Pub Date : 2014-02-01DOI: 10.1504/IJAISC.2014.059287
S. Abid, M. Chtourou, M. Djemel
This work presents two methods of selection of neural models for identification of dynamic systems. Initially, a strategy of selection based on statistical tests, which relates to training and generalisation performances of a neural model is analysed. In the second time, a new constructive approach of neural model selection, which the training begins with minimal structure and then incrementally adds new hidden units and/or layers, is described. The simulation and the application of these methods for selection of neural models are also considered.
{"title":"Statistical and incremental methods for neural models selection","authors":"S. Abid, M. Chtourou, M. Djemel","doi":"10.1504/IJAISC.2014.059287","DOIUrl":"https://doi.org/10.1504/IJAISC.2014.059287","url":null,"abstract":"This work presents two methods of selection of neural models for identification of dynamic systems. Initially, a strategy of selection based on statistical tests, which relates to training and generalisation performances of a neural model is analysed. In the second time, a new constructive approach of neural model selection, which the training begins with minimal structure and then incrementally adds new hidden units and/or layers, is described. The simulation and the application of these methods for selection of neural models are also considered.","PeriodicalId":364571,"journal":{"name":"Int. J. Artif. Intell. Soft Comput.","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125036492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-01DOI: 10.1504/IJAISC.2013.056829
N. Arbaiy, J. Watada
Real-life applications face simultaneously hybrid uncertainty namely fuzziness and randomness, or ambiguous and vague information that makes the existing decision-making model incapable of handling such uncertainties. This paper presents the possibilistic programming for decision-making using a fractile approach. Some real world problems are formulated as a necessity measure model to deal with the uncertainties, which come from vague aspiration and ambiguous coefficients. Thus, the proposed methodology is important in building the model and finding the solution. The vagueness and ambiguity are properly treated in the paper and the fractile approach is used to solve fuzzy linear programming problem. An illustrative example explains the proposed model. The analytical results of the proposed method reveal the improvement of conventional decision-making approaches to appropriately handle inherent uncertainties contained in the real world situation.
{"title":"A fractile optimisation approach for possibilistic programming problem in fuzzy random environment","authors":"N. Arbaiy, J. Watada","doi":"10.1504/IJAISC.2013.056829","DOIUrl":"https://doi.org/10.1504/IJAISC.2013.056829","url":null,"abstract":"Real-life applications face simultaneously hybrid uncertainty namely fuzziness and randomness, or ambiguous and vague information that makes the existing decision-making model incapable of handling such uncertainties. This paper presents the possibilistic programming for decision-making using a fractile approach. Some real world problems are formulated as a necessity measure model to deal with the uncertainties, which come from vague aspiration and ambiguous coefficients. Thus, the proposed methodology is important in building the model and finding the solution. The vagueness and ambiguity are properly treated in the paper and the fractile approach is used to solve fuzzy linear programming problem. An illustrative example explains the proposed model. The analytical results of the proposed method reveal the improvement of conventional decision-making approaches to appropriately handle inherent uncertainties contained in the real world situation.","PeriodicalId":364571,"journal":{"name":"Int. J. Artif. Intell. Soft Comput.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130232691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-01DOI: 10.1504/IJAISC.2013.056826
M. Paulraj, C. Hema, S. Yaacob, Mohd Shuhanaz Zanar Azalan, R. Palaniappan
This paper presents a simple sign language recognition system that has been developed using skin colour segmentation and Elman neural network. A simple segmentation process is carried out to separate the right and left hand. The 2D-invariant moments of the right and left hand segmented image are obtained as features. Using the 2D-invariant moment features, an Elman neural network model was developed. The system has been implemented and tested for its validity. Experimental results show that the system has a recognition rate of 90.63%.
