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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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Comparative analysis of ARIMA and LSTM for predicting fluctuating time series data Taslim, Deddy Gunawan; Murwantara, I Made
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6034

Abstract

The investigation of time series data forecasting is a critical topic within the realms of economics and business. The autoregressive integrated moving average (ARIMA) model has been prevalently utilized, notwithstanding its limitations, which include the necessity for a substantial quantity of data points and the presumption of data linearity. However, with recent developments, the long short-term memory (LSTM) network has emerged as a promising alternative, potentially overcoming these limitations. The objective of this study is to determine an effective approach for managing time series data characterized by volatility and missing values. Evaluation was conducted using RMSE for accuracy assessment, and the execution time measured using the Python Timeit library. The findings indicates that in a dataset comprising 60 data points, the LSTM model (RMSE 0.037618) surpasses the ARIMA model (RMSE 0.062667) in terms of accuracy. However, this trend reverses in a larger dataset of 228 data points, where the ARIMA model demonstrates superior accuracy (RMSE 0.006949) compared to the LSTM model (RMSE 0.036025). In scenarios with missing data, the LSTM model consistently outperforms the ARIMA model, although the accuracy of both models diminishes with an increase in the number of missing values. The ARIMA model significantly outpaces the LSTM model.
Prediction of mental illness using ensemble model and grid search hyperparameter optimization Kudlapura Shivaiah, Srinath; Krishnappa, Kiran; Kumar Boraiah, Naveen; Deepa Shenoy, Punjalkatte; Kuppanna Rajuk, Venugopal
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7309

Abstract

The early prediction of mental illnesses reduces the severity of the disease. The symptoms like poor concentration, unstable energy of the body, pressure, and loss of interest cause depression. A large-scale group decision making (LSGDM) method is proposed in this paper along with the ensemble classifier model by combining convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM) for effective classification of depression, anxiety and stress (DAS) levels. The data is collected from the depression anxiety stress scale-42 (DASS-42) dataset for efficient classification and predictions of mental health problems. The min-max normalization is used to pre-process the data, and the feature selection is done for extracting informative features. The extracted features are provided as input to the ensemble classifier. The proposed LSGDM model maximizes the classification accuracy with the help of grid search CV hyperparameter tuning, and results in an accuracy of 98.88%, precision of 98.21%, recall of 99.62%, F1-Score of 98.90%, and MCC of 99.41%. The proposed LSGDM method gives superior results when compared to the existing machine learning (ML) based ensemble model, a principal component analysis along with modified fast correlation based filtering (PCA-mFCBF), and LSTM based RNN (LSTM- RNN).
Software defined networking for internet of things: review, techniques, challenges, and future directions Al-Shareeda, Mahmood A.; Abdullah Alsadhan, Abeer; H. Qasim, Hamzah; Manickam, Selvakumar
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6386

Abstract

Security networks as one of the biggest issue for network managers with the exponential growth of devices connected to the internet. Keeping a big and diverse network running smoothly and securely is no easy feat. With this in mind, emerging technologies like software defined networking (SDN) and internet of things (IoT) hold considerable promise for information service innovation in the cloud and big data era. Therefore, this paper describes the model of SDN and the architecture of IoT. Then this review does not only review the research studies in SDN-IoT but also provides an explanation of the SDN-IoT solution in terms of architecture, main consideration, model, and the implementation of SDN controllers for IoT. Finally, this review discusses the challenges and future directions. This paper can be used as a starting point for thinking about how to improve SDN-IoT security and privacy.
Kernel rootkit detection multi class on deep learning techniques Srinivasan, Suresh Kumar; Thalavaipillai, SudalaiMuthu
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6802

