cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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 64 Documents
 3   4   5   6   7 
Search results for , issue "Vol 12, No 1: February 2023" : 64 Documents clear
Signal multiple encodings by using autoencoder deep learning Anaz, Ammar Sameer; Al-Ridha, Moatasem Yaseen; Al-Nima, Raid Rafi Omar
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4229

Abstract

Encryption is a substantial phase in information security. It permits only approved persons to get private information. This study suggests a signal multi-encryptions system (SMES) technique for coding and decoding signals created by a deep autoencoder network (DAN). The DAN of four layers is employed for a coding package of signals multiple times before decoding or restructuring the original signals again. The suggested SMES offers a high level of security as it can produce and exploit multiple encryptions for signals. Many statistical calculations are applied to measure the reliability of the system. The outcomes are promising where noteworthy encryptions-decryptions are obtained.
Complex predictive analysis for health care: a comprehensive review Srivastava, Dolley; Pandey, Himanshu; Agarwal, Ambuj Kumar
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4373

Abstract

Healthcare organizations accept information technology in a management system. A huge volume of data is gathered by healthcare system. Analytics offers tools and approaches for mining information from this complicated and huge data. The extracted information is converted into data which assist decision-making in healthcare. The use of big data analytics helps achievement of improved service quality and reduces cost. Both data mining and big data analytics are applied to pharma co-vigilance and methodological perspectives. Using effective load balancing and as little resources as possible, obtained data is accessible to improve analysis. Data prediction analysis is performed throughout the patient data extraction procedure to achieve prospective outcomes. Data aggregation from huge datasets is used for patient information prediction. Most current studies attempt to improve the accuracy of patient risk prediction by using a commercial model facilitated by big data analytics. Privacy concerns, security risks, limited resources, and the difficulty of dealing with massive amounts of data have all slowed the adoption of big data analytics in the healthcare industry. This paper reviews the various effective predictive analytics methods for diverse diseases like heart disease, blood pressure, and diabetes.
Security of private cloud using machine learning and cryptography Jabbar, Ali Abdulsattar; Bhaya, Wesam Sameer
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4383

Abstract

There are increased security challenges that target cloud systems. One of the most important requirements of users in cloud storage is protecting their cloud from attacks and keeping data secure. Modern technologies of machine learning are providing the ability to analyze and classify data perfectly. This paper proposes a model placed between users and the cloud, which is based on two phases. The first of which is protecting the cloud from different types of network attacks and detecting normal and abnormal flow. The second one is categorizing the users' data and then encrypting it based on its importance using different encryption algorithms. The accuracy results of random forest (RF) and decision tree (DT) are 100% of attack detection for each one. For the second phase of classifying data, the algorithms used are the logistic regression (LR) and stochastic gradient descent (SGD) learning which resulted in 98% accuracy for both. Besides, the encryption algorithms that have been adopted are rivest cipher (RC4), triple data encryption (3DES), and advanced encryption standard (AES) for encryption of the classified data according to the importance which will be then stored in the cloud in its secure form.
Synthesis of sliding mode control for flexible-joint manipulators based on serial invariant manifolds Thang, Le Tran; Son, Tran Van; Khoa, Truong Dang; Chiem, Nguyen Xuan
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4363

Abstract

This paper focuses on synthesizing sliding mode control (SMC) for flexible-joint manipulators (FJM) based on serial invariant manifolds in order to increase the control quality for the system. SMC based on the serial invariant manifolds is proposed. The control law is found based on synergetic control theory (SCT) and analytical design of aggregated regulators (ADAR) method. In order to improve the control quality due to the effect of the stiffness value between two links in the system, a mechanism for constructing manifolds is built. The time response of the outer loop manifolds close to the actuator will be larger in the next round. The control quality of the system can be pre-evaluated through the parameters of the designed manifolds. Global stability is demonstrated by using the Lyapunov function in the design process. Finally, the effectiveness of the proposed controller based on SCT is demonstrated by numerical simulation results and compared with the traditional SMC.
Stock market analysis with the usage of machine learning and deep learning algorithms Sarma, Seethiraju L. V. V. D.; Sekhar, Dorai Venkata; Murali, Gudipatu
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4305

