In previous researches on a model-based diagnostic system, the components are assumed mutually independent. Howerver , the assumption is not always the case because the information about whether a component is faulty ...In previous researches on a model-based diagnostic system, the components are assumed mutually independent. Howerver , the assumption is not always the case because the information about whether a component is faulty or not usually influences our knowledge about other components. Some experts may draw such a conclusion that 'if component m 1 is faulty, then component m 2 may be faulty too'. How can we use this experts' knowledge to aid the diagnosis? Based on Kohlas's probabilistic assumption-based reasoning method, we use Bayes networks to solve this problem. We calculate the posterior fault probability of the components in the observation state. The result is reasonable and reflects the effectiveness of the experts' knowledge.展开更多
Magnetic resonance imaging is a highly sensitive approach for diagnosis of multiple sclerosis, and T2-weighted images can reveal lesions in the cerebral white matter, gray matter, and spinal cord. However, the lesions...Magnetic resonance imaging is a highly sensitive approach for diagnosis of multiple sclerosis, and T2-weighted images can reveal lesions in the cerebral white matter, gray matter, and spinal cord. However, the lesions have a poor correlation with measurable clinical disability. In this study, we performed a large-scale epidemiological survey of 238 patients with multiple sclerosis in eleven districts by network member hospitals in Shanghai, China within 1 year. The involved patients were scanned for position and size of lesions by MRI. Results showed that lesions in the cerebrum, spina cord, or supratentorial position had an impact on the activities of daily living in multiple sclerosis patients, as assessed by the Bayes network. On the other hand, brainstem lesions were very unlikely to influence the activities of daily living, and were not associated with the position of lesion, patient＇s gender, and patient＇s living place.展开更多
The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed wo...The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.展开更多
Mass spectrometry is one of the widely utilized important methods to study protein functions and components. The challenge of mono-isotope pattern recognition from large scale protein mass spectral data needs computat...Mass spectrometry is one of the widely utilized important methods to study protein functions and components. The challenge of mono-isotope pattern recognition from large scale protein mass spectral data needs computational algorithms and tools to speed up the analysis and improve the analytic results. We utilized na¨？ve Bayes network as the classifier with the assumption that the selected features are independent to predict monoisotope pattern from mass spectrometry. Mono-isotopes detected from validated theoretical spectra were used as prior information in the Bayes method. Three main features extracted from the dataset were employed as independent variables in our model. The application of the proposed algorithm to public Mo dataset demonstrates that our na¨？ve Bayes classifier is advantageous over existing methods in both accuracy and sensitivity.展开更多
文摘In previous researches on a model-based diagnostic system, the components are assumed mutually independent. Howerver , the assumption is not always the case because the information about whether a component is faulty or not usually influences our knowledge about other components. Some experts may draw such a conclusion that 'if component m 1 is faulty, then component m 2 may be faulty too'. How can we use this experts' knowledge to aid the diagnosis? Based on Kohlas's probabilistic assumption-based reasoning method, we use Bayes networks to solve this problem. We calculate the posterior fault probability of the components in the observation state. The result is reasonable and reflects the effectiveness of the experts' knowledge.
基金supported by the National Natural Science Foundation of China,No.30872179 and 81070958a grant from Shanghai Science and Technology Commission,No.08410702200a grant from Shanghai Key Discipline Construction,No.08GWEX0201
文摘Magnetic resonance imaging is a highly sensitive approach for diagnosis of multiple sclerosis, and T2-weighted images can reveal lesions in the cerebral white matter, gray matter, and spinal cord. However, the lesions have a poor correlation with measurable clinical disability. In this study, we performed a large-scale epidemiological survey of 238 patients with multiple sclerosis in eleven districts by network member hospitals in Shanghai, China within 1 year. The involved patients were scanned for position and size of lesions by MRI. Results showed that lesions in the cerebrum, spina cord, or supratentorial position had an impact on the activities of daily living in multiple sclerosis patients, as assessed by the Bayes network. On the other hand, brainstem lesions were very unlikely to influence the activities of daily living, and were not associated with the position of lesion, patient＇s gender, and patient＇s living place.
文摘The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.
基金supported by an NSF Science and Technology Center, under Grant Agreement CCF0939370 and 2 G12 RR003048 from the RCMI program, Division of Research Infrastructure, National Center for Research Resources, NIH
文摘Mass spectrometry is one of the widely utilized important methods to study protein functions and components. The challenge of mono-isotope pattern recognition from large scale protein mass spectral data needs computational algorithms and tools to speed up the analysis and improve the analytic results. We utilized na¨？ve Bayes network as the classifier with the assumption that the selected features are independent to predict monoisotope pattern from mass spectrometry. Mono-isotopes detected from validated theoretical spectra were used as prior information in the Bayes method. Three main features extracted from the dataset were employed as independent variables in our model. The application of the proposed algorithm to public Mo dataset demonstrates that our na¨？ve Bayes classifier is advantageous over existing methods in both accuracy and sensitivity.