{"title":"Gesture recognition system using 2D-invariant moment feature and Elman neural network","authors":"M. Paulraj, C. Hema, S. Yaacob, Mohd Shuhanaz Zanar Azalan, R. Palaniappan","doi":"10.1504/IJAISC.2013.056826","DOIUrl":"https://doi.org/10.1504/IJAISC.2013.056826","url":null,"abstract":"This paper presents a simple sign language recognition system that has been developed using skin colour segmentation and Elman neural network. A simple segmentation process is carried out to separate the right and left hand. The 2D-invariant moments of the right and left hand segmented image are obtained as features. Using the 2D-invariant moment features, an Elman neural network model was developed. The system has been implemented and tested for its validity. Experimental results show that the system has a recognition rate of 90.63%.","PeriodicalId":364571,"journal":{"name":"Int. J. Artif. Intell. Soft Comput.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126345802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-01DOI: 10.1504/IJAISC.2013.056841
T. A. Fadil, S. Yaakob, R. Badlishah Ahmad, A. Yahya
In this paper, a cipher algorithm based on chaotic neural network CNN is used and integrated inside MPEG-2 video codec system to encrypt and decrypt the quantised coefficients and the motion vector data. This symmetric cipher algorithm was used to transform the plaintext into an unintelligible form under the control of the key. Chaos theory property and its effect on cipher algorithm have been investigated. Result shows that a minor-key modification of the receiver side will lead to unclear video scene with very low PSNR value of -18.363 dB. To reduce the required execution time for CNN cipher algorithm; a motion vector of video signal was selected for encryption and decryption instead of the quantised coefficients. Results indicate little execution time for motion vector encryption and decryption process of 5.498 and 5.381 seconds respectively, but the entropy value decreases to 7.645 as compared to the entropy value of the quantised coefficients encryption. The whole system model can control bit rate and video quality depending on the available bandwidth channel. It can be shown from results that by increasing video quality value the PSNR and the compressed bit rate values will increase also, but with penalty of compression ratio decreasing.
{"title":"A chaotic neural network-based encryption algorithm for MPEG-2 encoded video signal","authors":"T. A. Fadil, S. Yaakob, R. Badlishah Ahmad, A. Yahya","doi":"10.1504/IJAISC.2013.056841","DOIUrl":"https://doi.org/10.1504/IJAISC.2013.056841","url":null,"abstract":"In this paper, a cipher algorithm based on chaotic neural network CNN is used and integrated inside MPEG-2 video codec system to encrypt and decrypt the quantised coefficients and the motion vector data. This symmetric cipher algorithm was used to transform the plaintext into an unintelligible form under the control of the key. Chaos theory property and its effect on cipher algorithm have been investigated. Result shows that a minor-key modification of the receiver side will lead to unclear video scene with very low PSNR value of -18.363 dB. To reduce the required execution time for CNN cipher algorithm; a motion vector of video signal was selected for encryption and decryption instead of the quantised coefficients. Results indicate little execution time for motion vector encryption and decryption process of 5.498 and 5.381 seconds respectively, but the entropy value decreases to 7.645 as compared to the entropy value of the quantised coefficients encryption. The whole system model can control bit rate and video quality depending on the available bandwidth channel. It can be shown from results that by increasing video quality value the PSNR and the compressed bit rate values will increase also, but with penalty of compression ratio decreasing.","PeriodicalId":364571,"journal":{"name":"Int. J. Artif. Intell. Soft Comput.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128204447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-01DOI: 10.1504/IJAISC.2013.056828
B. M. Ghandi, R. Nagarajan, S. Yaacob, D. Hazry
We recently proposed the guided particle swarm optimisation GPSO algorithm as a modification to the popular particle swarm optimisation PSO algorithm with the objective of solving the facial emotion recognition problem. A real-time facial emotion recognition software was implemented using GPSO and tested with 25 subjects. The result was found to be good both in terms of recognition success rate and recognition speed. As a follow-up, we decided to investigate how our novel GPSO approach compares with existing popular classification methods, such as genetic algorithm GA. We re-implement our emotion recognition software using GA and tested it using the video recordings of the same 25 subjects that were used to test the GPSO-based system. Our results show that while the recognition success rate achieved using GA is still reasonable, the recognition speed is very slow, suggesting that the GA method may not be suitable for real-time emotion recognition applications.
