K. Venkatesan
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View article: Customized Spectro-Temporal CNN Feature Extraction and ELM-Based Classifier for Accurate Respiratory Obstruction Detection
Customized Spectro-Temporal CNN Feature Extraction and ELM-Based Classifier for Accurate Respiratory Obstruction Detection Open
The accurate prediction based on lung auscultation of respiratory obstruction conditions (ROC), such as chronic obstructive pulmonary disease (COPD) and asthma, is a challenging task due to the availability of small datasets, ambient noise…
View article: Multimodal Fusion and Cutting-Edge AI-Based Smart Vending Machines for Electronic Component Management
Multimodal Fusion and Cutting-Edge AI-Based Smart Vending Machines for Electronic Component Management Open
Managing electronic components efficiently in a laboratory environment poses a tedious problem, and the same can hinder research efficiency and productivity. These challenges stem from the diversity of electronic components, which can rang…
View article: Comparison Analysis of CXR Images in Detecting Pneumonia Using VGG16 and ResNet50 Convolution Neural Network Model
Comparison Analysis of CXR Images in Detecting Pneumonia Using VGG16 and ResNet50 Convolution Neural Network Model Open
Pneumonia is a lung disease that causes serious fatalities worldwide. Pneumonia can be complicated for medical professionals to identify since it shares similarities with other lung diseases like lung cancer and cardiomegaly. Hospitals fac…
View article: Supplementary Table 5 from FGFR Genetic Alterations Predict for Sensitivity to NVP-BGJ398, a Selective Pan-FGFR Inhibitor
Supplementary Table 5 from FGFR Genetic Alterations Predict for Sensitivity to NVP-BGJ398, a Selective Pan-FGFR Inhibitor Open
XML file - 56K, High throughput pharmacological screen of BGJ398 in 517 cancer cell lines
View article: Data from FGFR Genetic Alterations Predict for Sensitivity to NVP-BGJ398, a Selective Pan-FGFR Inhibitor
Data from FGFR Genetic Alterations Predict for Sensitivity to NVP-BGJ398, a Selective Pan-FGFR Inhibitor Open
Patient stratification biomarkers that enable the translation of cancer genetic knowledge into clinical use are essential for the successful and rapid development of emerging targeted anticancer therapeutics. Here, we describe the identifi…
View article: Data from FGFR Genetic Alterations Predict for Sensitivity to NVP-BGJ398, a Selective Pan-FGFR Inhibitor
Data from FGFR Genetic Alterations Predict for Sensitivity to NVP-BGJ398, a Selective Pan-FGFR Inhibitor Open
Patient stratification biomarkers that enable the translation of cancer genetic knowledge into clinical use are essential for the successful and rapid development of emerging targeted anticancer therapeutics. Here, we describe the identifi…
View article: Supplementary Figure Legends 1-5 from FGFR Genetic Alterations Predict for Sensitivity to NVP-BGJ398, a Selective Pan-FGFR Inhibitor
Supplementary Figure Legends 1-5 from FGFR Genetic Alterations Predict for Sensitivity to NVP-BGJ398, a Selective Pan-FGFR Inhibitor Open
PDF file - 139K
View article: Supplementary Figures 1-5 from FGFR Genetic Alterations Predict for Sensitivity to NVP-BGJ398, a Selective Pan-FGFR Inhibitor
Supplementary Figures 1-5 from FGFR Genetic Alterations Predict for Sensitivity to NVP-BGJ398, a Selective Pan-FGFR Inhibitor Open
PDF file - 1.4MB, Response to NVP-BGJ398; FGFR pathway modulation by NVP-BGJ398; NVP-BGJ398 - predictive features; FGFR2-c3 mRNA expression in cell lines and FGFR2 DNA copy number in tumors; FGF19 DNA copy number in CCLE cell lines and FGF…
View article: Supplementary Table 5 from FGFR Genetic Alterations Predict for Sensitivity to NVP-BGJ398, a Selective Pan-FGFR Inhibitor
