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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 64 Documents
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Search results for , issue "Vol 29, No 2: February 2023" : 64 Documents clear
Review of current artificial intelligence methods and metaheuristic algorithms for wind power prediction Doha Bouabdallaoui; Touria Haidi; Mariam El Jaadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp626-634

Abstract

Due to the insufficient fossil resources and the increasing environmental challenges, the world is heading for a more use-oriented to renewable energy sources, specifically to wind energy. A number of predictive techniques are available for the efficient use of wind energy. This article, which is a review of methods of artificial intelligence (AI) and meta-heuristic algorithms for wind energy prediction, fits into this context. There are two distinct categories: the first consists of traditional methods that are commonly used in this context, like different types of artificial neural networks (ANN), support vector machines (SVM) and fuzzy logic; the second is a combined approach which mixes the classic artificial intelligence methods and the meta-heuristic algorithms for the optimization of the forecast output. Then, a summary and comparison between the methodologies are established, and the advantages and limits of each technique are defined. The combination of the classic artificial intelligence and metaheuristic algorithms has a greater performance than the utilization of classic methods only. Nevertheless, using hybrid metaheuristic algorithms with classic artificial intelligence prediction methods can provide a higher precision.
Hybrid structure of u bent optical fiber local surface plasmon resonance sensor based on graphene Saffana Zeiab Maseer; Bushra Razooky Mahdi; Nahla Abd Aljbar
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp644-651

Abstract

In this paper, a fiber optic sensor was designed and implemented to detect the change in refractive index of sodium chloride salt solution based on the local surface plasmon resonance (LSPR) phenomenon. This sensor was manufactured using a plastic optical fiber (POF), this optical fiber was bent in a U-shape with 0.5cm bending diameter, and then the cladding and part from core of the fiber were removed by polishing at the sensor head to become as a D-shape in cross section. The sensor was coated with 30 nm thickness of gold nano particles (GNPs) by DC plasms coating technology and it was tested with sodium chloride solution, the detection sensitivity was 466.66 nm/RIU. To enhancement the sensitivity, the latest sensor was coated with 20nm thickness of graphene nano material and retested with same samples of sodium chloride solutions. It was found that graphene improved the sensitivity by an excellent amount, where shift in wavelength was 20nm and highest sensitivity obtained was 666.666 nm/RIU.
An extensible framework for recurrent breast cancer prognosis using deep learning techniques Reddy Shiva Shankar; Ravi Swaroop Chigurupati; Priyadarshini Voosala; Neelima Pilli
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp931-941

Abstract

Due to population growth, early illness detection is getting more challenging. Breast cancer is the second-deadliest malignancy. An estimated one million people are newly diagnosed with the disease annually in India. Most cases are never diagnosed because they are either ignored or not reported. Also, secondary malignancies may develop after a breast cancer recurrence, including those of the brain, lungs, and bones. Early detection and treatment of people with recurring breast cancer may help prevent secondary cancers and other disorders. By examining cell and tumour data as well as data from other diseases, this project hopes to overcome this obstacle and more accurately diagnose breast cancer. Accurate diagnosis of breast cancer may be achieved with the use of machine learning techniques. The effort focuses on recurring breast cancer and aims to efficiently identify it. In ensemble learning, decision trees filter out non-essential qualities. Cancer recurrences and non-recurrences are distinguished using voting classifiers. The soft voting classifier classifies a variety of data sets with 98.24% accuracy. The proposed model has an accuracy of 0.97, a recall of 0.97, an F1-Score of 0.969, and a Choen kappa score of 0.9655, as stated by the recommended model.
Simulation model of proportional integral controller-PWM DC-DC power converter for DC motor using MATLAB Salam Waley Shneen; Ahlam Luaibi Shuraiji; Kassim Rasheed Hameed
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp725-734

Abstract

Smoothly speed range changing, easily speed controlling, and swiftly dynamic response for load torque changing are the main merits which are delivered by direct current (DC) motors. They are also distinguished by their versatility. All these characteristics make the DC motors suitable candidates for various applications. An accurate high-speed control with a good dynamic response, would be of demand for many applications of the DC motors. Controlling the speed of motors using conventional systems is one of the most important method that is adopted and it can be more efficient when used with electronic power devices to control the output voltage. Hence, this paper introduces an efficient proportional integral (PI) speed controller for DC motor fed by direct current-direct current (DC-DC) convertor, which is switched by pulse-width modulation technique. MATLAB/Simulink environment is used to build the whole system. Two operation scenarios have been conducted including constant load with variable speed and variable load with constant speed.
Characterization of tin selenide nanoparticle films generating from plasma arc penetration of a temperature-varying field Hakima A. Abdulla; Nadheer Jassim Mohammed; Aseel Mustafa Abdul Majeed
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp678-686

Abstract

Using the pulsed laser deposition (PLD) method, tin selenide (SnSe) nanoparticles thin films are created on quartz substrates, which are held at different penetration field temperatures Tp-f (Tp-f is the penetration field temperature to which the plasma arc is exposed) (300, 373, 473, and 573) K. X-ray powder diffraction (XRD) reveals a phase transformation from a hexagonal to an orthorhombic structure. The energy gap, which ranges from 1.748-3.15 eV with direct electronic transmission, is calculated using transmittance spectra. Particle size increases by Tp-f increases. Photoluminescence (PL) intensity and film thickness are inversely proportional to each other. Changing the ratio of the compositions provides an essential strategy for altering a material's melting point as well as its energy gap.
Low-cost battery monitoring circuit for a photovoltaic system based on LoRa/LoRaWAN network Zena Ez Dallalbashi; Shaymaa Alhayalir; Mohannad Jabbar Mnati; Alhan Abd-aljbar Alhayali
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp669-677

