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1999 146 Computer-aided Diagnosis Applied to US of Solid Breast Nodules by Using Neural Networks

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Computer ApplicationsComputer-aided Diagnosis Applied to US of Solid Breast Nodules by Using Neural Networks1PURPOSE:To increase the capabilities of ultrasonographic(US)technology for the differential diagnosis of solid breast tumors by using a neural network.MATERIALS AND METHODS:One hundred forty US images of solid breast nodules were evaluated.When a sonogram was obtained,an analog video signal from the VCR output of the scanner was transmitted to a notebook computer.A frame grabber connected to the printer port of the computer was then used to digitize the data.The suspicious tumor region on the digitized US image was manually selected.The texture information of the subimage was extracted,and a neural network classifier with autocorrelation features was used to classify the tumor as benign or malignant.In this experiment,140pathologically proved tumors(52malignant and 88benign tumors)were sampled with k-fold cross-validation(kϭ10)to evaluate the performance with receiver operating characteristic curves.RESULTS:The accuracy of neural networks for classifying malignancies was95.0% (133of140tumors),the sensitivity was98%(51of52),the specificity was93%(82 of88),the positive predictive value was89%(51of57),and the negative predictive value was99%(82of83).CONCLUSION:This system differentiated solid breast nodules with relatively high accuracy and helped inexperienced operators to avoid misdiagnoses.Because the neural network is trainable,it could be optimized if a larger set of tumor images is supplied.Breast cancer ranks first in the causes of cancer deaths among women in developed countries and is second to cervical cancer in developing countries(1).The best way to reduce deaths due to breast cancer is to treat the disease at an earlier stage.Earlier treatment requires early diagnosis,and early diagnosis requires an accurate and reliable diagnostic procedure that allows physicians to differentiate benign breast tumors from malignant ones.Current procedures for detecting and diagnosing breast cancers,however,illustrate the difficulty in maximizing both sensitivity and specificity.Although the smaller hospitals use conventional ultrasonographic(US)equipment to obtain medical images quickly,estimates of the accuracy of US diagnostic methods are controversial,and the role of US in current practice is not yet defined(2,3).One of the important reasons for this controversy is the considerable overlap of benign and malignant findings on US images;interpretation is subjective and depends on the operator.The US examination described by Stavros et al(4)is much more extensive than the usual examinations performed at most breast imaging centers.In their study,Stavros et al(4) found that the sensitivity of breast US for malignancy was98.4%(123of125findings),the specificity was67.8%(424of625),the overall accuracy was72.9%(547of750),the positive predictive value was38.0%(123of324),and the negative predictive value was99.5%(424 of426).However,these improved diagnostic results were achieved by experienced radiologists.In practice,many invasive diagnostic procedures are still required in most cases.Most of these procedures are avoidable because the rate of positive findings at biopsy for cancer is low—between10%and31%(5–7).To overcome the aforementioned407shortcomings of US and to fully expand the capability of US,only an efficient computerized model offers objective evi-dence and avoids interobserver varia-tions;therefore,an optimal and stable high diagnostic rate can be achieved by using such a model.Both mammography(8–11)and US (12)can be used to detect and classify breast tumors.Although mammography can be used to visualize nonpalpable and small tumors,US is a convenient and safe tool to use in the classification of tumors, especially palpable tumors.Garra et al (13)suggest that analysis of US image texture is a simple means of markedly reducing the number of biopsies per-formed for benign lesions.Garra et al(13) used a co-occurrence matrix and the lin-ear Bayesian classifier.Neural network techniques have been applied to detect microcalcifications and have even been applied to distinguish benign and malig-nant microcalcifications on digital mam-mographic images(9–11).Moreover, Choong et al(14)used neural networks to make prognoses for women with breast cancer.The multilayer feed-forward neural net-work can be used to extract higher-order statistics by adding one or more hidden layers.This model has become extremely popular in terms of classification and prediction.The powerful error back-propagation algorithm proposed by Rumelhart el al(15)and Hirose et al(16) is the most widely used algorithm for multilayer feed-forward neural networks. We used this neural network model as a classifier to determine whether the breast tumors were benign or malignant.The diagnostic model proposed herein can exploit the nonlinear property of the learning algorithm of the neural network to classify the US images of the breast more accurately.The multilayer feed-forward neural network is a reliable choice for the proposed model because it trains well and computes efficiently. MATERIALS AND METHODSIn general,a physician can easily identify a tumor on a US image by the shape of the tumor and by the contrast of internal echoes,but the computer cannot easily segment a tumor from a digitized image. However,if a physician has already iden-tified the tumor,a computer can be used to diagnose the tumor with information about intensity variation and texture on the image.In this study,a physician (D.R.C.)first extracted the subimage of the region of interest,and then the com-puter was used to analyze the subimageby using the information about intensityvariation and texture to make a differen-tial diagnosis.Data AcquisitionThe US image database contained140images of pathologically proved tumors:benign breast tumors from88patientsand carcinomas from52patients(tumorFigure1.A,Transverse digital US image depicts a malignant tumor,with a resolution of736ϫ556pixels.Note that there are58ϫ58ϭ3,364pixels in a1ϫ1-cm rectangle.B,The region-of-interest rectangle is approximately3.1ϫ1.5cm and is captured with a resolution of 179ϫ88pixels.ROIϭregion ofinterest.a. b.Figure 2.Typical transverse US region-of-interest subimages in the breast depict(a)abenign lesion(arrows)and(b)a malignantlesion(arrows).TABLE1Mean,SD,and Mean Difference between Autocorrelation Coefficients for Malignant and Benign TumorsAutocorrelationAutocorrelation Coefficientsfor Malignant TumorsAutocorrelation Coefficientsfor Benign TumorsMeanDifferenceMean SD Mean SDAЈ(1,0)0.9660.0120.9580.0160.008 AЈ(2,0)0.9160.0270.9010.0330.015 AЈ(3,0)0.8750.0380.8570.0430.018 AЈ(4,0)0.8480.0450.8270.0500.021 AЈ(0,1)0.9150.0210.8610.0370.054 AЈ(1,1)0.8960.0270.8390.0400.057 AЈ(2,1)0.8650.0360.8050.0450.060 AЈ(3,1)0.8380.0420.7760.0490.062 AЈ(4,1)0.8180.0470.7550.0520.064 AЈ(0,2)0.8180.0390.7280.0570.091 AЈ(1,2)0.8120.0410.7200.0560.092 AЈ(2,2)0.8020.0440.7090.0560.093 AЈ(3,2)0.7920.0470.6980.0560.094 AЈ(4,2)0.7820.0500.6870.0560.095 AЈ(0,3)0.7690.0440.6560.0620.113 AЈ(1,3)0.7660.0450.6510.0600.115 AЈ(2,3)0.7620.0460.6460.0580.117 AЈ(3,3)0.7570.0480.6400.0570.118 AЈ(4,3)0.7520.0500.6340.0570.118 AЈ(0,4)0.7490.0450.6210.0620.128 AЈ(1,4)0.7460.0450.6160.0600.130 AЈ(2,4)0.7410.0470.6100.0580.131 AЈ(3,4)0.7370.0480.6050.0570.132 AЈ(4,4)0.7320.0490.6000.0560.132408•Radiology•November1999Chen et alsize,Ͼ1cm in all patients).The database contained only one image from each patient.The US images that depicted the largest diameter of the tumor were cap-tured.The images were collected from January 1,1997,to May 31,1998,and the patients’ages ranged from 18to was performed by using an Aloka SSD 1200scanner (Tokyo,Japan)and a 7.5-MHz linear transducer with freeze-frame capability.No acoustic standoff pad was used with any of the patients.The US gain setting remained unchanged throughout the entire study,except for changes made to obtain the best view.The following description explains how digital US images were obtained.When a sonogram was obtained,an analog video signal was transmitted from the VCR output of the scanner to a notebook computer.The data were then digitized by means of a Video CATcher frame grab-ber (Top Solution Technology;Taipei,Tai-wan,Republic of China)that was con-nected to the printer port of the computer.The capturing resolutions of the portable computer and the external frame grabber was 736ϫ566pixels for a National Television Systems Committee video-screen picture.We used the ProImagesoftware package from Prolab (version 2.0;Taipei,Taiwan,Republic of China),which was bundled with the frame grab-ber,to capture the real-time digital image.The monochrome US image was quan-tized to 8bits (ie,256gray levels).The subimage of the region of interest was manually selected by using the ProImage (Prolab)package.That is,the software package was used to capture the full im-age from the US scanner and to select the region-of-interest subimage manually.The region-of-interest subimage was then saved as a file for later analysis with the neural network program.Figure 1illus-trates a real-time digitized monochrome US image of a malignant tumor,with 58ϫ58pixels in a 1ϫ1-cm rectangle (Fig 1,A )and an image of a tumor (approxi-mately 3.1ϫ1.5cm)that was captured with a resolution of 179ϫ88pixels (Fig 1,B ).Image AnalysisA US image consists of many points with different values of gray-level inten-sity.Different tissues have markedly differ-ent textures.Benign tumors are classi-cally described as regular masses with homogeneous internal echoes,but carci-nomas are described as masses with fuzzy borders and heterogeneous internal ech-oes.Figure 2illustrates digital images of a benign tumor and a malignant tumor.We used the correlation between neigh-boring pixels on the images as classifying features of the tumor.The normalized autocorrelation coefficients (17)can be used to reflect the interpixel correlation on an image.The two-dimensional nor-malized autocorrelation coefficient ␥be-tween pixel (i ,j )and pixel (i ϩ⌬m ,j ϩ⌬n )on an image with size m ϫn can be defined as␥(⌬m ,⌬n )ϭA (⌬m ,⌬n )A (0,0),(1)whereA (⌬m ,⌬n )ϭ1(m Ϫ⌬m )(n Ϫ⌬n )(2)ϫ͚x ϭ0m Ϫ1Ϫ⌬m ͚y ϭ0n Ϫ1Ϫ⌬nf (x ,y )ϫf (x ,y )ϫf (x ϩ⌬m ,y ϩ⌬n ).Moreover,the two-dimensional auto-correlation coefficients were further modi-fied to a mean-removed version to gener-ate the similar autocorrelation features for images with different brightnessbutFigure 3.Structural graph depicts the multilayer feed-forward neu-ral network used in this study.Numbers indicate the number ofnodes.Figure 4.Diagram depicts the structure of the neural network model for tissue classification.ROI ϭregion of interest,2D ϭtwo-dimensional.Volume 213•Number 2Computer-aided Diagnosis of Solid Breast Nodules •409with similar texture.This modified ver-sion was expressed asAЈ(⌬m,⌬n)ϭ1(mϪ⌬m)(nϪ⌬n)(3)ϫ͚xϭ0mϪ1Ϫ⌬m͚yϭ0nϪ1Ϫ⌬nϫ0[f(x,y)Ϫf]ϫ[f(xϩ⌬m,yϩ⌬n)Ϫf0,whereƒwas the mean value ofƒ(x,y). The absolute value was used in Equation (3)because a negative value may be pro-duced when the mean is subtracted from the gray level of a pixel.We determined these two-dimensional autocorrelation coefficients for each US image of a breast tumor and used these coefficients as the interpixel features to distinguish between benign and malignant tumors.Neural Network ClassificationA multilayer feed-forward neural net-work contains one or more hidden layers. The function of neurons in the hidden layer is to arbitrate between the input and the output of the neural network.The input feature vector is fed into the source nodes in the input layer of the neural network.The neurons of the input layer constitute the input signals applied to the neurons in the hidden layer.The output signals of the hidden layer can be used as inputs to the next hidden or output layer. Finally,the output layer produces the output result and terminates the neural computing process.Among the algorithms used to design the multilayer feed-forward neural net-works,the back-propagation algorithm is the most popular.In general,there are two different phases in the back-propaga-tion algorithm:the forward phase and the backward phase.In the forward phase, the input signals are computed and passed through the neural network,layer by layer.Then,the neurons in the output layer produce the output signals of the neural network.During this phase,com-paring