Subjects -> ENGINEERING (Total: 2656 journals)
    - CHEMICAL ENGINEERING (235 journals)
    - CIVIL ENGINEERING (237 journals)
    - ELECTRICAL ENGINEERING (176 journals)
    - ENGINEERING (1316 journals)
    - HYDRAULIC ENGINEERING (56 journals)
    - INDUSTRIAL ENGINEERING (98 journals)
    - MECHANICAL ENGINEERING (112 journals)

ELECTRICAL ENGINEERING (176 journals)                     

Showing 1 - 168 of 168 Journals sorted alphabetically
3C TIC     Open Access   (Followers: 1)
Acta Electronica Malaysia     Open Access  
Acta Universitatis Sapientiae Electrical and Mechanical Engineering     Open Access   (Followers: 1)
Actuators     Open Access   (Followers: 3)
Advanced Electromagnetics     Open Access   (Followers: 15)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 9)
Advances in Electrical Engineering     Open Access   (Followers: 62)
Advances in Microelectronic Engineering     Open Access   (Followers: 14)
Advances in Signal Processing     Open Access   (Followers: 20)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 31)
American Journal of Sensor Technology     Open Access   (Followers: 2)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 8)
Archives of Electrical Engineering     Open Access   (Followers: 16)
Atom Indonesia     Open Access  
Australian Journal of Electrical and Electronics Engineering     Hybrid Journal   (Followers: 4)
Balkan Journal of Electrical and Computer Engineering     Open Access   (Followers: 1)
Bulletin of Electrical Engineering and Informatics     Open Access   (Followers: 7)
Carpathian Journal of Electronic and Computer Engineering     Open Access  
CES Transactions on Electrical Machines and Systems     Open Access   (Followers: 1)
Chinese Journal of Electrical Engineering     Open Access   (Followers: 1)
Circuits, Systems, and Signal Processing     Hybrid Journal   (Followers: 18)
Computers & Electrical Engineering     Hybrid Journal   (Followers: 10)
CPSS Transactions on Power Electronics and Applications     Open Access   (Followers: 3)
CSEE Journal of Power and Energy Systems     Open Access   (Followers: 1)
Current Trends in Signal Processing     Full-text available via subscription   (Followers: 10)
e-Prime : Advances in Electrical Engineering, Electronics and Energy     Open Access   (Followers: 3)
ECTI Transactions on Electrical Engineering, Electronics, and Communications     Open Access   (Followers: 1)
Edu Elektrika Journal     Open Access   (Followers: 1)
Electric Power Components and Systems     Hybrid Journal   (Followers: 7)
Electric Power Systems Research     Partially Free   (Followers: 15)
Electrical and Electronic Engineering     Open Access   (Followers: 69)
Electrical Engineering     Hybrid Journal   (Followers: 22)
Electrical Engineering and Automation     Open Access   (Followers: 9)
Electrical Engineering and Power Engineering     Open Access   (Followers: 3)
Electrical Engineering in Japan     Hybrid Journal   (Followers: 8)
Electrical, Control and Communication Engineering     Open Access   (Followers: 12)
Electrochemical Energy Reviews     Hybrid Journal   (Followers: 3)
Elektron     Open Access  
Elektronika ir Elektortechnika     Open Access  
Elkha : Jurnal Teknik Elektro     Open Access  
Emerging and Selected Topics in Circuits and Systems     Hybrid Journal   (Followers: 7)
Emitor : Jurnal Teknik Elektro     Open Access  
ETRI Journal     Open Access  
EURASIP Journal on Advances in Signal Processing     Open Access   (Followers: 10)
Exploration     Open Access   (Followers: 2)
Ferroelectrics     Hybrid Journal   (Followers: 1)
Ferroelectrics Letters Section     Hybrid Journal   (Followers: 1)
Frontiers in Electronics     Open Access   (Followers: 7)
Frontiers of Electrical and Electronic Engineering     Hybrid Journal   (Followers: 7)
Frontiers of Information Technology & Electronic Engineering     Hybrid Journal  
IEEE Access     Open Access   (Followers: 148)
IEEE Electrical Insulation Magazine     Full-text available via subscription   (Followers: 97)
IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology     Hybrid Journal   (Followers: 1)
IEEE Journal of Photovoltaics     Hybrid Journal   (Followers: 16)
IEEE Journal of Radio Frequency Identification     Hybrid Journal   (Followers: 5)
IEEE Journal of Selected Topics in Signal Processing     Hybrid Journal   (Followers: 47)
IEEE Journal on Miniaturization for Air and Space Systems     Hybrid Journal   (Followers: 4)
IEEE Letters on Electromagnetic Compatibility Practice and Applications     Hybrid Journal   (Followers: 1)
IEEE Networking Letters     Hybrid Journal   (Followers: 1)
IEEE Open Access Journal of Power and Energy     Open Access   (Followers: 1)
IEEE Open Journal of Antennas and Propagation     Open Access   (Followers: 5)
IEEE Open Journal of Circuits and Systems     Open Access  
IEEE Open Journal of Intelligent Transportation Systems     Open Access   (Followers: 4)
IEEE Open Journal of Power Electronics     Open Access   (Followers: 11)
IEEE Open Journal of Signal Processing     Open Access   (Followers: 5)
IEEE Sensors Journal     Hybrid Journal   (Followers: 123)
IEEE Sensors Letters     Hybrid Journal   (Followers: 5)
IEEE Signal Processing Magazine     Full-text available via subscription   (Followers: 100)
IEEE Solid-State Circuits Letters     Hybrid Journal  
IEEE Transactions on Control of Network Systems     Hybrid Journal   (Followers: 30)
IEEE Transactions on Dielectrics and Electrical Insulation     Hybrid Journal   (Followers: 39)
IEEE Transactions on Green Communications and Networking     Hybrid Journal   (Followers: 3)
IEEE Transactions on Network Science and Engineering     Hybrid Journal   (Followers: 1)
IEEE Transactions on Quantum Engineering     Open Access   (Followers: 3)
IEEE Transactions on Radiation and Plasma Medical Sciences     Hybrid Journal  
IEEE Transactions on Signal and Information Processing over Networks     Hybrid Journal   (Followers: 16)
IEEE Transactions on Sustainable Energy     Hybrid Journal   (Followers: 13)
IEEJ Transactions on Electrical and Electronic Engineering     Hybrid Journal   (Followers: 19)
IET Control Theory & Applications     Open Access   (Followers: 28)
IET Electric Power Applications     Open Access   (Followers: 56)
IET Electrical Systems in Transportation     Open Access   (Followers: 12)
IET Energy Systems Integration     Open Access   (Followers: 1)
IET Nanodielectrics     Open Access  
IET Smart Grid     Open Access   (Followers: 2)
IETE Journal of Education     Open Access   (Followers: 3)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
InComTech : Jurnal Telekomunikasi dan Komputer     Open Access  
Industrial Technology Research Journal Phranakhon Rajabhat University     Open Access  
InfoMat     Open Access  
Infotekmesin : Media Komunikasi Ilmiah Politeknik Cilacap     Open Access  
Ingeniería Electrónica, Automática y Comunicaciones     Open Access  
Integrated Ferroelectrics: An International Journal     Hybrid Journal  
International Journal of Advanced Electronics and Communication Systems     Open Access   (Followers: 11)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 10)
International Journal of Electrical and Computer Engineering     Open Access   (Followers: 8)
International Journal of Electrical Engineering Education     Hybrid Journal   (Followers: 5)
International Journal of Electrical Power & Energy Systems     Open Access   (Followers: 30)
International Journal of Microwave Engineering and Technology     Full-text available via subscription   (Followers: 11)
International Journal of Monitoring and Surveillance Technologies Research     Full-text available via subscription   (Followers: 3)
International Journal of Nuclear Security     Open Access   (Followers: 1)
International Journal of Turbomachinery, Propulsion and Power     Open Access   (Followers: 23)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
International Transactions on Electrical Energy Systems     Hybrid Journal   (Followers: 6)
Iranian Journal of Science and Technology, Transactions of Electrical Engineering     Hybrid Journal  
Izvestiya Vysshikh Uchebnykh Zavedenii. Materialy Elektronnoi Tekhniki : Materials of Electronics Engineering     Full-text available via subscription  
J3eA     Open Access   (Followers: 2)
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Journal of Control, Automation and Electrical Systems     Hybrid Journal   (Followers: 12)
Journal of Electrical and Computer Engineering     Open Access   (Followers: 6)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 41)
Journal of Electrical Engineering     Open Access   (Followers: 47)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 4)
Journal of Electrical Engineering & Technology     Hybrid Journal  
Journal of Electrical Systems and Information Technology     Open Access   (Followers: 4)
Journal of Field Robotics     Hybrid Journal   (Followers: 5)
Journal of International Council on Electrical Engineering     Open Access  
Journal of Micro-Bio Robotics     Hybrid Journal  
Journal of Power Technologies     Open Access   (Followers: 6)
Journal of the Society for Information Display     Hybrid Journal  
Journal of World's Electrical Engineering and Technology     Open Access   (Followers: 2)
Journal on Today's Ideas - Tomorrow's Technologies     Open Access   (Followers: 1)
Jurnal Ilmiah Mahasiswa SPEKTRUM     Open Access  
Jurnal Rekayasa Elektrika     Open Access  
Jurnal Teknik Elektro     Open Access  
Jurnal Teknik Elektro     Open Access   (Followers: 1)
Jurnal Teknologi Elektro     Open Access  
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access   (Followers: 7)
La Rivista del Nuovo Cimento     Hybrid Journal  
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 1)
Majlesi Journal of Electrical Engineering     Open Access   (Followers: 1)
Material Design & Processing Communications     Hybrid Journal  
Materials Today Electronics     Open Access   (Followers: 2)
Metrology and Instruments / Метрологія та прилади     Open Access  
Micro and Nano Systems Letters     Open Access   (Followers: 6)
Nanotechnology Development     Open Access   (Followers: 21)
npj Flexible Electronics     Open Access  
npj Materials Degradation     Open Access  
npj Quantum Materials     Open Access   (Followers: 1)
Open Electrical & Electronic Engineering Journal     Open Access   (Followers: 1)
Open Signal Processing Journal     Open Access   (Followers: 2)
Periodica Polytechnica Electrical Engineering and Computer Science     Open Access  
Presence: Virtual and Augmented Reality     Hybrid Journal   (Followers: 3)
Progress in Additive Manufacturing     Hybrid Journal   (Followers: 9)
Protek : Jurnal Ilmiah Teknik Elektro     Open Access   (Followers: 3)
Quantum Beam Science     Open Access   (Followers: 1)
Radio Science     Full-text available via subscription   (Followers: 44)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 7)
Russian Electrical Engineering     Hybrid Journal   (Followers: 5)
SAIEE Africa Research Journal     Open Access  
Sampling Theory, Signal Processing, and Data Analysis     Hybrid Journal  
Scientific Bulletin of Electrical Engineering Faculty     Open Access  
SID Symposium Digest of Technical Papers     Hybrid Journal  
Signal Processing     Hybrid Journal   (Followers: 12)
Signals     Open Access   (Followers: 1)
Simetris : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer     Open Access  
Sustainable Energy, Grids and Networks     Hybrid Journal   (Followers: 4)
Synthesis Lectures on Electrical Engineering     Full-text available via subscription   (Followers: 2)
System analysis and applied information science     Open Access  
Telematique     Open Access  
Transactions of the International Society for Music Information Retrieval     Open Access  
Transactions on Electrical and Electronic Materials     Hybrid Journal   (Followers: 2)
Transactions on Environment and Electrical Engineering     Open Access  
Trends in Electrical Engineering     Full-text available via subscription   (Followers: 4)
Tri Dasa Mega : Jurnal Teknologi Reaktor Nuklir     Open Access  
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 7)
Ural Radio Engineering Journal     Open Access   (Followers: 1)
Wireless Engineering and Technology     Open Access   (Followers: 3)
Електротехніка і Електромеханіка     Open Access   (Followers: 1)