{"title":"GPSO versus GA in facial emotion detection","authors":"B. M. Ghandi, R. Nagarajan, S. Yaacob, D. Hazry","doi":"10.1504/IJAISC.2013.056828","DOIUrl":"https://doi.org/10.1504/IJAISC.2013.056828","url":null,"abstract":"We recently proposed the guided particle swarm optimisation GPSO algorithm as a modification to the popular particle swarm optimisation PSO algorithm with the objective of solving the facial emotion recognition problem. A real-time facial emotion recognition software was implemented using GPSO and tested with 25 subjects. The result was found to be good both in terms of recognition success rate and recognition speed. As a follow-up, we decided to investigate how our novel GPSO approach compares with existing popular classification methods, such as genetic algorithm GA. We re-implement our emotion recognition software using GA and tested it using the video recordings of the same 25 subjects that were used to test the GPSO-based system. Our results show that while the recognition success rate achieved using GA is still reasonable, the recognition speed is very slow, suggesting that the GA method may not be suitable for real-time emotion recognition applications.","PeriodicalId":364571,"journal":{"name":"Int. J. Artif. Intell. Soft Comput.","volume":"174 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122249294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-01DOI: 10.1504/IJAISC.2013.056838
M. Paulraj, A. M. Andrew
In this research, a Proton model cars noise comfort level classification system has been developed to detect the noise comfort level in cars using artificial neural network. This research focuses on developing a database consisting of car sound samples measured from different Proton make models in stationary and moving state. In the stationary condition, the sound pressure level is measured at 1,300 RPM, 2,000 RPM and 3,000 RPM while in moving condition, the sound is recorded using dB Orchestra while the car is moving at constant speed from 30 km/h up to 110 km/h. Subjective test is conducted to find the jury's evaluation for the specific sound sample. The feature set is then feed to the neural network model to classify the comfort level. The spectral power feature gives the highest classification accuracy of 88.42%.
{"title":"Classification of interior noise comfort level of Proton model cars using feedforward neural network","authors":"M. Paulraj, A. M. Andrew","doi":"10.1504/IJAISC.2013.056838","DOIUrl":"https://doi.org/10.1504/IJAISC.2013.056838","url":null,"abstract":"In this research, a Proton model cars noise comfort level classification system has been developed to detect the noise comfort level in cars using artificial neural network. This research focuses on developing a database consisting of car sound samples measured from different Proton make models in stationary and moving state. In the stationary condition, the sound pressure level is measured at 1,300 RPM, 2,000 RPM and 3,000 RPM while in moving condition, the sound is recorded using dB Orchestra while the car is moving at constant speed from 30 km/h up to 110 km/h. Subjective test is conducted to find the jury's evaluation for the specific sound sample. The feature set is then feed to the neural network model to classify the comfort level. The spectral power feature gives the highest classification accuracy of 88.42%.","PeriodicalId":364571,"journal":{"name":"Int. J. Artif. Intell. Soft Comput.","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121142616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-01DOI: 10.1504/IJAISC.2013.056848
N. Azli, Norkharziana Mohd Nayan, S. Ayob
Particle swarm optimisation PSO algorithm is known for its easy implementation and has been empirically shown to perform well in many optimisation problems. Thus, it is expected to mitigate the computational burden associated with the solutions of non-linear transcendental equations relevant to problems related to power converter systems. This paper starts with an overview of the general concept of PSO and its variants. It then continues with a review on the application of the PSO algorithm in power converter systems. Then, a sample application is described, focusing on the implementation of the harmonic elimination pulse width modulation HEPWM technique on a single-phase inverter circuit. The results of a simulation and experimental work on the inverter operation have revealed that the PSO method employed is capable of accurately calculating the relevant switching angles and generating the gate signals for the inverter power devices and improving its overall system performance.
