic system’s accuracy,based on Enhanced Color Co-Occurrence Matrix.The study is aimed at identification and classification of chronic lymphocytic leukemia using microscopic images of WBCs based on Enhanced Virtual Neural Network(EVNN)classification.The proposed method achieved optimum accuracy in detection and classification of leukemia from WBC images.Thus,the study results establish the superiority of the proposed method in automated diagnosis of leukemia.The values achieved by the proposed method in terms of sensitivity,specificity,accuracy,and error rate were 97.8%,89.9%,76.6%,and 2.2%,respectively.Furthermore,the system could predict the disease in prior through images,and the probabilities of disease detection are also highly optimistic.展开更多
Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspe...Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspects of the items thus leading to more sophisticated and justifiable recommendations. However, most Collaborative Filtering (CF) techniques rely mainly on the overall preferences of users toward items only. And there is lack of conceptual and computational framework that enables an understandable aspect-based AI approach to recommending items to users. In this paper, we propose concepts and computational tools that can sharpen the logic of recommendations and that rely on users’ sentiments along various aspects of items. These concepts include: The sentiment of a user towards a specific aspect of a specific item, the emphasis that a given user places on a specific aspect in general, the popularity and controversy of an aspect among groups of users, clusters of users emphasizing a given aspect, clusters of items that are popular among a group of users and so forth. The framework introduced in this study is developed in terms of user emphasis, aspect popularity, aspect controversy, and users and items similarity. Towards this end, we introduce the Aspect-Based Collaborative Filtering Toolbox (ABCFT), where the tools are all developed based on the three-index sentiment tensor with the indices being the user, item, and aspect. The toolbox computes solutions to the questions alluded to above. We illustrate the methodology using a hotel review dataset having around 6000 users, 400 hotels and 6 aspects.展开更多
In this paper,the choice and parametrisation of finite deformation polyconvex isotropic hyperelastic models to describe the behaviour of a class of defect-free monocrystalline metal materials at the molecular level is...In this paper,the choice and parametrisation of finite deformation polyconvex isotropic hyperelastic models to describe the behaviour of a class of defect-free monocrystalline metal materials at the molecular level is examined.The article discusses some physical,mathematical and numerical demands which in our opinion should be fulfilled by elasticity models to be useful.A set of molecular numerical tests for aluminium and tungsten providing data for the fitting of a hyperelastic model was performed,and an algorithm for parametrisation is discussed.The proposed models with optimised parameters are superior to those used in non-linear mechanics of crystals.展开更多
In this work,a system for recognition of newspaper printed in Gurumukhi script is presented.Four feature extraction techniques,namely,zoning features,diagonal features,parabola curve fitting based features,and power c...In this work,a system for recognition of newspaper printed in Gurumukhi script is presented.Four feature extraction techniques,namely,zoning features,diagonal features,parabola curve fitting based features,and power curve fitting based features are considered for extracting the statistical properties of the characters printed in the newspaper.Different combinations of these features are also applied to improve the recognition accuracy.For recognition,four classification techniques,namely,k-NN,linear-SVM,decision tree,and random forest are used.A database for the experiments is collected from three major Gurumukhi script newspapers which are Ajit,Jagbani and Punjabi Tribune.Using 5-fold cross validation and random forest classifier,a recognition accuracy of 96.19%with a combination of zoning features,diagonal features and parabola curve fitting based features has been reported.A recognition accuracy of 95.21%with a partitioning strategy of data set(70%data as training data and remaining 30%data as testing data)has been achieved.展开更多
The numerical characteristics of fuzzy numbers include the optimistic value, pessimistic value, expected value, and the variance. We mainly provide the calculation formulae of several numerical characteristics of fuzz...The numerical characteristics of fuzzy numbers include the optimistic value, pessimistic value, expected value, and the variance. We mainly provide the calculation formulae of several numerical characteristics of fuzzy numbers based on credibility measure. Especially, the variance of symmetric fuzzy numbers is formulated, and a super bound for the variance of fuzzy numbers is presented. Meanwhile, some conclusions relative to credibility measure, optimistic and pessimistic values are also given.展开更多
Let T be the multiplier operator associated to a multiplier m, and [b, T] be the commutator generated by T and a BMO function b. In this paper, the authors have proved that [b,T] is bounded from the Hardy space H^1（...Let T be the multiplier operator associated to a multiplier m, and [b, T] be the commutator generated by T and a BMO function b. In this paper, the authors have proved that [b,T] is bounded from the Hardy space H^1（R^n） into the weak L^1 （R^n） space and from certain atomic Hardy space Hb^1 （R^n） into the Lebesgue space L^1 （R^n）, when the multiplier m satisfies the conditions of Hoermander type.展开更多
