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

平息

Dimensionality Reduction-Based Breast Cancer Classification Using Machine Learning which not only complicates the diagnostic process but also finds difficulty in deriving results. Therefore, computational diagnostic techniques must be introduced with the support of artificial intelligence and machine learning. Breast cancer, being one of the second-leading cause of deaths in wome

誘使

A New Heuristic for Degree-Constrained Minimum Spanning Tree Problemplete graph in such a way that the degree of each vertex in . should not exceed ., where . is a positive integer. The DCMST is a .-Hard problem for . . 2. This paper presents a new problem-specific heuristic (.DCMST). .DCMST first builds a feasible degree-constrained spanning tree (.) with the help

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thalamus

Trajectory Tracking of Quad-Rotor UAV Using Fractional Order , Controllertential to control the systems behavior. The quad-rotor control problem presents a test bed for developing and testing new control design methodologies. Fractional- order controllers are being widely used to achieve the robust performance of nonlinear system. These approaches provides greater flexib

Immunization

Evaluation of Security Metrics for System Security Analysis with which the network security has become more challenging. Even though practically we cannot build a perfect system which is fully secure, we can ensure the security level of the system by quantitatively evaluating it, so that the system can be protected against many attacks. Security evaluation

febrile

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Strategische Unternehmungsführungblocks of the network are stacked autoencoder for the multiple modalities. The performance of deep learning-based models with and without multimodal fusion and shared learning are compared. The results indicates that the use of multimodal fusion and shared learning help to improve deep learning-based medical image analysis.

PALSY

偽證

機(jī)警

Feature Learning Using Stacked Autoencoder for Shared and Multimodal Fusion of Medical Imagesblocks of the network are stacked autoencoder for the multiple modalities. The performance of deep learning-based models with and without multimodal fusion and shared learning are compared. The results indicates that the use of multimodal fusion and shared learning help to improve deep learning-based medical image analysis.