the output response of the neural network with the desired response can generate error signals.During the backward phase of the back-propagation algorithm,some free param-eters can be adjusted by referring to the error signals.This work can be used to minimize the distortion of the neural network.The multilayer feed-forward neural network has a high capability for learning and for computationalefficiency.Figure5.Illustration of the program for the neural network diagnostic model.The weights of the neural network are loaded,and then the region-of-interest subimage file is downloaded.If the output of neural network is larger than a predefined threshold of20,the result will be‘‘probably malignant.’’If the output is less than the threshold,the result will be‘‘probably benign.’’Note that the output of the neural network is multiplied by100.NNϭneural network,ROIϭregion ofinterest.Figure6.Plot depicts the mean differences between the autocorrelation coefficients for the malignant and benign tumors.The mean differences are increasing for larger⌬m and⌬n.Figure7.Plot depictsthe ROC curve for theneural network in theclassification of malig-nant and benign tumors.The A Z value of the ROCcurve is0.9560Ϯ0.0183.FPFϭfalse-posi-tive fraction,TPFϭtrue-positive fraction.410•Radiology•November1999Chen et alWe iteratively executed the back-propaga-tion learning algorithm for the training set and then produced the synaptic weight vectors that were applied to the neural network.We classified the benign and malignant tumors in the diagnostic model by applying the final synaptic weight vectors to the multilayer feed-forward neural network.We used the modified version of the two-dimensional normalized autocorrela-tion matrix for the input of the neural network.The dimension of the matrix can be fixed for an image of any size.Inthis study,both⌬m and⌬n were5,soprocessing a US image produced a5ϫ5autocorrelation matrix(ie,25autocorrela-tion coefficients).The value of␥(0,0)wasalways1for a normalized autocorrelationmatrix.Thus,except for the element␥(0,0),other autocorrelation coefficientswere formed as a24-dimensional imagefeature vector.We used a multilayer feed-forward neu-ral network with25input nodes,10hidden nodes,and one output node,asillustrated in Figure 3.The24-dimen-sional image feature vector and a pre-defined threshold of the input layer wereused as the input signals for the neuralnetwork.Moreover,the value producedby the output node was used to decidewhether a tumor was benign or malig-nant.The output value of the neuralnetwork was either0or 1.When theoutput value of a US breast image wasnear enough to1,the system classifiedthe tumor on the image as malignant.Conversely,when the output value wasclose to0,the system classified the tumoras benign.Figure4illustrates the struc-ture of the neural network model fortissue classification.Figure5illustratesthe program of the neural network diag-nostic model.In this program,the outputof the neural network was multiplied by100.Training and TestingThe receiver operating characteristic(ROC)curve and the k-fold cross-valida-tion method(18)were used to estimatethe performance of the neural network.In this study,the software packageLABROC1developed by Professor C.E.Metz,University of Chicago,Ill,was used to fitthe ROC curve.With the k-fold cross-validation method,the140images in thedatabase were randomly divided into kgroups.The first group was set aside,andthe remaining(kϪ1)groups were used totrain the neural network.The trainingprocedure was stopped when the improve-ment of error distortion was smaller than1ϫ10Ϫ6.Moreover,the maximal numberof iterations was limited to10,000.Oncetrained,the network was then tested withthe group that was set aside.The secondgroup was then removed,the remaining(kϪ1)groups were trained,and thenetwork was tested with the excludedgroup.This process was repeated until allk groups were used,in turn,as the groupthat was set aside and used for testing.Inthe simulations,k was10,and each grouphad14images.RESULTSTable1lists the mean and SD of theautocorrelation coefficients for malig-nant and benign tumors and the meandifference between the two groups.Thesubstantial mean differences of the twogroups were used to prove whether auto-correlation coefficients were good fea-tures for distinguishing malignant andbenign tumors.Moreover,with larger⌬mand⌬n,the mean difference was larger,asillustrated in Figure6.Table2lists thenumber of training iterations and errordistortions for each training set.The errordistortion was defined as the absolutedifference between the desired outputand the actual output of the neural net-work.Figure7illustrates the ROC curvefor the neural network in the classifica-tion of malignant and benign tumors.The overall performance of the neuralnetwork was evaluated by examining theROC area index A Z over the testing out-put values.Our method had a high A Zvalue of0.9560Ϯ0.0183(SD).Table3lists the performance for different thresh-old values.With a threshold of0.2,thenetwork correctly identified51of52ma-lignant tumors and82of88benign tu-TABLE2Number of Malignant and Benign Tumors,Iterations,and Error Distortions for Each Training SetTraining SetNo.ofMalignantTumorsNo.ofBenignTumorsNo.ofIterationsErrorDistortions1477910,0000.2285 2477910,000 1.1098 3477910,0000.1210 4477910,000 2.1424 547794,721 3.0023 6477910,0000.1747 7477910,000 1.2138 8477910,000 1.1468 9468010,000 1.1850 10468010,000 1.1071 TABLE3Performance of the Neural Network for Different Threshold ValuesThresholdNo.ofTumorsIncluded SensitivityNo.of BenignTumorsIncluded SpecificityNo.ofFalse-NegativeFindingsNo.ofFalse-PositiveFindings1.0500.8840.95640.9530.9250.94450.8560.9660.93260.2570.9860.93160.1580.9870.92170.0140 1.00880.00088TABLE4Number of Misdiagnosed Tumors byUsing the Neural Network at aThreshold of0.2Test Set MalignantTumorsBenignTumors10of50of921of50of930of51of940of50of950of51of960of53of970of50of980of50of990of60of8100of61of8Volume213•Number2Computer-aided Diagnosis of Solid Breast Nodules•411mors.Table4lists the number of tumors that were misdiagnosed in each test set by using the neural network at a threshold of 0.2.The accuracy of the neural network for detecting malignant tumors was95.0% (133of140),the sensitivity was98%(51 of52),the specificity was93%(82of88), the positive predictive value was89%(51 of57),and the negative predictive value was99%(82of83),as illustrated in Table 5.Table6lists the number of tumors of various specific types in this study.From the limited data in this study,the tumor type seemed to have no relationship with the ability of the network to differentiate benign and malignant tumors. DISCUSSIONWe proposed an efficient neural network diagnostic system in which interpixel cor-relations on the US image were used to differentiate benign and malignant tu-mors.The proposed system was used to diagnose breast tumors.The co-occur-rence features and the linear classifier (A Zϭ0.91)proposed by Garra et al(13) was replaced with the autocorrelation features and the neural network(A Zϭ0.96)to obtain a better result.This system adopted the multilayer feed-forward neu-ral network to classify US tumor images to distinguish benign and malignant tu-mors more accurately.The back-propaga-tion learning algorithm was used to con-struct the synaptic weights for the neural network classifier in our diagnostic sys-tem.The architecture of the neural network scheme was simple,redressed easily,and was appropriate for hardware design.Our scheme used only one type of neural network model,that is,the multilayer feed-forward neural network.Moreover, when the performance is suboptimal for new US images,these images can be added to the original training set to pro-duce a new set of synaptic weight vectorsby adjusting the free parameters.A newsynaptic weight vector for the neuralnetwork can control the performance ofthe system.Notably,the neural networkmodules can redress synaptic weight vec-tors without modifying the other func-tions.According to the experimental results,the performance of our diagnostic modelin making differential diagnoses was verygood.These results indicate that benignand malignant tumors can be distin-guished by using interpixel correlationson digital US images.From the highlysatisfactory specificity and sensitivity ofthe results,our system is expected to be auseful computer-aided diagnostic tool inthe classification of benign and malig-nant tumors on sonograms.It can pro-vide a second reading to help reducemisdiagnoses.Further studies of largertest sets of tumor images are underway.References1.Pisani P,Parkin DM,Ferlay J.Estimates ofthe worldwide mortality from eighteenmajor cancers in1985:implications forprevention and projections of future bur-den.Int J Cancer1993;55:891–903.2.Blickstein I,Goldchmit R,Strano SD,Gold-man RD,Barzili N.Echogenicity of fibro-adenoma and carcinoma of the breast:quantitative comparison using gain-as-sisted densitometric evaluation of sono-grams.J Ultrasound Med1995;14:661–664.3.Blickstein I,Goldman R.The relation be-tween different system gain settings andthe accuracy of densitometric assessmentof echogenicity.J Ultrasound Med1994;13:675–678.4.Stavros AT,Thickman D,Rapp CL,DennisMA,Parker SH,Sisney GA.Solid breastnodules:use of sonography to distinguishbetween benign and malignant lesions.Radiology1995;196:123–134.5.Gisvold JJ,Martin JK Jr.Prebiopsy localiza-tion of nonpalpable breast lesions.AJR1984;143:477–481.6.Rosenberg AL,Schwartz GF,Feig SA,Patchefsky AS.Clinically occult breast le-sions:localization and significance.Radi-ology1987;162:167–170.7.Bassett LW,Liu TH,Giuliano AE,Gold RH.The prevalence of carcinoma in palpablevs impalpable,mammographically de-tected lesions.AJR1991;157:21–24.8.Petrick N,Chan HP,Sahiner B,Wei D.Anadaptive density-weighted contrast en-hancement filter for mammographicbreast mass detection.IEEE Trans MedImaging1996;15:59–67.9.Dhawan AP,Chitre Y,Kaiser-Bonasso C,Moskowitz M.Analysis of mammographicmicrocalcifications using gray-level imagestructure features.IEEE Trans Med Imag-ing1996;15:246–259.10.Zheng B,Qian W,Clarke LP.Digital mam-mography:mixed feature neural networkwith spectral entropy decision for detec-tion of microcalcifications.IEEE TransMed Imaging1996;15:589–597.11.Sahiner B,Chan HP,Petrick N,Wei D,Helvi MA,Adler DD,Goodsitt MM.Classi-fication of mass and normal breast tissue:a convolution neural network classifierwith spatial domain and texture images.IEEE Trans Med Imaging1996;15:598–610.12.Shankar PM,Reid JM,Ortega H,PiccoliCW,Goldberg e of non-Rayleighstatistics for identification of tumors inultrasonic B-scans of the breast.IEEE TransMed Imaging1993;12:687–692.13.Garra BS,Krasner BH,Horii SC,Ascher S,Muk SK,Zeman RK.Improving the distinc-tion between benign and malignant breastlesions:the value of sonographic textureanalysis.Ultrasound Imaging1993;15:267–285.14.Choong PL,deSilva CJS,Dawkins HJS,Sterrett GF.Entropy maximization net-works:an application to breast cancer prog-nosis.Neural Networks1996;7:568–577.15.Rumelhart DE,Hinton GE,Williams RJ.Learning representation by back-propaga-tion errors.Nature1986;323:533–536.16.Hirose Y,Yamashita K,Hijiva S.Back-propagation algorithm which varies thenumber of hidden units.Neural Networks1991;4:61–66.17.Gonzalez RC,Woods RE.Image compres-sion.In:Digital image processing.Read-ing,Mass:Wesley,1992;312–315.18.Weiss SM,Kapouleas I.An empirical com-parison of pattern recognition neural netsand machine learning classification meth-ods.In:Proceedings of the11th Interna-tional Joint Conference on Artificial Intel-ligence.Detroit,Mich:IJCAI,1989;234–237.TABLE5Performance of the Neural Network at a Threshold of0.2Threshold Benign Findings Malignant Findings Neural network outputϽ0.282true-negative1false-negative Neural network outputՆ0.26false-positive51true-positive Total8852 Note.—Accuracyϭ(true-positive findingsϩtrue-negative findings)/(true-positiveϩtrue-negativeϩfalse-positive findingsϩfalse-negative findings)ϭ133/140ϭ95.0%.Sensitivityϭtrue-positive findings/(true-positive findingsϩfalse-negative findings)ϭ51/52ϭ98%.Specificityϭtrue-negative findings/(true-negative findingsϩfalse-positive findings)ϭ82/88ϭ93%.Positive predictive valueϭtrue-positive findings/(true-positive findingsϩfalse-positive findings)ϭ51/57ϭ89%.Negative predictive valueϭtrue-negative findings/(true-negative findingsϩfalse-negative findings)ϭ82/83ϭ99%.TABLE6Types of TumorsType No. BenignFibroadenoma78 Fibrocystic nodule10Total88 MalignantInvasive ductal carcinoma46 Intraductal carcinoma3 Invasive lobular carcinoma3Total52412•Radiology•November1999Chen et al。

