It is shown that if a 'max-subadditive funtional' p(x) defined on some symmetric neighborhood U0 of zero vector θ in a 'b.f.-toplological group' X is 'upper semi-cotinuous' at a point x0 ∈ U0...It is shown that if a 'max-subadditive funtional' p(x) defined on some symmetric neighborhood U0 of zero vector θ in a 'b.f.-toplological group' X is 'upper semi-cotinuous' at a point x0 ∈ U0, or 'lower semi-continuous' in some neighborhood V(x0) U0 and X is of second category; then p(x) can attain its supremum in U0. And there is a similar conclusion for the γ-max-subadditive functional when its supremum is 0 and if U0 is 'pseudo-bounded' set in X.展开更多
Tuna is superior among the marine fishes that are exported in the forms of raw fish and processed food.Separation of Tuna into their species is done in industries manually,and the process is tiresome.This work propose...Tuna is superior among the marine fishes that are exported in the forms of raw fish and processed food.Separation of Tuna into their species is done in industries manually,and the process is tiresome.This work proposes an automated system for classifying Tuna spe-cies based on their images.An ensemble of region-based deep neural networks is used.A sub region contrast stretching operation is applied to enhance the images.Each fish image is then divided into three regions and is augmented before giving as input to pre-trained convolutional neural networks(CNN).After fine-tuning the models,the output from the last convolutional layer is given to a grouped 2D-local binary pattern descriptor(G2DLBP).Statistical features from the descriptor are applied to different classifiers,and the best clas-sifier for each image region model is identified.Different ensemble methods are subse-quently used to combine the three CNN-G2DLBP models.Among the ensemble techniques,super learner ensemble method with random forest(RF)classifier using 5-fold cross-validation shows the highest classification accuracy of 97.32%.The perfor-mance of different ensemble methods is analyzed in terms of accuracy,precision,recall,and f-score.The proposed system shows an accuracy of 93.91% when evaluated with an independent test dataset.An ensemble of region-based CNN with textural features from G2DLBP is applied for the first time for fish classification.展开更多
文摘It is shown that if a 'max-subadditive funtional' p(x) defined on some symmetric neighborhood U0 of zero vector θ in a 'b.f.-toplological group' X is 'upper semi-cotinuous' at a point x0 ∈ U0, or 'lower semi-continuous' in some neighborhood V(x0) U0 and X is of second category; then p(x) can attain its supremum in U0. And there is a similar conclusion for the γ-max-subadditive functional when its supremum is 0 and if U0 is 'pseudo-bounded' set in X.
文摘Tuna is superior among the marine fishes that are exported in the forms of raw fish and processed food.Separation of Tuna into their species is done in industries manually,and the process is tiresome.This work proposes an automated system for classifying Tuna spe-cies based on their images.An ensemble of region-based deep neural networks is used.A sub region contrast stretching operation is applied to enhance the images.Each fish image is then divided into three regions and is augmented before giving as input to pre-trained convolutional neural networks(CNN).After fine-tuning the models,the output from the last convolutional layer is given to a grouped 2D-local binary pattern descriptor(G2DLBP).Statistical features from the descriptor are applied to different classifiers,and the best clas-sifier for each image region model is identified.Different ensemble methods are subse-quently used to combine the three CNN-G2DLBP models.Among the ensemble techniques,super learner ensemble method with random forest(RF)classifier using 5-fold cross-validation shows the highest classification accuracy of 97.32%.The perfor-mance of different ensemble methods is analyzed in terms of accuracy,precision,recall,and f-score.The proposed system shows an accuracy of 93.91% when evaluated with an independent test dataset.An ensemble of region-based CNN with textural features from G2DLBP is applied for the first time for fish classification.