Abstract

The harmful code application known as a rootkit is designed to be loaded and run directly from the operating system's (OSs') Kernel. Rootkits deployed in the Kernel, called Kernel-mode rootkits, can alter the OS. The intention behind these Kernel changes is to conceal the hack. Detecting a Kernel rootkit in a target machine is found to be quite challenging. Numerous techniques can be employed to modify the Kernel of a system. Kernel rootkits also create hidden access for attacks, enabling unauthorized entry to be gained by attackers on the machine. The ultimate consequence is that essential computer data can be modified, personal information can be gathered, and hackers can observe behavior. Synthetic neural networks support artificial intelligence, a branch of deep learning that models the human brain and operates on large datasets. This study proposed the Kernel rootkit detection multi-class deep learning techniques (KRDMCDLT). Deep learning algorithms are utilized to recognize the Kernel rootkit from a batch of data by selecting essential properties for learning tracking models. Thus, by identifying the OS malware, trojan assaults can be stopped before they can access infected data. This Kernel rootkit detection was tested in a Google Cloud Platform (GCP) computing system.
A system for monitoring human postures, seizures, and falls from bed using radio and surface electromyography signals Intongkum, Chawakorn; Sengchuai, Kiattisak; Booranawong, Apidet
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7241

Abstract

In this work, a system for monitoring human postures, seizures, and falls from bed using received signal strength indicator (RSSI) and surface electromyography (sEMG) signals is studied through experiments. In this proposed system, a person who is located inside a wireless link is monitored by considering the change in measured RSSI signals as the 2.4 GHz IEEE 802.15.4 signals received at a receiver. Human motions in bed that affect RSSI levels can be captured. Thus, with this technique, it does not raise a privacy concern compared with vision-based technology. Additionally, sEMG signals associated with muscle movements from human postures are recorded from the human body’s abdominal muscles. Eight different activities, including normal and critical events, are tested and evaluated. Experimental results indicate that the proposed system could automatically monitor different human postures in real-time. RSSI and sEMG signals correlated to each posture have their own patterns. Furthermore, the relationship between human behaviors and RSSI and sEMG levels is summarized.
Active filter harmonic compensator with proportional resonant current controller for photovoltaic inverter Nam, Le Khoa; Lam, Le Hong; Hieu, Trinh Trung; Hieu, Nguyen Huu
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8167

Abstract

The strong development of renewable energy sources (RES), especially distributed energy sources, brings many benefits to the power system. Single-phase photovoltaic (PV) systems are the fastest growing type of distributed energy source worldwide today. Besides the beneficial factors for the distribution power system, the high penetration rate of solar power systems also causes negative impacts, especially power quality issues. PV inverters generate harmonics during the high-frequency switching of semiconductor elements. Traditionally available passive filters are not effective enough to ensure output power quality when the PV system generates power to the distribution grid. Therefore, this study presents the design of a proportional resonant current controller combined with active filter harmonic compensated (PR+HC) for a single-phase PV inverter. This controller, when integrated into traditional PV inverter, will provide better output power quality, contributing to reducing total harmonic distortion (THD) on the distribution grid. This study analyzes the parameters affecting the harmonic attenuation effect of the PR+HC controller, then simulates it on MATLAB Simulink to evaluate the results. The results of the study show that the PR+HC controller is not only effective in reducing the amplitude of odd harmonics, but also operates reliably even when the grid frequency fluctuates widely.
Squirrel search method for deep learning-based anomaly identification in videos Malphedwar, Laxmikant; Rajesh Kumar, Thevasigamani
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.5933

Abstract

The monitoring of human behavior and traffic surveillance in various locations has become increasingly important in recent years. However, identifying abnormal activity in real-world settings is a challenging task due to the many different types of worrisome and abnormal actions, including theft, violence, and accidents. To address this issue, this paper proposes a new framework for deep learning-based anomaly identification in videos using the squirrel search algorithm and bidirectional long short-term memory (BiLSTM). The proposed method combines the squirrel search algorithm, an optimization technique inspired by nature, with BiLSTM for anomaly recognition. The framework uses the knowledge gained from a sequence of frames to categorize the video as either typical or abnormal. The proposed method was exhaustively tested in several benchmark datasets for anomaly detection to confirm its functionality in challenging surveillance circumstances. The results show that the proposed framework outperforms existing methods in terms of area under curve (AUC) values, with a test set AUC score of 93.1%. The paper also discusses the importance of feature selection and the benefits of using BiLSTM over traditional unidirectional long short-term memory (LSTM) models for anomaly detection in videos. Overall, the proposed framework provides a highly precise computerization of the system, making it an effective tool for identifying abnormal human behavior in surveillance footage.
6G networks: insights and reliability analysis Basahel, Ahmed Abdullah; Islam, Md. Rafiqul; Habaebi, Mohamed Hadi
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7896

