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31,377 Article Results

Development of real time internet of things (IoT) based air quality monitoring system

10.11591/ijeecs.v13.i3.pp1039-1047
Huzein Fahmi Hawari , Aideed Ahmad Zainal , Mohammad Radzi Ahmad
The atmospheric air pollution is a major concern in modern cities, especially in developing countries like Malaysia. In this paper, we have reported an effective implementation for Internet of Things used for monitoring the level of air pollution based on Malaysia Air Pollution Index (API). The low-cost and real time system would be able to monitor regular air quality pollutants including Particulate Matter (PM) of PM2.5, PM10 and Carbon Monoxide (CO) gas as well as the temperatures and humidity of the surroundings. The system has capability to detect Good, Moderate, Unhealthy, Very Unhealthy and Hazardous API status. Based on 5 weeks of experimental API monitoring result on specified test location, the system was able to demonstrate promising results in providing a reliable real time monitoring of the air quality condition.
Volume: 13
Issue: 3
Page: 1039-1047
Publish at: 2019-03-01

An improved radial basis function networks in networks weights adjustment for training real-world nonlinear datasets

10.11591/ijai.v8.i1.pp63-76
Lim Eng Aik , Tan Wei Hong , Ahmad Kadri Junoh
In neural networks, the accuracies of its networks are mainly relying on two important factors which are the centers and the networks weight. The gradient descent algorithm is a widely used weight adjustment algorithm in most of neural networks training algorithm. However, the method is known for its weakness for easily trap in local minima. It suffers from a random weight generated for the networks during initial stage of training at input layer to hidden layer networks. The performance of radial basis function networks (RBFN) has been improved from different perspectives, including centroid initialization problem to weight correction stage over the years. Unfortunately, the solution does not provide a good trade-off between quality and efficiency of the weight produces by the algorithm. To solve this problem, an improved gradient descent algorithm for finding initial weight and improve the overall networks weight is proposed. This improved version algorithm is incorporated into RBFN training algorithm for updating weight. Hence, this paper presented an improved RBFN in term of algorithm for improving the weight adjustment in RBFN during training process. The proposed training algorithm, which uses improved gradient descent algorithm for weight adjustment for training RBFN, obtained significant improvement in predictions compared to the standard RBFN. The proposed training algorithm was implemented in MATLAB environment. The proposed improved network called IRBFN was tested against the standard RBFN in predictions. The experimental models were tested on four literatures nonlinear function and four real-world application problems, particularly in Air pollutant problem, Biochemical Oxygen Demand (BOD) problem, Phytoplankton problem, and forex pair EURUSD. The results are compared to IRBFN for root mean square error (RMSE) values with standard RBFN. The IRBFN yielded a promising result with an average improvement percentage more than 40 percent in RMSE.
Volume: 8
Issue: 1
Page: 63-76
Publish at: 2019-03-01

Rainfall–landslide early warning system (RLEWS) using TRMM precipitation estimates

10.11591/ijeecs.v13.i3.pp1259-1266
Norsuzila Ya’acob , Noraisyah Tajudin , Aziean Mohd Azize
This paper presents Rainfall–Landslide Early Warning System (RLEWS) using Tropical Rainfall Measuring Mission (TRMM) precipitation estimates to notify the warning level for the possibility of landslide occurrences in Ulu Kelang, Selangor. In this study, RLEWS is developed to monitor the possibility of rainfall-induced landslide occurrences by comparing real time TRMM rainfall data with a landslide rainfall threshold. The landslide rainfall threshold is constructed by using the accumulated rainfall-accumulated rainfall (E-E) diagram method. The warning levels of rainfall threshold are classified into three levels; high, moderate and low. The analysis and notification are updating every 24 hours to provide the initial potential landslide information signal. The rainfall threshold analysis was able to detect the early signal of initial potential landslide occurrences. The aims of this study are to develop a low-cost, sustainable early warning system and web base application to send notification and awareness for residential areas in Ulu Kelang, Selangor.
Volume: 13
Issue: 3
Page: 1259-1266
Publish at: 2019-03-01

Influence of pole number on the characteristics of permanent magnet synchronous motor (PMSM)

