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27,860 Article Results

Behavioral Tracking in E-Learning by Using Learning Styles Approach

10.11591/ijeecs.v8.i1.pp17-26
Amira Fatiha Baharudin , Noor Azida Sahabudin , Adzhar Kamaludin
Currently, e-learning is becoming an option as it can save the cost of education, time, and more flexible in its implementation. The main problem that arises is how to create e-learning content that is interesting and really fit the needs of the users. One way that can be done to optimize the content of e-learning is to analyze the user behavior. This study aims to analyze user (student) behavior in KALAM UMP, based on logs report (activity history), which is often called as behavioral tracking. First, the learning style of the students is determined based on Honey and Mumford Learning Styles Model by using Learning Styles Questionnaire. The analysis is done using SPSS 16.0 for Windows. The results shows that student with Reflector and Theorist learning styles access e-learning materials the most. From Spearman Correlation analysis, the relationship between learning styles and students’ behavior in e-learning is found to be very weak (rs=.276, p=.000), but statistically significant (p<0.05). In other words, students’ learning styles and behavior in e-learning have significant impacts on the improvement or degradation of students’ performance. Therefore, from the results of this study, an adaptive KALAM e-learning system which can suits the learning styles of UMP students is proposed. In adaptive e-learning system, students can access learning materials that match the students' learning needs and preferences.
Volume: 8
Issue: 1
Page: 17-26
Publish at: 2017-10-01

A Performance Review of Intra and Inter-Group MANET Routing Protocols under Varying Speed of Nodes

10.11591/ijece.v7i5.pp2721-2730
Dilip Singh Sisodia , Riya Singhal , Vijay Khandal
Mobile Ad-hoc Networks (MANETs) are a cluster of self-organizing and self-governing wireless nodes without any backbone infrastructure and centralized administration. The various nodes in MANET move randomly, and this node mobility may pose challenges on the performance of routing protocols.  In this paper, an Intra and intergroup performance review of various MANET routing protocols are performed under varying speed of nodes. The routing protocols included in this study are reactive, proactive, and hybrid protocols. This performance review is done using the NS2 simulator and random waypoint model. The routing protocols performance is assessed through standard performance measure metrics including packet delivery ratio, throughput, routing overhead and end to end delivery with varying speed of nodes. The simulations result shows that there is no significant impact of varying speed of nodes on standard performance evaluation metrics.
Volume: 7
Issue: 5
Page: 2721-2730
Publish at: 2017-10-01

Optimization of Fuzzy Tsukamoto Membership Function using Genetic Algorithm to Determine the River Water

10.11591/ijece.v7i5.pp2838-2846
Qoirul Kotimah , Wayan Firdaus Mahmudy , Vivi Nur Wijayaningrum
Some aquatic ecosystems in rivers depend on the river water, so it needs to be maintained by measuring and analyzing the river water quality. STORET is one of the methods used to measure the river water quality, but it takes a quite high of time and costs. Fuzzy Tsukamoto is an alternative method that works by grouping the river water data, but it is difficult to determine the membership function value. The solution offered in this study is the use of genetic algorithm to determine the membership function value of each criterion. Based on the test results, the optimization of fuzzy membership function using genetic algorithm provides higher accuracy value that is 95%, while the accuracy value without optimization process is 90%. The parameters used in genetic algorithm are as follows: population size is 80, generation number is 175, crossover rate (cr) is 0.6, and mutation rate (mr) is 0.4.
Volume: 7
Issue: 5
Page: 2838-2846
Publish at: 2017-10-01

Series-Loaded Resonant Converter DC-DC Buck Operating for Low Power

10.11591/ijeecs.v8.i1.pp159-168
M. F. Omar , H. C. M. Haris
This paper presents the functions of Series-Loaded Resonant Converter (SLRC). Series Loaded Resonant DC-DC converter is a type of soft-switching topology widely known for providing improved efficiency. Zero voltage switching (ZVS) buck converter is more preferable over hard switched buck converter for low power, high frequency DC-DC conversion applications. Zero Voltage switching techniques will be used to improve the efficiency of current and voltage at the series loaded half-bridge rectifier. The results will be described from PSIM simulation, Programming of MATLAB calculation and hardware testing.
Volume: 8
Issue: 1
Page: 159-168
Publish at: 2017-10-01