Abstract

In this work we are focusing on listing out various works in the understanding of various parameters and context to get the overview of stock market analysis in the context of machine learning (ML) and deep learning (DL) models. The work focusses on the stock market analysis along with methodologies and algorithms used to understand the trends and the corresponding results as part of those studies. The importance of this work is to summarize and analyse the parameters which are highly influenced the understandingof the stock market trends. The outcome of the work is understanding the important factors which directly and indirectly influences the stock value raise and drop. The work highlights the methodologies and the algorithms used to stock market data analysis and efficient and effective recommendation of stable stocks to the customers. Further we are listing out the research gaps and future enhancements of the studies which are left over in the earlier works. The work pops up the limitations of some of the works in the existing works along with significance of hyper parameter techniques to clearly identify the features through which we can get more possibilities of better analysis of the data.
A novel approach for new architecture for green data centre Al-Fatlawi, Ahmed Abdulhassan; Al-Barazanchi, Israa
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4421

Abstract

The massive energy usage of data centers may be traced in part to the growing number of data centers and workstations because of economies of scale for cloud computing. It does, though, indicate wasteful power usage. Consequently, researching ways to improve the energy efficiency of datacenter equipment is now a crucial aspect in reducing datacenter power consumption. In this study, we describe methodologies and algorithms for flexible, energy-efficient, and effective load balancing in data centers, resulting in lower energy consumption by systems. Because of difficulties related with energy consumption, such as capital expenditure, operational costs, and environmental effect, renewable energy is becoming an increasing major consideration in data centers. With the rise in environmental consequences around the world, data centers must consider energy efficiency as one of the most critical factors. Cloud computing techniques have made a huge impact all over the world. Green DC is a concept that has been tested. We suggested an algorithm in this paper based on certain current algorithm limitations that will help to reduce environmental effect. We investigated the efficiency of our program further and used a load balancing method to improve its performance.
Man-in-the-middle and denial of service attacks detection using machine learning algorithms Al-Juboori, Sura Abdulmunem Mohammed; Hazzaa, Firas; Jabbar, Zinah Sattar; Salih, Sinan; Gheni, Hassan Muwafaq
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4555

Abstract

Network attacks (i.e., man-in-the-middle (MTM) and denial of service (DoS) attacks) allow several attackers to obtain and steal important data from physical connected devices in any network. This research used several machine learning algorithms to prevent these attacks and protect the devices by obtaining related datasets from the Kaggle website for MTM and DoS attacks. After obtaining the dataset, this research applied preprocessing techniques like fill the missing values, because this dataset contains a lot of null values. Then we used four machine learning algorithms to detect these attacks: random forest (RF), eXtreme gradient boosting (XGBoost), gradient boosting (GB), and decision tree (DT). To assess the performance of the algorithms, there are many classification metrics are used: precision, accuracy, recall, and f1-score. The research achieved the following results in both datasets: i) all algorithms can detect the MTM attack with the same performance, which is greater than 99% in all metrics; and ii) all algorithms can detect the DoS attack with the same performance, which is greater than 97% in all metrics. Results showed that these algorithms can detect MTM and DoS attacks very well, which is prompting us to use their effectiveness in protecting devices from these attacks.
A parametric study on strawberry radiated shaped monopole antenna for ultrawide-band applications Al-Gburi, Ahmed Jamal Abdullah; Zakaria, Zahriladha; Ibrahim, Imran Mohd; Akbar, Muhammad Firdaus; Al-Obaidi, Aymen Dheyaa Khaleel; Khabba, Asma
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4547

Abstract

This article gives a parametric study on strawberry-shaped monopole antennas for ultra-wideband (UWB) systems. The antenna design consisted of three different parametric design steps to structure the strawberry radiated monopole antenna. The scheduled strawberry monopole antenna was simulated on an FR4 substrate in a low profile for UWB applications. The total physical dimension is 26 mm×26 mm×1.6 mm, corresponding to the centre frequency of 7.5 GHz. The strawberry antenna is fed via a coplanar waveguide (CPW) to attain the best impedance matching for UWB systems. The presented monopole antenna has an impedance UWB bandwidth of 11.0 GHz from 2.6 GHz up to 13.6 GHz at −10 dB return loss. The simulated UWB strawberry monopole antenna displays an omnidirectional radiation behaviour with a simulated gain of 7.3 dB at 13.6 GHz, a directivity of 7.5 dBi at 13.6 GHz and favourable radiation efficiency of 97%. The proposed monopole UWB strawberry antenna has the technological possibility to be used for UWB applications.
Design and implementation of a driver circuit for three-phase induction motor based on STM32F103C8T6 Salih, Nahla Abdul Jalil; Altaie, Hayder Tareq Rajab; Al-Azzawi, Waleed Khalid; Mnati, Mohannad Jabbar
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4276