10.11591/ijeecs.v13.i3.pp1318-1323
S. Raj , R. Aziz , M.Z. Ahmad
This paper present the influence of pole number on the characteristics of permanent magnet synchronous motor (PMSM). This study is devoted to construct three different motors with varying pole numbers and investigating its effect on the characteristics of permanent magnet synchronous motor (PMSM). It is a study on an influence of pole numbers on electromagnetic and thermal characteristics of the PMSMs all while maintaining the same motor dimensions, parameters and slot number. The study is conducted to analyse the best slot-pole combination for a given dimension to determine if pole numbers have a role in the motor performance. The analysis for these permanent magnet motors is done via finite element analysis (FEA) in which JMAG Designer software is used. The software is used to analyse the motor performance in terms of cogging torque, speed, power, iron loss, copper loss as well as the efficiency of the motor itself. All three motors were simulated in no load and load condition.
Volume: 13
Issue: 3
Page: 1318-1323
Publish at: 2019-03-01

Detection of keratoconus in anterior segment photographed images using corneal curvature features

10.11591/ijeecs.v13.i3.pp1191-1198
Marizuana Mat Daud , Wan Mimi Diyana Wan Zaki , Aini Hussain , Haliza Abdul Mutalib
Keratoconus is a corneal ectatic disorder with complex aetiology and may induce mild to severe visual impairment and consequently decrease the quality of life. This paper presents a new keratoconus detection method using corneal curvature features to differentiate normal and keratoconus cases. In this study, the eye images known as anterior segmented photographed images (ASPIs) are captured from side view using a smartphone’s camera. For the side-view images, the corneal curvature is segmented using spline function to measure the corneal curvature. A template disc method is implemented to quantitatively measure the steepening of the corneal curvature of the captured ASPIs. Parameters obtained from three different template disc methods, namely, nonlinear, , crossover point, , and trigonometric, , are investigated to represent the most suitable curvature feature. SVM is then employed to classify normal and keratoconus eyes. Results reveal that a standalone nonlinear method gives a reliable parameter with 90% accuracy in classifying the data. However, the classification performance has increased to 99.5% accuracy with the use of all combined features known as a feature vector, . Additionally, classification with the proposed  has successfully distinguished normal and keratoconus cases with sensitivity and specificity rates of 99% and 100%, respectively. The results portray the bright potential of this method in assisting experts during ocular screening specifically to detect keratoconus disease.
Volume: 13
Issue: 3
Page: 1191-1198
Publish at: 2019-03-01

Anomaly-based intrusion detector system using restricted growing self organizing map

10.11591/ijeecs.v13.i3.pp919-926
Tomi Yahya Christyawan , Ahmad Afif Supianto , Wayan Firdaus Mahmudy
The rapid development of internet and network technology followed by malicious threats and attacks on networks and computers. Intrusion detection system (IDS) was developed to solve that problems. The development of IDS using machine learning is needed for classifying the attacks. One method of the classification is Self-Organizing Map (SOM). SOM able to perform classification and visualization in learning process to gain new knowledge. However, the SOM has less efficient in learning process when applied in Big Data. This study proposes Restricted Growing SOM method with clustering reference vector (RGSOM-CRV) and Parallel RGSOM-CRV to improve SOM efficiency in classification with accuracy consideration to solve Big Data problem. Growing process in RGSOM is restricted by maximum nodes and growing threshold, the reupdate weight process will update unused reference vector when map size already maximum, these two processes solve the consuming time of regular GSOM. From the results of this research against KDD Cup 1999 dataset, proposed method Parallel RGSOM-CRV able to give 91.86% accuracy, 20.58% false alarm rate, 95.32% recall or detection rate, and precision is 94.35% and time consuming is outperform than regular Growing SOM. This proposed method is very promising to handle big data problems compared with other methods.
Volume: 13
Issue: 3
Page: 919-926
Publish at: 2019-03-01

Pre-service teachers’ inclusion of climate change education

10.11591/ijere.v8i1.16923
Ronnel Joseph Tracena Competente
Findings from the study of pre-service teachers’ (PSTs) attitudes to climate change education, knowledge of climate change and potential inclusion in their future teachings are reported in this paper. Using adopted and self-made questionnaire, a sample of 180 pre-service teachers participated in a survey study in the College of Education, University of Nueva Caceres, divided into freshmen and senior year to examine developments in their attitudes to environmental education and their knowledge of climate change. Results showed that their attitudes towards climate change education were low and their climate change science knowledge had not changed. Data on preservice teachers’ sources of information for climate change, their views on essential climate change topics for their future students and their perceptions of gaps in their own training in relation to climate change education were also examined in order to substantiate the survey data. Results show that there is a need to triangulate climate change education not only in the program outcomes but also to the student and faculty development programs. Further results are discussed in this paper.
Volume: 8
Issue: 1
Page: 119-126
Publish at: 2019-03-01