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BIOMEDICAL ENGINEERINGIEEE T BIO-MED ENGIEEE TRANSACTIONS ON BROADCAST AND TELEVISION RECEIVERS IEEE T BROADC TELEVIEEE TRANSACTIONS ON BROADCASTINGIEEE T BROADCASTIEEE TRANSACTIONS ON CIRCUIT THEORYIEEE T CIRCUITS SYSTIEEE TRANSACTIONS ON CIRCUITS AND SYSTEMSIEEE T CIRCUITS SYSTIEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGYIEEE T CIRC SYST VIDIEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-FUNDAMENTAL THEORY AND APPLICATIONSIEEE T CIRCUITS-IIEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-ANALOG AND DIGITAL SIGNAL PROCESSINGIEEE T CIRCUITS-IIIEEE TRANSACTIONS ON COMMUNICATION AND ELECTRONICS IEEE T COMMUN ELECTRIEEE TRANSACTIONS ON COMMUNICATION TECHNOLOGYIEEE T COMMUN TECHNIEEE TRANSACTIONS ON COMMUNICATIONSIEEE T COMMUNIEEE TRANSACTIONS ON COMMUNICATIONS SYSTEMS IEEE T COMMUN SYSTIEEE TRANSACTIONS ON COMPONENT PARTSIEEE T COMPON PARTSIEEE TRANSACTIONS ON COMPONENTS AND PACKAGING TECHNOLOGIESIEEE T COMPON PACK TIEEE TRANSACTIONS ON COMPONENTS HYBRIDS AND MANUFACTURING TECHNOLOGY IEEE T COMPON HYBRIEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY PART AIEEE T COMPON PACK AIEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY PART B-ADVANCEDPACKAGINGIEEE T COMPON PACK BIEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMSIEEE T COMPUT AID DIEEE TRANSACTIONS ON COMPUTERSIEEE T COMPUTIEEE TRANSACTIONS ON CONSUMER ELECTRONICSIEEE T CONSUM ELECTRIEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY IEEE T CONTR SYST T IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION IEEE T DIELECT EL INIEEE TRANSACTIONS ON EDUCATIONIEEE T EDUCIEEE TRANSACTIONS ON ELECTRICAL INSULATIONIEEE T ELECTR INSULIEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY IEEE T ELECTROMAGN CIEEE TRANSACTIONS ON ELECTRON DEVICESIEEE T ELECTRON DEVIEEE TRANSACTIONS ON ELECTRONIC COMPUTERS IEEE TRANS ELECTRONIEEE TRANSACTIONS ON ELECTRONICS PACKAGING MANUFACTURINGIEEE T ELECTRON PA MIEEE TRANSACTIONS ON ENERGY CONVERSIONIEEE T ENERGY CONVERIEEE TRANSACTIONS ON ENGINEERING MANAGEMENT IEEE T ENG MANAGEIEEE TRANSACTIONS ON ENGINEERING WRITING AND SPEECH IEEE T PROF COMMUNIEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION IEEE T EVOLUT COMPUT IEEE TRANSACTIONS ON FUZZY SYSTEMSIEEE T FUZZY SYSTIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSINGIEEE T GEOSCI REMOTEIEEE TRANSACTIONS ON GEOSCIENCE ELECTRONICS IEEE T GEOSCI ELECT IEEE TRANSACTIONS ON HUMAN FACTORS IN ELECTRONICS IEEE TRANS HUM FACTIEEE TRANSACTIONS ON HUMAN FACTORS IN ENGINEERING IEEE T HUM FACT ENGIEEE TRANSACTIONS ON IMAGE PROCESSINGIEEE T IMAGE PROCESSIEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICSIEEE T IND ELECTRONIEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS AND CONTROL INSTRUMENTATIONIEEE T IND EL CON INIEEE TRANSACTIONS ON INDUSTRY AND GENERAL APPLICATIONS IEEE TRANS IND GEN AIEEE TRANSACTIONS ON INDUSTRY APPLICATIONSIEEE T IND APPLIEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE IEEE T INF TECHNOL BIEEE TRANSACTIONS ON INFORMATION THEORYIEEE T INFORM THEORYIEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT IEEE T INSTRUM MEASIEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERINGIEEE T KNOWL DATA ENIEEE TRANSACTIONS ON MAGNETICSIEEE T MAGNIEEE TRANSACTIONS ON MAN-MACHINE SYSTEMS IEEE T MAN MACHINEIEEE TRANSACTIONS ON MANUFACTURING TECHNOLOGYIEEE T MANUF TECHIEEE TRANSACTIONS ON MEDICAL IMAGING IEEE T MED IMAGINGIEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES IEEE T MICROW THEORYIEEE TRANSACTIONS ON MILITARY ELECTRONICS IEEE T MIL ELECTRONIEEE TRANSACTIONS ON NEURAL NETWORKSIEEE T NEURAL NETWORIEEE TRANSACTIONS ON NUCLEAR SCIENCEIEEE T NUCL SCIIEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS IEEE T PARALL DISTRIEEE TRANSACTIONS ON PARTS HYBRIDS AND PACKAGING IEEE T PARTS HYB PACIEEE TRANSACTIONS ON PARTS MATERIALS AND PACKAGINGIEEE TR PARTS MATERIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCEIEEE T PATTERN ANALIEEE TRANSACTIONS ON PLASMA SCIENCEIEEE T PLASMA SCIIEEE TRANSACTIONS ON POWER APPARATUS AND SYSTEMS IEEE T POWER AP SYSTIEEE TRANSACTIONS ON POWER DELIVERYIEEE T POWER DELIVERIEEE TRANSACTIONS ON POWER ELECTRONICS IEEE T POWER ELECTRIEEE TRANSACTIONS ON POWER SYSTEMSIEEE T POWER SYSTIEEE TRANSACTIONS ON PRODUCT ENGINEERING AND PRODUCTIONIEEE T PROD ENG PRODIEEE TRANSACTIONS ON PROFESSIONAL COMMUNICATION IEEE T PROF COMMUN IEEE TRANSACTIONS ON REHABILITATION ENGINEERING IEEE T REHABIL ENG IEEE TRANSACTIONS ON RELIABILITYIEEE T RELIABIEEE TRANSACTIONS ON ROBOTICS AND AUTOMATIONIEEE T ROBOTIC AUTOMIEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING IEEE T SEMICONDUCT MIEEE TRANSACTIONS ON SIGNAL PROCESSINGIEEE T SIGNAL PROCESIEEE TRANSACTIONS ON SOFTWARE ENGINEERINGIEEE T SOFTWARE ENGIEEE TRANSACTIONS ON SONICS AND ULTRASONICS IEEE T SON ULTRASONIEEE TRANSACTIONS ON SPACE ELECTRONICS AND TELEMETRY IEEE T SPACE EL TELIEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING IEEE T SPEECH AUDIPIEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS IEEE T SYST MAN CYB IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANSIEEE T SYST MAN CY AIEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS IEEE T SYST MAN CY BIEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWSIEEE T SYST MAN CY CIEEE TRANSACTIONS ON SYSTEMS SCIENCE AND CYBERNETICSIEEE T SYST SCI CYBIEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROLIEEE T ULTRASON FERRIEEE TRANSACTIONS ON VEHICULAR COMMUNICATIONS IEEE T VEH COMMUN IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY IEEE T VEH TECHNOLIEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION VLSI SYSTEMSIEEE T VLSI SYSTIEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS IEEE T VIS COMPUT GRIEEE VEHICULAR TECHNOLOGY GROUP-ANNUAL CONFERENCE IEEE VEH TECHNOL GRIEEE-ACM TRANSACTIONS ON NETWORKINGIEEE ACM T NETWORKIEEE-ASME TRANSACTIONS ON MECHATRONICSIEEE-ASME T MECHJournal of Optoelectronics . LaserJ. Optoelectronics . LaserJOURNAL OF THE OPTICAL SOCIETY OF AMERICAJ OPT SOC AMJOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISIONJ OPT SOC AM AJOURNAL OF THE OPTICAL SOCIETY OF AMERICA B-OPTICAL PHYSICSJ OPT SOC AM BOPTICAL TECHNOLOGYOPT. TECHNOL.OPTICS LETTERSOPT LETTOPTICAL TECHNICSOPT. TECH.OPTICS AND PRECISION ENGINEERINGOPT. PRECISION ENG.OPTICA ACTAOPT ACTAOPTICA APPLICATAOPT APPLOPTICAL AND QUANTUM ELECTRONICS OPT QUANT ELECTRON OPTICAL ENGINEERING OPT ENGOPTICAL FIBER TECHNOLOGYOPT FIBER TECHNOLOPTICAL IMAGING OF BRAIN FUNCTION AND METABOLISM 2 ADV EXP MED BIOLOPTICAL INFORMATION SYSTEMS OPT INF SYSTOPTICAL MATERIALSOPT MATEROPTICAL PROPERTIES OF SEMICONDUCTOR QUANTUM DOTS SPRINGER TR MOD PHYSOPTICAL REVIEWOPT REVOPTICAL SPECTRAOPT SPECTRAOPTICS & PHOTONICS NEWS OPT PHOTONICS NEWS OPTICS AND LASER TECHNOLOGY OPT LASER TECHNOL OPTICS AND LASERS IN ENGINEERINGOPT LASER ENGOPTICS AND SPECTROSCOPY OPT SPECTROSC+OPTICS AND SPECTROSCOPY-USSROPT SPECTROSC-USSROPTICS COMMUNICATIONS OPT COMMUNOPTICS EXPRESSOPT EXPRESSOPTICS LETTERSOPT LETTOPTIKOPTIKOPTIKA I SPEKTROSKOPIYA OPT SPEKTROSK+ PATTERN ANALYSIS AND APPLICATIONSPATTERN ANAL APPLPATTERN FORMATION IN GRANULAR MATERIALS SPRINGER TR MOD PHYSPATTERN RECOGNITION PATTERN RECOGNPATTERN RECOGNITION LETTERS PATTERN RECOGN LETT PROGRESS IN OPTICS PROG OPTICSPROGRESS IN OPTICS, VOL 33 PROG OPTICSPROGRESS IN OPTICS, VOL 35 PROG OPTICSPROGRESS IN OPTICS, VOL 38PROG OPTICSPROGRESS IN OPTICS, VOL XLPROG OPTICSPROGRESS IN OPTICS, VOL XXXII PROG OPTICSPROGRESS IN OPTICS, VOL XXXIXPROG OPTICSPROGRESS IN OPTICS, VOL XXXVIPROG OPTICSPROGRESS IN OPTICS, VOL. 37PROG OPTICSSpacecraft Recovery & Remote SensingSOLAR ENERGY MATERIALSSOL ENERG MATERSOLAR ENERGY MATERIALS AND SOLAR CELLS SOL ENERG MAT SOL C VISION RESEARCHVISION RESVISION TECNOLOGICAVIS TECNOL。

