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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) APTIKOM Journal on Computer Science and Information Technologies (CSIT)
Pattanayak, Binod Kumar
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Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Materials Science & Nanotechnology

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QUALITY OF SERVICE BASED MULTICASTING ROUTING PROTOCOLS FOR MANETS: A SURVEY Sahu, Prabhat Kumar; Pattanayak, Binod Kumar
APTIKOM Journal on Computer Science and Information Technologies Vol 2 No 1 (2017): APTIKOM Journal on Computer Science and Information Technologies (CSIT)
Publisher : APTIKOM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The modern electronic age consists of many new approaches for the communication among the humancivilizations and the AD HOC network is one of the successful self-configuring and infrastructure-lesscommunicating approach. Mobile ad hoc networks (MANETs) comprised of mobile nodes organize themselves insuch a manner that the can move freely inside the network and frequently change their position. Open architectureand dynamic nature of the network enhance its use in multimedia applications that needs graphics, audio, data,image, video and animation. Now a day?s the increasing use of multimedia and Internet technology needs quality ofservice (QoS) in MANETs in order to provide better service for the companies. This type of applications lendsthemselves well to multicast operations. As multicasting supports group oriented computing it can improve the QoSof the wireless medium by means of sending multiple copies of packets by exploring the internet broadcast propertiesof wireless transmission. Hence QoS multicasting plays a great role in MANETs for multimedia applications.However it is very difficult and challenging task to provide QoS multicasting. With unique features and by means ofdifferent recovery mechanisms, many researchers have been proposed various QoS based multicasting routingprotocols for MANETs. In order to assist QoS multicasting routing protocols design for MANETs, we characterizethe taxonomy of the multicast routing protocols and design features in this paper.
Precision medicine in hepatology: harnessing IoT and machine learning for personalized liver disease stage prediction Swain, Satyaprakash; Mohanty, Mihir Narayan; Pattanayak, Binod Kumar
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i3.pp724-734

Abstract

In this research, we used a dataset from Siksha ‘O’ Anusandhan (S’O’A) University Medical Laboratory containing 6,780 samples collected manually and through internet of things (IoT) sensor sources from 6,780 patients to perform a thorough investigation into liver disease stage prediction. The dataset was carefully cleaned before being sent to the machine learning pipeline. We utilised a range of machine learning models, such as Naïve Bayes (NB), sequential minimal optimisation (SMO), K-STAR, random forest (RF), and multi-class classification (MCC), using Python to predict the stages of liver disease. The results of our simulations demonstrated how well the SMO model performed in comparison to other models. We then expanded our analysis using different machine learning boosting models with SMO as the base model: adaptive boosting (AdaBoost), gradient boost, extreme gradient boosting (XGBoost), CatBoost, and light gradient boosting model (LightGBM). Surprisingly, gradient boost proved to be the most successful, producing an astounding 96% accuracy. A closer look at the data showed that when AdaBoost was combined with the SMO base model, the accuracy results were 94.10%, XGBoost 90%, CatBoost 92%, and LightGBM 94%. These results highlight the effectiveness of proposed model i.e. gradient boosting in improving the prediction of liver disease stage and provide insightful information for improving clinical decision support systems in the field of medical diagnostics.
Co-Authors Mihir Narayan Mohanty Sahu, Prabhat Kumar Swain, Satyaprakash