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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 67 Documents
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Search results for , issue "Vol 22, No 2: May 2021" : 67 Documents clear
Analyzing impact of number of features on efficiency of hybrid model of lexicon and stack based ensemble classifier for twitter sentiment analysis using WEKA tool Sangeeta Rani; Nasib Singh Gill; Preeti Gulia
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp1041-1051

Abstract

Twitter is used by millions of people across the world, so the data collected from Twitter can be highly valuable for research and helpful in decision support. Here in this paper ‘Twitter US Airline data’ from Kaggle data repository is used for sentiment classification of customers’ reviews. The current research aims to implement various machine learning classifiers, Stack-based ensemble classifiers and hybrid of lexicon classifier with other classifiers. 11 different classification models are implemented for different sized feature sets. Also, all the 11 models are re-implemented by adding sentiment score of lexicon based classifier as one of the features in the feature set. Results are analyzed by varying number of input feature variables used in the classification. Four different size feature sets having 301,501, 701, and 1301 number of features are used to analyze the variations in the final findings. Chi-Square and Information gain techniques are used for feature selection. The results show that an increase in the number of features increases the accuracy up to 701 features. After that, accuracy is stable or decreases with increase in feature set size. Also, the cost of adding sentiment score of lexicon classifier to the input feature set is nominal, but the results are improved consistently. WEKA and R Studio tools are used for analysis and implementation. Accuracy and Kappa are used for representing and comparing the efficiency of models.
Deepenz: prediction of enzyme classification by deep learning Hamza Chehili; Salah Eddine Aliouane; Abdelhafedh Bendahmane; Mohamed Abdelhafid Hamidechi
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp1108-1115

Abstract

Previously, the classification of enzymes was carried out by traditional heuritic methods, however, due to the rapid increase in the number of enzymes being discovered, new methods aimed to classify them are required. Their goal is to increase the speed of processing and to improve the accuracy of predictions. The Purpose of this work is to develop an approach that predicts the enzymes’ classification. This approach is based on two axes of artificial intelligence (AI): natural language processing (NLP) and deep learning (DL). The results obtained in the tests  show the effectiveness of this approach. The combination of these two tools give a model with a great capacity to extract knowledge from enzyme data to predict and classify them. The proposed model learns through intensive training by exploiting enzyme sequences. This work highlights the contribution of this approach to improve the precision of enzyme classification.
Gray level co-occurrence matrix feature extraction and histogram in breast cancer classification with ultrasonographic imagery Karina Djunaidi; Herman Bedi Agtriadi; Dwina Kuswardani; Yudhi S. Purwanto
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp795-800

Abstract

One way to detect breast cancer is using the Ultrasonography (USG) procedure, but the ultrasound image is susceptible to the noise speckles so that the interpretation and diagnosis results are different. This paper discusses the classification of breast cancer ultrasound images that aims to improve the accuracy of the identification of the type and level of cancer malignancies based on the features of its texture. The feature extraction process uses a histogram which then the results are calculated using the Gray Level Co-Occurrence Matrix (GLCM). The results of the two extraction features are then classified using K-Nearest Neighbors (KNN) to obtain accurate figures from those images. The results of this study is that the accuracy in detecting cancer types is 80%.
Video mosaic watermarking using plasma key Nidaa Flaih Hassan; Akbas Ezaldeen Ali; Teaba Wala Aldeen; Ayad Al-Adhami
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp619-628

Abstract

Video watermarking is one of the most widespread techniques amongst the many watermarking techniques presently are used; this is because the extreme existences of copyright abuse and misappropriation occur for video content. In this paper, a new watermarking algorithm is proposed to embed logo in digital video for copyright protection. To make the watermarks more robust to attack, host frame and host embedding indices must be changeable. A new algorithm is proposed to determined host frames by plasma function, Host location indices in frames are also determined by another plasma function. Logo is divided using the mosaic principle, the size of mosaic blocks is determined initially according to the degree of protection, whenever the size of mosaic blocks is small, it leads to safe embedding, and vice versa. Digital watermarks are embedded easily without any degradation for video quality, In the other side, the watermarked is retrieved by applying the reverse of proposed embedding algorithm and extracted watermark is still recognizable. The experimental results confirm that watermark is robust against three types of attacks which are addition of Gaussian noise, JPEG compression, and rotation process.
Isolated Arabic handwritten words recognition using EHD and HOG methods Mamoun Jassim Mohammed; Suphian Mohammed Tariq; Hayder Ayad
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp801-808

Abstract

Handwriting recognition is a growing field of study in computer vision, artificial intelligence and pattern recognition technology aimed to recognizing texts and handwritings of hefty amount of produced official documents and paper works by institutes or governments. Using computer to distinguish and make these documents accessible and approachable is the goal of these efforts. Moreover, recognition of text has accomplished practically a major progress in many domains such as security sector and e-government structure and more. A system for recognition text’s handwriting was presented here relied on edge histogram descriptor (EHD), histogram of orientated gradients (HOG) features extraction and support vector machine (SVM) as a classifier is proposed in this paper. HOG and EHD give an optimal features of the Arabic hand-written text by extracting the directional properties of the text. Besides that, SVM is a most common machine learning classifier that obtaining an essential classification results within various kernel functions. The experimental evaluation is carried out for Arabic handwritten images from IESK-ArDB database using HOG, EHD features and proposed work provides 85% recognition rate.
Capacitance study of integrated circuits matrix interconnects Ahcene Lakhlef; Arezki Benfdila; Lounas Belhimer
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp1156-1164

