Tuesday, September 19, 2023

Dr. R. GNANAKUMARAN IEEE JOURNALS

 1. Robust Extreme Learning Machine based Sentiment Analysis and Classification


Abstract:
In recent times, S entimentalAnalysis (SA) acquires important attention in the process of decision making, primarily implied for the classification and extraction of the sentiments exist in the online reviews posted by the user. SA could be assumed as a sentiment classification (SC)issue where the online reviews experiences classification into negative and positive polarities based on the words available in the online reviews. This study focuses on the design of Robust Extreme Learning Machine Based Sentiment Analysis and Classification (RLM-SAC) model. The presented REIM-SAC model majorly aims to determine the nature of sentiments exist in the input data. Primarily, the input data is thoroughly pre-processed to get rid of unwanted data, which helps in enhancing the classification accuracy and minimizing the computational complexity. In addition, the presented REIM-SAC model applies ELM model to allocate proper class labels to it. To adjust the parameters of the ELM model, comprehensive learning particle swarm optimization (CLPSO) technique was used. The performance assessment of the RELM-SAC model is experimented with using benchmark database and the outcomes are scrutinized under numerous aspects. The simulation outcomes pointed out that the RELM-SAC method has obtained improved outcomes than other models.


Abstract:
The epidemic of coronavirus disease 2019 (COVID-19) has caused an ever-growing demand for treatment, testing, and diagnosis. Chest x-rays are a fast and low-cost test that can detect COVID19 but chest imaging is not a first-line test for COVID19 because of lower diagnosis performance and confounding with other viral pneumonia. Current studies using deep learning (DL) might assist in overcoming these issues as convolution neural networks (CNN) have illustrated higher performance of COVID19 diagnoses at the earlier phase. This study develops a new Firefly Optimization with Bidirectional Gated Recurrent Unit (FFO-BGRU) for COVID19 diagnoses on Chest Radiographs. The main intention of the FFO-BGRU technique lies in the recognition and classification of COVID-19 on Chest X-ray images. At the initial stage, the presented FFO-BGRU technique applies Wiener filtering (WF) technique for noise removal process. Followed, the hyperparameter tuning process takes place by using FFO algorithm and SqueezeNet architecture is applied for feature extraction. Lastly, the BGRU model is applied for COVID19 recognition and classification. A wide range of simulations were performed to demonstrate the betterment of the FFO-BGRU model. The comprehensive comparison study highlighted the improved outcomes of the FFO-BGRU algorithm over other recent approaches.

Tuesday, March 7, 2023

I(EEE) PUBLISED PAPER

 Firefly Optimization with Bidirectional Gated Recurrent Unit for COVID-19 Diagnosis on Chest Radiographs

 

Abstract:

The epidemic of coronavirus disease 2019 (COVID-19) has caused an ever-growing demand for treatment, testing, and diagnosis. Chest x-rays are a fast and low-cost test that can detect COVID19 but chest imaging is not a first-line test for COVID19 because of lower diagnosis performance and confounding with other viral pneumonia. Current studies using deep learning (DL) might assist in overcoming these issues as convolution neural networks (CNN) have illustrated higher performance of COVID19 diagnoses at the earlier phase. This study develops a new Firefly Optimization with Bidirectional Gated Recurrent Unit (FFO-BGRU) for COVID19 diagnoses on Chest Radiographs. The main intention of the FFO-BGRU technique lies in the recognition and classification of COVID-19 on Chest X-ray images. At the initial stage, the presented FFO-BGRU technique applies Wiener filtering (WF) technique for noise removal process. Followed, the hyperparameter tuning process takes place by using FFO algorithm and SqueezeNet architecture is applied for feature extraction. Lastly, the BGRU model is applied for COVID19 recognition and classification. A wide range of simulations were performed to demonstrate the betterment of the FFO-BGRU model. The comprehensive comparison study highlighted the improved outcomes of the FFO-BGRU algorithm over other recent approaches.

 

 

Date Added to IEEE Xplore: 16 January 2023
 
ISBN Information:
 
INSPEC Accession Number: 22539008
 
 
Publisher: IEEE

 
 Author
PG & Research Dept. Of Computer Science, Annai Vailankanni Arts and Science College, Thanjavur, Tamilnadu, India
 
 
 

Wednesday, February 1, 2023

SINGLE-NODE ARCHITECTURE - ppt download

SINGLE-NODE ARCHITECTURE - ppt download: Controller: The controller is the core of a wireless sensor node, it process all the relevant data, capable of executing arbitrary code. It collects data from the sensors, processes this data, decides when and where to send it, similarly receives data from other sensor nodes and decides on the actuator’s behavior. It has to execute various programs, hence it is the Central Processing Unit (CPU) of the node. For General-purpose processors applications microcontrollers are used. These are highly overpowered, and their energy consumption is excessive. These are used in embedded systems. NAVEEN RAJA.V

Tuesday, August 2, 2022

Dr. R. GnanaKumaran Internet Of Things


Dr R.GNANAKUMARAN


IOT IMAGE ISBN:9798886848946

One Part of the Author -Dr. R.Gnanakumaran Presently working as Assistant Professor of PG & Research Department of Computer Science at Annai Vailankanni Arts and Science College, Thanjavur Dist,Tamilnadu. He Post graduated as M.C.A., from Anna University, Chennai and as M.Tech (CSE) from PRIST University Thanjavur, Ph.D. in Computer Science from Bharathidasan University Tiruchirappalli. His Areas of Interest includes Network, Wireless Communication and Mobile computing.