Similar Journals
Journal Cover
Bulletin of Electrical Engineering and Informatics
Number of Followers: 7  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2089-3191 - ISSN (Online) 2302-9285
Published by Universitas Ahmad Dahlan Homepage  [6 journals]
  • Loss reduction of transmission lines using PSO-based optimum performance
           of UPFC

    • Authors: Shaimaa A. Hussein, Dhari Yousif Mahmood, Ali Hussein Numan
      Pages: 1237 - 1247
      Abstract: Transmission line losses are one of the essential topics and issues in power systems research. Several methods and techniques have been used to reduce these losses, and one of these modern techniques is flexible alternating current transmission systems (FACTS). In this paper, one of the most important types of this technology, the unified power flow controller (UPFC), was used to reduce losses in the Iraqi national grid (ING) 400 kV. This paper presents an efficient method for minimizing losses of transmission lines in the ING system (400 kV) 46-bus approach. A particle swarm optimization (PSO)-based optimum proportional-integral (PI) controller with UPFC was proposed to obtain the optimal location of UPFC and optimum parameters of the PI controller to achieve the objective function of the research. MATLAB coded the algorithm. The Newton-Raphson method was employed to perform load flow analysis. The results showed that the best place for UPFC is buses (14-17) named BGE4 (Baghdad)-AMN4 (Baghdad), and the total active power and reactive power losses decreased from 727.4593 to 579.3874 MW and from 5155.9 to 3971.1 MVAR, respectively and also led to voltage regulation.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4559
      Issue No: Vol. 12, No. 3 (2023)
  • Arduino-based design and implementation of experimental rooms with a
           trombe wall for solar cells applications

    • Authors: Raid W. Daoud, Obed Majeed Ali, Omer Khalil Ahmed, Ihab A. Satam
      Pages: 1248 - 1255
      Abstract: The simplicity of design and construction following the researcher's or company's notion is the most typical description of solar panels. There will be a set of sensors in every design to derive information about the environment's shifting seasons and days. Two chambers of 1 m2 and 2 m in height were constructed for this study. A solar panel made from a unique exchangeable material has been installed instead of one of the walls, allowing a space between them for experimental reasons. Several temperature sensors were mounted inside and outside the chamber, as well as on the surface of the solar panel and within the air openings, in this work to record the temperature readings in various places. The used controller, an Arduino, is in charge of several operations, including controlling the solar panel's cooling device, reading and recording sensor data and storing it in RAM, controlling the orientation of the solar panel, controlling the vacuums, and regulating the on-off time of the motors. The findings show that by using sensor data, the system can keep the temperature constant when it is turned on. Additionally, the battery life will be preserved to the greatest extent feasible thanks to the well-balanced regulation of the loads.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4522
      Issue No: Vol. 12, No. 3 (2023)
  • Arduino-based design and implementation of experimental rooms with a
           trombe wall for solar cells applications

    • Authors: Raid W. Daoud, Obed Majeed Ali, Omer Khalil Ahmed, Ihab A. Satam
      Pages: 1248 - 1255
      Abstract: The simplicity of design and construction following the researcher's or company's notion is the most typical description of solar panels. There will be a set of sensors in every design to derive information about the environment's shifting seasons and days. Two chambers of 1 m2 and 2 m in height were constructed for this study. A solar panel made from a unique exchangeable material has been installed instead of one of the walls, allowing a space between them for experimental reasons. Several temperature sensors were mounted inside and outside the chamber, as well as on the surface of the solar panel and within the air openings, in this work to record the temperature readings in various places. The used controller, an Arduino, is in charge of several operations, including controlling the solar panel's cooling device, reading and recording sensor data and storing it in RAM, controlling the orientation of the solar panel, controlling the vacuums, and regulating the on-off time of the motors. The findings show that by using sensor data, the system can keep the temperature constant when it is turned on. Additionally, the battery life will be preserved to the greatest extent feasible thanks to the well-balanced regulation of the loads.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4522
      Issue No: Vol. 12, No. 3 (2023)
  • A heuristic optimization approach for the scheduling home appliances

    • Authors: Basil H. Jasim, Anwer Mossa AL-Aaragee, Ahmed Abdulmahdi Abdulkareem Alawsi, Adel M. Dakhil
      Pages: 1256 - 1266
      Abstract: In order to develop and execute a demand response (DR) system for a household energy management system, an effective and adaptable energy management architecture is provided in this study. Several issues related to the current home energy management system (HEMS) are among those that do not give their consumers a choice to assure user comfort (UC) or a long-term answer to lowered carbon emissions. Our research suggests a programmable heuristic-based energy management controller (HPEMC) to manage a residential building in order to minimize power costs, reduce carbon emissions, increase UC, and lower the peak-to-average ratio (PAR). In this study, the demand-responsive appliance scheduling problem is solved using an energy management system to reduce the cost and a PAR. Numerous case studies have been used to demonstrate the viability of the suggested method. The simulation results confirmed the effectiveness of the proposed method and that it is capable of running a hybrid microgrid in various modes. The findings indicate that the proposed schedule controller saved 25.98% of energy.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.3989
      Issue No: Vol. 12, No. 3 (2023)
  • Widespread compact fluorescent lamp evaluations in 50 Hz electrical

    • Authors: Ruhaizad Ishak, Ahmad Syahiman Mohd Shah, Mohd Ikhwan Muhammad Ridzuan, Noraslinda Muhamad Bunnori
      Pages: 1267 - 1275
      Abstract: Rapid development in electrical technology has imposed strong challenges to modern power system. Power quality has become a great concern due to proliferation of power electronic technology in modern electrical loads. Specifically for lighting load such as compact fluorescent lamps (CFLs), one of the concerning issues is harmonics. CFL is a cost-competitive and energy efficient compared to incandescent lamp. Inevitably, CFL produces harmonics current due to nonlinearity behaviour of the electronic ballast circuit. This paper presents a study on the widespread installation of CFL lamps in electrical power network. Initially, the harmonic current characteristics of local-branded CFL was identified from laboratory measurement. Then, a simulated CFL model was developed in MATLAB/Simulink to replicate the identified characteristics. The same step was repeated for other two different brands where eventually all models were embedded into a distribution network. The results show that at low voltage level, with installation more than 50 units for each type of CFL, the harmonic voltage distortion exceeded the 8% total harmonic distortion (THD) limit as stipulated in EN50160 standard. However, at higher voltage, the amount of THD decreased to average 0.94% and further down to average 0.28% at small transmission voltage level.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4904
      Issue No: Vol. 12, No. 3 (2023)
  • Minimizing electricity cost by optimal location and power of battery
           energy storage system using wild geese algorithm

    • Authors: Thuan Thanh Nguyen, Thang Trung Nguyen, Trung Dung Nguyen
      Pages: 1276 - 1284
      Abstract: The mismatch between load demand and supply power may increase when distributed generation based on renewable energy sources is connected to the distribution system (DS). This paper shows the optimal battery energy storage system (BESS) placement problem on the DS to minimize the electricity cost. Diverse electricity prices are considered for normal, off-peak and peak hours in a day. Wild geese algorithm (WGA) is applied to optimize the location and power of the BESS. The problem and the efficiency of WGA is validated on the 18-bus DS four scenarios consisting of the DS without BESS placement, the DS with BESS placement, the DS existing photovoltaic system (PVS) without BESS placement and the DS existing PVS with BESS placement. The numerical results show that optimal BESS placement is an effective solution for minimizing electricity cost on the DS with and without PVS. In addition, the results have also shown that WGA is a potential method for the BESS placement problem.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4779
      Issue No: Vol. 12, No. 3 (2023)
  • Acidity improvement of refined-bleached used vegetable oils as dielectric
           liquid using two-level factorial design

    • Authors: Muhammad Syahrani Johal, Sharin Ab Ghani, Imran Sutan Chairul, Mohd Shahril Ahmad Khiar, Mohamad Nazri Mohamad Din
      Pages: 1285 - 1292
      Abstract: Recent studies have shown that modifying the chemical structure of used vegetable oils (UVOs) as an alternative dielectric liquid for oil-immersed transformers has improved these oils' physical, electrical and chemical properties. However, previous researchers have implemented the one-factor-at-a-time method as their experimental design approach. Therefore, they overlooked the possibility that combining mixing process parameters at optimum ratios will yield a better result. Hence, in this study, the two-level (2k) factorial design is applied to achieve the lowest acidity level of UVOs through chemical refining process namely as refined-bleached used vegetable oils (RBUVOs). The involved process parameters are oil temperature, mixing speed and mixing time. Based on the results of 23 factorial design, it is found that oil temperature has the most significant effect on acidity, with a percentage contribution of 35.76%. The result also shows that the best mixing process parameters of RBUVOs were: oil temperature (60 °C), mixing speed (1,000 rpm) and mixing time (30 min). Note that these mixing process parameters produced better RBUVOs with an acidity value of 0.0221 mg KOH/g. A regression model is also developed to predict the acidity of RBUVOs as a function of oil temperature, mixing speed and mixing time.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4922
      Issue No: Vol. 12, No. 3 (2023)
  • Adaptive position control of DC motor for brush-based Adaptive position
           control of DC motor for brush-based

    • Authors: Ummi Sorfina, Syed Zahurul Islam, Kok Boon Ching, Dur Muhammad Soomro, Jabbar Al-Fatta Yahaya
      Pages: 1293 - 1301
      Abstract: In this paper, we have developed an automatic brush-based PV cleaning system to control and synchronize the 3 motors together with a smooth periodic of cleaning while moving it horizontally over the PV surface. The mechanical design involved installing linear guides at the top and bottom of the rail to support the aluminium plate that holds the carrier motors and rotating brush. Two different movements of translational and rotational motion of the motors are managed by an algorithm programmed in Arduino Mega. In investigating the performance of motor parameters and dust removal rate, we conducted an experiment by spreading dry sand over the PV surface. Results showed that the torque of the cleaning brush motor increases with the increase in load. The obtained torque of the carrier motor was found to be 9.167 Nm (> stall torque, 9.8 Nm) with a full load of 18 brushes. The torque is inversely proportional to the speed but directly proportional to power. The required power to move the 2.93 kg of cleaning system was 19.20 W with 3.015 Nm of torque. The system achieved 86.8% of the dust removal rate from the four cycles of cleaning operations.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4926
      Issue No: Vol. 12, No. 3 (2023)
  • Reliability enhancement of radial distribution system by placing the
           reactive power compensators and distribution systems