Abstract

A three-phase induction motor is an electrical machine that can be used for many industrial applications. A three-phase driver circuit, also known as a three-wire series circuit or three-wire delta circuit is an electrical power system. These motors typically rely on power supplied using electric cables, which carry alternating currents. The three-phase motor inverter driver circuit is a simple circuit consisting of three half-wave rectifiers, which are connected in a bridge. When the input voltage level of the DC power supply to the inverter is high enough, this arrangement can provide a large current through the induction motor. This paper will show how to build a three-phase inverter driver circuit from scratch for a threephase induction motor by using (transistors and diodes) for photovoltaic application. The paper will guide people through each step with diagrams and schematics that make it easy for anyone to understand every part of this project and ensure people's safety by learning how these components work before putting them into their own design. The focus of this project will be on the use of STM32F103C8T6 as microcontrollers to generate the pulse width modulation (PWM) signals.
Design security architecture for unmanned aerial vehicles by 5G cloud network based implementation of SDN with NFV and AI Lehmoud, Ahssan Ahmed Mohammed; Obeis, Nawfal Turki; Mutar, Ahmed Fakhir
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4239

Abstract

A recent progression of unmanned aerial vehicles (UAV) augmentation its employments for different applications. It’s also vulnerable to being, stolen, lost, stray, or destroyed at status of a security infringements for the UAV network. The proposed strategy is defending against of different attacks through using artificial intelligence by implements five steps: RGSK, GCSCS, SEDC, HSSC, and FVNF. UAV authentication is happened in the first step through the Curve448. We performance deep reinforcement learning to run with GCS for packet assignment as it implemented for switch current state identification before updating. In our work we ability to alleviate for attack of flow table overloading by assigned of packets as an under loaded or idle switches. Then, selected the least loaded switch by applied 5 tuples. Hence, we divided SDN to SEDCs and HSSC forms. First in the SEDC we using Shannon entropy to achieve classified of input packet in to regular and suspicious packets. Last will forwarded regular packets to cloud layer. By growing multiple self-organizing maps for maintained in NFV that used to classify suspicious packets as classes normal or malicious packet. The proposed performance work evaluates using NS3.26 show up the better strategy to secure UAV for different attacks.

Page 6 of 7 | Total Record : 64

 3   4   5   6   7 

Filter by Year

2023 2023


Reset
Filter By Issues
All Issue Vol 14, No 6: December 2025 Vol 14, No 5: October 2025 Vol 14, No 4: August 2025 Vol 14, No 3: June 2025 Vol 14, No 2: April 2025 Vol 14, No 1: February 2025 Vol 13, No 6: December 2024 Vol 13, No 5: October 2024 Vol 13, No 4: August 2024 Vol 13, No 3: June 2024 Vol 13, No 2: April 2024 Vol 13, No 1: February 2024 Vol 12, No 6: December 2023 Vol 12, No 5: October 2023 Vol 12, No 4: August 2023 Vol 12, No 3: June 2023 Vol 12, No 2: April 2023 Vol 12, No 1: February 2023 Vol 11, No 6: December 2022 Vol 11, No 5: October 2022 Vol 11, No 4: August 2022 Vol 11, No 3: June 2022 Vol 11, No 2: April 2022 Vol 11, No 1: February 2022 Vol 10, No 6: December 2021 Vol 10, No 5: October 2021 Vol 10, No 4: August 2021 Vol 10, No 3: June 2021 Vol 10, No 2: April 2021 Vol 10, No 1: February 2021 Vol 9, No 6: December 2020 Vol 9, No 5: October 2020 Vol 9, No 4: August 2020 Vol 9, No 3: June 2020 Vol 9, No 2: April 2020 Vol 9, No 1: February 2020 Vol 8, No 4: December 2019 Vol 8, No 3: September 2019 Vol 8, No 2: June 2019 Vol 8, No 1: March 2019 Vol 7, No 4: December 2018 Vol 7, No 3: September 2018 Vol 7, No 2: June 2018 Vol 7, No 1: March 2018 Vol 6, No 4: December 2017 Vol 6, No 3: September 2017 Vol 6, No 2: June 2017 Vol 6, No 1: March 2017 Vol 5, No 4: December 2016 Vol 5, No 3: September 2016 Vol 5, No 2: June 2016 Vol 5, No 1: March 2016 Vol 4, No 4: December 2015 Vol 4, No 3: September 2015 Vol 4, No 2: June 2015 Vol 4, No 1: March 2015 Vol 3, No 4: December 2014 Vol 3, No 3: September 2014 Vol 3, No 2: June 2014 Vol 3, No 1: March 2014 Vol 2, No 4: December 2013 Vol 2, No 3: September 2013 Vol 2, No 2: June 2013 Vol 2, No 1: March 2013 Vol 1, No 4: December 2012 Vol 1, No 3: September 2012 Vol 1, No 2: June 2012 Vol 1, No 1: March 2012 List of Accepted Papers (with minor revisions) More Issue