The Experts below are selected from a list of 139395 Experts worldwide ranked by ideXlab platform
W Moniaci - One of the best experts on this subject based on the ideXlab platform.
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short term local meteorological forecasting using type 2 fuzzy systems
Lecture Notes in Computer Science, 2006Co-Authors: Arianna Mencattini, S Bertazzoni, Eros Gian Alessandro Pasero, Marcello Salmeri, R. Lojacono, W MoniaciAbstract:Meteorological forecasting is an important issue in research. Typically, the forecasting is performed at global level, by gathering data in a large Geographical Region and by studying their evolution, thus foreseeing the meteorological situation in a certain place. In this paper a local level approach, based on time series forecasting using Type-2 Fuzzy Systems, is proposed. In particular temperature forecasting is inspected. The Fuzzy System is trained by means of historical local time series. The algorithm uses a detrend procedure in order to extract the chaotic component to be predicted.
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WIRN/NAIS - Short term local meteorological forecasting using type-2 fuzzy systems
Neural Nets, 2005Co-Authors: Arianna Mencattini, S Bertazzoni, Eros Gian Alessandro Pasero, Marcello Salmeri, R. Lojacono, W MoniaciAbstract:Meteorological forecasting is an important issue in research. Typically, the forecasting is performed at “global level,” by gathering data in a large Geographical Region and by studying their evolution, thus foreseeing the meteorological situation in a certain place. In this paper a “local level” approach, based on time series forecasting using Type-2 Fuzzy Systems, is proposed. In particular temperature forecasting is inspected. The Fuzzy System is trained by means of historical local time series. The algorithm uses a detrend procedure in order to extract the chaotic component to be predicted.
Arianna Mencattini - One of the best experts on this subject based on the ideXlab platform.
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short term local meteorological forecasting using type 2 fuzzy systems
Lecture Notes in Computer Science, 2006Co-Authors: Arianna Mencattini, S Bertazzoni, Eros Gian Alessandro Pasero, Marcello Salmeri, R. Lojacono, W MoniaciAbstract:Meteorological forecasting is an important issue in research. Typically, the forecasting is performed at global level, by gathering data in a large Geographical Region and by studying their evolution, thus foreseeing the meteorological situation in a certain place. In this paper a local level approach, based on time series forecasting using Type-2 Fuzzy Systems, is proposed. In particular temperature forecasting is inspected. The Fuzzy System is trained by means of historical local time series. The algorithm uses a detrend procedure in order to extract the chaotic component to be predicted.
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WIRN/NAIS - Short term local meteorological forecasting using type-2 fuzzy systems
Neural Nets, 2005Co-Authors: Arianna Mencattini, S Bertazzoni, Eros Gian Alessandro Pasero, Marcello Salmeri, R. Lojacono, W MoniaciAbstract:Meteorological forecasting is an important issue in research. Typically, the forecasting is performed at “global level,” by gathering data in a large Geographical Region and by studying their evolution, thus foreseeing the meteorological situation in a certain place. In this paper a “local level” approach, based on time series forecasting using Type-2 Fuzzy Systems, is proposed. In particular temperature forecasting is inspected. The Fuzzy System is trained by means of historical local time series. The algorithm uses a detrend procedure in order to extract the chaotic component to be predicted.
Sam S Chang - One of the best experts on this subject based on the ideXlab platform.
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immediate surgical outcomes for radical prostatectomy in the university healthsystem consortium clinical data base the impact of hospital case volume hospital size and Geographical Region on 48 000 patients
BJUI, 2009Co-Authors: Robert E Mitchell, Byron Lee, Michael S Cookson, Daniel A Barocas, Duke S Herrell, Peter E Clark, Joseph A Smith, Sam S ChangAbstract:OBJECTIVE To determine the impact of hospital variables on immediate surgical outcomes for patients treated with radical prostatectomy (RP) in academic centres. PATIENTS AND METHODS The University HealthSystem Consortium (UHC) Clinical Data Base was queried for data corresponding to patients who had RP at one of 130 academic medical centres nationwide between 2003 and the second quarter of 2007 (48 086). RP case volume (1–99, 100–499 and >500), total discharges (1–49 999, 50 000–99 999, >100 000), and Geographical Region (five categories) were determined and categorized for each academic centre. Analysis of variance and the Tukey statistic were used to assess the results. Length of stay (LOS), intensive care unit (ICU) rate, complication rate (CR) and in-hospital mortality (IHM) were analysed. RESULTS Case volume was a significant predictor of LOS, ICU and CR. The mean LOS was 3.77, 2.65 and 2.09 days, respectively, for centres from three tiers of lowest to highest case volumes (P < 0.001). ICU rates for the three tiers were 18.57, 3.61, and 1.30 (P < 0.001); CRs were 15.93, 8.79 and 5.76 (P < 0.001). Tukey analysis showed a ‘ceiling’ effect for ICU and CRs; there were no differences between the two higher case-volume groups. IHM was not significantly different between groups stratified by case volume. Stratification by total discharges showed differences in ICU rates only (P = 0.003). Stratification by Geographical Region showed no differences in outcome. CONCLUSIONS RP case volume was an important variable in predicting three of the four outcome variables. CRs and ICU rates showed a ‘ceiling effect’ suggesting that an unknown ‘critical volume’ of cases portends improved surgical outcomes.
