Regular expression matching is playing an important role in deep inspection. The rapid development of SDN and NFV makes the network more dynamic, bringing serious challenges to traditional deep inspection matching eng...Regular expression matching is playing an important role in deep inspection. The rapid development of SDN and NFV makes the network more dynamic, bringing serious challenges to traditional deep inspection matching engines. However, state-of-theart matching methods often require a significant amount of pre-processing time and hence are not suitable for this fast updating scenario. In this paper, a novel matching engine called BFA is proposed to achieve high-speed regular expression matching with fast pre-processing. Experiments demonstrate that BFA obtains 5 to 20 times more update abilities compared to existing regular expression matching methods, and scales well on multi-core platforms.展开更多
Microarray data is inherently noisy due to the noise contaminated from various sources during the preparation of microarray slide and thus it greatly affects the accuracy of the gene expression. How to eliminate the e...Microarray data is inherently noisy due to the noise contaminated from various sources during the preparation of microarray slide and thus it greatly affects the accuracy of the gene expression. How to eliminate the effect of the noise constitutes a challenging problem in microarray analysis. Efficient denoising is often a necessary and the first step to be taken before the image data is analyzed to compensate for data corruption and for effective utilization for these data. Hence preprocessing of microarray image is an essential to eliminate the background noise in order to enhance the image quality and effective quantification. Existing denoising techniques based on transformed domain have been utilized for microarray noise reduction with their own limitations. The objective of this paper is to introduce novel preprocessing techniques such as optimized spatial resolution (OSR) and spatial domain filtering (SDF) for reduction of noise from microarray data and reduction of error during quantification process for estimating the microarray spots accurately to determine expression level of genes. Besides combined optimized spatial resolution and spatial filtering is proposed and found improved denoising of microarray data with effective quantification of spots. The proposed method has been validated in microarray images of gene expression profiles of Myeloid Leukemia using Stanford Microarray Database with various quality measures such as signal to noise ratio, peak signal to noise ratio, image fidelity, structural content, absolute average difference and correlation quality. It was observed by quantitative analysis that the proposed technique is more efficient for denoising the microarray image which enables to make it suitable for effective quantification.展开更多
The Low Earth Orbit(LEO)remote sensing satellite mega-constellation has the characteristics of large quantity and various types which make it have unique superiority in the realization of concurrent multiple tasks.How...The Low Earth Orbit(LEO)remote sensing satellite mega-constellation has the characteristics of large quantity and various types which make it have unique superiority in the realization of concurrent multiple tasks.However,the complexity of resource allocation is increased because of the large number of tasks and satellites.Therefore,the primary problem of implementing concurrent multiple tasks via LEO mega-constellation is to pre-process tasks and observation re-sources.To address the challenge,we propose a pre-processing algorithm for the mega-constellation based on highly Dynamic Spatio-Temporal Grids(DSTG).In the first stage,this paper describes the management model of mega-constellation and the multiple tasks.Then,the coding method of DSTG is proposed,based on which the description of complex mega-constellation observation resources is realized.In the third part,the DSTG algorithm is used to realize the processing of concurrent multiple tasks at multiple levels,such as task space attribute,time attribute and grid task importance evaluation.Finally,the simulation result of the proposed method in the case of constellation has been given to verify the effectiveness of concurrent multi-task pre-processing based on DSTG.The autonomous processing process of task decomposition and task fusion and mapping to grids,and the convenient indexing process of time window are verified.展开更多
Forecasting river flow is crucial for optimal planning,management,and sustainability using freshwater resources.Many machine learning(ML)approaches have been enhanced to improve streamflow prediction.Hybrid techniques...Forecasting river flow is crucial for optimal planning,management,and sustainability using freshwater resources.Many machine learning(ML)approaches have been enhanced to improve streamflow prediction.Hybrid techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone approaches.Current researchers have also emphasised using hybrid models to improve forecast accuracy.Accordingly,this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years,summarising data preprocessing,univariate machine learning modelling strategy,advantages and disadvantages of standalone ML techniques,hybrid models,and performance metrics.This study focuses on two types of hybrid models:parameter optimisation-based hybrid models(OBH)and hybridisation of parameter optimisation-based and preprocessing-based hybridmodels(HOPH).Overall,this research supports the idea thatmeta-heuristic approaches precisely improveML techniques.It’s also one of the first efforts to comprehensively examine the efficiency of various meta-heuristic approaches(classified into four primary classes)hybridised with ML techniques.This study revealed that previous research applied swarm,evolutionary,physics,and hybrid metaheuristics with 77%,61%,12%,and 12%,respectively.Finally,there is still room for improving OBH and HOPH models by examining different data pre-processing techniques and metaheuristic algorithms.展开更多
With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality pred...With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.展开更多
This research concentrates to model an efficient thyroid prediction approach,which is considered a baseline for significant problems faced by the women community.The major research problem is the lack of automated mod...This research concentrates to model an efficient thyroid prediction approach,which is considered a baseline for significant problems faced by the women community.The major research problem is the lack of automated model to attain earlier prediction.Some existing model fails to give better prediction accuracy.Here,a novel clinical decision support system is framed to make the proper decision during a time of complexity.Multiple stages are followed in the proposed framework,which plays a substantial role in thyroid prediction.These steps include i)data acquisition,ii)outlier prediction,and iii)multi-stage weight-based ensemble learning process(MS-WEL).The weighted analysis of the base classifier and other classifier models helps bridge the gap encountered in one single classifier model.Various classifiers aremerged to handle the issues identified in others and intend to enhance the prediction rate.The proposed model provides superior outcomes and gives good quality prediction rate.The simulation is done in the MATLAB 2020a environment and establishes a better trade-off than various existing approaches.The model gives a prediction accuracy of 97.28%accuracy compared to other models and shows a better trade than others.展开更多