    • Authors: Manjunatha Babu Pattabhi, Krishna Shanmukha Sundar, Bengaluru Rangappa Lakshmikantha
      Pages: 1302 - 1309
      Abstract: Distribution systems (DSs) are highly stressed with addition of newer loads like electric vehicle charging stations and lower scope for expansion due to urbanization. Any line outage could cause interruption to major loads. Reliability studies have gained importance for lowering the frequency and lowering the duration of interruption for supply systems. In this paper a bi-stage method for optimum placement of reactive power compensation devices and distributed generations (DGs) for enhancing voltage stability and system reliability. A new method named delta analysis method is used to optimally locate the reactive power compensation devices and DGs. IEEE-33 radial DS, which is taken as experimental system. Based on the study, the fixing of reactive power compensation devices and DGs are to increase voltage outline of buses and decrease power fatalities. After the placement of DGs, the enhancement in reliability indices following line contingency is studied using MATLAB simulation.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4515
      Issue No: Vol. 12, No. 3 (2023)
  • A new robust SIDA-PBC approach to control a DFIG

    • Authors: Hamiani Hichem, Tadjeddine Ali Abderrazak, Arbaoui Iliace, Mohammed Sofiane Bendelhoum, Benali Abdelkrim
      Pages: 1310 - 1317
      Abstract: Credible control and overall stabilization of closed-loop nonlinear systems presented in a port-controlled hamiltonian structure (PCH-D) were forever the development of the simultaneous interconnection and damping assignment passivity-based control (SIDA-PBC) method. The robustness and reliability of the method called into question against noise and certainly modelling errors. Indeed, a new scheme has been presented to control a doubly fed induction generator (DFIG) based on the energy form and exploiting the electrical parameters of the closed loop system. The results obtained provide a new technique and implement more freedom when designing the diagram of the advanced controller during the production of active power. The contribution of this paper is researching on the advanced nonlinear methodes used, many simulations were carried out in simulation using the MATLAB/Simulink environment under important operating conditions, allowing to demonstrate the feasibility of the proposed method and verify the performance considering the robustness.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.2155
      Issue No: Vol. 12, No. 3 (2023)
  • Designing a power system stabilizer using a hybrid algorithm by genetics
           and bacteria for the multi-machine power system

    • Authors: Mahmoud Zadehbagheri, Tole Sutikno, Mohammad Javad Kiani, Meysam Yousefi
      Pages: 1318 - 1331
      Abstract: This research creates an optimal power grid stabilizer for four machines. Rotor speed signals are system inputs in the proposed design. To improve response, this system uses a conventional power system stabilizer (CPSS) and an optimal CPSS (OCPSS). The genetic optimization hybrid has a new structure, and the network generators use the bacterial foraging algorithm (BFA) with stabilizer system. The system set is tested by MATLAB software under various conditions to evaluate the designs. The experiment starts with a three-phase fault in line 3 that is fixed by breaking the line after 0.2 seconds. The simulation results show that after a short circuit in line 3, the proposed OCPSS design reaches damping after about a cycle of oscillation in 4 seconds. However, the conventional CPSS design achieves damping after four oscillations in six seconds. Simulations show that the proposed method is better than genetic algorithm (GA) and BFA. Power system oscillations are dampened faster and with lower amplitude when power system stabilizer (PSS) coordinate with the proposed optimization method. It also improves power system dynamics. We demonstrate with the proposed OCPSS stabilizer that advanced optimization systems can maximize system control capacity by utilizing conventional CPSS system advantages.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4704
      Issue No: Vol. 12, No. 3 (2023)
  • Color enhancement of refined-bleached used vegetable oils as dielectric
           liquid: two-level factorial design approach

    • Authors: Muhammad Syahrani Johal, Sharin Ab Ghani, Imran Sutan Chairul, Mohd Shahril Ahmad Khiar, Muhamad Falihan Bahari
      Pages: 1332 - 1339
      Abstract: Number of findings have shown that the used vegetable oils (UVOs) properties can be enhanced by changing their chemical structure and can be utilized as dielectric liquid in oil-immersed transformers. However, earlier researchers used the one-factor-at-a-time (OFAT) method for their experimental design approach. Nevertheless, they failed to consider the possibility that combining the mixing process parameters at the highest ratios could produce a more favorable outcome. Hence, in this study, two-level (2k) factorial design is applied to achieve the highest color reduction of UVOs through chemical refining process known as refined-bleached UVOs (RBUVOs). The involved process parameters are oil temperature, mixing speed and mixing time. Based on the results of 23 factorial design, it is found that mixing time and oil temperature has the most significant effects on color reduction, with a percentage contribution of 35.00% and 32.51%, respectively. The result also shows that the best mixing process parameters of RBUVOs were oil temperature (80 °C), mixing speed (1,000 rpm) and mixing time (60 min). These resulted in the highest color reduction of RBUVOs by 79.27%.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4988
      Issue No: Vol. 12, No. 3 (2023)
  • Design and DSP implementation in HIL of nonlinear observers for a doubly
           fed induction aero-generator

    • Authors: Bouchaib Rached, Mustapha Elharoussi, Elhassane Abdelmounim, Mounir Bensaid
      Pages: 1340 - 1351
      Abstract: This paper reports on the conception and digital signal processor (DSP)-based hardware-in-the-loop realisation of a nonlinear observer for a grid-connected variable speed wind turbine using a doubly fed induction generator (DFIG). The objective of this work is to build some observation structure based on the extended Kalman filter (EKF), the sliding mode observer (SMO) and the adaptive model reference adaptive system (MRAS) observer to observe certain quantities of the wind power system connected to the electrical grid in order to reduce the complexity and the cost of the hardware, to achieve an increased mechanical robustness, to operate in hostile environments, to have a higher reliability and to ensure an unchanged inertia of the system. The proposed observers will be combined with robust nonlinear controls based on the backstepping approach to control the wind energy conversion system (WECS). The whole will be implemented on a TMS320F28335 DSP board. A comparative analysis of the proposed observers will be carried out. Through the results of the DSP implementation in hardware in the loop (HIL), we will prove that this improved combination increases the desired performance.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4562
      Issue No: Vol. 12, No. 3 (2023)
  • Fish drying machine with PV system for fisherman to support blue economy

    • Authors: I Gusti Made Ngurah Desnanjaya, I Komang Arya Ganda Wiguna, I Made Aditya Nugraha
      Pages: 1352 - 1358
      Abstract: The abundance of fish catches in Indonesia is excellent potential. Still, if the abundant results cannot be adequately managed and are just wasted, it will eventually lead to bad things. This problem was also found in Seraya village, Karangasem, Bali, where large fish yields and the fish processing process were still constrained by weather and environmental conditions causing the expected results to not be achieved. To overcome this, a photovoltaic (PV) system-based fish dryer was developed that can assist the fish drying process. Utilization of this system is also supported by good solar energy potential. The system can generate 402.78 Wh of electrical energy per day, covering 104.89% of the electrical energy demand of the fish dryer. The results of statistical tests using the Mann-Whitney test for fish weight and unpaired t-test for fish moisture content showed no significant results (p>0.05). This value states that there is no difference in the results of drying fish with the PV system and the traditional method. From this, we can conclude that fish drying using a solar power system works similarly to conventional fish drying methods.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4690
      Issue No: Vol. 12, No. 3 (2023)
  • High-performance Cuk converter with turn-on switching at zero voltage and
           zero current

    • Authors: Basim Talib Kadhem, Sumer S. Hardan, Khalid M. Abdulhassan
      Pages: 1359 - 1370
      Abstract: The soft-switching technique has the potential to significantly enhance the performance of the power converter. This is primarily because it allows for an increase in the switching frequency, which ultimately leads to improved modulation quality. This raises extra concerns, particularly in high-power applications, because in a standard hard-switching converter structure, components can often not function at frequencies higher than a few hundred hertz. This paper presents a high-efficiency soft switching CUK converter.When the main and auxiliary switches are turned on and off at zero voltage, the proposed converter yields zero voltage and zero current. The suggested method is ideal for a DC-DC converter based on IGBTs or MOSFETs. The recommended systems are described using theoretical analysis, the results of computer simulations, and experimental data derived from a prototype. The design parameters of the inductance and capacitor circuit for edge-resonant soft switching were obtained using the output power and the switching duty ratio. In the end, soft-switching is better than hard-switching in terms of efficiency, particularly when operating under full load.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4499
      Issue No: Vol. 12, No. 3 (2023)
  • Five parameters extraction of single diode PV model by metaheuristic
           optimization method by identified built-up data

    • Authors: Supriya R. Patil, Prakash G. Burade, Deepak Prakash Kadam
      Pages: 1371 - 1387
      Abstract: Precision calculation of unknown photovoltaic (PV) modules or single diode models for PV cell specifications under various environmental conditions is needed to build a sunlight-based PV framework. Installing a PV system requires knowledge of all parameters, modeling, and optimization techniques because PV system analysis and configuration help generate renewable energy. This concept requires accurate modeling and calculation of identified and unknown parameters. The single-diode model is simple and accurate for different mathematical equations. Streamlining calculations requires distinguishing this nonlinear model. The current investigation calculated five unknown parameters and compared them with particle swarm optimization (PSO) and wind-driven optimization (WDO) optimization results. The said approach utilizes MATLAB software, analytical as well as optimization methods, and manufacturing data. The suggested method is simple, fast, and accurate for calculating diode ideality factor (A),
      output currents (Io), series resistance (Rs), Shunt resistance (Rsh), and photocurrent (Iph).
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4876
      Issue No: Vol. 12, No. 3 (2023)
  • A novel pulse charger with intelligent battery management system for fast
           charging of electric vehicle

    • Authors: Sunil Somnath Kadlag, Mohan P. Thakre, Rahul Mapari, Rakesh Shriwastava, Pawan C. Tapre, Deepak P. Kadam
      Pages: 1388 - 1396
      Abstract: Electric vehicles contribute a major role in building an eco-friendly environment. Li-ion batteries are most widely used in electric vehicles. It is very important to maintain the operation of Li-ion batteries within their “safety operation area (SOA)”. Hence implementing a battery management system (BMS) becomes a necessity while using Li-ion batteries. This paper proposes an intelligent BMS for electric vehicles using proportional integral derivative (PID) control action along with artificial neural network (ANN). It prefers the improved pulse charging technique. The design consists of a battery pack containing four 12 V Li-ion batteries, MOSFETs, Arduino Uno, a transformer, a temperature sensor, a liquid-crystal displays (LCD), a cooling fan, and four relay circuit are used. Arduino Uno is used as a master controller for controlling the whole operation. Using this design approximately 38 minutes are required to fully charge the battery. Implementation results validate the system performance and efficiency of the design.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4890
      Issue No: Vol. 12, No. 3 (2023)
  • An ingenious MMC topology appropriate for motor drives across their entire
           frequency spectrum

