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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 63 Documents
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Search results for , issue "Vol 10, No 2: April 2021" : 63 Documents clear
Design and development of intelligent waste bin system with advertisement solution Tarig Faisal; Moath Awawdeh; Anees Bashir
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2753

Abstract

In cities where a large geographical area of the city is densely populated, the process of waste collection is cumbersome, tiresome and expensive. Often, the burden of manually tracking and collecting of waste causes waste management companies enormous wasted effort and get them involved in tasks that are not necessary. No doubt, a digital interaction between waste management companies and targeted waste collection areas could ensure the process becomes fast, efficient and traceable as they become aware of the states of the wastes, aptly. It will considerably reduce any discrepancies that may occur due to the lack of information available during a particular time. Accordingly, this paper proposes a novel approach towards waste management combined with the internet of things to reduce the problems that would occur due to the accumulation of wastes and hence improvise waste collection/management process. Additionally, an innovative feature which generates revenue and creates business opportunities for waste management companies is introduced via advertisement solution based on network-attached storage technology.
BCH codes in UFMC: A new contender candidate for 5G communication systems Ghasan Ali Hussain; Lukman Audah
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2080

Abstract

Nowadays, fifth generation (5G) wireless network is considered one of the most important research topics in wireless industry and it will be substituting with fourth generation (4G) in several aspects. Although the robustness of orthogonal frequency division multiplexing (OFDM) system against channel delays which is the reason behind using it in LTE/LTE Advanced however, it is suffering from high peak to average power ration (PAPR) and out of band side lobes. So, universal filtered multi-carrier (UFMC) technique is considered a new modulation scheme for 5G wireless communication system to overcome on the common OFDM demits. In contrast, to achieve reliable data transmission in digital communication systems, using error correcting codes are considered an essential over noisy channels. In this paper, BCH code has been used for UFMC system over AWGN. The results showed that using BCH codes in UFMC contributed in enhancing BER performance while could decreasing both of PAPR and OOBE values better than conventional OFDM system.
The search for science and technology verses in Qur’an and hadith Ichsan Taufik; Mohamad Jaenudin; Fatimah Ulwiyatul Badriyah; Beki Subaeki; Opik Taupik Kurahman
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2629

Abstract

Currently, the Vector Space Model algorithm has been widely implemented for the document search feature because of its reliability in retrieving information. One of them in the search for verses of the Qur'an based on the translation. However, if the phrase or word used is different (even though it has one meaning) with the word in the document in the database, the system will not display the verse. As we know that the Qur'an has a very deep meaning, so an interpretation of the verse is needed. Therefore, this research focuses on implementing the Vector Space Model (VSM) algorithm for searching verses and hadiths in science and technology by using the discussion parameters of these verses or hadiths. The test results obtained with 20 keyword samples using metric recall were 81% with an average time of 2.24 seconds.
Towards a formal analysis of the multi-robot task allocation problem using set theory Farouq Zitouni; Ramdane Maamri; Saad Harous
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2395

Abstract

Nowadays, the multi-robot task allocation problem is one of the most challenging problems in multi-robot systems. It concerns the optimal assignment of a set of tasks to several robots while optimizing a given criterion subject to some constraints. This problem is very complex, particularly when handling large groups of robots and tasks. We propose a formal analysis of the task allocation problem in a multi-robot system, based on set theory concepts. We believe that this analysis will help researchers understand the nature of the problem, its time complexity, and consequently develop efficient solutions. Also, we used that formal analysis to formulate two well-known taxonomies of multi-robot task allocation problems. Finally, a generic solving scheme of multi-robot task allocation problems is proposed and illustrated on assigning papers to reviewers within a journal.
The COVID-19 fake news detection in Thai social texts Pakpoom Mookdarsanit; Lawankorn Mookdarsanit
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2745

Abstract

One important obstruction against Thai COVID-19 recovery is fake news shared on social media that is one of the “Artificial Intelligence Open Issues against COVID-19” reported by Montreal.AI. Misinformation spread is one of the main cyber-security threats that should be filtered out as the IDS for maintaining COVID-19 information quality. To detect fake news in Thai texts, Thai-NLP techniques are necessary. This paper proposes a state-of-the-art Thai COVID-19 fake news detection among word relations using transfer learning models. For pre-training from the global open COVID-19 datasets, the source dataset is constructed by English to Thai translating. The novel feature shifting is formulated to enlarge Thai text examples in target dataset. Machine translation can be used for constructing Thai source dataset to cope with the lack of local dataset for future Thai-NLP applications. To lead the knowledge in Thai text understanding forward, feature shifting is a promising accuracy improvement in fine-tuning stage.
Automated diagnosis of attacks in internet of things using machine learning and frequency distribution techniques Toufik Ghrib; Mohamed Benmohammed; Purnendu.Shekhar Pandey
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2766

