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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 2,901 Documents
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Comparative analysis of machine learning approaches in Kazakh banknote classification Sadyk, Ualikhan; Yerzhan, Makhambet; Baimukashev, Rashid; Turan, Cemil
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.8004

Abstract

Nowadays, smartphones seamlessly blend into every aspect of our lives, including as handheld assistants for individuals with disabilities. Therefore, this research addresses the need for a robust system that can classify Kazakh banknotes. By capitalizing on the availability of smartphones and the ability to integrate detectors with classifiers this study introduces classifiers of Kazakh banknote images specifically designed for banknotes ranging from 500 KZT to 20,000 KZT. It compares traditional and hybrid machine learning (ML) approaches, utilizing a dataset of diverse banknote images, aiming for both lightweight and high accuracy. Competitive performance is demonstrated by the traditional approach, enhanced by thoughtful feature engineering. The hybrid approach, utilizing features from a pre-trained ResNet-18 model, showcases remarkable accuracy and robustness. Evaluation metrics reveal significant achievements, with the traditional approach attaining 94.00% accuracy and the hybrid approach excelling at 99.11%. Model stacking, combining classifiers from both approaches, outperforms individual classifiers, achieving 95.00% and 99.55% accuracy for the traditional and hybrid ML approaches, respectively. Our methodology’s comparable outcome in classifying Thai banknotes and coffee beans roasting levels demonstrates their versatility in image classification tasks that rely on color differentiation, showcasing the potential beyond banknote recognition.
Ensemble learning classifiers hybrid feature selection for enhancing performance of intrusion detection system Ali Al Essa, Hasanain; S. Bhaya, Wesam
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5844

Abstract

Feature selection (FS) plays an important role in the construction of efficient ensemble classifiers; particularly for intrusion detection system (IDS). An IDS is a utilized in a network architecture to protect the availability of sensitive information. However, existing IDSs suffer from redundancy, high dimensionality, and high false alarm rate (FAR). Also, lots of models are constructed for outdated datasets, which makes them less flexible to deal with new assaults. Therefore, this paper proposes a new IDS relies on hybrid FS and ensemble classifiers. A hybrid FS approach consists of two techniques, hard-voting and mean. In contrast to recent papers, we use three different FS approaches: extra tree classifier importance as an embedded FS, recursive feature elimination (RFE) as a wrapper FS, and mutual information (MI) as a filter FS. Then, a hard-voting technique has been used to fuse output of these approaches and obtain a reduced subset of features. Since each feature has three weights, a mean technique has been utilized to assign one weight to each feature and obtain an optimal subset of features. The experimental outcomes, utilizing the modern InSDN dataset, confirm that the proposed hybrid FS with ensemble soft voting classifier achieves better results than other ensemble and individual classifiers due to several measures.
Intelligent agriculture system using low energy and based on the use of the internet of things Elhattab, Kamal; Abouelmehdi, Karim
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6346

Abstract

The field of smart agriculture is ranked among the top areas that uses the internet of things (IoT), whose goal is to increase the quantity and quality of agricultural productivity. The aim of this work is to realize a new device that will be cost-effective, reliable, and autonomous using a solar panel to provide electricity in large-scale agricultural fields, ESP32 to interconnect IoT sensors and the long range (LoRa) data transmission protocol to guarantee connectivity in places where there is no internet, whose objective is to monitor and irrigate agricultural fields only when there is a need for water. The data received by the sensors is sent to mobile app users via the Blynk cloud. The performance of our new approach is measured in terms of energy savings. This new model of irrigation and smart monitoring will improve the efficiency of farming techniques.
Security-based low-density parity check encoder for 5G communication Rajangam, Balamurugan; Alagarsamy, Manjunathan; Radhakrishnan, Chirakkal Rathish; Assegie, Tsehay Admassu; Salau, Ayodeji Olalekan; Quansah, Andrew; Chowdhury, Nur Mohammad; Chowdhury, Ismatul Jannat
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7019

