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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 2,901 Documents
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Explicit kissing scene detection in cartoon using convolutional long short-term memory Muhammad Arif Haikal Muhammad Fadzli; Mohd Fadzil Abu Hassan; Norazlin Ibrahim
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3542

Abstract

The main concern of this study is due to certain cartoon content consisting of explicit scenes such as kissing, sex, violence. That are somehow not suitable for kids and may contradict to some religions and cultures. There are some reasons the film industry does not expel the kissing scene in a cartoon movie. It is categorized as a romance sequence and love scene. These could be a double-edged weapon that will ruin an individual’s childhood through excessive exposure to explicit content. This paper proposes a deep learning-based classifier to detect the kissing scene in the cartoon by using Darknet-19 for frame-level feature extraction, while the feature aggregation in the temporal domain is using convolutional long short-term memory (conv-LSTM). This paper also has discussed a few steps related to evaluation and analysis regarding the performance of the models. Extensive experiments prove that the proposed system provides excellent results of 96.43% accuracy to detect the kissing scene in the cartoon. Due to high accuracy performance, the model is suitable to be a kissing scene filter feature in a digital video player that may able to decrease the excessive exposure to explicit content for kids.
Digital watermarking image using three-level discrete wavelet transform under attacking noise Lita Lidyawati; Arsyad Ramadhan Darlis; Lucia Jambola; Lisa Kristiana; Rea Ramada Jayandanu
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3565

Abstract

The authentication, identification, and copyright protection can be obtained by constructing the digital image watermarking technique. Watermark robustness and imperceptibility account for the capability of the hidden watermark to survive the manipulation. The proposed paper is a robust algorithm for digital image watermarking with 3-level discrete wavelet transform (DWT) with some attacks method. The 3-level DWT method was used constants α=0.01 and 0.03 as a function of how depth the watermark inserts to the host image in the insertion and extraction process. The algorithm was evaluated using 8 bits per pixel (bpp) grayscale, 1024x1024 pixels for the host image, and 256x256 pixels for the watermark image. The method is also implemented some experimental with attacks such as gaussian, salt and pepper, blurring, and compression. The algorithm is relatively acceptable of good quality, achieves low-value mean squared error (MSE), high peak signals to noise ratio (PSNR), and structural similarity index metric (SSIM) value approach to 1. It is found that the highest image quality measurements by using α=0.03 with the attacking method of salt and pepper yield MSE=0.01, PSNR=45.6 dB and SSIM=0.95, respectively. 
A visual framework for software requirements traceability Abdulkadir Ahmad Madaki; Wan Mohd Nazmee Wan Zainon
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3269

Abstract

Requirement traceability supports several activities of software development processes such as impact analysis, requirement changes, maintenance, verification, and validation of a software system. For its effective use in those activities, the graphical representation of traceability data plays an important role. However, several traceability tools lack an excellent visual representation to present these type of data. Therefore, this paper presents a visual framework which has been designed and proposed as a prototype tool that can visualize traceability data. The framework applies data visualization techniques to represent requirements and its artefacts relationships as colour-coded symbols on a node-link diagram; users can traverse the graph with an impact analysis method to understand data and make decisions. The evaluation result shows that the proposed tool is useful and easy enough in terms of improving user interaction and to better understand requirement traceability data.
Comparison study of channel coding on non-orthogonal multiple access techniques Sarmad Khaleel Ibrahim; Nooruldeen Q. Ismaeel; Saif A. Abdulhussien
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3348

Abstract

Some of the benefits of fifth-generation (5G) mobile communications include low latency, fast data rates, and increased amount of perceived service quality of users and base station capacity. The purpose of this paper is to solve some of the problems in the traditional mobile system by increasing the channel capacity, non-orthogonal multiple access (NOMA), has a chance of winning the race, power-domain NOMA (PD-NOMA) is widely used in but it requires a large power imbalance between the signals allocated to various users to work. This paper also proposes an improved mobile system model and compares it with a traditional mobile system, then evaluates the effect of channel coding types on the spectrum efficiency performance. A proposed mobile system relied on increasing the number of users as well as increasing the frequency spectrum and is also proposed to improve the error rate, which is incorporated into NOMA and orthogonal frequency division multiplexing (OFDM) schemes at the same time to provide great flexibility and compatibility with other services, such as the 5G and sixth-generation (6G) systems. The mobile gully system (MGS) system is compared to a traditional system, the result is demonstrated that the proposed outperforms the orthogonal multiple access (OMA) system in terms of sum-rate capacity, and bit error rate (BER) performance.
Multimodal deep learning model for human handover classification Islam A Monir; Mohamed W. Fakhr; Nashwa El-Bendary
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3690

Abstract

Giving and receiving objects between humans and robots is a critical task which collaborative robots must be able to do. In order for robots to achieve that, they must be able to classify different types of human handover motions. Previous works did not mainly focus on classifying the motion type from both giver and receiver perspectives. However, they solely focused on object grasping, handover detection, and handover classification from one side only (giver/receiver). This paper discusses the design and implementation of different deep learning architectures with long short term memory (LSTM) network; and different feature selection techniques for human handover classification from both giver and receiver perspectives. Classification performance while using unimodal and multimodal deep learning models is investigated. The data used for evaluation is a publicly available dataset with four different modalities: motion tracking sensors readings, Kinect readings for 15 joints positions, 6-axis inertial sensor readings, and video recordings. The multimodality added a huge boost in the classification performance; achieving 96% accuracy with the feature selection based deep learning architecture.
Colorectal multi-class image classification using deep learning models Mallela Siva Naga Raju; Battula Srinivasa Rao
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3299

