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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 9,174 Documents
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Kidney stones detection based on deep learning and discrete wavelet transform Fouad Shaker Tahir; Asma Abdulelah Abdulrahman
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1829-1838

Abstract

The problem of the research is to find medical images of purity, high quality and free of impurities, which contributes to enabling doctors to obtain the results of analyzing the health status of each patient according to his disease data. Therefore, it was necessary to use discrete first chebysheve wavelets transform (DFCWT) technique in order to remove the associated impurities that appear in the medical images, and then analyze the results for all of the above, the algorithm DFCWT has been combined with and linking it to a neural network based on convolutional neural network (CNN) and this contributes to obtaining the results of analyzing image data with high accuracy and speed. The new algorithm proposed in this paper is based on deep learning finding the identification of kidney stones using DFCWT and the same process can be repeated on skin cancer, bones and fractures, processing by discrete first chebyshev wavelet transformation convolution neural network (DFCWTCNN).
Purchasing planning for pharmaceuticals inventory: a case study of drug warehouse in hospital Praphan Yawara; Naratip Supattananon; Pinpicha Siwapornrak; Raknoi Akarrungruangkul
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1496-1506

Abstract

Lack of purchasing planning and proper demand forecasting causes hospitals to suffer from drug inventory mismatches with actual demand; in other words, the inventory management cost is high if the quantity exceeds or less the demand. Therefore, this research aimed to plan an appropriate inventory purchase to reduce inventory costs and effectively meet the hospital's pharmaceutical inventory needs in a case study: i) demand forecasting for 29 AV drugs using Minitab 19, ii) economic order quantity (EOQ) and Newsboy form when drug demand is stable and non-steady, respectively, and iii) design a ready-made program using Excel program to help control, make purchase decisions and be easy to use. There were 5 forecasting methods used. Each drug forecasting method was selected from the one with the slightest error. Twenty-four drugs and five drugs were determined using EOQ and Newsboy forms for re-order point (ROP), safety stock (SS), and total costs. The total cost of drug inventory management per year was 1,780,336.98 baht; compared with the current method, it reduced the cost by 506,569.10 baht per year or a 22.15% reduction.
Leukemia detection system using convolutional neural networks by means of microscopic pictures Pathan Mohd Shafi; Vijaykumar Bidve; Haribhau Bhapkar; Prashant Dhotre; Veer Bhadra Pratap Singh
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1616-1623

Abstract

All over the world, there are a significant number of patients suffering every year from blood cancer. Most of the people are unaware of the risk involved in such a disease. A majority of these diseases are dangerous and may cause death. The patient who have been diagnosed with such a disease, feels very afraid. The patient may feel that the disease is very uncontrolled. Such diseases are very uncommon, and the patient may get very less assistance and information available about this disease. This symptom is called as acute lymphocytic leukemia (ALL) in medical science. In such a kind of cancer, white blood cells are mostly affected. In case of children, this disease is mainly detected i.e. children are more prone to this disease. If the disease is diagnosed in the early stage, the chances of recovery are maximum. Hence, there should be an accurate and guaranteed mechanism available to detect such type of blood cancers in the patients. This work proposes a system to distinguish the three different types of ALL using a convolutional neural network (CNN) by means of microscopic pictures of peripheral blood smears (PBS) and obtain accuracy levels that surpass those of practicing physicians.
Enhancing attendance tracking using animated QR codes: a case study Mustafa Saad Mohammed; Khamis A. Zidan
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1716-1723

Abstract

This research paper explores the effectiveness of quick response (QR) code-based attendance systems with the added security measure of generating two QR codes per second. With traditional attendance tracking methods being time-consuming and inefficient, QR codes have become increasingly popular as a quick and efficient alternative. However, one concern with QR code-based attendance systems is the potential for fraud and misuse. To address this issue, this study proposes generating two QR codes per second to ensure that only the current and legitimate QR code is recognized. The purpose of this study is to assess the impact of this technology on student attendance rates, the accuracy and reliability of attendance data, and the overall user experience for both students and instructors. Through data analysis and surveys, we found that the use of QR codes with the added security measure resulted in increased student attendance rates, improved accuracy and reliability of attendance data, and a positive user experience for both students and instructors. This research provides practical insights for educational institutions considering the implementation of QR code-based attendance systems and contributes to the growing body of literature on the use of QR codes in education.
Automated decision classification model for tax appeals commission in Morocco using latent dirichlet allocation Soufiane Aouichaty; Yassine Maleh; Abdelmajid Hajami; Hakim Allali
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1811-1820

Abstract

This research paper focuses on extracting and classifying information from the Moroccan National Tax Appeals Commission, which is presently nonexistent in the country's legal and tax landscape. This study examines 201 decisions selected from a pool of 562, released between 1999 and 2018, pertaining to corporate tax and involving 550 disputes spanning various corporate tax classifications. The paper aims to propose latent dirichlet allocation (LDA) for topic modeling and compare it with our previous results obtained from the bidirectional encoder representations from transformers (BERT) model. The findings suggest that the rulings can be classified into two primary classifications: those that uphold or reject the tax administration's position. The proposed model shows a good performance, achieving a precision of 9.25% and an accuracy of 9.51%. This highlights the effectiveness of both LDA and BERT models for understanding and classifying topics in tax decision analysis.
Identification of medicinal plant using hybrid transfer learning technique Sukanta Ghosh; Amar Singh; Shakti Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1605-1615