Academic performance prediction algorithm based on fuzzy data mining

10.11591/ijai.v8.i1.pp26-32
Anil Kumar Tiwari , G. Ramakrishna , Lokesh Kumar Sharma , Sunil Kumar Kashyap
This paper presents an algorithm for prediction of academic performance of students by fuzzy data mining. The fuzzy-trace concept applied to predict the academic performance of the students. An algorithm is proposed in this paper lies with this idea. The fuzzy academic set is generated from the student’s academic data. This is analyzed by the fuzzy-matrix set. The prediction academic data is referred as the management of data or data mining. Data mining is the science of analyzing the data for obtaining more information than Keywords the current information. The hidden information appears by this technique.
Volume: 8
Issue: 1
Page: 26-32
Publish at: 2019-03-01

Fault analysis for renewable energy power system in micro-grid distributed generation

10.11591/ijeecs.v13.i3.pp1117-1123
Ameerul A. J. Jeman , Naeem M. S. Hannoon , Nabil Hidayat , Mohamed.M.H. Adam , Ismail Musirin , Vijayakumar. V
In distribution system, wind power plants are becoming popular renewable energy sources. It employs Doubly Fed Induction Generator (DFIG) to generate power based on wind conversion. Short and long transmission lines, presence of faults and presence of Static Synchronous Compensator (STATCOM) are highlighted issues in this paper. Basically, this research develops investigations on some electrical variables such as voltage and current to control them. Distribution Static Synchronous Compensator (DSTATCOM) is proposed in this paper. Wind farm acts as a source while DSTATCOM is connected to the distribution system with a DFIG based wind farm. The controller proposed is DSTATCOM is modeled and simulated in MATLAB/SIMULINK and the results are given. A microgrid based small signal analysis is performed in the laboratory using MATLAB and different comparisons are made and simulation case studies are presented and validated.
Volume: 13
Issue: 3
Page: 1117-1123
Publish at: 2019-03-01

Speech recognition of moroccan dialect using hidden markov models

10.11591/ijai.v8.i1.pp7-13
Bezoui Mouaz , Beni-hssane Abderrahim , Elmoutaouakkil Abdelmajid
This paper addresses the development of an Automatic Speech Recognition (ASR) system for the Moroccan Dialect. Dialectal Arabic (DA) refers to the day-to-day vernaculars spoken in the Arab world. In fact, Moroccan Dialect is very different from the Modern Standard Arabic (MSA) because it is highly influenced by the French Language. It is observed throughout all Arab countries that standard Arabic widely written and used for official speech, news papers, public administration and school but not used in everyday conversation and dialect is widely spoken in everyday life but almost never written. we propose to use the Mel Frequency Cepstral Coefficient (MFCC) features to specify the best speaker identification system. The extracted speech features are quantized to a number of centroids using vector quantization algorithm. These centroids constitute the codebook of that speaker. MFCC’s are calculated in training phase and again in testing phase. Speakers uttered same words once in a training session and once in a testing session later. The Euclidean distance between the MFCC’s of each speaker in training phase to the centroids of individual speaker in testing phase is measured and the speaker is identified according to the minimum Euclidean distance. The code is developed in the MATLAB environment and performs the identification satisfactorily.
Volume: 8
Issue: 1
Page: 7-13
Publish at: 2019-03-01

Comparing the linear and logarithm normalized extreme learning machine in flow curve modeling of magnetorheological fluid

10.11591/ijeecs.v13.i3.pp1065-1072
Irfan Bahiuddin , Abdul Y Abd Fatah , Saiful A Mazlan , Mohd I Shapiai , Fitrian Imaduddin , Ubaidillah Ubaidillah , Dewi Utami , Mohd N Muhtazaruddin
The extreme learning machine (ELM) plays an important role to predict magnetorheological (MR) fluid behavior and to reduce the computational fluid dynamics (CFD) calculation cost while simulating the MR fluid flow of an MR actuator. This paper presents a logarithm normalized method to enhance the prediction of ELM of the flow curve representing the MR fluid rheological properties. MRC C1L was used to test the performance of the proposed method, and different activation functions of ELMs were chosen to be the neural networks setting. The Normalized Root Mean Square Error (NRMSE) was selected as the indicator of the ELM prediction accuracy. NRMSE of the proposed method is found to improve the model accuracy up to 77.10 % for the prediction or testing case while comparing with the linear normalized ELM
Volume: 13
Issue: 3
Page: 1065-1072
Publish at: 2019-03-01