Abstract

The fifth generation (5G) of mobile telecommunication standards is intended to offer better performance and efficiency. One of the most significant difficulties in delivering safe data transfer through the transmission channel in the emerging 5G technology is channel-coding security. This research primarily focused on offering information transmission that is secure in the place of novel assaults such as side-channel attacks. In this research, we present a low-density parity check (LDPC) encoder that is constructed using the multiplicative masking method to overcome side-channel attacks, one of the most significant security concerns for the upcoming 5G system. As a result, it offers greater security, reduced complexity, and higher performance. Power, area, and delay can all be calculated using LDPC codes. Comparing multiplicative masking implemented LDPC encoders to ordinary channel coding techniques in terms of security seen that multiplicative masking implemented LDPC encoders offer more security. The program Xilinx ISE 14.7 can synthesize the analysis. The advantage of multiplicative masking is that it offers a promising level of security through the principle of randomization, which is the foundation of the procedure. According to the analysis, the secured transmission of the data by the proposed multiplicative masking implemented LDPC encoder is increased.
Design optimization and trajectory planning of a strawberry harvesting manipulator Saoud, Inas; Jaafari, Hatim Idriss; Chahboun, Asaad; Raissouni, Naoufal; Achhab, Nizar Ben; Azyat, Abdelilah
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7957

Abstract

This paper presents a systematic approach to optimizing the structural parameters of a 4-degree-of-freedom (DoF) strawberry harvesting manipulator to minimize its workspace. Unlike previous research that primarily concentrated on the spatial needs related to fruit distribution areas, this work addresses the spatial dynamics of different stages of the fruit-picking process. This is achieved by combining the workspace model method, mathematical modeling, and the GlobalSearch algorithm in the optimization process. A comprehensive verification was conducted using the Denavit-Hartenberg method to simulate the workspace of the optimal manipulator structure. This ensured that the manipulator effectively covered the entire harvesting space. The research design involves exploring an optimal trajectory planning method by adopting a modified sine jerk profile that minimizes overall trajectory duration while maintaining good smoothness. The effectiveness of this method is demonstrated through a simulation of the trajectory of the four joints to drive the end effector from the initial position to the position of the strawberry. This approach yields execution times up to 27% shorter than in previous studies. The proposed method is useful for optimizing the physical and trajectory design of the harvesting manipulator that operates in confined and restricted environments to enhance efficiency, adaptability, and safety in harvesting operations.
Effective privacy preserving in cloud computing using position aware Merkle tree model Gangadharaiah, Shruthi; Shrinivasacharya, Purohit
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6636

Abstract

In this research manuscript, a new protocol is proposed for predicting the available space in the cloud and verifying the security of stored data. The protocol is utilized for learning the available data, and based on this learning, the available storage space is identified, after which the cloud service providers allow for data storage. The Integrity verification separates the private and the public data, which avoids privacy issues. The integration of the private data is done with the help of cloud service providers with respect to the third-party auditing (TPA). Earlier, public key cryptography and bilinear map technologies have been combined by the researchers, but the computation time and costs were high. To secure the integrity of the data storage, the client should execute several computations. Therefore, this research suggests a reliable and effective method called position-aware Merkle tree (PMT), which is implemented for ensuring data integrity. The proposed system uses a PMT that enables the TPA to perform multiple auditing tasks with high efficiency, less computational cost and computation time. Simulation results clearly shows that the developed PMT method consumed 0.00459 milliseconds of computation time, which is limited when compared to the existing models.
CaO:Tb3+ green-emitting phosphor for white light-emitted diode-phosphor applications: the improvement of light output intensity Tung, Ha Thanh; Nguyen Thi, Dieu An
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.4753

Abstract

The use of CaO:Tb3+ with light green emission on for improvement of both luminescent output and chromatic fidelity of the white light emitted from a light-emitting diode (LED). The CaO:Tb3+ is combined with the yellow-emitting phosphor of YAG:Ce3+ to provide sufficient colored spectral proportion for the white light generation, enhancing the color performance. The phosphor combination is utilized for the three most applied LED structures: conformal, in-cup, and remote phosphor structures. The changes in optical properties of these three LEDs are monitored with adjustments in the proportion of CaO:Tb3+. The higher proportion of the green phosphor results in higher scattering efficiency in all structures, offering better color coordination and stronger luminous flux. The color quality scale is somehow reduced when CaO:Tb3+ concentration is more than a certain level. Therefore, depending on the phosphor configuration of the white light-emitting diode (WLED), the concentration of CaO:Tb3+ should be modified to achieve a good color rendition with improved color consistency and luminous properties.
An interpretable machine learning-based breast cancer classification using XGBoost, SHAP, and LIME Dutta, Monoronjon; Mehedi Hasan, Khondokar Md.; Akter, Alifa; Rahman, Md. Hasibur; Assaduzzaman, Md.
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7866