It is well known that economic policy uncertainty prompts the volatility of the high-yield bond market.However,the correlation between economic policy uncertainty and volatility of high-yield bonds is still not clear....It is well known that economic policy uncertainty prompts the volatility of the high-yield bond market.However,the correlation between economic policy uncertainty and volatility of high-yield bonds is still not clear.In this paper,we employ GARCH-MIDAS models to investigate their correlation with US economic policy uncertainty index and S&P high-yield bond index.The empirical studies show that mixed volatility models can effectively capture the realized volatility of high-yield bonds,and economic policy uncertainty and macroeconomic factors have significant effects on the long-term component of high-yield bonds volatility.展开更多
In convex metric spaces, the sufficient and necessary conditions for Ishikawa iterative sequences of uniformly quasi-Lipschitzian mapping T with mixed errors to converge to a fixed point ate proved, and as a special c...In convex metric spaces, the sufficient and necessary conditions for Ishikawa iterative sequences of uniformly quasi-Lipschitzian mapping T with mixed errors to converge to a fixed point ate proved, and as a special case, in which T need not be continuous. The results of this paper improve and extend some recent results.展开更多
Incremental Newton（IN） iteration, proposed by Iannazzo, is stable for computing the matrix pth root, and its computational cost is O（n-3p） flops per iteration. In this paper, a cost-efficient variant of IN iterati...Incremental Newton（IN） iteration, proposed by Iannazzo, is stable for computing the matrix pth root, and its computational cost is O（n-3p） flops per iteration. In this paper, a cost-efficient variant of IN iteration is presented. The computational cost of the variant well agrees with O（n-3logp） flops per iteration, if p is up to at least 100.展开更多
We report accurate, calculated electronic, transport, and bulk properties of zinc blende gallium arsenide (GaAs). Our ab-initio, non-relativistic, self-con-sistent calculations employed a local density approximation (...We report accurate, calculated electronic, transport, and bulk properties of zinc blende gallium arsenide (GaAs). Our ab-initio, non-relativistic, self-con-sistent calculations employed a local density approximation (LDA) potential and the linear combination of atomic orbital (LCAO) formalism. We strictly followed the Bagayoko, Zhao, and William (BZW) method, as enhanced by Ekuma and Franklin (BZW-EF). Our calculated, direct band gap of 1.429 eV, at an experimental lattice constant of 5.65325 Å, is in excellent agreement with the experimental values. The calculated, total density of states data reproduced several experimentally determined peaks. We have predicted an equilibrium lattice constant, a bulk modulus, and a low temperature band gap of 5.632 Å, 75.49 GPa, and 1.520 eV, respectively. The latter two are in excellent agreement with corresponding, experimental values of 75.5 GPa (74.7 GPa) and 1.519 eV, respectively. This work underscores the capability of the local density approximation (LDA) to describe and to predict accurately properties of semiconductors, provided the calculations adhere to the conditions of validity of DFT.展开更多
Recently, smoothed particle hydrodynamics (SPH) method has become popular in computational fluid dynamic and heat transfer simulation. The simplicity offered by this method made some complex system in physics such as ...Recently, smoothed particle hydrodynamics (SPH) method has become popular in computational fluid dynamic and heat transfer simulation. The simplicity offered by this method made some complex system in physics such as moving interface in multiphase flow, heat conductivity jumping in multiple material boundaries and many geometrical difficulties become relative easy to calculate. We will treat a relative easy example of melting process to test the method in solving fluid motion equation coupled by heat transfer process. The main heat transfer processes are caused by solid-liquid (medium to medium) heat diffusion and convection. System interaction with ambient temperature can be modeled by gas surrounding fluid-solid system. For the ambient temperature, we proposed surface particle heat transfer governed by convectional heat flux. Using local particle number density value as surface detection method, we applied cooling and heating to surface particle on the melting ice cube and water system. The simulation result is also verified by experiment.展开更多
This investigation is focused on conducting a thorough analysis of Municipal Solid Waste Management (MSWM). MSWM encompasses a range of interdisciplinary measures that govern the various stages involved in managing un...This investigation is focused on conducting a thorough analysis of Municipal Solid Waste Management (MSWM). MSWM encompasses a range of interdisciplinary measures that govern the various stages involved in managing unwanted or non-utilizable solid materials, commonly known as rubbish, trash, junk, refuse, and garbage. These stages include generation, storage, collection, recycling, transportation, handling, disposal, and monitoring. The waste materials mentioned in this context exhibit a wide range of items, such as organic waste from food and vegetables, paper, plastic, polyethylene, iron, tin cans, deceased animals, byproducts from demolition activities, manure, and various other discarded materials. This study aims to provide insights into the possibilities of enhancing solid waste management in the Farmgate area of Dhaka North City Corporation (DNCC). To accomplish this objective, the research examines the conventional waste management methods employed in this area. It conducts extensive field surveys, collecting valuable data through interviews with local residents and key individuals involved in waste management, such as waste collectors, dealers, intermediate dealers, recyclers, and shopkeepers. The results indicate that significant amounts of distinct waste categories are produced daily. These include food and vegetable waste, which amount to 52.1 tons/day;polythene and plastic, which total 4.5 tons/day;metal and tin-can waste, which amounts to 1.4 tons/day;and paper waste, which totals 5.9 tons/day. This study highlights the significance of promoting environmental consciousness to effectively shape the attitudes of urban residents toward waste disposal and management. It emphasizes the need for collaboration between authorities and researchers to improve the current waste management system.展开更多
