International Journal of Electrical and Computer Engineering (IJECE)

Journal Information
ISSN / EISSN : 2088-8708 / 2088-8708
Current Publisher: Institute of Advanced Engineering and Science (10.11591)
Total articles ≅ 3,126
Current Coverage
SCOPUS
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SHERPA/ROMEO
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Latest articles in this journal

Idrees S. Al-Kofahi, Zaid AlBataineh, Ahmad Dagamseh
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 772-779; doi:10.11591/ijece.v11i1.pp772-779

Abstract:
In this paper, a two-stage 0.18 μm CMOS power amplifier (PA) for ultra-wideband (UWB) 3 to 5 GHz based on common source inductive degeneration with an auxiliary amplifier is proposed. In this proposal, an auxiliary amplifier is used to place the 2nd harmonic in the core amplified in order to make up for the gain progression phenomena at the main amplifier output node. Simulation results show a power gain of 16 dB with a gain flatness of 0.4 dB and an input 1 dB compression of about -5 dBm from 3 to 5 GHz using a 1.8 V power supply consuming 25 mW. Power added efficiency (PAE) of around 47% at 4 GHz with 50 Ω load impedance was also observed.
Youssra Zahidi, Yacine El Younoussi, Yassine Al-Amrani
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 745-752; doi:10.11591/ijece.v11i1.pp745-752

Abstract:
Deep learning (DL) is a machine learning (ML) subdomain that involves algorithms taken from the brain function named artificial neural networks (ANNs). Recently, DL approaches have gained major accomplishments across various Arabic natural language processing (ANLP) tasks, especially in the domain of Arabic sentiment analysis (ASA). For working on Arabic SA, researchers can use various DL libraries in their projects, but without justifying their choice or they choose a group of libraries relying on their particular programming language familiarity. We are basing in this work on Java and Python programming languages because they have a large set of deep learning libraries that are very useful in the ASA domain. This paper focuses on a comparative analysis of different valuable Python and Java libraries to conclude the most relevant and robust DL libraries for ASA. Throw this comparative analysis, and we find that: TensorFlow, Theano, and Keras Python frameworks are very popular and very used in this research domain.
Walaiporn Sornkliang, Thimaporn Phetkaew
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 575-588; doi:10.11591/ijece.v11i1.pp575-588

Abstract:
The benefit of exploratory testing and ad hoc testing by tester’s experience is that crucial bugs are found quickly. Regression testing and test case prioritization are important processes of software testing when software functions have been changed. We propose a test path prioritization method to generate a sequence of test paths that would match the testers’ interests and focuses on the target area of interest or on the changed area. We generate test paths form the activity diagrams and survey the test path prioritization from testers. We define node and edge weight to the symbols of activity diagrams by applying Time management, Pareto, Buffett, Binary, and Bipolar method. Then we propose a test path score equation to prioritize test paths. We also propose evaluation methods i.e., the difference and the similarity of test path prioritization to testers’ interests. Our proposed method had the least average of the difference and the most average of the similarity compare with the tester’s prioritization of test paths. The Bipolar method was the most suitable for assigning weights to match test path rank by the tester. Our proposed method also has given the affected path by changing area higher priority than the other test path.
M. A. Abo-Sennah, M. A. El-Dabah, Ahmed El-Biomey Mansour
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 57-73; doi:10.11591/ijece.v11i1.pp57-73

Abstract:
Photovoltaic systems (PV) are one of the most important renewable energy resources (RER). It has limited energy efficiency leading to increasing the number of PV units required for certain input power i.e. to higher initial cost. To overcome this problem, maximum power point tracking (MPPT) controllers are used. This work introduces a comparative study of seven MPPT classical, artificial intelligence (AI), and bio-inspired (BI) techniques: perturb and observe (P&O), modified perturb and observe (M-P&O), incremental conductance (INC), fuzzy logic controller (FLC), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and cuckoo search (CS). Under the same climatic conditions, a comparison between these techniques in view of some criteria’s: efficiencies, tracking response, implementation cost, and others, will be performed. Simulation results, obtained using MATLAB/SIMULINK program, show that the MPPT techniques improve the lowest efficiency resulted without control. ANFIS is the highest efficiency, but it requires more sensors. CS and ANN produce the best performance, but CS provided significant advantages over others in view of low implementation cost, and fast computing time. P&O has the highest oscillation, but this drawback is eliminated using M-P&O. FLC has the longest computing time due to software complexity, but INC has the longest tracking time.
Hena Iqbal, Sujni Paul, Khaliquzzaman Khan
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 498-497; doi:10.11591/ijece.v11i1.pp498-497

Abstract:
Evaluation is an analytical and organized process to figure out the present positive influences, favourable future prospects, existing shortcomings and ulterior complexities of any plan, program, practice or a policy. Evaluation of policy is an essential and vital process required to measure the performance or progression of the scheme. The main purpose of policy evaluation is to empower various stakeholders and enhance their socio-economic environment. A large number of policies or schemes in different areas are launched by government in view of citizen welfare. Although, the governmental policies intend to better shape up the life quality of people but may also impact their every day’s life. A latest governmental scheme Saubhagya launched by Indian government in 2017 has been selected for evaluation by applying opinion mining techniques. The data set of public opinion associated with this scheme has been captured by Twitter. The primary intent is to offer opinion mining as a smart city technology that harness the user-generated big data and analyse it to offer a sustainable governance model.
Muhamet Kastrati, Marenglen Biba
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 716-727; doi:10.11591/ijece.v11i1.pp716-727