Development of Hybrid Artificial Neural Network for Quantifying Energy Saving using Measurement and Verification

10.11591/ijeecs.v8.i1.pp137-145
Wan n Nazirah Wan Md Adna , Nofri Yenita Dahlan , Ismail Musirin
This paper presents a Hybrid Artificial Neural Network (HANN) for chiller system Measurement and Verification (M&V) model development. In this work, hybridization of Evolutionary Programming (EP) and Artificial Neural Network (ANN) are considered in modeling the baseline electrical energy consumption for a chiller system hence quantifying saving. EP with coefficient of correlation (R) objective function is used in optimizing the neural network training process and selecting the optimal values of ANN initial weights and biases. Three inputs that are affecting energy use of the chiller system are selected; 1) operating time, 2) refrigerant tonnage and 3) differential temperature. The output is hourly energy use of building air-conditioning system. The HANN model is simulated with 16 different structures and the results reveal that all HANN structures produce higher prediction performance with R is above 0.977. The best structure with the highest value of R is selected as the baseline model hence is used to determine the saving. The avoided energy calculated from this model is 132944.59 kWh that contributes to 1.38% of saving percentage.
Volume: 8
Issue: 1
Page: 137-145
Publish at: 2017-10-01

Reconfigurable Feeding Network with Dual-band Filter for WiMAX Application

10.11591/ijece.v7i5.pp2411-2419
N. Edward , Z. Zakaria , N.A. Shairi
Design and simulation for reconfigurable Wilkinson Power Divider (WPD) related to WiMAX application is proposed in this paper. This proposed design relates to dual band WiMAX frequencies at 2.5 GHz and 3.5 GHz. The main purpose of this design is to design a switchable feeding network that can cover the WiMAX standards by reconfiguring the microstrip line length using PIN diode switches. Besides, the power divider also can be design and develop as power combiner due to the passive component structure and hence reciprocal. In this proposed Wilkinson power divider, different band of frequencies for WiMAX application are obtained by using PIN diode. By turning ‘ON’ and ‘OFF’ the PIN diode, different frequencies are achieved between 2.5 and 3.5 GHz. This proposed design used Rogers RO4350 (er = 3.48, h = 0.508mm) as a substrate material and copper (thickness = 0.002 mm) related to patch of design. This simulation results showed that the S11 is less than -15dB; and S12 and S13 are better than -5dB. Based on these simulation results, the proposed WPD design using dual-band filter was well applied where it has better return loss (S11) with less than -15 dB for both WiMAX frequencies.
Volume: 7
Issue: 5
Page: 2411-2419
Publish at: 2017-10-01

The Effect of Bandwidth on Speech Intelligibility in Albanian Language by Using Multimedia Applications like Skype and Viber

10.11591/ijece.v7i5.pp2514-2519
Sabrije Osmanaj , Altin Shala , Blerta Prevalla
This paper intends to analyze subjective measurements of intelligibility of speech on Albanian language during the conversation between two people using applications which today are very used for communication such as Skype and Viber. The measurement is done as follows: on the entry part of the transmission system sentences or words are spoken or just syllables while on receiving part is recorded what is heard; the percentage of words, sentences or syllables correctly received, on proportion to those imposed on the entry of the system, providing the percentage of intelligibility (the words, sentences or syllables). Methods of measurements are made at different speed of the Internet, in an environment without noise and with noise, in order to see the impact on understanding of the speech with different target parameters.
Volume: 7
Issue: 5
Page: 2514-2519
Publish at: 2017-10-01

A PAPR Reduction for OFDM Signals Based on Self-Adaptive Multipopulation DE algorithm

