Internet of Medical Things is a smart provision of medical services to patients interacting with the doctors in harmony to uplift healthcare facilities. It enables the automated diagnosis of diseases for patients in remote areas. Alzheimer's disease is one of the most chronic diseases and the main cause of dementia in human beings. Dementia affects the patient by a process of gradual degeneration of the human brain and results in an inability to perform daily routine tasks and actions. An automated system needs to be developed, to classify the subject with dementia and to determine the prodromal stage of dementia. Considering such requirement, a fully automated classification system is proposed. The proposed algorithm works on the hybrid feature vector combining the textural, statistical, and shape features extracted from three-dimensional views. The feature length is reduced using principal component analysis and relevant features are extracted for classification. The proposed algorithm is tested for both binary and multi-class problems. The method achieves the average precision of 99.2% and 99.02% for binary and multi-class classifications, respectively. The results outperform the existing methods. The algorithm showed accurate results with the average computational time of 0.05 s per magnetic resonance imaging scan.

This paper proposes a new method of creating 3D visual data cubes for high volume/dimension OLAP data analysis with intuitive region selection. Previous methods construct data cubes directly from a data warehouse and build table format cubes with multi-dimensional attributes, in order to specify target ranges for analysis. However, it is a difficult task to select appropriate attributes and their ranges from high cardinality of dimensions with hierarchical structure. The new method reduces the number of dimensions according to the levels of relationship, then confines analysis target ranges with intuitive 3D graphical interface to build an analysis target cube.

In this pape, we develop a new Homotopy method called the individual Homotopy method to solve the symmetric eigenproblem. The individual Homotopy method overcomes notable drawbacks of the existing Homotopy method, namely, (i) the possibility of breakdown or having a slow rate of convergence in the presence of clustering of the eigenvalues and (ii) the absence of a definite criterion to choose a step size that guarantees the convergence of the method. On the other hand, we also have a good approximations of the largest eigenvalue of a symmetric matrix from Lanczos algorithm. We apply it for the extremal eigenproblem of a very large symmetric matrix with good initial points.

We propose to develope a preconditioner HC for solving the toeplitz T linear system,. Since toeplitz matrices have a nice structure, it has been researched in toeplitz matrix properties and developed precoditioners for the linear toeplitz system. In this paper, we develope a hermitian circulant matrix as a preconditioner HC for the toeplitz linear system. We have two conditions such as (i) ∥HC - T∥F is minimized (ii) HC has also a nice structure such as given matrix T. This preconditioner has an advantage contrary to the existing preconditioners. That is, all eigenvalues of the given toeplitz matrix T are very close to all eigenvalues of our preconditioner HC. Also all eigenvalues of HC-1T are very close to 1. It supports it is a good choice as a preconditioner HC.