A new formula for conjugate parameter computation based on the quadratic model

10.11591/ijeecs.v13.i3.pp954-961
Basim Abbas Hassan
The conjugancy coefficient is the very basis of a diversity of the conjugate gradient methods. In this research, we derivation a new formula of conjugate gradient methods based on the quadratic model. Our arithmetical findings have revealed that, our new method has the most excellent performance contrast to the other standard CG methods. Also give proof viewing that this method converges globally.
Volume: 13
Issue: 3
Page: 954-961
Publish at: 2019-03-01

Distance weighted K-Means algorithm for center selection in training radial basis function networks

10.11591/ijai.v8.i1.pp54-62
Lim Eng Aik , Tan Wei Hong , Ahmad Kadri Junoh
The accuracies rates of the neural networks mainly depend on the selection of the correct data centers. The K-means algorithm is a widely used clustering algorithm in various disciplines for centers selection. However, the method is known for its sensitivity to initial centers selection. It suffers not only from a high dependency on the algorithm's initial centers selection but, also from data points. The performance of K-means has been enhanced from different perspectives, including centroid initialization problem over the years. Unfortunately, the solution does not provide a good trade-off between quality and efficiency of the centers produces by the algorithm. To solve this problem, a new method to find the initial centers and improve the sensitivity to the initial centers of K-means algorithm is proposed. This paper presented a training algorithm for the radial basis function network (RBFN) using improved K-means (KM) algorithm, which is the modified version of KM algorithm based on distance-weighted adjustment for each centers, known as distance-weighted K-means (DWKM) algorithm. The proposed training algorithm, which uses DWKM algorithm select centers for training RBFN obtained better accuracy in predictions and reduced network architecture compared to the standard RBFN. The proposed training algorithm was implemented in MATLAB environment; hence, the new network was undergoing a hybrid learning process. The network called DWKM-RBFN was tested against the standard RBFN in predictions. The experimental models were tested on four literatures nonlinear function and four real-world application problems, particularly in Air pollutant problem, Biochemical Oxygen Demand (BOD) problem, Phytoplankton problem, and forex pair EURUSD. The results are compared to proposed method for root mean square error (RMSE) in radial basis function network (RBFN). The proposed method yielded a promising result with an average improvement percentage more than 50 percent in RMSE.
Volume: 8
Issue: 1
Page: 54-62
Publish at: 2019-03-01

A new class of BFGS updating formula based on the new quasi-newton equation

10.11591/ijeecs.v13.i3.pp945-953
Basim Abbas Hassan , Hussein K. Khalo
Quasi-Newton methods” are amongst the mainly useful and competent iterative process for solving unrestrained minimization functions. In this paper we derive a new quasi-Newton equation with on the Hessian estimate updates and alterations intended at developing their performance. The “Numerical results” illustrate that the proposed technique useful for the known test functions.
Volume: 13
Issue: 3
Page: 945-953
Publish at: 2019-03-01

Autonomous coop cooling system using renewable energy and water recycling

10.11591/ijeecs.v13.i3.pp1303-1310
Shamsul Kamal Ahmad Khalid , Nurul Shafiqah Che Dan , Noor Azah Samsudin , Muhammad Syariff Aripin , Nor Amirul Amri Nordin
Extreme temperature in a chicken coop can significantly affect the growth and productivity of poultry. Therefore, the temperature inside the chicken coop need to be controlled to protect it from extreme temperatures. Most of the technology use electrical energy supplied to an evaporative cooling system to control the temperature of a coop. This paper presents an autonomous chicken coop cooling system using renewable energy and water recycling (REMACT). In this study, a monitoring system with necessary hardware, control application, powered with solar power source and water recycling, has been developed. The proposed cooling system consists of hardware part such as an Internet of Things (IOT) controller platform, temperature sensor, solar panel, water pump, water storage, water drain and pipe. When the temperature sensor detects extreme temperature more than 28℃ in a chicken coop, the water in storage tank will flow throughout the pipe and pass into water pump before it irrigates the chicken coop roof. When the temperature is below 22℃, the bulb will light up to transfer heat to the chicken coop and cause the temperature drop back to a healthy range. The water drain that is attached to the roof will collect the water and return the water back to the water storage again. The software components required by the project are Arduino IDE, Thinger.io, and Android Studio Framework. Several experiments have been conducted with hot and cold scenarios. The system was able to stabilise the temperature back to a healthy range. A usability testing result demonstrates 80% satisfactory rate. The findings from the experiments show that IoT, renewable energy and water recycling have the potential for temperature control of a chicken coop.
Volume: 13
Issue: 3
Page: 1303-1310
Publish at: 2019-03-01
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