10.11591/ijece.v7i5.pp2651-2660
Hocine Ait-Saadi , Jean-Yves Chouinard , Abderrazak Guessoum
One of major drawbacks of orthogonal frequency division multiplexing (OFDM) systems is the high peak-to-average power ratio (PAPR). A signal with high PAPR leads to nonlinear distortion caused mainly by power amplifiers in wireless transmitters. Partial transmit sequence (PTS) is one of the most attractive methods to reduce the PAPR in OFDM systems. It achieves considerable PAPR reduction without distortion, but it requires an exhaustive search over all the combinations of the given phase factors, which results in a computational complexity that increases exponentially with the number of partitions. For this optimization problem, we propose in this paper a suboptimal PTS method based on the self-adaptive multipopulation differential evolution algorithm (SAMDE). The self adaptation of control parameters and structured population, is able to obtain high quality solutions with low computational cost by evolving each sub-population of individuals over successive generations.
Volume: 7
Issue: 5
Page: 2651-2660
Publish at: 2017-10-01

A Deconvolution Approach to the Three Dimensional Identification of Cracks in Magnetic Slabs

10.11591/ijece.v7i5.pp2357-2364
Amr A. Adly , Salwa K. Abd-El-Hafiz
Nondestructive assessment of cracks in metallic parts has always been a topic of industrial interest. In the past, different approaches have been proposed to assess such cracks. Recently, semi-orthogonal compactly supported spline wavelets were utilized to efficiently identify the 3D spatial location of cracks in conducting slabs of finite thicknesses. Within this proposed approach a horizontally oriented field sensor is employed on top of a magnetic slab subject to uniform horizontal magnetic field. In this paper the 3D spatial identification of cracks in conducting slabs is carried out through the Fourier transform by de-convoluting sensor response. In comparison to the previously adopted approach, the approach proposed in this paper is capable of identifying cracks that span over a relatively larger distance.  Details of the crack detection methodology and simulations are given in the paper.
Volume: 7
Issue: 5
Page: 2357-2364
Publish at: 2017-10-01

Black Box Model based Self Healing Solution for Stuck at Faults in Digital Circuits

10.11591/ijece.v7i5.pp2451-2458
S. Meyyappan , V. Alamelumangai
The paper proposes a design strategy to retain the true nature of the output in the event of occurrence of stuck at faults at the interconnect levels of digital circuits. The procedure endeavours to design a combinational architecture which includes attributes to identify stuck at faults present in the intermediate lines and involves a healing mechanism to redress the same. The simulated fault injection procedure introduces both single as well as multiple stuck-at faults at the interconnect levels of a two level combinational circuit in accordance with the directives of a control signal. The inherent heal facility attached to the formulation enables to reach out the fault free output even in the presence of faults. The Modelsim based simulation results obtained for the Circuit Under Test [CUT] implemented using a Read Only Memory [ROM], proclaim the ability of the system to survive itself from the influence of faults. The comparison made with the traditional Triple Modular Redundancy [TMR] exhibits the superiority of the scheme in terms of fault coverage and area overhead.   
Volume: 7
Issue: 5
Page: 2451-2458
Publish at: 2017-10-01

Inertial Navigation for Quadrotor Using Kalman Filter with Drift Compensation

10.11591/ijece.v7i5.pp2596-2604
Lasmadi Lasmadi , Adha Imam Cahyadi , Samiadji Herdjunanto , Risanuri Hidayat
The main disadvantage of an Inertial Navigation System is a low accuracy due to noise, bias, and drift error in the inertial sensor. This research aims to develop the accelerometer and gyroscope sensor for quadrotor navigation system, bias compensation, and Zero Velocity Compensation (ZVC). Kalman Filter is designed to reduce the noise on the sensor while bias compensation and ZVC are designed to eliminate the bias and drift error in the sensor data. Test results showed the Kalman Filter design is acceptable to reduce the noise in the sensor data. Moreover, the bias compensation and ZVC can reduce the drift error due to integration process as well as improve the position estimation accuracy of the quadrotor. At the time of testing, the system provided the accuracy above 90 % when it tested indoor.
Volume: 7
Issue: 5
Page: 2596-2604
Publish at: 2017-10-01

Enabling External Factors for Inflation Rate Forecasting Using Fuzzy Neural System

