Nowadays, a large amount of information has to be transmitted or processed. This implies
high-power processing, large memory density, and increased energy consumption. In …

With the development of intelligent networks such as the Internet of Things, network scales
are becoming increasingly larger, and network environments increasingly complex, which …

Abstract Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System (MKL with
ANFIS) based deep learning method is proposed in this paper for heart disease diagnosis …

Electrocardiographic (ECG) signals often consist of unwanted noises and speckles. In order
to remove the noises, various image processing filters are used in various studies. In this …

The main objective of this research is to study the integration of Analytic Hierarchy Process
(AHP) into Delphi framework in neutrosophic environment and present a new technique for …

Geospatial data analytical model is developed in this paper to model the spatial suitability of
malaria outbreak in Vellore, Tamil Nadu, India. In general, Disease control strategies are …

Recently, visible light positioning and visible light communication are becoming a promising
technology for integrated sensing and communication. However, the isolated design of …

Single-pixel imaging (SPI) uses a single-pixel detector instead of a detector array with a lot
of pixels in traditional imaging techniques to realize two-dimensional or even multi …

In this paper, we study the orthogonal least squares (OLS) algorithm for sparse recovery. On
one hand, we show that if the sampling matrix A satisfies the restricted isometry property of …

Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or
compressible signals. Sparse coding represents a signal as a sparse linear combination of …