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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal 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.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6,301 Documents
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Comparison between incremental conductance and perturb and observe algorithms in photovoltaic system under low temperature and irradiation levels Abssane, Sara; Outzourhit, Abdelkader; Amatoul, Fatima Zahra
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp4897-4906

Abstract

This paper compares two mostly used maximum power point tracking (MPPT) methods, perturb and observe (P&O), and incremental conductance (INC), for a photovoltaic (PV) system integrated with a step-up converter and resistive load. The evaluation was conducted using MATLAB/Simulink under two specific environmental conditions: low irradiation and temperature levels. The results indicate that under irradiation levels below 200 W/m², the INC algorithm outperforms P&O by exhibiting minimal fluctuations and achieving higher efficiency. Conversely, under low temperature (below 25 °C) the P&O method reaches the highest efficiency, exceeding 99%. These findings highlight the importance of selecting the appropriate MPPT algorithm based on specific environmental conditions to optimize the energy output of PV systems.
Detecting anomalies in security cameras with 3D-convolutional neural network and convolutional long short-term memory Mahareek, Esraa A.; ElSayed, Eman K.; ElDesouky, Nahed M.; ElDahshan, Kamal A.
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp993-1004

Abstract

This paper presents a novel deep learning-based approach for anomaly detection in surveillance films. A deep network that has been trained to recognize objects and human activity in movies forms the foundation of the suggested approach. In order to detect anomalies in surveillance films, the proposed method combines the strengths of 3D-convolutional neural network (3DCNN) and convolutional long short-term memory (ConvLSTM). From the video frames, the 3DCNN is utilized to extract spatiotemporal features,while ConvLSTM is employed to record temporal relationships between frames. The technique was evaluated on five large-scale datasets from the actual world (UCFCrime, XDViolence, UBIFights, CCTVFights, UCF101) that had both indoor and outdoor video clips as well as synthetic datasets with a range of object shapes, sizes, and behaviors. The results further demonstrate that combining 3DCNN with ConvLSTM can increase precision and reduce false positives, achieving a high accuracy and area under the receiver operating characteristic-area under the curve (ROC-AUC) in both indoor and outdoor scenarios when compared to cuttingedge techniques mentioned in the comparison.
Comparing Mask R-CNN backbone architectures for human detection using thermal imaging Trinh, Tan Dat; Cung Le Thien Vu, Pham; The Bao, Pham
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3962-3970

Abstract

We introduce a method for detecting humans in thermal imaging using an end-to-end deep learning model. Our objective is to optimize the human detection process in thermal imaging by investigating the mask region-based convolutional neural network (Mask R-CNN). The model, an advancement of the faster region-based convolutional neural network (Faster R-CNN), not only captures bounding boxes encompassing human subjects but also delineates segmentation masks around them. Our investigation extends to the evaluation and comparison of various convolutional neural networks for feature learning, like residual network (ResNet) and Inception ResNet, all integrated into the Mask R-CNN framework. Furthermore, the experimental results show that our proposed technique achieves high accuracy. Specifically, the Mask R-CNN model using ResNet50-V1 achieved the best results, with an F-value of 87.85%, a recall of 79.33%, and a precision of 98.41%.
Expert system for diagnosing learning disorders in children Andrade-Arenas, Laberiano; Yactayo-Arias, Cesar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2965-2975

Abstract

Given the urgent need for early detection of learning disorders such as dysgraphia, dyslexia, and dyscalculia in children, this study aimed to evaluate an expert system developed in Python to facilitate early diagnosis of these disorders. The background highlights the importance of providing parents, educators, and health professionals with an effective tool for early detection of these disorders. In 21 simulated cases, the system showed impressive performance with an accuracy rate of 95%, a precision of 100%, a sensitivity of 93%, and a specificity of 100%. Furthermore, the acceptability evaluation, conducted with 15 parents selected by convenience sampling, showed a high level of satisfaction, with an overall mean of 4.78 and a standard deviation of 0.45, indicating consistency in responses. In conclusion, this study confirms the effectiveness of the expert system in the early diagnosis of learning disabilities, providing parents, educators, and health professionals with a valuable tool. Despite these encouraging results, the need for additional research is recognized to address limitations and improve the external validity of the system to ensure its widespread utility and adaptability in real clinical settings.
Revolutionizing brain tumor diagnoses: a ResNet18 and focal loss approach to magnetic resonance imaging-based classification in neuro-oncology Kempanna, Shashi Raj; Rangappa, Aswatha Anoor; Maheshappa, Shruthi; Kumar Siddaraju, Druva; Gowda, Kumar Puttaswamy; Ramachandragowda, Santhosh Kumar; Tagare, Trupti Shripad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6551-6559

Abstract

Brain tumor diagnosis remains a critical challenge in neuro- oncology, where accurate and timely identification of malignancies can significantly impact patient outcomes. This research explores the integration of deep learning techniques, specifically leveraging the ResNet18 architecture coupled with focal loss, to enhance the classification accuracy of magnetic resonance imaging (MRI)-based brain tumor diagnoses. ResNet18, known for its powerful feature extraction capabilities, was employed to analyze MRI scans, while focal loss was utilized to address class imbalance issues prevalent in medical datasets. The model was trained on a comprehensive dataset, achieving an accuracy of 95.54%. These results demonstrate the potential of this approach in providing robust and precise diagnostic support in clinical settings, potentially revolutionizing the current methodologies in brain tumor detection and classification. The integration of advanced neural networks with specialized loss functions presents a significant advancement in the field, paving the way for more reliable and automated neuro-oncological diagnostics.
Development of system for generating questions, answers, distractors using transformers Barlybayev, Alibek; Matkarimov, Bakhyt
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1851-1863