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刊名简称刊名全称ISSN小类名称（中文）小类名称（英文）小类分区HUM-COMPUT I HUMAN-COMPUTER I0737-0024计算机：控制论COMPUTER SCIENCE, CYBER1 IEEE T SYST IEEE TRANSACTION1083-4419计算机：控制论COMPUTER SCIENCE, CYBER2INT J HUM-CO INTERNATIONAL JO1071-5819计算机：控制论COMPUTER SCIENCE, CYBER2 USER MODEL U USER MODELING AN0924-1868计算机：控制论COMPUTER SCIENCE, CYBER2 ACM T COMPUT ACM Transactions1073-0516计算机：控制论COMPUTER SCIENCE, CYBER3 BIOL CYBERN B IOLOGICAL CYBER0340-1200计算机：控制论COMPUTER SCIENCE, CYBER3 IEEE T SYST IEEE TRANSACTION1083-4427计算机：控制论COMPUTER SCIENCE, CYBER3 IEEE T SYST IEEE TRANSACTION1094-6977计算机：控制论COMPUTER SCIENCE, CYBER3 MACH VISION MACHINE VISION A0932-8092计算机：控制论COMPUTER SCIENCE, CYBER3 BEHAV INFORM BEHAVIOUR & INFO0144-929X计算机：控制论COMPUTER SCIENCE, CYBER4 CONTROL CYBE CONTROL AND CYBE0324-8569计算机：控制论COMPUTER SCIENCE, CYBER4 CYBERNET SYS CYBERNETICS AND 0196-9722计算机：控制论COMPUTER SCIENCE, CYBER4INT J HUM-CO INTERNATIONAL JO1044-7318计算机：控制论COMPUTER SCIENCE, CYBER4 INTERACT COM INTERACTING WITH0953-5438计算机：控制论COMPUTER SCIENCE, CYBER4J COMPUT SYS JOURNAL OF COMPU1064-2307计算机：控制论COMPUTER SCIENCE, CYBER4 KYBERNETESKYBERNETES0368-492X计算机：控制论COMPUTER SCIENCE, CYBER4 KYBERNETIKA K YBERNETIKA0023-5954计算机：控制论COMPUTER SCIENCE, CYBER4 MODEL IDENT MODELING IDENTIF0332-7353计算机：控制论COMPUTER SCIENCE, CYBER4 PRESENCE-TEL PRESENCE-TELEOPE1054-7460计算机：控制论COMPUTER SCIENCE, CYBER4 IEEE T EVOLU IEEE TRANSACTION1089-778X计算机：人工智能COMPUTER SCIENCE, ARTIF1 IEEE T FUZZY IEEE TRANSACTION1063-6706计算机：人工智能COMPUTER SCIENCE, ARTIF1 IEEE T PATTE IEEE TRANSACTION0162-8828计算机：人工智能COMPUTER SCIENCE, ARTIF1INT J COMPUT INTERNATIONAL JO0920-5691计算机：人工智能COMPUTER SCIENCE, ARTIF1 SIAM J IMAGI SIAM Journal on 1936-4954计算机：人工智能COMPUTER SCIENCE, ARTIF1 ADV ENG INFO ADVANCED ENGINEE1474-0346计算机：人工智能COMPUTER SCIENCE, ARTIF2 APPL SOFT CO APPLIED SOFT COM1568-4946计算机：人工智能COMPUTER SCIENCE, ARTIF2 ARTIF INTELL ARTIFICIAL INTEL0004-3702计算机：人工智能COMPUTER SCIENCE, ARTIF2 ARTIF LIFEARTIFICIAL LIFE1064-5462计算机：人工智能COMPUTER SCIENCE, ARTIF2 COMPUT INTEL COMPUTATIONAL IN0824-7935计算机：人工智能COMPUTER SCIENCE, ARTIF2 COMPUT LINGU COMPUTATIONAL LI0891-2017计算机：人工智能COMPUTER SCIENCE, ARTIF2 COMPUT VIS I COMPUTER VISION 1077-3142计算机：人工智能COMPUTER SCIENCE, ARTIF2 DECIS SUPPOR DECISION SUPPORT0167-9236计算机：人工智能COMPUTER SCIENCE, ARTIF2 EVOL COMPUT E VOLUTIONARY COM1063-6560计算机：人工智能COMPUTER SCIENCE, ARTIF2 EXPERT SYST EXPERT SYSTEMS W0957-4174计算机：人工智能COMPUTER SCIENCE, ARTIF2 IEEE COMPUT IEEE Computation1556-603X计算机：人工智能COMPUTER SCIENCE, ARTIF2 IEEE INTELL IEEE INTELLIGENT1541-1672计算机：人工智能COMPUTER SCIENCE, ARTIF2 IEEE T IMAGE IEEE TRANSACTION1057-7149计算机：人工智能COMPUTER SCIENCE, ARTIF2 IEEE T KNOWL IEEE TRANSACTION1041-4347计算机：人工智能COMPUTER SCIENCE, ARTIF2 IEEE T NEURA IEEE TRANSACTION1045-9227计算机：人工智能COMPUTER SCIENCE, ARTIF2 IEEE T SYST IEEE TRANSACTION1083-4419计算机：人工智能COMPUTER SCIENCE, ARTIF2 INFORM FUSIO Information Fusi1566-2535计算机：人工智能COMPUTER SCIENCE, ARTIF2INT J INNOV International Jo1349-4198计算机：人工智能COMPUTER SCIENCE, ARTIF2INT J NEURAL International 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APPL0952-1976计算机：人工智能COMPUTER SCIENCE, ARTIF3 IEEE T SYST IEEE TRANSACTION1094-6977计算机：人工智能COMPUTER SCIENCE, ARTIF3 IMAGE VISION IMAGE AND VISION0262-8856计算机：人工智能COMPUTER SCIENCE, ARTIF3 INT J APPROX INTERNATIONAL JO0888-613X计算机：人工智能COMPUTER SCIENCE, ARTIF3 INT J INF TE INTERNATIONAL JO0219-6220计算机：人工智能COMPUTER SCIENCE, ARTIF3 INT J SEMANT International Jo1552-6283计算机：人工智能COMPUTER SCIENCE, ARTIF3 J AMB INTEL Journal of Ambie1876-1364计算机：人工智能COMPUTER SCIENCE, ARTIF3 J ARTIF INTE JOURNAL OF ARTIF1076-9757计算机：人工智能COMPUTER SCIENCE, ARTIF3 J AUTOM REAS JOURNAL OF AUTOM0168-7433计算机：人工智能COMPUTER SCIENCE, ARTIF3 J HEURISTICS JOURNAL OF HEURI1381-1231计算机：人工智能COMPUTER SCIENCE, ARTIF3 J INTELL MAN JOURNAL OF INTEL0956-5515计算机：人工智能COMPUTER SCIENCE, ARTIF3 KNOWL INF SY KNOWLEDGE AND IN0219-1377计算机：人工智能COMPUTER SCIENCE, ARTIF3 MACH LEARNMACHINE LEARNING0885-6125计算机：人工智能COMPUTER SCIENCE, ARTIF3 MACH VISION MACHINE VISION A0932-8092计算机：人工智能COMPUTER SCIENCE, ARTIF3 NETWORK-COMP NETWORK-COMPUTAT0954-898X计算机：人工智能COMPUTER SCIENCE, ARTIF3 NEURAL NETWO NEURAL NETWORKS0893-6080计算机：人工智能COMPUTER SCIENCE, ARTIF3 SOFT COMPUT S OFT COMPUTING1432-7643计算机：人工智能COMPUTER SCIENCE, ARTIF3 ADV ELECTR C Advances in Elec1582-7445计算机：人工智能COMPUTER SCIENCE, ARTIF4 AI COMMUN AI COMMUNICATION0921-7126计算机：人工智能COMPUTER SCIENCE, ARTIF4 AI EDAM AI EDAM-ARTIFICI0890-0604计算机：人工智能COMPUTER SCIENCE, ARTIF4 AI MAG AI MAGAZINE0738-4602计算机：人工智能COMPUTER SCIENCE, ARTIF4 ANN MATH ART ANNALS OF MATHEM1012-2443计算机：人工智能COMPUTER SCIENCE, ARTIF4 APPL ARTIF I APPLIED ARTIFICI0883-9514计算机：人工智能COMPUTER SCIENCE, ARTIF4 APPL INTELL A PPLIED INTELLIG0924-669X计算机：人工智能COMPUTER SCIENCE, ARTIF4 ARTIF INTELL ARTIFICIAL INTEL0933-3657计算机：人工智能COMPUTER SCIENCE, ARTIF4 ARTIF INTELL ARTIFICIAL INTEL0269-2821计算机：人工智能COMPUTER SCIENCE, ARTIF4 COGN SYST RE Cognitive System1389-0417计算机：人工智能COMPUTER SCIENCE, ARTIF4 COMPUT INFOR COMPUTING AND IN1335-9150计算机：人工智能COMPUTER SCIENCE, ARTIF4 COMPUT SPEEC COMPUTER SPEECH 0885-2308计算机：人工智能COMPUTER SCIENCE, ARTIF4 CONNECT SCI C ONNECTION SCIEN0954-0091计算机：人工智能COMPUTER SCIENCE, ARTIF4 EXPERT SYST E XPERT SYSTEMS0266-4720计算机：人工智能COMPUTER SCIENCE, ARTIF4 FUZZY OPTIM Fuzzy Optimizati1568-4539计算机：人工智能COMPUTER SCIENCE, ARTIF4 GENET PROGRA Genetic Programm1389-2576计算机：人工智能COMPUTER SCIENCE, ARTIF4 IET COMPUT V IET Computer Vis1751-9632计算机：人工智能COMPUTER SCIENCE, ARTIF4 INF TECHNOL Information Tech1392-124X计算机：人工智能COMPUTER SCIENCE, ARTIF4 INT ARAB J I International Ar1683-3198计算机：人工智能COMPUTER SCIENCE, ARTIF4 INT J AP MAT International Jo1641-876X计算机：人工智能COMPUTER SCIENCE, ARTIF4 INT J ARTIF International Jo0218-2130计算机：人工智能COMPUTER SCIENCE, ARTIF4 INT J COMPUT International Jo1875-6883计算机：人工智能COMPUTER SCIENCE, ARTIF4 INT J DOC AN International Jo1433-2833计算机：人工智能COMPUTER SCIENCE, ARTIF4 INT J FUZZY International Jo1562-2479计算机：人工智能COMPUTER SCIENCE, ARTIF4 INT J INTELL INTERNATIONAL JO0884-8173计算机：人工智能COMPUTER SCIENCE, ARTIF4 INT J PATTER INTERNATIONAL JO0218-0014计算机：人工智能COMPUTER SCIENCE, ARTIF4 INT J SOFTW INTERNATIONAL JO0218-1940计算机：人工智能COMPUTER SCIENCE, ARTIF4 INT J UNCERT INTERNATIONAL JO0218-4885计算机：人工智能COMPUTER SCIENCE, ARTIF4INTELL DATA Intelligent Data1088-467X计算机：人工智能COMPUTER SCIENCE, ARTIF4 J CHEMOMETR J OURNAL OF CHEMO0886-9383计算机：人工智能COMPUTER