Cloud-based setups are intertwined with the Internet of Things and advanced,and technologies such as blockchain revolutionize conventional healthcare infrastructure.This digitization has major advantages,mainly enhanc...Cloud-based setups are intertwined with the Internet of Things and advanced,and technologies such as blockchain revolutionize conventional healthcare infrastructure.This digitization has major advantages,mainly enhancing the security barriers of the green tree infrastructure.In this study,we conducted a systematic review of over 150 articles that focused exclusively on blockchain-based healthcare systems,security vulnerabilities,cyberattacks,and system limitations.In addition,we considered several solutions proposed by thousands of researchers worldwide.Our results mostly delineate sustained threats and security concerns in blockchain-based medical health infrastructures for data management,transmission,and processing.Here,we describe 17 security threats that violate the privacy and data integrity of a system,over 21 cyber-attacks on security and QoS,and some system implementation problems such as node compromise,scalability,efficiency,regulatory issues,computation speed,and power consumption.We propose a multi-layered architecture for the future healthcare infrastructure.Second,we classify all threats and security concerns based on these layers and assess suggested solutions in terms of these contingencies.Our thorough theoretical examination of several performance criteria—including confidentiality,access control,interoperability problems,and energy efficiency—as well as mathematical verifications establishes the superiority of security,privacy maintenance,reliability,and efficiency over conventional systems.We conducted in-depth comparative studies on different interoperability parameters in the blockchain models.Our research justifies the use of various positive protocols and optimization methods to improve the quality of services in e-healthcare and overcome problems arising fromlaws and ethics.Determining the theoretical aspects,their scope,and future expectations encourages us to design reliable,secure,and privacy-preserving systems.展开更多
Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process.At the same time,breast cancer becomes the deadliest disease among women and can be detected by the use ...Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process.At the same time,breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques.Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate.But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives.For resolving the issues of false positives of breast cancer diagnosis,this paper presents an automated deep learning based breast cancer diagnosis(ADL-BCD)model using digital mammograms.The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms.The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation.In addition,Deep Convolutional Neural Network based Residual Network(ResNet 34)is applied for feature extraction purposes.Specifically,a hyper parameter tuning process using chimp optimization algorithm(COA)is applied to tune the parameters involved in ResNet 34 model.The wavelet neural network(WNN)is used for the classification of digital mammograms for the detection of breast cancer.The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures.The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.展开更多
Diabetic retinopathy (DR) is a complication of diabetesmellitus thatappears in the retina. Clinitians use retina images to detect DR pathologicalsigns related to the occlusion of tiny blood vessels. Such occlusion bri...Diabetic retinopathy (DR) is a complication of diabetesmellitus thatappears in the retina. Clinitians use retina images to detect DR pathologicalsigns related to the occlusion of tiny blood vessels. Such occlusion brings adegenerative cycle between the breaking off and the new generation of thinnerand weaker blood vessels. This research aims to develop a suitable retinalvasculature segmentation method for improving retinal screening proceduresby means of computer-aided diagnosis systems. The blood vessel segmentationmethodology relies on an effective feature selection based on SequentialForward Selection, using the error rate of a decision tree classifier in theevaluation function. Subsequently, the classification process is performed bythree alternative approaches: artificial neural networks, decision trees andsupport vector machines. The proposed methodology is validated on threepublicly accessible datasets and a private one provided by Hospital Sant Joanof Reus. In all cases we obtain an average accuracy above 96% with a sensitivityof 72% in the blood vessel segmentation process. Compared with the state-ofthe-art, our approach achieves the same performance as other methods thatneed more computational power.Our method significantly reduces the numberof features used in the segmentation process from 20 to 5 dimensions. Theimplementation of the three classifiers confirmed that the five selected featureshave a good effectiveness, independently of the classification algorithm.展开更多