Abstract

The goal of this article is to develop a multiple-choice questions generation system that has a number of advantages, including quick scoring, consistent grading, and a short exam period. To overcome this difficulty, we suggest treating the problem of question creation as a sequence-to-sequence learning problem, where a sentence from a text passage can directly mapped to a question. Our approach is data-driven, which eliminates the need for manual rule implementation. This strategy is more effective and gets rid of potential errors that could result from incorrect human input. Our work on question generation, particularly the usage of the transformer model, has been impacted by recent developments in a number of domains, including neural machine translation, generalization, and picture captioning.
A novel approach to simplified and secure message cryptography using chaotic logistic maps and index keys Al-Ofeishat, Hussein Ahmad; Alkasassbeh, Jawdat S.; Alzyoud, Khalaf Y.; Al-Taweel, Farouq M.; Alrawashdeh, Hisham; Al-Rawashdeh, Ayman Y.
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5139-5152

Abstract

This paper proposes a novel method of message cryptography aiming to provide a simple, secure, and highly efficient approach to encryption and decryption. Unlike existing methods that rely on complex logical operations, our method utilizes simple rearrangement operations, reducing computational complexity while ensuring robust security. It employs a sophisticated, high-entropy private key designed to withstand hacking attempts. This key generates two chaotic keys using chaotic logistic map models, which are sorted to form two index keys essential for rearranging message blocks and characters during encryption and decryption. The process is facilitated by two simple operations, Get_index and Get_min, based on the index keys. These operations achieve streamlined procedures without compromising security. The method's speed is evaluated across various message lengths, demonstrating significant improvements in encryption time and throughput. The comparative analysis highlights the superior efficiency of this method compared to existing methods. Rigorous testing confirms that the proposed method meets stringent quality and sensitivity requirements, ensuring robust cryptographic standards. This innovative approach offers a promising solution for secure message encryption and decryption, combining simplicity, security and efficiency to meet the evolving needs of secure communication systems.
Development of energy conversion and lightning strike protection simulation for photovoltaic-wind turbine on grid Satria, Habib; Mungkin, Moranain; Dayana, Indri; Ramdan, Dadan; Maizana, Dina; Syafii, Syafii
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp66-74

Abstract

Photovoltaic (PV) installations and wind turbines that are installed on the rooftops of buildings need to be protected because the layout is in a high position and there is a risk of being struck by lightning. Therefore, a more effective protection system is designed to anticipate electronic damage and fire on all materials in the distribution network, especially the addition of PV and wind turbine installations on building roofs. The purpose of this study is to simulate a lightning protection system on the distribution network and the results of on-grid PV energy conversion using electrical transient analyzer program (ETAP) software. Feeder relay delay times and cascade coordination patterns between outgoing and incoming relays do not overlap. the delay time of the relay working on the feeder is 0.31 s and the coordination pattern of the outgoing relay and incoming relay does not touch each other, so the delay time for the incoming relay is 2.73 s. Then testing the results of PV energy conversion connected to the grid using MATLAB Simulink monitoring obtained data reaching 1.600 Wp at peak power with sun conditions parallel to the PV installation layout.
Email subjects generation with large language models: GPT-3.5, PaLM 2, and BERT Loukili, Soumaya; Fennan, Abdelhadi; Elaachak, Lotfi
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4655-4663

Abstract

In order to enhance marketing efforts and improve the performance of marketing campaigns, the effectiveness of language generation models needs to be evaluated. This study examines the performance of large language models (LLMs), namely GPT-3.5, PaLM 2, and bidirectional encoder representations from transformers (BERT), in generating email subjects for advertising campaigns. By comparing their results, the authors evaluate the efficacy of these models in enhancing marketing efforts. The objective is to explore how LLMs contribute to creating compelling email subject lines and improving opening rates and campaign performance, which gives us an insight into the impact of these models in digital marketing. In this paper, the authors first go over the different types of language models and the differences between them, before giving an overview of the most popular ones that will be used in the study, such as GPT-3.5, PaLM 2, and BERT. This study assesses the relevance, engagement, and uniqueness of GPT-3.5, PaLM 2, and BERT by training and fine-tuning them on marketing texts. The findings provide insights into the major positive impact of artificial intelligence (AI) on digital marketing, enabling informed decision-making for AI-driven email marketing strategies.
Medication dispenser with touch screen and consumption authenticator for monitoring adherence to medication in the elderly Linder Rubiños Jimenez, Santiago; Adolfo Vidal Sánchez, Ricardo Sergio; Enrique Solis Farfán, Roberto; Alfredo Vallejos Zuta, Alex; Junior Grados Espinoza, Herbert; Linett Velasquez Jimenez, Angélica
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2634-2646

Abstract

The elderly population continues to grow, this population has many chronic conditions, which is why they take multiple pills at the same time, this situation makes it difficult for them to take their medications at the appropriate time, which can cause health complications and even death. death. The objective of this research is to improve medication adherence using a medication dispenser that allows authenticating medication consumption accurately with a high level of usability. To do this, we implemented a dispenser with an alarm system that can be configured from a graphical interface, with internet of things (IoT) to remotely monitor the intake of pills and authenticate their consumption through artificial vision that will use the instant messaging system to inform the caregiver about the situation and finally measure the dispenser usability.

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