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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 9,138 Documents
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A framework for dynamic monitoring of distributed systems featuring adaptive security Periyasamy, Sudhakar; Kaliyaperumal, Prabu; Alagarsamy, Abinaya; Elumalai, Thenmozhi; Karuppiah, Tamilarasi
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp660-669

Abstract

Distributed systems play a crucial role in today’s information-based society, enabling seamless communication among governmental, industrial, social, and non-governmental institutions. As information becomes increasingly complex, the software industry is highly concerned about the heterogeneity and dynamicity of distributed systems. It is common for various types of information and services to be disseminated on different sites, especially in web 2.0. Since ‘information’ has become a prime tool for organizations to achieve their vision and mission, a high level of quality of service (QoS) is mandatory to disseminate and access information and services over remote sites, despite an unsecure communication system. These systems are expected to have security mechanisms in place, render services within an acceptable response time, dynamically adapt to environmental requirements, and secure key information. This research article proposes a framework for evaluating and determining a threshold up to which distributed systems can collect data to adapt to the environment. The study also proposes a dynamic security metric to determine the level of security disturbance caused by the monitoring system for adaptation and the measures to be implemented. Additionally, the paper details the role of the monitoring system in safeguarding the adaptive distributed system and proposes an adaptive monitoring system that can modify its functionality as per the environment.
Four quadrant operation of bidirectional DC-DC converter for light electric vehicles Ann Sam, Caroline; Jegathesan, Varghese
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp740-748

Abstract

This paper discusses the closed-loop control of a bidirectional full bridge DC-DC converter which aids in the four-quadrant operation of an electric vehicle (EV). Several topologies of bidirectional converters have been recently investigated for optimizing vehicle performance. The bidirectional converters with buck and boost modes of operation aid the four-quadrant operation of drives. The proposed bidirectional converter aids buck and boost modes of operation in both forward and reverse directions of the drive. The buck/boost operation in the forward direction is suitable to operate the traction drive in motoring mode. Also, the buck/boost operation in the reverse direction aids the drive to operate in charging mode. The performance analysis of the bi-directional converter-fed EV drive is done using MATLAB/Simulink software. The different modes of operation of the converter which is utilized for the four-quadrant operation of the drive are validated using a 12-60V hardware prototype. DSP TMS2837D controller is used to control the bi-directional converter and the code generation for the controller is done in MATLAB-DSP integrated platform. The hardware results validate theoretical analysis and simulation studies.
Detection of diabetic retinopathy and classification of its stages by using convolutional neural network Gaur, Sachin; Kandwal, Anirudh; Pandey, Bhaskar
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1284-1293

Abstract

Diabetes detection is pivotal in disease management and complication prevention. Traditional screening methods, like blood tests, are invasive and time-consuming. Deep learning has emerged as a non-invasive and automated alternative for diabetes detection. Convolutional neural networks (CNNs) excel in image analysis tasks, making them ideal for this purpose. This paper employs a CNN-based method for diabetes prediction using retinal images, utilizing the DenseNet169 architecture for feature extraction and diabetic retinopathy (DR) prediction. The APTOS 2019 blindness detection dataset from Kaggle, containing around 13,000 retinal images, is used for training. Pre-processing and normalization precede feature extraction, followed by the prediction of the DR stage. The model aims to classify retinal images into five stages of DR (0 to 4), ranging from no DR to proliferative DR. Our model achieved over 82% accuracy, outperforming advanced algorithms. Model evaluation includes accuracy, precision, recall, and F1 score measures.
Backstepping approach for the control of the double-fed asynchronous generator in a wind power system Chahboun, Mbarek; Abouyaakoub, Mohcine; Ali, Ali Ait; El Mrabet, Aziz; Hihi, Hicham; Ouabi, Hassan; El Bid, Youssef
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp78-89

Abstract

This paper aims to model and control the dual-fed asynchronous generator (DFIG). The modeling and vector control were simulated using MATLAB, followed by the application of the Backstepping control strategy. A comparative study between two DFIG control strategies, fuzzy logic control (FLC) and Back-stepping control, was conducted. The results for the Backstepping approach are discussed and compared with FLC, highlighting that the Backstepping technique addresses robustness issues regarding variations in operating conditions and internal parameters. Both control strategies are applied to a wind turbine system, and the simulation results and robustness tests are analyzed.
EMSPLA for accurate feature molecular extraction from protein-ligand interactions Kulkarni V, Srinidhi; Dhandapani, Ganesh; Ramesh, Kureeckal V.
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp580-589

Abstract

Protein-ligand interactions are fundamental in various biological and medical fields, influencing drug discovery and therapeutic development. In recent years, deep learning (DL) has revolutionized the study of these interactions, but significant challenges remain in accurately representing molecular structures for DL models. Traditional featurization techniques often depend on handcrafted features, requiring expert knowledge and potentially missing crucial molecular aspects. This work addresses these challenges by developing and evaluating a novel protein-ligand feature extraction system using an enhanced molecular similarity protein-ligand aligner (EMSPLA). The primary objective is to leverage EMSPLA for similarity matching in protein-ligand interactions, improving predictive model accuracy. The methodology combines convolutional neural networks (CNN) for local feature extraction with an attention module to capture long-distance dependencies, enhancing binding site predictions. Using the PDBbind v.2020 dataset, the EMSPLA model demonstrated superior performance with a root mean square error (RMSE) of 0.67, surpassing current state-of-the-art models. These findings highlight the system’s potential for efficient deployment and scalability, positioning it as a powerful tool in computational biology and drug discovery, ultimately advancing our understanding of protein-ligand interactions.
Early skin disease diagnosis by using artificial neural network for internet of healthcare things Wan Bejuri, Wan Mohd Yaakob; Mohamad, Mohd Murtadha; Tang, Michelle; Ahmad Khair, Aina Khairina; Adriyansyah, Yusuf Athallah; Kasmin, Fauziah; Tahir, Zulkifli
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1032-1041

