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31,291 Article Results

M7 subtype leukemic cell edge detection techniques with threshold value comparison and noise filters

10.11591/ijeecs.v13.i3.pp1294-1302
A.S.A. Salam , M.N.M. Isa , M.I. Ahmad
The aim of this paper is to study and identify various threshold values for two prevalently used edge detection techniques, which are Sobel and Canny. The purpose is to determine which value gives an accurate result for identifying a leukemic cell. Moreover, evaluating suitability of edge detectors is also essential as feature extraction of cell depends greatly on image segmentation (edge detection). Firstly, an image of M7 subtype of Acute Myelocytic Leukemia (AML) is selected due to its diagnosing which were found lacking. Next, apply noise filters for the best of image quality. Thus by comparing image with no filter, median and average filters, useful information can be acquired. Each edge detectors is fixed with threshold value of 0-0.5 but for Cann edge detection the value can increase until 0.9. From the research, it is found that Canny edge with no filter and a threshold value of 0.7 gives a clearer image with less noise reduction.
Volume: 13
Issue: 3
Page: 1294-1302
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

Internet of things (IoT) based I-V curve tracer for photovoltaic monitoring systems

10.11591/ijeecs.v13.i3.pp1022-1030
H. B. Chi , M. F. N. Tajuddin , N. H. Ghazali , A. Azmi , M. U. Maaz
This paper presents a low-cost PV current-voltage or I-V curve tracer that has the Internet of Things (IoT) capability. Single ended primary inductance converter (SEPIC) is used to develop the I-V tracer, which is able to cope with rapidly changing irradiation conditions. The I-V tracer control software also has the ability to automatically adapt to the varying irradiation conditions. The performance of the I-V curve tracer is evaluated and verified using simulation and experimental tests.
Volume: 13
Issue: 3
Page: 1022-1030
Publish at: 2019-03-01

The evaluation of AdBlock technique implementation for enterprise network environment

10.11591/ijeecs.v13.i3.pp1102-1109
Mohd Iskandar Bin Samsuddin , Mohamad Yusof Darus , Shamsul J Elias , Abidah Hj Mat Taib , Norkhushaini Awang , Roshidi Din
This paper presents the evaluation of AdBlock technique implementation for enterprise network environment. This study has presented the impact of web browsing activities where it is the most active traffic where is consumed the highest inbound bandwidth usage in enterprise network environment. We can conclude that DNS AdBlock is the best solution for enterprise network environment in term of blocking advertisement compare to extension adblock. Adblock technique also reduce network data request by comparing front-end solution (browser extension AdBlock) at client web browser and networks level adblock. The parameters such as HTTP request, TCP connection and network bandwidth are being examined to measure the effectiveness of blocking online advertisement. Both techniques perform the reduction of traffics and bandwidth utilization. The result shows that DNS AdBlock is the most effective in blocking online advertisement using the examined parameters. DNS AdBlock can sustain the usage of web browsing activity for enterprise network and also generate substantial saving across several fonts. This study has identified current web browsing trends traffic in enterprise network where it consumed 50 percent in average. This number increased when industries are moving to cloud web-based consumption. However, industries such as educational sector, web browsing traffic is one of connectivity that enterprises network should be investing to support openness and heavy traffic from educational users.
Volume: 13
Issue: 3
Page: 1102-1109
Publish at: 2019-03-01

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

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

Retinal blood vessel segmentation from retinal image using B-COSFIRE and adaptive thresholding

10.11591/ijeecs.v13.i3.pp1199-1207
Aziah Ali , Wan Mimi Diyana Wan Zaki , Aini Hussain
Segmentation of blood vessels (BVs) from retinal image is one of the important steps in developing a computer-assisted retinal diagnosis system and has been widely researched especially for implementing automatic BV segmentation methods. This paper proposes an improvement to an existing retinal BV (RBV) segmentation method by combining the trainable B-COSFIRE filter with adaptive thresholding methods. The proposed method can automatically configure its selectivity given a prototype pattern to be detected. Its segmentation performance is comparable to many published methods with the advantage of robustness against noise on retinal background. Instead of using grid search to find the optimal threshold value for a whole dataset, adaptive thresholding (AT) is used to determine the threshold for each retinal image. Two AT methods investigated in this study were ISODATA and Otsu’s method. The proposed method was validated using 40 images from two benchmark datasets for retinal BV segmentation validation, namely DRIVE and STARE. The validation results indicated that the segmentation performance of the proposed unsupervised method is comparable to the original B-COSFIRE method and other published methods, without requiring the availability of ground truth data for new dataset. The Sensitivity and Specificity values achieved for DRIVE and STARE are 0.7818, 0.9688, 0.7957 and 0.9648, respectively.
Volume: 13
Issue: 3
Page: 1199-1207
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

Cartoon to solve teaching problem on mathematics

10.11591/ijere.v8i1.17609
Yasin Gokbulut , Sultan Kus
The aim of this study is to determine the effect of mathematics teaching with cartoons on the problem solving skills of primary school 2nd grade students based on addition and substraction. In the research, pretest-posttest control group design of the experimental model was used. In the classroom where the experimental group students were present, cartoon supported education was applied and the current program based teaching method was used in the control group class. The target population of the study consisted of 2nd grade students of all primary schools of the Ministry of National Education of Mersin. The population of the study consisted of 2nd grade students of all primary schools of the Ministry of National Education of Mersin. The study was conducted for 4 weeks in the fall semester of the 2015-2016 academic year. The experimental group consisted of 17 students and the control group consisted of 13 students. In order to determine the validity and reliability of the achievement test used in the study, item analysis was performed with the TAB program. The t-test was used to determine whether there was a significant difference between the pre-test and post-test scores of the groups. As a result of the research, it was observed that the success of the students in the problem solving in addition and substraction education has increased.
Volume: 8
Issue: 1
Page: 145-150
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

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

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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