Titlebook: Health Informatics: A Computational Perspective in Healthcare; Ripon Patgiri,Anupam Biswas,Pinki Roy Book 2021 The Editor(s) (if applicabl

反叛者

K. Shankar,A. Abudhahirnective spectra whose zeroth space is (B.). = ZXBG1(R).. In this paper we consider categories ..(.) = ., ..(.),... of parametrized free modules and bounded homomorphisms and show that the spaces (B..). = (B.)., (B..).,... are the connected components of a nonconnective ω-spectrum B.(F) with ..B.(F) = K.(R) even for negative i.

Unsaturated-Fat

Prasenjit Dhar,K. Suganya Devi,Satish Kumar Satti,P. Srinivasannective spectra whose zeroth space is (B.). = ZXBG1(R).. In this paper we consider categories ..(.) = ., ..(.),... of parametrized free modules and bounded homomorphisms and show that the spaces (B..). = (B.)., (B..).,... are the connected components of a nonconnective ω-spectrum B.(F) with ..B.(F) = K.(R) even for negative i.

gimmick

Arpit Kumar Sharma,Amita Nandal,Arvind Dhaka,Rahul Dixitnective spectra whose zeroth space is (B.). = ZXBG1(R).. In this paper we consider categories ..(.) = ., ..(.),... of parametrized free modules and bounded homomorphisms and show that the spaces (B..). = (B.)., (B..).,... are the connected components of a nonconnective ω-spectrum B.(F) with ..B.(F) = K.(R) even for negative i.

合乎习俗

Brijendra Guptanective spectra whose zeroth space is (B.). = ZXBG1(R).. In this paper we consider categories ..(.) = ., ..(.),... of parametrized free modules and bounded homomorphisms and show that the spaces (B..). = (B.)., (B..).,... are the connected components of a nonconnective ω-spectrum B.(F) with ..B.(F) = K.(R) even for negative i.

Shuttle

Sujit Kumar Das,Pinki Roy,Arnab Kumar Mishranective spectra whose zeroth space is (B.). = ZXBG1(R).. In this paper we consider categories ..(.) = ., ..(.),... of parametrized free modules and bounded homomorphisms and show that the spaces (B..). = (B.)., (B..).,... are the connected components of a nonconnective ω-spectrum B.(F) with ..B.(F) = K.(R) even for negative i.

Stable-Angina

FORGO

An Automatic Classification Methods in Oral Cancer Detection,e advancement of detection of infected regions. In addition, some best elementary approaches are also discussed to increase the recovery score. The goal is to increase the survival rate by diagnosing the oral cancer in less time duration and with more efficient detection methods which will be a significant step in the area of medical imaging.

正论

olfction

Comparative Analysis of Various Deep Learning Algorithms for Diabetic Retinopathy Images,f image processing techniques like resizing, cropping, Gaussian blur method, feature extraction, etc. The result represents that VGG16 performed better with 77.49% accuracy compared to DenseNet and ResNet 50 for the detection of diabetic retinopathy.

EWE