Abstract

Propagation delays and couplings between nearby lines affect the circuit performances (speed, power consumption) and operations. Propagation delays in longer lines can become critical compared to the clock frequency and can induce unwanted signals in neighboring lines ("crosstalk" phenomenon). Induced line capacitances can induce parasitic signals. Hence characterizing of these capacitances is of paramount importance. The present work deals with the analysis of capacitance of a multilayer conductor interconnect aiming for their possible exact extraction. We used three topologies of a microstrip conductor interconnects and identified the potential distributor and then computed the capacitance and inductance matrix using a finite element method. The first analysis dealt with parallel microstrip conductors and the second with two levels (plan) of a microstrip conductors the results are compared to those obtained by other methods and found quite encouraging.
Analysis and evaluation of symmetrical key ciphers for internet of things smart home Lujain S. Abdulla; Musaria K. Mahmood; Abbas F. Salih; Sulaiman M. Karim
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp1191-1198

Abstract

A large number of sensors and intelligent devices are interconnected via the internet to collect data as part of the internet of things (IoT) applications. Data security is one of the important challenges for these applications due to vulnerability of the internet. IoT devices limiting factors, such as delay-sensitivity, restricted memory, and low computing capability, make choosing the appropriate data encryption standard extremely important. The current research focuses on evaluating four data security, block cipher standards for the IoT smart home application. Considering the encryption/decryption speed, DES, TDES, AES, and SAFER+ standards are evaluated by implementing the algorithms with MATLAB to determine the best security solution. The simulation of the four standards shows the superiority of SAFER+ standard in term of encryption speed compared to others added to its capabilities on security, and software implementation opportunity. The use of classical symmetric key standards for real time data security in the IoT application can be validated through the selection of SAFER+.
On active anti-islanding techniques: survey Yasser Ahmed Elshrief; Sameh Abd-Elhaleem; Belal Abo Zalam; Amin D. Asham
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp609-618

Abstract

The phenomenon of feeding loads from any distributed generators (DGs) with a total disconnection of utility grid at the point of common coupling is called Islanding. The DGs are usually independently controlled. Hence, when the islanding problem occurs, the electric utility loses the control and supervision over that section of the power grid. Furthermore, prolonged islanding can prevent reconnection to the power grid and may cause damage due to voltage and frequency excursions. Therefore, the islanding detection, which is also called anti-islanding (AI), is one of the most critical aspects of the integration of DG sources into the power grid. In this paper, a comprehensive survey on the local AI techniques is illustrated, especially active type which is used for improving the performance regarding the size of the non-detection zone and detection speed. Extensive comparisons are provided to demonstrate the effectiveness of each technique.
Dynamic composition components based on machine learning: architecture design and process Younes Zouani; Abdelmounaim Abdali; Charafeddine Ait Zaouiat
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp1135-1143

Abstract

The dynamic composition of components is an emerging concept that aims to allow a new application to be constructed based on a user’s request. Three main ingredients must be used to achieve the dynamic composition of components: goal, scenario, and context-awareness. These three ingredients must be completed by artificial intelligence (AI) techniques that help process discovery and storage. This paper presents framework architecture for the dynamic composition of components that can extract expressed goals, deduce implicit ones using AI. The goal will be combined with pertinent contextual data, to compose the relevant components that meet the real requirements of the user. The core element of our proposed architecture is the composer component that (i) negotiate user goal, (ii) load the associated scenarios and choose the most suitable one based on user goal and profile, (iii) get binding information of scenario’s actions, (iv) compose the loaded actions, and (v) store the new component as a tree of actions enabled by contextual or process constraint. In our e-learning proven of concept, we consider five components: composer component, reader component, formatter component, matcher component, and executor component. These five components stipulate that a course is the combination of existing/scrapped chapters that have been adapted to a user profile in terms of language, level of difficulty, and prerequisite. The founding result shows that AI is not only an element that enhances system performance in terms of timing response but a crucial ingredient that guides the dynamic composition of components. 
A review of codebook design methods for sparse code multiple access Syed Aamer Hussain; Norulhusna Ahmad; Ibraheem Shayea; Hazilah Mad Kaidi; Liza Abdul Latiff; Norliza Mohamed; Suriani Mohd Sam
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp927-935

Abstract

The progressions in telecommunication beyond the 5th generation have created a need to improve research drifts. The current 5G study has an important focus on non-orthogonal multiple access (NOMA) technology. sparse code multiple access (SCMA) is a promising technique within NOMA, enhancing the multi-user handling capability of next-generation communication. In the SCMA sphere, codebook designing and optimisation are essential research matters. This study conversed with different codebook design practises existing in the literature, analysing them for numerous parameters, including bit error rate (BER), an optimisation technique, and channel settings. From the analysis, the paper presents the efficiency of different approaches. The article also discusses the prospects and challenges of SCMA optimisation in practical implementation in various domains.

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