Abstract

Internet of healthcare things (IoHT) represents a burgeoning field that leverages pervasive technologies to create technology driven environments for healthcare professionals, thereby enhancing the delivery of efficient healthcare services. In remote and isolated areas, such as rural communities and boarding schools, access to healthcare professionals (especially dermatologists) can be particularly challenging. However, these areas often lack the specialized expertise required for effective skin disease consultations. Thus, the purpose of this research is to design a scheme of early skin disease diagnosis for internet of healthcare things that is accessible anywhere and anytime. In this research, the image of skin disease from patient will be taken by using a mobile phone for predicting and identifying the disease. This proposed scheme will diagnose skin disease and convert it be meaningful information. As a result, it show our proposed scheme can be the most consistent in term of accuracy and loss compared to others method. Overall, this research represents a significant step toward improving healthcare accessibility and empowering individuals to manage their own health. Furthermore, the proposed scheme is anticipated to contribute significantly to the IoHT field, benefiting both academia and societal health outcomes.
Compressor performance prediction: gradient boosting regression model and sensitivity analysis Liao, Kuo-Chien; Wu, Hom-Yu; Wen, Hung-Ta; Sung, Jui-Tang; Hidayat, Muhamad; Wang, Will Wei-Juen
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1201-1208

Abstract

This study introduces the use of gradient boosting regression (GBR) models to estimate the compressor performance of aero-engines. The model exhibits a mean absolute error (MAE) of 0.078, showcasing superior performance compared to previous studies. Through sensitivity analysis, optimal values for three key parameters were determined: 280 estimators, a max depth of 9, and a learning rate of 0.085. Furthermore, a comparison with a prior study revealed an impressive MAE value lower than 0.002, highlighting the GBR model’s success in accurately predicting compressor performance. This demonstrates the model’s effectiveness and predictive accuracy, making it a valuable tool for aero-engine compressor performance estimation.
Implementing zero-knowledge proof authentication on Hyperledger fabric to enhance patient privacy and access control Joshi, Praveena Bolly; Natesan, Arivazhagan
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp498-506

Abstract

In recent years, the healthcare sector has encountered significant challenges in authenticating identities for online medical services. A predominant reliance on centralized identity management systems (IDMs) has presented obstacles to the seamless exchange of patient identities among various healthcare institutions, often resulting in data isolation within individual silos. Of paramount concern are the potential privacy breaches associated with centralized IDMs, which may compromise patient confidentiality. In response to these challenges, we propose a novel approach to securely sharing patient details across multiple hospitals utilizing the zero-knowledge access protocol (MediCrypt-ZKAP) within the Hyperledger Fabric blockchain framework. By adopting MediCrypt-ZKAP, hospitals can effectively verify the identities of requesting entities without disclosing sensitive patient information, thereby ensuring the highest levels of confidentiality and privacy protection. The proposed system represents a proactive step towards addressing the critical need for secure and interoperable patient data exchange within the healthcare sector. Through the integration of MediCrypt-ZKAP into existing blockchain infrastructure, our solution aims to enhance data security and privacy while promoting seamless collaboration among healthcare institutions.
Machine learning-based emotions recognition model using peripheral signals Kumar, Tarun; Kumar, Rajendra; Chandra Singh, Ram
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp976-984

Abstract

This work proposes a system for emotion recognition using four peripheral signals electromyography, galvanic skin response, blood volume pulse, and respiration. Peripheral signals cannot be modified, unlike other expression like voice and facial expression. The proposed method is applied to the DEAP datasets to verify the accuracy of emotion recognition. The proposed model focuses on accuracy and F1-score. DEAP dataset has more signals but only thirty-seven features from four peripheral signals were extracted for each trail and each video. On the DEAP datasets, the implementation found that the classification accuracy for arousal, valence, liking, and dominance was, respectively, 80%, 75%, 71%, and 78%. For two classes of problems, the corresponding F1-scores for arousal, valence, liking, and dominance are 0.50, 0.49, 0.47, and 0.47. The proposed model was implemented in MATLAB R2017a.
Building knowledge graph for relevant degree recommendations using semantic similarity search and named entity recognition Zineb, Elkaimbillah; Zineb, Mcharfi; Mohamed, Khoual; Bouchra, El Asri
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp463-474

Abstract

Career guidance is a critical and often daunting process, particularly during the transition from high school to higher education within the Moroccan education system. Faced with a vast array of university programs and career options, students frequently struggle to make informed decisions that align with their aspirations and skills. To address this challenge, our research introduces an innovative system that combines semantic similarity search with knowledge graph (KG) construction to enhance the precision and personalization of academic recommendations. By utilizing Sentence-BERT (SBERT) for semantic similarity, we generate embedding vectors that capture nuanced relationships between student profiles and degree descriptions. Subsequently, named entity recognition (NER) is applied to extract essential information such as skills, fields of study, and career opportunities from these profiles and descriptions. The extracted entities and their interrelationships are then structured into a coherent KG, stored in a Neo4j database, enabling efficient querying and visualization of complex data connections. This approach provides a transparent and explainable framework, ultimately delivering tailored advice that aligns with students’ individual needs and educational goals.

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