Abstract:
The main objective of this paper is to provide a state-of-the-art review, analyze and discuss stochastic local search techniques used for solving hard combinatorial problems. It begins with a short introduction, motivation and some basic notation on combinatorial problems, search paradigms and other relevant features of searching techniques as needed for background. In the following a brief overview of the stochastic local search methods along with an analysis of the state-of-the-art stochastic local search algorithms is given. Finally, the last part of the paper present and discuss some of the most latest trends in application of stochastic local search algorithms in machine learning, data mining and some other areas of science and engineering. We conclude with a discussion on capabilities and limitations of stochastic local search algorithms.
Achmad Ridok, Nashi Widodo, Wayan Firdaus Mahmudy, Muhaimin Rifa’I
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 728-735; doi:10.11591/ijece.v11i1.pp728-735

Abstract:
Breast cancer may cause a death due to the late diagnosis. A cheap and accurate tool for early detection of this disease is essential to prevent fatal incidence. In general, the cheap and less invasive method to diagnose the disease could be done by biopsy using fine needle aspirates from breast tissue. However, rapid and accurate identification of the cancer cell pattern from the cell biopsy is still challenging task. This diagnostic tool can be developed using machine learning as a classification problem. The performance of the classifier depends on the interrelationship between sample sizes, some features, and classifier complexity. Thus, the removal of some irrelevant features may increase classification accuracy. In this study, a new hybrid feature selection fast correlation based feature (FCBF) and information gain (IG) was used to select features on identifying breast cancer using AIRS algorithm. The results of 10 times the crossing (CF) of our validation on various AIRS seeds indicate that the proposed method can achieve the best performance with accuracy =0.9797 and AUC=0.9777 at k=6 and seed=50.
Sang Dang Ho, Petr Palacky, Martin Kuchar, Pavel Brandstetter, Cuong Dinh Tran
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 815-826; doi:10.11591/ijece.v11i1.pp815-826

Abstract:
This paper presents a different technique for the online stator resistance estimation using a particle swarm optimization (PSO) based algorithm for rotor flux oriented control schemes of induction motor drives without a rotor speed sensor. First, a conventional proportional-integral controller-based stator resistance estimation technique is used for a speed sensorless control scheme with two different model reference adaptive system (MRAS) concepts. Finally, a novel method for the stator resistance estimation based on the PSO algorithm is presented for the two MRAS-type observers. Simulation results in the Matlab/Simulink environment show good adaptability of the proposed estimation model while the stator resistance is varied to 200% of the nominal value. The results also confirm more accurate stator resistance and rotor speed estimation in comparison with the conventional technique.
Kashish Ara Shakil, Mansaf Alam, Samiya Khan
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 671-685; doi:10.11591/ijece.v11i1.pp671-685

Abstract:
Cloud computing is an emerging distributed computing paradigm. However, it requires certain initiatives that need to be tailored for the cloud environment such as the provision of an on-the-fly mechanism for providing resource availability based on the rapidly changing demands of the customers. Although, resource allocation is an important problem and has been widely studied, there are certain criteria that need to be considered. These criteria include meeting user’s quality of service (QoS) requirements. High QoS can be guaranteed only if resources are allocated in an optimal manner. This paper proposes a latency-aware max-min algorithm (LAM) for allocation of resources in cloud infrastructures. The proposed algorithm was designed to address challenges associated with resource allocation such as variations in user demands and on-demand access to unlimited resources. It is capable of allocating resources in a cloud-based environment with the target of enhancing infrastructure-level performance and maximization of profits with the optimum allocation of resources. A priority value is also associated with each user, which is calculated by analytic hierarchy process (AHP). The results validate the superiority for LAM due to better performance in comparison to other state-of-the-art algorithms with flexibility in resource allocation for fluctuating resource demand patterns.
Kevin Alejandro Hernández, D. Cárdenas Peña, Álvaro A. Orozco
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 620-627; doi:10.11591/ijece.v11i1.pp620-627

Abstract:
The development of analysis methods for categorical data begun in 90's decade, and it has been booming in the last years. On the other hand, the performance of many of these methods depends on the used metric. Therefore, determining a dissimilarity measure for categorical data is one of the most attractive and recent challenges in data mining problems. However, several similarity/dissimilarity measures proposed in the literature have drawbacks due to high computational cost, or poor performance. For this reason, we propose a new distance metric for categorical data. We call it: Weighted pairing (W-P) based on feature space-structure, where the weights are understood like a degree of contribution of an attribute to the compact cluster structure. The performance of W-P metric was evaluated in the unsupervised learning framework in terms of cluster quality index. We test the W-P in six real categorical datasets downloaded from the public UCI repository, and we make a comparison with the distance metric (DM3) method and hamming metric (H-SBI). Results show that our proposal outperforms DM3 and H-SBI in different experimental configurations. Also, the W-P achieves highest rand index values and a better clustering discriminant than the other methods.
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