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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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Semantic segmentation and thermal imaging for forest fires detection and monitoring by drones Yandouzi, Mimoun; Berrahal, Mohammed; Grari, Mounir; Boukabous, Mohammed; Moussaoui, Omar; Azizi, Mostafa; Ghoumid, Kamal; Kerkour Elmiad, Aissa
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.v13i4.7663

Abstract

Forest ecosystems play a crucial role in providing a wide range of ecological, social, and economic benefits. However, the increasing frequency and severity of forest fires pose a significant threat to the sustainability of forests and their functions, highlighting the need for early detection and swift action to mitigate damage. The combination of drones and artificial intelligence, particularly deep learning, proves to be a cost-effective solution for accurately and efficiently detecting forest fires in real-time. Deep learning-based image segmentation models can not only be employed for forest fire detection but also play a vital role in damage assessment and support reforestation efforts. Furthermore, the integration of thermal cameras on drones can significantly enhance the sensitivity in forest fire detection. This study undertakes an in-depth analysis of recent advancements in deep learning-based semantic segmentation, with a particular focus on model’s mask region convolutional neural network (Mask R-CNN) and you only look once (YOLO) v5, v7, and v8 variants. Emphasis is placed on their suitability for forest fire monitoring using drones equipped with RGB and/or thermal cameras. The conducted experiments have yielded encouraging outcomes across various metrics, underscoring its significance as an invaluable asset for both fire detection and continuous monitoring endeavors.
Quasi resonant topology based highly efficient solar-powered induction cooker Ahmad, Shameem; Awalin, Lilik Jamilatul; Nahid Hasan, Sheikh Md.; Saha, Arghya; Ali, Mohd Syukri; Syafiq, Amirul; Wang, Li
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.v13i6.7925

Abstract

The energy crisis is a major issue in developing countries, with fossil fuels being the main source of cooking. Induction cookers have received attention due to their safe operation and eco-friendliness, but traditional AC induction cookers are costly and inefficient due to an inverter and rectifier. In this regard, this paper aims to model and develop a solar-powered, low-cost, and highly efficient induction cooker that can be operated directly by solar panels through a battery. By utilizing the solar panels’ maximum output, a maximum power point tracking (MPPT)-based solar power controller has been utilized to charge the battery. A modified coil structure for the cooker is proposed to decrease the coil’s excitation time and increase the resonant frequency. A quasi-resonant converter topology has been used in the proposed induction cooker, as it operates at high frequencies above 20 kHz to avert audible noise and below 100 kHz to minimize losses in switching. The performance of the suggested induction cooktop has been validated by modifying the circuit and the coil of a traditional 220 V, 2 kW induction cooker. Based on the outcomes, it is observed that the efficiency of the proposed induction cooker reached 93%, which is better than that of existing induction cookers.
A novel particle swarm optimization-based intelligence link prediction algorithm in real world networks Choudhury, Deepjyoti; Acharjee, Tapodhir
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6761

Abstract

Link prediction in social network is an important topic due to its applications like finding collaborations and recommending friends. Among existing link prediction methods, similarity-based approaches are found to be most effective since they examine the number of common neighbours (CN). Current work presents a novel link prediction algorithm based on particle swarm optimization (PSO) and implemented on four real world datasets namely, Zachary’s karate club (ZKC), bottlenose dolphin network (BDN), college football network (CFN) and Krebs’ books on American politics (KBAP). It consists of three experiments: i) to find the measures on existing methods and compare them with our proposed algorithm; ii) to find the measured values of the existing methods along with our proposed one to determine future links among nodes that have no CN; and iii) to find the measures of the methods to determine future links among nodes having same number of CN. In experiment 1, our proposed approach achieved 75.88%, 78.34%, 82.63% and 78.36% accuracy for ZKC, BDN, CFN, and KBAP respectively. These results beat the performances of traditional algorithms. In experiment 2, the accuracies are found as 75.53%, 74.25%, 81.63% and 78.34% respectively. In experiment 3, accuracies are detected as 72.75%, 81.53%, 78.35% and 75.13% respectively.
A novel approach to analyzing the impact of AI, ChatGPT, and chatbot on education using machine learning algorithms Hasan, Nahid; Polin, Johora Akter; Ahmmed, Md. Rayhan; Sakib, Md. Mamun; Jahin, Md. Farhan; Rahman, Md. Mahfuzur
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.v13i4.7158

