摘要 :
Background Epileptic seizures are characterized by aberrant synchronization. We hypothesized that higher synchronization across the seizure onset zone (SOZ) channels during a temporal lobe seizure contributes to impaired conscious...
展开
Background Epileptic seizures are characterized by aberrant synchronization. We hypothesized that higher synchronization across the seizure onset zone (SOZ) channels during a temporal lobe seizure contributes to impaired consciousness. New method All symmetric bivariate synchronization measures were extended to multivariate measure by a principal component analysis (PCA) based technique. A novel nonparametric method has been proposed to test the statistical significance between increased synchronization across the seizure onset zone (SOZ) channels and reduced consciousness. Results Increased synchronization in the gamma band towards seizure termination significantly contributes to impaired consciousness (p < 0.1). Synchronization reaches its peak in the extratemporal region (frontal lobe) ahead of the temporal region (p < 0.05). Synchronization is prominent in beta and gamma bands by most methods and it is more in the second half of seizure duration than in the first (p < 0.05). Conclusions Mutual information is the only synchronization measure out of the six that we studied, whose increase can be associated with the loss of consciousness in a statistically significant way.
收起
摘要 :
Important machine learning classifiers viz., Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbor's and Naive-Bayes were subjected for studing their accuracy, precision and recall accommodating 100 dataset each of...
展开
Important machine learning classifiers viz., Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbor's and Naive-Bayes were subjected for studing their accuracy, precision and recall accommodating 100 dataset each of 12 varieties (Awrodhi, C 235, JG14, K 850, KWR108, Pragati, PG 186, Pusa 362, Radhey, Shubhra, Udai, Ujjawal) of chickpea, 10 of lentil (DPL 62, HUL 57, IPL 316, K75, NDL 1, PL 6, PL 8, PL 406, Pusa Vaibhav, Shekhar), 11 of fieldpea (Adarsh, Aman, Arkel, Azad pea 1, HUDP 15, Indra, EPF 4-9, IP ID 10-12, Prakash, Rachna, Vikash) 11 moong (PDM 139, HUM 12, HUM 16, IPM 02-3, IPM 02-4, Meha, NDM 1, Pant Moong 6, Pusa Vishal, Samrat, Sweta) and 11 of urdbean (Azad 1, Azad 2, Azad 3, IPU 02-43, NDU 1, PU 31, PU 40, Shekhar 1, Shekhar 2, T 9, Uttara) on the basis of their most important metric traits viz., plant height, size of leaf/leaflets, number of branches per plant, days to 50 per cent flowering, number of pods per plant, pod length, number of seed per pod and seed size inorder to find out comparatively the best one model for characterizing the varieties. The average accuracy of Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbor's and Na'ive-Bayes were varied over the pulse crops. The precision and recall of test data set of all crops varieties were 100%. The K-NN model was thus found to be out performed over other models under studied and could therefore effectively be utilized for characterizing, classifying and/or identifying the varieties of pulsecrops.
收起
摘要 :
Several time series generated from agriculture can be effectively modelled using various time-series modelling techniques such as ARIMA (Box-Jenkins) modelling technique, State-Space modelling technique, Structural Time Series mod...
展开
Several time series generated from agriculture can be effectively modelled using various time-series modelling techniques such as ARIMA (Box-Jenkins) modelling technique, State-Space modelling technique, Structural Time Series modelling and other timeseries modelling depend on the properties of the given time series. Modelling and related forecasting for thetime serieswere performed using Autoregressive Moving Average (ARIMA), Autoregressive Neural Network(ARNN) and ARIMA-ARNN hybrid models. First,to maintain the stationarity property of the data (1950-51 to 2017-18)as a necessary step, the datasetwas tested,and thefirst order difference series were considered for modelling using the Box-Jenkins approach. ARIMA (0,1,1) were found suitable for theproduction and yield databased on the least value of Schwarz-Bayesian Criterion (SBC). Secondly, Autoregressive Neural Network (ARNN) of orderARNN (2,2) wasselected for both the dataset. Lastly, ARIMA (0,1,1) - ARNN (4,6) for both production and yield were found suitable. All the three models were tested for their forecast accuracy using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Accordingly, the ARIMA-ARNN hybrid model was found to be best as compared to the individual ARIMA and ARNN model. Based on the ARIMA-ARNN model, the forecasting of the production and yield for the year 2050 was found to be 35.84 million tonnes and 1062.01 kg/ha, respectively of pulses in India.
收起
原文获取