Brain tumor classification using deep CNN features via transfer learning
… (1) P r e c i s i o n = T P T P + F P R e c a l l = T P T P + F N S p e c i f i c i t y = T N T N + F …
(2) F a v g = 2 3 ∑ c = 1 3 P r e c i s i o n c * R e c a l l c P r e c i s i o n c + R e c a l l c …
(2) F a v g = 2 3 ∑ c = 1 3 P r e c i s i o n c * R e c a l l c P r e c i s i o n c + R e c a l l c …
Does improving a firm's environmental management system and environmental performance result in a higher stock price?
SJ Feldman, PA Soyka, PG Ameer - The Journal of Investing, 1997 - pm-research.com
… Ameer … Ameer … Ameer holds an MPP from Harvard University and BS in mechanical
engineering from Cornell University. He is a chartered finandal analyst. …
engineering from Cornell University. He is a chartered finandal analyst. …
Automated categorization of brain tumor from mri using cnn features and svm
Automated tumor characterization has a prominent role in the computer-aided diagnosis (CAD)
system for the human brain. Despite being a well-studied topic, CAD of brain tumors …
system for the human brain. Despite being a well-studied topic, CAD of brain tumors …
Brain tumor categorization from imbalanced MRI dataset using weighted loss and deep feature fusion
Deep learning-based brain tumor classification from brain magnetic resonance imaging (MRI)
is a significant research problem. The research problem encounters a major challenge. …
is a significant research problem. The research problem encounters a major challenge. …
An enhanced deterministic K-Means clustering algorithm for cancer subtype prediction from gene expression data
… The GEO2R tool [21] provided by NCBI GEO was used to compute the adjusted p-values (…
We depended on the adjusted p-value based gene/probe selection for the GEO datasets. …
We depended on the adjusted p-value based gene/probe selection for the GEO datasets. …
Localization and recognition of leukocytes in peripheral blood: A deep learning approach
MR Reena, PM Ameer - Computers in Biology and Medicine, 2020 - Elsevier
… + F N (5) s p e c i f i c i t y = T N T N + F P (6) F 1 s c o r e = 2 × p r e c i s i o n × r e c a l l p
r e c i s i o n + r e c a l l where TP and TN represent the number of true positives and true …
r e c i s i o n + r e c a l l where TP and TN represent the number of true positives and true …
Localization using ray tracing for underwater acoustic sensor networks
PM Ameer, L Jacob - IEEE Communications Letters, 2010 - ieeexplore.ieee.org
… cation, National Institute of Technology Calicut, India (e-mail: {ameer, lilly}@nitc.ac.in). …
We can reduce these equations to first order form by introducing auxiliary variables (p, ζ) …
We can reduce these equations to first order form by introducing auxiliary variables (p, ζ) …
Retrieval of brain MRI with tumor using contrastive loss based similarity on GoogLeNet encodings
… which is a hyperparameter and d ( x p 1 , x p 2 ) represents Euclidean distance between x
p 1 a n d x p 2 . The loss function is small when x p 1 a n d x p 2 are from the same class and it …
p 1 a n d x p 2 . The loss function is small when x p 1 a n d x p 2 are from the same class and it …
Segmentation of leukocyte by semantic segmentation model: A deep learning approach
RM Roy, PM Ameer - Biomedical Signal Processing and Control, 2021 - Elsevier
… p is the number of pixels that must be incorporated in the segmented outcome but are not, E
p … from the segmented outcome but incorporated and A p is the number of pixels that should …
p … from the segmented outcome but incorporated and A p is the number of pixels that should …
Performance evaluation of an IEEE 802.15. 4 sensor network with a star topology
One class of applications envisaged for the IEEE 802.15.4 LR-WPAN (low data rate—wireless
personal area network) standard is wireless sensor networks for monitoring and control …
personal area network) standard is wireless sensor networks for monitoring and control …