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Immediate surgical outcomes for radical prostatectomy in the University HealthSystem Consortium Clinical Data Base: the impact of hospital case volume, hospital size and Geographical Region on 48 000 patients
BJU international, 2009Co-Authors: Robert E Mitchell, Byron Lee, Michael S Cookson, Daniel A Barocas, Peter E Clark, Joseph A Smith, S. Duke Herrell, Sam S ChangAbstract:OBJECTIVE To determine the impact of hospital variables on immediate surgical outcomes for patients treated with radical prostatectomy (RP) in academic centres. PATIENTS AND METHODS The University HealthSystem Consortium (UHC) Clinical Data Base was queried for data corresponding to patients who had RP at one of 130 academic medical centres nationwide between 2003 and the second quarter of 2007 (48 086). RP case volume (1–99, 100–499 and >500), total discharges (1–49 999, 50 000–99 999, >100 000), and Geographical Region (five categories) were determined and categorized for each academic centre. Analysis of variance and the Tukey statistic were used to assess the results. Length of stay (LOS), intensive care unit (ICU) rate, complication rate (CR) and in-hospital mortality (IHM) were analysed. RESULTS Case volume was a significant predictor of LOS, ICU and CR. The mean LOS was 3.77, 2.65 and 2.09 days, respectively, for centres from three tiers of lowest to highest case volumes (P
S Bertazzoni - One of the best experts on this subject based on the ideXlab platform.
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short term local meteorological forecasting using type 2 fuzzy systems
Lecture Notes in Computer Science, 2006Co-Authors: Arianna Mencattini, S Bertazzoni, Eros Gian Alessandro Pasero, Marcello Salmeri, R. Lojacono, W MoniaciAbstract:Meteorological forecasting is an important issue in research. Typically, the forecasting is performed at global level, by gathering data in a large Geographical Region and by studying their evolution, thus foreseeing the meteorological situation in a certain place. In this paper a local level approach, based on time series forecasting using Type-2 Fuzzy Systems, is proposed. In particular temperature forecasting is inspected. The Fuzzy System is trained by means of historical local time series. The algorithm uses a detrend procedure in order to extract the chaotic component to be predicted.
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WIRN/NAIS - Short term local meteorological forecasting using type-2 fuzzy systems
Neural Nets, 2005Co-Authors: Arianna Mencattini, S Bertazzoni, Eros Gian Alessandro Pasero, Marcello Salmeri, R. Lojacono, W MoniaciAbstract:Meteorological forecasting is an important issue in research. Typically, the forecasting is performed at “global level,” by gathering data in a large Geographical Region and by studying their evolution, thus foreseeing the meteorological situation in a certain place. In this paper a “local level” approach, based on time series forecasting using Type-2 Fuzzy Systems, is proposed. In particular temperature forecasting is inspected. The Fuzzy System is trained by means of historical local time series. The algorithm uses a detrend procedure in order to extract the chaotic component to be predicted.
Eros Gian Alessandro Pasero - One of the best experts on this subject based on the ideXlab platform.
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short term local meteorological forecasting using type 2 fuzzy systems
Lecture Notes in Computer Science, 2006Co-Authors: Arianna Mencattini, S Bertazzoni, Eros Gian Alessandro Pasero, Marcello Salmeri, R. Lojacono, W MoniaciAbstract:Meteorological forecasting is an important issue in research. Typically, the forecasting is performed at global level, by gathering data in a large Geographical Region and by studying their evolution, thus foreseeing the meteorological situation in a certain place. In this paper a local level approach, based on time series forecasting using Type-2 Fuzzy Systems, is proposed. In particular temperature forecasting is inspected. The Fuzzy System is trained by means of historical local time series. The algorithm uses a detrend procedure in order to extract the chaotic component to be predicted.
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WIRN/NAIS - Short term local meteorological forecasting using type-2 fuzzy systems
Neural Nets, 2005Co-Authors: Arianna Mencattini, S Bertazzoni, Eros Gian Alessandro Pasero, Marcello Salmeri, R. Lojacono, W MoniaciAbstract:Meteorological forecasting is an important issue in research. Typically, the forecasting is performed at “global level,” by gathering data in a large Geographical Region and by studying their evolution, thus foreseeing the meteorological situation in a certain place. In this paper a “local level” approach, based on time series forecasting using Type-2 Fuzzy Systems, is proposed. In particular temperature forecasting is inspected. The Fuzzy System is trained by means of historical local time series. The algorithm uses a detrend procedure in order to extract the chaotic component to be predicted.
