Titlebook: Computational Intelligence: Theories, Applications and Future Directions - Volume I; ICCI-2017 Nishchal K. Verma,A. K. Ghosh Conference pro

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辭職

H. I. Ansoff,R. P. Declerck,R. L. Hayesanisms comprised of Escherichia coli MG1655 and Streptococcus sanguinis SK36 and another classifier based on PCR method were implemented. The performance of the model based on principal component regression (PCR) method for both set of target organisms resulted in lower AUC values. It shows that the

Asparagus

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https://doi.org/10.1007/978-3-8350-9234-1NNs) with training input size of 15?×?15 and 50?×?50. The result of the study shows that deep learning using CNN is able to detect the melanoma lesion efficiently. The best performance has been achieved using CNN with 15?×?15 training input size. The performances obtained using this network is Jacca

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https://doi.org/10.1007/978-3-8350-9234-1to segment and separate the core and halo parts of the sperm cell. Using centroid-based measures, the difference in diameters between the core and halo parts were calculated. Based on the range of diameter differences, assessment was made on the number of sperm cells with small halo, medium halo, bi

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一起平行

A New Computational Approach to Identify Essential Genes in Bacterial Organisms Using Machine Learnianisms comprised of Escherichia coli MG1655 and Streptococcus sanguinis SK36 and another classifier based on PCR method were implemented. The performance of the model based on principal component regression (PCR) method for both set of target organisms resulted in lower AUC values. It shows that the

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Automatic ECG Signals Recognition Based on Time Domain Features Extraction Using Fiducial Mean Square intervals between the peaks are calibrated and computed by fiducial mean features and (ii) Computation of QRS detection, intervals of QRS, and R-peak amplitude. The procedure of feature extraction of database is also applied on test ECG signals. The Euclidean distance is used to compare database a

PAC

Segmentation and Border Detection of Melanoma Lesions Using Convolutional Neural Network and SVMNNs) with training input size of 15?×?15 and 50?×?50. The result of the study shows that deep learning using CNN is able to detect the melanoma lesion efficiently. The best performance has been achieved using CNN with 15?×?15 training input size. The performances obtained using this network is Jacca

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