    • Authors: Manoj Dhondiram Patil, Mohan P. Thakre, Haridarshan S. Sonawane, Pawan C. Tapre, Sunil Somnath Kadlag, Deepak P. Kadam
      Pages: 1397 - 1412
      Abstract: Modular multilevel converter (MMC) modules have popped up as among the best choices for medium and high-powered uses. This paper proposes a control scheme for the entire frequency range of operation for the MMC, focusing on supplying a three-phase machine. The machine is required to be controlled in the outer as well as the inner loop. Standard field oriented control (FOC) manages the three-phase machine in the outer closed loop while the inner control has to come up against the problem of energy balancing. That is unevenly distributed and stored in the capacitance of the upper and lower arms of the converter. There are two operating methods used in the inner control loop: a low-frequency method is used for start-up and low-speed operation, and a high-frequency method is for higher speed. In low-frequency mode (LF-mode), a special control strategy has to be implemented to minimize the energy oscillation in the capacitances of the converter arms. It makes utilization of the 3-phase machine's common mode voltage (Vc) as well as internal circulatory currents to verify a symmetrical energy distribution inside this MMC arms and also to avert whatever AC currents inside the DC source.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4872
      Issue No: Vol. 12, No. 3 (2023)
  • A binary classification model of COVID-19 based on convolution neural

    • Authors: Reham Sabah Saeed, Bushra Kadhim Oleiwi Chabor Alwawi
      Pages: 1413 - 1417
      Abstract: The outbreak of the new coronavirus (COVID-19) had resulted in the creation of a disaster all over the world and it had become a highly acute and severe illness. The prevalence of this disease is increasing rapidly worldwide. The technology of deep learning (DL) became one of the hot topics in the computing context and it is widely implemented in a variety of the medical applications. Those techniques proved to be sufficient tools for the clinicians in automatic COVID-19 diagnosis. In the present study, a DL technology that is based on convolution neural networks (CNN) models had been suggested for the binary COVID-19 classification. In the initial step of the suggested model, COVID-19 data-set of chest X-ray (CXR) images have been obtained then preprocessed. Whereas in the second stage, a new CNN model has been built and trained for diagnosing COVID-19 data-set as (positive) infection or (negative) normal cases. The suggested architecture had a success in classifying COVID-19 with the training model accuracy that had reached 96.57% for the training data-set and 92.29% for validating data-set and could reach the target point with a minimal learning rate for training this model with promising results.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4832
      Issue No: Vol. 12, No. 3 (2023)
  • Real-time monitoring system based on integration of internet of things and
           global system of mobile using Raspberry Pi

    • Authors: Hamzah H. Qasim, Ali M. Jasim, Khalid A. Hashim
      Pages: 1418 - 1426
      Abstract: Security and safety of homes remain critical issues in all countries. The majority of individuals have to deal with significant issues like fire and theft at some point in their lives, particularly in families that spend the majority of their time and engage in most of their activities outside the house. There is a pressing need to use cutting-edge technology in order to upgrade and strengthen the security system, as well as to remotely monitor the living environment for potential mishaps. In this paper, we proposed two integrated techniques, which are the internet of things (IoT) and short message service (SMS), to monitor the home for hazards to take the necessary actions, by Raspberry Pi 4 model B as a controller and phone app to monitor. Global system of mobile (GSM) sends SMS alerts to users, and the Blynk application monitors the data of sensors. Our outcome of this demonstrates that the proposed had the capability and high efficiency to monitor and detect undesirable situations in real-time before disasters occur.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4699
      Issue No: Vol. 12, No. 3 (2023)
  • Noise estimation using an artificial neural network in the urban area of
           Jaen, Cajamarca

    • Authors: Wendy Díaz, Anali Tarrillo, Candy Ocaña, Lenin Quiñones
      Pages: 1427 - 1434
      Abstract: Jaen is a city in constant urban growth which generates an increase in vehicular traffic and active noise pollution. The research presents the development of an artificial neural network (ANN) to estimate the noise produced by vehicular traffic in the urban area of the city. Consequently, information was collected from two investigations coded as T1 and T2, for which a matrix of 10 variables was elaborated with 210 and 273 data respectively. Random random sampling was performed to divide the data matrix into 80% (training) and 20% (validation). Weka software and the multi-layer perceptron (MLP) training algorithm were used to model the ANN. An ANN for T1 with 6-19-1 architecture and an ANN for T2 with 6-15-1 architecture were obtained. The performance of the ANNs was evaluated using the correlation coefficient (R), coefficient of determination (R2) and root mean square error (RMSE). The results show that the MLP networks are able to estimate the sound pressure level with values of
      R=0.9927, R2=0.9854 and RMSE=0.7313 for T1, R=0.9989, R2=0.9978, and RMSE=0.1515 for T2.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4633
      Issue No: Vol. 12, No. 3 (2023)
  • IoT-based smart monitoring and management system for fish farming

    • Authors: Abdallah Waddah Al-Mutairi, Kasim Mousa Al-Aubidy
      Pages: 1435 - 1446
      Abstract: Fish farming is still controlled and managed in the traditional way where water quality and fish feeding are manually controlled. There is a need to use computer and communication technology in fish farms for remote monitoring and control. This paper deals with the design and implementation of an internet of things (IoT) based system for real-time monitoring, control and management of fish farming. The design of such a system is based on measuring different types of variables and using the information to control fish growth and increase productivity. Each fish pond is a node in a wireless sensor network. The node contains an embedded microcontroller connected to a set of sensors and actuators and a wireless communication module. Two fuzzy controllers are designed to control the water quality in the ponds as well as the environment using five sensors in each pond plus three environmental sensors. Practical results indicate the accuracy of the measurement system compared to the results obtained from commercial devices used on the farm. These results also showed that the proposed approach achieves the best performance of the real-time monitoring and control system in fish ponds.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.3365
      Issue No: Vol. 12, No. 3 (2023)
  • Fast terminal sliding mode control for dual arm manipulators

    • Authors: Minh Đức Dương, Trần Đức Chuyển, Tùng Lâm Nguyễn
      Pages: 1447 - 1457
      Abstract: In this paper, present the recent advances in bimanual industrial manipulators have led to an increased interest in the specific problems pertaining to dual arm manipulation. This paper presents a control algorithm for dual arm robot that can move the object in a working plane both in translation and rotation ways. Different from other research that extend the control algorithms for a single robot to a dual arm robot because of fixed grasp assumption, this research has considered the frictional contact constraints to guarantee object grasping during moving of the object. Fast terminal sliding mode control (FTSMC) technique is used to design the controller and comparison to traditional and super-twisting sliding mode controls have been done. Simulations show the effectiveness and outperformance of the proposed control algorithm in comparison to considered sliding mode control techniques.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4981
      Issue No: Vol. 12, No. 3 (2023)
  • The impacts of green LaBSiO5: Tb3+, Ce3+ phosphor on lumen output of white

    • Authors: Ha Thanh Tung, Huu Phuc Dang, Phung Ton That
      Pages: 1458 - 1463
      Abstract: The traditional solid-state technique was used to create LaBSiO5 phosphors doped with Ce3+ and Tb3+ at 1,100 °C. These phosphors' phase purity and luminous characteristics are looked at. Under ultraviolet (UV) light stimulation, LaBSiO5: Tb3+ phosphors emit bright green light, whereas LaBSiO5 samples incorporated with Ce3+ emit blue-violet light. With UV ray stimulation, LaBSiO5 samples incorporated with Ce3+ as well as Tb3+ emit blue-violet as well as green illumination. The 5d-4f shift for Ce3+ is responsible for the blue-violet radiation, while the 5D4→7F5 transition of Tb3+ is responsible for the green radiation. The mechanism for power conversion between Ce3+ and Tb3+ was examined since there is a spectral overlap among the stimulation line for Tb3+ and the emitting line for Ce3+.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4772
      Issue No: Vol. 12, No. 3 (2023)
  • Blue-yellow emissive LaAlO3:Dy3+ phosphor for high bright white
           light-emitting diodes

    • Authors: Ha Thanh Tung, Huu Phuc Dang
      Pages: 1464 - 1470
      Abstract: LaAlO3:Dy3+ (LAO:Dy3+) with blue-yellow emission for brilliant white light of light-emitting diodes (LEDs) was prepared using gel-combustion process. The chemical content of the created material was confirmed using energy dispersive X-ray analysis (EDX). The transmission electron microscopy was used to examine the phosphor crystal size and morphology. The luminescence analysis showed that LAO:Dy3+ phosphors exhibited peaks at 482 nm (blue) and 574 nm (yellow), attributed to the transitions 4F 9/2à6H 15/2 and 4F 9/2à6H 13/2, respectively. The investigating of correlated color temperature (CCT) and Commission International de L'Eclairage (CIE) described that the phosphor LAO:Dy3+ helped to attained good CCT coordination and bright cool-white emission under ultraviolet (UV) stimulation. However, when using in white LED fabrication, the concentration of LAO:Dy3+ should be modified to fulfill the need from manufactures. According to results from this work, the white light luminescence intensity and color rendering performance may be reduced if the LAO:Dy3+ is high (>10%) because of too much scattering. On the other hand, such scattering improvements offered by LAO:Dy3+ phosphor is beneficial to reduce the color deviance for better color uniformity of light.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4949
      Issue No: Vol. 12, No. 3 (2023)
  • Classification of potatoes according to their cultivated field by SVM and
           KNN approaches using an electronic nose

    • Authors: Ali Amkor, Noureddine El Barbri
      Pages: 1471 - 1477
      Abstract: In this article, we propose a homemade electronic nose to distinguish between two types of potatoes: the first type is traditionally treated with donkey and sheep manure, and the other type is treated with chicken manure. The proposed tool consists of a network of commercial metal oxide sensors, a data acquisition card, and a personal computer for data pre-processing and processing. Two methods were used, namely, support vector machines (SVM) and k-nearest neighbors (KNN) with 5-fold cross-validation and which achieved the same success rate of 97.5%. These results demonstrate that our concept, which is quick, simple, and inexpensive, can discriminate between potatoes based on the method of fertilization used in the field.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.5116
      Issue No: Vol. 12, No. 3 (2023)
  • Performance analysis for a suitable propagation model in outdoor with 2.5
           GHz band

    • Authors: Zaenab Shakir, Abbas Al-Thaedan, Ruaa Alsabah, Monera Salah, Ali AlSabbagh, Josko Zec
      Pages: 1478 - 1485
      Abstract: As demand for mobile wireless network services continues to rise, network planning
      and optimization significantly affect development. One of the critical elements
      in network planning is predicting pathloss. Thus, propagation models
      predict pathloss in indoor and outdoor environments. Choosing the appropriate
      propagation model for the area out of existing models is essential for network
      planning. Selected propagation models suitable with 2.5GHz, such as Friis Free
      Space Propagation Model (FSPL), Sandford University Interim (SUI), Ericsson,
      Okumura, and COST-231 HATA models, are utilized for evaluation and compared
      with empirical data collected from LTE networks in urban areas. The best
      acceptable model is chosen based on statistical results such as mean, standard
      deviation, and Root Mean Square Errors (RMSE). The analytical results show
      Cost-231 Hata model fits the empirical pathloss with a minimum RMSE of 5.27
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.5006
      Issue No: Vol. 12, No. 3 (2023)
  • OFDM-based Wideband Hybrid Beamformer for mmWave Massive MIMO Multiuser 5G