Biomedical image processing acts as an essential part of severalmedical applications in supporting computer aided disease diagnosis. MagneticResonance Image (MRI) is a commonly utilized imaging tool used tosave glioma...Biomedical image processing acts as an essential part of severalmedical applications in supporting computer aided disease diagnosis. MagneticResonance Image (MRI) is a commonly utilized imaging tool used tosave glioma for clinical examination. Biomedical image segmentation plays avital role in healthcare decision making process which also helps to identifythe affected regions in the MRI. Though numerous segmentation models areavailable in the literature, it is still needed to develop effective segmentationmodels for BT. This study develops a salp swarm algorithm with multi-levelthresholding based brain tumor segmentation (SSAMLT-BTS) model. Thepresented SSAMLT-BTS model initially employs bilateral filtering based onnoise removal and skull stripping as a pre-processing phase. In addition,Otsu thresholding approach is applied to segment the biomedical imagesand the optimum threshold values are chosen by the use of SSA. Finally,active contour (AC) technique is used to identify the suspicious regions in themedical image. A comprehensive experimental analysis of the SSAMLT-BTSmodel is performed using benchmark dataset and the outcomes are inspectedin many aspects. The simulation outcomes reported the improved outcomesof the SSAMLT-BTS model over recent approaches with maximum accuracyof 95.95%.展开更多
The aviation industry is one of the most competitive markets. Themost common approach for airline service providers is to improve passengersatisfaction. Passenger satisfaction in the aviation industry occurs whenpasse...The aviation industry is one of the most competitive markets. Themost common approach for airline service providers is to improve passengersatisfaction. Passenger satisfaction in the aviation industry occurs whenpassengers’ expectations are met during flights. Airline service quality iscritical in attracting new passengers and retaining existing ones. It is crucialto identify passengers’ pain points and enhance their satisfaction with theservices offered. The airlines used a variety of techniques to improve servicequality. They used data analysis approaches to analyze the passenger pointdata. These solutions have focused simply on surveys;consequently, deeplearningapproaches have received insufficient attention. In this study, deepneural networks with the adaptive moment estimation Adam optimizationalgorithm were applied to enhance classification performance. In previousstudies, the quality of the dataset has been ignored. The proposed approachwas applied to the airline passenger satisfaction dataset from the Kagglerepository. It was validated by applying artificial neural networks (ANNs),random forests, and support vector machine techniques to the same dataset. Itwas compared with other research papers that used the same dataset and had asimilar problem. The experimental results showed that the proposed approachoutperformed previous studies. It has achieved an accuracy of 99.3%.展开更多
ESystems based on EHRs(Electronic health records)have been in use for many years and their amplified realizations have been felt recently.They still have been pioneering collections of massive volumes of health data.D...ESystems based on EHRs(Electronic health records)have been in use for many years and their amplified realizations have been felt recently.They still have been pioneering collections of massive volumes of health data.Duplicate detections involve discovering records referring to the same practical components,indicating tasks,which are generally dependent on several input parameters that experts yield.Record linkage specifies the issue of finding identical records across various data sources.The similarity existing between two records is characterized based on domain-based similarity functions over different features.De-duplication of one dataset or the linkage of multiple data sets has become a highly significant operation in the data processing stages of different data mining programmes.The objective is to match all the records associated with the same entity.Various measures have been in use for representing the quality and complexity about data linkage algorithms,and many other novel metrics have been introduced.An outline of the problem existing in themeasurement of data linkage and de-duplication quality and complexity is presented.This article focuses on the reprocessing of health data that is horizontally divided among data custodians,with the purpose of custodians giving similar features to sets of patients.The first step in this technique is about an automatic selection of training examples with superior quality from the compared record pairs and the second step involves training the reciprocal neuro-fuzzy inference system(RANFIS)classifier.Using the Optimal Threshold classifier,it is presumed that there is information about the original match status for all compared record pairs(i.e.,Ant Lion Optimization),and therefore an optimal threshold can be computed based on the respective RANFIS.Febrl,Clinical Decision(CD),and Cork Open Research Archive(CORA)data repository help analyze the proposed method with evaluated benchmarks with current techniques.展开更多
In recent years,Peripheral blood smear is a generic analysis to assess the person’s health status.Manual testing of Peripheral blood smear images are difficult,time-consuming and is subject to human intervention and ...In recent years,Peripheral blood smear is a generic analysis to assess the person’s health status.Manual testing of Peripheral blood smear images are difficult,time-consuming and is subject to human intervention and visual error.This method encouraged for researchers to present algorithms and techniques to perform the peripheral blood smear analysis with the help of computer-assisted and decision-making techniques.Existing CAD based methods are lacks in attaining the accurate detection of abnormalities present in the images.In order to mitigate this issue Deep Convolution Neural Network(DCNN)based automatic classification technique is introduced with the classification of eight groups of peripheral blood cells such as basophil,eosinophil,lymphocyte,monocyte,neutrophil,erythroblast,platelet,myocyte,promyocyte and metamyocyte.The proposed DCNN model employs transfer learning approach and additionally it carries three stages such as pre-processing,feature extraction and classification.Initially the pre-processing steps are incorporated to eliminate noisy contents present in the image by using Histogram Equalization(HE).It is enclosed to improve an image contrast.In order to distinguish the dissimilar class and segmentation approach is carried out with the help of Fuzzy C-Means(FCM)model whereas its centroid point optimality method with Slap Swarm based optimization strategy.Moreover some specific set of Gray Level Co-occurrence Matrix(GLCM)features of the segmented images are extracted to augment the performance of proposed detection algorithm.Finally the extracted features are recorded by DCNN and the proposed classifier has the capability to extract their own features.Based on this the diverse set of classes are classified and distinguished from qualitative abnormalities found in the image.展开更多
Software is unavoidable in software development and maintenance.In literature,many methods are discussed which fails to achieve efficient software bug detection and classification.In this paper,efficient Adaptive Deep...Software is unavoidable in software development and maintenance.In literature,many methods are discussed which fails to achieve efficient software bug detection and classification.In this paper,efficient Adaptive Deep Learning Model(ADLM)is developed for automatic duplicate bug report detection and classification process.The proposed ADLM is a combination of Conditional Random Fields decoding with Long Short-Term Memory(CRF-LSTM)and Dingo Optimizer(DO).In the CRF,the DO can be consumed to choose the efficient weight value in network.The proposed automatic bug report detection is proceeding with three stages like pre-processing,feature extraction in addition bug detection with classification.Initially,the bug report input dataset is gathered from the online source system.In the pre-processing phase,the unwanted information from the input data are removed by using cleaning text,convert data types and null value replacement.The pre-processed data is sent into the feature extraction phase.In the feature extraction phase,the four types of feature extraction method are utilized such as contextual,categorical,temporal and textual.Finally,the features are sent to the proposed ADLM for automatic duplication bug report detection and classification.The proposed methodology is proceeding with two phases such as training and testing phases.Based on the working process,the bugs are detected and classified from the input data.The projected technique is assessed by analyzing performance metrics such as accuracy,precision,Recall,F_Measure and kappa.展开更多