Lupus Nephritis(LN)is a significant risk factor for morbidity and mortality in systemic lupus erythematosus,and nephropathology is still the gold standard for diagnosing LN.To assist pathologists in evaluating histopa...Lupus Nephritis(LN)is a significant risk factor for morbidity and mortality in systemic lupus erythematosus,and nephropathology is still the gold standard for diagnosing LN.To assist pathologists in evaluating histopathological images of LN,a 2D Rényi entropy multi-threshold image segmentation method is proposed in this research to apply to LN images.This method is based on an improved Cuckoo Search(CS)algorithm that introduces a Diffusion Mechanism(DM)and an Adaptiveβ-Hill Climbing(AβHC)strategy called the DMCS algorithm.The DMCS algorithm is tested on 30 benchmark functions of the IEEE CEC2017 dataset.In addition,the DMCS-based multi-threshold image segmentation method is also used to segment renal pathological images.Experimental results show that adding these two strategies improves the DMCS algorithm's ability to find the optimal solution.According to the three image quality evaluation metrics:PSNR,FSIM,and SSIM,the proposed image segmentation method performs well in image segmentation experiments.Our research shows that the DMCS algorithm is a helpful image segmentation method for renal pathological images.展开更多
Authorization management is important precondition and foundation for coordinating and resource sharing in open networks. Recently, authorization based on trust is widely used whereby access rights to shared resource ...Authorization management is important precondition and foundation for coordinating and resource sharing in open networks. Recently, authorization based on trust is widely used whereby access rights to shared resource are granted on the basis of their trust relation in distributed environment. Nevertheless, dynamic change of the status of credential and chain of trust induces to uncertainty of trust relation. Considering uncertainty of authorization and analyzing deficiency of authorization model only based on trust, we proposes joint trust-risk evaluation and build the model based on fuzzy set theory, and make use of the membership grade of fuzzy set to express joint trust-risk relation. Finally, derivation principle and constraint principle of joint trust-risk relationships are presented. The authorization management model is defined based on joint trust-risk evaluation, proof of compliance and separation of duty are analyzed. The proposed model depicts not only trust relationship between principals, but also security problem of authorization.展开更多
Nowadays,quality improvement and increased accessibility to patient data,at a reasonable cost,are highly challenging tasks in healthcare sector.Internet of Things(IoT)and Cloud Computing(CC)architectures are utilized ...Nowadays,quality improvement and increased accessibility to patient data,at a reasonable cost,are highly challenging tasks in healthcare sector.Internet of Things(IoT)and Cloud Computing(CC)architectures are utilized in the development of smart healthcare systems.These entities can support real-time applications by exploiting massive volumes of data,produced by wearable sensor devices.The advent of evolutionary computation algorithms andDeep Learning(DL)models has gained significant attention in healthcare diagnosis,especially in decision making process.Skin cancer is the deadliest disease which affects people across the globe.Automatic skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin lesions.The current research article presents a new skin lesion diagnosis model i.e.,Deep Learning with Evolutionary Algorithm based Image Segmentation(DL-EAIS)for IoT and cloud-based smart healthcare environments.Primarily,the dermoscopic images are captured using IoT devices,which are then transmitted to cloud servers for further diagnosis.Besides,Backtracking Search optimization Algorithm(BSA)with Entropy-Based Thresholding(EBT)i.e.,BSA-EBT technique is applied in image segmentation.Followed by,Shallow Convolutional Neural Network(SCNN)model is utilized as a feature extractor.In addition,Deep-Kernel Extreme LearningMachine(D-KELM)model is employed as a classification model to determine the class labels of dermoscopic images.An extensive set of simulations was conducted to validate the performance of the presented method using benchmark dataset.The experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures.展开更多
Computer-aided diagnosis(CAD)models exploit artificial intelligence(AI)for chest X-ray(CXR)examination to identify the presence of tuberculosis(TB)and can improve the feasibility and performance of CXR for TB screenin...Computer-aided diagnosis(CAD)models exploit artificial intelligence(AI)for chest X-ray(CXR)examination to identify the presence of tuberculosis(TB)and can improve the feasibility and performance of CXR for TB screening and triage.At the same time,CXR interpretation is a time-consuming and subjective process.Furthermore,high resemblance among the radiological patterns of TB and other lung diseases can result in misdiagnosis.Therefore,computer-aided diagnosis(CAD)models using machine learning(ML)and deep learning(DL)can be designed for screening TB accurately.With this motivation,this article develops a Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification(WSODTL-TBC)model on Chest X-rays(CXR).The presented WSODTL-TBC model aims to detect and classify TB on CXR images.Primarily,the WSODTL-TBC model undergoes image filtering techniques to discard the noise content and U-Net-based image segmentation.Besides,a pre-trained residual network with a two-dimensional convolutional neural network(2D-CNN)model is applied to extract feature vectors.In addition,the WSO algorithm with long short-term memory(LSTM)model was employed for identifying and classifying TB,where the WSO algorithm is applied as a hyperparameter optimizer of the LSTM methodology,showing the novelty of the work.The performance validation of the presented WSODTL-TBC model is carried out on the benchmark dataset,and the outcomes were investigated in many aspects.The experimental development pointed out the betterment of the WSODTL-TBC model over existing algorithms.展开更多
基金supported by DST-Science and Engineering Research Board(SERB)Early carrier research grant ECR/2015/000240Core research grant CRG/2020/003257,the University Grants Commission(UGC start-up grant F.30-318/2016)the Central University of Punjab RSM grant-CUPB/CC/16/00/13.