    • Authors: Faez Fawwaz Shareef, Manal Jamil Al-Kindi
      Pages: 1486 - 1494
      Abstract: Employing massive antennas array at the user terminals can be feasible by using millimeter wave (mmWave) transmission which significantly reduce the antennas array size. The implementation of massive multiple input multiple output (MIMO) at the user terminals facilitates accurate beamforming. In this paper, a modified orthogonal matching pursuit (OMP) algorithm is used to design a wideband hybrid combiner based on the sparse structure of mmWave channel and orthogonal frequency-division multiplexing (OFDM). Based on OFDM, the wideband channel considered as multiple narrowband channels so a modified narrowband hybrid combiner can be implemented for each subcarrier channel in a manner where the RF combiner is the same for all subcarriers, whilst the baseband combiner is obtained for each subcarrier. For the multiuser 5G system, a wideband hybrid precoder based on the block diagonalization (BD) method is used at the base station (BS) to cancel the interference at each user due to the other users. The performance of this hybrid beamformers (precoder/combiner) are tested for different scenarios of base station antennas number, numbers of users’ antennas, and number of users.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4094
      Issue No: Vol. 12, No. 3 (2023)
  • Compact low profile 5.8 GHz MPA for on-body applications

    • Authors: Siba Monther Yousif, Anwer Sabah Mekki, Ahmed Jumaa Lafta
      Pages: 1495 - 1501
      Abstract: A compact microstrip patch antenna (MPA) with a T shape monopole technique is designed, simulated, and measured. By using fire retardant material (FR-4) as a substrate with a low profile, the proposed antenna is designed and simulated to be used for on-body biomedical applications. A center frequency of 5.78 is achieved with a gain of 11.78 dB and a matching impedance of -47.47 dB. A 1.48 W/Kg (10 gm) as a specific absorption rate (SAR) is achieved and 29.69 dB front to back ratio with a bandwidth of 3.376 GHz. The antenna was examined in free space as well as on-body using CST-MW software. The proposed antenna is fabricated and examined. Finally, a comparison is done among simulated results, measured results, and the dual-band dual-mode antenna. The proposed antenna overcomes the latter work in terms of small size, high matching impedance, high front to back ratio, and operating bandwidth.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4518
      Issue No: Vol. 12, No. 3 (2023)
  • Improving FEC layer frame for DVB-S2 link system based on 5G NR polar

    • Authors: Omar M Salih, Ashwaq Q Hameed
      Pages: 1502 - 1512
      Abstract: Within the scope of this investigation, the MATLAB simulation code for digital video broadcasting–for satellite–second generation (DVB-S2) has been constructed. Forward error correction (FEC) rates as 3/5, 2/3, and 3/4 across additive white Gaussian noise (AWGN) and Rayleigh fading channels are used to evaluate the system's performance, mainly while working on quadrature phase-shift keying (QPSK), 8-ary phase-shift keying (8PSK), 16-ary amplitude phase-shift keying (16APSK), and 32-ary amplitude phase-shift keying (32APSK) official modulation types. The system's redesign has been achieved to investigate high performance and reliability based on a cascade of new radio (NR) fifth generation (5G) Polar coding with low-density parity-check (LDPC). Some signal-to-noise ratio (SNR) levels were changed when evaluated in contrast to the conventional model. It has been determined that five iterations of the LDPC decoder were performed. In comparison to the traditional model. The proposed design's performance accomplished the highest possible value for reducing the bit error rate (BER) value and investigated better-transmitted power gain for most testing cases.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4713
      Issue No: Vol. 12, No. 3 (2023)
  • Parametric study of a dual-band quasi-Yagi antenna for LTE application

    • Authors: Md Ashraful Haque, Mohd Azman Zakariya, Narinderjit Singh Sawaran Singh, Md. Afzalur Rahman, Liton Chandra Paul
      Pages: 1513 - 1522
      Abstract: Due to small size and lightweight properties, the development of microstrip (MS) patch antennas for wireless communication has become one of the mobile application devices in demand in recent years. This has helped to reduce reliance on wired cables. In this paper, a study has been performed on MS antenna by developing a quasi-Yagi structure on a fire retardant-4
      (FR-4) substrate with a MS to coplanar strip line (CPS) transition feeding technique. The antenna is designed by using computer simulation technology (CST) to achieve the desired resonant frequency and bandwidth. The proposed dual band quasi-Yagi antenna has impedance bandwidths of approximately 0.3 GHz and 0.22 GHz resonating at 1.80 GHz and 2.60 GHz, respectively, which makes it suitable for long-term evolution (LTE) applications. Eight-director elements in four pairs are constructed to achieve directivity with magnitudes of 6 dB and 8.3 dBi at both resonant frequencies, 1.80 GHz and 2.60 GHz, respectively. Different parametric studies have also been performed to characterize the antenna radiation characteristics. The return loss, voltage standing wave ratio (VSWR), front to back (F/B) ratio and far-field radiation are analyzed and discussed.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4639
      Issue No: Vol. 12, No. 3 (2023)
  • Design and planning of a 5G fixed wireless network

    • Authors: Silas Soo Tyokighir, Joseph M. Mom, Kingsley Eghonghon Ukhurebor, Gabriel A. Igwue
      Pages: 1523 - 1527
      Abstract: This research explains how to design and plan fixed wireless access connections in an urban setting using 5th generation (5G) technology in a multi-user urban scenario. Although the antennas used had a high gain, the 28 GHz carrier frequency proved incompatible with the connections due to path loss. The additional loss due to foliage led to a drop in the receiver sensitivity to -84 dBm. The loss due to weather conditions resulted in lower received signal strength. The lower frequency of 3.5 GHz performed better and is recommended to establish successful communication over multi-kilometer distances. As a result, this study demonstrates how vulnerable high 5G carrier frequencies are to typical path loss impairments.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4901
      Issue No: Vol. 12, No. 3 (2023)
  • A multiband triangular antenna for wireless communication applications

    • Authors: Nagnath Biradar, Kishan Singh
      Pages: 1528 - 1535
      Abstract: Modern technology has made it easier to perform many tasks, including data, voice, video, and short-range device-to-device communication. These characteristics operate at different frequencies. In order to realize compact electronic devices and give users ease of movement, the antenna should operate on various frequency bands. This paper discusses the design of quad band antenna operating from 2.1 to 2.6 GHz, 3.9 to 4.9 GHz, 5.1 to 6.3 GHz, and 7.4 to 11.2 GHz. The realization of multiband is achieved using slots as parasitic elements on the radiator. These slots alter the antenna's regular current flow by generating a local, out-of-phase current channel with the same amplitude. The presented antenna has an overall electrical dimension of 0.22×0.16×0.01 λ3 (λ is determined using the frequency of 2.1 GHz). The 1.6 mm thickness FR4 substrate serves as the development platform for the proposed multiband antenna. The antenna has good reflection coefficient (S11), voltage standing wave ratio (VSWR), radiation characteristics, anda peak gain of around 5.1 dB. The results show design usefulness for wireless applications and are consistent with the measured values.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4844
      Issue No: Vol. 12, No. 3 (2023)
  • Prospects for developing digital telecommunication complexes for storing
           and analyzing media data

    • Authors: Vladimir Kuklin, Islam Alexandrov, Dmitry Polezhaev, Aslan Tatarkanov
      Pages: 1536 - 1549
      Abstract: In today's digital world, saturated with data flows, universal multifunctional systems are developing, capable of solving various problems related to optimizing the use of available computing resources. A distinctive feature of such systems is the heterogeneity of incoming flows of user requests due to the multifunctionality of modern information systems, expressed in supporting various multimedia services on a single platform. Data heterogeneity and large volumes of data create many problems related to the speed of digital systems and data storage security. The solutions can be found in artificial intelligence (AI) technologies, particularly machine learning. Therefore, development and implementation of digital telecommunication complexes for storing, processing, and forming a dynamic flow of multiformat data using AI technologies are becoming more relevant. This paper aims to identify trends and prospects for developing these complexes, and develop proposals on their perspective characteristics. The authors focused on review the experience of Russian organizations developing multi-object analytics systems and analyze the technical and functional characteristics of existing systems. The result of the review and analysis is a table with a comparison of the technical characteristics of existing complexes and proposals for characteristics that are promising for further implementation.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4840
      Issue No: Vol. 12, No. 3 (2023)
  • Simulations on the effects of an optimized bowtie dipole antenna with an
           adaptive FIR filter

    • Authors: Aaron Don Munsayac Africa, Benjamin Emmanuel Uy, Bianca Clarisse Tan
      Pages: 1550 - 1559
      Abstract: In the evolution of technology through the years, antennas are use in varying wireless systems have been in demand. Antennas play a great role in transmitting and receiving signals. As its application is heavily used in many days to day activities, it is important to create a cost-efficient and quick way to analyze its performance, characteristics, and relationship to different variables. As many radiation pattern acquisition devices are expensive, this simulation proposes a quick, reliable, and cost-friendly way to simulate 2D patterns in the E-plane and H-plane of a bowtie dipole antenna with an adaptive finite impulse response (FIR) filter. Through this study, the software MATLAB will be utilized to successfully simulate the radiation patterns of antennas with varying lengths. With the use of MATLAB toolboxes, the researchers aim to be able to compare different antenna lengths and determine the relationship and effect of it in the obtained 2D radiation pattern. If this method is successful various antenna applications may be implemented in the future with the use of 2D radiation pattern results.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4406
      Issue No: Vol. 12, No. 3 (2023)
  • Discover human poses similarity and action recognition based on machine

    • Authors: Mohammed Moath Abdulghani, Mohammed Talal Ghazal, Anmar Burhan M. Salih
      Pages: 1570 - 1577
      Abstract: In the computer vision field, human action recognition depending on pose estimation recently made considerable progress, especially by using deep learning, which improves recognition performance. Therefore, it has been employed in various applications, including sports and physical activity follow-up. This paper presents a technique for recognizing the human posture in different images and matching their pose similarity. This aims to evaluate the viability of employing computer vision techniques to verify a person's body pose during exercise and determine whether the pose is executed properly. Exercise is one strategy we use to maintain our health throughout life. Gymnastics and yoga are two examples of this type of exercise. The proposed algorithm identifies human action by recognizing the body's key points. The OpenPose library has been used to detect 18 key points of the human body. The action classification task is performed using the support vector machine (SVM) algorithm. Then, the algorithm computes the similarity of the human pose by comparing a model image to a test image to determine the matching score. Evaluations show that our method can perform at a competitive or state-of-the-art performance on a number of body pose datasets.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4930
      Issue No: Vol. 12, No. 3 (2023)
  • Protection of images by combination of vernam stream cipher, AES and LSB
           steganography in a video clip

    • Authors: Marwah Kamil Hussein, Haleh Amintoosi
      Pages: 1578 - 1585
      Abstract: Visual communication has become more popular in recent years, and because data must be transferred safely over a restricted bandwidth, techniques of data security and preservation, such as masking and encryption, have to be included after the optimization process for the image in question. The two most common methods of data protection are encryption and steganography. Steganography is a way for covering data that is hidden in another medium without leaving any proof of the data being changed, whereas cryptography converts regular data into incomprehensible data, which is known as scrambled data. Using the least significant bit (LSB) technique, the information was scrambled with graphics. The Vernam encryption algorithm and the advanced encryption standard (AES) will have a side in the proposed method in the encryption step, and the three improvement proposals using quality standards and encryption will be compared with the Vernam encryption algorithm and the AES encryption algorithm, and the effect of the improvement ratio and the size of the encrypted data with different threshold values will be investigated.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4039
      Issue No: Vol. 12, No. 3 (2023)
  • K-mean clustering and local binary pattern techniques for automatic brain
           tumor detection