Abstract

The Internet of Things (IoT) is the interconnection of things around us to make our daily process more efficient by providing more comfort and productivity. However, these connections also reveal a lot of sensitive data. Therefore, thinking about the methods of information security and coding are important as the security approaches that rely heavily on coding are not a strong match for these restricted devices. Consequently, this research aims to contribute to filling this gap, which adopts machine learning techniques to enhance network-level security in the low-power devices that use the lightweight MQTT protocol for their work. This study used a set of tools tools and, through various techniques, trained the proposed system ranging from Ensemble methods to deep learning models. The system has come to know what type of attack has occurred, which helps protect IoT devices. The log loss of the Ensemble methods is 0.44, and the accuracy of multi-class classification is 98.72% after converting the table data into an image set. The work also uses a Convolution Neural Network, which has a log loss of 0.019 and an accuracy of 99.3%. It also aims to implement these functions in IDS.
Experimental analysis of non-Gaussian noise resistance on global method optical flow using bilateral in reverse confidential Darun Kesrarat; Vorapoj Patanavijit
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2740

Abstract

This paper presents the analytical of non-Gaussian noise resistance with the aid of the use of bilateral in reverse confidential with the optical flow. In particular, optical flow is the sample of the image’s motion from the consecutive images caused by the object’s movement. It is a 2-D vector where every vector is a displacement vector displaying the motion from the first image to the second. When the noise interferes with the image flow, the approximated performance on the vector in optical flow is poor. We ensure greater appropriate noise resistance by applying bilateral in reverse confidential in optical flow in the experiment by concerning the error vector magnitude (EVM). Many noise resistance models of the global method optical flow are using for comparison in our experiment. And many sequenced image data sets where they are interfered with by several types of non-Gaussian noise are used for experimental analysis.
Proposition of local automatic algorithm for landmark detection in 3D cephalometry Mohammed Ed-dhahraouy; Hicham Riri; Manal Ezzahmouly; Abdelmajid El moutaouakkil; Hakima Aghoutan; Farid Bourzgui
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.1827

Abstract

This study proposes a new contribution to solve the problem of automatic landmarks detection in three-dimensional cephalometry. 3D images obtained from CBCT (cone beam computed tomography) equipment were used for automatic identification of twelve landmarks. The proposed method is based on a local geometry and intensity criteria of skull structures. After the step of preprocessing and binarization, the algorithm segments the skull into three structures using the geometry information of nasal cavity and intensity information of the teeth. Each targeted landmark was detected using local geometrical information of the volume of interest containing this landmark. The ICC and confidence interval (95% CI) for each direction were 0, 91 (0.75 to 0.96) for x- direction; 0.92 (0.83 to 0.97) for y-direction; 0.92 (0.79 to 0.97) for z-direction. The mean error of detection was calculated using the Euclidian distance between the 3D coordinates of manually and automatically detected landmarks. The overall mean error of the algorithm was 2.76 mm with a standard deviation of 1.43 mm. Our proposed approach for automatic landmark identification in 3D cephalometric was capable of detecting 12 landmarks on 3D CBCT images which can be facilitate the use of 3D cephalometry to orthodontists.
Pre-convoluted neural networks for fashion classification Mustafa Amer Obaid; Wesam M. Jasim
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2750

Abstract

In this work, concept of the fashion-MNIST images classification constructed on convolutional neural networks is discussed. Whereas, 28×28 grayscale images of 70,000 fashion products from 10 classes, with 7,000 images per category, are in the fashion-MNIST dataset. There are 60,000 images in the training set and 10,000 images in the evaluation set. The data has been initially pre-processed for resizing and reducing the noise. Then, this data is normalized for ensuring that all the data are on the same scale and this usually improves the performance. After normalizing the data, it is augmented where one image will be in three forms of output. The first output image is obtained by rotating the actual one; the second output image is obtained as acute angle image; and the third is obtained as tilt image. The new data set is of 180,000 images for training phase and 30,000 images for the testing phase. Finally, data is sent to training process as input for training model of the pre-convolution network. The pre-convolution neural network with the five layered convoluted deep neural network and do the training with the augmented data, The performance of the proposed system shows 94% accuracy where it was 93% in VGG16 and 92% in AlexNetnetworks.
Vietnamese character recognition based on CNN model with reduced character classes Thi Ha Phan; Duc Chung Tran; Mohd Fadzil Hassan
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2810

Abstract

This article will detail the steps to build and train the convolutional neural network (CNN) model for Vietnamese character recognition in educational books. Based on this model, a mobile application for extracting text content from images in Vietnamese textbooks was built using OpenCV and Canny edge detection algorithm. There are 178 characters classes in Vietnamese with accents. However, within the scope of Vietnamese character recognition in textbooks, some classes of characters only differ in terms of actual sizes, such as “c” and “C”, “o” and “O”. Therefore, the authors built the classification model for 138 Vietnamese character classes after filtering out similar character classes to increase the model's effectiveness.

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