文摘Objective:To examine the effect of an neurokinin 3 receptor(NK3R)agonist,senktide,on neuronal nitric oxide synthase(nNOS)activation in the median eminence-arcuate nucleus(ME-ARC)and preoptic area(POA)regions of the hypothalamus across proestrus,diestrus,and ovariectomized states in female rats and its correlation with luteinizing hormone(LH)secretion.Methods:Adult female Sprague-Dawley rats were examined for proestrus and diestrus phases of the estrous cycle.Female rats were categorized into proestrus and diestrus groups,and each was further divided into four subgroups(n=4).In both the diestrus and proestrus categories,Group 1 was the control group.Groups 2,3,and 4 received senktide(100μg/kg-1),NK3R antagonist SB222200(10 mg/kg-1),and SB222200 followed by senktide,respectively.To evaluate the effect of sex steroids on NK3R agonist-induced nNOS activation,female rats underwent bilateral ovariectomy and were divided into four groups(n=3).Group 1 served as the control.Group 2 received a subcutaneous injection of 17β-estradiol 3-benzoate(E2,3μg/rat).Group 3 received E2 and progesterone(30μg/rat).Group 4 was administered senktide(100μg/kg).Female rats from each group were sacrificed,blood was collected for LH ELISA,and hypothalamic tissues were collected for Western blotting.Results:Senktide increased nNOS phosphorylation in the ME-ARC during both the proestrus and diestrus phases.In the POA,senktide increased nNOS phosphorylation only during the diestrus phase.In ovariectomized rats,senktide activated nNOS independent of sex steroid levels.Senktide also increased serum LH concentration in diestrus and ovariectomized female rats.Conclusions:Senktide,an NK3R agonist,activates nNOS in the POA and ME-ARC regions of the hypothalamus in a phase dependent manner.The activation of nNOS by senktide suggests a potential mechanism by which neurokinin B triggers nNOS activation in the ARC and POA regions and regulates GnRH/LH secretion.
文摘White Blood Cell(WBC)cancer or leukemia is one of the serious cancers that threaten the existence of human beings.In spite of its prevalence and serious consequences,it is mostly diagnosed through manual practices.The risks of inappropriate,sub-standard and wrong or biased diagnosis are high in manual methods.So,there is a need exists for automatic diagnosis and classification method that can replace the manual process.Leukemia is mainly classified into acute and chronic types.The current research work proposed a computer-based application to classify the disease.In the feature extraction stage,we use excellent physical properties to improve the diagnostic system’s accuracy,based on Enhanced Color Co-Occurrence Matrix.The study is aimed at identification and classification of chronic lymphocytic leukemia using microscopic images of WBCs based on Enhanced Virtual Neural Network(EVNN)classification.The proposed method achieved optimum accuracy in detection and classification of leukemia from WBC images.Thus,the study results establish the superiority of the proposed method in automated diagnosis of leukemia.The values achieved by the proposed method in terms of sensitivity,specificity,accuracy,and error rate were 97.8%,89.9%,76.6%,and 2.2%,respectively.Furthermore,the system could predict the disease in prior through images,and the probabilities of disease detection are also highly optimistic.
文摘Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspects of the items thus leading to more sophisticated and justifiable recommendations. However, most Collaborative Filtering (CF) techniques rely mainly on the overall preferences of users toward items only. And there is lack of conceptual and computational framework that enables an understandable aspect-based AI approach to recommending items to users. In this paper, we propose concepts and computational tools that can sharpen the logic of recommendations and that rely on users’ sentiments along various aspects of items. These concepts include: The sentiment of a user towards a specific aspect of a specific item, the emphasis that a given user places on a specific aspect in general, the popularity and controversy of an aspect among groups of users, clusters of users emphasizing a given aspect, clusters of items that are popular among a group of users and so forth. The framework introduced in this study is developed in terms of user emphasis, aspect popularity, aspect controversy, and users and items similarity. Towards this end, we introduce the Aspect-Based Collaborative Filtering Toolbox (ABCFT), where the tools are all developed based on the three-index sentiment tensor with the indices being the user, item, and aspect. The toolbox computes solutions to the questions alluded to above. We illustrate the methodology using a hotel review dataset having around 6000 users, 400 hotels and 6 aspects.
文摘In this paper,the choice and parametrisation of finite deformation polyconvex isotropic hyperelastic models to describe the behaviour of a class of defect-free monocrystalline metal materials at the molecular level is examined.The article discusses some physical,mathematical and numerical demands which in our opinion should be fulfilled by elasticity models to be useful.A set of molecular numerical tests for aluminium and tungsten providing data for the fitting of a hyperelastic model was performed,and an algorithm for parametrisation is discussed.The proposed models with optimised parameters are superior to those used in non-linear mechanics of crystals.