    • Authors: Faiq Sabbar Baji, Saleema Baji Abdullah, Fatimah S. Abdulsattar
      Pages: 1586 - 1594
      Abstract: Tumors in brains are caused by the unregulated emergence of tissue cells inside the brain. The early diagnosis and determining the precise location of the tumor in magnetic resonance imaging (MRI) and its size are essential for the teams of physicians. Image segmentation is often considered a preliminary step in medical image analyses. K-means clustering has been widely adopted for brain tumor detection. The result of this technique is a list of cluster images. The challenge of this method is the difficulty of selecting the appropriate cluster section that depicts the tumor. In this work, we analyze the influence of different image clusters. Each cluster is then split into the left and right parts. After that, the texture features are depicted in each part. Furthermore, the bilateral symmetry measure is applied to estimate the cluster that contains the tumor. Finally, the connected component labeling is employed to determine the target cluster for brain tumor detection. The developed technique is applied to 30 MRI images. The encouraging accuracy of 87% is obtained.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4404
      Issue No: Vol. 12, No. 3 (2023)
  • Improve steganography system using agents software based on statistical
           and classification technique

    • Authors: Estabraq Hussein Jasim Halboos, Abbas M. Albakry
      Pages: 1595 - 1606
      Abstract: In digital communications, information security is a paramount necessity. In the hiding algorithm, there are three basic parameters: security, capacity, and imperceptibility. Therefore, there are many ways to design the steganography algorithm, such as least significant bit (LSB), discrete wave transformation (DWT), and discrete cosine transform (DCT). The aim of this paper is to improve agent software design based on a steganography system. It proposed an agent system based on a support vector machine (SVM) classifier to hide a secret message in a certain cover image. The common dataset for steganography uses 80% training and 20% testing to get accurate results. Developing an agent system depends on six statistical parameters such as energy, standard deviation, histogram, variance, mean, and entropy. This resulted in features classified by the SVM classifier to predict the best cover image to be nominated for embedding. Worthy results were obtained in terms of imperceptibility, attack, and cover image prediction by statistical issues.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4540
      Issue No: Vol. 12, No. 3 (2023)
  • Colour image encryption based on hybrid bit-level scrambling, ciphering,
           and public key cryptography

    • Authors: Ahmed Kamil Hasan Al-Ali, Jafaar Mohammed Daif Alkhasraji
      Pages: 1607 - 1619
      Abstract: This paper proposes an image encryption technique using three stages algorithms based on hyper-chaotic maps. In the first scenario, bit-level scrambling (BLS) using a 2D coupled chaotic map (2D-CCM) is used to encrypt the bits of the basic colour image. In the second strategy, the scrambled bit level is XORed with pseudo random bit generator (PRBG). The PRBG is designed using a combination of chaotic maps, including, logistic map (LM), sine map (SM), 5D chaotic map (5D-CM), enhanced quadratic map (EQM), and 2D henon SM (2D-HSM). The pubic key based on the Chebyshev polynomial chaotic map is used as the final phase of the encryption algorithms. The performance analysis of the proposed image encryption technique is validated through various criteria such as fundamental space analysis, correlation coefficient, entropy, the number of pixels changes rate (NPCR), and unified average-changing intensity (UACI). Also, the obtained results are compared with other recent studies. The simulation results demonstrated that the proposed technique has robust security and it provides the image with high protection against various attacks.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4728
      Issue No: Vol. 12, No. 3 (2023)
  • Curriculum learning based overcomplete U-Net for liver tumor segmentation
           from computed tomography images

    • Authors: Bindu Madhavi Tummala, Soubhagya Sankar Barpanda
      Pages: 1620 - 1629
      Abstract: In this paper, we have proposed an overcomplete U-Net to perform liver tumor segmentation jointly using a curriculum learning strategy. Liver tumor segmentation is the most prominent and primary step in treating liver cancer and can also help doctors with proper diagnosis and therapy planning. However, it is challenging because of variations in shape, position, and depth of tumors and adjacent boundaries with internal organs around the liver. We have presented a promising solution by designing a U-Net-based segmentation network with two branches: an overcomplete branch to fine grade the small structures and an undercomplete branch to fine grade the high-level structures. This combination allows the network to learn all types of tumor artifacts more accurately. We also changed the conventional learning paradigm to curriculum learning where the input images are fed to the network from easy to hard ones to achieve faster convergence. Finally, our network segments the tumors directly from the whole medical images without the need for segmented liver region of interests (ROIs). The proposed network achieved a DICE score of 75% in tumor segmentation which is a decent value when compared with some existing deep learning methods for liver tumor segmentation.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4676
      Issue No: Vol. 12, No. 3 (2023)
  • Image and video-based crime prediction using object detection and deep

    • Authors: Mohammed Boukabous, Mostafa Azizi
      Pages: 1630 - 1638
      Abstract: In recent years, the use of artificial intelligence (AI) for image and video-based crime detection has gained significant attention from law enforcement agencies and security experts. Indeed, deep learning (DL) models can learn complex patterns from data and help law enforcement agencies save time and resources by automatically identifying and tracking potential criminals. This contributes to make deep investigations and better steer their targets’ searches. Among others, handheld firearms and bladed weapons are the most frequent objects encountered at crime scenes. In this paper, we propose a DL-based surveillance system that can detect the presence of tracked objects, such as handheld firearms and bladed weapons, as well as may proceed to alert authorities regarding eventual threats before an incident occurs. After making a comparison of different DL-based object detection techniques, such as you only look once (YOLO), single shot multibox detector (SSD), or faster region-based convolutional neural networks (R-CNN), YOLO achieves the optimal balance of mean average precision (mAP) and inference speed for real-time prediction. Thus, we retain YOLOv5 for the implementation of our solution.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.5157
      Issue No: Vol. 12, No. 3 (2023)
  • Analysis and description S-box generation for the AES algorithm-a new 3D
           hyperchaotic system

    • Authors: Hayder Kadhim Zghair, Mehdi Ebady Manaa, Safa Saad A. AL-Murieb, Fryal Jassim Abd Al-Razaq
      Pages: 1639 - 1647
      Abstract: In this paper, a description, and analysis of a novel 3-D dimension hyperchaotic system is implemented. The proposed system oscillation is two-order autonomous and consisted of a nine-term and symmetric oscillation w.r.t x-axis. It is proved analysis by Kaplan-York dimension, waveform analysis, phase portrait, and Lyapunov exponent. This work-study stability and equilibrium point and Routh stability criteria produced that the new system has one unstable point from the type saddle-focus point. One of the characteristics of the proposed system is hyperchaotic since this system has two Lyapunov large than zero. This system is applied to generate a chaotic  (S-box) based in advanced encryption standard (AES) algorithm for text encryption and gives a high level of security. In addition to the description, and analysis S-box. Therefore. the proposed algorithm is satisfied the high randomness of entropy value and passes the National Institute of Standards and Technology (NIST) parameters and another test. Mathematica and MATLAB programs simulated some results.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4824
      Issue No: Vol. 12, No. 3 (2023)
  • Predicting COVID-19 vaccinators based on machine learning and sentiment

    • Authors: Hadab Khalid Obayes, Khaldoon Hasan Alhussayni, Saba Mohammed Hussain
      Pages: 1648 - 1656
      Abstract: In the past two years, the world witnessed the spread of the coronavirus (COVID-19) pandemic that disrupted the entire world, the only solution to this epidemic was health isolation, and with it everything stopped. When announcing the availability of a vaccine, the world was divided over the effectiveness and harms of this vaccine. This article provides an analysis of vaccinators and analysis of people's opinions of the vaccine's efficacy and whether negative or positive. Then a model is built to predict the future numbers of vaccinators and a model that predicts the number of negative opinions or tweets. The model consists of three stages: first, converting data sets into a synchronized time series, that is, the same place and time for vaccination and tweets. The second stage is building a prediction model and the third stage was descripting analysis of the prediction results. The autoregressive integrated moving averages (ARIMA) method was used after decomposing the components of ARIMA and choosing the optimal model, the best results obtained from seasonal ARIMA (SARIMA) for both predictions, the last stage is the descriptive analysis of the results and linking them together to obtain an analysis describing the change in the number of vaccinators and the number of negative tweets.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4278
      Issue No: Vol. 12, No. 3 (2023)
  • Auto-correction of multiple spatial conflicts in multimedia authoring

    • Authors: Marvin Chandra Wijaya, Zulisman Maksom, Muhammad Haziq Lim Abdullah
      Pages: 1657 - 1665
      Abstract: Multimedia authoring tool serves to facilitate an author to create multimedia presentations. Multimedia authoring tool will convert the multimedia presentation into a document. Mobile devices with different screen sizes that can cause visual media to overlap are called Spatio-temporal conflict. The process in the multimedia authoring tool involves a Spatio-temporal verification process to detect Spatio-temporal conflicts. This study proposes a method for correcting the conflict using a conflict region detection process, Spatio-temporal verification, and auto-correction. The auto-correction method uses two stages of area relocation for vertical conflict and horizontal conflict. The two stages are repeated for the ability to correct up to four conflicting regions. Visual media objects such as overlapping images and videos were successfully separated into non-overlapping media objects. The proposed method succeeded in separating the four media objects that were previously overlapping.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4894
      Issue No: Vol. 12, No. 3 (2023)
  • Machine learning with task-technology fit theory factors for predicting
           students’ adoption in video-based learning

    • Authors: Suraya Masrom, Rahayu Abdul Rahman, Norhayati Baharun, Syed Redzwan Sayed Rohani, Abdullah Sani Abd Rahman
      Pages: 1666 - 1673
      Abstract: Nowadays, various innovative educational and instructional tools have been created to deliver learning material including video content. One of the important issues with video-based learning is to devise effective teaching strategies to ensure higher level of learning can be achieved by the students. Getting insight and predicting the students’ video-based learning adoption will help the educators. Thus, this study aims to examine the potential of using machine learning prediction models on video-based learning adoption in higher education institutions. Five machine learning algorithms were used to be empirically compared namely generalized linear model (GLM), random forest (RF), decision tree (DT), gradient boosted tree (GBT), and support vector machine (SVM). The performance of each machine learning algorithm in predicting the students’ learning adoption with video-based learning has been observed based on the attributes of task-technology fit theory. The findings indicated that the task-technology fit is useful in helping the machine learning algorithm to achieve high accuracy in the prediction of video-based learning adoption. The GBT is the best outperforming algorithm, followed with RF and SVM. This paper presents a fundamental research framework useful for helping educators and researchers to enhance student interest and retention on video-based learning.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.5037
      Issue No: Vol. 12, No. 3 (2023)
  • Surveillance detection of anomalous activities with optimized deep
           learning technique in crowded scenes

    • Authors: Omobayo Ayokunle Esan, Dorcas Oladayo Esan, Munienge Mbodila, Elegbeleye Femi Abiodun, Kasewa Koranteng
      Pages: 1674 - 1683
      Abstract: The performance of conventional surveillance systems is challenged by high error detection rates in busy scenes, which has significantly affected the accurate detection of the current surveillance system. Feature representation and object pattern extraction from different scenes have made deep learning (DL) promising methods in surveillance systems, compared to the approaches where features are created manually. To improve the detection accuracy, this paper presents an intelligent DL technique that combines convolutional neural network (CNN) and long short-term memory (LSTM). CNN extracts and learns the object features from a set of raw images, while the LSTM is then used by gated mechanisms to store important information from the extracted features. The proposed method was validated using datasets from the University of California San Diego (UCSD). The result shows that the model achieves 95% accuracy, which is superior compared to other conventional detection models.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4471
      Issue No: Vol. 12, No. 3 (2023)
  • An innovative method for enhancing advanced encryption standard algorithm
           based on magic square of order 6