Abstract

Artificial intelligence (AI) is one of the most common and essential technologies in this modern era, especially in the education and research sectors. It mimics machine-processed human intellect. In modern times, ChatGPT is one of the most effective and beneficial tools developed by OpenAI. Provides prompt answers and feedback to help academics and researchers. Using ChatGPT has various advantages, including improving methods of instruction, preparing interactive lessons, assessment, and advanced problem-solving. Threats against ChatGPT, however, include diminishing creativity, and analytical thinking. Additionally, students would adopt unfair procedures when submitting any tests or assignments online, which would increase their dependency on AI systems rather than thinking analytically. In this study, we have demonstrated arguments on both sides of AI technology. We believe that our study would provide a depth of knowledge and more informed discussion. Data is collected via an offline platform and then machine learning algorithms such as K-nearest neighbour (K-NN), support vector machine (SVM), naive bayes (NB), decision tree (DT), and random forest (RF) are used to analyze the data which helps to improve teaching and learning techniques where SVM shows best performance. The results of the study would offer several significant learning and research directions as well as ensure safe and responsible adoption.
Evaluation of structural failure resistance of glass fiber reinforced concrete beams Getachew Chikol, Yilachew; Assegie, Tsehay Admassu; Mohmmad, Shaimaa Hadi; Salau, Ayodeji Olalekan; Yanhui, Liu; Braide, Sepiribo Lucky
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.v13i1.6620

Abstract

Glass fiber reinforced concrete (GFRC) is a composite material that is widely used in construction due to its high strength and durability. GFRC is made by adding glass fibers to the concrete mix, which increases the tensile strength of the material. This paper evlautes the shear resistance (SR) of sliced glass fiber (30 mm) GFRC beams. The shear resistance of GFRC beams can be significantly improved by adding glass fibers to the concrete mix. However, further research is needed to fully understand the shear behavior of GFRC and to optimize its design for maximum shear resistance. The result indicates that shear fracture glass fiber is a better alternative for increasing a shear resistance input mechanism.
Bidirectional recommendation in HR analytics through text summarization Arandi, Channabasamma; Yeresime, Suresh
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.v13i2.5650

Abstract

For over a decade, online job portals have been providing their services to both job seekers and employers in search of hiring opportunities. Because of the high demand for recruitment, it is insufficient to use conventional hiring methods to find a suitable candidate to fill the position. Validating resumes online is challenging due to the potential for manual errors, making the process inherently risky. The bidirectional method comprises named entity recognition (NER) for extracting the required resumes for recruiters. Cosine similarity shows the match percentage of resumes for the job requirements and vice versa. In an attempt to tackle an issue of unregistered words, a solution called decoder attention with pointer network (DA-PN) has been introduced. This method incorporates the use of coverage mechanism to prevent word repetition through generated text summary. DA-PN+Cover method with mixed learning objective (MLO) (DA-PN+Cover+MLO) is utilized for protecting grow of increasing faults in generated text summary. Performance of proposed method is estimated using evaluation indicator recall oriented understudy for gisting evaluation (ROUGE) and attains an average of 27.47 which is comparatively higher than existing methods.
An intelligent obfuscated mobile malware detection using deep supervised learning algorithms Ganapathi, Padmavathi; Arumugam, Roshni; Dhathathri, Shanmugapriya
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.v13i4.6974