Abstract

Colorectal image classification is a novel application area in medical image processing. Colorectal images are one of the most prevalent malignant tumour disease type in the world. However, due to the complexity of histopathological imaging, the most accurate and effective classification still needs to be addressed. In this work we proposed a novel architecture of convolution neural network with deep learning models for the multiclass classification of histopathology images. We achieved the findings using three deep learning models, including the vgg16 with 96.16% and a modified version of Resnet50 with 97.08%, however the proposed Adaptive Resnet152 model generated the best accuracy of 98.38%. The colorectal image multiclass dataset is publicly available which has 5000 images with 8 classes. In this study we have increased all classes equally, total 15000 images have been generated using image augmentation technique. This dataset consists of 60% training images and 40% testing images. The suggested method in this paper produced better results than the existing histopathology image categorization methods with the lowest error rate. For histopathological image categorization, it is a straightforward, effective, and efficient method. We were able to attain state-of-the-art outcomes by efficiently utilizing the resourced dataset.
Information technology governance: an analysis of the approach in Ecuador Andrés Gavilanes- Molina; Vicente Merchán- Rodríguez
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3449

Abstract

This work aims to show the Ecuadorian IT governance reality using descriptive research to analyze the approach of IT decisions. Indeed, one hundred and one private and governmental organizations were surveyed to examine their IT governance approach through a decision-making matrix model of responsibilities. The purpose is to distinguish IT governance perspectives and archetypes for IT decision-making, so that we collect relevant information about decision-making of executives, IT executives, C-level, and business unit leader business in a developing economy context. The survey results are conclusive; business monarchy is the centralized approach for decision-making in Ecuador. For instance, IT governance in Ecuador is different from other Latin American nations in terms of digital culture, maturity, and effectiveness. On the other hand, this work encourages practitioners and scholars to increase the research scope to create, adopt, or adapt IT governance decision-making models for low-income countries. This is another step on the ongoing discussion in the extant IT governance literature, rather than as a final answer. Finally, future work will analyze and contrast the past normality with the post-pandemic period in Ecuador. Hence, using a survey on Ecuadorian IT governance structures, practices and behaviors will show the IT governance changes, perceptions, and trends.
Impact of NILM-based energy efficiency on environmental degradation and kuznets hypothesis analysis Keh-Kim Kee; Yun Seng Lim; Jianhui Wong; Kein-Huat Chua
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3136

Abstract

Nonintrusive load monitoring (NILM) breaks down the aggregated electrical consumption data into individual appliances. The feedback of disaggregated data to the consumers enables awareness and behaviour change to conserve electricity, consequently reducing CO2 emissions to the environment. However, the limited literature regarding the impact of NILM and Kuznets hypothesis (EKC) analysis on CO2 emissions reduction has restricted policymakers in developing effective mitigation measures. This work aims to assess the impact of NILM-based based energy efficiency (EE) on environmental improvement. The combined approach of scenario simulation and EKC analysis was adopted to gauge the effectiveness of NILM that leads to sustainable development. The monotonically increase relationship between environmental degradation and economic growth in Malaysia without peaking beyond 2030 implies that the current mitigation measures and policies imposed may not effectively cope with the future power demands for sustainable development. NILM-based EE measures could be a great potential for reducing CO2 emissions by 10.2%. The inverted-U curves and reduced turning points of environmental degradation from the income level of USD 20,063.36 to USD 16,305.19. Therefore, NILM approach can accelerate sustainable development with lower environmental deterioration. The work may beneficial to policymakers to analyse the impact and effectiveness of mitigation measures quantitatively.
Hyper parameter tuning based gradient boosting algorithm for detection of diabetic retinopathy: an analytical review Parul Datta; Prasenjit Das; Abhishek Kumar
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3559

Abstract

The pipelines of approaches for classifying diabetic retinopathy were examined in this study. The effort entails developing appropriate transformations and estimators that can be used to automate the process of diabetic retinopathy detection. The segmentation of the blood vessels was done using a hybrid algorithm that uses Otsu and median filter to get the region of interest. Further, ten classifiers were investigated in order to develop an automated pipeline for diabetic retinopathy detection. The ten classifiers were reviewed based on earlier work in a similar setting and on an exploration of new ways for identifying diabetic retinopathy. To overcome the challenge of low volume of dataset, data argumentation was done so that a generic classifier can be configured. Extensive hyper parameter tuning was performed, and it was shown that the gradient boosting approach is the most stable technique for detecting diabetic retinopathy. This was validated using a 10K fold cross validation method on many metrics (accuracy, recall, precision, and v-measure score). Hyper-parameter tuning helped in achieving accuracy of 0.96.
Prediction of COVID-19 disease severity using machine learning techniques Alaa H. Ahmed; Mokhaled N. A. Al-Hamadani; Ihab A. Satam
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3272

Abstract

A terrifying spread of COVID-19 (which is also known as severe acute respiratory syndrome coronavirus 2 or SARS-COV-2) led scientists to conduct tremendous efforts to reduce the pandemic effects. COVID-19 has been announced pandemic discovered in 2019 and affected millions of people. Infected people may experience headache, body pain, and sometimes difficulty in breathing. For older people, the symptoms can get worse. Also, it can cause death because of the huge effect on some parts of the human body, particularly for those who have chronic diseases like diabetes. Machine learning algorithms are applied to patients diagnosed with Corona Virus to estimate the severity of the disease depending on their chronic diseases at an early stage. Chronic diseases could raise the severity of COVID-19 and that is what has been proved in this paper. This paper applies different machine learning techniques such as random forest, decision tree, linear regression, binary search, and k-nearest neighbor on Mexican patients’ dataset to find out the impact of lifelong illnesses on increasing the symptoms of the virus in the human body. Besides, the paper demonstrates that in some cases, especially for older people, the virus can cause inevitable death.

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