Emotion represents the feeling of an individual in a given situation. There are various ways to express the emotions of an individual. It can be categorized into verbal expressions, written expressions, facial express...Emotion represents the feeling of an individual in a given situation. There are various ways to express the emotions of an individual. It can be categorized into verbal expressions, written expressions, facial expressions and gestures. Among these various ways of expressing the emotion, the written method is a challenging task to extract the emotions, as the data is in the form of textual dat. Finding the different kinds of emotions is also a tedious task as it requires a lot of pre preparations of the textual data taken for the research. This research work is carried out to analyse and extract the emotions hidden in text data. The text data taken for the analysis is from the social media dataset. Using the raw text data directly from the social media will not serve the purpose. Therefore, the text data has to be pre-processed and then utilised for further processing. Pre-processing makes the text data more efficient and would infer valuable insights of the emotions hidden in it. The preprocessing steps also help to manage the text data for identifying the emotions conveyed in the text. This work proposes to deduct the emotions taken from the social media text data by applying the machine learning algorithm. Finally, the usefulness of the emotions is suggested for various stake holders, to find the attitude of individuals at that moment, the data is produced. .展开更多
Liquid leakage from pipelines is a critical issue in large-scale process plants.Damage in pipelines affects the normal operation of the plant and increases maintenance costs.Furthermore,it causes unsafe and hazardous ...Liquid leakage from pipelines is a critical issue in large-scale process plants.Damage in pipelines affects the normal operation of the plant and increases maintenance costs.Furthermore,it causes unsafe and hazardous situations for operators.Therefore,the detection and localization of leakages is a crucial task for maintenance and condition monitoring.Recently,the use of infrared(IR)cameras was found to be a promising approach for leakage detection in large-scale plants.IR cameras can capture leaking liquid if it has a higher(or lower)temperature than its surroundings.In this paper,a method based on IR video data and machine vision techniques is proposed to detect and localize liquid leakages in a chemical process plant.Since the proposed method is a vision-based method and does not consider the physical properties of the leaking liquid,it is applicable for any type of liquid leakage(i.e.,water,oil,etc.).In this method,subsequent frames are subtracted and divided into blocks.Then,principle component analysis is performed in each block to extract features from the blocks.All subtracted frames within the blocks are individually transferred to feature vectors,which are used as a basis for classifying the blocks.The k-nearest neighbor algorithm is used to classify the blocks as normal(without leakage)or anomalous(with leakage).Finally,the positions of the leakages are determined in each anomalous block.In order to evaluate the approach,two datasets with two different formats,consisting of video footage of a laboratory demonstrator plant captured by an IR camera,are considered.The results show that the proposed method is a promising approach to detect and localize leakages from pipelines using IR videos.The proposed method has high accuracy and a reasonable detection time for leakage detection.The possibility of extending the proposed method to a real industrial plant and the limitations of this method are discussed at the end.展开更多
Various post-harvest processes of rice are commonly employed,especially during the off-season,to ensure its consumption feasibility,which often affect the grain quality.Different forms of drying,storage and processing...Various post-harvest processes of rice are commonly employed,especially during the off-season,to ensure its consumption feasibility,which often affect the grain quality.Different forms of drying,storage and processing of rice are evaluated to identify their effects on grain quality.Microwave drying has emerged as an alternative to the widely-used intermittent-drying and fixed-bed-dryer methods of drying paddy rice.Control of drying-air temperatures(between 40℃ and 60℃)according to the rice variety can improve quality,especially for exotic varieties.Keeping stored grain in hygroscopic balance,with water content between 11%to 15%,at temperatures between 16℃ and 20℃ and with intergranular relative humidity near 60%,allows 12 months of storage in a controlled environment without significant deterioration.Other innovations,notably the application of artificial refrigeration to grain stored in bulk in vertical cylindrical silos and the use of impermeable packaging for storage,ensure the conservation of grain mass.The different stages and equipments used to obtain polished,brown and parboiled rice result in significant changes in the nutritional value of rice because of the removal of the outermost layers of the grains.Polishing reduces the nutritional value and physical homogeneity of rice.Brown rice retains more bioactive compounds and nutrients because it does not lose the outer layer of the grains in the polishing processes.Parboiled rice,although less nutritious than brown rice,has better grain integrity and milling yield and less loss of nutrients than white rice.展开更多
Aiming at the problem of coal gangue identifcation in the current fully mechanized mining face and coal washing,this article proposed a convolution neural network(CNN)coal and rock identifcation method based on hypers...Aiming at the problem of coal gangue identifcation in the current fully mechanized mining face and coal washing,this article proposed a convolution neural network(CNN)coal and rock identifcation method based on hyperspectral data.First,coal and rock spectrum data were collected by a near-infrared spectrometer,and then four methods were used to flter 120 sets of collected data:frst-order diferential(FD),second-order diferential(SD),standard normal variable transformation(SNV),and multi-style smoothing.The coal and rock refectance spectrum data were pre-processed to enhance the intensity of spectral refectance and absorption characteristics,as well as efectively remove the spectral curve noise generated by instrument performance and environmental factors.A CNN model was constructed,and its advantages and disadvantages were judged based on the accuracy of the three parameter combinations(i.e.,the learning rate,the number of feature extraction layers,and the dropout rate)to generate the best CNN classifer for the hyperspectral data for rock recognition.The experiments show that the recognition accuracy of the one-dimensional CNN model proposed in this paper reaches 94.6%.Verifcation of the advantages and efectiveness of the method were proposed in this article.展开更多