SCIENCE, ARTIF4 J COMPUT SYS JOURNAL OF COMPU1064-2307计算机：人工智能COMPUTER SCIENCE, ARTIF4 J EXP THEOR JOURNAL OF EXPER0952-813X计算机：人工智能COMPUTER SCIENCE, ARTIF4 J INTELL FUZ JOURNAL OF INTEL1064-1246计算机：人工智能COMPUTER SCIENCE, ARTIF4 J INTELL INF JOURNAL OF INTEL0925-9902计算机：人工智能COMPUTER SCIENCE, ARTIF4 J INTELL ROB JOURNAL OF INTEL0921-0296计算机：人工智能COMPUTER SCIENCE, ARTIF4 J MATH IMAGI JOURNAL OF MATHE0924-9907计算机：人工智能COMPUTER SCIENCE, ARTIF4 J MULT-VALUE JOURNAL OF MULTI1542-3980计算机：人工智能COMPUTER SCIENCE, ARTIF4 J REAL-TIME Journal of Real-1861-8200计算机：人工智能COMPUTER SCIENCE, ARTIF4 KNOWL ENG RE KNOWLEDGE ENGINE0269-8889计算机：人工智能COMPUTER SCIENCE, ARTIF4 KNOWL-BASED KNOWLEDGE-BASED 0950-7051计算机：人工智能COMPUTER SCIENCE, ARTIF4 MALAYS J COM Malaysian Journa0127-9084计算机：人工智能COMPUTER SCIENCE, ARTIF4 MECHATRONICS MECHATRONICS0957-4158计算机：人工智能COMPUTER SCIENCE, ARTIF4 MIND MACH MINDS AND MACHIN0924-6495计算机：人工智能COMPUTER SCIENCE, ARTIF4 NEURAL COMPU NEURAL COMPUTING0941-0643计算机：人工智能COMPUTER SCIENCE, ARTIF4 NEURAL NETW Neural Network W1210-0552计算机：人工智能COMPUTER SCIENCE, ARTIF4 NEURAL PROCE NEURAL PROCESSIN1370-4621计算机：人工智能COMPUTER SCIENCE, ARTIF4 NEUROCOMPUTI NEUROCOMPUTING0925-2312计算机：人工智能COMPUTER SCIENCE, ARTIF4 PATTERN ANAL PATTERN ANALYSIS1433-7541计算机：人工智能COMPUTER SCIENCE, ARTIF4 PATTERN RECO PATTERN RECOGNIT0167-8655计算机：人工智能COMPUTER SCIENCE, ARTIF4 ROBOT AUTON ROBOTICS AND AUT0921-8890计算机：人工智能COMPUTER SCIENCE, ARTIF4 TRAIT SIGNAL Traitement du Si0765-0019计算机：人工智能COMPUTER SCIENCE, ARTIF4 TURK J ELECT Turkish Journal 1300-0632计算机：人工智能COMPUTER SCIENCE, ARTIF42010年总被引频次09年总被引频大类名称大类分区2010年影响因子009年影响因子2010年平均影响008年影响因子工程技术1 4.000 6.190 2.905 4.36511751601工程技术2 2.699 3.007 2.361 2.68945224901工程技术2 1.600 2.380 1.769 1.91622452539工程技术2 3.074 2.345 1.483 2.301552672工程技术3 1.857 1.1940.000 1.526662875工程技术3 1.667 1.697 1.935 1.76644894624工程技术3 2.093 2.033 1.350 1.82522922379工程技术3 2.105 2.016 1.375 1.83213901507工程技术3 1.5490.952 1.485 1.329702574工程技术40.8350.7670.9150.839824873工程技术40.3000.3780.6890.456454415工程技术40.6620.7800.4940.645363534工程技术40.6810.5870.3210.530425534工程技术3 1.192 1.698 1.103 1.331821980工程技术40.1910.1680.0820.14712793工程技术40.3030.3080.2350.282441544工程技术40.4610.4450.2810.396463526工程技术40.4070.4350.0000.421158110工程技术4 1.097 1.2410.750 1.029********工程技术1 4.403 4.589 3.736 4.24346815038工程技术2 2.695 3.343 3.624 3.22148755961工程技术1 5.308 4.378 5.960 5.2152506022851工程技术1 5.151 3.508 5.358 4.67298987324工程技术1 4.5000.0000.000 4.5003220工程技术3 1.400 1.730 1.848 1.659495460工程技术2 2.097 2.415 1.909 2.14012061079工程技术2 2.533 3.036 3.397 2.98962659016工程技术3 2.143 1.960 1.164 1.7568161070工程技术20.704 5.378 3.310 3.1314081223工程技术2 2.971 2.212 2.656 2.61313201467工程技术2 2.534 1.676 2.220 2.14328592369工程技术2 2.135 2.622 1.873 2.21032333376工程技术2 2.652 3.103 3.000 2.91819922397工程技术2 1.926 2.908 2.596 2.47766155421工程技术2 2.905 2.622 2.535 2.687385283工程技术2 2.570 3.144 2.278 2.66416612214工程技术2 2.918 2.848 3.315 3.0271277411472工程技术2 1.851 2.285 2.236 2.12436544262工程技术2 2.633 2.889 3.726 3.0839********工程技术2 2.699 3.007 2.361 2.68945224901工程技术2 1.621 2.212 2.057 1.963620536工程技术2 1.667 2.932 2.791 2.46319361690工程技术2 4.237 2.9880.901 2.709816669工程技术3 2.122 2.0420.617 1.594304254工程技术2 2.974 2.789 3.116 2.96047664132工程技术2 2.789 3.412 3.023 3.075578651工程技术1 4.364 3.093 3.602 3.68629012307工程技术2 2.290 2.175 2.378 2.28175817456工程技术2 2.682 2.554 3.279 2.8381172611703工程技术3 1.000 1.3640.000 1.182108114工程技术2 2.103 1.510 2.125 1.913524551工程技术3 2.033 1.235 1.500 1.58913191215工程技术2 2.222 2.111 1.940 2.0913*******工程技术3 1.410 1.2970.879 1.195268307工程技术3 1.722 1.745 1.480 1.64913121398工程技术2 1.238 2.950 2.421 2.20315091822工程技术3 1.352 1.444 1.397 1.39814261483工程技术3 2.105 2.016 1.375 1.83213901507工程技术3 1.578 1.474 1.496 1.51632362926工程技术3 1.684 2.090 1.708 1.82713841686工程技术3 3.139 1.3120.953 1.801534322工程技术2 1.679 2.345 1.893 1.972123167工程技术3 1.5000.0000.000 1.500430工程技术3 1.691 1.981 1.611 1.76117392238工程技术2 2.280 1.926 1.691 1.966579522工程技术3 1.623 1.264 1.064 1.317629764工程技术4 1.0810.938 1.018 1.012854979工程技术2 2.008 2.211 1.733 1.984727773工程技术2 1.967 1.663 2.326 1.98563107305工程技术3 1.5490.952 1.485 1.329702574工程技术30.957 1.536 1.333 1.275742792工程技术2 1.972 1.879 2.656 2.16958256488工程技术3 1.512 1.3280.984 1.27510451096工程技术40.7000.5090.0000.6058347工程技术40.8370.7550.6080.733294461工程技术40.6400.6360.4770.584299357工程技术40.525 1.0180.6910.7457121180工程技术40.4300.8930.7220.682472734工程技术40.5630.5800.7950.646429621工程技术40.8930.9880.7750.885508534工程技术3 1.568 1.645 1.960 1.72411101475工程技术40.4290.0570.1190.202462592工程技术4 1.0000.571 1.581 1.051223251工程技术40.3560.4560.4920.435104131工程技术3 1.353 1.034 1.413 1.267626636工程技术4 1.0570.8060.8840.916358420工程技术40.733 1.2310.7170.894241332工程技术40.702 1.0590.0000.881147136工程技术3 1.167 1.0910.000 1.12910258工程技术40.4470.9690.6670.6943733工程技术40.6380.4950.0000.5678254工程技术40.0650.0000.0000.065250工程技术40.7940.6840.0000.739418407工程技术40.3300.4360.6670.478235244工程技术4 1.4710.3120.0000.89212410工程技术4 1.091 1.2130.909 1.071253311工程技术3 1.362 1.0900.000 1.226207215工程技术3 1.331 1.1940.860 1.12810871309工程技术40.6850.5120.6600.619698831工程技术40.2480.3270.4470.341185258工程技术40.850 1.147 1.0000.999718990工程技术40.4120.9290.4260.589427526化学4 1.377 1.291 1.415 1.36121862095工程技术40.1910.1680.0820.14712793工程技术40.6550.5330.3410.510207312工程技术40.6480.7400.6490.679318336工程技术40.8750.980 1.0750.977399545工程技术40.7570.8580.5600.725575568工程技术3 1.285 1.437 1.331 1.35110631074工程技术40.3330.3430.3080.3288570工程技术40.9620.0000.0000.9621060工程技术3 1.257 1.143 1.588 1.329635954工程技术3 1.574 1.3080.924 1.269914933工程技术40.3640.0000.0000.364210工程技术30.944 1.198 1.434 1.19211731214工程技术40.6180.7830.3400.580216266工程技术40.5630.8120.7670.714401474工程技术40.5110.4750.3950.460200180工程技术4 1.088 1.0150.942 1.015567634工程技术3 1.442 1.440 1.234 1.37240354108工程技术3 1.097 1.293 1.367 1.252433471工程技术3 1.235 1.303 1.559 1.36646984545工程技术3 1.313 1.361 1.214 1.29616651526工程技术40.0780.1210.0000.1004978工程技术40.2860.0000.0000.28681008年总被引频备注1030399118614814259189911696246064013843185846443237591838495211246748660354606712075581512862548263318163032123179612214381398833991472907493154332843822536730111493625 178 **** **** 1192 1169 3067 1249 234 78 0 1766 470 586 782 550 6211 624 666 5706 723 0 310 274 814 583 484 407 1025 482 194 95 461 373 261 0 0 9 0 0 0 204 0 204 0 1039 760 235 75064 230 338 529 497 804 49 0 701 680 0 1051 166 362 174 564 2946 402 4256 1364 0 0。