    • Authors: Suhad Muhajer Kareem, Abdul Monem S. Rahma
      Pages: 1684 - 1692
      Abstract: This paper introduces an improved advanced encryption standard (AES) cipher algorithm by proposing a new algorithm based on magic square to decrease the AES execution time. This is done by replacing Mixcolumn function with magic square of order 6. This paper raises the security level of AES cryptosystem by using another key, which is generated using magic square while decreasing the execution time. For application of encrypting a colouring image, visual studio and MATLAB programs have been used as means for computing results. The results of complexity, time execution, National Institute of Standards and Technology (NIST) tests, histogram, differential attacks and peak signal to noise ratio (PSNR) are computed and compared with the original AES cryptosystem, the original and the proposed algorithms. The proposed algorithm results in reasonable findings under several evaluation metrics. For instance, the complexity of our proposed algorithm is higher than the basic AES while decreases the time execution. The experimental results show that the suggested algorithm provides an efficient and secure way for image encryption. The suggested algorithm leads to leverage the complexity of cipher process as well as to make the linear and differential cryptanalysis harder by pre-process the input image initial step of the proposed AES.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4498
      Issue No: Vol. 12, No. 3 (2023)
  • Digital handwriting characteristics for dysgraphia detection using
           artificial neural network

    • Authors: Mohamed Ikermane, Abdelkrim El Mouatasim
      Pages: 1693 - 1699
      Abstract: Despite all of the technical advancements in writing and text editing with keyboards on numerous devices, writing with a pen remains a fundamental ability in modern human existence. Handwriting disabilities are referred to as dysgraphia. Nonetheless, how well they are taught to write in school, 10-30% of children never attain a respectable level of handwriting. Early identification is critical because it can help children avoid difficulties in their behavioral and academic development. On blank papers attached to digital tablets, 280 individuals were asked to complete the concise evaluation scale for children’s handwriting (BHK), with 218 having typical handwriting and 62 having dysgraphia. In addition to their age and BHK quality and speed scores, 12 variables identifying digital handwriting across several domains (static, kinematic, pressure, and tilt) were collected. In this paper, we provided a rapid and automated dysgraphia classification approach using an artificial neural network (ANN) model. Using digital handwriting traits as an input to the ANN approach, the prediction findings were encouraging and very accurate, reaching 96% accuracy, and they could lead to the development of a new self-administered dysgraphia screening tool.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4571
      Issue No: Vol. 12, No. 3 (2023)
  • A missing data imputation method based on salp swarm algorithm for
           diabetes disease

    • Authors: Geehan Sabah Hassan, Noora Jamal Ali, Asma Khazaal Abdulsahib, Farah Jasim Mohammed, Hassan Muwafaq Gheni
      Pages: 1700 - 1710
      Abstract: Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve Bayesian classifier (NBC) have been enhanced as compared to the dataset before applying the proposed method. Moreover, the results indicated that issa was performed better than the statistical imputation techniques such as deleting the samples with missing values, replacing the missing values with zeros, mean, or random values.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4528
      Issue No: Vol. 12, No. 3 (2023)
  • Framework for selecting the best software quality model for a smart health
           application based on intelligent approach

    • Authors: Ashraf Mousa Saleh, Odai Enaizan
      Pages: 1711 - 1727
      Abstract: There is difficulty in knowing how to weigh the factors of software quality models so that decision-making can be eased. Furthermore, previous work was limited to undertake evaluation and selection of appropriate software quality model based upon multi-criteria in the context of smart health applications. This paper aims to evaluate and select an appropriate model of software quality based on multi-criteria decision-making (MCDM) by three phases of framework. Firstly, investigation of software quality models and factors that were identified based on ‘fuzzy delphi’. Secondly, identification of quality models that have uniform multi-criteria so that a decision matrix could be established. Uniform multi-criteria were used in the decision matrix as the basis of the models of quality and the multi-criteria. Subsequently, MCDM approach is adopted and the bases used in the employment of the MCDM approach for the eva luation and selection of the software quality model were technique for order preference by similarity to ideal solution (TOPSIS) and fuzzy analytical hierarchy process (FAHP). The results demonstrated that seven quality factors could be considered as the key factors based upon fuzzy delphi, i.e., usability, maintainability, reliability, interoperability, portability, modifiability, and efficiency. Also, reults shows that McCall is the most appropriate model.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4945
      Issue No: Vol. 12, No. 3 (2023)
  • Comparative analysis of predictive machine learning algorithms for
           diabetes mellitus

    • Authors: Kirti Kangra, Jaswinder Singh
      Pages: 1728 - 1737
      Abstract: Diabetes mellitus (DM) is a serious worldwide health issue, and its prevalence is rapidly growing. It is a spectrum of metabolic illnesses defined by perpetually increased blood glucose levels. Undiagnosed diabetes can lead to a variety of problems, including retinopathy, nephropathy, neuropathy, and other vascular abnormalities. In this context, machine learning (ML) technologies may be particularly useful for early disease identification, diagnosis, and therapy monitoring. The core idea of this study is to identify the strong ML algorithm to predict it. For this several ML algorithms were chosen i.e., support vector machine (SVM), Naïve Bayes (NB), K nearest neighbor (KNN), random forest (RF), logistic regression (LR), and decision tree (DT), according to studied work. Two, Pima Indian diabetic (PID) and Germany diabetes datasets were used and the experiment was performed using Waikato environment for knowledge analysis (WEKA) 3.8.6 tool. This article discussed about performance matrices and error rates of classifiers for both datasets. The results showed that for PID database (PIDD), SVM works better with an accuracy of 74% whereas for Germany KNN and RF work better with 98.7% accuracy. This study can aid healthcare facilities and researchers in comprehending the value and application of ML algorithms in predicting diabetes at an early stage.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4412
      Issue No: Vol. 12, No. 3 (2023)
  • A solution approach to minimum spanning tree problem under fermatean fuzzy

    • Authors: Francis Remigius Perpetua Mary, Swaminathan Mohanaselvi, Said Broumi
      Pages: 1738 - 1746
      Abstract: In classical graph theory, the minimal spanning tree (MST) is a subgraph with no cycles that connects each vertex with minimum edge weights. Calculating minimum spanning tree of a graph has always been a common problem throughout ages. Fuzzy minimum spanning tree (FMST) is able to handle uncertainty existing in edge weights for a fuzzy graph which occurs in real world situations. In this article, we have studied the MST problem of a directed and undirected fuzzy graph whose edge weights are represented by fermatean fuzzy numbers (FFN). We focus on determining an algorithmic approach for solving fermatean fuzzy minimum spanning tree (FFMST) using the modified Prim’s algorithm for an undirected graph and modified optimum branching algorithm for a directed graph under FFN environment. Since the proposed algorithm includes FFN ranking and arithmetic operations, we use FFNs improved scoring function to compare the weights of the edges of the graph. With the help of numerical examples, the solution technique for the proposed FFMST model is described.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4794
      Issue No: Vol. 12, No. 3 (2023)
  • Intelligent deep learning algorithm for lung cancer detection and

    • Authors: N. Sudhir Reddy, V. Khanaa
      Pages: 1747 - 1754
      Abstract: Lung cancer is one of the leading causes of cancer mortality. The overlapping of cancer cells makes early diagnosis difficult. When lung cancer is found early, many therapy choices are reduced, the danger of invasive surgery is reduced, and the chance of survival increases. The primary goal of this study work is to identify early-stage lung cancer and categories using an intelligent deep learning algorithm. Following a thorough review of the literature, we discovered that certain classifiers are ineffective while others are almost perfect. In general, several different kinds of images are employed, but computer tomography scanned images are preferable due to their reduced noise. Intelligent deep learning algorithm is one such approach that employs convolutional neural network techniques and has been shown to be the most effective way for medical image processing, lung nodule identification, classification, feature extraction, and lung cancer prediction. The characteristics are taken from the segmented images and classified using intelligent deep learning algorithm. The suggested techniques' performances are assessed based on their accuracy, sensitivity, specificity, recall, and precision.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4579
      Issue No: Vol. 12, No. 3 (2023)
  • Identifying clickbait in online news using deep learning

    • Authors: Andry Chowanda, Nadia Nadia, Lie Maximilianus Maria Kolbe
      Pages: 1755 - 1761
      Abstract: Several industries use clickbait techniques as their strategy to increase the number of readers for their news. Some news companies implement catchy headlines and images in their news article links, with the expectation that the readers will be interested in reading the news and click the provided link. The majority of the news is not hoax news. However, the content might not be as grand as the catchy headlines and images provided to the readers. This research aims to explore the classification model using machine learning to identify if the headlines are classified as clickbait in online news. This research explores several machine learning techniques to classify clickbait in online news and comprehensively explain the results. Several popular machine learning techniques were implemented and explored in this research. The results demonstrate that the model trained with fast large margin provides the best accuracy and classification error (90% and 10%, respectively). Moreover, to improve the performance, bidirectional encoder representations from transformers architecture was used to model clickbait in online news. The best BERT model achieved 98.86% in the test accuracy. BERT model requires more time to train (0.9 hour) compared to machine learning (0.4 hour).
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4444
      Issue No: Vol. 12, No. 3 (2023)
  • Bitcoin trading indicator: a machine learning driven real time bitcoin
           trading indicator for the crypto market

    • Authors: Ashikur Rahaman, Abu Kowshir Bitto, Khalid Been Md. Badruzzaman Biplob, Md. Hasan Imam Bijoy, Nusrat Jahan, Imran Mahmud
      Pages: 1762 - 1772
      Abstract: As opposed to other fiat currencies, bitcoin has no relationship with banks. Its price fluctuation is largely influenced by fresh blocks, news, mining information, support or resistance levels, and public opinion. Therefore, a machine-learning model will be fantastic if it learns from data and tells or indicates if we need to purchase or sell for a little period. In this study, we attempted to create a tool or indicator that can gather tweets in real-time using tweepy and the Twitter application programming interface (API) and report the sentiment at the time. Using the renowned Python module "FBProphet," we developed a model in the second phase that can gather historical price data for the bitcoin to US dollar (BTCUSD) pair and project the price of bitcoin. In order to provide guidance for an intelligent forex trader, we finally merged all of the models into one form. We traded with various models for a very little number of days to validate our bitcoin trading indicator (BTI), and we discovered that the combined version of this tool is more profitable. With the combined version of the instrument, we quickly and with little error root mean square error (RMSE: 1,480.58) generated a profit of $1,000.71 USD.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4486
      Issue No: Vol. 12, No. 3 (2023)
  • COVID-19 classification using CNN-BiLSTM based on chest X-ray images