Abstract

Ayurveda is one of the oldest holistic treatment systems in the world. Finding an accurate and correct plant is the key to the working of Ayurvedic treatment. Identification of medicinal plants is a tedious job due to the look-alike feature and availability issue of medicinal plants with other plants. This paper emphases on the ideal identification and classification of plants of medicinal use using deep learning approaches. Previously researchers have used traditional machine learning techniques to identify medicinal plants, which lead to mixed results. Such results are good but not enough as the identification of medicinal plants may lead to a worsening situation for patients. This research is conducted to get results closer to an ayurvedic expert. The dataset used for this research has been taken from Mendeley Data. The dataset comprises 30 different species of medicinal plants. Hybrid transfer learning has been applied to this dataset. The model has generated a test accuracy of 95.25% which is better than the other popular transfer learning techniques.
Implementing and developing secure low-cost long-range system using speech signal processing Samer Alabed; Amer Alsaraira; Nour Mostafa; Mohammad Al-Rabayah; Yehia Kotb; Omar A. Saraereh
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1408-1419

Abstract

In the proposed work, we present a secure low-cost speech communication system for long-distance communication. The system utilizes long range (LoRa) technology to transmit speech signals. LoRa technology uses spread-spectrum modulation to enable long-range communication with low power consumption. LoRa modulation allows for data transfer at a slow speed, typically below 22 kbps, which makes it infeasible for transmitting speech. To address this limitation, we suggest a speech coding technique that reduces the overall data rate of speech signals to below 7.5 kbps. This lower rate is more compatible with the LoRa module and ideal for transmitting speech. Moreover, this technique can improve the LoRa transmission range. Additionally, we have developed an encryption-decryption method to ensure the privacy of the messages and prevent unauthorized access by third parties.
Investigation into facial expression recognition methods: a review Ajaykumar Devarapalli; Jora M. Gonda
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1754-1762

Abstract

Facial expression recognition (FER) is a rapidly emerging topic in computer vision that has gotten a lot of interest because of its numerous applications in fields including psychology, sociology, human-computer interaction (HCI), and security. FER seeks to recognise and analyse human facial expressions in order to determine emotions and other mental states. Several strategies, including feature-based, kernel-based, and deep learning-based methods, have been developed and implemented in FER in recent years. FER’s major goal is to extract and identify the most discriminating elements that accurately represent the emotions expressed by facial expressions. The literature reviewed in this field shows that deep learning-based methods have outperformed traditional feature-based and kernel-based methods in terms of accuracy and robustness in recognizing facial expressions. However, these deep learning-based methods also pose several challenges, such as the need for large labeled-data-sets, robustness to different facial poses and illumination conditions, and generalization to unseen data. Despite these challenges, the field of FER is expected to continue growing, and future research will likely focus on addressing these challenges and improving the accuracy and robustness of FER systems.
New conception of 3×4 circularly polarized antenna patch network for RF energy harvesting at 2.45 GHz Walid En-Naghma; Hanan Halaq; Abdelghani El Ougli
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1451-1463

Abstract

This paper presents a developed design of a 3×4 circular polarization microstrip antenna patch array whose its resonance frequency is 2.45 GHz in the ISM band. This array is printed on a FR4 substrate of 1.58 mm thickness with a dielectric permittivity of 4.4. The basic antenna patch is a square shape inserted by a rectangular slot inclined positioned diagonally at the center to achieve circular polarization. In addition, a triangular-shaped slit is added at each of the four corners of the patch. The antenna array performance is tested numerically using two different software packages, CST MWS and HFSS. The antenna’s array size is compacted to a 22.7 cm×13.5 cm. The simulated results give good performance results in terms of return loss and gain; directivity is found to be 9.66 dBi at 2.45 GHz; the axial ratio value is 1.69 dB; and total efficiency is about 95.55% at 2.45 GHz. Then, the simulated results obtained by these two programs are in good agreement, which makes this proposed array design very suitable for radio frequency energy harvesting and its various applications to power supply different devices in a clean way for our environment.
Conceptualising flood warning system for connected vehicles Feu Che Sung; Siti Fatimah Abdul Razak; Sumendra Yogarayan; Mohd. Fikri Azli Abdullah; Noor Hisham Kamis; Afizan Azman
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1684-1695

Abstract

Floods are a common natural hazard in Malaysia during the monsoon season. It affects millions of people each year that leads to severe deaths and infrastructure destruction. In recent time, flood warning system (FWS) has been a notable topic but it has not been extensively implemented in Malaysia. In this study, we developed a FWS that can interface with connected vehicles in order to provide alerts to drivers while also sending warnings to end users. This type of FWS enables vehicles to connect with one another within a particular radius to broadcast flood information via long range (LoRa) communication technology. When the water level rises over a certain point, the system sends a warning to drivers indicated through a mobile application. Drivers have the option to take alternative route, reducing the likelihood of damage when driving into or near a flooded area. The developed application demonstrates that the warning was able to be instantly displayed to the driver if there is a significant increase in water level. Experimental evidence shows that the driver was able to receive the water level alert and a visual interpretation of the immediate area affected by flood through the application.

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