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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,174 Documents
 545   546   547   548   549 
A class skew-insensitive ACO-based decision tree algorithm for imbalanced data sets Muhamad Hasbullah Bin Mohd Razali; Rizauddin Bin Saian; Yap Bee Wah; Ku Ruhana Ku-Mahamud
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 1: January 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i1.pp412-419

Abstract

Ant-tree-miner (ATM) has an advantage over the conventional decision tree algorithm in terms of feature selection. However, real world applications commonly involved imbalanced class problem where the classes have different importance. This condition impeded the entropy-based heuristic of existing ATM algorithm to develop effective decision boundaries due to its biasness towards the dominant class. Consequently, the induced decision trees are dominated by the majority class which lack in predictive ability on the rare class. This study proposed an enhanced algorithm called hellinger-ant-tree-miner (HATM) which is inspired by ant colony optimization (ACO) metaheuristic for imbalanced learning using decision tree classification algorithm. The proposed algorithm was compared to the existing algorithm, ATM in nine (9) publicly available imbalanced data sets. Simulation study reveals the superiority of HATM when the sample size increases with skewed class (Imbalanced Ratio < 50%). Experimental results demonstrate the performance of the existing algorithm measured by BACC has been improved due to the class skew-insensitiveness of hellinger distance. The statistical significance test shows that HATM has higher mean BACC score than ATM.
Detection and classification of various pest attacks and infection on plants using RBPN with GA based PSO algorithm Kapilya Gangadharan; G. Rosline Nesa Kumari; D. Dhanasekaran; K. Malathi
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1278-1288

Abstract

Machine learning methodologies are commonly used in the field of precession farming. It prospects greatly in the plant safety measure like disease detection and classification of pest attacks. It highly influences the crop production and management. The venture of our system is to produce healthy plantation. The proposed system involves enhanced feature fractal texture analysis, Statistical feature selection and machine learning methodology for classification. Hence more than ever there is a need for such a tool that combines image processing methodologies and the neural network concepts and that is supported by huge cloud of structured data which makes the diagnosis and classification part much easier and convenient. The proposed system recognizes and classifies the plant taxonomy and the infection based on the selected statistical features. The neural network concept followed in our proposed system is focused on artificial neural network which uses recursive back propagation neural network to speed up the training process as well as reduce multiclass problem in the network and optimize the weights on hidden layers of the Network using Genetic algorithm based particle swarm optimization technique. We have used MATLAB to implement the concept and obtained better accuracy in disease/pest detection and proved to be an efficient method.
A new approach for improving clustering algorithms performance Anfal F. N. Alrammahi; Kadhim B. S. Aljanabi
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1569-1575

Abstract

Clustering represents one of the most popular and used Data Mining techniques due to its usefulness and the wide variations of the applications in real world. Defining the number of the clusters required is an application oriented context, this means that the number of clusters k is an input to the whole clustering process. The proposed approach represents a solution for estimating the optimum number of clusters. It is based on the use of iterative K-means clustering under three different criteria; centroids convergence, total distance between the objects and the cluster centroid and the number of migrated objects which can be used effectively to ensure better clustering accuracy and performance. A total of 20000 records available on the internet were used in the proposed approach to test the approach. The results obtained from the approach showed good improvement on clustering accuracy and algorithm performance over the other techniques where centroids convergence represents a major clustering criteria. C# and Microsoft Excel were the software used in the approach.
Naïve Bayes and linear discriminate analysis based diagnostic analytic of harmonic source identification M. H Jopri; MR Ab Ghani; A.R Abdullah; Tole Sutikno; M Manap; J. Too
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1626-1633

Abstract

The diagnostic analytic type of harmonic source is a vital research due to diagnose and identify type of harmonic source that exist in the power system. This paper presents a comparison of machine learning (ML) algorithm namely as the Naïve Bayes (NB) and linear discriminate analysis (LDA) in identifying and diagnosing the harmonic sources.  The MLs inputs are the voltage and current feature sets that estimated from the time-frequency representation (TFR) of S-transform analysis. Four specific cases of harmonic source location are considered in this research, whereas harmonic voltage (HV) and harmonic current (HC) source type-load are used in the diagnosing process. The sufficiency of the proposed methodology is tested and verified on the IEEE 4-bust test feeder, and to prevent overfitting, the K-fold cross-validation technique is implemented for performance evaluation. To identify the best ML, the performance measurement consist of the accuracy, precision, geometric mean, F-measure, sensitivity, and specificity are conducted.
Total energy consumption analysis in wireless Mobile ad hoc network with varying mobile nodes Mohammed Ayad Saad; Sameer A. S. Lafta; Raed Khalid Al-Azzawi; Adnan Hussein Ali; Sameer Alani; M. M. Hashim; Bassam Hasan
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1397-1405

Abstract

The energy protocols that have a mechanisms of shortest path routing considered predominant in the networking scenarios. The interesting matter in the routing protocols designing deal with mobile ad hoc network (MANET) must have an energy efficient network for better network performances. The Performances of such routing protocols that can be assessed will be focused on many metrics like delay, throughput, and packet delivery.  MANET is a distribution network, having no infrastructure and network decentralization. There routing protocols are utilized for detecting paths among mobile nodes to simplify network communication. The performance comparison of three protocols are Optimized Link State Routing (OLSR), the second is Ad hoc On-Demand Distance Vector (AODV), while the third is Dynamic Source Routing (DSR) routing protocols concerning to average energy consumption and mobile node numbers are described thoroughly by NS-3 simulator.  The nodes number is changing between 10 and 25 nodes, with various mobility models. The performance analysis shows that the suggested protocols are superior in relations to the energy consumption for networking data transmission and the performance of the wireless network can be improved greatly.
Robust watermarking scheme based LWT and SVD using artificial bee colony optimization Adnan Mohsin Abdulazeez; Diyar Qader Zeebaree; Dilan M. Hajy; Dilovan Asaad Zebari
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 2: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i2.pp1218-1229