The aim of this study was to model and validate a new concept of a silo-dryer-aerator for the drying of soybean seeds and determine the quality of the seeds in function of the air temperatures in the drying. Soybeans ...The aim of this study was to model and validate a new concept of a silo-dryer-aerator for the drying of soybean seeds and determine the quality of the seeds in function of the air temperatures in the drying. Soybeans with water contents of 17%(w.b.) were dried and stored in a silo-dryer-aerator system that was designed with a drying chamber and four independent storage cells in the air drying temperatures at 30, 40 and 50℃ in silo-dryer-aerator. The drying in the air temperature at 30℃ in the cell C1 the diffusion approximation model was the one that best fit the data, in the cell C2 the Newton model prevailed and in the cells C3 and C4 the Midilli model. In the drying with air temperature of 40℃ in the cell C1 the Page model was the one that better adjusted the data, whereas in the cell C2 the model of diffusion approximation determined the best fit, while in the cells C3 and C4 the Page model obtained better fit. In the drying with air temperature of 50℃ in the cells C1, C2, C3 and C4 the logarithm model was the one that best represented the fit of the data. The increase in the drying air temperature to 50 °C decreased the quality of soybeans seeds. In the upper and middle part of the cells there was an increase in electrical conductivity (140.02 μS/cm/g) and germination (53%) compared with the lower dryer position.展开更多
DSP operation in a Biomedical related therapeutic hardware need to beperformed with high accuracy and with high speed. Portable DSP hardware’s likepulse/heart beat detectors must perform with reduced operational powe...DSP operation in a Biomedical related therapeutic hardware need to beperformed with high accuracy and with high speed. Portable DSP hardware’s likepulse/heart beat detectors must perform with reduced operational power due to lack ofconventional power sources. This work proposes a hybrid biomedical hardware chip inwhich the speed and power utilization factors are greatly improved. Multipliers are thecore operational unit of any DSP SoC. This work proposes a LUT based unsignedmultiplication which is proven to be efficient in terms of high operating speed. For n bitinput multiplication n*n memory array of 2 n bit size is required to memorize all thepossible input and output combination. Various literature works claims to be achieve highspeed multiplication with reduced LUT size by integrating a barrel shifter mechanism.This paper work address this problem, by reworking the multiplier architecture with aparallel operating pre-processing unit which used to change the multiplier and multiplicandorder with respect to the number of computational addition and subtraction stages required.Along with LUT multiplier a low power bus encoding scheme is integrated to limit the powerconstraint of the on chip DSP unit. This paper address both the speed and power optimizationtechniques and tested with various FPGA device families.展开更多
In early December 2019,the city of Wuhan,China,reported an outbreak of coronavirus disease(COVID-19),caused by a novel severe acute respiratory syndrome coronavirus-2(SARS-CoV-2).On January 30,2020,the World Health Or...In early December 2019,the city of Wuhan,China,reported an outbreak of coronavirus disease(COVID-19),caused by a novel severe acute respiratory syndrome coronavirus-2(SARS-CoV-2).On January 30,2020,the World Health Organization(WHO)declared the outbreak a global pandemic crisis.In the face of the COVID-19 pandemic,the most important step has been the effective diagnosis and monitoring of infected patients.Identifying COVID-19 using Machine Learning(ML)technologies can help the health care unit through assistive diagnostic suggestions,which can reduce the health unit's burden to a certain extent.This paper investigates the possibilities of ML techniques in identifying/detecting COVID-19 patients including both conventional and exploring from chest X-ray images the effect of viral infection.This approach includes preprocessing,feature extraction,and classification.However,the features are extracted using the Histogram of Oriented(HOG)and Local Binary Pattern(LBP)feature descriptors.Furthermore,for the extracted features classification,six ML models of Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)is used.Experimental results show that the diagnostic accuracy of random forest classifier(RFC)on extracted HOG plusLBP features is as high as 94%followed by SVM at 93%.The sensitivity of the K-nearest neighbour model has reached an accuracy of 88%.Overall,the predicted approach has shown higher classification accuracy and effective diagnostic performance.It is a highly useful tool for clinical practitioners and radiologists to help them in diagnosing and tracking the cases of COVID-19.展开更多
Computational fluid dynamics(CFD)has become the main method for the prediction of the properties of the external wind environment in cities and other urban contexts.A review is presented of the existing literature in ...Computational fluid dynamics(CFD)has become the main method for the prediction of the properties of the external wind environment in cities and other urban contexts.A review is presented of the existing literature in terms of boundary conditions,building models,computational domains,computational grids,and turbulence models.Some specific issues,such as the accuracy/computational cost ratio and the exploitation of existing empirical correlations,are also examined.展开更多
Fingerprints are an extraordinary source for recognizable proof of people. Unique finger impression acknowledgment is one of the most seasoned types of biometric identification. However, getting a decent unique finger...Fingerprints are an extraordinary source for recognizable proof of people. Unique finger impression acknowledgment is one of the most seasoned types of biometric identification. However, getting a decent unique finger impression picture isn’t that simple. So we must process unique finger impression picture before coordinating. A crucial advance in measurements of fingerprint minutiae is to obtain minutiae from the finger impression pictures dependably. However, fingerprint images are occasionally of perfect quality. They might be debased and defiled because of varieties in skin and impression conditions. Along these lines, image enhancement strategies utilize other details extraction to acquire a more reliable estimation of minutiae areas. The primary objective of this research work is to introduce a superior and improved unique fingerprint image. We studied the elements identifying with getting elite component focuses detection algorithm, for example, picture quality, segmentation, picture upgrade and highlight recognition. Usually utilized features for enhancing unique finger impression picture quality are Fourier spectrum energy, Sobel filter energy, and local orientation. Precise segmentation of unique finger impression edges from a broad foundation is vital. For productive improvement and feature extraction algorithms, we zero the commotion in segmented features. As a pre-processing method, we need to perform comprising of field introduction, ridge frequency estimation, Sobel filtering, division. Then connect the resulting picture to a thinning algorithm and consequent minutiae extraction. After resultant extraction of these minutiae focuses, we will utilize the picture with focuses for coordinating or finding the offenders and also for other security issues. The procedure of image pre-processing and minutiae extraction is explored. The simulations are performed in the MATLAB environment to assess the execution of the implemented algorithm.展开更多
基金supported by the National Key Technology R&D Program of China under Grant No. 2015BAK34B00the National Key Research and Development Program of China under Grant No. 2016YFB1000102
文摘Regular expression matching is playing an important role in deep inspection. The rapid development of SDN and NFV makes the network more dynamic, bringing serious challenges to traditional deep inspection matching engines. However, state-of-theart matching methods often require a significant amount of pre-processing time and hence are not suitable for this fast updating scenario. In this paper, a novel matching engine called BFA is proposed to achieve high-speed regular expression matching with fast pre-processing. Experiments demonstrate that BFA obtains 5 to 20 times more update abilities compared to existing regular expression matching methods, and scales well on multi-core platforms.