Abstract

Obfuscated mobile malware (OMM) is a malicious software in mobile that hides to avoid detection and annihilation. These types of malwares are thorny to identify due to their inevitable nature. Deep learning (DL) algorithms are the most desirable to detect obfuscated malware based on the ‘n’ number of iterations with adjustable weights and neurons. This study investigates the accurate detection of OMM using significant DL algorithms such as multi-layer perceptron (MLP), self-organizing maps (SOM), long short-term memory (LSTM) networks, auto encoders (AE), and convolutional neural network (CNN) based on appropriate parameter tuning. The dataset taken for the study is CICMalMem2022 that contains 58,596 samples with 57 features which is basically designed for OMM detection. The dataset comprises Spyware, Ransomware, Trojan horse, and Benign. The DL models are evaluated based on performance metrics such as precision, recall, accuracy, training accuracy, test accuracy, validation accuracy, training loss, validation loss and receiver operating characteristic (ROC) curve. Based on the experimental evaluation, the study reveals that LSTM outperforms with 100% accuracy and MLP achieves 99.9% accuracy in detecting and classifying the OMM using deep supervised learning (SL) mechanism.
A frequency adaptive multiple complex coefficient filter for grid connected applications Boumediene, Bachir; Araria, Rabah; Chedjara, Zakaria; Mekhloufi, Belkacem; Bey, Mohamed; Wira, Patrice
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.v13i6.7245

Abstract

In the realm of synchronization techniques, the dichotomy between open loops (OLSs) and closed loops (CLSs) presents a perennial challenge: how to enhance dynamic performance without sacrificing stability and disturbance rejection. While OLS techniques offer rapid dynamic response and unwavering stability, they often falter in non-nominal frequency scenarios. Conversely, CLS techniques grapple with the delicate balance of bolstering dynamic performance while maintaining robust stability. To surmount these obstacles, this study proposes an innovative approach: the integration of a frequency locked loop (FLL) as a secondary frequency detector within synchronization structures, coupled with the multiple-complex coefficient-filter (MCCF). This amalgamation bestows notable advantages. Firstly, from a control perspective, the resultant synchronization technique resembles a quasi-OLS, obviating the need for intricate stability analyses. Moreover, it exhibits commendable disturbances rejection alongside swift dynamic response. Through comprehensive simulation, our proposed technique showcases superiority over existing counterparts, evidencing enhanced settling time, disturbances rejection, and efficiency in the face of frequency drifts.
Empowering customer satisfaction chatbot using deep learning and sentiment analysis Merizig, Abdelhak; Belouaar, Houcine; Mghezzi Bakhouche, Mohamed; Kazar, Okba
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6966

Abstract

The rapid advancement of technology holds great promise for various types of users, clients, or service providers. Intelligent robots, whether virtual or physical, can simplify the reservation process. With the development of machines and processing tools, natural language processing (NLP) and natural language understanding (NLU) have emerged to help people comprehend spoken language through machines. In order to facilitate seamless human-machine interaction, we aim to address customer needs through a chatbot. The objective of this paper is to incorporate sentiment analysis techniques with deep learning algorithms to cater to customers’ needs during message exchanges. This study aims to create an intelligent chatbot to engage customers during their routine operations and offer support. In addition, it offers to companies a manner to detect sarcastic messages. The proposed chatbot utilizes deep learning techniques to predict users’ intentions based on the questions asked and provide a helpful and convenient answer. A new chatbot for the customer is presented to overcome with challenges related to a wrong statement like sarcastic one and feedback towards user messages. A comparison between deep and transfer learning gives a new insight to include sentiments and sarcasm detection in the conversion process.
A flexible paper based strain sensors drawn by pencil for low-cost pressure sensing applications Mat Nawi, Mohd Norzaidi; Ho Lau, Jim Tze
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.v13i4.8001

Abstract

Paper-based strain sensors, offering a cost-effective and environmentally friendly solution, are in demand for pressure sensing applications. Here, we present a simple sensor design comprising a piece of paper, a graphite pencil, and a copper plate. The proposed fabrication process is simple and eco-friendly. Beyond design and fabrication, our study explores the performance of paper-based sensors in effectively measuring and monitoring pressure changes induced by varying deflection angles. Our findings show that as the deflection angle increases, the sensor exhibits a proportional increase in the relative change in resistance. Furthermore, the practical applicability of the fabricated sensor is demonstrated through real-world testing on a human finger, considering different positions. In essence, our research positions paper-based strain sensors as a promising and practical choice for affordable, eco-friendly, and responsive pressure sensing.

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