{"title":"Particle swarm optimisation and its applications in power converter systems","authors":"N. Azli, Norkharziana Mohd Nayan, S. Ayob","doi":"10.1504/IJAISC.2013.056848","DOIUrl":"https://doi.org/10.1504/IJAISC.2013.056848","url":null,"abstract":"Particle swarm optimisation PSO algorithm is known for its easy implementation and has been empirically shown to perform well in many optimisation problems. Thus, it is expected to mitigate the computational burden associated with the solutions of non-linear transcendental equations relevant to problems related to power converter systems. This paper starts with an overview of the general concept of PSO and its variants. It then continues with a review on the application of the PSO algorithm in power converter systems. Then, a sample application is described, focusing on the implementation of the harmonic elimination pulse width modulation HEPWM technique on a single-phase inverter circuit. The results of a simulation and experimental work on the inverter operation have revealed that the PSO method employed is capable of accurately calculating the relevant switching angles and generating the gate signals for the inverter power devices and improving its overall system performance.","PeriodicalId":364571,"journal":{"name":"Int. J. Artif. Intell. Soft Comput.","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125468255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-04-01DOI: 10.1504/IJAISC.2013.053384
M. Ghosh, D. Das, C. Chakraborty, A. Ray
This paper aims at introducing a textural pattern analysis approach to Plasmodium vivax P. vivax detection from Leishman stained thin blood film. This scheme follows retrospective study design protocol where patients were selected at random in the clinic. The scheme consists of four stages - artefacts reduction, fuzzy divergence-based segmentation of P. vivax infected regions and normal erythrocytes, textural feature extraction using grey level co-occurrence matrix and fractal dimension, finally classification. Here, we have extracted seven features, out of which five are statistically significant in discriminating textures between malaria and normal classes based on light microscopic blood images at 100× resolutions. Finally, Bayesian and support vector machine-based classifiers are trained and validated with 100 cases and 100 control subjects. In effect, it is hereby observed that the significant textural features lead to discriminate P. vivax with 95% and 98% accuracies for SVM and Bayesian classifiers respectively. Results are studied and compared.
{"title":"Quantitative characterisation of Plasmodium vivax in infected erythrocytes: a textural approach","authors":"M. Ghosh, D. Das, C. Chakraborty, A. Ray","doi":"10.1504/IJAISC.2013.053384","DOIUrl":"https://doi.org/10.1504/IJAISC.2013.053384","url":null,"abstract":"This paper aims at introducing a textural pattern analysis approach to Plasmodium vivax P. vivax detection from Leishman stained thin blood film. This scheme follows retrospective study design protocol where patients were selected at random in the clinic. The scheme consists of four stages - artefacts reduction, fuzzy divergence-based segmentation of P. vivax infected regions and normal erythrocytes, textural feature extraction using grey level co-occurrence matrix and fractal dimension, finally classification. Here, we have extracted seven features, out of which five are statistically significant in discriminating textures between malaria and normal classes based on light microscopic blood images at 100× resolutions. Finally, Bayesian and support vector machine-based classifiers are trained and validated with 100 cases and 100 control subjects. In effect, it is hereby observed that the significant textural features lead to discriminate P. vivax with 95% and 98% accuracies for SVM and Bayesian classifiers respectively. Results are studied and compared.","PeriodicalId":364571,"journal":{"name":"Int. J. Artif. Intell. Soft Comput.","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117290867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-04-01DOI: 10.1504/IJAISC.2013.053406
K. Sujatha, N. Pappa, U. Nambi, K. S. Kumar, C. R. R. Dinakaran
This research work aims at monitoring and control of the combustion quality in a power station coal fired boiler using a combination of Fisher's linear discriminant FLD analysis and radial basis network RBN. The flame video is acquired with CCD camera. The features of the flame images like average intensity, area of the flame, brightness of the flame, orientation of the flame, etc. are extracted from the preprocessed images. The FLD is applied to reduce the n-dimensional feature size to two-dimensional feature size for faster learning by the RBN. The results of the proposed technique are compared with the conventional Euclidean distance classifier EDC, which is also used to find the distance between the three groups of images. Three groups of images corresponding to different combustion conditions of the flames have been extracted from a continuous video. The corresponding temperatures and the carbon monoxide CO in the flue gas have been obtained through measurements. Training and testing of Fisher's linear discriminant radial basis network FLDRBN with the data collected have been done and the performances of the various algorithms are evaluated.