10.11591/ijece.v7i5.pp2746-2756
Nadia Roosmalita Sari , Wayan Firdaus Mahmudy , Aji Prasetya Wibawa , Elta Sonalitha
Inflation is the tendency of increasing prices of goods in general and happens continuously. Indonesia's economy will decline if inflation is not controlled properly. To control the inflation rate required an inflation rate forecasting in Indonesia. The forecasting result will be used as information to the government in order to keep the inflation rate stable. This study proposes Fuzzy Neural System (FNS) to forecast the inflation rate. This study uses historical data and external factors as the parameters. The external factor using in this study is very important, which inflation rate is not only affected by the historical data. External factor used are four external factors which each factor has two fuzzy set. While historical data is divided into three input variables with three fuzzy sets. The combination of three input variables and four external factors will generate too many rules. Generate of rules with too many amounts will less effective and have lower accuracy. The novelty is needed to minimalize the amount of rules by using two steps fuzzy. To evaluate the forecasting results, Root Means Square Error (RMSE) technique is used. Fuzzy Inference System Sugeno used as the comparison method. The study results show that FNS has a better performance than the comparison method with RMSE that is 1.81.
Volume: 7
Issue: 5
Page: 2746-2756
Publish at: 2017-10-01

A Novel Integrated Framework to Ensure Better Data Quality in Big Data Analytics over Cloud Environment

10.11591/ijece.v7i5.pp2798-2805
C.S. Sindhu , Nagaratna P. Hegde
With advent of Big Data Analytics, the healthcare system is increasingly adopting the analytical services that is ultimately found to generate massive load of highly unstructured data. We reviewed the existing system to find that there are lesser number of solutions towards addressing the problems of data variety, data uncertainty, and data speed. It is important that an error-free data should arrive in analytics. Existing system offers single-hand solution towards single platform. Therefore, we introduced an integrated framework that has the capability to address all these three problems in one execution time. Considering the synthetic big data of healthcare, we carried out the investigation to find that our proposed system using deep learning architecture offers better optimization of computational resources. The study outcome is found to offer comparatively better response time and higher accuracy rate as compared to existing optimization technqiues that is found and practiced widely in literature.
Volume: 7
Issue: 5
Page: 2798-2805
Publish at: 2017-10-01

Insights on Research Techniques towards Cost Estimation in Software Design

10.11591/ijece.v7i5.pp2883-2894
Praveen Naik , Shantaram Nayak
Software cost estimation is of the most challenging task in project management in order to ensuring smoother development operation and target achievement. There has been evolution of various standards tools and techniques for cost estimation practiced in the industry at present times. However, it was never investigated about the overall picturization of effectiveness of such techniques till date. This paper initiates its contribution by presenting taxonomies of conventional cost-estimation techniques and then investigates the research trends towards frequently addressed problems in it. The paper also reviews the existing techniques in well-structured manner in order to highlight the problems addressed, techniques used, advantages associated and limitation explored from literatures. Finally, we also brief the explored open research issues as an added contribution to this manuscript.
Volume: 7
Issue: 5
Page: 2883-2894
Publish at: 2017-10-01

Optimized High-Utility Itemsets Mining for Effective Association Mining Paper

10.11591/ijece.v7i5.pp2911-2918
K Rajendra Prasad
Association rule mining is intently used for determining the frequent itemsets of transactional database; however, it is needed to consider the utility of itemsets in market behavioral applications. Apriori or FP-growth methods generate the association rules without utility factor of items. High-utility itemset mining (HUIM) is a well-known method that effectively determines the itemsets based on high-utility value and the resulting itemsets are known as high-utility itemsets. Fastest high-utility mining method (FHM) is an enhanced version of HUIM. FHM reduces the number of join operations during itemsets generation, so it is faster than HUIM. For large datasets, both methods are very expenisve. Proposed method addressed this issue by building pruning based utility co-occurrence structure (PEUCS) for elimatination of low-profit itemsets, thus, obviously it process only optimal number of high-utility itemsets, so it is called as optimal FHM (OFHM). Experimental results show that OFHM takes less computational runtime, therefore it is more efficient when compared to other existing methods for benchmarked large datasets.
Volume: 7
Issue: 5
Page: 2911-2918
Publish at: 2017-10-01
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