文摘Microarray data is inherently noisy due to the noise contaminated from various sources during the preparation of microarray slide and thus it greatly affects the accuracy of the gene expression. How to eliminate the effect of the noise constitutes a challenging problem in microarray analysis. Efficient denoising is often a necessary and the first step to be taken before the image data is analyzed to compensate for data corruption and for effective utilization for these data. Hence preprocessing of microarray image is an essential to eliminate the background noise in order to enhance the image quality and effective quantification. Existing denoising techniques based on transformed domain have been utilized for microarray noise reduction with their own limitations. The objective of this paper is to introduce novel preprocessing techniques such as optimized spatial resolution (OSR) and spatial domain filtering (SDF) for reduction of noise from microarray data and reduction of error during quantification process for estimating the microarray spots accurately to determine expression level of genes. Besides combined optimized spatial resolution and spatial filtering is proposed and found improved denoising of microarray data with effective quantification of spots. The proposed method has been validated in microarray images of gene expression profiles of Myeloid Leukemia using Stanford Microarray Database with various quality measures such as signal to noise ratio, peak signal to noise ratio, image fidelity, structural content, absolute average difference and correlation quality. It was observed by quantitative analysis that the proposed technique is more efficient for denoising the microarray image which enables to make it suitable for effective quantification.
基金supported by the National Natural Science Foundation of China(Nos.62003115 and 11972130)the Shenzhen Science and Technology Program,China(JCYJ20220818102207015)the Heilongjiang Touyan Team Program,China。
文摘The Low Earth Orbit(LEO)remote sensing satellite mega-constellation has the characteristics of large quantity and various types which make it have unique superiority in the realization of concurrent multiple tasks.However,the complexity of resource allocation is increased because of the large number of tasks and satellites.Therefore,the primary problem of implementing concurrent multiple tasks via LEO mega-constellation is to pre-process tasks and observation re-sources.To address the challenge,we propose a pre-processing algorithm for the mega-constellation based on highly Dynamic Spatio-Temporal Grids(DSTG).In the first stage,this paper describes the management model of mega-constellation and the multiple tasks.Then,the coding method of DSTG is proposed,based on which the description of complex mega-constellation observation resources is realized.In the third part,the DSTG algorithm is used to realize the processing of concurrent multiple tasks at multiple levels,such as task space attribute,time attribute and grid task importance evaluation.Finally,the simulation result of the proposed method in the case of constellation has been given to verify the effectiveness of concurrent multi-task pre-processing based on DSTG.The autonomous processing process of task decomposition and task fusion and mapping to grids,and the convenient indexing process of time window are verified.
基金This paper’s logical organisation and content quality have been enhanced,so the authors thank anonymous reviewers and journal editors for assistance.
文摘Forecasting river flow is crucial for optimal planning,management,and sustainability using freshwater resources.Many machine learning(ML)approaches have been enhanced to improve streamflow prediction.Hybrid techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone approaches.Current researchers have also emphasised using hybrid models to improve forecast accuracy.Accordingly,this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years,summarising data preprocessing,univariate machine learning modelling strategy,advantages and disadvantages of standalone ML techniques,hybrid models,and performance metrics.This study focuses on two types of hybrid models:parameter optimisation-based hybrid models(OBH)and hybridisation of parameter optimisation-based and preprocessing-based hybridmodels(HOPH).Overall,this research supports the idea thatmeta-heuristic approaches precisely improveML techniques.It’s also one of the first efforts to comprehensively examine the efficiency of various meta-heuristic approaches(classified into four primary classes)hybridised with ML techniques.This study revealed that previous research applied swarm,evolutionary,physics,and hybrid metaheuristics with 77%,61%,12%,and 12%,respectively.Finally,there is still room for improving OBH and HOPH models by examining different data pre-processing techniques and metaheuristic algorithms.
基金supported by the National Science and Technology Innovation 2030 Next-Generation Artifical Intelligence Major Project(2018AAA0101801)the National Natural Science Foundation of China(72271188)。
文摘With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.
文摘This research concentrates to model an efficient thyroid prediction approach,which is considered a baseline for significant problems faced by the women community.The major research problem is the lack of automated model to attain earlier prediction.Some existing model fails to give better prediction accuracy.Here,a novel clinical decision support system is framed to make the proper decision during a time of complexity.Multiple stages are followed in the proposed framework,which plays a substantial role in thyroid prediction.These steps include i)data acquisition,ii)outlier prediction,and iii)multi-stage weight-based ensemble learning process(MS-WEL).The weighted analysis of the base classifier and other classifier models helps bridge the gap encountered in one single classifier model.Various classifiers aremerged to handle the issues identified in others and intend to enhance the prediction rate.The proposed model provides superior outcomes and gives good quality prediction rate.The simulation is done in the MATLAB 2020a environment and establishes a better trade-off than various existing approaches.The model gives a prediction accuracy of 97.28%accuracy compared to other models and shows a better trade than others.