最新版继续教育公需科目：人工智能与健康试题及答案(第五套)

最新版继续教育公需科目：人工智能与健康试题及答案(第五套)

最新版继续教育公需科目：人工智能与健康试题及答案（第五套）一、单选题1.（）是研究用计算机系统解释图，像实现类似人类视觉系统理解外部世界的一种技术，所讨论的问题是为了完成某一任务需要从图像中获取哪些信息，以及如何利用这些信息获得必要的解释。

A.立体视觉B.图像理解C.姿态估计D.轨迹跟踪参考答案：B2.（）是一种处理时序数据的神经网络，常用于语音识别、机器翻译等领域。

A.前馈神经网络B.卷积神经网络C.循环神经网络D.对抗神经网络参考答案：C3.据2005年美国一份癌症统计报告表明，在女性的所有死亡原因中，排在第二位的是（）。

A.肺癌B.肝癌C.乳腺癌D.淋巴癌参考答案：C4.最经典的西方健康研究——佛雷明翰研究开始于（）。

A.1948年B.1971年C.1989年D.2000年参考答案：A5.2017年，卡内基梅隆大学开发的一个人工智能程序在（）大赛上战胜了四位人类玩家，这在人工智能发展史上具有里程碑式的意义。

A.五子棋B.国际象棋C.德州扑克D.围棋参考答案：C6.根据国际评判健康的标准，（）的韩国中年人心血管呈理想状态。

A.0.1%B.0.2%C.0.4%D.0.67%参考答案：D7.古代把计量叫“度量衡”，其中，“度”是测量（）的过程。

A.长度B.容积C.温度D.轻重参考答案：A8.下列对人工智能芯片的表述，不正确的是（）。

A.一种专门用于处理人工智能应用中大量计算任务的芯片B.能够更好地适应人工智能中大量矩阵运算C.目前处于成熟高速发展阶段D.相对于传统的CPU处理器，智能芯片具有很好的并行计算性能参考答案：C9.下列选项中，不属于生物特征识别技术的是（）。

A.步态识别B.声纹识别C.文本识别D.虹膜识别参考答案：C10.在大数据隐私保护生命周期模型中，大数据使用的风险是（）。

A.被第三方偷窥或篡改B.如何确保合适的数据及属性在合适的时间地点给合适的用户访问C.匿名处理后经过数据挖掘仍可被分析出隐私D.如何在发布时去掉用户隐私并保证数据可用参考答案：B11.在2016年，ImageNet测试的识别错误率为（）。

