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International Journal of Electrical and Computer Engineering

IJECE
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Published by Institute of Advanced Engineering and Science

ISSN : 20888708 EISSN : 27222578 DOI : -

Core Subject : Science, Engineering,

Computer Science & IT Electrical & Electronics Engineering

International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.

Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)

Articles 6,301 Documents

148 149 150 151 152

An ensemble multi-model technique for predicting chronic kidney disease Komal Kumar N; R. Lakshmi Tulasi; Vigneswari D
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 2: April 2019
Publisher : Institute of Advanced Engineering and Science

Chronic Kidney Disease (CKD) is a type of lifelong kidney disease that leads to the gradual loss of kidney function over time; the main function of the kidney is to filter the wastein the human body. When the kidney malfunctions, the wastes accumulate in our body leading to complete failure. Machine learning algorithms can be used in prediction of the kidney disease at early stages by analyzing the symptoms. The aim of this paper is to propose an ensemble learning technique for predicting Chronic Kidney Disease (CKD). We propose a new hybrid classifier called as ABC4.5, which is ensemble learning for predicting Chronic Kidney Disease (CKD). The proposed hybrid classifier is compared with the machine learning classifiers such as Support Vector Machine (SVM), Decision Tree (DT), C4.5, Particle Swarm Optimized Multi Layer Perceptron (PSO-MLP). The proposed classifier accurately predicts the occurrences of kidney disease by analysis various medical factors. The work comprises of two stages, the first stage consists of obtaining weak decision tree classifiers from C4.5 and in the second stage, the weak classifiers are added to the weighted sum to represent the final output for improved performance of the classifier.

Indian Classical Dance Mudra Classification Using HOG Features and SVM Classifier K.V.V. Kumar; P.V.V. Kishore
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 5: October 2017
Publisher : Institute of Advanced Engineering and Science

Digital understanding of Indian classical dance is least studied work, though it has been a part of Indian Culture from around 200BC. This work explores the possibilities of recognizing classical dance mudras in various dance forms in India. The images of hand mudras of various classical dances are collected form the internet and a database is created for this job. Histogram of oriented (HOG) features of hand mudras input the classifier. Support vector machine (SVM) classifies the HOG features into mudras as text messages. The mudra recognition frequency (MRF) is calculated for each mudra using graphical user interface (GUI) developed from the model. Popular feature vectors such as SIFT, SURF, LBP and HAAR are tested against HOG for precision and swiftness. This work helps new learners and dance enthusiastic people to learn and understand dance forms and related information on their mobile devices.

Design and control technique for single phase bipolar H-bridge inverter connected to the grid Linda Hassaine; Mohamed Rida Bengourina
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

The power quality injected into the grid and the performance of the converter system depend on the quality of the inverter current control. This paper proposes a design and control technique for a photovoltaic inverter connected to the grid based on the digital pulse-width modulation (DSPWM) which can synchronise a sinusoidal output current with a grid voltage and control a power factor. The current injected must be sinusoidal with reduced harmonic distortion. The connected PV system is based on H-Bridge inverter controlled by bipolar PWM Switching. The current control technique and functional structure of this system are presented and simulated. Detailed analysis, Simulations results of output voltage and current waveform demonstrate the contribution of this approach to determinate the suitable control of the system. A digital design of a generator PWM using VHDL is proposed and implemented on an Xilinx FPGA and it has been validated with experimental results. As a result, the proposed inverter implementation is simple, and it becomes an attractive solution for low power grid connected applications.

Dense Wavelength Division Multiplexing Optical Network System Md. Masud Rana; Muhammad Abdul Goffar Khan
International Journal of Electrical and Computer Engineering (IJECE) Vol 2, No 2: April 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (59.099 KB)

In this paper, a dense wavelength division multiplexing (DWDM) system with 64 modulated channels 50 GHz spacing covering 25.2-nm bandwidth has been demonstrated. When optical signals are to travel over long distances, it’s be faded and spread out. So, it is necessary to strengthen the signal at intervals. To keep the signal strength at same level in the DWMD system, Er-doped fiber amplifier (EDFA) has been used. The EDFA provides a gain across the bandwidth with 10 dB average gain and a gain shape variation peak-to-peak of about 1 dB. OptSim’s physical EDFA model has been used for DWMD systems.DOI:http://dx.doi.org/10.11591/ijece.v2i2.202