文摘In this work,a system for recognition of newspaper printed in Gurumukhi script is presented.Four feature extraction techniques,namely,zoning features,diagonal features,parabola curve fitting based features,and power curve fitting based features are considered for extracting the statistical properties of the characters printed in the newspaper.Different combinations of these features are also applied to improve the recognition accuracy.For recognition,four classification techniques,namely,k-NN,linear-SVM,decision tree,and random forest are used.A database for the experiments is collected from three major Gurumukhi script newspapers which are Ajit,Jagbani and Punjabi Tribune.Using 5-fold cross validation and random forest classifier,a recognition accuracy of 96.19%with a combination of zoning features,diagonal features and parabola curve fitting based features has been reported.A recognition accuracy of 95.21%with a partitioning strategy of data set(70%data as training data and remaining 30%data as testing data)has been achieved.
文摘The numerical characteristics of fuzzy numbers include the optimistic value, pessimistic value, expected value, and the variance. We mainly provide the calculation formulae of several numerical characteristics of fuzzy numbers based on credibility measure. Especially, the variance of symmetric fuzzy numbers is formulated, and a super bound for the variance of fuzzy numbers is presented. Meanwhile, some conclusions relative to credibility measure, optimistic and pessimistic values are also given.
基金Supported by the Research Funds of Zhejiaug Sci-Tech University (No. 0313055-Y).
文摘Let T be the multiplier operator associated to a multiplier m, and [b, T] be the commutator generated by T and a BMO function b. In this paper, the authors have proved that [b,T] is bounded from the Hardy space H^1（R^n） into the weak L^1 （R^n） space and from certain atomic Hardy space Hb^1 （R^n） into the Lebesgue space L^1 （R^n）, when the multiplier m satisfies the conditions of Hoermander type.
基金This work was supported by National Natural Science Foundation of China(Nos.71461005,71561008)National Social Science Foundation of China(No.17BGL234)Innovation Project of Guangxi Graduate Education(No.YC-SW2017143).
文摘It is well known that economic policy uncertainty prompts the volatility of the high-yield bond market.However,the correlation between economic policy uncertainty and volatility of high-yield bonds is still not clear.In this paper,we employ GARCH-MIDAS models to investigate their correlation with US economic policy uncertainty index and S&P high-yield bond index.The empirical studies show that mixed volatility models can effectively capture the realized volatility of high-yield bonds,and economic policy uncertainty and macroeconomic factors have significant effects on the long-term component of high-yield bonds volatility.
文摘In convex metric spaces, the sufficient and necessary conditions for Ishikawa iterative sequences of uniformly quasi-Lipschitzian mapping T with mixed errors to converge to a fixed point ate proved, and as a special case, in which T need not be continuous. The results of this paper improve and extend some recent results.
文摘Incremental Newton（IN） iteration, proposed by Iannazzo, is stable for computing the matrix pth root, and its computational cost is O（n-3p） flops per iteration. In this paper, a cost-efficient variant of IN iteration is presented. The computational cost of the variant well agrees with O（n-3logp） flops per iteration, if p is up to at least 100.
文摘We report accurate, calculated electronic, transport, and bulk properties of zinc blende gallium arsenide (GaAs). Our ab-initio, non-relativistic, self-con-sistent calculations employed a local density approximation (LDA) potential and the linear combination of atomic orbital (LCAO) formalism. We strictly followed the Bagayoko, Zhao, and William (BZW) method, as enhanced by Ekuma and Franklin (BZW-EF). Our calculated, direct band gap of 1.429 eV, at an experimental lattice constant of 5.65325 Å, is in excellent agreement with the experimental values. The calculated, total density of states data reproduced several experimentally determined peaks. We have predicted an equilibrium lattice constant, a bulk modulus, and a low temperature band gap of 5.632 Å, 75.49 GPa, and 1.520 eV, respectively. The latter two are in excellent agreement with corresponding, experimental values of 75.5 GPa (74.7 GPa) and 1.519 eV, respectively. This work underscores the capability of the local density approximation (LDA) to describe and to predict accurately properties of semiconductors, provided the calculations adhere to the conditions of validity of DFT.
文摘Recently, smoothed particle hydrodynamics (SPH) method has become popular in computational fluid dynamic and heat transfer simulation. The simplicity offered by this method made some complex system in physics such as moving interface in multiphase flow, heat conductivity jumping in multiple material boundaries and many geometrical difficulties become relative easy to calculate. We will treat a relative easy example of melting process to test the method in solving fluid motion equation coupled by heat transfer process. The main heat transfer processes are caused by solid-liquid (medium to medium) heat diffusion and convection. System interaction with ambient temperature can be modeled by gas surrounding fluid-solid system. For the ambient temperature, we proposed surface particle heat transfer governed by convectional heat flux. Using local particle number density value as surface detection method, we applied cooling and heating to surface particle on the melting ice cube and water system. The simulation result is also verified by experiment.