计算机科学ESI期刊汇总

计算机科学ESI期刊汇总

期刊全称期刊简称Acm Computing Surveys ACM COMPUT SURV Acm Journal On Emerging Technologies in Computing Systems ACM J EMERG TECH COM Acm Sigcomm Computer Communication Review ACM SIGCOMM COMP COMAcm Sigplan Notices ACM SIGPLAN NOTICES Acm Transactions On Algorithms ACM T ALGORITHMS Acm Transactions On Applied Perception ACM T APPL PERCEPT Acm Transactions On Architecture and Code Optimization ACM T ARCHIT CODE OPAcm Transactions On Autonomous and Adaptive Systems ACM T AUTON ADAP SYS Acm Transactions On Computational Logic ACM T COMPUT LOGAcm Transactions On Computer Systems ACM T COMPUT SYST Acm Transactions On Computer-Human Interaction ACM T COMPUT-HUM INT Acm Transactions On Database Systems ACM T DATABASE SYST Acm Transactions On Design Automation of Electronic Systems ACM T DES AUTOMAT EL Acm Transactions On Embedded Computing Systems ACM T EMBED COMPUT S Acm Transactions On Graphics ACM T GRAPHIC Acm Transactions On Information and System Security ACM T INFORM SYST SE Acm Transactions On Information Systems ACM T INFORM SYST Acm Transactions On Intelligent Systems and Technology ACM T INTEL SYST TEC Acm Transactions On Internet Technology ACM T INTERNET TECHN Acm Transactions On Knowledge Discovery From Data ACM T KNOWL DISCOV D Acm Transactions On Mathematical Software ACM T MATH SOFTWARE Acm Transactions On Modeling and Computer Simulation ACM T MODEL COMPUT S Acm Transactions On Multimedia Computing Communications and Applications ACM T MULTIM COMPUT Acm Transactions On Programming Languages and Systems ACM T PROGR LANG SYS Acm Transactions On Reconfigurable Technology and Systems ACM T RECONFIG TECHN Acm Transactions On Sensor Networks ACM T SENSOR NETWORK Acm Transactions On Software Engineering and Methodology ACM T SOFTW ENG METH Acm Transactions On Storage ACM T STORAGEAcm Transactions On the Web ACM T WEBActa Informatica ACTA INFORMAd Hoc & Sensor Wireless Networks AD HOC SENS WIREL NEAd Hoc Networks AD HOC NETWAdvances in Computers ADV COMPUTAdvances in Engineering Software ADV ENG SOFTW Aeu-International Journal of Electronics and Communications AEU-INT J ELECTRON C Annals of Telecommunications-Annales Des Telecommunications ANN TELECOMMUNApplied Clinical Informatics APPL CLIN INFORMApplied Ontology APPL ONTOLApplied Soft Computing APPL SOFT COMPUTArtificial Intelligence ARTIF INTELLArtificial Intelligence Review ARTIF INTELL REVArtificial Life ARTIF LIFEAutomated Software Engineering AUTOMAT SOFTW ENG Autonomous Agents and Multi-Agent Systems AUTON AGENT MULTI-AG Bell Labs Technical Journal BELL LABS TECH JBioinformatics BIOINFORMATICSBmc Bioinformatics BMC BIOINFORMATICSBriefings in Bioinformatics BRIEF BIOINFORM Business & Information Systems Engineering BUS INFORM SYST ENG+China Communications CHINA COMMUN Cluster Computing-the Journal of Networks Software Tools and Applications CLUSTER COMPUT Cmc-Computers Materials & Continua CMC-COMPUT MATER CONCmes-Computer Modeling in Engineering & Sciences CMES-COMP MODEL ENGCognitive Computation COGN COMPUTCognitive Systems Research COGN SYST RESCommunications of the Acm COMMUN ACMComputational Biology and Chemistry COMPUT BIOL CHEMComputational Geosciences COMPUTAT GEOSCIComputational Linguistics COMPUT LINGUISTComputer COMPUTERComputer Aided Geometric Design COMPUT AIDED GEOM D Computer Animation and Virtual Worlds COMPUT ANIMAT VIRT W Computer Applications in Engineering Education COMPUT APPL ENG EDUCComputer Communications COMPUT COMMUNComputer Graphics Forum COMPUT GRAPH FORUMComputer Journal COMPUT JComputer Languages Systems & Structures COMPUT LANG SYST STR Computer Methods and Programs in Biomedicine COMPUT METH PROG BIO Computer Methods in Applied Mechanics and Engineering COMPUT METHOD APPL M Computer Methods in Biomechanics and Biomedical Engineering COMPUT METHOD BIOMECComputer Music Journal COMPUT MUSIC JComputer Networks COMPUT NETW Computer Science and Information Systems COMPUT SCI INF SYSTComputer Speech and Language COMPUT SPEECH LANGComputer Standards & Interfaces COMPUT STAND INTER Computer Supported Cooperative Work-the Journal of Collaborative Computing COMPUT SUPP COOP W J Computer Systems Science and Engineering COMPUT SYST SCI ENGComputer Vision and Image Understanding COMPUT VIS IMAGE UNDComputer-Aided Design COMPUT AIDED DESIGN Computers & Chemical Engineering COMPUT CHEM ENGComputers & Education COMPUT EDUCComputers & Electrical Engineering COMPUT ELECTR ENGComputers & Fluids COMPUT FLUIDSComputers & Geosciences COMPUT GEOSCI-UKComputers & Graphics-Uk COMPUT GRAPH-UKComputers & Industrial Engineering COMPUT IND ENGComputers & Operations Research COMPUT OPER RESComputers & Security COMPUT SECURComputers & Structures COMPUT STRUCTComputers and Concrete COMPUT CONCRETE Computers and Electronics in Agriculture COMPUT ELECTRON AGRComputers and Geotechnics COMPUT GEOTECHComputers in Biology and Medicine COMPUT BIOL MEDComputers in Industry COMPUT INDComputing COMPUTINGComputing and Informatics COMPUT INFORMComputing in Science & Engineering COMPUT SCI ENG Concurrency and Computation-Practice & Experience CONCURR COMP-PRACT EConnection Science CONNECT SCIConstraints CONSTRAINTS Cryptography and Communications-Discrete-Structures Boolean Functions and Sequences CRYPTOGR COMMUNCryptologia CRYPTOLOGIACurrent Bioinformatics CURR BIOINFORMData & Knowledge Engineering DATA KNOWL ENG Data Mining and Knowledge Discovery DATA MIN KNOWL DISCDecision Support Systems DECIS SUPPORT SYST Design Automation For Embedded Systems DES AUTOM EMBED SYST Designs Codes and Cryptography DESIGN CODE CRYPTOGRDigital Investigation DIGIT INVESTDisplays DISPLAYSDistributed and Parallel Databases DISTRIB PARALLEL DATDistributed Computing DISTRIB COMPUTEmpirical Software Engineering EMPIR SOFTW ENGEngineering With Computers ENG COMPUT-GERMANYEnterprise Information Systems ENTERP INF SYST-UK Environmental Modelling & Software ENVIRON MODELL SOFTWEtri Journal ETRI J Eurasip Journal On Wireless Communications and Networking EURASIP J WIREL COMM European Journal of Information Systems EUR J INFORM SYSTEvolutionary Bioinformatics EVOL BIOINFORMEvolutionary Computation EVOL COMPUTExpert Systems EXPERT SYSTFormal Aspects of Computing FORM ASP COMPUTFormal Methods in System Design FORM METHOD SYST DES Foundations and Trends in Information Retrieval FOUND TRENDS INF RETFrontiers in Neurorobotics FRONT NEUROROBOTICSFrontiers of Computer Science FRONT COMPUT SCI-CHI Frontiers of Information Technology & Electronic Engineering FRONT INFORM TECH ELFundamenta Informaticae FUND INFORMture Generation Computer Systems-the International Journal of Grid Computing and EscienFUTURE GENER COMP SY Genetic Programming and Evolvable Machines GENET PROGRAM EVOL MGraphical Models GRAPH MODELSHuman-Computer Interaction HUM-COMPUT INTERACT Ibm Journal of Research and Development IBM J RES DEVIcga Journal ICGA JIeee Annals of the History of Computing IEEE ANN HIST COMPUTIeee Communications Letters IEEE COMMUN LETTIeee Communications Magazine IEEE COMMUN MAG Ieee Communications Surveys and Tutorials IEEE COMMUN SURV TUTIeee Computational Intelligence Magazine IEEE COMPUT INTELL M Ieee Computer Architecture Letters IEEE COMPUT ARCHIT L Ieee Computer Graphics and Applications IEEE COMPUT GRAPHIeee Design & Test IEEE DES TESTIeee Internet Computing IEEE INTERNET COMPUT Ieee Journal of Biomedical and Health Informatics IEEE J BIOMED HEALTHIeee Journal On Selected Areas in Communications IEEE J SEL AREA COMMIeee Micro IEEE MICROIeee Multimedia IEEE MULTIMEDIAIeee Network IEEE NETWORKIeee Pervasive Computing IEEE PERVAS COMPUTIeee Security & Privacy IEEE SECUR PRIVIeee Software IEEE SOFTWAREIeee Systems Journal IEEE SYST JIeee Transactions On Affective Computing IEEE T AFFECT COMPUT Ieee Transactions On Autonomous Mental Development IEEE T AUTON MENT DE Ieee Transactions On Broadcasting IEEE T BROADCASTIeee Transactions On Communications IEEE T COMMUN Ieee Transactions On Computational Intelligence and Ai in Games IEEE T COMP INTEL AIIeee Transactions On Computers IEEE T COMPUTIeee Transactions On Cybernetics IEEE T CYBERNETICS Ieee Transactions On Dependable and Secure Computing IEEE T DEPEND SECURE Ieee Transactions On Evolutionary Computation IEEE T EVOLUT COMPUT Ieee Transactions On Haptics IEEE T HAPTICS Ieee Transactions On Information Forensics and Security IEEE T INF FOREN SEC Ieee Transactions On Information Theory IEEE T INFORM THEORYIeee Transactions On Learning Technologies IEEE T LEARN TECHNOLIeee Transactions On Mobile Computing IEEE T MOBILE COMPUT Ieee Transactions On Multimedia IEEE T MULTIMEDIA Ieee Transactions On Neural Networks and Learning Systems IEEE T NEUR NET LEAR Ieee Transactions On Parallel and Distributed Systems IEEE T PARALL DISTR Ieee Transactions On Services Computing IEEE T SERV COMPUTIeee Transactions On Software Engineering IEEE T SOFTWARE ENG Ieee Transactions On Visualization and Computer Graphics IEEE T VIS COMPUT GR Ieee Transactions On Wireless Communications IEEE T WIREL COMMUN Ieee Wireless Communications IEEE WIREL COMMUN Ieee-Acm Transactions On Computational Biology and Bioinformatics IEEE ACM T COMPUT BI Ieee-Acm Transactions On Networking IEEE ACM T NETWORKIeice Transactions On Communications IEICE T COMMUN Ieice Transactions On Information and Systems IEICE T INF SYSTIet Biometrics IET BIOMETRICSIet Computer Vision IET COMPUT VIS Iet Computers and Digital Techniques IET COMPUT DIGIT TEC Iet Information Security IET INFORM SECUR Iet Radar Sonar and