Clustering Prediction Techniques in Defining and Predicting Customers Defection: The Case of E-Commerce Context Ait Daoud Rachid; Amine Abdellah; Bouikhalene Belaid; Lbibb Rachid
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 4: August 2018
Publisher : Institute of Advanced Engineering and Science

With the growth of the e-commerce sector, customers have more choices, a fact which encourages them to divide their purchases amongst several e-commerce sites and compare their competitors’ products, yet this increases high risks of churning. A review of the literature on customer churning models reveals that no prior research had considered both partial and total defection in non-contractual online environments. Instead, they focused either on a total or partial defect. This study proposes a customer churn prediction model in an e-commerce context, wherein a clustering phase is based on the integration of the k-means method and the Length-Recency-Frequency-Monetary (LRFM) model. This phase is employed to define churn followed by a multi-class prediction phase based on three classification techniques: Simple decision tree, Artificial neural networks and Decision tree ensemble, in which the dependent variable classifies a particular customer into a customer continuing loyal buying patterns (Non-churned), a partial defector (Partially-churned), and a total defector (Totally-churned). Macro-averaging measures including average accuracy, macro-average of Precision, Recall, and F-1 are used to evaluate classifiers’ performance on 10-fold cross validation. Using real data from an online store, the results show the efficiency of decision tree ensemble model over the other models in identifying both future partial and total defection.

A Predictive Model for Mining Opinions of an Educational Database Using Neural Networks M R Narasinga Rao; Deepthi Gurram; Sai Mahathi Vadde; Sathish Tallam; N. Sai Chand; L. Kiran
International Journal of Electrical and Computer Engineering (IJECE) Vol 5, No 5: October 2015
Publisher : Institute of Advanced Engineering and Science

Assessing the performance of an educational institute is a prime concern in an educational scenario. Educational Data Mining (EDM) considers several tasks originated from an educational context. One of the tasks identified is providing feedback for supporting instructors, administrators, teachers, course authors in decision making and thereby enable them to take appropriate remedial action. In this research, we have developed a prototype Neural Network Model which is trained to predict the performance of an educational institution. A Multilayer Perceptron Neural Network (MLP) model had been developed for this proposed research. The network is trained by back propagation algorithm. Data was obtained from a well-defined questionnaire consisting of 14 questions in the domains namely Academic Schedule, International Exposure, Jobs and Internship, Quality of the college, and Life at Campus. The results of these questions have been taken as inputs and performance of the institute has been considered as the output. To, validate the results generated by the network, statistical techniques have been used for the purpose. In this proposed research performance of an educational institution has been predicted. The results generated by the Neural Network and the statistical techniques have been compared in this research and it is observed that, both the methods have generated accurate results. The results have been considered based on the Normalized System Error (NSE) values of the network. A prototype Neural Network model has been developed to assess the performance of an educational institution.

Interactive on smart classroom system using beacon technology Hu Jia Dong; Raed Abdulla; Sathish Kumar Selvaperumal; Shankar Duraikannan; Ravi Lakshmanan; Maythem K. Abbas
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 5: October 2019
Publisher : Institute of Advanced Engineering and Science

The emergence of many internet industries ushers in IOT era, and about to bring us to the point of universal connectivity. In the field of education, the IOT technology has a broad applicable prospect for a more interactive and intelligent way by improving the quality of teaching and management. The proposed class affair management system is mean to enrich the interaction between lecturers and students which in an efficient and smart way. Based on the existing model, a layered architecture is proposed to build the beacon based campus management system. Backend device and protocols compose the physical layer to collect the raw data from physical objects. Data link layer and control layer are responsible for forming required package and sending to corresponding layer. Beacon technology used for proposed design applies Bluetooth low energy 4.0 standard which allowing devices exchange data through Bluetooth at an extremely low power consumption-using a single coin cell battery can last for several years. Saved up to 97 percentage energy compared with similar system. The entire proposed platform allows participants to bring personally owned devices to access campus management system. Through location information, teaching activities and personalized information notification can be automatically accomplished, which will inspire the innovation and development of classroom teaching mode. Beacon technology has a great potential that can be completely transplanted into other scenario such as the hypermarket and library.