Supplementary Table 5 from FGFR Genetic Alterations Predict for Sensitivity to NVP-BGJ398, a Selective Pan-FGFR Inhibitor Open
XML file - 56K, High throughput pharmacological screen of BGJ398 in 517 cancer cell lines
View article: Supplementary Figure Legends 1-5 from FGFR Genetic Alterations Predict for Sensitivity to NVP-BGJ398, a Selective Pan-FGFR Inhibitor
Supplementary Figure Legends 1-5 from FGFR Genetic Alterations Predict for Sensitivity to NVP-BGJ398, a Selective Pan-FGFR Inhibitor Open
PDF file - 139K
View article: Supplementary Tables 1-4 from FGFR Genetic Alterations Predict for Sensitivity to NVP-BGJ398, a Selective Pan-FGFR Inhibitor
Supplementary Tables 1-4 from FGFR Genetic Alterations Predict for Sensitivity to NVP-BGJ398, a Selective Pan-FGFR Inhibitor Open
XML file - 99K, Kinase activity and selectivity for NVP-BGJ398; CCLE cell lines sensitive to NVP-BGJ398; GeneSet expression signatures; FGFR genetic alterations and concomitant mutations
View article: Supplementary Tables 1-4 from FGFR Genetic Alterations Predict for Sensitivity to NVP-BGJ398, a Selective Pan-FGFR Inhibitor
Supplementary Tables 1-4 from FGFR Genetic Alterations Predict for Sensitivity to NVP-BGJ398, a Selective Pan-FGFR Inhibitor Open
XML file - 99K, Kinase activity and selectivity for NVP-BGJ398; CCLE cell lines sensitive to NVP-BGJ398; GeneSet expression signatures; FGFR genetic alterations and concomitant mutations
View article: Supplementary Figures 1-5 from FGFR Genetic Alterations Predict for Sensitivity to NVP-BGJ398, a Selective Pan-FGFR Inhibitor
Supplementary Figures 1-5 from FGFR Genetic Alterations Predict for Sensitivity to NVP-BGJ398, a Selective Pan-FGFR Inhibitor Open
PDF file - 1.4MB, Response to NVP-BGJ398; FGFR pathway modulation by NVP-BGJ398; NVP-BGJ398 - predictive features; FGFR2-c3 mRNA expression in cell lines and FGFR2 DNA copy number in tumors; FGF19 DNA copy number in CCLE cell lines and FGF…
View article: Supervised regression modelling for mitigation of four-wave mixing in dense wavelength-division multiplexing systems
Supervised regression modelling for mitigation of four-wave mixing in dense wavelength-division multiplexing systems Open
A recent global crisis associated with COVID-19 has encouraged millions of people to work from home, thus causing a drastic increase in overall network traffic, data-rate requirements and end network capabilities.This has also produced mor…
View article: Machine Learning Based DWDM Design Using Regression Modelling
Machine Learning Based DWDM Design Using Regression Modelling Open
In this paper, we discuss the non-linearity problems such as Four Wave Mixing (FWM) and high signal distortion with low Output Signal to Noise Ratio (OSNR) in the design of a 64-channel DWDM system using Regression learning technique. The …
View article: Analysis of EDFA Parameters Amenable for Ultra High Bitrate Based DWDM System
Analysis of EDFA Parameters Amenable for Ultra High Bitrate Based DWDM System Open
Exponential growth in data centric services often leads to overhaul of backbone and access networks. Design of high transmission capacity of up to 640Gbps DWDM system with EDFA was analyzed to meet less distortion in an optical backhaul sy…
View article: Design and Development of a Novel Miniaturized Fractal based 3-Bitloaded Line Phase Shifter
Design and Development of a Novel Miniaturized Fractal based 3-Bitloaded Line Phase Shifter Open
This paper proposes a new miniaturized 3-bit loaded line phase shifter for 2.45 GHz WLAN applications. The design use Koch fractal curves to reduce the size of the conventional loaded line phase shifter. The concept has been tested at 2.45…