Abstract

This paper presents a watermarking scheme for grayscale images, in which lifting wavelet transform and singular value decomposition are exploited based on multi-objective artificial bee colony optimization to produce a robust watermarking method. Furthermore, for increasing security encryption of the watermark is done prior to the embedding operation. In the proposed scheme, the actual image is altered to four sub-band over three levels of lifting wavelet transform then the singular value of the watermark image is embedded to the singular value of LH sub-band of the transformed original image. In the embedding operation, multiple scaling factors are utilized on behalf of the single scaling element to get the maximum probable robustness without changing watermark lucidity. Multi-objective artificial bee colony optimization is utilized for the determination of the optimal values for multiple scaling components, which are examined against various types of attacks. For making the proposed scheme more secure, the watermark is encrypted chaotically by logistic chaotic encryption before embedding it to the host (original) image. The experimental results show excellent imperceptibility and good resiliency against a wide range of image processing attacks.
Enhancement in resource allocation system for cloud environment using modified grey wolf technique Soukaina Ouhame; Youssef Hadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1530-1537

Abstract

Cloud computing is new trend of technology which provides services with the help of internet based on specific rules.VM is one of the main elements of cloud computing it work on virtualizations concept. Due to the growth of cloud computing user demands for better service are increasing and it make different kind of issues in cloud environment. Data allocation sysytem in VM is one of them for that reason in this paper a new technique used for improvment of data allocation system in VM for cloud computing. The improvement took place GWO algorithm two main section of this algorithm are modified which are local search section and fitness function value. The above proposed technique used to improve three main parameter of scheduling which are energy consumption, throughput and average network executation time in VM for cloud computing. The proposed technique result are compare with ABC algorithm and GWO algorithm based on those result the proposed algorithm improved the three main parameter of load balancing technique in cloud computing.
Biometric authenticator algorithm based on multiresolution analysis Kerrache Soumia; Beladgham Mohammed; Hamza Aymen; Kadri Ibrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1332-1341

Abstract

In this paper, we propose a feature extraction method for two-dimensional imageauthentication algorithm using curvelet transform and principal component analysis(PCA). Since wavelet transform can not adequately describe facial curves features,Theproposed approach involves image denoising applying a 2D-Curvelet transform toachieve compact representations of curves singularities. To assess the performanceof the presented method, we have employed three classifification techniques: Neuralnetworks (NN), K-Nearest Neighbor (KNN) and Support Vector machines (SVM).Extensive experimental results and comparison with the existing methods show the effectiveness of the proposed recognition method in the ORL face database and CASIAiris database.
Bangla language textual image description by hybrid neural network model Md. Asifuzzaman Jishan; Khan Raqib Mahmud; Abul Kalam Al Azad; Mohammad Rifat Ahmmad Rashid; Bijan Paul; Md. Shahabub Alam
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 2: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i2.pp757-767

Abstract

Automatic image captioning task in different language is a challenging task which has not been well investigated yet due to the lack of dataset and effective models. It also requires good understanding of scene and contextual embedding for robust semantic interpretation of images for natural language image descriptor. To generate image descriptor in Bangla, we created a new Bangla dataset of images paired with target language label, named as Bangla Natural Language Image to Text (BNLIT) dataset. To deal with the image understanding, we propose a hybrid encoder-decoder model based on encoder-decoder architecture and the model is evaluated on our newly created dataset. This proposed approach achieves significance performance improvement on task of semantic retrieval of images. Our hybrid model uses the Convolutional Neural Network as an encoder whereas the Bidirectional Long Short Term Memory is used for the sentence representation that decreases the computational complexities without trading off the exactness of the descriptor. The model yielded benchmark accuracy in recovering Bangla natural language and we also conducted a thorough numerical analysis of the model performance on the BNLIT dataset.
A fast spectral conjugate gradient method for solving nonlinear optimization problems Ali A. Al-Arbo; Rana Z. Al-Kawaz
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 1: January 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i1.pp429-439

Abstract

This paper proposes a new spectral conjugate gradient (SCG) approach for solving unregulated nonlinear optimization problems. Our approach proposes Using Wolfe's rapid line scan to adjust the standard conjugate descent (CD) algorithm. A new spectral parameter is a mixture of new gradient and old search path. The path provided by the modified method provides a path of descent for the solution of objective functions. The updated method fits the traditional CD method if the line check is correct. The stability and global convergence properties of the current new SCG are technically obtained from applying certain well-known and recent mild assumptions. We test our approach with eight recently published CD and SCG methods on 55 optimization research issues from the CUTE library. The suggested and all other algorithms included in our experimental research were implemented in FORTRAN language with double precision arithmetic and all experiments were conducted on a PC with 8 GB ram Processor Intel Core i7. The results indicate that our proposed solution outperforms recently reported algorithms by processing and performing fewer iterations in a shorter time.

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