基金The author would like to express their gratitude to the Ministry of Education and the Deanship of Scientific Research-Najran University-Kingdom of Saudi Arabia for their financial and technical support under code number:NU/NRP/SERC/11/3.
文摘Biomedical image processing acts as an essential part of severalmedical applications in supporting computer aided disease diagnosis. MagneticResonance Image (MRI) is a commonly utilized imaging tool used tosave glioma for clinical examination. Biomedical image segmentation plays avital role in healthcare decision making process which also helps to identifythe affected regions in the MRI. Though numerous segmentation models areavailable in the literature, it is still needed to develop effective segmentationmodels for BT. This study develops a salp swarm algorithm with multi-levelthresholding based brain tumor segmentation (SSAMLT-BTS) model. Thepresented SSAMLT-BTS model initially employs bilateral filtering based onnoise removal and skull stripping as a pre-processing phase. In addition,Otsu thresholding approach is applied to segment the biomedical imagesand the optimum threshold values are chosen by the use of SSA. Finally,active contour (AC) technique is used to identify the suspicious regions in themedical image. A comprehensive experimental analysis of the SSAMLT-BTSmodel is performed using benchmark dataset and the outcomes are inspectedin many aspects. The simulation outcomes reported the improved outcomesof the SSAMLT-BTS model over recent approaches with maximum accuracyof 95.95%.
文摘The aviation industry is one of the most competitive markets. Themost common approach for airline service providers is to improve passengersatisfaction. Passenger satisfaction in the aviation industry occurs whenpassengers’ expectations are met during flights. Airline service quality iscritical in attracting new passengers and retaining existing ones. It is crucialto identify passengers’ pain points and enhance their satisfaction with theservices offered. The airlines used a variety of techniques to improve servicequality. They used data analysis approaches to analyze the passenger pointdata. These solutions have focused simply on surveys;consequently, deeplearningapproaches have received insufficient attention. In this study, deepneural networks with the adaptive moment estimation Adam optimizationalgorithm were applied to enhance classification performance. In previousstudies, the quality of the dataset has been ignored. The proposed approachwas applied to the airline passenger satisfaction dataset from the Kagglerepository. It was validated by applying artificial neural networks (ANNs),random forests, and support vector machine techniques to the same dataset. Itwas compared with other research papers that used the same dataset and had asimilar problem. The experimental results showed that the proposed approachoutperformed previous studies. It has achieved an accuracy of 99.3%.
基金This research project was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R234),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘ESystems based on EHRs(Electronic health records)have been in use for many years and their amplified realizations have been felt recently.They still have been pioneering collections of massive volumes of health data.Duplicate detections involve discovering records referring to the same practical components,indicating tasks,which are generally dependent on several input parameters that experts yield.Record linkage specifies the issue of finding identical records across various data sources.The similarity existing between two records is characterized based on domain-based similarity functions over different features.De-duplication of one dataset or the linkage of multiple data sets has become a highly significant operation in the data processing stages of different data mining programmes.The objective is to match all the records associated with the same entity.Various measures have been in use for representing the quality and complexity about data linkage algorithms,and many other novel metrics have been introduced.An outline of the problem existing in themeasurement of data linkage and de-duplication quality and complexity is presented.This article focuses on the reprocessing of health data that is horizontally divided among data custodians,with the purpose of custodians giving similar features to sets of patients.The first step in this technique is about an automatic selection of training examples with superior quality from the compared record pairs and the second step involves training the reciprocal neuro-fuzzy inference system(RANFIS)classifier.Using the Optimal Threshold classifier,it is presumed that there is information about the original match status for all compared record pairs(i.e.,Ant Lion Optimization),and therefore an optimal threshold can be computed based on the respective RANFIS.Febrl,Clinical Decision(CD),and Cork Open Research Archive(CORA)data repository help analyze the proposed method with evaluated benchmarks with current techniques.
文摘In recent years,Peripheral blood smear is a generic analysis to assess the person’s health status.Manual testing of Peripheral blood smear images are difficult,time-consuming and is subject to human intervention and visual error.This method encouraged for researchers to present algorithms and techniques to perform the peripheral blood smear analysis with the help of computer-assisted and decision-making techniques.Existing CAD based methods are lacks in attaining the accurate detection of abnormalities present in the images.In order to mitigate this issue Deep Convolution Neural Network(DCNN)based automatic classification technique is introduced with the classification of eight groups of peripheral blood cells such as basophil,eosinophil,lymphocyte,monocyte,neutrophil,erythroblast,platelet,myocyte,promyocyte and metamyocyte.The proposed DCNN model employs transfer learning approach and additionally it carries three stages such as pre-processing,feature extraction and classification.Initially the pre-processing steps are incorporated to eliminate noisy contents present in the image by using Histogram Equalization(HE).It is enclosed to improve an image contrast.In order to distinguish the dissimilar class and segmentation approach is carried out with the help of Fuzzy C-Means(FCM)model whereas its centroid point optimality method with Slap Swarm based optimization strategy.Moreover some specific set of Gray Level Co-occurrence Matrix(GLCM)features of the segmented images are extracted to augment the performance of proposed detection algorithm.Finally the extracted features are recorded by DCNN and the proposed classifier has the capability to extract their own features.Based on this the diverse set of classes are classified and distinguished from qualitative abnormalities found in the image.