文摘This investigation is focused on conducting a thorough analysis of Municipal Solid Waste Management (MSWM). MSWM encompasses a range of interdisciplinary measures that govern the various stages involved in managing unwanted or non-utilizable solid materials, commonly known as rubbish, trash, junk, refuse, and garbage. These stages include generation, storage, collection, recycling, transportation, handling, disposal, and monitoring. The waste materials mentioned in this context exhibit a wide range of items, such as organic waste from food and vegetables, paper, plastic, polyethylene, iron, tin cans, deceased animals, byproducts from demolition activities, manure, and various other discarded materials. This study aims to provide insights into the possibilities of enhancing solid waste management in the Farmgate area of Dhaka North City Corporation (DNCC). To accomplish this objective, the research examines the conventional waste management methods employed in this area. It conducts extensive field surveys, collecting valuable data through interviews with local residents and key individuals involved in waste management, such as waste collectors, dealers, intermediate dealers, recyclers, and shopkeepers. The results indicate that significant amounts of distinct waste categories are produced daily. These include food and vegetable waste, which amount to 52.1 tons/day;polythene and plastic, which total 4.5 tons/day;metal and tin-can waste, which amounts to 1.4 tons/day;and paper waste, which totals 5.9 tons/day. This study highlights the significance of promoting environmental consciousness to effectively shape the attitudes of urban residents toward waste disposal and management. It emphasizes the need for collaboration between authorities and researchers to improve the current waste management system.
文摘Cloud-based setups are intertwined with the Internet of Things and advanced,and technologies such as blockchain revolutionize conventional healthcare infrastructure.This digitization has major advantages,mainly enhancing the security barriers of the green tree infrastructure.In this study,we conducted a systematic review of over 150 articles that focused exclusively on blockchain-based healthcare systems,security vulnerabilities,cyberattacks,and system limitations.In addition,we considered several solutions proposed by thousands of researchers worldwide.Our results mostly delineate sustained threats and security concerns in blockchain-based medical health infrastructures for data management,transmission,and processing.Here,we describe 17 security threats that violate the privacy and data integrity of a system,over 21 cyber-attacks on security and QoS,and some system implementation problems such as node compromise,scalability,efficiency,regulatory issues,computation speed,and power consumption.We propose a multi-layered architecture for the future healthcare infrastructure.Second,we classify all threats and security concerns based on these layers and assess suggested solutions in terms of these contingencies.Our thorough theoretical examination of several performance criteria—including confidentiality,access control,interoperability problems,and energy efficiency—as well as mathematical verifications establishes the superiority of security,privacy maintenance,reliability,and efficiency over conventional systems.We conducted in-depth comparative studies on different interoperability parameters in the blockchain models.Our research justifies the use of various positive protocols and optimization methods to improve the quality of services in e-healthcare and overcome problems arising fromlaws and ethics.Determining the theoretical aspects,their scope,and future expectations encourages us to design reliable,secure,and privacy-preserving systems.
文摘Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process.At the same time,breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques.Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate.But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives.For resolving the issues of false positives of breast cancer diagnosis,this paper presents an automated deep learning based breast cancer diagnosis(ADL-BCD)model using digital mammograms.The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms.The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation.In addition,Deep Convolutional Neural Network based Residual Network(ResNet 34)is applied for feature extraction purposes.Specifically,a hyper parameter tuning process using chimp optimization algorithm(COA)is applied to tune the parameters involved in ResNet 34 model.The wavelet neural network(WNN)is used for the classification of digital mammograms for the detection of breast cancer.The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures.The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.
基金This work has been funded by the research project PI18/00169 from Instituto de Salud Carlos III&FEDER funds.University Rovira i.Virgili also provided funds with Project 2019PFR-B2-61.
文摘Diabetic retinopathy (DR) is a complication of diabetesmellitus thatappears in the retina. Clinitians use retina images to detect DR pathologicalsigns related to the occlusion of tiny blood vessels. Such occlusion brings adegenerative cycle between the breaking off and the new generation of thinnerand weaker blood vessels. This research aims to develop a suitable retinalvasculature segmentation method for improving retinal screening proceduresby means of computer-aided diagnosis systems. The blood vessel segmentationmethodology relies on an effective feature selection based on SequentialForward Selection, using the error rate of a decision tree classifier in theevaluation function. Subsequently, the classification process is performed bythree alternative approaches: artificial neural networks, decision trees andsupport vector machines. The proposed methodology is validated on threepublicly accessible datasets and a private one provided by Hospital Sant Joanof Reus. In all cases we obtain an average accuracy above 96% with a sensitivityof 72% in the blood vessel segmentation process. Compared with the state-ofthe-art, our approach achieves the same performance as other methods thatneed more computational power.Our method significantly reduces the numberof features used in the segmentation process from 20 to 5 dimensions. Theimplementation of the three classifiers confirmed that the five selected featureshave a good effectiveness, independently of the classification algorithm.