Abstract

As we are living in a fast-moving dynamic world. Emerging technologies such as artificial intelligence (AI), internet of things (IoT), virtual reality (VR), augmented reality (AR), fourth industrial revolution (Industry 4.0), metaverse, and edge computing are expected to play an essential role in our daily life. These technologies require high-speed, sustainable, and reliable communications networks which are expected by sixth generation (6G) wireless communications networks. 6G will be the backbone for these emerging technologies as well as for the technology-driven digital infrastructure. Governments as well as research and development (RD) of the technology companies are gearing up to conduct a regulatory framework to standardize 6G networks; studying and conducting experimental setups to examine and evaluate the deployment of 6G networks; both in which they will have opportunities and challenges. This paper provides insights and guidelines for 6G networks in terms of standards, implementations, applications, and research trends. In addition, it provides reliability analysis for terrestrial 6G networks. A carrier class availability could be achieved over a maximum of 4 km link distance. These insights and availability figures may be used as a useful tool for researchers and industry stakeholders for the deployment and rollout of the next generation 6G wireless communications networks.
Pre-processing technique of Aquilaria species from Malaysia for four different qualities Mohd Huzir, Siti Mariatul Hazwa; Hasnu Al-Hadi, Anis Hazirah 'Izzati; Zaidi, Amir Hussairi; Ismail, Nurlaila; Mohd Yusoff, Zakiah; Haron, Mohamad Hushni; Almisreb, Ali Abd; Taib, Mohd Nasir
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5577

Abstract

The paper interprets data distribution by using boxplot pre-processing in classify the quality of Agarwood oil for eleven chemical substances into four different qualities. The varieties usage of Agarwood oil makes it considered as an expensive and valuable product on the essential oil market. Perfumes, fragrances, incense, aromatherapy, and traditional medicine are the most popular Agarwood oil applications. However, the classification of Agarwood oil grades does not yet have standard grading method. This because it has been graded manually into different qualities by using human sensory evaluation. Boxplot analysis involving eleven chemical subtances that will be focusing in this study by concerned the quality for low, medium low, medium high and high. ɤ-eudesmol, ar-curcumene, β-dihydro agarofuran, ϒ-cadinene, α-agarofuran, allo aromadendrene epoxide, valerianol, α-guaiene, 10-epi-ɤ-eudesmol, β-agarofuran, and dihydrocollumellarin compounds are the selected significant compounds that represent the input for boxplot. Agarwood oil consist 660 data samples from low, medium low, medium high, and high quality. The result in this study showed that the four selected significant compounds (ɤ-eudesmol, 10-epi-ɤ-eudesmol, β-agarofuran, and dihydrocollumellarin) are important as a marker for Agarwood oil quality classification. The identification of chemical substances on high quality done as reference for future research studies.
Pre-slippage detection and counter-slippage for e-pattern omniwheeled cellular conveyor Keek, Joe Siang; Loh, Ser Lee; Hanafi, Ainain Nur; Cheong, Tau Han
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6996

Abstract

This paper presents continuation work of e-pattern omniwheeled cellular conveyor (EOCC) since its first introduction. EOCC is a conveyor that is modular and is made up of omniwheels arranged horizontally and vertically. Although in the last published paper, the EOCC had been proven to be capable of transporting box omnidirectionally and achieving yaw control concurrently, however, due to the natural properties of omniwheel, the performance is jeopardized by slippage. While minor slippage can be negligible, but a major slippage can eventually destroy the whole trajectory tracking performance. Therefore, counter-slippage methods are proposed in this paper. The simulation results show that the proposed counter-slippage method significantly improves the trajectory tracking performance up to 42% of reduction in integral of absolute error. Moreover, in this paper, pre-slippage detection method, which aims to perform early detection of slippage, is being presented as well. Although these proposed methods are simple, but they are proven to have achieved improved tracking performance than conventional controller, as presented in this paper.

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