Abstract

In this paper, an inexpensive electronic circuit will be designed to monitor the cells of battery cells’ voltage in a Lithium-ion bato monitors paper, we will use three battery packs made up of lithium cells connected in a series, and we will test the individual cell voltages, design a simple circuit with op-amps, and send the results via long-range (LoRa) technology to an Arduino base station, where they will be displayed on an liquid crystal display (LCD) screen. This paper deals with the development of a photovoltaic (PV) system battery performance-monitoring unit. This will be done by using an Arduino and long-range wide area networking (LoRaWAN) as a control and monitoring system. The measurement values and data will be sent to a personal station by using an RFM95 LoRa module (as a Universal Asynchronous unit) and the data can be visualized in LCD. One of the parameters of the battery monitoring system is the voltage of the PV system. The goal of the project is to design a new circuit for monitoring the charging condition, discharge depth, and ampere-hour (AH) of the battery, and this all will be analyzed to prove the performance of the battery in the independent photovoltaic system.
Preliminary study on agarwood essential oil and its classification techniques using machine learning Anis Hazirah 'Izzati Hasnu Al-Hadi; Aqib Fawwaz Mohd Amidon; Siti Mariatul Hazwa Mohd Huzir; Nurlaila Ismail; Zakiah Mohd Yusoff; Saiful Nizam Tajuddin; Mohd Nasir Taib
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp753-760

Abstract

Using essential oils derived from trees for pharmaceutical purposes, incense, aromatherapy, and other areas has expanded its popularity on the international market. However, since human sensory evaluation is still the primary technique used to grade essential oils in Malaysia, the classification technique for determining their grade is still below standard. Nonetheless, prior studies established new approaches for classifying the grade of essential oils by studying their chemical compounds. Therefore, agarwood essential oil was selected for the suggested model due to the increasing demand and the high cost of the world's natural raw materials. The support vector machine (SVM) using one versus all (OVA) approach was selected as the classifier for agarwood essential oil. This study provides an overview of essential oils and their prior research techniques. In addition, a review of SVM is conducted to demonstrate that the technique is appropriate for future studies.
A parallel algorithm of multiple face detection on multi-core system Mohammed Wajid Al-Neama; Abeer A. Mohamad Alshiha; Mustafa Ghanem Saeed
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp1166-1173

Abstract

This work offers a graphics processing unit (GPU)-based system for real-time face recognition, which can detect and identify faces with high accuracy. This work created and implemented novel parallel strategies for image integral, computation scan window processing, and classifier amplification and correction as part of the face identification phase of the Viola-Jones cascade classifier. Also, the algorithm and parallelized a portion of the testing step during the facial recognition stage were experimented with. The suggested approach significantly improves existing facial recognition methods by enhancing the performance of two crucial components. The experimental findings show that the proposed method, when implemented on an NVidia GTX 570 graphics card, outperforms the typical CPU program by a factor of 19.72 in the detection phase and 1573 in the recognition phase, with only 2000 images trained and 40 images tested. The recognition rate will plateau when the hardware's capabilities are maxed out. This demonstrates that the suggested method works well in real-time.
Human ear print recognition based on fusion of difference theoretic texture and gradient direction pattern features Kawther Thabt Saleh; Raniah Ali Mustafa; Haitham Salman Chyad
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp1017-1029

Abstract

Human ear recognition can be defined as a branch of biometrics that uses images of the ears to identify people. This paper provides a new ear print recognition approach depending on the combination of gradient direction pattern (GDP2) and difference theoretic texture features (DTTF) features. The region of interest (ROI), the gray scale of the ear print was cut off, noise removal by the median filter, histogram equalization, and local normalization (LN) are the first steps in this approach. After the image has been processed, it is used as input for the fusion of GDP2 and DTTF for extracting the features of ear print images. Lastly, the Gaussian distribution (GD) was utilized to compute the distance among fusion feature vectors (FV) for ear print images for recognizing ear print images for people using a set of images that had been trained and tested. The unconstrained ear recognition challenge (UERC) database, which comprises 330 subjects for ear print images, provides the approach that was suggested by employing ear print databases. Furthermore, experimental results on images from a benchmark dataset reveal that statistical-rely super-resolution methods outperform other algorithms in ear recognition accuracy, which was around 93.70% in this case.
Airlines fleet assignment prediction model for new flights using deep neural network Abdallah A. Abouzeid; Mostafa Mohei Eldin; Mohammed Abdel Razek
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp973-980

Abstract

Airline fleet assignment is the process of allocating different types of aircraft to different scheduled flight legs in order to reduce operating costs and increase revenue. In this research, flights data records from Egypt Air airlines was employed to build an intelligent fleet assignment model to predict the optimal fleet type for new flights. Deep neural network (DNN) and support vector machines (SVM) was used for model formulations. We evaluated the performance of models on a fleet type prediction. The research results showed that various accuracy levels of fleet type multiclass classifications were attained by the models. In terms of accuracy, the deep neural network performed better than support vector machines. Besides, airline companies can use our proposed model for fleet type prediction for new flight with desired parameter values 5, 20 and 250 for hidden layers, number of neuron and number of epochs respectively if they use the same structure for data attributes.

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