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All Journal International Journal of Electrical and Computer Engineering International Journal of Informatics and Communication Technology (IJ-ICT) PROSIDING SENATEK FAKULTAS TEKNIK UMP
Seong-Cheol Kim, Seong-Cheol
School of Chemical Engineering, Yeungnam University
Computer Science & IT Electrical & Electronics Engineering Engineering

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CARBON MATERIALS FOR ELECTRONIC, ENVIRONMENTAL AND BIOMEDICAL APPLICATION Kim, Seong-Cheol
PROSIDING SENATEK FAKULTAS TEKNIK UMP 2015: PROSIDING SENATEK TAHUN 2015, 28 November 2015
Publisher : PROSIDING SENATEK FAKULTAS TEKNIK UMP

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Abstract

The carbon materials are the focal point of materials science especially in the field of environmental, biomedical and electrical materials. The focus of my presentation is the introduction of new carbon materials developed in last two decades. The materials include fullerenes, carbon nanotubes, and graphenes. The basic physical properties of the carbon nanomaterials will be introduced in the order of fullerene, nanotube and graphenes. Some of these properties are unique and superior to those of metals, polymers and ceramics. The properties of these materials make them useful for enhancing surface-to-volume ratio, reactivity, strength and durability. Some of the applications of these properties are included in the presentation.
MATERI PPT KEY NOTE SPEAKER Prof. SEONG CHEOL KIM Kim, Seong-Cheol
PROSIDING SENATEK FAKULTAS TEKNIK UMP 2015: PROSIDING SENATEK TAHUN 2015, 28 November 2015
Publisher : PROSIDING SENATEK FAKULTAS TEKNIK UMP

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Abstract

POLY(ETHYLENE) OXIDE SYNTHETIC POLYMER HYDROGELS FOR MEDICAL APPLICATIONS
Advancements in energy storage technologies for smart grid development Sharma, Pankaj; Reddy Salkuti, Surender; Kim, Seong-Cheol
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3421-3429

Abstract

In the modern world, the consumption of oil, coal natural gas, and nuclear energy has been causing by a serious environmental problem and an ongoing energy crisis. The generation and consumption of renewable energy sources (RESs) such as solar and wind tidal, can resolve the problem but the nature of the RESs is fluctuating and intermitted. This evolution brings a lot of challenges in the management of electrical grids. The paper reviewed the advancements in energy storage technologies for the development of a smart grid (SG). More attention was paid to the classification of energy storage technologies based on the form of energy storage and based on the form of discharge duration. The evaluation criteria for the energy storage technologies have been carried out based on technological dimensions such as storage capacity, efficiency, response time, energy density, and power density, the economic dimension such as input cost and economic benefit; and the environmental dimension such as emission and stress on ecosystem, social demission such as job creation and social acceptance were also presented in this paper.
Data analysis and visualization on titanic and student’s performance datasets-an exploratory study Kim, Seong-Cheol; Salkuti, Surender Reddy; Suresh, Alka Manvayalar; Sankaran, Madhu Sree
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i1.pp68-76

Abstract

Exploratory data analysis (EDA) is all about exploring the data in order to identify any underlying pattern before you try to use it to make a predictive model. It also plays a major role in the data discovery process as it is used to analyze data and to recapitulate their different characteristics, which is displayed efficiently with the help of data visualization methods. This paper aims to identify errors in the dataset, to understand the existing hidden structure and to identify new ones, to detect points in a dataset that deviate to a greater extent from the collected data (outliers), and also to find any relationship or intersection between the variables and constants. Two datasets are used namely ‘Titanic’ and ‘student’s performance’ to perform data analysis and ‘data visualization’ to depict ‘exploratory data analysis’ which acts as an important set of tools for recognizing a qualitative understanding. The datasets were explored and hence it assisted with identifying patterns, outliers, corrupt data, and discovering the relationship between the fields in the dataset.
AI-based federated learning for heart disease prediction: a collaborative and privacy-preserving approach Bhatt, Stuti; Salkuti, Surender Reddy; Kim, Seong-Cheol
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp751-759

Abstract

People with symptoms like diabetes, high BP, and high cholesterol are at an increased risk for heart disease and stroke as they get older. To mitigate this threat, predictive fashions leveraging machine learning (ML) and artificial intelligence (AI) have emerged as a precious gear; however, heart disease prediction is a complicated task, and diagnosis outcomes are hardly ever accurate. Currently, the existing ML tech says it is necessary to have data in certain centralized locations to detect heart disease, as data can be found centrally and is easily accessible. This review introduces federated learning (FL) to answer data privacy challenges in heart disease prediction. FL, a collaborative technique pioneered by Google, trains algorithms across independent sessions using local datasets. This paper investigates recent ML methods and databases for predicting cardiovascular disease (heart attack). Previous research explores algorithms like region-based convolutional neural network (RCNN), convolutional neural network (CNN), and federated logistic regressions (FLRs) for heart and other disease prediction. FL allows the training of a collaborative model while keeping patient info spread out among various sites, ensuring privacy and security. This paper explores the efficacy of FL, a collaborative technique, in enhancing the accuracy of cardiovascular disease (CVD) prediction models while preserving data privacy across distributed datasets.
Co-Authors Bhatt, Stuti Reddy Salkuti, Surender Salkuti, Surender Reddy Sankaran, Madhu Sree Sharma, Pankaj Suresh, Alka Manvayalar