Blockchain technology and raft consensus for secure physician prescriptions and improved diagnoses in electronic healthcare systems.

With electronic healthcare systems undergoing rapid change, optimizing the crucial process of recording physician prescriptions is a task with major implications for patient care. The power of blockchain technology and the precision of the Raft consensus algorithm are combined in this article to create a revolutionary solution for this problem. In addition to addressing these issues, the proposed framework, by focusing on the challenges associated with physician prescriptions, is a breakthrough in a new era of security and dependability for the healthcare sector.

Forward propagation dropout in deep neural networks using Jensen-Shannon and random forest feature importance ranking.

Dropout is a mechanism to prevent deep neural networks from overfitting and improving their generalization. Random dropout is the simplest method, where nodes are randomly terminated at each step of the training phase, which may lead to network accuracy reduction. In dynamic dropout, the importance of each node and its impact on the network performance is calculated, and the important nodes do not participate in the dropout.

Robust microarray data feature selection using a correntropy based distance metric learning approach.

Classification of high-dimensional microarray data is a challenge in bioinformatics and genetic data processing. One of the challenging issues of feature selection is the presence of outliers. The Euclidean distance metric is sensitive to outliers.

DMRFusion: A differentially methylated region detection tool based on the ranked fusion method.

DNA methylation is an important epigenetic modification involved in many biological processes and diseases. Computational analysis of differentially methylated regions (DMRs) could explore the underlying reasons of methylation. DMRFusion is presented as a useful tool for comprehensive DNA methylation analysis of DMRs on methylation sequencing data.

A hybrid gene selection approach for microarray data classification using cellular learning automata and ant colony optimization.

This paper proposes an approach for gene selection in microarray data. The proposed approach consists of a primary filter approach using Fisher criterion which reduces the initial genes and hence the search space and time complexity. Then, a wrapper approach which is based on cellular learning automata (CLA) optimized with ant colony method (ACO) is used to find the set of features which improve the classification accuracy.

Robust Feature Selection from Microarray Data Based on Cooperative Game Theory and Qualitative Mutual Information.

High dimensionality of microarray data sets may lead to low efficiency and overfitting. In this paper, a multiphase cooperative game theoretic feature selection approach is proposed for microarray data classification. In the first phase, due to high dimension of microarray data sets, the features are reduced using one of the two filter-based feature selection methods, namely, mutual information and Fisher ratio.

Robust and stable feature selection by integrating ranking methods and wrapper technique in genetic data classification.

High dimensional data increase the dimension of space and consequently the computational complexity and result in lower generalization. From these types of classification problems microarray data classification can be mentioned. Microarrays contain genetic and biological data which can be used to diagnose diseases including various types of cancers and tumors.