One-dimensional Lumped-Circuit for Transient Thermal Study of an Induction Electric Motor Radhia Jebahi; Helmi Aloui; Moez Ayadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 4: August 2017
Publisher : Institute of Advanced Engineering and Science

Electrical machines lifetime and performances could be improved when along the design process both electromagnetic and thermal behaviors are taken into account. Moreover, real time information about the device thermal state is necessary to an appropriate control with minimized losses. Models based on lumped parameter thermal circuits are: generic, rapid, accurate and qualified as a convenient solution for power systems. The purpose of the present paper is to validate a simulation platform intended for the prediction of the thermal state of an induction motor covering all operation regimes. To do so, in steady state, the proposed model is validated using finite element calculation and experimental records. Then, in an overload situation, obtained temperatures are compared to finite element’s ones. It has been found that, in both regimes, simulation results are with closed proximity to finite element’s ones and experimental records.

Swarm robotics:design and implementation Ashraf Abuelhaija; Ayham Jebrein; Tarik Baldawi
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

This project presents a swarming and herding behaviour using simple robots. The main goal is to demonstrate the applicability of artificial intelligence (AI) in simple robotics that can then be scaled to industrial and consumer markets to further the ability of automation. AI can be achieved in many different ways; this paper explores the possible platforms on which to build a simple AI robots from consumer grade microcontrollers. Emphasis on simplicity is the main focus of this paper. Cheap and 8 bit microcontrollers were used as the brain of each robot in a decentralized swarm environment were each robot is autonomous but still a part of the whole. These simple robots don’t communicate directly with each other. They will utilize simple IR sensors to sense each other and simple limit switches to sense other obstacles in their environment. Their main objective is to assemble at certain location after initial start from random locations, and after converging they would move as a single unit without collisions. Using readily available microcontrollers and simple circuit design, semiconsistent swarming behaviour was achieved. These robots don’t follow a set path but will react dynamically to different scenarios, guided by their simple AI algorithm.

A transient current based micro-grid connected power system protection scheme using wavelet approach S. Chandra Shekar; G.Ravi Kumar; S.V.N.L Lalitha
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 1: February 2019
Publisher : Institute of Advanced Engineering and Science

Micro-grids comprise Distributed Energy Resources (DER’s) with low voltage distribution networks having controllable loads those can operate with different voltage levels are connected to the micro-grid and operated in grid mode or islanding mode in a coordinated way of control. DER’s provides clear environment-economical benefits for society and consumer utilities. But their development poses great technical challenges mainly protection of main and micro grid. Protection scheme must have to respond to both the main grid and micro-grid faults. If the fault is occurs on main grid, the response must isolate the DER’s from the main grid rapidly to protect the system loads. If the fault ocuurs within the micro-grid, the protection scheme must coordinate and isolates the least priority possible part of the grid to eliminate the fault. In order to deal with the bidirectional energy flow due to large numbers of micro sources new protection schemes are required. The system is simulated using MATLAB Wavelet Tool box and Wavelet based Multi-resolution Analysis is considered. Wavelet based Multi-resolution Analysis is used for detection, discrimination and location of faults on transmission network. This paper is discussed a transient current based micro-grid connected power system protection scheme using Wavelet Approach described on wavelet detailed-coefficients of Mother Biorthogonal 1.5 wavelet. The proposed algorithm is tested in micro-grid connected power systems environment and proved for the detection, discrimination and location of faults which is almost independent of fault impedance, fault inception angle (FIA) and fault distance of feeder line.

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Home Page

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Contact Name Tole Sutikno

Contact Email ijece@iaesjournal.com

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Location Kota yogyakarta, Daerah istimewa yogyakarta INDONESIA

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Contact Name
Tole Sutikno

Contact Email
ijece@iaesjournal.com

Phone
-

Journal Mail Official
ijece@iaesjournal.com

Editorial Address
-

Location
Kota yogyakarta,

Daerah istimewa yogyakarta

INDONESIA