Navigation IET RADAR SONAR NAVIet Software IET SOFTWInformatica INFORMATICA-LITHUAN Information and Computation INFORM COMPUT Information and Software Technology INFORM SOFTWARE TECH Information Fusion INFORM FUSION Information Processing Letters INFORM PROCESS LETT Information Retrieval INFORM RETRIEVALInformation Sciences INFORM SCIENCESInformation Systems INFORM SYSTInformation Systems Frontiers INFORM SYST FRONTInformation Systems Management INFORM SYST MANAGEInformation Technology and Control INF TECHNOL CONTROL Information Visualization INFORM VISUAL Informs Journal On Computing INFORMS J COMPUT Integrated Computer-Aided Engineering INTEGR COMPUT-AID E Integration-the Vlsi Journal INTEGRATIONIntelligent Data Analysis INTELL DATA ANALInteracting With Computers INTERACT COMPUT International Arab Journal of Information Technology INT ARAB J INF TECHN International Journal For Numerical Methods in Biomedical Engineering INT J NUMER METH BIO International Journal of Ad Hoc and Ubiquitous Computing INT J AD HOC UBIQ CO International Journal of Approximate Reasoning INT J APPROX REASONInternational Journal of Bio-Inspired Computation INT J BIO-INSPIR COM International Journal of Computational Intelligence Systems INT J COMPUT INT SYS International Journal of Computer Networks and Communications INT J COMPUT NETW CO International Journal of Computers Communications & Control INT J COMPUT COMMUN International Journal of Cooperative Information Systems INT J COOP INF SYSTInternational Journal of Data Mining and Bioinformatics INT J DATA MIN BIOINInternational Journal of Data Warehousing and Mining INT J DATA WAREHOUSInternational Journal of Distributed Sensor Networks INT J DISTRIB SENS NInternational Journal of Foundations of Computer Science INT J FOUND COMPUT S International Journal of General Systems INT J GEN SYST International Journal of High Performance Computing Applications INT J HIGH PERFORM C International Journal of Information Security INT J INF SECUR International Journal of Information Technology & Decision Making INT J INF TECH DECIS International Journal of Machine Learning and Cybernetics INT J MACH LEARN CYB International Journal of Network Management INT J NETW MANAG International Journal of Neural Systems INT J NEURAL SYST International Journal of Parallel Programming INT J PARALLEL PROG International Journal of Pattern Recognition and Artificial Intelligence INT J PATTERN RECOGN International Journal of Satellite Communications and Networking INT J SATELL COMM N International Journal of Sensor Networks INT J SENS NETW International Journal of Software Engineering and Knowledge Engineering INT J SOFTW ENG KNOW International Journal of Uncertainty Fuzziness and Knowledge-Based Systems INT J UNCERTAIN FUZZ International Journal of Unconventional Computing INT J UNCONV COMPUT International Journal of Wavelets Multiresolution and Information Processing INT J WAVELETS MULTI International Journal of Web and Grid Services INT J WEB GRID SERVInternational Journal of Web Services Research INT J WEB SERV RESInternational Journal On Artificial Intelligence Tools INT J ARTIF INTELL T International Journal On Document Analysis and Recognition INT J DOC ANAL RECOG International Journal On Semantic Web and Information Systems INT J SEMANT WEB INFInternet Research INTERNET RESIt Professional IT PROF Journal of Ambient Intelligence and Humanized Computing J AMB INTEL HUM COMP Journal of Ambient Intelligence and Smart Environments J AMB INTEL SMART EN Journal of Applied Logic J APPL LOGIC Journal of Artificial Intelligence Research J ARTIF INTELL RES Journal of Automated Reasoning J AUTOM REASONING Journal of Bioinformatics and Computational Biology J BIOINF COMPUT BIOL Journal of Biomedical Informatics J BIOMED INFORMJournal of Cellular Automata J CELL AUTOMJournal of Cheminformatics J CHEMINFORMATICS Journal of Communications and Networks J COMMUN NETW-S KOR Journal of Communications Technology and Electronics J COMMUN TECHNOL EL+Journal of Computational Analysis and Applications J COMPUT ANAL APPL Journal of Computational Science J COMPUT SCI-NETH Journal of Computer and System Sciences J COMPUT SYST SCI Journal of Computer and Systems Sciences International J COMPUT SYS SC INT+ Journal of Computer Information Systems J COMPUT INFORM SYSTJournal of Computer Science and Technology J COMPUT SCI TECH-CHJournal of Computers J COMPUTJournal of Cryptology J CRYPTOLJournal of Database Management J DATABASE MANAGE Journal of Experimental & Theoretical Artificial Intelligence J EXP THEOR ARTIF IN Journal of Functional Programming J FUNCT PROGRAMJournal of Grid Computing J GRID COMPUTJournal of Heuristics J HEURISTICS Journal of Information Science and Engineering J INF SCI ENGJournal of Information Technology J INF TECHNOLJournal of Intelligent & Fuzzy Systems J INTELL FUZZY SYSTJournal of Intelligent Information Systems J INTELL INF SYST Journal of Internet Technology J INTERNET TECHNOLJournal of Logic and Computation J LOGIC COMPUT Journal of Logical and Algebraic Methods in Programming J LOG ALGEBR METHODS Journal of Machine Learning Research J MACH LEARN RES Journal of Management Information Systems J MANAGE INFORM SYSTJournal of Mathematical Imaging and Vision J MATH IMAGING VISJournal of Molecular Graphics & Modelling J MOL GRAPH MODEL Journal of Multiple-Valued Logic and Soft Computing J MULT-VALUED LOG S Journal of Network and Computer Applications J NETW COMPUT APPLJournal of Network and Systems Management J NETW SYST MANAG Journal of New Music Research J NEW MUSIC RES Journal of Next Generation Information Technology J NEXT GENER INF TEC Journal of Optical Communications and Networking J OPT COMMUN NETW Journal of Organizational and End User Computing J ORGAN END USER COM Journal of Organizational Computing and Electronic Commerce J ORG COMP ELECT COM Journal of Parallel and Distributed Computing J PARALLEL DISTR COM Journal of Real-Time Image Processing J REAL-TIME IMAGE PR Journal of Research and Practice in Information Technology J RES PRACT INF TECH Journal of Software-Evolution and Process J SOFTW-EVOL PROC Journal of Statistical Software J STAT SOFTW Journal of Strategic Information Systems J STRATEGIC INF SYST Journal of Supercomputing J SUPERCOMPUT Journal of Systems and Software J SYST SOFTWAREJournal of Systems Architecture J SYST ARCHITECTJournal of the Acm J ACM Journal of the Association For Information Systems J ASSOC INF SYST Journal of the Institute of Telecommunications Professionals J I TELECOMMUN PROF Journal of Universal Computer Science J UNIVERS COMPUT SCI Journal of Visual Communication and Image Representation J VIS COMMUN IMAGE R Journal of Visual Languages and Computing J VISUAL LANG COMPUT Journal of Visualization J VISUAL-JAPANJournal of Web Engineering J WEB ENGJournal of Web Semantics J WEB SEMANT Journal of Zhejiang University-Science C-Computers & Electronics J ZHEJIANG U-SCI C Journal On Multimodal User Interfaces J MULTIMODAL USER INKnowledge and Information Systems KNOWL INF SYSTKnowledge Engineering Review KNOWL ENG REVKnowledge-Based Systems KNOWL-BASED SYST Ksii Transactions On Internet and Information Systems KSII T INTERNET INF Language Resources and Evaluation LANG RESOUR EVALLogical Methods in Computer Science LOG METH COMPUT SCIMalaysian Journal of Computer Science MALAYS J COMPUT SCI Mathematical and Computer Modelling of Dynamical Systems MATH COMP MODEL DYN Mathematical Modelling of Natural Phenomena MATH MODEL NAT PHENO Mathematical Programming MATH PROGRAM Mathematical Structures in Computer Science MATH STRUCT COMP SCI Medical Image Analysis MED IMAGE ANALMemetic Computing MEMET COMPUT Microprocessors and Microsystems MICROPROCESS MICROSY Minds and Machines MIND MACHMobile Information Systems MOB INF SYSTMobile Networks & Applications MOBILE NETW APPLMultidimensional Systems and Signal Processing MULTIDIM SYST SIGN PMultimedia Systems MULTIMEDIA SYST Multimedia Tools and Applications MULTIMED TOOLS APPLNatural Computing NAT COMPUTNetworks NETWORKSNeural Computation NEURAL COMPUTNeural Network World NEURAL NETW WORLDNeural Networks NEURAL NETWORKSNeural Processing Letters NEURAL PROCESS LETTNeurocomputing NEUROCOMPUTINGNew Generation Computing NEW GENERAT COMPUT New Review of Hypermedia and Multimedia NEW REV HYPERMEDIA M Online Information Review ONLINE INFORM REV Optical Switching and Networking OPT SWITCH NETWOptimization Methods & Software OPTIM METHOD SOFTWParallel Computing PARALLEL COMPUT Peer-To-Peer Networking and Applications PEER PEER NETW APPLPerformance Evaluation PERFORM EVALUATION Personal and Ubiquitous Computing PERS UBIQUIT COMPUTPervasive and Mobile Computing PERVASIVE MOB COMPUTPhotonic Network Communications PHOTONIC NETW COMMUN Presence-Teleoperators and Virtual Environments PRESENCE-TELEOP VIRT Problems of Information Transmission PROBL INFORM TRANSM+Programming and Computer Software PROGRAM COMPUT SOFT+ Rairo-Theoretical Informatics and Applications RAIRO-THEOR INF APPLReal-Time Systems REAL-TIME SYSTRequirements Engineering REQUIR ENGResearch Synthesis Methods RES SYNTH METHODS Romanian Journal of Information Science and Technology ROM J INF SCI TECH Science China-Information Sciences SCI CHINA INFORM SCIScience of Computer Programming SCI COMPUT PROGRAMScientific Programming SCI PROGRAMMING-NETH Security and Communication Networks SECUR COMMUN NETW Siam Journal On Computing SIAM J COMPUTSiam Journal On 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