文摘Software is unavoidable in software development and maintenance.In literature,many methods are discussed which fails to achieve efficient software bug detection and classification.In this paper,efficient Adaptive Deep Learning Model(ADLM)is developed for automatic duplicate bug report detection and classification process.The proposed ADLM is a combination of Conditional Random Fields decoding with Long Short-Term Memory(CRF-LSTM)and Dingo Optimizer(DO).In the CRF,the DO can be consumed to choose the efficient weight value in network.The proposed automatic bug report detection is proceeding with three stages like pre-processing,feature extraction in addition bug detection with classification.Initially,the bug report input dataset is gathered from the online source system.In the pre-processing phase,the unwanted information from the input data are removed by using cleaning text,convert data types and null value replacement.The pre-processed data is sent into the feature extraction phase.In the feature extraction phase,the four types of feature extraction method are utilized such as contextual,categorical,temporal and textual.Finally,the features are sent to the proposed ADLM for automatic duplication bug report detection and classification.The proposed methodology is proceeding with two phases such as training and testing phases.Based on the working process,the bugs are detected and classified from the input data.The projected technique is assessed by analyzing performance metrics such as accuracy,precision,Recall,F_Measure and kappa.
文摘Emotion represents the feeling of an individual in a given situation. There are various ways to express the emotions of an individual. It can be categorized into verbal expressions, written expressions, facial expressions and gestures. Among these various ways of expressing the emotion, the written method is a challenging task to extract the emotions, as the data is in the form of textual dat. Finding the different kinds of emotions is also a tedious task as it requires a lot of pre preparations of the textual data taken for the research. This research work is carried out to analyse and extract the emotions hidden in text data. The text data taken for the analysis is from the social media dataset. Using the raw text data directly from the social media will not serve the purpose. Therefore, the text data has to be pre-processed and then utilised for further processing. Pre-processing makes the text data more efficient and would infer valuable insights of the emotions hidden in it. The preprocessing steps also help to manage the text data for identifying the emotions conveyed in the text. This work proposes to deduct the emotions taken from the social media text data by applying the machine learning algorithm. Finally, the usefulness of the emotions is suggested for various stake holders, to find the attitude of individuals at that moment, the data is produced. .
基金funded by the German Federal Ministry for Economic Affairs and Energy(BMWi)(01MD15009F).
文摘Liquid leakage from pipelines is a critical issue in large-scale process plants.Damage in pipelines affects the normal operation of the plant and increases maintenance costs.Furthermore,it causes unsafe and hazardous situations for operators.Therefore,the detection and localization of leakages is a crucial task for maintenance and condition monitoring.Recently,the use of infrared(IR)cameras was found to be a promising approach for leakage detection in large-scale plants.IR cameras can capture leaking liquid if it has a higher(or lower)temperature than its surroundings.In this paper,a method based on IR video data and machine vision techniques is proposed to detect and localize liquid leakages in a chemical process plant.Since the proposed method is a vision-based method and does not consider the physical properties of the leaking liquid,it is applicable for any type of liquid leakage(i.e.,water,oil,etc.).In this method,subsequent frames are subtracted and divided into blocks.Then,principle component analysis is performed in each block to extract features from the blocks.All subtracted frames within the blocks are individually transferred to feature vectors,which are used as a basis for classifying the blocks.The k-nearest neighbor algorithm is used to classify the blocks as normal(without leakage)or anomalous(with leakage).Finally,the positions of the leakages are determined in each anomalous block.In order to evaluate the approach,two datasets with two different formats,consisting of video footage of a laboratory demonstrator plant captured by an IR camera,are considered.The results show that the proposed method is a promising approach to detect and localize leakages from pipelines using IR videos.The proposed method has high accuracy and a reasonable detection time for leakage detection.The possibility of extending the proposed method to a real industrial plant and the limitations of this method are discussed at the end.
基金CAPES(Coordination for the Improvement of Higher Education Personnel)(Financial Code 001)CNPq(National Council for Scientific Technological Development)+1 种基金FAPERGS-RS(Research Support Foundation of the State of Rio Grande do Sul)UFSM(Federal University of Santa Maria)-Research Group at Postharvest Innovation:Technology,Quality and Sustainability,for their financial contributions。
文摘Various post-harvest processes of rice are commonly employed,especially during the off-season,to ensure its consumption feasibility,which often affect the grain quality.Different forms of drying,storage and processing of rice are evaluated to identify their effects on grain quality.Microwave drying has emerged as an alternative to the widely-used intermittent-drying and fixed-bed-dryer methods of drying paddy rice.Control of drying-air temperatures(between 40℃ and 60℃)according to the rice variety can improve quality,especially for exotic varieties.Keeping stored grain in hygroscopic balance,with water content between 11%to 15%,at temperatures between 16℃ and 20℃ and with intergranular relative humidity near 60%,allows 12 months of storage in a controlled environment without significant deterioration.Other innovations,notably the application of artificial refrigeration to grain stored in bulk in vertical cylindrical silos and the use of impermeable packaging for storage,ensure the conservation of grain mass.The different stages and equipments used to obtain polished,brown and parboiled rice result in significant changes in the nutritional value of rice because of the removal of the outermost layers of the grains.Polishing reduces the nutritional value and physical homogeneity of rice.Brown rice retains more bioactive compounds and nutrients because it does not lose the outer layer of the grains in the polishing processes.Parboiled rice,although less nutritious than brown rice,has better grain integrity and milling yield and less loss of nutrients than white rice.
基金supported by the Theory and Method of Excavation-Support-Anchor Parallel Control for Intelligent Excavation Complex System(2021101030125)Green,intelligent,and safe mining of coal resources(52121003)the Mining Robotics Engineering Discipline Innovation and Intelligence Base(B21014).
文摘Aiming at the problem of coal gangue identifcation in the current fully mechanized mining face and coal washing,this article proposed a convolution neural network(CNN)coal and rock identifcation method based on hyperspectral data.First,coal and rock spectrum data were collected by a near-infrared spectrometer,and then four methods were used to flter 120 sets of collected data:frst-order diferential(FD),second-order diferential(SD),standard normal variable transformation(SNV),and multi-style smoothing.The coal and rock refectance spectrum data were pre-processed to enhance the intensity of spectral refectance and absorption characteristics,as well as efectively remove the spectral curve noise generated by instrument performance and environmental factors.A CNN model was constructed,and its advantages and disadvantages were judged based on the accuracy of the three parameter combinations(i.e.,the learning rate,the number of feature extraction layers,and the dropout rate)to generate the best CNN classifer for the hyperspectral data for rock recognition.The experiments show that the recognition accuracy of the one-dimensional CNN model proposed in this paper reaches 94.6%.Verifcation of the advantages and efectiveness of the method were proposed in this article.