    • Authors: Denis Eka Cahyani, Anjar Dwi Hariadi, Faisal Farris Setyawan, Langlang Gumila, Samsul Setumin
      Pages: 1773 - 1782
      Abstract: Cases of the COVID-19 virus continue to spread still needs to be considered even though we have entered the post-pandemic era. Rapid identification of COVID-19 cases is necessary to prevent the virus from spreading further. This study developed a chest X-ray-based (CXR) COVID-19 classification for COVID-19 detection using the convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) combination model and compared the CNN-BiLSTM combination model with CNN models. The CNN models used in this study are the transfer learning models, namely Resnet50, VGG19, InceptionV3, Xception, and AlexNet. This research classifies CXR into three groups: COVID-19, normal, and viral pneumonia. In comparison to other models, the Resnet50-BiLSTM model is the most accurate and hence the best. The accuracy of the Resnet50-BiLSTM model was 98.48%. The model that obtains the next highest accuracy i.e Resnet50, VGG19-BiLSTM, VGG19, InceptionV3-BiLSTM, InceptionV3, Xception-BiLSTM, Xception, AlexNet-BiLSTM, and AlexNet. In this study, precision, recall, and F1-measure are also employed to demonstrate that Resnet50-BiLSTM achieves the highest value compared to other approaches. When compared to previous studies, this study enhances classification performance results.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4848
      Issue No: Vol. 12, No. 3 (2023)
  • Design and analysis of trans Z-source inverter for electric vehicle
           applications using neural network-clustering

    • Authors: Manish Bharat, ASR Murty, Ritesh Dash
      Pages: 1783 - 1796
      Abstract: The presented paper analyzes the detailed design of a trans Z-source inverter (ZSI) with an input from solar photovoltaic (SPV) system. Increase in SPV uses requires highly efficient SPV enabled inverters under varying weather parameters are in high demand in modern smart grid applications. The SPV-trans ZSI has high conversion efficiency because of the single-stage voltage boost conversion capability. In contradiction, the conventional voltage source inverter (VSI) requires an additional step-up transformer to boost the output voltage of inverter. This reduces the efficiency by increasing the volume of set up and also increase the cost of the system. In the proposed SPV system it provides a better output against VSI. The increase in inverter output voltage is because of shoot through time period present in ZSI. It also reduces the voltage stress and harmonics content as compared to VSI. The proposed model has been validated through MATLAB simulation.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4818
      Issue No: Vol. 12, No. 3 (2023)
  • Simulation of SDN in mininet and detection of DDoS attack using machine

    • Authors: P. Karthika, Karmel Arockiasamy
      Pages: 1797 - 1805
      Abstract: Most contemporary businesses are embracing software defined networking (SDN), a developing architecture that enables an aerial-like perspective of the entire network. SDN operates by virtualizing the network and provides advantages including improved performance, visibility, speed, and scalability. SDN attempts to divide the network control plane from the forwarding plane. The control plane, which includes one or more controllers and incorporates complete intelligence, is thought of as the brain of the SDN. However, SDN has challenges with controller vulnerability, flexibility, and hardware security. But distributed denial of service (DDoS) assaults constitutes a serious threat to the SDN. Transmission control protocol-synchronized (TCP-SYN) floods, a common cyberattack that can harm SDNs, can deplete network resources by opening an excessive number of illegitimate TCP connections. In this research, we provide an OpenFlow port statistic-based architecture for machine learning (ML) enabled TCP-SYN flood detection. This research showed that ML models like support vector machine (SVM), Navie Bayes, and multi-layered perceptron can distinguish between regular traffic and SYN flood traffic and can mitigate the impacts of the attacking node on the network. Results showed that the multilayered perceptron can classify the traffic with highest accuracy of 99.75% for the simulation dataset.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.5232
      Issue No: Vol. 12, No. 3 (2023)
  • An accurate traffic flow prediction using long-short term memory and gated
           recurrent unit networks

    • Authors: Mohamed S. Sawah, Shereen Aly Taie, Mohamed Hasan Ibrahim, Shereen A. Hussein
      Pages: 1806 - 1816
      Abstract: Congestion on roadways is an issue in many cities, especially at peak times, which causes air and noise pollution and cause pressure on citizens. So, the implementation of intelligent transportation systems (ITSs) is a very important part of smart cities. As a result, the importance of making accurate short-term predictions of traffic flow has significantly increased in recent years. However, the current methods for predicting short-term traffic flow are incapable of effectively capturing the complex non-linearity of traffic flow that affects prediction accuracy. To overcome this problem, this study introduces two novel models. The first model uses two long-short term memory (LSTM) units that can extract the traffic flow temporal features followed by four dense layers to perform the traffic flow prediction. The second model uses two gated recurrent unit (GRU) units that can extract the traffic flow temporal features followed by three dense layers to perform the traffic flow prediction. The two proposed models give promising results on performance measurement system (PEMS), traffic and congestions (TRANCOS) dataset that is firstly used as metadata. So, the two models can do this in specific cases and are able to suddenly capture trend changes.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.5080
      Issue No: Vol. 12, No. 3 (2023)
  • Improving sentiment reviews classification performance using support
           vector machine-fuzzy matching algorithm

    • Authors: Vivine Nurcahyawati, Zuriani Mustaffa
      Pages: 1817 - 1824
      Abstract: High dimensionality in data sets is one of the challenges faced in classification, data mining, and sentiment analysis. In the data set, many dimensionalities require effort to simplify. Many of these dimensionalities have a major impact on the complexity and performance of the algorithms used for classification. Various challenges were encountered, including how to determine the optimal combination of pre-processing techniques, how to clean the dataset, and determine the best classification algorithm. This study uses a new approach based on the combination of three powerful techniques which are: tokenizing-lowercasing-stemming (for series of preprocessing), support vector machine (SVM) for supervised classification, and fuzzy matching (FM) for dimensionality reduction. The proposed model was realized using 3 different datasets, namely Amazon product review, movie review, and airline review from Twitter. This study provides better findings than the previous results. Improved performance is generated by SVM combined with FM, resulting in 96% accuracy. So that the SVM-FM combination can be said to be the best combination for sentiment analysis on the given data set.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4830
      Issue No: Vol. 12, No. 3 (2023)
  • Usability measures used to enhance user experience in using digital health
           technology among elderly: a systematic review

    • Authors: Azaliza Zainal, Nur Farhanum Abdul Aziz, Nahdatul Akma Ahmad, Fariza Hanis Abdul Razak, Fadia Razali, Noor Hidayah Azmi, Haily Liduin Koyou
      Pages: 1825 - 1832
      Abstract: In 2030, it is expected that 15% of the country's population will be classified as elderly and there is driving up demand for elderly healthcare services. The evolution of digital health technology has emerged as a solution to this issue. However, there has been a recent decline in the elderly adoption of digital health technologies. This issue is worsened by the emergence of interfaces and interaction styles in newly developed technologies. A systematic review was conducted in this article to investigate the usability measures used to improve the user experience of digital health technology among the elderly. This study includes articles selected from the Web of Science and Scopus databases, both of which are well-established. Using thematic analysis, data from 29 articles were analyzed, yielding four main themes: i) effectiveness; i) efficiency; iii) satisfaction; and iv) learnability. The four main themes generated 12 sub-themes. The appearance, functionality, and structure of new digital health technology are the primary barriers to adoption. User interface (UI) design should take into account the limitations of elderly users. Additionally, elderly users require motivation, support, and training to utilize digital health technologies effectively. This study's findings make significant contributions to digital health and gerontechnology fields.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4773
      Issue No: Vol. 12, No. 3 (2023)
  • Evaluation of feature scaling for improving the performance of supervised
           learning methods

    • Authors: Tsehay Admassu Assegie, Vadivel Elanangai, Josephin Shermila Paulraj, Mani Velmurugan, Daya Florance Devesan
      Pages: 1833 - 1838
      Abstract: This article evaluates the performance of the support vector machine (SVM), decision tree (DT), and random forest (RF) on the dataset that contains the medical records of 299 patients with heart failure (HF) collected at the Faisalabad Institute of Cardiology and the Allied hospital in Pakistan. The dataset contains 13 descriptive features of physical, clinical, and lifestyle information. The study compared the performance of three classification algorithms employing pre-processing techniques such as min-max scaling, and principal component analysis (PCA). The simulation result shows that the performance of the DT, and RF decreased with dimensionality reduction while the SVM improved with dimensionality reduction. The SVM achieved 84.44%. Thus, feature scaling improves the performance of the SVM. The RF performs at 82.22%, the DT at 81.11%, and the SVM shows an improvement of 1.64% with scaled features, compared to the original dataset.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.5170
      Issue No: Vol. 12, No. 3 (2023)
  • Mobile game model for monitoring Malaysian food calories intake using
           image recognition

    • Authors: Nurmaisarah Ismail, Sazilah Salam, Siti Nurul Mahfuzah Mohamad, Bambang Pudjoatmodjo, Norazlina Shafie, Rashidah Lip, Mohd Adili Norasikin, Faaizah Shahbodin
      Pages: 1839 - 1848
      Abstract: Two important problems related to food consumption were reported in Malaysia: Malaysia was the sixth rank in Asia for the highest adult obesity rate; and the United Nation reported that Malaysian consumed an average of 2,910 calories per day. An imbalanced diet and high intake of calorie-dense food problems that need attention to reduce obesity. These problems affect national economies by lowering productivity, increasing disability, raising health care expenses, and shortening life spans. Although, there are food calorie tracking applications available, however, existing apps are less engaging and to recognize Malaysian food due to its not versatile databases. This can be solved using game technologies. Hence, this study will propose mobile game model as a solution to the underlying problems. There are 4 phases in the method: expert validation, initial model, expert verification, and final model. The proposed parameters were validated by dietitians, and nutritionists. The model was verified by game experts. A low fidelity prototype was developed based on the proposed model to assist the expert verification process. The model was finalized based on the expert’s feedback. The proposed game model resolves the limited recognition of Malaysian food and monitoring the food calories intake in an engaging way.
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.4916
      Issue No: Vol. 12, No. 3 (2023)
  • Development of playfair cryptosystem based on generation a
           multi-dimensional key matrix

    • Authors: Mustafa Dhiaa Al-Hassani, Methaq Talib Gaata
      Pages: 1849 - 1856
      Abstract: Playfair is considered as one of the classical encryption symmetric methods, it has a limitation of using just 5×5 matrix, which means only 25 English letters could be represented. In this work, a 2D and 3D method is adopted as an expanded matrix that encompass the overall American standard code for information interchange (ASCII) codes in a permuted manner for all symbols of any language. Any sort of the multi-dimensional matrix will enhance the security by increasing the complexity on the attacker to try 256! patterns of keys probabilities instead of 25!. The key-matrix is generated from the chaotic maps for some control parameters as patterns of non-repeating random numbers from 0 to 255 equivalent to their ASCII code values. The security of the proposed method not rely only on the number of key probabilities, but exceed that to: matrix dimensionality, encryption/decryption algorithms, initial chaotic parameters, and key-matrix values permutation. The efficiency of the proposed cryptosystem has been investigated when tested on 784 samples according to security measurements in which the obtained number of pixels change rate (NPCR) (99.609) is very close to the ideal value, while the correlation plotting close to zero (0.00058) and entropy near from 8 (7.9998).
      PubDate: 2023-06-01
      DOI: 10.11591/eei.v12i3.5052
      Issue No: Vol. 12, No. 3 (2023)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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