Abstract

Globally, breast cancer is among the most prevalent and deadly tumors that affect women. Early and accurate identification of breast cancer is essential for effective treatment planning and improving patient outcomes. This research focuses on improving breast cancer classification accuracy through machine learning (ML) methodologies, emphasizing interpretability. The study utilized the chi-square method to enhance model testing performance by pinpointing the most significant features for further analysis. The study also improved data quality by identifying and removing outliers, thus minimizing the influence of data irregularities on the performance of the models. For classification, the study evaluated six different ML algorithms—namely extreme gradient boosting (XGBoost), decision tree (DT), AdaBoost (AB), support vector machine (SVM), gradient boosting (GB), and K-nearest neighbors (KNN)—each applied to distinguish between the two variants of breast cancer. Among these, the XGBoost classifier emerged as the most accurate, achieving an impressive 99.30% accuracy rate. Moreover, the research incorporated shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) methods to boost the interpretability of the proposed model, offering crucial insights into the model’s decision-making process. Applying these interpretability techniques provided significant insights into the predictive factors influencing healthcare outcomes, ensuring the classification approach’s transparency and reliability.
Challenges in implementing free software in small and medium-sized enterprises in the city of Montería: a case study Baena-Navarro, Rubén; Vergara-Villadiego, Juan; Carriazo-Regino, Yulieth; Crawford-Vidal, Richard; Barreiro-Pinto, Francisco
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6710

Abstract

This study investigates challenges and opportunities in adopting free and open-source software (FOSS) in small and medium-sized enterprises (SMEs) in Monteria, Colombia. The research reveals that around 77.5% of SMEs prefer free software, yet surprisingly, 80% are unaware of the benefits of open-source licenses, with nearly 45% not adopting them due to lack of knowledge. Implementing FOSS in SMEs offers legal and economic advantages, including reduced software acquisition costs, compliance with data protection and privacy regulations, and fostering innovation. However, adoption barriers persist, necessitating further research for enhancing implementation in Colombian SMEs. Notably, Colombia's ethical framework for AI serves as a guide for ethical AI and open-source software deployment, aligned with sustainable development goals. This study highlights free software usage prevalence in Monteria's SMEs and critical factors hindering full adoption. Addressing challenges and leveraging potential benefits can improve efficiency, regulatory compliance, and contribute to sustainable development. Continued research in this field can promote broader and stronger implementation of FOSS in Colombian SMEs.
Human blood group type detection prototype focusing on agglutinin using microcontroller based photodiode Lubis, Arif Ridho; Harefa, Hafid Rahman; Al-Khowarizmi, Al-Khowarizmi; Julham, Julham; Lubis, Muharman; Rahmat, Romi Fadillah
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7007

Abstract

Blood is a fluid in the body that mainly serves as a medium for transporting various substances in the body. Detection of human blood group types with this microcontroller utilizes dark and light properties. The dark character appears due to agglomeration, while the light nature arises because of no agglomeration, for this to happen, a liquid reagent is needed. Administration of this liquid uses the aviator's breathing oxygen (ABO) system, which consists of reagent a, reagent b, and reagent c and mixing it with blood on the test paper. The number of blood samples in each reagent is based on blood lancet. Furthermore, the sensors used to detect these properties are photodiode and light emitting diode (LED) each of 3 pieces. The Arduino Uno is used to process sensor input while at the same time producing displayed human blood group type on the display screen. The test is carried out involving 12 blood samples and a medical officer. Medical officer are tasked reading directly the results of mixing between reagents and blood samples, after that are compared with the system. The results show that the deviation of the system reading is 0.167 for the sensor reading distance with the sample as far as 0.5 cm.

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