{"title":"Automation of combustion monitoring in boilers using discriminant radial basis network","authors":"K. Sujatha, N. Pappa, U. Nambi, K. S. Kumar, C. R. R. Dinakaran","doi":"10.1504/IJAISC.2013.053406","DOIUrl":"https://doi.org/10.1504/IJAISC.2013.053406","url":null,"abstract":"This research work aims at monitoring and control of the combustion quality in a power station coal fired boiler using a combination of Fisher's linear discriminant FLD analysis and radial basis network RBN. The flame video is acquired with CCD camera. The features of the flame images like average intensity, area of the flame, brightness of the flame, orientation of the flame, etc. are extracted from the preprocessed images. The FLD is applied to reduce the n-dimensional feature size to two-dimensional feature size for faster learning by the RBN. The results of the proposed technique are compared with the conventional Euclidean distance classifier EDC, which is also used to find the distance between the three groups of images. Three groups of images corresponding to different combustion conditions of the flames have been extracted from a continuous video. The corresponding temperatures and the carbon monoxide CO in the flue gas have been obtained through measurements. Training and testing of Fisher's linear discriminant radial basis network FLDRBN with the data collected have been done and the performances of the various algorithms are evaluated.","PeriodicalId":364571,"journal":{"name":"Int. J. Artif. Intell. Soft Comput.","volume":"26 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131017819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-04-01DOI: 10.1504/IJAISC.2013.053407
J. Virmani, Vinod Kumar, N. Kalra, N. Khandelwal
Early diagnosis of liver cirrhosis is essential as cirrhosis is an irreversible disease most often seen as precursor to development of hepatocellular carcinoma. Early diagnosis helps radiologist in better disease management by adequate scheduling of treatment options. In the present work, features derived from GLCM mean matrix, GLCM range matrix and singular value decomposition of GLCM matrix have been used along with SVM classifier for designing an efficient computer-aided diagnostic system to characterise normal and cirrhotic liver. The study has been carried out on 120 regions of interest ROIs extracted from 31 clinically acquired B-mode liver ultrasound images. It is observed that the first four singular values obtained by singular value decomposition of GLCM matrix result in highest accuracy and sensitivity of 98.33% and 100%, respectively. The promising results obtained by the proposed computer-aided diagnostic system indicate its usefulness to assist radiologists in diagnosis of liver cirrhosis.
{"title":"SVM-based characterisation of liver cirrhosis by singular value decomposition of GLCM matrix","authors":"J. Virmani, Vinod Kumar, N. Kalra, N. Khandelwal","doi":"10.1504/IJAISC.2013.053407","DOIUrl":"https://doi.org/10.1504/IJAISC.2013.053407","url":null,"abstract":"Early diagnosis of liver cirrhosis is essential as cirrhosis is an irreversible disease most often seen as precursor to development of hepatocellular carcinoma. Early diagnosis helps radiologist in better disease management by adequate scheduling of treatment options. In the present work, features derived from GLCM mean matrix, GLCM range matrix and singular value decomposition of GLCM matrix have been used along with SVM classifier for designing an efficient computer-aided diagnostic system to characterise normal and cirrhotic liver. The study has been carried out on 120 regions of interest ROIs extracted from 31 clinically acquired B-mode liver ultrasound images. It is observed that the first four singular values obtained by singular value decomposition of GLCM matrix result in highest accuracy and sensitivity of 98.33% and 100%, respectively. The promising results obtained by the proposed computer-aided diagnostic system indicate its usefulness to assist radiologists in diagnosis of liver cirrhosis.","PeriodicalId":364571,"journal":{"name":"Int. J. Artif. Intell. Soft Comput.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125543160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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