文摘The aim of this study was to model and validate a new concept of a silo-dryer-aerator for the drying of soybean seeds and determine the quality of the seeds in function of the air temperatures in the drying. Soybeans with water contents of 17%(w.b.) were dried and stored in a silo-dryer-aerator system that was designed with a drying chamber and four independent storage cells in the air drying temperatures at 30, 40 and 50℃ in silo-dryer-aerator. The drying in the air temperature at 30℃ in the cell C1 the diffusion approximation model was the one that best fit the data, in the cell C2 the Newton model prevailed and in the cells C3 and C4 the Midilli model. In the drying with air temperature of 40℃ in the cell C1 the Page model was the one that better adjusted the data, whereas in the cell C2 the model of diffusion approximation determined the best fit, while in the cells C3 and C4 the Page model obtained better fit. In the drying with air temperature of 50℃ in the cells C1, C2, C3 and C4 the logarithm model was the one that best represented the fit of the data. The increase in the drying air temperature to 50 °C decreased the quality of soybeans seeds. In the upper and middle part of the cells there was an increase in electrical conductivity (140.02 μS/cm/g) and germination (53%) compared with the lower dryer position.
文摘DSP operation in a Biomedical related therapeutic hardware need to beperformed with high accuracy and with high speed. Portable DSP hardware’s likepulse/heart beat detectors must perform with reduced operational power due to lack ofconventional power sources. This work proposes a hybrid biomedical hardware chip inwhich the speed and power utilization factors are greatly improved. Multipliers are thecore operational unit of any DSP SoC. This work proposes a LUT based unsignedmultiplication which is proven to be efficient in terms of high operating speed. For n bitinput multiplication n*n memory array of 2 n bit size is required to memorize all thepossible input and output combination. Various literature works claims to be achieve highspeed multiplication with reduced LUT size by integrating a barrel shifter mechanism.This paper work address this problem, by reworking the multiplier architecture with aparallel operating pre-processing unit which used to change the multiplier and multiplicandorder with respect to the number of computational addition and subtraction stages required.Along with LUT multiplier a low power bus encoding scheme is integrated to limit the powerconstraint of the on chip DSP unit. This paper address both the speed and power optimizationtechniques and tested with various FPGA device families.
基金supported by the Information Technology Department,College of Computer,Qassim University,6633,Buraidah 51452,Saudi Arabia.
文摘In early December 2019,the city of Wuhan,China,reported an outbreak of coronavirus disease(COVID-19),caused by a novel severe acute respiratory syndrome coronavirus-2(SARS-CoV-2).On January 30,2020,the World Health Organization(WHO)declared the outbreak a global pandemic crisis.In the face of the COVID-19 pandemic,the most important step has been the effective diagnosis and monitoring of infected patients.Identifying COVID-19 using Machine Learning(ML)technologies can help the health care unit through assistive diagnostic suggestions,which can reduce the health unit's burden to a certain extent.This paper investigates the possibilities of ML techniques in identifying/detecting COVID-19 patients including both conventional and exploring from chest X-ray images the effect of viral infection.This approach includes preprocessing,feature extraction,and classification.However,the features are extracted using the Histogram of Oriented(HOG)and Local Binary Pattern(LBP)feature descriptors.Furthermore,for the extracted features classification,six ML models of Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)is used.Experimental results show that the diagnostic accuracy of random forest classifier(RFC)on extracted HOG plusLBP features is as high as 94%followed by SVM at 93%.The sensitivity of the K-nearest neighbour model has reached an accuracy of 88%.Overall,the predicted approach has shown higher classification accuracy and effective diagnostic performance.It is a highly useful tool for clinical practitioners and radiologists to help them in diagnosing and tracking the cases of COVID-19.
基金This work was supported by the National Natural Science Foundation of China(No.51976139).
文摘Computational fluid dynamics(CFD)has become the main method for the prediction of the properties of the external wind environment in cities and other urban contexts.A review is presented of the existing literature in terms of boundary conditions,building models,computational domains,computational grids,and turbulence models.Some specific issues,such as the accuracy/computational cost ratio and the exploitation of existing empirical correlations,are also examined.
文摘Fingerprints are an extraordinary source for recognizable proof of people. Unique finger impression acknowledgment is one of the most seasoned types of biometric identification. However, getting a decent unique finger impression picture isn’t that simple. So we must process unique finger impression picture before coordinating. A crucial advance in measurements of fingerprint minutiae is to obtain minutiae from the finger impression pictures dependably. However, fingerprint images are occasionally of perfect quality. They might be debased and defiled because of varieties in skin and impression conditions. Along these lines, image enhancement strategies utilize other details extraction to acquire a more reliable estimation of minutiae areas. The primary objective of this research work is to introduce a superior and improved unique fingerprint image. We studied the elements identifying with getting elite component focuses detection algorithm, for example, picture quality, segmentation, picture upgrade and highlight recognition. Usually utilized features for enhancing unique finger impression picture quality are Fourier spectrum energy, Sobel filter energy, and local orientation. Precise segmentation of unique finger impression edges from a broad foundation is vital. For productive improvement and feature extraction algorithms, we zero the commotion in segmented features. As a pre-processing method, we need to perform comprising of field introduction, ridge frequency estimation, Sobel filtering, division. Then connect the resulting picture to a thinning algorithm and consequent minutiae extraction. After resultant extraction of these minutiae focuses, we will utilize the picture with focuses for coordinating or finding the offenders and also for other security issues. The procedure of image pre-processing and minutiae extraction is explored. The simulations are performed in the MATLAB environment to assess the execution of the implemented algorithm.