基金supported in part by the Natural Science Foundation of Zhejiang Province(LZ22F020005,LTGS23E070001)National Natural Science Foundation of China(62076185,U1809209).
文摘Lupus Nephritis(LN)is a significant risk factor for morbidity and mortality in systemic lupus erythematosus,and nephropathology is still the gold standard for diagnosing LN.To assist pathologists in evaluating histopathological images of LN,a 2D Rényi entropy multi-threshold image segmentation method is proposed in this research to apply to LN images.This method is based on an improved Cuckoo Search(CS)algorithm that introduces a Diffusion Mechanism(DM)and an Adaptiveβ-Hill Climbing(AβHC)strategy called the DMCS algorithm.The DMCS algorithm is tested on 30 benchmark functions of the IEEE CEC2017 dataset.In addition,the DMCS-based multi-threshold image segmentation method is also used to segment renal pathological images.Experimental results show that adding these two strategies improves the DMCS algorithm's ability to find the optimal solution.According to the three image quality evaluation metrics:PSNR,FSIM,and SSIM,the proposed image segmentation method performs well in image segmentation experiments.Our research shows that the DMCS algorithm is a helpful image segmentation method for renal pathological images.
基金Supported by the National Natural Science Foundation of China (60403027)
文摘Authorization management is important precondition and foundation for coordinating and resource sharing in open networks. Recently, authorization based on trust is widely used whereby access rights to shared resource are granted on the basis of their trust relation in distributed environment. Nevertheless, dynamic change of the status of credential and chain of trust induces to uncertainty of trust relation. Considering uncertainty of authorization and analyzing deficiency of authorization model only based on trust, we proposes joint trust-risk evaluation and build the model based on fuzzy set theory, and make use of the membership grade of fuzzy set to express joint trust-risk relation. Finally, derivation principle and constraint principle of joint trust-risk relationships are presented. The authorization management model is defined based on joint trust-risk evaluation, proof of compliance and separation of duty are analyzed. The proposed model depicts not only trust relationship between principals, but also security problem of authorization.
文摘Nowadays,quality improvement and increased accessibility to patient data,at a reasonable cost,are highly challenging tasks in healthcare sector.Internet of Things(IoT)and Cloud Computing(CC)architectures are utilized in the development of smart healthcare systems.These entities can support real-time applications by exploiting massive volumes of data,produced by wearable sensor devices.The advent of evolutionary computation algorithms andDeep Learning(DL)models has gained significant attention in healthcare diagnosis,especially in decision making process.Skin cancer is the deadliest disease which affects people across the globe.Automatic skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin lesions.The current research article presents a new skin lesion diagnosis model i.e.,Deep Learning with Evolutionary Algorithm based Image Segmentation(DL-EAIS)for IoT and cloud-based smart healthcare environments.Primarily,the dermoscopic images are captured using IoT devices,which are then transmitted to cloud servers for further diagnosis.Besides,Backtracking Search optimization Algorithm(BSA)with Entropy-Based Thresholding(EBT)i.e.,BSA-EBT technique is applied in image segmentation.Followed by,Shallow Convolutional Neural Network(SCNN)model is utilized as a feature extractor.In addition,Deep-Kernel Extreme LearningMachine(D-KELM)model is employed as a classification model to determine the class labels of dermoscopic images.An extensive set of simulations was conducted to validate the performance of the presented method using benchmark dataset.The experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures.
文摘Computer-aided diagnosis(CAD)models exploit artificial intelligence(AI)for chest X-ray(CXR)examination to identify the presence of tuberculosis(TB)and can improve the feasibility and performance of CXR for TB screening and triage.At the same time,CXR interpretation is a time-consuming and subjective process.Furthermore,high resemblance among the radiological patterns of TB and other lung diseases can result in misdiagnosis.Therefore,computer-aided diagnosis(CAD)models using machine learning(ML)and deep learning(DL)can be designed for screening TB accurately.With this motivation,this article develops a Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification(WSODTL-TBC)model on Chest X-rays(CXR).The presented WSODTL-TBC model aims to detect and classify TB on CXR images.Primarily,the WSODTL-TBC model undergoes image filtering techniques to discard the noise content and U-Net-based image segmentation.Besides,a pre-trained residual network with a two-dimensional convolutional neural network(2D-CNN)model is applied to extract feature vectors.In addition,the WSO algorithm with long short-term memory(LSTM)model was employed for identifying and classifying TB,where the WSO algorithm is applied as a hyperparameter optimizer of the LSTM methodology,showing the novelty of the work.The performance validation of the presented WSODTL-TBC model is carried out on the benchmark dataset,and the outcomes were investigated in many aspects.The experimental development pointed out the betterment of the WSODTL-TBC model over existing algorithms.
